04142nas a2200757 4500008004100000022001400041245008700055210006900142260001300211300001100224490000600235520194100241653002202182653002102204653002102225653001902246653002302265653004002288653003202328653001302360653001102373653001402384653001902398653003602417653002002453653001802473100002002491700002202511700001802533700002302551700002602574700002202600700002402622700001602646700002002662700001802682700002102700700001902721700002202740700002402762700002502786700002002811700001802831700002302849700002402872700002902896700002302925700002002948700002802968700002102996700002003017700002103037700001903058700002103077700002203098700002203120700002203142700002703164700002103191700002203212700002403234700002103258700002303279710004603302856003603348 2010 eng d a1942-326800aCandidate gene association resource (CARe): design, methods, and proof of concept.0 aCandidate gene association resource CARe design methods and proo c2010 Jun a267-750 v33 a
BACKGROUND: The National Heart, Lung, and Blood Institute's Candidate Gene Association Resource (CARe), a planned cross-cohort analysis of genetic variation in cardiovascular, pulmonary, hematologic, and sleep-related traits, comprises >40,000 participants representing 4 ethnic groups in 9 community-based cohorts. The goals of CARe include the discovery of new variants associated with traits using a candidate gene approach and the discovery of new variants using the genome-wide association mapping approach specifically in African Americans.
METHODS AND RESULTS: CARe has assembled DNA samples for >40,000 individuals self-identified as European American, African American, Hispanic, or Chinese American, with accompanying data on hundreds of phenotypes that have been standardized and deposited in the CARe Phenotype Database. All participants were genotyped for 7 single-nucleotide polymorphisms (SNPs) selected based on prior association evidence. We performed association analyses relating each of these SNPs to lipid traits, stratified by sex and ethnicity, and adjusted for age and age squared. In at least 2 of the ethnic groups, SNPs near CETP, LIPC, and LPL strongly replicated for association with high-density lipoprotein cholesterol concentrations, PCSK9 with low-density lipoprotein cholesterol levels, and LPL and APOA5 with serum triglycerides. Notably, some SNPs showed varying effect sizes and significance of association in different ethnic groups.
CONCLUSIONS: The CARe Pilot Study validates the operational framework for phenotype collection, SNP genotyping, and analytic pipeline of the CARe project and validates the planned candidate gene study of approximately 2000 biological candidate loci in all participants and genome-wide association study in approximately 8000 African American participants. CARe will serve as a valuable resource for the scientific community.
10aAfrican Americans10aCholesterol, HDL10aCholesterol, LDL10aCohort Studies10aDatabases, Genetic10aEuropean Continental Ancestry Group10aGenetic Association Studies10aGenotype10aHumans10aPhenotype10aPilot Projects10aPolymorphism, Single Nucleotide10aResearch Design10aTriglycerides1 aMusunuru, Kiran1 aLettre, Guillaume1 aYoung, Taylor1 aFarlow, Deborah, N1 aPirruccello, James, P1 aEjebe, Kenechi, G1 aKeating, Brendan, J1 aYang, Qiong1 aChen, Ming-Huei1 aLapchyk, Nina1 aCrenshaw, Andrew1 aZiaugra, Liuda1 aRachupka, Anthony1 aBenjamin, Emelia, J1 aCupples, Adrienne, L1 aFornage, Myriam1 aFox, Ervin, R1 aHeckbert, Susan, R1 aHirschhorn, Joel, N1 aNewton-Cheh, Christopher1 aNizzari, Marcia, M1 aPaltoo, Dina, N1 aPapanicolaou, George, J1 aPatel, Sanjay, R1 aPsaty, Bruce, M1 aRader, Daniel, J1 aRedline, Susan1 aRich, Stephen, S1 aRotter, Jerome, I1 aTaylor, Herman, A1 aTracy, Russell, P1 aVasan, Ramachandran, S1 aWilson, James, G1 aKathiresan, Sekar1 aFabsitz, Richard, R1 aBoerwinkle, Eric1 aGabriel, Stacey, B1 aNHLBI Candidate Gene Association Resource uhttps://chs-nhlbi.org/node/118803576nas a2200673 4500008004100000022001400041245014900055210006900204260001300273300001300286490000600299520159100305653001201896653003401908653002501942653001101967653001701978653003401995653001102029653000902040653003602049653003602085100002402121700002002145700001802165700002102183700001902204700002502223700002702248700001902275700001802294700002602312700002502338700001902363700002102382700002102403700002302424700002202447700001802469700001702487700001802504700002102522700001902543700002302562700002002585700002102605700002502626700001802651700001802669700002402687700002802711700002102739700002002760700002002780700002102800700002502821700002002846856003602866 2011 eng d a1553-740400aGenetic loci associated with plasma phospholipid n-3 fatty acids: a meta-analysis of genome-wide association studies from the CHARGE Consortium.0 aGenetic loci associated with plasma phospholipid n3 fatty acids c2011 Jul ae10021930 v73 aLong-chain n-3 polyunsaturated fatty acids (PUFAs) can derive from diet or from α-linolenic acid (ALA) by elongation and desaturation. We investigated the association of common genetic variation with plasma phospholipid levels of the four major n-3 PUFAs by performing genome-wide association studies in five population-based cohorts comprising 8,866 subjects of European ancestry. Minor alleles of SNPs in FADS1 and FADS2 (desaturases) were associated with higher levels of ALA (p = 3 x 10⁻⁶⁴) and lower levels of eicosapentaenoic acid (EPA, p = 5 x 10⁻⁵⁸) and docosapentaenoic acid (DPA, p = 4 x 10⁻¹⁵⁴). Minor alleles of SNPs in ELOVL2 (elongase) were associated with higher EPA (p = 2 x 10⁻¹²) and DPA (p = 1 x 10⁻⁴³) and lower docosahexaenoic acid (DHA, p = 1 x 10⁻¹⁵). In addition to genes in the n-3 pathway, we identified a novel association of DPA with several SNPs in GCKR (glucokinase regulator, p = 1 x 10⁻⁸). We observed a weaker association between ALA and EPA among carriers of the minor allele of a representative SNP in FADS2 (rs1535), suggesting a lower rate of ALA-to-EPA conversion in these subjects. In samples of African, Chinese, and Hispanic ancestry, associations of n-3 PUFAs were similar with a representative SNP in FADS1 but less consistent with a representative SNP in ELOVL2. Our findings show that common variation in n-3 metabolic pathway genes and in GCKR influences plasma phospholipid levels of n-3 PUFAs in populations of European ancestry and, for FADS1, in other ancestries.
10aAlleles10aContinental Population Groups10aFatty Acids, Omega-310aFemale10aGenetic Loci10aGenome-Wide Association Study10aHumans10aMale10aMetabolic Networks and Pathways10aPolymorphism, Single Nucleotide1 aLemaitre, Rozenn, N1 aTanaka, Toshiko1 aTang, Weihong1 aManichaikul, Ani1 aFoy, Millennia1 aKabagambe, Edmond, K1 aNettleton, Jennifer, A1 aKing, Irena, B1 aWeng, Lu-Chen1 aBhattacharya, Sayanti1 aBandinelli, Stefania1 aBis, Joshua, C1 aRich, Stephen, S1 aJacobs, David, R1 aCherubini, Antonio1 aMcKnight, Barbara1 aLiang, Shuang1 aGu, Xiangjun1 aRice, Kenneth1 aLaurie, Cathy, C1 aLumley, Thomas1 aBrowning, Brian, L1 aPsaty, Bruce, M1 aChen, Yii-der, I1 aFriedlander, Yechiel1 aDjoussé, Luc1 aH Y Wu, Jason1 aSiscovick, David, S1 aUitterlinden, André, G1 aArnett, Donna, K1 aFerrucci, Luigi1 aFornage, Myriam1 aTsai, Michael, Y1 aMozaffarian, Dariush1 aSteffen, Lyn, M uhttps://chs-nhlbi.org/node/131107169nas a2202257 4500008004100000022001400041245010400055210006900159260001600228300001200244490000700256520086500263653001001128653004001138653003401178653001101212653004301223653003101266100002601297700001801323700002001341700002001361700001901381700001601400700001801416700001901434700002601453700002501479700001801504700002601522700002001548700001701568700002001585700002101605700002001626700001801646700002401664700001901688700001901707700002001726700002501746700002101771700002401792700002301816700002001839700002201859700002901881700001901910700002801929700002101957700002201978700002602000700002302026700002402049700001802073700001902091700001902110700002102129700002302150700002102173700001802194700001602212700002802228700002202256700001702278700002002295700001702315700002002332700001902352700002602371700001602397700001702413700001702430700002002447700001802467700001902485700001902504700002102523700002202544700001802566700001902584700002002603700001802623700001802641700002202659700001902681700001902700700002002719700001702739700002302756700001902779700002102798700002002819700001802839700002402857700001902881700002102900700001602921700002302937700001902960700002202979700001903001700001803020700002403038700001803062700002303080700002103103700002203124700002103146700002303167700002203190700002103212700001903233700002603252700002003278700002203298700001603320700001803336700002403354700001703378700001903395700002803414700002003442700002203462700002203484700002003506700001903526700001703545700002103562700002603583700002203609700001903631700002403650700001903674700001903693700002003712700002703732700001803759700002603777700001803803700002003821700002803841700002003869700002003889700002203909700001603931700002403947700001903971700001803990700002004008700001704028700002204045700001904067700002504086700002604111700002204137700001904159700001904178700001904197700002004216700001604236700002204252700002204274700002004296700001504316700002204331700001904353700002204372700002304394700002604417700001904443700002104462700002204483700002004505700002204525700002004547700002304567700001804590700002704608700002604635700001804661700002404679700002304703700002504726700001704751700002404768700002104792710004104813710002104854856003604875 2011 eng d a1546-171800aGenome-wide association and large-scale follow up identifies 16 new loci influencing lung function.0 aGenomewide association and largescale follow up identifies 16 ne c2011 Sep 25 a1082-900 v433 aPulmonary function measures reflect respiratory health and are used in the diagnosis of chronic obstructive pulmonary disease. We tested genome-wide association with forced expiratory volume in 1 second and the ratio of forced expiratory volume in 1 second to forced vital capacity in 48,201 individuals of European ancestry with follow up of the top associations in up to an additional 46,411 individuals. We identified new regions showing association (combined P < 5 × 10(-8)) with pulmonary function in or near MFAP2, TGFB2, HDAC4, RARB, MECOM (also known as EVI1), SPATA9, ARMC2, NCR3, ZKSCAN3, CDC123, C10orf11, LRP1, CCDC38, MMP15, CFDP1 and KCNE2. Identification of these 16 new loci may provide insight into the molecular mechanisms regulating pulmonary function and into molecular targets for future therapy to alleviate reduced lung function.
10aChild10aEuropean Continental Ancestry Group10aGenome-Wide Association Study10aHumans10aPulmonary Disease, Chronic Obstructive10aRespiratory Function Tests1 aArtigas, Maria, Soler1 aLoth, Daan, W1 aWain, Louise, V1 aGharib, Sina, A1 aObeidat, Ma'en1 aTang, Wenbo1 aZhai, Guangju1 aZhao, Jing Hua1 aSmith, Albert, Vernon1 aHuffman, Jennifer, E1 aAlbrecht, Eva1 aJackson, Catherine, M1 aEvans, David, M1 aCadby, Gemma1 aFornage, Myriam1 aManichaikul, Ani1 aLopez, Lorna, M1 aJohnson, Toby1 aAldrich, Melinda, C1 aAspelund, Thor1 aBarroso, Inês1 aCampbell, Harry1 aCassano, Patricia, A1 aCouper, David, J1 aEiriksdottir, Gudny1 aFranceschini, Nora1 aGarcia, Melissa1 aGieger, Christian1 aGislason, Gauti, Kjartan1 aGrkovic, Ivica1 aHammond, Christopher, J1 aHancock, Dana, B1 aHarris, Tamara, B1 aRamasamy, Adaikalavan1 aHeckbert, Susan, R1 aHeliövaara, Markku1 aHomuth, Georg1 aHysi, Pirro, G1 aJames, Alan, L1 aJankovic, Stipan1 aJoubert, Bonnie, R1 aKarrasch, Stefan1 aKlopp, Norman1 aKoch, Beate1 aKritchevsky, Stephen, B1 aLauner, Lenore, J1 aLiu, Yongmei1 aLoehr, Laura, R1 aLohman, Kurt1 aLoos, Ruth, J F1 aLumley, Thomas1 aBalushi, Khalid, A Al1 aAng, Wei, Q1 aBarr, Graham1 aBeilby, John1 aBlakey, John, D1 aBoban, Mladen1 aBoraska, Vesna1 aBrisman, Jonas1 aBritton, John, R1 aBrusselle, Guy, G1 aCooper, Cyrus1 aCurjuric, Ivan1 aDahgam, Santosh1 aDeary, Ian, J1 aEbrahim, Shah1 aEijgelsheim, Mark1 aFrancks, Clyde1 aGaysina, Darya1 aGranell, Raquel1 aGu, Xiangjun1 aHankinson, John, L1 aHardy, Rebecca1 aHarris, Sarah, E1 aHenderson, John1 aHenry, Amanda1 aHingorani, Aroon, D1 aHofman, Albert1 aHolt, Patrick, G1 aHui, Jennie1 aHunter, Michael, L1 aImboden, Medea1 aJameson, Karen, A1 aKerr, Shona, M1 aKolcic, Ivana1 aKronenberg, Florian1 aLiu, Jason, Z1 aMarchini, Jonathan1 aMcKeever, Tricia1 aMorris, Andrew, D1 aOlin, Anna-Carin1 aPorteous, David, J1 aPostma, Dirkje, S1 aRich, Stephen, S1 aRing, Susan, M1 aRivadeneira, Fernando1 aRochat, Thierry1 aSayer, Avan Aihie1 aSayers, Ian1 aSly, Peter, D1 aSmith, George Davey1 aSood, Akshay1 aStarr, John, M1 aUitterlinden, André, G1 aVonk, Judith, M1 aWannamethee, Goya1 aWhincup, Peter, H1 aWijmenga, Cisca1 aWilliams, Dale1 aWong, Andrew1 aMangino, Massimo1 aMarciante, Kristin, D1 aMcArdle, Wendy, L1 aMeibohm, Bernd1 aMorrison, Alanna, C1 aNorth, Kari, E1 aOmenaas, Ernst1 aPalmer, Lyle, J1 aPietiläinen, Kirsi, H1 aPin, Isabelle1 aEk, Ozren, Pola Sbrev1 aPouta, Anneli1 aPsaty, Bruce, M1 aHartikainen, Anna-Liisa1 aRantanen, Taina1 aRipatti, Samuli1 aRotter, Jerome, I1 aRudan, Igor1 aRudnicka, Alicja, R1 aSchulz, Holger1 aShin, So-Youn1 aSpector, Tim, D1 aSurakka, Ida1 aVitart, Veronique1 aVölzke, Henry1 aWareham, Nicholas, J1 aWarrington, Nicole, M1 aWichmann, H-Erich1 aWild, Sarah, H1 aWilk, Jemma, B1 aWjst, Matthias1 aWright, Alan, F1 aZgaga, Lina1 aZemunik, Tatijana1 aPennell, Craig, E1 aNyberg, Fredrik1 aKuh, Diana1 aHolloway, John, W1 aBoezen, Marike1 aLawlor, Debbie, A1 aMorris, Richard, W1 aProbst-Hensch, Nicole1 aKaprio, Jaakko1 aWilson, James, F1 aHayward, Caroline1 aKähönen, Mika1 aHeinrich, Joachim1 aMusk, Arthur, W1 aJarvis, Deborah, L1 aGläser, Sven1 aJarvelin, Marjo-Riitta1 aStricker, Bruno, H Ch1 aElliott, Paul1 aO'Connor, George, T1 aStrachan, David, P1 aLondon, Stephanie, J1 aHall, Ian, P1 aGudnason, Vilmundur1 aTobin, Martin, D1 aInternational Lung Cancer Consortium1 aGIANT Consortium uhttps://chs-nhlbi.org/node/609605378nas a2201249 4500008004100000022001400041245010600055210006900161260001600230300001100246490000800257520186000265653000902125653001102134653002902145653003402174653001102208653000902219653001602228653002602244653003602270653004302306653002502349653003202374653001202406653001902418100001902437700002202456700002002478700001902498700002102517700002002538700002602558700002302584700002202607700001602629700001802645700001902663700001602682700002002698700002602718700002102744700002102765700001502786700002002801700002002821700002302841700002202864700002402886700001902910700001802929700002002947700001702967700001902984700002303003700001903026700002103045700002203066700001903088700001903107700001903126700001803145700001903163700002203182700002203204700001703226700002003243700002003263700001903283700002603302700002203328700001903350700002403369700002003393700002203413700001903435700002203454700002003476700002103496700002603517700002003543700002203563700002603585700001903611700002803630700002503658700002003683700001803703700002503721700002403746700001803770700002003788700002503808700002403833700001703857700002003874700002303894700002503917700001903942700002603961700002003987700001704007700002404024700002104048700002304069856003604092 2012 eng d a1535-497000aGenome-wide association studies identify CHRNA5/3 and HTR4 in the development of airflow obstruction.0 aGenomewide association studies identify CHRNA53 and HTR4 in the c2012 Oct 01 a622-320 v1863 aRATIONALE: Genome-wide association studies (GWAS) have identified loci influencing lung function, but fewer genes influencing chronic obstructive pulmonary disease (COPD) are known.
OBJECTIVES: Perform meta-analyses of GWAS for airflow obstruction, a key pathophysiologic characteristic of COPD assessed by spirometry, in population-based cohorts examining all participants, ever smokers, never smokers, asthma-free participants, and more severe cases.
METHODS: Fifteen cohorts were studied for discovery (3,368 affected; 29,507 unaffected), and a population-based family study and a meta-analysis of case-control studies were used for replication and regional follow-up (3,837 cases; 4,479 control subjects). Airflow obstruction was defined as FEV(1) and its ratio to FVC (FEV(1)/FVC) both less than their respective lower limits of normal as determined by published reference equations.
MEASUREMENTS AND MAIN RESULTS: The discovery meta-analyses identified one region on chromosome 15q25.1 meeting genome-wide significance in ever smokers that includes AGPHD1, IREB2, and CHRNA5/CHRNA3 genes. The region was also modestly associated among never smokers. Gene expression studies confirmed the presence of CHRNA5/3 in lung, airway smooth muscle, and bronchial epithelial cells. A single-nucleotide polymorphism in HTR4, a gene previously related to FEV(1)/FVC, achieved genome-wide statistical significance in combined meta-analysis. Top single-nucleotide polymorphisms in ADAM19, RARB, PPAP2B, and ADAMTS19 were nominally replicated in the COPD meta-analysis.
CONCLUSIONS: These results suggest an important role for the CHRNA5/3 region as a genetic risk factor for airflow obstruction that may be independent of smoking and implicate the HTR4 gene in the etiology of airflow obstruction.
10aAged10aFemale10aForced Expiratory Volume10aGenome-Wide Association Study10aHumans10aMale10aMiddle Aged10aNerve Tissue Proteins10aPolymorphism, Single Nucleotide10aPulmonary Disease, Chronic Obstructive10aReceptors, Nicotinic10aReceptors, Serotonin, 5-HT410aSmoking10aVital Capacity1 aWilk, Jemma, B1 aShrine, Nick, R G1 aLoehr, Laura, R1 aZhao, Jing Hua1 aManichaikul, Ani1 aLopez, Lorna, M1 aSmith, Albert, Vernon1 aHeckbert, Susan, R1 aSmolonska, Joanna1 aTang, Wenbo1 aLoth, Daan, W1 aCurjuric, Ivan1 aHui, Jennie1 aCho, Michael, H1 aLatourelle, Jeanne, C1 aHenry, Amanda, P1 aAldrich, Melinda1 aBakke, Per1 aBeaty, Terri, H1 aBentley, Amy, R1 aBorecki, Ingrid, B1 aBrusselle, Guy, G1 aBurkart, Kristin, M1 aChen, Ting-Hsu1 aCouper, David1 aCrapo, James, D1 aDavies, Gail1 aDupuis, Josée1 aFranceschini, Nora1 aGulsvik, Amund1 aHancock, Dana, B1 aHarris, Tamara, B1 aHofman, Albert1 aImboden, Medea1 aJames, Alan, L1 aKhaw, Kay-Tee1 aLahousse, Lies1 aLauner, Lenore, J1 aLitonjua, Augusto1 aLiu, Yongmei1 aLohman, Kurt, K1 aLomas, David, A1 aLumley, Thomas1 aMarciante, Kristin, D1 aMcArdle, Wendy, L1 aMeibohm, Bernd1 aMorrison, Alanna, C1 aMusk, Arthur, W1 aMyers, Richard, H1 aNorth, Kari, E1 aPostma, Dirkje, S1 aPsaty, Bruce, M1 aRich, Stephen, S1 aRivadeneira, Fernando1 aRochat, Thierry1 aRotter, Jerome, I1 aArtigas, Maria, Soler1 aStarr, John, M1 aUitterlinden, André, G1 aWareham, Nicholas, J1 aWijmenga, Cisca1 aZanen, Pieter1 aProvince, Michael, A1 aSilverman, Edwin, K1 aDeary, Ian, J1 aPalmer, Lyle, J1 aCassano, Patricia, A1 aGudnason, Vilmundur1 aBarr, Graham1 aLoos, Ruth, J F1 aStrachan, David, P1 aLondon, Stephanie, J1 aBoezen, Marike1 aProbst-Hensch, Nicole1 aGharib, Sina, A1 aHall, Ian, P1 aO'Connor, George, T1 aTobin, Martin, D1 aStricker, Bruno, H uhttps://chs-nhlbi.org/node/609205536nas a2201405 4500008004100000022001400041245012000055210006900175260000900244300001300253490000600266520151700272653002901789653002001818653001801838653003401856653002001890653002301910653001101933653000901944653002601953653003601979653004402015653004302059653002802102653001202130653003002142653001902172100002102191700002602212700002002238700001802258700002102276700002602297700001802323700001902341700001602360700002202376700002102398700002202419700001702441700001602458700001902474700001802493700001902511700002002530700002402550700001902574700002202593700001802615700001702633700002202650700002102672700001802693700001902711700001802730700002002748700001702768700002002785700002202805700002602827700001902853700002202872700002402894700002202918700001902940700002402959700002302983700002203006700002203028700002003050700001903070700002003089700001903109700002103128700001903149700002403168700002303192700002603215700002103241700002803262700001703290700001903307700002003326700002003346700002803366700002003394700002103414700002003435700002403455700001903479700002103498700001903519700002203538700001803560700002803578700002803606700001803634700002303652700001703675700002503692700001903717700002503736700002403761700002903785700002003814700002703834700002303861700002403884700001903908700001703927700002503944700002303969700002003992700001704012700001904029700002104048700002504069856003604094 2012 eng d a1553-740400aGenome-wide joint meta-analysis of SNP and SNP-by-smoking interaction identifies novel loci for pulmonary function.0 aGenomewide joint metaanalysis of SNP and SNPbysmoking interactio c2012 ae10030980 v83 aGenome-wide association studies have identified numerous genetic loci for spirometic measures of pulmonary function, forced expiratory volume in one second (FEV(1)), and its ratio to forced vital capacity (FEV(1)/FVC). Given that cigarette smoking adversely affects pulmonary function, we conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on FEV(1) and FEV(1)/FVC across 19 studies (total N = 50,047). We identified three novel loci not previously associated with pulmonary function. SNPs in or near DNER (smallest P(JMA = )5.00×10(-11)), HLA-DQB1 and HLA-DQA2 (smallest P(JMA = )4.35×10(-9)), and KCNJ2 and SOX9 (smallest P(JMA = )1.28×10(-8)) were associated with FEV(1)/FVC or FEV(1) in meta-analysis models including SNP main effects, smoking main effects, and SNP-by-smoking (ever-smoking or pack-years) interaction. The HLA region has been widely implicated for autoimmune and lung phenotypes, unlike the other novel loci, which have not been widely implicated. We evaluated DNER, KCNJ2, and SOX9 and found them to be expressed in human lung tissue. DNER and SOX9 further showed evidence of differential expression in human airway epithelium in smokers compared to non-smokers. Our findings demonstrated that joint testing of SNP and SNP-by-environment interaction identified novel loci associated with complex traits that are missed when considering only the genetic main effects.
10aForced Expiratory Volume10aGene Expression10aGenome, Human10aGenome-Wide Association Study10aHLA-DQ Antigens10aHLA-DQ beta-Chains10aHumans10aLung10aNerve Tissue Proteins10aPolymorphism, Single Nucleotide10aPotassium Channels, Inwardly Rectifying10aPulmonary Disease, Chronic Obstructive10aReceptors, Cell Surface10aSmoking10aSOX9 Transcription Factor10aVital Capacity1 aHancock, Dana, B1 aArtigas, Maria, Soler1 aGharib, Sina, A1 aHenry, Amanda1 aManichaikul, Ani1 aRamasamy, Adaikalavan1 aLoth, Daan, W1 aImboden, Medea1 aKoch, Beate1 aMcArdle, Wendy, L1 aSmith, Albert, V1 aSmolonska, Joanna1 aSood, Akshay1 aTang, Wenbo1 aWilk, Jemma, B1 aZhai, Guangju1 aZhao, Jing Hua1 aAschard, Hugues1 aBurkart, Kristin, M1 aCurjuric, Ivan1 aEijgelsheim, Mark1 aElliott, Paul1 aGu, Xiangjun1 aHarris, Tamara, B1 aJanson, Christer1 aHomuth, Georg1 aHysi, Pirro, G1 aLiu, Jason, Z1 aLoehr, Laura, R1 aLohman, Kurt1 aLoos, Ruth, J F1 aManning, Alisa, K1 aMarciante, Kristin, D1 aObeidat, Ma'en1 aPostma, Dirkje, S1 aAldrich, Melinda, C1 aBrusselle, Guy, G1 aChen, Ting-Hsu1 aEiriksdottir, Gudny1 aFranceschini, Nora1 aHeinrich, Joachim1 aRotter, Jerome, I1 aWijmenga, Cisca1 aWilliams, Dale1 aBentley, Amy, R1 aHofman, Albert1 aLaurie, Cathy, C1 aLumley, Thomas1 aMorrison, Alanna, C1 aJoubert, Bonnie, R1 aRivadeneira, Fernando1 aCouper, David, J1 aKritchevsky, Stephen, B1 aLiu, Yongmei1 aWjst, Matthias1 aWain, Louise, V1 aVonk, Judith, M1 aUitterlinden, André, G1 aRochat, Thierry1 aRich, Stephen, S1 aPsaty, Bruce, M1 aO'Connor, George, T1 aNorth, Kari, E1 aMirel, Daniel, B1 aMeibohm, Bernd1 aLauner, Lenore, J1 aKhaw, Kay-Tee1 aHartikainen, Anna-Liisa1 aHammond, Christopher, J1 aGläser, Sven1 aMarchini, Jonathan1 aKraft, Peter1 aWareham, Nicholas, J1 aVölzke, Henry1 aStricker, Bruno, H C1 aSpector, Timothy, D1 aProbst-Hensch, Nicole, M1 aJarvis, Deborah1 aJarvelin, Marjo-Riitta1 aHeckbert, Susan, R1 aGudnason, Vilmundur1 aBoezen, Marike1 aBarr, Graham1 aCassano, Patricia, A1 aStrachan, David, P1 aFornage, Myriam1 aHall, Ian, P1 aDupuis, Josée1 aTobin, Martin, D1 aLondon, Stephanie, J uhttps://chs-nhlbi.org/node/608803041nas a2200541 4500008004100000022001400041245007600055210006900131260000900200300001100209490000600220520147100226653002201697653002101719653002101740653004001761653003201801653001701833653001101850653003601861653001801897100002001915700002401935700002201959700002101981700002102002700002102023700002202044700002502066700002202091700002102113700001902134700002002153700002802173700002102201700001502222700002202237700002202259700002302281700002002304700002502324700002202349700002102371700002802392700002202420700002102442856003602463 2012 eng d a1932-620300aMulti-ethnic analysis of lipid-associated loci: the NHLBI CARe project.0 aMultiethnic analysis of lipidassociated loci the NHLBI CARe proj c2012 ae364730 v73 aBACKGROUND: Whereas it is well established that plasma lipid levels have substantial heritability within populations, it remains unclear how many of the genetic determinants reported in previous studies (largely performed in European American cohorts) are relevant in different ethnicities.
METHODOLOGY/PRINCIPAL FINDINGS: We tested a set of ∼50,000 polymorphisms from ∼2,000 candidate genes and genetic loci from genome-wide association studies (GWAS) for association with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) in 25,000 European Americans and 9,000 African Americans in the National Heart, Lung, and Blood Institute (NHLBI) Candidate Gene Association Resource (CARe). We replicated associations for a number of genes in one or both ethnicities and identified a novel lipid-associated variant in a locus harboring ICAM1. We compared the architecture of genetic loci associated with lipids in both African Americans and European Americans and found that the same genes were relevant across ethnic groups but the specific associated variants at each gene often differed.
CONCLUSIONS/SIGNIFICANCE: We identify or provide further evidence for a number of genetic determinants of plasma lipid levels through population association studies. In many loci the determinants appear to differ substantially between African Americans and European Americans.
10aAfrican Americans10aCholesterol, HDL10aCholesterol, LDL10aEuropean Continental Ancestry Group10aGenetic Association Studies10aGenetic Loci10aHumans10aPolymorphism, Single Nucleotide10aTriglycerides1 aMusunuru, Kiran1 aRomaine, Simon, P R1 aLettre, Guillaume1 aWilson, James, G1 aVolcik, Kelly, A1 aTsai, Michael, Y1 aTaylor, Herman, A1 aSchreiner, Pamela, J1 aRotter, Jerome, I1 aRich, Stephen, S1 aRedline, Susan1 aPsaty, Bruce, M1 aPapanicolaou, George, J1 aOrdovas, Jose, M1 aLiu, Kiang1 aKrauss, Ronald, M1 aGlazer, Nicole, L1 aGabriel, Stacey, B1 aFornage, Myriam1 aCupples, Adrienne, L1 aBuxbaum, Sarah, G1 aBoerwinkle, Eric1 aBallantyne, Christie, M1 aKathiresan, Sekar1 aRader, Daniel, J uhttps://chs-nhlbi.org/node/138803560nas a2200709 4500008004100000022001400041245008800055210006900143260000900212300001100221490000600232520160600238653001001844653001201854653002101866653001901887653003401906653001001940653001101950653001901961653001301980653001301993653001002006653001102016653000902027653004402036653003602080653001602116653001602132653002702148100002002175700001302195700002402208700002302232700001902255700002002274700001702294700002302311700002502334700002002359700002402379700002202403700002202425700001902447700002202466700001702488700001702505700001902522700002002541700002002561700002102581700002602602700002402628700002102652700002402673700002302697700002102720700003002741700002202771700002102793856003602814 2013 eng d a1932-620300aBest practices and joint calling of the HumanExome BeadChip: the CHARGE Consortium.0 aBest practices and joint calling of the HumanExome BeadChip the c2013 ae680950 v83 aGenotyping arrays are a cost effective approach when typing previously-identified genetic polymorphisms in large numbers of samples. One limitation of genotyping arrays with rare variants (e.g., minor allele frequency [MAF] <0.01) is the difficulty that automated clustering algorithms have to accurately detect and assign genotype calls. Combining intensity data from large numbers of samples may increase the ability to accurately call the genotypes of rare variants. Approximately 62,000 ethnically diverse samples from eleven Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium cohorts were genotyped with the Illumina HumanExome BeadChip across seven genotyping centers. The raw data files for the samples were assembled into a single project for joint calling. To assess the quality of the joint calling, concordance of genotypes in a subset of individuals having both exome chip and exome sequence data was analyzed. After exclusion of low performing SNPs on the exome chip and non-overlap of SNPs derived from sequence data, genotypes of 185,119 variants (11,356 were monomorphic) were compared in 530 individuals that had whole exome sequence data. A total of 98,113,070 pairs of genotypes were tested and 99.77% were concordant, 0.14% had missing data, and 0.09% were discordant. We report that joint calling allows the ability to accurately genotype rare variation using array technology when large sample sizes are available and best practices are followed. The cluster file from this experiment is available at www.chargeconsortium.com/main/exomechip.
10aAging10aAlleles10aCluster Analysis10aCohort Studies10aContinental Population Groups10aExome10aFemale10aGene Frequency10aGenomics10aGenotype10aHeart10aHumans10aMale10aOligonucleotide Array Sequence Analysis10aPolymorphism, Single Nucleotide10aSample Size10aSelf Report10aSequence Analysis, DNA1 aGrove, Megan, L1 aYu, Bing1 aCochran, Barbara, J1 aHaritunians, Talin1 aBis, Joshua, C1 aTaylor, Kent, D1 aHansen, Mark1 aBorecki, Ingrid, B1 aCupples, Adrienne, L1 aFornage, Myriam1 aGudnason, Vilmundur1 aHarris, Tamara, B1 aKathiresan, Sekar1 aKraaij, Robert1 aLauner, Lenore, J1 aLevy, Daniel1 aLiu, Yongmei1 aMosley, Thomas1 aPeloso, Gina, M1 aPsaty, Bruce, M1 aRich, Stephen, S1 aRivadeneira, Fernando1 aSiscovick, David, S1 aSmith, Albert, V1 aUitterlinden, Andre1 aDuijn, Cornelia, M1 aWilson, James, G1 aO'Donnell, Christopher, J1 aRotter, Jerome, I1 aBoerwinkle, Eric uhttps://chs-nhlbi.org/node/606710597nas a2203385 4500008004100000022001400041245009500055210006900150260001300219300001200232490000700244520112400251653002501375653002101400653002101421653002801442653001101470653003601481653001701517653001801534100001201552700002301564700002201587700002201609700001301631700002001644700002301664700002201687700001801709700001401727700002601741700001601767700002501783700003101808700002001839700001901859700002601878700002401904700002001928700001601948700002101964700002101985700002002006700002402026700002002050700002302070700002202093700002102115700002102136700001802157700002102175700001902196700001802215700002102233700002302254700002202277700001602299700001802315700002802333700002702361700002102388700002102409700002202430700003002452700001902482700002602501700002302527700001902550700002602569700001802595700001802613700002202631700001602653700002002669700001802689700001302707700002502720700001702745700002002762700002402782700002502806700002802831700002402859700002102883700002202904700001702926700001302943700001802956700001802974700001902992700001903011700002103030700002403051700002503075700002103100700002103121700002203142700002103164700002103185700002003206700001803226700002403244700003203268700001903300700002203319700002103341700002303362700002703385700002103412700002803433700002203461700002003483700002103503700001603524700001703540700001803557700002303575700002203598700002503620700001803645700001403663700001803677700002203695700001803717700002403735700002203759700001703781700002203798700002003820700002003840700002203860700002303882700002503905700002303930700002203953700001903975700002503994700002404019700002304043700001604066700001904082700002504101700002204126700001804148700002104166700002404187700002004211700002604231700001604257700001904273700001904292700002204311700001804333700002004351700002504371700002304396700001804419700002004437700002804457700002004485700002204505700002504527700002004552700001904572700002304591700001904614700002104633700002404654700001904678700002004697700002404717700002904741700002504770700002204795700002104817700002304838700002304861700002304884700002504907700001804932700002004950700002004970700002604990700002205016700002205038700002405060700002305084700001705107700002205124700001805146700002105164700002005185700002005205700002305225700002205248700001905270700002405289700002005313700002005333700002205353700002105375700002405396700001905420700001805439700002405457700002405481700002005505700002005525700002205545700002705567700001605594700002005610700001905630700002305649700001905672700002205691700002405713700002205737700001505759700002205774700002205796700001905818700001905837700001505856700002505871700002405896700002005920700002205940700002305962700002005985700002106005700002006026700002206046700002406068700002106092700002306113700001706136700002606153700002106179700002006200700002406220700002106244700002106265700002006286700002706306700002006333700002406353700002106377700002306398700002106421700002006442700002106462700002306483700002206506700001906528700002306547700002006570700002306590700002406613700002006637700002506657700002306682700003006705700002106735700002106756700002106777700002306798700002806821700002306849700002206872700002006894700002006914700002506934700002506959700002106984700002107005700002007026700002107046700002007067700002507087700001807112700002307130700002207153856003607175 2013 eng d a1546-171800aCommon variants associated with plasma triglycerides and risk for coronary artery disease.0 aCommon variants associated with plasma triglycerides and risk fo c2013 Nov a1345-520 v453 aTriglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiological studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P < 5 × 10(-8) for each) to examine the role of triglycerides in risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglyceride levels, and we show that the direction and magnitude of the associations with both traits are factors in determining CAD risk. Second, we consider loci with only a strong association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol (HDL-C) levels, the strength of a polymorphism's effect on triglyceride levels is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.
10aBiological Transport10aCholesterol, HDL10aCholesterol, LDL10aCoronary Artery Disease10aHumans10aPolymorphism, Single Nucleotide10aRisk Factors10aTriglycerides1 aDo, Ron1 aWiller, Cristen, J1 aSchmidt, Ellen, M1 aSengupta, Sebanti1 aGao, Chi1 aPeloso, Gina, M1 aGustafsson, Stefan1 aKanoni, Stavroula1 aGanna, Andrea1 aChen, Jin1 aBuchkovich, Martin, L1 aMora, Samia1 aBeckmann, Jacques, S1 aBragg-Gresham, Jennifer, L1 aChang, Hsing-Yi1 aDemirkan, Ayse1 aHertog, Heleen, M Den1 aDonnelly, Louise, A1 aEhret, Georg, B1 aEsko, Tõnu1 aFeitosa, Mary, F1 aFerreira, Teresa1 aFischer, Krista1 aFontanillas, Pierre1 aFraser, Ross, M1 aFreitag, Daniel, F1 aGurdasani, Deepti1 aHeikkilä, Kauko1 aHyppönen, Elina1 aIsaacs, Aaron1 aJackson, Anne, U1 aJohansson, Asa1 aJohnson, Toby1 aKaakinen, Marika1 aKettunen, Johannes1 aKleber, Marcus, E1 aLi, Xiaohui1 aLuan, Jian'an1 aLyytikäinen, Leo-Pekka1 aMagnusson, Patrik, K E1 aMangino, Massimo1 aMihailov, Evelin1 aMontasser, May, E1 aMüller-Nurasyid, Martina1 aNolte, Ilja, M1 aO'Connell, Jeffrey, R1 aPalmer, Cameron, D1 aPerola, Markus1 aPetersen, Ann-Kristin1 aSanna, Serena1 aSaxena, Richa1 aService, Susan, K1 aShah, Sonia1 aShungin, Dmitry1 aSidore, Carlo1 aSong, Ci1 aStrawbridge, Rona, J1 aSurakka, Ida1 aTanaka, Toshiko1 aTeslovich, Tanya, M1 aThorleifsson, Gudmar1 avan den Herik, Evita, G1 aVoight, Benjamin, F1 aVolcik, Kelly, A1 aWaite, Lindsay, L1 aWong, Andrew1 aWu, Ying1 aZhang, Weihua1 aAbsher, Devin1 aAsiki, Gershim1 aBarroso, Inês1 aBeen, Latonya, F1 aBolton, Jennifer, L1 aBonnycastle, Lori, L1 aBrambilla, Paolo1 aBurnett, Mary, S1 aCesana, Giancarlo1 aDimitriou, Maria1 aDoney, Alex, S F1 aDöring, Angela1 aElliott, Paul1 aEpstein, Stephen, E1 aEyjolfsson, Gudmundur, Ingi1 aGigante, Bruna1 aGoodarzi, Mark, O1 aGrallert, Harald1 aGravito, Martha, L1 aGroves, Christopher, J1 aHallmans, Göran1 aHartikainen, Anna-Liisa1 aHayward, Caroline1 aHernandez, Dena1 aHicks, Andrew, A1 aHolm, Hilma1 aHung, Yi-Jen1 aIllig, Thomas1 aJones, Michelle, R1 aKaleebu, Pontiano1 aKastelein, John, J P1 aKhaw, Kay-Tee1 aKim, Eric1 aKlopp, Norman1 aKomulainen, Pirjo1 aKumari, Meena1 aLangenberg, Claudia1 aLehtimäki, Terho1 aLin, Shih-Yi1 aLindström, Jaana1 aLoos, Ruth, J F1 aMach, François1 aMcArdle, Wendy, L1 aMeisinger, Christa1 aMitchell, Braxton, D1 aMüller, Gabrielle1 aNagaraja, Ramaiah1 aNarisu, Narisu1 aNieminen, Tuomo, V M1 aNsubuga, Rebecca, N1 aOlafsson, Isleifur1 aOng, Ken, K1 aPalotie, Aarno1 aPapamarkou, Theodore1 aPomilla, Cristina1 aPouta, Anneli1 aRader, Daniel, J1 aReilly, Muredach, P1 aRidker, Paul, M1 aRivadeneira, Fernando1 aRudan, Igor1 aRuokonen, Aimo1 aSamani, Nilesh1 aScharnagl, Hubert1 aSeeley, Janet1 aSilander, Kaisa1 aStančáková, Alena1 aStirrups, Kathleen1 aSwift, Amy, J1 aTiret, Laurence1 aUitterlinden, André, G1 avan Pelt, Joost1 aVedantam, Sailaja1 aWainwright, Nicholas1 aWijmenga, Cisca1 aWild, Sarah, H1 aWillemsen, Gonneke1 aWilsgaard, Tom1 aWilson, James, F1 aYoung, Elizabeth, H1 aZhao, Jing Hua1 aAdair, Linda, S1 aArveiler, Dominique1 aAssimes, Themistocles, L1 aBandinelli, Stefania1 aBennett, Franklyn1 aBochud, Murielle1 aBoehm, Bernhard, O1 aBoomsma, Dorret, I1 aBorecki, Ingrid, B1 aBornstein, Stefan, R1 aBovet, Pascal1 aBurnier, Michel1 aCampbell, Harry1 aChakravarti, Aravinda1 aChambers, John, C1 aChen, Yii-Der Ida1 aCollins, Francis, S1 aCooper, Richard, S1 aDanesh, John1 aDedoussis, George1 ade Faire, Ulf1 aFeranil, Alan, B1 aFerrieres, Jean1 aFerrucci, Luigi1 aFreimer, Nelson, B1 aGieger, Christian1 aGroop, Leif, C1 aGudnason, Vilmundur1 aGyllensten, Ulf1 aHamsten, Anders1 aHarris, Tamara, B1 aHingorani, Aroon1 aHirschhorn, Joel, N1 aHofman, Albert1 aHovingh, Kees1 aHsiung, Chao, Agnes1 aHumphries, Steve, E1 aHunt, Steven, C1 aHveem, Kristian1 aIribarren, Carlos1 aJarvelin, Marjo-Riitta1 aJula, Antti1 aKähönen, Mika1 aKaprio, Jaakko1 aKesäniemi, Antero1 aKivimaki, Mika1 aKooner, Jaspal, S1 aKoudstaal, Peter, J1 aKrauss, Ronald, M1 aKuh, Diana1 aKuusisto, Johanna1 aKyvik, Kirsten, O1 aLaakso, Markku1 aLakka, Timo, A1 aLind, Lars1 aLindgren, Cecilia, M1 aMartin, Nicholas, G1 aMärz, Winfried1 aMcCarthy, Mark, I1 aMcKenzie, Colin, A1 aMeneton, Pierre1 aMetspalu, Andres1 aMoilanen, Leena1 aMorris, Andrew, D1 aMunroe, Patricia, B1 aNjølstad, Inger1 aPedersen, Nancy, L1 aPower, Chris1 aPramstaller, Peter, P1 aPrice, Jackie, F1 aPsaty, Bruce, M1 aQuertermous, Thomas1 aRauramaa, Rainer1 aSaleheen, Danish1 aSalomaa, Veikko1 aSanghera, Dharambir, K1 aSaramies, Jouko1 aSchwarz, Peter, E H1 aSheu, Wayne, H-H1 aShuldiner, Alan, R1 aSiegbahn, Agneta1 aSpector, Tim, D1 aStefansson, Kari1 aStrachan, David, P1 aTayo, Bamidele, O1 aTremoli, Elena1 aTuomilehto, Jaakko1 aUusitupa, Matti1 aDuijn, Cornelia, M1 aVollenweider, Peter1 aWallentin, Lars1 aWareham, Nicholas, J1 aWhitfield, John, B1 aWolffenbuttel, Bruce, H R1 aAltshuler, David1 aOrdovas, Jose, M1 aBoerwinkle, Eric1 aPalmer, Colin, N A1 aThorsteinsdottir, Unnur1 aChasman, Daniel, I1 aRotter, Jerome, I1 aFranks, Paul, W1 aRipatti, Samuli1 aCupples, Adrienne, L1 aSandhu, Manjinder, S1 aRich, Stephen, S1 aBoehnke, Michael1 aDeloukas, Panos1 aMohlke, Karen, L1 aIngelsson, Erik1 aAbecasis, Goncalo, R1 aDaly, Mark, J1 aNeale, Benjamin, M1 aKathiresan, Sekar uhttps://chs-nhlbi.org/node/801410661nas a2203397 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2013 eng d a1546-171800aDiscovery and refinement of loci associated with lipid levels.0 aDiscovery and refinement of loci associated with lipid levels c2013 Nov a1274-12830 v453 aLevels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol are heritable, modifiable risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,577 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5 × 10(-8), including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipid levels are often associated with cardiovascular and metabolic traits, including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio and body mass index. Our results demonstrate the value of using genetic data from individuals of diverse ancestry and provide insights into the biological mechanisms regulating blood lipids to guide future genetic, biological and therapeutic research.
10aAfrican Continental Ancestry Group10aAsian Continental Ancestry Group10aCholesterol, HDL10aCholesterol, LDL10aCoronary Artery Disease10aEuropean Continental Ancestry Group10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aGenotype10aHumans10aLipids10aTriglycerides1 aWiller, Cristen, J1 aSchmidt, Ellen, M1 aSengupta, Sebanti1 aPeloso, Gina, M1 aGustafsson, Stefan1 aKanoni, Stavroula1 aGanna, Andrea1 aChen, Jin1 aBuchkovich, Martin, L1 aMora, Samia1 aBeckmann, Jacques, S1 aBragg-Gresham, Jennifer, L1 aChang, Hsing-Yi1 aDemirkan, Ayse1 aHertog, Heleen, M Den1 aDo, Ron1 aDonnelly, Louise, A1 aEhret, Georg, B1 aEsko, Tõnu1 aFeitosa, Mary, F1 aFerreira, Teresa1 aFischer, Krista1 aFontanillas, Pierre1 aFraser, Ross, M1 aFreitag, Daniel, F1 aGurdasani, Deepti1 aHeikkilä, Kauko1 aHyppönen, Elina1 aIsaacs, Aaron1 aJackson, Anne, U1 aJohansson, Asa1 aJohnson, Toby1 aKaakinen, Marika1 aKettunen, Johannes1 aKleber, Marcus, E1 aLi, Xiaohui1 aLuan, Jian'an1 aLyytikäinen, Leo-Pekka1 aMagnusson, Patrik, K E1 aMangino, Massimo1 aMihailov, Evelin1 aMontasser, May, E1 aMüller-Nurasyid, Martina1 aNolte, Ilja, M1 aO'Connell, Jeffrey, R1 aPalmer, Cameron, D1 aPerola, Markus1 aPetersen, Ann-Kristin1 aSanna, Serena1 aSaxena, Richa1 aService, Susan, K1 aShah, Sonia1 aShungin, Dmitry1 aSidore, Carlo1 aSong, Ci1 aStrawbridge, Rona, J1 aSurakka, Ida1 aTanaka, Toshiko1 aTeslovich, Tanya, M1 aThorleifsson, Gudmar1 avan den Herik, Evita, G1 aVoight, Benjamin, F1 aVolcik, Kelly, A1 aWaite, Lindsay, L1 aWong, Andrew1 aWu, Ying1 aZhang, Weihua1 aAbsher, Devin1 aAsiki, Gershim1 aBarroso, Inês1 aBeen, Latonya, F1 aBolton, Jennifer, L1 aBonnycastle, Lori, L1 aBrambilla, Paolo1 aBurnett, Mary, S1 aCesana, Giancarlo1 aDimitriou, Maria1 aDoney, Alex, S F1 aDöring, Angela1 aElliott, Paul1 aEpstein, Stephen, E1 aEyjolfsson, Gudmundur, Ingi1 aGigante, Bruna1 aGoodarzi, Mark, O1 aGrallert, Harald1 aGravito, Martha, L1 aGroves, Christopher, J1 aHallmans, Göran1 aHartikainen, Anna-Liisa1 aHayward, Caroline1 aHernandez, Dena1 aHicks, Andrew, A1 aHolm, Hilma1 aHung, Yi-Jen1 aIllig, Thomas1 aJones, Michelle, R1 aKaleebu, Pontiano1 aKastelein, John, J P1 aKhaw, Kay-Tee1 aKim, Eric1 aKlopp, Norman1 aKomulainen, Pirjo1 aKumari, Meena1 aLangenberg, Claudia1 aLehtimäki, Terho1 aLin, Shih-Yi1 aLindström, Jaana1 aLoos, Ruth, J F1 aMach, François1 aMcArdle, Wendy, L1 aMeisinger, Christa1 aMitchell, Braxton, D1 aMüller, Gabrielle1 aNagaraja, Ramaiah1 aNarisu, Narisu1 aNieminen, Tuomo, V M1 aNsubuga, Rebecca, N1 aOlafsson, Isleifur1 aOng, Ken, K1 aPalotie, Aarno1 aPapamarkou, Theodore1 aPomilla, Cristina1 aPouta, Anneli1 aRader, Daniel, J1 aReilly, Muredach, P1 aRidker, Paul, M1 aRivadeneira, Fernando1 aRudan, Igor1 aRuokonen, Aimo1 aSamani, Nilesh1 aScharnagl, Hubert1 aSeeley, Janet1 aSilander, Kaisa1 aStančáková, Alena1 aStirrups, Kathleen1 aSwift, Amy, J1 aTiret, Laurence1 aUitterlinden, André, G1 avan Pelt, Joost1 aVedantam, Sailaja1 aWainwright, Nicholas1 aWijmenga, Cisca1 aWild, Sarah, H1 aWillemsen, Gonneke1 aWilsgaard, Tom1 aWilson, James, F1 aYoung, Elizabeth, H1 aZhao, Jing Hua1 aAdair, Linda, S1 aArveiler, Dominique1 aAssimes, Themistocles, L1 aBandinelli, Stefania1 aBennett, Franklyn1 aBochud, Murielle1 aBoehm, Bernhard, O1 aBoomsma, Dorret, I1 aBorecki, Ingrid, B1 aBornstein, Stefan, R1 aBovet, Pascal1 aBurnier, Michel1 aCampbell, Harry1 aChakravarti, Aravinda1 aChambers, John, C1 aChen, Yii-Der Ida1 aCollins, Francis, S1 aCooper, Richard, S1 aDanesh, John1 aDedoussis, George1 ade Faire, Ulf1 aFeranil, Alan, B1 aFerrieres, Jean1 aFerrucci, Luigi1 aFreimer, Nelson, B1 aGieger, Christian1 aGroop, Leif, C1 aGudnason, Vilmundur1 aGyllensten, Ulf1 aHamsten, Anders1 aHarris, Tamara, B1 aHingorani, Aroon1 aHirschhorn, Joel, N1 aHofman, Albert1 aHovingh, Kees1 aHsiung, Chao, Agnes1 aHumphries, Steve, E1 aHunt, Steven, C1 aHveem, Kristian1 aIribarren, Carlos1 aJarvelin, Marjo-Riitta1 aJula, Antti1 aKähönen, Mika1 aKaprio, Jaakko1 aKesäniemi, Antero1 aKivimaki, Mika1 aKooner, Jaspal, S1 aKoudstaal, Peter, J1 aKrauss, Ronald, M1 aKuh, Diana1 aKuusisto, Johanna1 aKyvik, Kirsten, O1 aLaakso, Markku1 aLakka, Timo, A1 aLind, Lars1 aLindgren, Cecilia, M1 aMartin, Nicholas, G1 aMärz, Winfried1 aMcCarthy, Mark, I1 aMcKenzie, Colin, A1 aMeneton, Pierre1 aMetspalu, Andres1 aMoilanen, Leena1 aMorris, Andrew, D1 aMunroe, Patricia, B1 aNjølstad, Inger1 aPedersen, Nancy, L1 aPower, Chris1 aPramstaller, Peter, P1 aPrice, Jackie, F1 aPsaty, Bruce, M1 aQuertermous, Thomas1 aRauramaa, Rainer1 aSaleheen, Danish1 aSalomaa, Veikko1 aSanghera, Dharambir, K1 aSaramies, Jouko1 aSchwarz, Peter, E H1 aSheu, Wayne, H-H1 aShuldiner, Alan, R1 aSiegbahn, Agneta1 aSpector, Tim, D1 aStefansson, Kari1 aStrachan, David, P1 aTayo, Bamidele, O1 aTremoli, Elena1 aTuomilehto, Jaakko1 aUusitupa, Matti1 aDuijn, Cornelia, M1 aVollenweider, Peter1 aWallentin, Lars1 aWareham, Nicholas, J1 aWhitfield, John, B1 aWolffenbuttel, Bruce, H R1 aOrdovas, Jose, M1 aBoerwinkle, Eric1 aPalmer, Colin, N A1 aThorsteinsdottir, Unnur1 aChasman, Daniel, I1 aRotter, Jerome, I1 aFranks, Paul, W1 aRipatti, Samuli1 aCupples, Adrienne, L1 aSandhu, Manjinder, S1 aRich, Stephen, S1 aBoehnke, Michael1 aDeloukas, Panos1 aKathiresan, Sekar1 aMohlke, Karen, L1 aIngelsson, Erik1 aAbecasis, Goncalo, R1 aGlobal Lipids Genetics Consortium uhttps://chs-nhlbi.org/node/615404169nas a2200709 4500008004100000022001400041245026000055210006900315260001300384300001100397490000600408520202600414653001002440653000902450653003102459653001902490653002102509653003002530653003302560653001102593653001702604653003402621653001302655653001102668653002702679653001602706653000902722653001602731653001502747653001802762653003602780653001802816100001802834700002402852700002102876700001702897700002002914700001902934700002502953700001802978700002102996700002203017700002303039700001903062700002003081700002203101700002103123700002103144700002703165700001803192700002503210700002103235700002303256700002103279700001703300700002103317700002003338700002003358700002003378700002503398856003603423 2013 eng d a1942-326800aGenome-wide association study identifies novel loci associated with concentrations of four plasma phospholipid fatty acids in the de novo lipogenesis pathway: results from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortiu0 aGenomewide association study identifies novel loci associated wi c2013 Apr a171-830 v63 aBACKGROUND- Palmitic acid (16:0), stearic acid (18:0), palmitoleic acid (16:1n-7), and oleic acid (18:1n-9) are major saturated and monounsaturated fatty acids that affect cellular signaling and metabolic pathways. They are synthesized via de novo lipogenesis and are the main saturated and monounsaturated fatty acids in the diet. Levels of these fatty acids have been linked to diseases including type 2 diabetes mellitus and coronary heart disease. METHODS AND RESULTS- Genome-wide association studies were conducted in 5 population-based cohorts comprising 8961 participants of European ancestry to investigate the association of common genetic variation with plasma levels of these 4 fatty acids. We identified polymorphisms in 7 novel loci associated with circulating levels of ≥1 of these fatty acids. ALG14 (asparagine-linked glycosylation 14 homolog) polymorphisms were associated with higher 16:0 (P=2.7×10(-11)) and lower 18:0 (P=2.2×10(-18)). FADS1 and FADS2 (desaturases) polymorphisms were associated with higher 16:1n-7 (P=6.6×10(-13)) and 18:1n-9 (P=2.2×10(-32)) and lower 18:0 (P=1.3×10(-20)). LPGAT1 (lysophosphatidylglycerol acyltransferase) polymorphisms were associated with lower 18:0 (P=2.8×10(-9)). GCKR (glucokinase regulator; P=9.8×10(-10)) and HIF1AN (factor inhibiting hypoxia-inducible factor-1; P=5.7×10(-9)) polymorphisms were associated with higher 16:1n-7, whereas PKD2L1 (polycystic kidney disease 2-like 1; P=5.7×10(-15)) and a locus on chromosome 2 (not near known genes) were associated with lower 16:1n-7 (P=4.1×10(-8)). CONCLUSIONS- Our findings provide novel evidence that common variations in genes with diverse functions, including protein-glycosylation, polyunsaturated fatty acid metabolism, phospholipid modeling, and glucose- and oxygen-sensing pathways, are associated with circulating levels of 4 fatty acids in the de novo lipogenesis pathway. These results expand our knowledge of genetic factors relevant to de novo lipogenesis and fatty acid biology.
10aAdult10aAged10aChromosomes, Human, Pair 210aCohort Studies10aCoronary Disease10aDiabetes Mellitus, Type 210aFatty Acids, Monounsaturated10aFemale10aGenetic Loci10aGenome-Wide Association Study10aGenotype10aHumans10aLinkage Disequilibrium10aLipogenesis10aMale10aMiddle Aged10aOleic Acid10aPalmitic Acid10aPolymorphism, Single Nucleotide10aStearic Acids1 aH Y Wu, Jason1 aLemaitre, Rozenn, N1 aManichaikul, Ani1 aGuan, Weihua1 aTanaka, Toshiko1 aFoy, Millennia1 aKabagambe, Edmond, K1 aDjoussé, Luc1 aSiscovick, David1 aFretts, Amanda, M1 aJohnson, Catherine1 aKing, Irena, B1 aPsaty, Bruce, M1 aMcKnight, Barbara1 aRich, Stephen, S1 aChen, Yii-der, I1 aNettleton, Jennifer, A1 aTang, Weihong1 aBandinelli, Stefania1 aJacobs, David, R1 aBrowning, Brian, L1 aLaurie, Cathy, C1 aGu, Xiangjun1 aTsai, Michael, Y1 aSteffen, Lyn, M1 aFerrucci, Luigi1 aFornage, Myriam1 aMozaffarian, Dariush uhttps://chs-nhlbi.org/node/588005943nas a2201657 4500008004100000022001400041245014100055210006900196260001600265300001100281490000700292520126000299653005101559653001001610653003901620653000901659653001201668653001201680653002101692653002101713653001901734653002101753653004001774653001101814653001901825653003201844653001701876653002201893653001101915653001801926653000901944653000901953653002301962653003601985653001602021653001402037653002702051653001602078653001802094100002002112700001802132700001902150700001902169700002402188700002402212700002302236700002502259700002002284700002002304700001802324700002602342700002102368700001702389700002502406700002102431700001702452700001702469700001802486700002102504700002002525700001802545700001902563700002302582700001602605700001902621700002502640700002402665700002202689700002202711700002602733700002402759700002502783700002602808700002102834700001902855700002802874700002202902700002002924700002602944700002502970700002502995700001903020700002103039700002303060700001803083700001803101700002003119700002403139700001903163700002503182700002003207700001803227700002903245700002003274700001503294700002103309700002203330700002303352700001903375700002203394700001603416700002103432700002403453700002003477700001803497700002103515700001903536700001503555700002103570700002203591700001903613700002503632700002103657700001903678700001603697700002403713700002203737700002103759700001703780700001903797700002503816700002203841700001903863700001803882700002303900700001703923700002403940700002103964700002303985700002404008700002104032700002004053700002204073700003004095700001804125700002104143700002204164700002504186710003804211856003604249 2014 eng d a1537-660500aAssociation of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks.0 aAssociation of lowfrequency and rare codingsequence variants wit c2014 Feb 06 a223-320 v943 aLow-frequency coding DNA sequence variants in the proprotein convertase subtilisin/kexin type 9 gene (PCSK9) lower plasma low-density lipoprotein cholesterol (LDL-C), protect against risk of coronary heart disease (CHD), and have prompted the development of a new class of therapeutics. It is uncertain whether the PCSK9 example represents a paradigm or an isolated exception. We used the "Exome Array" to genotype >200,000 low-frequency and rare coding sequence variants across the genome in 56,538 individuals (42,208 European ancestry [EA] and 14,330 African ancestry [AA]) and tested these variants for association with LDL-C, high-density lipoprotein cholesterol (HDL-C), and triglycerides. Although we did not identify new genes associated with LDL-C, we did identify four low-frequency (frequencies between 0.1% and 2%) variants (ANGPTL8 rs145464906 [c.361C>T; p.Gln121*], PAFAH1B2 rs186808413 [c.482C>T; p.Ser161Leu], COL18A1 rs114139997 [c.331G>A; p.Gly111Arg], and PCSK7 rs142953140 [c.1511G>A; p.Arg504His]) with large effects on HDL-C and/or triglycerides. None of these four variants was associated with risk for CHD, suggesting that examples of low-frequency coding variants with robust effects on both lipids and CHD will be limited.
10a1-Alkyl-2-acetylglycerophosphocholine Esterase10aAdult10aAfrican Continental Ancestry Group10aAged10aAlleles10aAnimals10aCholesterol, HDL10aCholesterol, LDL10aCohort Studies10aCoronary Disease10aEuropean Continental Ancestry Group10aFemale10aGene Frequency10aGenetic Association Studies10aGenetic Code10aGenetic Variation10aHumans10aLinear Models10aMale10aMice10aMice, Inbred C57BL10aMicrotubule-Associated Proteins10aMiddle Aged10aPhenotype10aSequence Analysis, DNA10aSubtilisins10aTriglycerides1 aPeloso, Gina, M1 aAuer, Paul, L1 aBis, Joshua, C1 aVoorman, Arend1 aMorrison, Alanna, C1 aStitziel, Nathan, O1 aBrody, Jennifer, A1 aKhetarpal, Sumeet, A1 aCrosby, Jacy, R1 aFornage, Myriam1 aIsaacs, Aaron1 aJakobsdottir, Johanna1 aFeitosa, Mary, F1 aDavies, Gail1 aHuffman, Jennifer, E1 aManichaikul, Ani1 aDavis, Brian1 aLohman, Kurt1 aJoon, Aron, Y1 aSmith, Albert, V1 aGrove, Megan, L1 aZanoni, Paolo1 aRedon, Valeska1 aDemissie, Serkalem1 aLawson, Kim1 aPeters, Ulrike1 aCarlson, Christopher1 aJackson, Rebecca, D1 aRyckman, Kelli, K1 aMackey, Rachel, H1 aRobinson, Jennifer, G1 aSiscovick, David, S1 aSchreiner, Pamela, J1 aMychaleckyj, Josyf, C1 aPankow, James, S1 aHofman, Albert1 aUitterlinden, André, G1 aHarris, Tamara, B1 aTaylor, Kent, D1 aStafford, Jeanette, M1 aReynolds, Lindsay, M1 aMarioni, Riccardo, E1 aDehghan, Abbas1 aFranco, Oscar, H1 aPatel, Aniruddh, P1 aLu, Yingchang1 aHindy, George1 aGottesman, Omri1 aBottinger, Erwin, P1 aMelander, Olle1 aOrho-Melander, Marju1 aLoos, Ruth, J F1 aDuga, Stefano1 aMerlini, Piera, Angelica1 aFarrall, Martin1 aGoel, Anuj1 aAsselta, Rosanna1 aGirelli, Domenico1 aMartinelli, Nicola1 aShah, Svati, H1 aKraus, William, E1 aLi, Mingyao1 aRader, Daniel, J1 aReilly, Muredach, P1 aMcPherson, Ruth1 aWatkins, Hugh1 aArdissino, Diego1 aZhang, Qunyuan1 aWang, Judy1 aTsai, Michael, Y1 aTaylor, Herman, A1 aCorrea, Adolfo1 aGriswold, Michael, E1 aLange, Leslie, A1 aStarr, John, M1 aRudan, Igor1 aEiriksdottir, Gudny1 aLauner, Lenore, J1 aOrdovas, Jose, M1 aLevy, Daniel1 aChen, Y-D, Ida1 aReiner, Alexander, P1 aHayward, Caroline1 aPolasek, Ozren1 aDeary, Ian, J1 aBorecki, Ingrid, B1 aLiu, Yongmei1 aGudnason, Vilmundur1 aWilson, James, G1 aDuijn, Cornelia, M1 aKooperberg, Charles1 aRich, Stephen, S1 aPsaty, Bruce, M1 aRotter, Jerome, I1 aO'Donnell, Christopher, J1 aRice, Kenneth1 aBoerwinkle, Eric1 aKathiresan, Sekar1 aCupples, Adrienne, L1 aNHLBI GO Exome Sequencing Project uhttps://chs-nhlbi.org/node/659003165nas a2200505 4500008004100000022001400041245011400055210006900169260001300238300001000251490000800261520171800269653001001987653000901997653002802006653001802034653001302052653001102065653004302076653001502119653003202134653001302166653001102179653000902190653001602199653004402215653003602259653001602295100001802311700001802329700002102347700002302368700001902391700003002410700001702440700002502457700002302482700002202505700002302527700001802550700001602568700002102584700001802605856003602623 2014 eng d a1879-247200aA genetic association study of D-dimer levels with 50K SNPs from a candidate gene chip in four ethnic groups.0 agenetic association study of Ddimer levels with 50K SNPs from a c2014 Aug a462-70 v1343 aINTRODUCTION: D-dimer, a fibrin degradation product, is related to risk of cardiovascular disease and venous thromboembolism. Genetic determinants of D-dimer are not well characterized; notably, few data have been reported for African American (AA), Asian, and Hispanic populations.
MATERIALS AND METHODS: We conducted a large-scale candidate gene association study to identify variants in genes associated with D-dimer levels in multi-ethnic populations. Four cohorts, comprising 6,848 European Americans (EAs), 2,192 AAs, 670 Asians, and 1,286 Hispanics in the National Heart, Lung, and Blood Institute Candidate Gene Association Resource consortium, were assembled. Approximately 50,000 genotyped single nucleotide polymorphisms (SNPs) in 2,000 cardiovascular disease gene loci were analyzed by linear regression, adjusting for age, sex, study site, and principal components in each cohort and ethnic group. Results across studies were combined within each ethnic group by meta-analysis.
RESULTS: Twelve SNPs in coagulation factor V (F5) and 3 SNPs in the fibrinogen alpha chain (FGA) were significantly associated with D-dimer level in EAs with p<2.0×10(-6). The signal for the most associated SNP in F5 (rs6025, factor V Leiden) was replicated in Hispanics (p=0.023), while that for the top functional SNP in FGA (rs6050) was replicated in AAs (p=0.006). No additional SNPs were significantly associated with D-dimer.
CONCLUSIONS: Our study replicated previously reported associations of D-dimer with SNPs in F5 and FGA in EAs; we demonstrated replication of the association of D-dimer with FGA rs6050 in AAs and the factor V Leiden variant in Hispanics.
10aAdult10aAged10aCardiovascular Diseases10aEthnic Groups10aFactor V10aFemale10aFibrin Fibrinogen Degradation Products10aFibrinogen10aGenetic Association Studies10aGenotype10aHumans10aMale10aMiddle Aged10aOligonucleotide Array Sequence Analysis10aPolymorphism, Single Nucleotide10aYoung Adult1 aWeng, Lu-Chen1 aTang, Weihong1 aRich, Stephen, S1 aSmith, Nicholas, L1 aRedline, Susan1 aO'Donnell, Christopher, J1 aBasu, Saonli1 aReiner, Alexander, P1 aDelaney, Joseph, A1 aTracy, Russell, P1 aPalmer, Cameron, D1 aYoung, Taylor1 aYang, Qiong1 aFolsom, Aaron, R1 aCushman, Mary uhttps://chs-nhlbi.org/node/661107147nas a2202257 4500008004100000022001400041245010000055210006900155260001300224300001100237490000700248520092000255653001901175653002301194653002201217653002901239653001701268653003801285653001801323653003401341653001101375653001801386653002701404653003601431653001401467653002801481653003101509653001501540653001901555100001801574700002601592700002001618700002001638700002301658700001601681700002301697700002601720700001501746700002101761700002001782700002301802700001901825700002501844700002201869700002601891700002501917700001901942700001801961700001901979700002001998700002202018700001802040700002002058700001702078700002402095700003102119700001902150700001902169700002002188700001502208700001802223700002202241700002002263700002002283700001202303700001902315700001602334700002102350700001902371700002202390700001902412700002402431700001602455700001702471700001902488700002002507700002402527700002002551700001802571700001802589700002102607700002702628700001702655700001702672700002702689700001902716700002202735700001702757700002002774700002102794700001802815700001902833700001902852700002002871700002602891700001902917700001902936700002402955700002402979700002203003700001303025700002203038700002003060700002103080700001903101700001903120700001803139700002003157700002003177700001703197700002503214700002803239700002003267700002303287700002203310700001503332700001803347700002303365700001603388700002603404700001703430700001603447700001703463700001903480700002303499700001903522700002003541700002403561700002003585700002103605700002103626700002103647700002203668700001903690700002003709700002103729700002203750700001903772700002003791700002003811700002203831700001803853700002803871700002403899700001703923700002803940700002403968700002103992700001904013700001904032700002204051700001804073700002204091700001904113700001804132700002304150700002504173700002004198700001904218700002404237700001904261700001604280700001904296700002204315700001804337700001904355700001704374700002704391700002104418700001504439700002304454700002204477700002404499700001704523700002504540700002104565700001704586700001904603700002204622700001704644700002404661700002504685700001704710700002204727700001904749700001704768700002204785700002104807700002504828856003604853 2014 eng d a1546-171800aGenome-wide association analysis identifies six new loci associated with forced vital capacity.0 aGenomewide association analysis identifies six new loci associat c2014 Jul a669-770 v463 aForced vital capacity (FVC), a spirometric measure of pulmonary function, reflects lung volume and is used to diagnose and monitor lung diseases. We performed genome-wide association study meta-analysis of FVC in 52,253 individuals from 26 studies and followed up the top associations in 32,917 additional individuals of European ancestry. We found six new regions associated at genome-wide significance (P < 5 × 10(-8)) with FVC in or near EFEMP1, BMP6, MIR129-2-HSD17B12, PRDM11, WWOX and KCNJ2. Two loci previously associated with spirometric measures (GSTCD and PTCH1) were related to FVC. Newly implicated regions were followed up in samples from African-American, Korean, Chinese and Hispanic individuals. We detected transcripts for all six newly implicated genes in human lung tissue. The new loci may inform mechanisms involved in lung development and the pathogenesis of restrictive lung disease.
10aCohort Studies10aDatabases, Genetic10aFollow-Up Studies10aForced Expiratory Volume10aGenetic Loci10aGenetic Predisposition to Disease10aGenome, Human10aGenome-Wide Association Study10aHumans10aLung Diseases10aMeta-Analysis as Topic10aPolymorphism, Single Nucleotide10aPrognosis10aQuantitative Trait Loci10aRespiratory Function Tests10aSpirometry10aVital Capacity1 aLoth, Daan, W1 aArtigas, Maria, Soler1 aGharib, Sina, A1 aWain, Louise, V1 aFranceschini, Nora1 aKoch, Beate1 aPottinger, Tess, D1 aSmith, Albert, Vernon1 aDuan, Qing1 aOldmeadow, Chris1 aLee, Mi, Kyeong1 aStrachan, David, P1 aJames, Alan, L1 aHuffman, Jennifer, E1 aVitart, Veronique1 aRamasamy, Adaikalavan1 aWareham, Nicholas, J1 aKaprio, Jaakko1 aWang, Xin-Qun1 aTrochet, Holly1 aKähönen, Mika1 aFlexeder, Claudia1 aAlbrecht, Eva1 aLopez, Lorna, M1 ade Jong, Kim1 aThyagarajan, Bharat1 aAlves, Alexessander, Couto1 aEnroth, Stefan1 aOmenaas, Ernst1 aJoshi, Peter, K1 aFall, Tove1 aViñuela, Ana1 aLauner, Lenore, J1 aLoehr, Laura, R1 aFornage, Myriam1 aLi, Guo1 aWilk, Jemma, B1 aTang, Wenbo1 aManichaikul, Ani1 aLahousse, Lies1 aHarris, Tamara, B1 aNorth, Kari, E1 aRudnicka, Alicja, R1 aHui, Jennie1 aGu, Xiangjun1 aLumley, Thomas1 aWright, Alan, F1 aHastie, Nicholas, D1 aCampbell, Susan1 aKumar, Rajesh1 aPin, Isabelle1 aScott, Robert, A1 aPietiläinen, Kirsi, H1 aSurakka, Ida1 aLiu, Yongmei1 aHolliday, Elizabeth, G1 aSchulz, Holger1 aHeinrich, Joachim1 aDavies, Gail1 aVonk, Judith, M1 aWojczynski, Mary1 aPouta, Anneli1 aJohansson, Asa1 aWild, Sarah, H1 aIngelsson, Erik1 aRivadeneira, Fernando1 aVölzke, Henry1 aHysi, Pirro, G1 aEiriksdottir, Gudny1 aMorrison, Alanna, C1 aRotter, Jerome, I1 aGao, Wei1 aPostma, Dirkje, S1 aWhite, Wendy, B1 aRich, Stephen, S1 aHofman, Albert1 aAspelund, Thor1 aCouper, David1 aSmith, Lewis, J1 aPsaty, Bruce, M1 aLohman, Kurt1 aBurchard, Esteban, G1 aUitterlinden, André, G1 aGarcia, Melissa1 aJoubert, Bonnie, R1 aMcArdle, Wendy, L1 aMusk, Bill1 aHansel, Nadia1 aHeckbert, Susan, R1 aZgaga, Lina1 avan Meurs, Joyce, B J1 aNavarro, Pau1 aRudan, Igor1 aOh, Yeon-Mok1 aRedline, Susan1 aJarvis, Deborah, L1 aZhao, Jing Hua1 aRantanen, Taina1 aO'Connor, George, T1 aRipatti, Samuli1 aScott, Rodney, J1 aKarrasch, Stefan1 aGrallert, Harald1 aGaddis, Nathan, C1 aStarr, John, M1 aWijmenga, Cisca1 aMinster, Ryan, L1 aLederer, David, J1 aPekkanen, Juha1 aGyllensten, Ulf1 aCampbell, Harry1 aMorris, Andrew, P1 aGläser, Sven1 aHammond, Christopher, J1 aBurkart, Kristin, M1 aBeilby, John1 aKritchevsky, Stephen, B1 aGudnason, Vilmundur1 aHancock, Dana, B1 aWilliams, Dale1 aPolasek, Ozren1 aZemunik, Tatijana1 aKolcic, Ivana1 aPetrini, Marcy, F1 aWjst, Matthias1 aKim, Woo, Jin1 aPorteous, David, J1 aScotland, Generation1 aSmith, Blair, H1 aViljanen, Anne1 aHeliövaara, Markku1 aAttia, John, R1 aSayers, Ian1 aHampel, Regina1 aGieger, Christian1 aDeary, Ian, J1 aBoezen, Marike1 aNewman, Anne1 aJarvelin, Marjo-Riitta1 aWilson, James, F1 aLind, Lars1 aStricker, Bruno, H1 aTeumer, Alexander1 aSpector, Timothy, D1 aMelén, Erik1 aPeters, Marjolein, J1 aLange, Leslie, A1 aBarr, Graham1 aBracke, Ken, R1 aVerhamme, Fien, M1 aSung, Joohon1 aHiemstra, Pieter, S1 aCassano, Patricia, A1 aSood, Akshay1 aHayward, Caroline1 aDupuis, Josée1 aHall, Ian, P1 aBrusselle, Guy, G1 aTobin, Martin, D1 aLondon, Stephanie, J uhttps://chs-nhlbi.org/node/658203759nas a2200649 4500008004100000022001400041245015900055210006900214260001300283300001200296490000600308520187100314653001002185653000902195653002202204653001002226653003202236653003202268653003102300653002702331653002502358653001102383653003402394653001302428653001902441653001102460653000902471653001602480653003602496653002402532653002702556100001702583700002202600700002402622700001802646700002002664700002102684700001902705700002102724700001302745700002702758700001802785700001702803700002502820700001902845700002202864700002002886700002102906700001802927700002202945700002002967700002002987700002503007700002103032700002003053856003603073 2014 eng d a1942-326800aGenome-wide association study of plasma N6 polyunsaturated fatty acids within the cohorts for heart and aging research in genomic epidemiology consortium.0 aGenomewide association study of plasma N6 polyunsaturated fatty c2014 Jun a321-3310 v73 aBACKGROUND: Omega6 (n6) polyunsaturated fatty acids (PUFAs) and their metabolites are involved in cell signaling, inflammation, clot formation, and other crucial biological processes. Genetic components, such as variants of fatty acid desaturase (FADS) genes, determine the composition of n6 PUFAs.
METHODS AND RESULTS: To elucidate undiscovered biological pathways that may influence n6 PUFA composition, we conducted genome-wide association studies and meta-analyses of associations of common genetic variants with 6 plasma n6 PUFAs in 8631 white adults (55% women) across 5 prospective studies. Plasma phospholipid or total plasma fatty acids were analyzed by similar gas chromatography techniques. The n6 fatty acids linoleic acid (LA), γ-linolenic acid (GLA), dihomo-GLA, arachidonic acid, and adrenic acid were expressed as percentage of total fatty acids. We performed linear regression with robust SEs to test for single-nucleotide polymorphism-fatty acid associations, with pooling using inverse-variance-weighted meta-analysis. Novel regions were identified on chromosome 10 associated with LA (rs10740118; P=8.1×10(-9); near NRBF2), on chromosome 16 with LA, GLA, dihomo-GLA, and arachidonic acid (rs16966952; P=1.2×10(-15), 5.0×10(-11), 7.6×10(-65), and 2.4×10(-10), respectively; NTAN1), and on chromosome 6 with adrenic acid after adjustment for arachidonic acid (rs3134950; P=2.1×10(-10); AGPAT1). We confirmed previous findings of the FADS cluster on chromosome 11 with LA and arachidonic acid, and further observed novel genome-wide significant association of this cluster with GLA, dihomo-GLA, and adrenic acid (P=2.3×10(-72), 2.6×10(-151), and 6.3×10(-140), respectively).
CONCLUSIONS: Our findings suggest that along with the FADS gene cluster, additional genes may influence n6 PUFA composition.
10aAdult10aAged10aAged, 80 and over10aAging10aChromosomes, Human, Pair 1010aChromosomes, Human, Pair 1610aChromosomes, Human, Pair 610aFatty Acid Desaturases10aFatty Acids, Omega-610aFemale10aGenome-Wide Association Study10aGenomics10aHeart Diseases10aHumans10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide10aProspective Studies10aSequence Analysis, DNA1 aGuan, Weihua1 aSteffen, Brian, T1 aLemaitre, Rozenn, N1 aH Y Wu, Jason1 aTanaka, Toshiko1 aManichaikul, Ani1 aFoy, Millennia1 aRich, Stephen, S1 aWang, Lu1 aNettleton, Jennifer, A1 aTang, Weihong1 aGu, Xiangjun1 aBandinelli, Stafania1 aKing, Irena, B1 aMcKnight, Barbara1 aPsaty, Bruce, M1 aSiscovick, David1 aDjoussé, Luc1 aChen, Yii-Der Ida1 aFerrucci, Luigi1 aFornage, Myriam1 aMozafarrian, Dariush1 aTsai, Michael, Y1 aSteffen, Lyn, M uhttps://chs-nhlbi.org/node/656705734nas a2201333 4500008004100000022001400041245007800055210006900133260001500202300001000217490000800227520196900235653003902204653002502243653002102268653004002289653001002329653001302339653001702352653001102369653001002380653001302390653001702403653002702420653001802447110010402465700001702569700002002586700001802606700002302624700002402647700002102671700001802692700002202710700001402732700001802746700002002764700002302784700002202807700001202829700001302841700001402854700002002868700001602888700001502904700002002919700001802939700002802957700002102985700002203006700002303028700002303051700001203074700001903086700002503105700002103130700002003151700002303171700001803194700002303212700002903235700002303264700002303287700001903310700002003329700001903349700001903368700002203387700002403409700002403433700002303457700002203480700001703502700002203519700002003541700001903561700001903580700002003599700001903619700002603638700002003664700002403684700003003708700002003738700002803758700002203786700002103808700001903829700001803848700002503866700002103891700002003912700002003932700002303952700002203975700002203997700002204019700002004041700002404061700002104085700002404106700002104130700002204151700001604173700002104189700002004210700002604230700002004256700002504276700002004301700002104321700002204342856003604364 2014 eng d a1533-440600aLoss-of-function mutations in APOC3, triglycerides, and coronary disease.0 aLossoffunction mutations in APOC3 triglycerides and coronary dis c2014 Jul 3 a22-310 v3713 aBACKGROUND: Plasma triglyceride levels are heritable and are correlated with the risk of coronary heart disease. Sequencing of the protein-coding regions of the human genome (the exome) has the potential to identify rare mutations that have a large effect on phenotype.
METHODS: We sequenced the protein-coding regions of 18,666 genes in each of 3734 participants of European or African ancestry in the Exome Sequencing Project. We conducted tests to determine whether rare mutations in coding sequence, individually or in aggregate within a gene, were associated with plasma triglyceride levels. For mutations associated with triglyceride levels, we subsequently evaluated their association with the risk of coronary heart disease in 110,970 persons.
RESULTS: An aggregate of rare mutations in the gene encoding apolipoprotein C3 (APOC3) was associated with lower plasma triglyceride levels. Among the four mutations that drove this result, three were loss-of-function mutations: a nonsense mutation (R19X) and two splice-site mutations (IVS2+1G→A and IVS3+1G→T). The fourth was a missense mutation (A43T). Approximately 1 in 150 persons in the study was a heterozygous carrier of at least one of these four mutations. Triglyceride levels in the carriers were 39% lower than levels in noncarriers (P<1×10(-20)), and circulating levels of APOC3 in carriers were 46% lower than levels in noncarriers (P=8×10(-10)). The risk of coronary heart disease among 498 carriers of any rare APOC3 mutation was 40% lower than the risk among 110,472 noncarriers (odds ratio, 0.60; 95% confidence interval, 0.47 to 0.75; P=4×10(-6)).
CONCLUSIONS: Rare mutations that disrupt APOC3 function were associated with lower levels of plasma triglycerides and APOC3. Carriers of these mutations were found to have a reduced risk of coronary heart disease. (Funded by the National Heart, Lung, and Blood Institute and others.).
10aAfrican Continental Ancestry Group10aApolipoprotein C-III10aCoronary Disease10aEuropean Continental Ancestry Group10aExome10aGenotype10aHeterozygote10aHumans10aLiver10aMutation10aRisk Factors10aSequence Analysis, DNA10aTriglycerides1 aTG and HDL Working Group of the Exome Sequencing Project, National Heart, Lung, and Blood Institute1 aCrosby, Jacy1 aPeloso, Gina, M1 aAuer, Paul, L1 aCrosslin, David, R1 aStitziel, Nathan, O1 aLange, Leslie, A1 aLu, Yingchang1 aTang, Zheng-Zheng1 aZhang, He1 aHindy, George1 aMasca, Nicholas1 aStirrups, Kathleen1 aKanoni, Stavroula1 aDo, Ron1 aJun, Goo1 aHu, Youna1 aKang, Hyun, Min1 aXue, Chenyi1 aGoel, Anuj1 aFarrall, Martin1 aDuga, Stefano1 aMerlini, Pier, Angelica1 aAsselta, Rosanna1 aGirelli, Domenico1 aOlivieri, Oliviero1 aMartinelli, Nicola1 aYin, Wu1 aReilly, Dermot1 aSpeliotes, Elizabeth1 aFox, Caroline, S1 aHveem, Kristian1 aHolmen, Oddgeir, L1 aNikpay, Majid1 aFarlow, Deborah, N1 aAssimes, Themistocles, L1 aFranceschini, Nora1 aRobinson, Jennifer1 aNorth, Kari, E1 aMartin, Lisa, W1 aDePristo, Mark1 aGupta, Namrata1 aEscher, Stefan, A1 aJansson, Jan-Håkan1 aVan Zuydam, Natalie1 aPalmer, Colin, N A1 aWareham, Nicholas1 aKoch, Werner1 aMeitinger, Thomas1 aPeters, Annette1 aLieb, Wolfgang1 aErbel, Raimund1 aKönig, Inke, R1 aKruppa, Jochen1 aDegenhardt, Franziska1 aGottesman, Omri1 aBottinger, Erwin, P1 aO'Donnell, Christopher, J1 aPsaty, Bruce, M1 aBallantyne, Christie, M1 aAbecasis, Goncalo1 aOrdovas, Jose, M1 aMelander, Olle1 aWatkins, Hugh1 aOrho-Melander, Marju1 aArdissino, Diego1 aLoos, Ruth, J F1 aMcPherson, Ruth1 aWiller, Cristen, J1 aErdmann, Jeanette1 aHall, Alistair, S1 aSamani, Nilesh, J1 aDeloukas, Panos1 aSchunkert, Heribert1 aWilson, James, G1 aKooperberg, Charles1 aRich, Stephen, S1 aTracy, Russell, P1 aLin, Dan-Yu1 aAltshuler, David1 aGabriel, Stacey1 aNickerson, Deborah, A1 aJarvik, Gail, P1 aCupples, Adrienne, L1 aReiner, Alex, P1 aBoerwinkle, Eric1 aKathiresan, Sekar uhttps://chs-nhlbi.org/node/660505814nas a2201345 4500008004100000022001400041245014200055210006900197260001300266300001300279490000700292520193700299653002202236653003002258653003402288653002002322653001802342653001102360653002802371653002902399653003602428653004202464100001902506700002002525700001902545700001402564700001802578700001702596700001802613700002602631700001902657700002202676700002002698700003102718700002302749700002102772700001602793700001302809700002102822700001802843700001702861700002402878700002302902700001802925700002402943700002602967700001902993700002303012700002003035700002303055700002603078700001503104700002203119700002203141700001903163700002203182700001903204700001603223700001803239700002103257700002103278700002303299700001803322700001803340700002303358700002203381700002503403700002303428700001903451700001903470700002503489700002403514700002203538700001803560700001703578700001903595700001403614700002403628700001603652700002503668700002403693700002003717700001603737700001503753700002103768700002103789700002503810700002203835700002003857700002103877700002003898700001703918700002003935700002303955700002403978700002404002700002004026700002004046700002204066700002204088700002104110700002004131700002104151700002204172700002204194700001504216700002304231700002204254710002004276710002204296710002304318710002204341710006904363856003604432 2014 eng d a1553-740400aMeta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes.0 aMetaanalysis of genomewide association studies in African Americ c2014 Aug ae10045170 v103 aType 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94)
10aAfrican Americans10aDiabetes Mellitus, Type 210aGenome-Wide Association Study10aHLA-B27 Antigen10aHMGA2 Protein10aHumans10aKCNQ1 Potassium Channel10aMutant Chimeric Proteins10aPolymorphism, Single Nucleotide10aTranscription Factor 7-Like 2 Protein1 aC Y Ng, Maggie1 aShriner, Daniel1 aChen, Brian, H1 aLi, Jiang1 aChen, Wei-Min1 aGuo, Xiuqing1 aLiu, Jiankang1 aBielinski, Suzette, J1 aYanek, Lisa, R1 aNalls, Michael, A1 aComeau, Mary, E1 aRasmussen-Torvik, Laura, J1 aJensen, Richard, A1 aEvans, Daniel, S1 aSun, Yan, V1 aAn, Ping1 aPatel, Sanjay, R1 aLu, Yingchang1 aLong, Jirong1 aArmstrong, Loren, L1 aWagenknecht, Lynne1 aYang, Lingyao1 aSnively, Beverly, M1 aPalmer, Nicholette, D1 aMudgal, Poorva1 aLangefeld, Carl, D1 aKeene, Keith, L1 aFreedman, Barry, I1 aMychaleckyj, Josyf, C1 aNayak, Uma1 aRaffel, Leslie, J1 aGoodarzi, Mark, O1 aChen, Y-D, Ida1 aTaylor, Herman, A1 aCorrea, Adolfo1 aSims, Mario1 aCouper, David1 aPankow, James, S1 aBoerwinkle, Eric1 aAdeyemo, Adebowale1 aDoumatey, Ayo1 aChen, Guanjie1 aMathias, Rasika, A1 aVaidya, Dhananjay1 aSingleton, Andrew, B1 aZonderman, Alan, B1 aIgo, Robert, P1 aSedor, John, R1 aKabagambe, Edmond, K1 aSiscovick, David, S1 aMcKnight, Barbara1 aRice, Kenneth1 aLiu, Yongmei1 aHsueh, Wen-Chi1 aZhao, Wei1 aBielak, Lawrence, F1 aKraja, Aldi1 aProvince, Michael, A1 aBottinger, Erwin, P1 aGottesman, Omri1 aCai, Qiuyin1 aZheng, Wei1 aBlot, William, J1 aLowe, William, L1 aPacheco, Jennifer, A1 aCrawford, Dana, C1 aGrundberg, Elin1 aRich, Stephen, S1 aHayes, Geoffrey1 aShu, Xiao-Ou1 aLoos, Ruth, J F1 aBorecki, Ingrid, B1 aPeyser, Patricia, A1 aCummings, Steven, R1 aPsaty, Bruce, M1 aFornage, Myriam1 aIyengar, Sudha, K1 aEvans, Michele, K1 aBecker, Diane, M1 aKao, Linda, W H1 aWilson, James, G1 aRotter, Jerome, I1 aSale, Michèle, M1 aLiu, Simin1 aRotimi, Charles, N1 aBowden, Donald, W1 aFIND Consortium1 aeMERGE Consortium1 aDIAGRAM Consortium1 aMuTHER Consortium1 aMEta-analysis of type 2 DIabetes in African Americans Consortium uhttps://chs-nhlbi.org/node/658503227nas a2200505 4500008004100000022001400041245012600055210006900181260001600250300001200266490000700278520166100285653003501946653002301981653004002004653001002044653001102054653003202065653002102097653002602118100002002144700002002164700002302184700002402207700002902231700001802260700002502278700001902303700002602322700002302348700001702371700002202388700002202410700002002432700002402452700002502476700003002501700002502531700002102556700002002577700002402597700002602621710003802647856003602685 2014 eng d a1460-208300aQuantifying rare, deleterious variation in 12 human cytochrome P450 drug-metabolism genes in a large-scale exome dataset.0 aQuantifying rare deleterious variation in 12 human cytochrome P4 c2014 Apr 15 a1957-630 v233 a
The study of genetic influences on drug response and efficacy ('pharmacogenetics') has existed for over 50 years. Yet, we still lack a complete picture of how genetic variation, both common and rare, affects each individual's responses to medications. Exome sequencing is a promising alternative method for pharmacogenetic discovery as it provides information on both common and rare variation in large numbers of individuals. Using exome data from 2203 AA and 4300 Caucasian individuals through the NHLBI Exome Sequencing Project, we conducted a survey of coding variation within 12 Cytochrome P450 (CYP) genes that are collectively responsible for catalyzing nearly 75% of all known Phase I drug oxidation reactions. In addition to identifying many polymorphisms with known pharmacogenetic effects, we discovered over 730 novel nonsynonymous alleles across the 12 CYP genes of interest. These alleles include many with diverse functional effects such as premature stop codons, aberrant splicesites and mutations at conserved active site residues. Our analysis considering both novel, predicted functional alleles as well as known, actionable CYP alleles reveals that rare, deleterious variation contributes markedly to the overall burden of pharmacogenetic alleles within the populations considered, and that the contribution of rare variation to this burden is over three times greater in AA individuals as compared with Caucasians. While most of these impactful alleles are individually rare, 7.6-11.7% of individuals interrogated in the study carry at least one newly described potentially deleterious alleles in a major drug-metabolizing CYP.
10aCytochrome P-450 Enzyme System10aDatabases, Genetic10aEuropean Continental Ancestry Group10aExome10aHumans10aPharmaceutical Preparations10aPharmacogenetics10aPolymorphism, Genetic1 aGordon, Adam, S1 aTabor, Holly, K1 aJohnson, Andrew, D1 aSnively, Beverly, M1 aAssimes, Themistocles, L1 aAuer, Paul, L1 aIoannidis, John, P A1 aPeters, Ulrike1 aRobinson, Jennifer, G1 aSucheston, Lara, E1 aWang, Danxin1 aSotoodehnia, Nona1 aRotter, Jerome, I1 aPsaty, Bruce, M1 aJackson, Rebecca, D1 aHerrington, David, M1 aO'Donnell, Christopher, J1 aReiner, Alexander, P1 aRich, Stephen, S1 aRieder, Mark, J1 aBamshad, Michael, J1 aNickerson, Deborah, A1 aNHLBI GO Exome Sequencing Project uhttps://chs-nhlbi.org/node/656504324nas a2200793 4500008004100000022001400041245017800055210006900233260001300302300001100315490000600326520200100332653001002333653000902343653002202352653001002374653001902384653001102403653002202414653003402436653001302470653002802483653001902511653001102530653000902541653001602550653004002566653003602606653002702642100002202669700002302691700001902714700001902733700001902752700001702771700001802788700002402806700001602830700002002846700001902866700001902885700002302904700001902927700002402946700002502970700002202995700002403017700001903041700002903060700003003089700002403119700002403143700002003167700002203187700002203209700002203231700002203253700002103275700002003296700002103316700002103337700002103358700002003379700002203399710002203421710004103443710001003484856003603494 2014 eng d a1942-326800aSequencing of SCN5A identifies rare and common variants associated with cardiac conduction: Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium.0 aSequencing of SCN5A identifies rare and common variants associat c2014 Jun a365-730 v73 aBACKGROUND: The cardiac sodium channel SCN5A regulates atrioventricular and ventricular conduction. Genetic variants in this gene are associated with PR and QRS intervals. We sought to characterize further the contribution of rare and common coding variation in SCN5A to cardiac conduction.
METHODS AND RESULTS: In Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study, we performed targeted exonic sequencing of SCN5A (n=3699, European ancestry individuals) and identified 4 common (minor allele frequency >1%) and 157 rare variants. Common and rare SCN5A coding variants were examined for association with PR and QRS intervals through meta-analysis of European ancestry participants from CHARGE, National Heart, Lung, and Blood Institute's Exome Sequencing Project (n=607), and the UK10K (n=1275) and by examining Exome Sequencing Project African ancestry participants (n=972). Rare coding SCN5A variants in aggregate were associated with PR interval in European and African ancestry participants (P=1.3×10(-3)). Three common variants were associated with PR and QRS interval duration among European ancestry participants and one among African ancestry participants. These included 2 well-known missense variants: rs1805124 (H558R) was associated with PR and QRS shortening in European ancestry participants (P=6.25×10(-4) and P=5.2×10(-3), respectively) and rs7626962 (S1102Y) was associated with PR shortening in those of African ancestry (P=2.82×10(-3)). Among European ancestry participants, 2 novel synonymous variants, rs1805126 and rs6599230, were associated with cardiac conduction. Our top signal, rs1805126 was associated with PR and QRS lengthening (P=3.35×10(-7) and P=2.69×10(-4), respectively) and rs6599230 was associated with PR shortening (P=2.67×10(-5)).
CONCLUSIONS: By sequencing SCN5A, we identified novel common and rare coding variants associated with cardiac conduction.
10aAdult10aAged10aAged, 80 and over10aAging10aCohort Studies10aFemale10aGenetic Variation10aGenome-Wide Association Study10aGenomics10aHeart Conduction System10aHeart Diseases10aHumans10aMale10aMiddle Aged10aNAV1.5 Voltage-Gated Sodium Channel10aPolymorphism, Single Nucleotide10aSequence Analysis, DNA1 aMagnani, Jared, W1 aBrody, Jennifer, A1 aPrins, Bram, P1 aArking, Dan, E1 aLin, Honghuang1 aYin, Xiaoyan1 aLiu, Ching-Ti1 aMorrison, Alanna, C1 aZhang, Feng1 aSpector, Tim, D1 aAlonso, Alvaro1 aBis, Joshua, C1 aHeckbert, Susan, R1 aLumley, Thomas1 aSitlani, Colleen, M1 aCupples, Adrienne, L1 aLubitz, Steven, A1 aSoliman, Elsayed, Z1 aPulit, Sara, L1 aNewton-Cheh, Christopher1 aO'Donnell, Christopher, J1 aEllinor, Patrick, T1 aBenjamin, Emelia, J1 aMuzny, Donna, M1 aGibbs, Richard, A1 aSantibanez, Jireh1 aTaylor, Herman, A1 aRotter, Jerome, I1 aLange, Leslie, A1 aPsaty, Bruce, M1 aJackson, Rebecca1 aRich, Stephen, S1 aBoerwinkle, Eric1 aJamshidi, Yalda1 aSotoodehnia, Nona1 aCHARGE Consortium1 aNHLBI Exome Sequencing Project (ESP)1 aUK10K uhttps://chs-nhlbi.org/node/658305955nas a2201657 4500008004100000022001400041245011000055210006900165260001600234300001100250490000700261520138800268653001001656653000901666653002201675653002101697653001901718653001801737653001001755653001101765653002201776653001901798653001701817653003401834653001301868653001101881653001101892653000901903653001601912653001401928653003601942653002801978653002702006653001902033653002702052653002602079100002102105700001402126700001402140700001602154700002202170700002202192700001702214700002002231700002102251700002102272700001302293700002002306700001702326700001502343700001702358700002002375700001402395700002702409700001802436700002202454700001602476700001902492700001702511700002102528700002302549700002002572700001802592700002202610700001802632700001502650700001202665700001702677700001502694700001802709700001902727700001902746700001902765700002502784700002602809700002102835700002502856700002102881700001902902700002502921700001702946700002502963700001202988700002303000700002003023700002603043700002903069700002303098700002803121700001803149700002003167700002303187700002103210700001903231700001803250700002803268700001903296700002403315700002303339700002803362700001703390700002203407700002203429700002403451700002403475700002003499700002303519700002203542700001603564700001703580700002403597700002003621700001803641700002003659700002103679700001603700700001903716700002203735700002803757700002303785700003003808700001903838700001903857700002503876700002103901700002003922700002103942700002203963700001603985700002004001700002504021700002404046700002104070700002604091700002504117700002104142700002204163700002304185710005304208856003604261 2014 eng d a1537-660500aWhole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol.0 aWholeexome sequencing identifies rare and lowfrequency coding va c2014 Feb 06 a233-450 v943 aElevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or <2(nd) percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments.
10aAdult10aAged10aApolipoproteins E10aCholesterol, LDL10aCohort Studies10aDyslipidemias10aExome10aFemale10aFollow-Up Studies10aGene Frequency10aGenetic Code10aGenome-Wide Association Study10aGenotype10aHumans10aLipase10aMale10aMiddle Aged10aPhenotype10aPolymorphism, Single Nucleotide10aProprotein Convertase 910aProprotein Convertases10aReceptors, LDL10aSequence Analysis, DNA10aSerine Endopeptidases1 aLange, Leslie, A1 aHu, Youna1 aZhang, He1 aXue, Chenyi1 aSchmidt, Ellen, M1 aTang, Zheng-Zheng1 aBizon, Chris1 aLange, Ethan, M1 aSmith, Joshua, D1 aTurner, Emily, H1 aJun, Goo1 aKang, Hyun, Min1 aPeloso, Gina1 aAuer, Paul1 aLi, Kuo-Ping1 aFlannick, Jason1 aZhang, Ji1 aFuchsberger, Christian1 aGaulton, Kyle1 aLindgren, Cecilia1 aLocke, Adam1 aManning, Alisa1 aSim, Xueling1 aRivas, Manuel, A1 aHolmen, Oddgeir, L1 aGottesman, Omri1 aLu, Yingchang1 aRuderfer, Douglas1 aStahl, Eli, A1 aDuan, Qing1 aLi, Yun1 aDurda, Peter1 aJiao, Shuo1 aIsaacs, Aaron1 aHofman, Albert1 aBis, Joshua, C1 aCorrea, Adolfo1 aGriswold, Michael, E1 aJakobsdottir, Johanna1 aSmith, Albert, V1 aSchreiner, Pamela, J1 aFeitosa, Mary, F1 aZhang, Qunyuan1 aHuffman, Jennifer, E1 aCrosby, Jacy1 aWassel, Christina, L1 aDo, Ron1 aFranceschini, Nora1 aMartin, Lisa, W1 aRobinson, Jennifer, G1 aAssimes, Themistocles, L1 aCrosslin, David, R1 aRosenthal, Elisabeth, A1 aTsai, Michael1 aRieder, Mark, J1 aFarlow, Deborah, N1 aFolsom, Aaron, R1 aLumley, Thomas1 aFox, Ervin, R1 aCarlson, Christopher, S1 aPeters, Ulrike1 aJackson, Rebecca, D1 aDuijn, Cornelia, M1 aUitterlinden, André, G1 aLevy, Daniel1 aRotter, Jerome, I1 aTaylor, Herman, A1 aGudnason, Vilmundur1 aSiscovick, David, S1 aFornage, Myriam1 aBorecki, Ingrid, B1 aHayward, Caroline1 aRudan, Igor1 aChen, Eugene1 aBottinger, Erwin, P1 aLoos, Ruth, J F1 aSætrom, Pål1 aHveem, Kristian1 aBoehnke, Michael1 aGroop, Leif1 aMcCarthy, Mark1 aMeitinger, Thomas1 aBallantyne, Christie, M1 aGabriel, Stacey, B1 aO'Donnell, Christopher, J1 aPost, Wendy, S1 aNorth, Kari, E1 aReiner, Alexander, P1 aBoerwinkle, Eric1 aPsaty, Bruce, M1 aAltshuler, David1 aKathiresan, Sekar1 aLin, Dan-Yu1 aJarvik, Gail, P1 aCupples, Adrienne, L1 aKooperberg, Charles1 aWilson, James, G1 aNickerson, Deborah, A1 aAbecasis, Goncalo, R1 aRich, Stephen, S1 aTracy, Russell, P1 aWiller, Cristen, J1 aNHLBI Grand Opportunity Exome Sequencing Project uhttps://chs-nhlbi.org/node/657704486nas a2200901 4500008004100000022001400041245009600055210006900151260001600220300001100236490000700247520184700254653001002101653002202111653002302133653002802156653001902184653004002203653001002243653001102253653001902264653003802283653003402321653003802355653001102393653000902404653001102413653003602424653002902460653001702489100002202506700001802528700001902546700001902565700001402584700002102598700002102619700002002640700002402660700001902684700002802703700002002731700002202751700001202773700002402785700002402809700002102833700002002854700002102874700001902895700002102914700001602935700002002951700001502971700001902986700002003005700001803025700002103043700001803064700002203082700002403104700002303128700002303151700001903174700002603193700002203219700001903241700001903260700002103279700002103300700002403321700002403345700002003369700002003389710006503409710007403474856003603548 2015 eng d a1460-208300aAssociation of exome sequences with plasma C-reactive protein levels in >9000 participants.0 aAssociation of exome sequences with plasma Creactive protein lev c2015 Jan 15 a559-710 v243 aC-reactive protein (CRP) concentration is a heritable systemic marker of inflammation that is associated with cardiovascular disease risk. Genome-wide association studies have identified CRP-associated common variants associated in ∼25 genes. Our aims were to apply exome sequencing to (1) assess whether the candidate loci contain rare coding variants associated with CRP levels and (2) perform an exome-wide search for rare variants in novel genes associated with CRP levels. We exome-sequenced 6050 European-Americans (EAs) and 3109 African-Americans (AAs) from the NHLBI-ESP and the CHARGE consortia, and performed association tests of sequence data with measured CRP levels. In single-variant tests across candidate loci, a novel rare (minor allele frequency = 0.16%) CRP-coding variant (rs77832441-A; p.Thr59Met) was associated with 53% lower mean CRP levels (P = 2.9 × 10(-6)). We replicated the association of rs77832441 in an exome array analysis of 11 414 EAs (P = 3.0 × 10(-15)). Despite a strong effect on CRP levels, rs77832441 was not associated with inflammation-related phenotypes including coronary heart disease. We also found evidence for an AA-specific association of APOE-ε2 rs7214 with higher CRP levels. At the exome-wide significance level (P < 5.0 × 10(-8)), we confirmed associations for reported common variants of HNF1A, CRP, IL6R and TOMM40-APOE. In gene-based tests, a burden of rare/lower frequency variation in CRP in EAs (P ≤ 6.8 × 10(-4)) and in retinoic acid receptor-related orphan receptor α (RORA) in AAs (P = 1.7 × 10(-3)) were associated with CRP levels at the candidate gene level (P < 2.0 × 10(-3)). This inquiry did not elucidate novel genes, but instead demonstrated that variants distributed across the allele frequency spectrum within candidate genes contribute to CRP levels.
10aAdult10aAfrican Americans10aC-Reactive Protein10aCardiovascular Diseases10aCohort Studies10aEuropean Continental Ancestry Group10aExome10aFemale10aGene Frequency10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHepatocyte Nuclear Factor 1-alpha10aHumans10aMale10aPlasma10aPolymorphism, Single Nucleotide10aReceptors, Interleukin-610aRisk Factors1 aSchick, Ursula, M1 aAuer, Paul, L1 aBis, Joshua, C1 aLin, Honghuang1 aWei, Peng1 aPankratz, Nathan1 aLange, Leslie, A1 aBrody, Jennifer1 aStitziel, Nathan, O1 aKim, Daniel, S1 aCarlson, Christopher, S1 aFornage, Myriam1 aHaessler, Jeffery1 aHsu, Li1 aJackson, Rebecca, D1 aKooperberg, Charles1 aLeal, Suzanne, M1 aPsaty, Bruce, M1 aBoerwinkle, Eric1 aTracy, Russell1 aArdissino, Diego1 aShah, Svati1 aWiller, Cristen1 aLoos, Ruth1 aMelander, Olle1 aMcPherson, Ruth1 aHovingh, Kees1 aReilly, Muredach1 aWatkins, Hugh1 aGirelli, Domenico1 aFontanillas, Pierre1 aChasman, Daniel, I1 aGabriel, Stacey, B1 aGibbs, Richard1 aNickerson, Deborah, A1 aKathiresan, Sekar1 aPeters, Ulrike1 aDupuis, Josée1 aWilson, James, G1 aRich, Stephen, S1 aMorrison, Alanna, C1 aBenjamin, Emelia, J1 aGross, Myron, D1 aReiner, Alex, P1 aCohorts for Heart and Aging Research in Genomic Epidemiology1 aNational Heart, Lung, and Blood Institute GO Exome Sequencing Project uhttps://chs-nhlbi.org/node/659703799nas a2200757 4500008004100000022001400041245019200055210006900247260001300316300001200329490000700341520158600348653002301934653002101957653004101978653001902019653000902038653002602047653002602073653002502099653002702124653001602151653002502167653001102192653001102203653000902214653001602223653003602239100002002275700002002295700002702315700001902342700001802361700001302379700002002392700002302412700001502435700001902450700002002469700002002489700002502509700002502534700001902559700002202578700002102600700001802621700002002639700001802659700002202677700002102699700002502720700002202745700001702767700002302784700002002807700002102827700001902848700001202867700001302879700001902892700002502911700002402936700002102960700002402981856003603005 2015 eng d a1613-413300aDietary fatty acids modulate associations between genetic variants and circulating fatty acids in plasma and erythrocyte membranes: Meta-analysis of nine studies in the CHARGE consortium.0 aDietary fatty acids modulate associations between genetic varian c2015 Jul a1373-830 v593 aSCOPE: Tissue concentrations of omega-3 fatty acids may reduce cardiovascular disease risk, and genetic variants are associated with circulating fatty acids concentrations. Whether dietary fatty acids interact with genetic variants to modify circulating omega-3 fatty acids is unclear. We evaluated interactions between genetic variants and fatty acid intakes for circulating alpha-linoleic acid, eicosapentaenoic acid, docosahexaenoic acid, and docosapentaenoic acid.
METHODS AND RESULTS: We conducted meta-analyses (N = 11 668) evaluating interactions between dietary fatty acids and genetic variants (rs174538 and rs174548 in FADS1 (fatty acid desaturase 1), rs7435 in AGPAT3 (1-acyl-sn-glycerol-3-phosphate), rs4985167 in PDXDC1 (pyridoxal-dependent decarboxylase domain-containing 1), rs780094 in GCKR (glucokinase regulatory protein), and rs3734398 in ELOVL2 (fatty acid elongase 2)). Stratification by measurement compartment (plasma versus erthyrocyte) revealed compartment-specific interactions between FADS1 rs174538 and rs174548 and dietary alpha-linolenic acid and linoleic acid for docosahexaenoic acid and docosapentaenoic acid.
CONCLUSION: Our findings reinforce earlier reports that genetically based differences in circulating fatty acids may be partially due to differences in the conversion of fatty acid precursors. Further, fatty acids measurement compartment may modify gene-diet relationships, and considering compartment may improve the detection of gene-fatty acids interactions for circulating fatty acid outcomes.
10aAcetyltransferases10aAcyltransferases10aAdaptor Proteins, Signal Transducing10aCarboxy-Lyases10aDiet10aDocosahexaenoic Acids10aEicosapentaenoic Acid10aErythrocyte Membrane10aFatty Acid Desaturases10aFatty Acids10aFatty Acids, Omega-310aFemale10aHumans10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide1 aSmith, Caren, E1 aFollis, Jack, L1 aNettleton, Jennifer, A1 aFoy, Millennia1 aH Y Wu, Jason1 aMa, Yiyi1 aTanaka, Toshiko1 aManichakul, Ani, W1 aWu, Hongyu1 aChu, Audrey, Y1 aSteffen, Lyn, M1 aFornage, Myriam1 aMozaffarian, Dariush1 aKabagambe, Edmond, K1 aFerruci, Luigi1 aChen, Yii-Der Ida1 aRich, Stephen, S1 aDjoussé, Luc1 aRidker, Paul, M1 aTang, Weihong1 aMcKnight, Barbara1 aTsai, Michael, Y1 aBandinelli, Stefania1 aRotter, Jerome, I1 aHu, Frank, B1 aChasman, Daniel, I1 aPsaty, Bruce, M1 aArnett, Donna, K1 aKing, Irena, B1 aSun, Qi1 aWang, Lu1 aLumley, Thomas1 aChiuve, Stephanie, E1 aSiscovick, David, S1 aOrdovas, Jose, M1 aLemaitre, Rozenn, N uhttps://chs-nhlbi.org/node/668706191nas a2201513 4500008004100000022001400041245010300055210006900158260001500227300001000242490000800252520194100260653001602201653001702217653001202234653002202246653002502268653002102293653002802314653001002342653001102352653003802363653002502401653001702426653001102443653000902454653001602463653001302479653002602492653005302518653001902571653001802590653001802608100001202626700002402638700001802662700002902680700001802709700002802727700001702755700002002772700001502792700001202807700002002819700002102839700002102860700002002881700001802901700002202919700002302941700002302964700002202987700002003009700002803029700002103057700001703078700002403095700002803119700001803147700002303165700002203188700002403210700002203234700001903256700002203275700001903297700001803316700002903334700001503363700002203378700002003400700002003420700002203440700001503462700002303477700001603500700001903516700001703535700001703552700002903569700001903598700002003617700002103637700002203658700002303680700002003703700001903723700002203742700001203764700002103776700002003797700002403817700002003841700002503861700002103886700002103907700002403928700002203952700002503974700002103999700002404020700002104044700001904065700002004084700002204104700002204126700002004148700002004168700002804188700002404216700002404240700002104264700002004285700002504305700002404330700002004354700002604374700002504400700001804425700002604443700002104469700002304490700003004513700002104543700002004564700002204584710003504606856003604641 2015 eng d a1476-468700aExome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction.0 aExome sequencing identifies rare LDLR and APOA5 alleles conferri c2015 Feb 5 a102-60 v5183 aMyocardial infarction (MI), a leading cause of death around the world, displays a complex pattern of inheritance. When MI occurs early in life, genetic inheritance is a major component to risk. Previously, rare mutations in low-density lipoprotein (LDL) genes have been shown to contribute to MI risk in individual families, whereas common variants at more than 45 loci have been associated with MI risk in the population. Here we evaluate how rare mutations contribute to early-onset MI risk in the population. We sequenced the protein-coding regions of 9,793 genomes from patients with MI at an early age (≤50 years in males and ≤60 years in females) along with MI-free controls. We identified two genes in which rare coding-sequence mutations were more frequent in MI cases versus controls at exome-wide significance. At low-density lipoprotein receptor (LDLR), carriers of rare non-synonymous mutations were at 4.2-fold increased risk for MI; carriers of null alleles at LDLR were at even higher risk (13-fold difference). Approximately 2% of early MI cases harbour a rare, damaging mutation in LDLR; this estimate is similar to one made more than 40 years ago using an analysis of total cholesterol. Among controls, about 1 in 217 carried an LDLR coding-sequence mutation and had plasma LDL cholesterol > 190 mg dl(-1). At apolipoprotein A-V (APOA5), carriers of rare non-synonymous mutations were at 2.2-fold increased risk for MI. When compared with non-carriers, LDLR mutation carriers had higher plasma LDL cholesterol, whereas APOA5 mutation carriers had higher plasma triglycerides. Recent evidence has connected MI risk with coding-sequence mutations at two genes functionally related to APOA5, namely lipoprotein lipase and apolipoprotein C-III (refs 18, 19). Combined, these observations suggest that, as well as LDL cholesterol, disordered metabolism of triglyceride-rich lipoproteins contributes to MI risk.
10aAge Factors10aAge of Onset10aAlleles10aApolipoproteins A10aCase-Control Studies10aCholesterol, LDL10aCoronary Artery Disease10aExome10aFemale10aGenetic Predisposition to Disease10aGenetics, Population10aHeterozygote10aHumans10aMale10aMiddle Aged10aMutation10aMyocardial Infarction10aNational Heart, Lung, and Blood Institute (U.S.)10aReceptors, LDL10aTriglycerides10aUnited States1 aDo, Ron1 aStitziel, Nathan, O1 aWon, Hong-Hee1 aJørgensen, Anders, Berg1 aDuga, Stefano1 aMerlini, Pier, Angelica1 aKiezun, Adam1 aFarrall, Martin1 aGoel, Anuj1 aZuk, Or1 aGuella, Illaria1 aAsselta, Rosanna1 aLange, Leslie, A1 aPeloso, Gina, M1 aAuer, Paul, L1 aGirelli, Domenico1 aMartinelli, Nicola1 aFarlow, Deborah, N1 aDePristo, Mark, A1 aRoberts, Robert1 aStewart, Alexander, F R1 aSaleheen, Danish1 aDanesh, John1 aEpstein, Stephen, E1 aSivapalaratnam, Suthesh1 aHovingh, Kees1 aKastelein, John, J1 aSamani, Nilesh, J1 aSchunkert, Heribert1 aErdmann, Jeanette1 aShah, Svati, H1 aKraus, William, E1 aDavies, Robert1 aNikpay, Majid1 aJohansen, Christopher, T1 aWang, Jian1 aHegele, Robert, A1 aHechter, Eliana1 aMärz, Winfried1 aKleber, Marcus, E1 aHuang, Jie1 aJohnson, Andrew, D1 aLi, Mingyao1 aBurke, Greg, L1 aGross, Myron1 aLiu, Yongmei1 aAssimes, Themistocles, L1 aHeiss, Gerardo1 aLange, Ethan, M1 aFolsom, Aaron, R1 aTaylor, Herman, A1 aOlivieri, Oliviero1 aHamsten, Anders1 aClarke, Robert1 aReilly, Dermot, F1 aYin, Wu1 aRivas, Manuel, A1 aDonnelly, Peter1 aRossouw, Jacques, E1 aPsaty, Bruce, M1 aHerrington, David, M1 aWilson, James, G1 aRich, Stephen, S1 aBamshad, Michael, J1 aTracy, Russell, P1 aCupples, Adrienne, L1 aRader, Daniel, J1 aReilly, Muredach, P1 aSpertus, John, A1 aCresci, Sharon1 aHartiala, Jaana1 aTang, W, H Wilson1 aHazen, Stanley, L1 aAllayee, Hooman1 aReiner, Alex, P1 aCarlson, Christopher, S1 aKooperberg, Charles1 aJackson, Rebecca, D1 aBoerwinkle, Eric1 aLander, Eric, S1 aSchwartz, Stephen, M1 aSiscovick, David, S1 aMcPherson, Ruth1 aTybjaerg-Hansen, Anne1 aAbecasis, Goncalo, R1 aWatkins, Hugh1 aNickerson, Deborah, A1 aArdissino, Diego1 aSunyaev, Shamil, R1 aO'Donnell, Christopher, J1 aAltshuler, David1 aGabriel, Stacey1 aKathiresan, Sekar1 aNHLBI Exome Sequencing Project uhttps://chs-nhlbi.org/node/669103195nas a2200577 4500008004100000022001400041245014300055210006900198260001300267300001100280490000700291520150000298653001001798653002201808653000901830653004001839653001601879653001101895653001701906653003401923653001101957653000901968653003501977653001602012653003602028653002702064653002602091100001802117700001802135700001702153700002102170700002102191700001602212700002202228700002402250700001702274700002102291700002402312700001802336700002202354700002102376700002302397700002302420700001802443700002102461700003002482700002302512700002502535700002102560856003602581 2015 eng d a1096-865200aGene-centric approach identifies new and known loci for FVIII activity and VWF antigen levels in European Americans and African Americans.0 aGenecentric approach identifies new and known loci for FVIII act c2015 Jun a534-400 v903 aCoagulation factor VIII and von Willebrand factor (VWF) are key proteins in procoagulant activation. Higher FVIII coagulant activity (FVIII :C) and VWF antigen (VWF :Ag) are risk factors for cardiovascular disease and venous thromboembolism. Beyond associations with ABO blood group, genetic determinants of FVIII and VWF are not well understood, especially in non European-American populations. We performed a genetic association study of FVIII :C and VWF:Ag that assessed 50,000 gene-centric single nucleotide polymorphisms (SNPs) in 18,556 European Americans (EAs) and 5,047 African Americans (AAs) from five population-based cohorts. Previously unreported associations for FVIII :C were identified in both AAs and EAs with KNG1 (most significantly associated SNP rs710446, Ile581Thr, Ile581Thr, P = 5.10 × 10(-7) in EAs and P = 3.88 × 10(-3) in AAs) and VWF rs7962217 (Gly2705Arg,P = 6.30 × 10(-9) in EAs and P = 2.98 × 10(-2) in AAs. Significant associations for FVIII :C were also observed with F8/TMLHE region SNP rs12557310 in EAs (P = 8.02 × 10(-10) ), with VWF rs1800380 in AAs (P = 5.62 × 10(-11) ), and with MAT1A rs2236568 in AAs (P51.69 × 10(-6) ). We replicated previously reported associations of FVIII :C and VWF :Ag with the ABO blood group, VWF rs1063856(Thr789Ala), rs216321 (Ala852Gln), and VWF rs2229446 (Arg2185Gln). Findings from this study expand our understanding of genetic influences for FVIII :C and VWF :Ag in both EAs and AAs.
10aAdult10aAfrican Americans10aAged10aEuropean Continental Ancestry Group10aFactor VIII10aFemale10aGenetic Loci10aGenome-Wide Association Study10aHumans10aMale10aMethionine Adenosyltransferase10aMiddle Aged10aPolymorphism, Single Nucleotide10aVenous Thromboembolism10avon Willebrand Factor1 aTang, Weihong1 aCushman, Mary1 aGreen, David1 aRich, Stephen, S1 aLange, Leslie, A1 aYang, Qiong1 aTracy, Russell, P1 aTofler, Geoffrey, H1 aBasu, Saonli1 aWilson, James, G1 aKeating, Brendan, J1 aWeng, Lu-Chen1 aTaylor, Herman, A1 aJacobs, David, R1 aDelaney, Joseph, A1 aPalmer, Cameron, D1 aYoung, Taylor1 aPankow, James, S1 aO'Donnell, Christopher, J1 aSmith, Nicholas, L1 aReiner, Alexander, P1 aFolsom, Aaron, R uhttps://chs-nhlbi.org/node/737602945nas a2200589 4500008004100000022001400041245009400055210006900149260001300218300001100231490000700242520132000249653001901569653001601588653001701604653002201621653003401643653001101677100002401688700001901712700002501731700001801756700002201774700002101796700001701817700001201834700002301846700001901869700001301888700001701901700002401918700002101942700002101963700002001984700001802004700002102022700001802043700001802061700001502079700002202094700001902116700002102135700002102156700002502177700001702202700001802219700001702237700002002254700002002274700002502294856003602319 2015 eng d a1539-726200aGenetic loci associated with circulating levels of very long-chain saturated fatty acids.0 aGenetic loci associated with circulating levels of very longchai c2015 Jan a176-840 v563 aVery long-chain saturated fatty acids (VLSFAs) are saturated fatty acids with 20 or more carbons. In contrast to the more abundant saturated fatty acids, such as palmitic acid, there is growing evidence that circulating VLSFAs may have beneficial biological properties. Whether genetic factors influence circulating levels of VLSFAs is not known. We investigated the association of common genetic variation with plasma phospholipid/erythrocyte levels of three VLSFAs by performing genome-wide association studies in seven population-based cohorts comprising 10,129 subjects of European ancestry. We observed associations of circulating VLSFA concentrations with common variants in two genes, serine palmitoyl-transferase long-chain base subunit 3 (SPTLC3), a gene involved in the rate-limiting step of de novo sphingolipid synthesis, and ceramide synthase 4 (CERS4). The SPTLC3 variant at rs680379 was associated with higher arachidic acid (20:0 , P = 5.81 × 10(-13)). The CERS4 variant at rs2100944 was associated with higher levels of 20:0 (P = 2.65 × 10(-40)) and in analyses that adjusted for 20:0, with lower levels of behenic acid (P = 4.22 × 10(-26)) and lignoceric acid (P = 3.20 × 10(-21)). These novel associations suggest an inter-relationship of circulating VLSFAs and sphingolipid synthesis.
10aCohort Studies10aFatty Acids10aGenetic Loci10aGenetic Variation10aGenome-Wide Association Study10aHumans1 aLemaitre, Rozenn, N1 aKing, Irena, B1 aKabagambe, Edmond, K1 aH Y Wu, Jason1 aMcKnight, Barbara1 aManichaikul, Ani1 aGuan, Weihua1 aSun, Qi1 aChasman, Daniel, I1 aFoy, Millennia1 aWang, Lu1 aZhu, Jingwen1 aSiscovick, David, S1 aTsai, Michael, Y1 aArnett, Donna, K1 aPsaty, Bruce, M1 aDjoussé, Luc1 aChen, Yii-der, I1 aTang, Weihong1 aWeng, Lu-Chen1 aWu, Hongyu1 aJensen, Majken, K1 aChu, Audrey, Y1 aJacobs, David, R1 aRich, Stephen, S1 aMozaffarian, Dariush1 aSteffen, Lyn1 aRimm, Eric, B1 aHu, Frank, B1 aRidker, Paul, M1 aFornage, Myriam1 aFriedlander, Yechiel uhttps://chs-nhlbi.org/node/661504501nas a2200649 4500008004100000022001400041245015600055210006900211260001300280300001200293490000800305520259500313653002202908653002102930653002002951653001502971653004002986653002503026653001903051653003203070653001703102653002603119653001103145653001803156653003603174653002203210100002503232700002503257700002603282700002403308700002103332700001203353700001903365700001303384700001903397700002503416700002103441700001503462700002203477700002303499700001903522700002003541700001703561700001903578700002203597700002003619700001803639700001903657700001803676700002403694700002003718700002103738700001803759700001703777700002103794856003603815 2015 eng d a1938-320700aGenetic loci associated with circulating phospholipid trans fatty acids: a meta-analysis of genome-wide association studies from the CHARGE Consortium.0 aGenetic loci associated with circulating phospholipid trans fatt c2015 Feb a398-4060 v1013 aBACKGROUND: Circulating trans fatty acids (TFAs), which cannot be synthesized by humans, are linked to adverse health outcomes. Although TFAs are obtained from diet, little is known about subsequent influences (e.g., relating to incorporation, metabolism, or intercompetition with other fatty acids) that could alter circulating concentrations and possibly modulate or mediate impacts on health.
OBJECTIVE: The objective was to elucidate novel biologic pathways that may influence circulating TFAs by evaluating associations between common genetic variation and TFA biomarkers.
DESIGN: We performed meta-analyses using 7 cohorts of European-ancestry participants (n = 8013) having measured genome-wide variation in single-nucleotide polymorphisms (SNPs) and circulating TFA biomarkers (erythrocyte or plasma phospholipids), including trans-16:1n-7, total trans-18:1, trans/cis-18:2, cis/trans-18:2, and trans/trans-18:2. We further evaluated SNPs with genome-wide significant associations among African Americans (n = 1082), Chinese Americans (n = 669), and Hispanic Americans (n = 657) from 2 of these cohorts.
RESULTS: Among European-ancestry participants, 31 SNPs in or near the fatty acid desaturase (FADS) 1 and 2 cluster were associated with cis/trans-18:2; a top hit was rs174548 (β = 0.0035, P = 4.90 × 10(-15)), an SNP previously associated with circulating n-3 and n-6 polyunsaturated fatty acid concentrations. No significant association was identified for other TFAs. rs174548 in FADS1/2 was also associated with cis/trans-18:2 in Hispanic Americans (β = 0.0053, P = 1.05 × 10(-6)) and Chinese Americans (β = 0.0028, P = 0.002) but not African Americans (β = 0.0009, P = 0.34); however, in African Americans, fine mapping identified a top hit in FADS2 associated with cis/trans-18:2 (rs174579: β = 0.0118, P = 4.05 × 10(-5)). The association between rs174548 and cis/trans-18:2 remained significant after further adjustment for individual circulating n-3 and n-6 fatty acids, except arachidonic acid. After adjustment for arachidonic acid concentrations, the association between rs174548 and cis/trans-18:2 was nearly eliminated in European-ancestry participants (β-coefficient reduced by 86%), with similar reductions in Hispanic Americans and Chinese Americans.
CONCLUSIONS: Our findings provide novel evidence for genetic regulation of cis/trans-18:2 by the FADS1/2 cluster and suggest that this regulation may be influenced/mediated by concentrations of arachidonic acid, an n-6 polyunsaturated fat.
10aAfrican Americans10aArachidonic Acid10aAsian Americans10aBiomarkers10aEuropean Continental Ancestry Group10aFatty Acids, Omega-610aGene Frequency10aGenetic Association Studies10aGenetic Loci10aGenotyping Techniques10aHumans10aPhospholipids10aPolymorphism, Single Nucleotide10aTrans Fatty Acids1 aMozaffarian, Dariush1 aKabagambe, Edmond, K1 aJohnson, Catherine, O1 aLemaitre, Rozenn, N1 aManichaikul, Ani1 aSun, Qi1 aFoy, Millennia1 aWang, Lu1 aWiener, Howard1 aIrvin, Marguerite, R1 aRich, Stephen, S1 aWu, Hongyu1 aJensen, Majken, K1 aChasman, Daniel, I1 aChu, Audrey, Y1 aFornage, Myriam1 aSteffen, Lyn1 aKing, Irena, B1 aMcKnight, Barbara1 aPsaty, Bruce, M1 aDjoussé, Luc1 aChen, Ida, Y-D1 aH Y Wu, Jason1 aSiscovick, David, S1 aRidker, Paul, M1 aTsai, Michael, Y1 aRimm, Eric, B1 aHu, Frank, B1 aArnett, Donna, K uhttps://chs-nhlbi.org/node/668504013nas a2201093 4500008004100000022001400041245012400055210006900179260000900248300000900257490000600266520085300272653003801125653001601163653001901179653003201198653001101230653002301241653001601264100003001280700002401310700001801334700001801352700002801370700001801398700002701416700002001443700001801463700001801481700002801499700002501527700002201552700002101574700002001595700002101615700002001636700002001656700002001676700002101696700002301717700002001740700002101760700002701781700002601808700002401834700001901858700002601877700001901903700001901922700002101941700002401962700002001986700002502006700001602031700001902047700002202066700002302088700001902111700002202130700002402152700002302176700001902199700002202218700002602240700002002266700001502286700002102301700002102322700002202343700001902365700002202384700001602406700002002422700002102442700002002463700002302483700002502506700002002531700002202551700001902573700001502592700002302607700002402630700002802654700002202682700002702704700002502731700002002756700002302776700002002799700002302819710004102842856003602883 2015 eng d a2041-172300aGenome of The Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels.0 aGenome of The Netherlands populationspecific imputations identif c2015 a60650 v63 aVariants associated with blood lipid levels may be population-specific. To identify low-frequency variants associated with this phenotype, population-specific reference panels may be used. Here we impute nine large Dutch biobanks (~35,000 samples) with the population-specific reference panel created by the Genome of The Netherlands Project and perform association testing with blood lipid levels. We report the discovery of five novel associations at four loci (P value <6.61 × 10(-4)), including a rare missense variant in ABCA6 (rs77542162, p.Cys1359Arg, frequency 0.034), which is predicted to be deleterious. The frequency of this ABCA6 variant is 3.65-fold increased in the Dutch and its effect (βLDL-C=0.135, βTC=0.140) is estimated to be very similar to those observed for single variants in well-known lipid genes, such as LDLR.
10aATP-Binding Cassette Transporters10aCholesterol10aGene Frequency10aGenetic Association Studies10aHumans10aMutation, Missense10aNetherlands1 avan Leeuwen, Elisabeth, M1 aKarssen, Lennart, C1 aDeelen, Joris1 aIsaacs, Aaron1 aMedina-Gómez, Carolina1 aMbarek, Hamdi1 aKanterakis, Alexandros1 aTrompet, Stella1 aPostmus, Iris1 aVerweij, Niek1 avan Enckevort, David, J1 aHuffman, Jennifer, E1 aWhite, Charles, C1 aFeitosa, Mary, F1 aBartz, Traci, M1 aManichaikul, Ani1 aJoshi, Peter, K1 aPeloso, Gina, M1 aDeelen, Patrick1 avan Dijk, Freerk1 aWillemsen, Gonneke1 ade Geus, Eco, J1 aMilaneschi, Yuri1 aPenninx, Brenda, W J H1 aFrancioli, Laurent, C1 aMenelaou, Androniki1 aPulit, Sara, L1 aRivadeneira, Fernando1 aHofman, Albert1 aOostra, Ben, A1 aFranco, Oscar, H1 aLeach, Irene, Mateo1 aBeekman, Marian1 ade Craen, Anton, J M1 aUh, Hae-Won1 aTrochet, Holly1 aHocking, Lynne, J1 aPorteous, David, J1 aSattar, Naveed1 aPackard, Chris, J1 aBuckley, Brendan, M1 aBrody, Jennifer, A1 aBis, Joshua, C1 aRotter, Jerome, I1 aMychaleckyj, Josyf, C1 aCampbell, Harry1 aDuan, Qing1 aLange, Leslie, A1 aWilson, James, F1 aHayward, Caroline1 aPolasek, Ozren1 aVitart, Veronique1 aRudan, Igor1 aWright, Alan, F1 aRich, Stephen, S1 aPsaty, Bruce, M1 aBorecki, Ingrid, B1 aKearney, Patricia, M1 aStott, David, J1 aCupples, Adrienne1 aJukema, Wouter1 aHarst, Pim1 aSijbrands, Eric, J1 aHottenga, Jouke-Jan1 aUitterlinden, André, G1 aSwertz, Morris, A1 avan Ommen, Gert-Jan, B1 ade Bakker, Paul, I W1 aSlagboom, Eline1 aBoomsma, Dorret, I1 aWijmenga, Cisca1 aDuijn, Cornelia, M1 aGenome of the Netherlands Consortium uhttps://chs-nhlbi.org/node/668205324nas a2201021 4500008004100000022001400041245012300055210006900178260001300247300001100260490000800271520242700279653001002706653002002716653001902736653001902755653002802774653000902802653002102811653001802832653004002850653002902890653001102919653003302930653003802963653001103001653000903012653001603021653001203037653003603049653001003085653001603095100002203111700002003133700002003153700002003173700001903193700002403212700001703236700002103253700002503274700002103299700001803320700002503338700001903363700002003382700002403402700001803426700002803444700003103472700001903503700001903522700002403541700001603565700002303581700001503604700001903619700002203638700002303660700001903683700002003702700002003722700001803742700002503760700002403785700002003809700002203829700002403851700002303875700002303898700002203921700002003943700002403963700002503987700002104012700002104033700001904054700002004073700002004093700002304113700002504136700001904161700002104180700002204201700002204223700002104245856003604266 2015 eng d a1938-320700aHabitual sleep duration is associated with BMI and macronutrient intake and may be modified by CLOCK genetic variants.0 aHabitual sleep duration is associated with BMI and macronutrient c2015 Jan a135-430 v1013 aBACKGROUND: Short sleep duration has been associated with greater risks of obesity, hypertension, diabetes, and cardiovascular disease. Also, common genetic variants in the human Circadian Locomotor Output Cycles Kaput (CLOCK) show associations with ghrelin and total energy intake.
OBJECTIVES: We examined associations between habitual sleep duration, body mass index (BMI), and macronutrient intake and assessed whether CLOCK variants modify these associations.
DESIGN: We conducted inverse-variance weighted, fixed-effect meta-analyses of results of adjusted associations of sleep duration and BMI and macronutrient intake as percentages of total energy as well as interactions with CLOCK variants from 9 cohort studies including up to 14,906 participants of European descent from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium.
RESULTS: We observed a significant association between sleep duration and lower BMI (β ± SE = 0.16 ± 0.04, P < 0.0001) in the overall sample; however, associations between sleep duration and relative macronutrient intake were evident in age- and sex-stratified analyses only. We observed a significant association between sleep duration and lower saturated fatty acid intake in younger (aged 20-64 y) adults (men: 0.11 ± 0.06%, P = 0.03; women: 0.10 ± 0.05%, P = 0.04) and with lower carbohydrate (-0.31 ± 0.12%, P < 0.01), higher total fat (0.18 ± 0.09%, P = 0.05), and higher PUFA (0.05 ± 0.02%, P = 0.02) intakes in older (aged 65-80 y) women. In addition, the following 2 nominally significant interactions were observed: between sleep duration and rs12649507 on PUFA intake and between sleep duration and rs6858749 on protein intake.
CONCLUSIONS: Our results indicate that longer habitual sleep duration is associated with lower BMI and age- and sex-specific favorable dietary behaviors. Differences in the relative intake of specific macronutrients associated with short sleep duration could, at least in part, explain previously reported associations between short sleep duration and chronic metabolic abnormalities. In addition, the influence of obesity-associated CLOCK variants on the association between sleep duration and macronutrient intake suggests that longer habitual sleep duration could ameliorate the genetic predisposition to obesity via a favorable dietary profile.
10aAdult10aBody Mass Index10aCLOCK Proteins10aCohort Studies10aCross-Sectional Studies10aDiet10aDietary Proteins10aEnergy Intake10aEuropean Continental Ancestry Group10aFatty Acids, Unsaturated10aFemale10aGene-Environment Interaction10aGenetic Predisposition to Disease10aHumans10aMale10aMiddle Aged10aObesity10aPolymorphism, Single Nucleotide10aSleep10aYoung Adult1 aDashti, Hassan, S1 aFollis, Jack, L1 aSmith, Caren, E1 aTanaka, Toshiko1 aCade, Brian, E1 aGottlieb, Daniel, J1 aHruby, Adela1 aJacques, Paul, F1 aLamon-Fava, Stefania1 aRichardson, Kris1 aSaxena, Richa1 aScheer, Frank, A J L1 aKovanen, Leena1 aBartz, Traci, M1 aPerälä, Mia-Maria1 aJonsson, Anna1 aFrazier-Wood, Alexis, C1 aKalafati, Ioanna-Panagiota1 aMikkilä, Vera1 aPartonen, Timo1 aLemaitre, Rozenn, N1 aLahti, Jari1 aHernandez, Dena, G1 aToft, Ulla1 aJohnson, Craig1 aKanoni, Stavroula1 aRaitakari, Olli, T1 aPerola, Markus1 aPsaty, Bruce, M1 aFerrucci, Luigi1 aGrarup, Niels1 aHighland, Heather, M1 aRallidis, Loukianos1 aKähönen, Mika1 aHavulinna, Aki, S1 aSiscovick, David, S1 aRäikkönen, Katri1 aJørgensen, Torben1 aRotter, Jerome, I1 aDeloukas, Panos1 aViikari, Jorma, S A1 aMozaffarian, Dariush1 aLinneberg, Allan1 aSeppälä, Ilkka1 aHansen, Torben1 aSalomaa, Veikko1 aGharib, Sina, A1 aEriksson, Johan, G1 aBandinelli, Stefania1 aPedersen, Oluf1 aRich, Stephen, S1 aDedoussis, George1 aLehtimäki, Terho1 aOrdovas, Jose, M uhttps://chs-nhlbi.org/node/661409013nas a2202797 4500008004100000022001400041245011400055210006900169260000900238300000900247490000600256520115900262653003901421653001801460653003001478653004001508653001001548653001201558653003201570653001701602653003801619653002201657653003701679653002601716653001101742653001201753653001801765653004401783653003601827100002101863700001901884700002101903700001601924700002301940700002301963700001801986700002502004700002402029700002202053700002002075700001502095700002502110700001302135700001802148700003102166700001802197700002102215700001102236700002602247700002502273700002002298700001902318700001702337700002302354700002002377700002302397700002302420700001702443700001602460700001802476700002602494700002102520700002002541700001202561700002002573700002102593700002402614700001902638700002502657700002502682700002302707700002102730700002102751700002202772700002302794700002502817700002002842700002002862700002302882700001602905700002402921700001402945700002302959700002502982700002103007700002303028700002203051700001903073700001703092700001903109700002003128700001903148700002203167700001903189700002003208700002403228700002203252700002503274700002303299700002203322700002303344700002403367700001503391700002003406700002003426700002303446700001403469700002103483700001703504700001803521700002303539700002303562700002503585700002303610700002403633700001903657700002403676700001903700700002203719700002003741700001403761700001803775700001703793700001703810700001603827700001703843700002503860700002103885700002203906700002203928700002003950700002203970700002003992700002704012700001704039700002304056700002104079700002004100700001904120700002604139700001804165700001904183700002104202700002004223700001404243700002004257700002004277700002704297700002304324700002004347700002804367700001804395700002304413700002304436700002704459700001704486700002104503700002704524700001804551700001904569700001904588700002104607700001904628700002004647700002504667700001904692700002004711700002204731700002004753700002304773700002004796700002004816700002104836700002104857700002204878700001704900700002604917700001904943700002404962700002204986700002005008700002005028700002105048700002005069700002205089700002405111700002005135700002205155700002605177700002205203700001905225700002405244700002405268700002205292700001705314700001905331700003005350700002305380700002505403700002105428700001905449700002505468700002105493700002005514700001605534700002505550700002005575700002805595700001705623700001805640700001805658700002405676700001905700700002305719700002305742700001905765700002005784700001905804700002305823700002505846700002405871700002105895700002005916700002005936700002005956700002105976700002505997700002406022700001906046700002206065700002006087700002106107700002206128710002906150856003606179 2015 eng d a2041-172300aLow-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility.0 aLowfrequency and rare exome chip variants associate with fasting c2015 a58970 v63 aFasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=-0.09±0.01 mmol l(-1), P=3.4 × 10(-12)), T2D risk (OR[95%CI]=0.86[0.76-0.96], P=0.010), early insulin secretion (β=-0.07±0.035 pmolinsulin mmolglucose(-1), P=0.048), but higher 2-h glucose (β=0.16±0.05 mmol l(-1), P=4.3 × 10(-4)). We identify a gene-based association with FG at G6PC2 (pSKAT=6.8 × 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l(-1), P=1.3 × 10(-8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
10aAfrican Continental Ancestry Group10aBlood Glucose10aDiabetes Mellitus, Type 210aEuropean Continental Ancestry Group10aExome10aFasting10aGenetic Association Studies10aGenetic Loci10aGenetic Predisposition to Disease10aGenetic Variation10aGlucagon-Like Peptide-1 Receptor10aGlucose-6-Phosphatase10aHumans10aInsulin10aMutation Rate10aOligonucleotide Array Sequence Analysis10aPolymorphism, Single Nucleotide1 aWessel, Jennifer1 aChu, Audrey, Y1 aWillems, Sara, M1 aWang, Shuai1 aYaghootkar, Hanieh1 aBrody, Jennifer, A1 aDauriz, Marco1 aHivert, Marie-France1 aRaghavan, Sridharan1 aLipovich, Leonard1 aHidalgo, Bertha1 aFox, Keolu1 aHuffman, Jennifer, E1 aAn, Ping1 aLu, Yingchang1 aRasmussen-Torvik, Laura, J1 aGrarup, Niels1 aEhm, Margaret, G1 aLi, Li1 aBaldridge, Abigail, S1 aStančáková, Alena1 aAbrol, Ravinder1 aBesse, Céline1 aBoland, Anne1 aBork-Jensen, Jette1 aFornage, Myriam1 aFreitag, Daniel, F1 aGarcia, Melissa, E1 aGuo, Xiuqing1 aHara, Kazuo1 aIsaacs, Aaron1 aJakobsdottir, Johanna1 aLange, Leslie, A1 aLayton, Jill, C1 aLi, Man1 aZhao, Jing, Hua1 aMeidtner, Karina1 aMorrison, Alanna, C1 aNalls, Mike, A1 aPeters, Marjolein, J1 aSabater-Lleal, Maria1 aSchurmann, Claudia1 aSilveira, Angela1 aSmith, Albert, V1 aSoutham, Lorraine1 aStoiber, Marcus, H1 aStrawbridge, Rona, J1 aTaylor, Kent, D1 aVarga, Tibor, V1 aAllin, Kristine, H1 aAmin, Najaf1 aAponte, Jennifer, L1 aAung, Tin1 aBarbieri, Caterina1 aBihlmeyer, Nathan, A1 aBoehnke, Michael1 aBombieri, Cristina1 aBowden, Donald, W1 aBurns, Sean, M1 aChen, Yuning1 aChen, Yii-DerI1 aCheng, Ching-Yu1 aCorrea, Adolfo1 aCzajkowski, Jacek1 aDehghan, Abbas1 aEhret, Georg, B1 aEiriksdottir, Gudny1 aEscher, Stefan, A1 aFarmaki, Aliki-Eleni1 aFrånberg, Mattias1 aGambaro, Giovanni1 aGiulianini, Franco1 aGoddard, William, A1 aGoel, Anuj1 aGottesman, Omri1 aGrove, Megan, L1 aGustafsson, Stefan1 aHai, Yang1 aHallmans, Göran1 aHeo, Jiyoung1 aHoffmann, Per1 aIkram, Mohammad, K1 aJensen, Richard, A1 aJørgensen, Marit, E1 aJørgensen, Torben1 aKaraleftheri, Maria1 aKhor, Chiea, C1 aKirkpatrick, Andrea1 aKraja, Aldi, T1 aKuusisto, Johanna1 aLange, Ethan, M1 aLee, I, T1 aLee, Wen-Jane1 aLeong, Aaron1 aLiao, Jiemin1 aLiu, Chunyu1 aLiu, Yongmei1 aLindgren, Cecilia, M1 aLinneberg, Allan1 aMalerba, Giovanni1 aMamakou, Vasiliki1 aMarouli, Eirini1 aMaruthur, Nisa, M1 aMatchan, Angela1 aMcKean-Cowdin, Roberta1 aMcLeod, Olga1 aMetcalf, Ginger, A1 aMohlke, Karen, L1 aMuzny, Donna, M1 aNtalla, Ioanna1 aPalmer, Nicholette, D1 aPasko, Dorota1 aPeter, Andreas1 aRayner, Nigel, W1 aRenstrom, Frida1 aRice, Ken1 aSala, Cinzia, F1 aSennblad, Bengt1 aSerafetinidis, Ioannis1 aSmith, Jennifer, A1 aSoranzo, Nicole1 aSpeliotes, Elizabeth, K1 aStahl, Eli, A1 aStirrups, Kathleen1 aTentolouris, Nikos1 aThanopoulou, Anastasia1 aTorres, Mina1 aTraglia, Michela1 aTsafantakis, Emmanouil1 aJavad, Sundas1 aYanek, Lisa, R1 aZengini, Eleni1 aBecker, Diane, M1 aBis, Joshua, C1 aBrown, James, B1 aCupples, Adrienne, L1 aHansen, Torben1 aIngelsson, Erik1 aKarter, Andrew, J1 aLorenzo, Carlos1 aMathias, Rasika, A1 aNorris, Jill, M1 aPeloso, Gina, M1 aSheu, Wayne, H-H1 aToniolo, Daniela1 aVaidya, Dhananjay1 aVarma, Rohit1 aWagenknecht, Lynne, E1 aBoeing, Heiner1 aBottinger, Erwin, P1 aDedoussis, George1 aDeloukas, Panos1 aFerrannini, Ele1 aFranco, Oscar, H1 aFranks, Paul, W1 aGibbs, Richard, A1 aGudnason, Vilmundur1 aHamsten, Anders1 aHarris, Tamara, B1 aHattersley, Andrew, T1 aHayward, Caroline1 aHofman, Albert1 aJansson, Jan-Håkan1 aLangenberg, Claudia1 aLauner, Lenore, J1 aLevy, Daniel1 aOostra, Ben, A1 aO'Donnell, Christopher, J1 aO'Rahilly, Stephen1 aPadmanabhan, Sandosh1 aPankow, James, S1 aPolasek, Ozren1 aProvince, Michael, A1 aRich, Stephen, S1 aRidker, Paul, M1 aRudan, Igor1 aSchulze, Matthias, B1 aSmith, Blair, H1 aUitterlinden, André, G1 aWalker, Mark1 aWatkins, Hugh1 aWong, Tien, Y1 aZeggini, Eleftheria1 aLaakso, Markku1 aBorecki, Ingrid, B1 aChasman, Daniel, I1 aPedersen, Oluf1 aPsaty, Bruce, M1 aTai, Shyong, E1 aDuijn, Cornelia, M1 aWareham, Nicholas, J1 aWaterworth, Dawn, M1 aBoerwinkle, Eric1 aKao, Linda, W H1 aFlorez, Jose, C1 aLoos, Ruth, J F1 aWilson, James, G1 aFrayling, Timothy, M1 aSiscovick, David, S1 aDupuis, Josée1 aRotter, Jerome, I1 aMeigs, James, B1 aScott, Robert, A1 aGoodarzi, Mark, O1 aEPIC-InterAct Consortium uhttps://chs-nhlbi.org/node/668605080nas a2201129 4500008004100000022001400041245007200055210006900127260001600196300001100212490000700223520191200230653002502142653002102167653002802188653001102216653001902227653001302246653002602259653001102285653000902296653003702305653001602342653003602358653002002394653001802414100002302432700002702455700001902482700001902501700002502520700002702545700002202572700002502594700001702619700002402636700002302660700002602683700002802709700001602737700002102753700001502774700002002789700002802809700001502837700002102852700001802873700002102891700002602912700002602938700002202964700001902986700002103005700002203026700001803048700002003066700002103086700001903107700002403126700002103150700002603171700002203197700002403219700002103243700002203264700001903286700002403305700002003329700002103349700001803370700001803388700001803406700002003424700002103444700002103465700001803486700002403504700001903528700001803547700002003565700002103585700002103606700002503627700002103652700002503673700002003698700002403718700001903742700002203761700002103783700002203804700002403826700002403850700001903874710002103893856003603914 2015 eng d a1522-964500aMendelian randomization of blood lipids for coronary heart disease.0 aMendelian randomization of blood lipids for coronary heart disea c2015 Mar 01 a539-500 v363 aAIMS: To investigate the causal role of high-density lipoprotein cholesterol (HDL-C) and triglycerides in coronary heart disease (CHD) using multiple instrumental variables for Mendelian randomization.
METHODS AND RESULTS: We developed weighted allele scores based on single nucleotide polymorphisms (SNPs) with established associations with HDL-C, triglycerides, and low-density lipoprotein cholesterol (LDL-C). For each trait, we constructed two scores. The first was unrestricted, including all independent SNPs associated with the lipid trait identified from a prior meta-analysis (threshold P < 2 × 10(-6)); and the second a restricted score, filtered to remove any SNPs also associated with either of the other two lipid traits at P ≤ 0.01. Mendelian randomization meta-analyses were conducted in 17 studies including 62,199 participants and 12,099 CHD events. Both the unrestricted and restricted allele scores for LDL-C (42 and 19 SNPs, respectively) associated with CHD. For HDL-C, the unrestricted allele score (48 SNPs) was associated with CHD (OR: 0.53; 95% CI: 0.40, 0.70), per 1 mmol/L higher HDL-C, but neither the restricted allele score (19 SNPs; OR: 0.91; 95% CI: 0.42, 1.98) nor the unrestricted HDL-C allele score adjusted for triglycerides, LDL-C, or statin use (OR: 0.81; 95% CI: 0.44, 1.46) showed a robust association. For triglycerides, the unrestricted allele score (67 SNPs) and the restricted allele score (27 SNPs) were both associated with CHD (OR: 1.62; 95% CI: 1.24, 2.11 and 1.61; 95% CI: 1.00, 2.59, respectively) per 1-log unit increment. However, the unrestricted triglyceride score adjusted for HDL-C, LDL-C, and statin use gave an OR for CHD of 1.01 (95% CI: 0.59, 1.75).
CONCLUSION: The genetic findings support a causal effect of triglycerides on CHD risk, but a causal role for HDL-C, though possible, remains less certain.
10aCase-Control Studies10aCholesterol, HDL10aCoronary Artery Disease10aFemale10aGene Frequency10aGenotype10aGenotyping Techniques10aHumans10aMale10aMendelian Randomization Analysis10aMiddle Aged10aPolymorphism, Single Nucleotide10aRisk Assessment10aTriglycerides1 aHolmes, Michael, V1 aAsselbergs, Folkert, W1 aPalmer, Tom, M1 aDrenos, Fotios1 aLanktree, Matthew, B1 aNelson, Christopher, P1 aDale, Caroline, E1 aPadmanabhan, Sandosh1 aFinan, Chris1 aSwerdlow, Daniel, I1 aTragante, Vinicius1 avan Iperen, Erik, P A1 aSivapalaratnam, Suthesh1 aShah, Sonia1 aElbers, Clara, C1 aShah, Tina1 aEngmann, Jorgen1 aGiambartolomei, Claudia1 aWhite, Jon1 aZabaneh, Delilah1 aSofat, Reecha1 aMcLachlan, Stela1 aDoevendans, Pieter, A1 aBalmforth, Anthony, J1 aHall, Alistair, S1 aNorth, Kari, E1 aAlmoguera, Berta1 aHoogeveen, Ron, C1 aCushman, Mary1 aFornage, Myriam1 aPatel, Sanjay, R1 aRedline, Susan1 aSiscovick, David, S1 aTsai, Michael, Y1 aKarczewski, Konrad, J1 aHofker, Marten, H1 aVerschuren, Monique1 aBots, Michiel, L1 aSchouw, Yvonne, T1 aMelander, Olle1 aDominiczak, Anna, F1 aMorris, Richard1 aBen-Shlomo, Yoav1 aPrice, Jackie1 aKumari, Meena1 aBaumert, Jens1 aPeters, Annette1 aThorand, Barbara1 aKoenig, Wolfgang1 aGaunt, Tom, R1 aHumphries, Steve, E1 aClarke, Robert1 aWatkins, Hugh1 aFarrall, Martin1 aWilson, James, G1 aRich, Stephen, S1 ade Bakker, Paul, I W1 aLange, Leslie, A1 aSmith, George, Davey1 aReiner, Alex, P1 aTalmud, Philippa, J1 aKivimaki, Mika1 aLawlor, Debbie, A1 aDudbridge, Frank1 aSamani, Nilesh, J1 aKeating, Brendan, J1 aHingorani, Aroon, D1 aCasas, Juan, P1 aUCLEB consortium uhttps://chs-nhlbi.org/node/656803753nas a2200601 4500008004100000022001400041245011800055210006900173260001300242300001100255490000700266520199300273653002202266653002502288653001902313653003802332653003402370653001102404653003602415653001702451653001102468100001902479700002002498700002002518700002202538700001802560700001602578700001902594700002302613700002102636700002102657700002302678700002202701700002302723700002302746700002202769700001702791700001602808700002002824700001702844700002202861700002102883700001802904700002002922700002502942700002002967700002402987700002203011700002503033700002003058710003703078856003603115 2015 eng d a1524-462800aMeta-Analysis of Genome-Wide Association Studies Identifies Genetic Risk Factors for Stroke in African Americans.0 aMetaAnalysis of GenomeWide Association Studies Identifies Geneti c2015 Aug a2063-80 v463 aBACKGROUND AND PURPOSE: The majority of genome-wide association studies (GWAS) of stroke have focused on European-ancestry populations; however, none has been conducted in African Americans, despite the disproportionately high burden of stroke in this population. The Consortium of Minority Population Genome-Wide Association Studies of Stroke (COMPASS) was established to identify stroke susceptibility loci in minority populations.
METHODS: Using METAL, we conducted meta-analyses of GWAS in 14 746 African Americans (1365 ischemic and 1592 total stroke cases) from COMPASS, and tested genetic variants with P<10(-6) for validation in METASTROKE, a consortium of ischemic stroke genetic studies in European-ancestry populations. We also evaluated stroke loci previously identified in European-ancestry populations.
RESULTS: The 15q21.3 locus linked with lipid levels and hypertension was associated with total stroke (rs4471613; P=3.9×10(-8)) in African Americans. Nominal associations (P<10(-6)) for total or ischemic stroke were observed for 18 variants in or near genes implicated in cell cycle/mRNA presplicing (PTPRG, CDC5L), platelet function (HPS4), blood-brain barrier permeability (CLDN17), immune response (ELTD1, WDFY4, and IL1F10-IL1RN), and histone modification (HDAC9). Two of these loci achieved nominal significance in METASTROKE: 5q35.2 (P=0.03), and 1p31.1 (P=0.018). Four of 7 previously reported ischemic stroke loci (PITX2, HDAC9, CDKN2A/CDKN2B, and ZFHX3) were nominally associated (P<0.05) with stroke in COMPASS.
CONCLUSIONS: We identified a novel genetic variant associated with total stroke in African Americans and found that ischemic stroke loci identified in European-ancestry populations may also be relevant for African Americans. Our findings support investigation of diverse populations to identify and characterize genetic risk factors, and the importance of shared genetic risk across populations.
10aAfrican Americans10aCase-Control Studies10aCohort Studies10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aPolymorphism, Single Nucleotide10aRisk Factors10aStroke1 aCarty, Cara, L1 aKeene, Keith, L1 aCheng, Yu-Ching1 aMeschia, James, F1 aChen, Wei-Min1 aNalls, Mike1 aBis, Joshua, C1 aKittner, Steven, J1 aRich, Stephen, S1 aTajuddin, Salman1 aZonderman, Alan, B1 aEvans, Michele, K1 aLangefeld, Carl, D1 aGottesman, Rebecca1 aMosley, Thomas, H1 aShahar, Eyal1 aWoo, Daniel1 aYaffe, Kristine1 aLiu, Yongmei1 aSale, Michèle, M1 aDichgans, Martin1 aMalik, Rainer1 aLongstreth, W T1 aMitchell, Braxton, D1 aPsaty, Bruce, M1 aKooperberg, Charles1 aReiner, Alexander1 aWorrall, Bradford, B1 aFornage, Myriam1 aCOMPASS and METASTROKE Consortia uhttps://chs-nhlbi.org/node/681204841nas a2200685 4500008004100000022001400041245011900055210006900174260001300243300001000256490000700266520281800273653000903091653001903100653001003119653001103129653003803140653002203178653003403200653001103234653000903245653001603254653002003270653005303290653002103343653002403364653002803388653001103416653001803427100001803445700001603463700002203479700002503501700002003526700002003546700002403566700002403590700002803614700001903642700001803661700002503679700002403704700002003728700001603748700001203764700002403776700002003800700001903820700002903839700002103868700002103889700002203910700002503932700002503957700002603982700001904008700002104027710007104048856003604119 2015 eng d a2168-615700aRare and Coding Region Genetic Variants Associated With Risk of Ischemic Stroke: The NHLBI Exome Sequence Project.0 aRare and Coding Region Genetic Variants Associated With Risk of c2015 Jul a781-80 v723 aIMPORTANCE: Stroke is the second leading cause of death and the third leading cause of years of life lost. Genetic factors contribute to stroke prevalence, and candidate gene and genome-wide association studies (GWAS) have identified variants associated with ischemic stroke risk. These variants often have small effects without obvious biological significance. Exome sequencing may discover predicted protein-altering variants with a potentially large effect on ischemic stroke risk.
OBJECTIVE: To investigate the contribution of rare and common genetic variants to ischemic stroke risk by targeting the protein-coding regions of the human genome.
DESIGN, SETTING, AND PARTICIPANTS: The National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP) analyzed approximately 6000 participants from numerous cohorts of European and African ancestry. For discovery, 365 cases of ischemic stroke (small-vessel and large-vessel subtypes) and 809 European ancestry controls were sequenced; for replication, 47 affected sibpairs concordant for stroke subtype and an African American case-control series were sequenced, with 1672 cases and 4509 European ancestry controls genotyped. The ESP's exome sequencing and genotyping started on January 1, 2010, and continued through June 30, 2012. Analyses were conducted on the full data set between July 12, 2012, and July 13, 2013.
MAIN OUTCOMES AND MEASURES: Discovery of new variants or genes contributing to ischemic stroke risk and subtype (primary analysis) and determination of support for protein-coding variants contributing to risk in previously published candidate genes (secondary analysis).
RESULTS: We identified 2 novel genes associated with an increased risk of ischemic stroke: a protein-coding variant in PDE4DIP (rs1778155; odds ratio, 2.15; P = 2.63 × 10(-8)) with an intracellular signal transduction mechanism and in ACOT4 (rs35724886; odds ratio, 2.04; P = 1.24 × 10(-7)) with a fatty acid metabolism; confirmation of PDE4DIP was observed in affected sibpair families with large-vessel stroke subtype and in African Americans. Replication of protein-coding variants in candidate genes was observed for 2 previously reported GWAS associations: ZFHX3 (cardioembolic stroke) and ABCA1 (large-vessel stroke).
CONCLUSIONS AND RELEVANCE: Exome sequencing discovered 2 novel genes and mechanisms, PDE4DIP and ACOT4, associated with increased risk for ischemic stroke. In addition, ZFHX3 and ABCA1 were discovered to have protein-coding variants associated with ischemic stroke. These results suggest that genetic variation in novel pathways contributes to ischemic stroke risk and serves as a target for prediction, prevention, and therapy.
10aAged10aBrain Ischemia10aExome10aFemale10aGenetic Predisposition to Disease10aGenetic Variation10aGenome-Wide Association Study10aHumans10aMale10aMiddle Aged10aMuscle Proteins10aNational Heart, Lung, and Blood Institute (U.S.)10aNuclear Proteins10aOpen Reading Frames10aPalmitoyl-CoA Hydrolase10aStroke10aUnited States1 aAuer, Paul, L1 aNalls, Mike1 aMeschia, James, F1 aWorrall, Bradford, B1 aLongstreth, W T1 aSeshadri, Sudha1 aKooperberg, Charles1 aBurger, Kathleen, M1 aCarlson, Christopher, S1 aCarty, Cara, L1 aChen, Wei-Min1 aCupples, Adrienne, L1 aDeStefano, Anita, L1 aFornage, Myriam1 aHardy, John1 aHsu, Li1 aJackson, Rebecca, D1 aJarvik, Gail, P1 aKim, Daniel, S1 aLakshminarayan, Kamakshi1 aLange, Leslie, A1 aManichaikul, Ani1 aQuinlan, Aaron, R1 aSingleton, Andrew, B1 aThornton, Timothy, A1 aNickerson, Deborah, A1 aPeters, Ulrike1 aRich, Stephen, S1 aNational Heart, Lung, and Blood Institute Exome Sequencing Project uhttps://chs-nhlbi.org/node/684905111nas a2200877 4500008004100000022001400041245025000055210006900305260001300374300001100387490000800398520248000406653002202886653003902908653002102947653001902968653000902987653002002996653002603016653002403042653001603066653003103082653001103113653001103124653003603135653003003171653001803201100001303219700002003232700002003252700002003272700002403292700001903316700002103335700001403356700001703370700001303387700001903400700002103419700002303440700002003463700002303483700002103506700002003527700002803547700002303575700001503598700002203613700002103635700001303656700002503669700002003694700002503714700001603739700002203755700002603777700002003803700001903823700002503842700002203867700002503889700002103914700002003935700002003955700002503975700001704000700002204017700003004039700002004069700002404089700001804113700002104131700002104152700002404173856003604197 2016 eng d a1938-320700aInteraction of methylation-related genetic variants with circulating fatty acids on plasma lipids: a meta-analysis of 7 studies and methylation analysis of 3 studies in the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium.0 aInteraction of methylationrelated genetic variants with circulat c2016 Feb a567-780 v1033 aBACKGROUND: DNA methylation is influenced by diet and single nucleotide polymorphisms (SNPs), and methylation modulates gene expression.
OBJECTIVE: We aimed to explore whether the gene-by-diet interactions on blood lipids act through DNA methylation.
DESIGN: We selected 7 SNPs on the basis of predicted relations in fatty acids, methylation, and lipids. We conducted a meta-analysis and a methylation and mediation analysis with the use of data from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium and the ENCODE (Encyclopedia of DNA Elements) consortium.
RESULTS: On the basis of the meta-analysis of 7 cohorts in the CHARGE consortium, higher plasma HDL cholesterol was associated with fewer C alleles at ATP-binding cassette subfamily A member 1 (ABCA1) rs2246293 (β = -0.6 mg/dL, P = 0.015) and higher circulating eicosapentaenoic acid (EPA) (β = 3.87 mg/dL, P = 5.62 × 10(21)). The difference in HDL cholesterol associated with higher circulating EPA was dependent on genotypes at rs2246293, and it was greater for each additional C allele (β = 1.69 mg/dL, P = 0.006). In the GOLDN (Genetics of Lipid Lowering Drugs and Diet Network) study, higher ABCA1 promoter cg14019050 methylation was associated with more C alleles at rs2246293 (β = 8.84%, P = 3.51 × 10(18)) and lower circulating EPA (β = -1.46%, P = 0.009), and the mean difference in methylation of cg14019050 that was associated with higher EPA was smaller with each additional C allele of rs2246293 (β = -2.83%, P = 0.007). Higher ABCA1 cg14019050 methylation was correlated with lower ABCA1 expression (r = -0.61, P = 0.009) in the ENCODE consortium and lower plasma HDL cholesterol in the GOLDN study (r = -0.12, P = 0.0002). An additional mediation analysis was meta-analyzed across the GOLDN study, Cardiovascular Health Study, and the Multi-Ethnic Study of Atherosclerosis. Compared with the model without the adjustment of cg14019050 methylation, the model with such adjustment provided smaller estimates of the mean plasma HDL cholesterol concentration in association with both the rs2246293 C allele and EPA and a smaller difference by rs2246293 genotypes in the EPA-associated HDL cholesterol. However, the differences between 2 nested models were NS (P > 0.05).
CONCLUSION: We obtained little evidence that the gene-by-fatty acid interactions on blood lipids act through DNA methylation.
10aApolipoproteins E10aATP Binding Cassette Transporter 110aCholesterol, HDL10aCohort Studies10aDiet10aDNA Methylation10aEicosapentaenoic Acid10aEpigenesis, Genetic10aFatty Acids10aGene Expression Regulation10aHumans10aLipids10aPolymorphism, Single Nucleotide10aPromoter Regions, Genetic10aTriglycerides1 aMa, Yiyi1 aFollis, Jack, L1 aSmith, Caren, E1 aTanaka, Toshiko1 aManichaikul, Ani, W1 aChu, Audrey, Y1 aSamieri, Cecilia1 aZhou, Xia1 aGuan, Weihua1 aWang, Lu1 aBiggs, Mary, L1 aChen, Yii-der, I1 aHernandez, Dena, G1 aBorecki, Ingrid1 aChasman, Daniel, I1 aRich, Stephen, S1 aFerrucci, Luigi1 aIrvin, Marguerite, Ryan1 aAslibekyan, Stella1 aZhi, Degui1 aTiwari, Hemant, K1 aClaas, Steven, A1 aSha, Jin1 aKabagambe, Edmond, K1 aLai, Chao-Qiang1 aParnell, Laurence, D1 aLee, Yu-Chi1 aAmouyel, Philippe1 aLambert, Jean-Charles1 aPsaty, Bruce, M1 aKing, Irena, B1 aMozaffarian, Dariush1 aMcKnight, Barbara1 aBandinelli, Stefania1 aTsai, Michael, Y1 aRidker, Paul, M1 aDing, Jingzhong1 aMstat, Kurt, Lohmant1 aLiu, Yongmei1 aSotoodehnia, Nona1 aBarberger-Gateau, Pascale1 aSteffen, Lyn, M1 aSiscovick, David, S1 aAbsher, Devin1 aArnett, Donna, K1 aOrdovas, Jose, M1 aLemaitre, Rozenn, N uhttps://chs-nhlbi.org/node/695105559nas a2201297 4500008004100000022001400041245013800055210006900193260001500262300001000277490000700287520188700294100002402181700002202205700002002227700002002247700001602267700002302283700001602306700002002322700001202342700002802354700002102382700002102403700002802424700002102452700001702473700002102490700002602511700002302537700002702560700002402587700002502611700001902636700001602655700002002671700002602691700002002717700002402737700002202761700002502783700002102808700001702829700001802846700001902864700002302883700002602906700001702932700001902949700002402968700002102992700002803013700002003041700002003061700002103081700002103102700002303123700001903146700002003165700002003185700001203205700002403217700002403241700002103265700002403286700002103310700002103331700002103352700002003373700002403393700002203417700001903439700001803458700001703476700002203493700002203515700001803537700002103555700002403576700002403600700002103624700002403645700002003669700002603689700002303715700002303738700001803761700001803779700001803797700002203815700002403837700002003861700002003881700002203901700002203923700001903945700002103964700002203985700002004007700001604027700002004043700002104063700001804084700002304102700002104125700001904146700002204165700002004187700001804207856003604225 2016 eng d a1537-660500aLarge-Scale Exome-wide Association Analysis Identifies Loci for White Blood Cell Traits and Pleiotropy with Immune-Mediated Diseases.0 aLargeScale Exomewide Association Analysis Identifies Loci for Wh c2016 Jul 7 a22-390 v993 aWhite blood cells play diverse roles in innate and adaptive immunity. Genetic association analyses of phenotypic variation in circulating white blood cell (WBC) counts from large samples of otherwise healthy individuals can provide insights into genes and biologic pathways involved in production, differentiation, or clearance of particular WBC lineages (myeloid, lymphoid) and also potentially inform the genetic basis of autoimmune, allergic, and blood diseases. We performed an exome array-based meta-analysis of total WBC and subtype counts (neutrophils, monocytes, lymphocytes, basophils, and eosinophils) in a multi-ancestry discovery and replication sample of ∼157,622 individuals from 25 studies. We identified 16 common variants (8 of which were coding variants) associated with one or more WBC traits, the majority of which are pleiotropically associated with autoimmune diseases. Based on functional annotation, these loci included genes encoding surface markers of myeloid, lymphoid, or hematopoietic stem cell differentiation (CD69, CD33, CD87), transcription factors regulating lineage specification during hematopoiesis (ASXL1, IRF8, IKZF1, JMJD1C, ETS2-PSMG1), and molecules involved in neutrophil clearance/apoptosis (C10orf54, LTA), adhesion (TNXB), or centrosome and microtubule structure/function (KIF9, TUBD1). Together with recent reports of somatic ASXL1 mutations among individuals with idiopathic cytopenias or clonal hematopoiesis of undetermined significance, the identification of a common regulatory 3' UTR variant of ASXL1 suggests that both germline and somatic ASXL1 mutations contribute to lower blood counts in otherwise asymptomatic individuals. These association results shed light on genetic mechanisms that regulate circulating WBC counts and suggest a prominent shared genetic architecture with inflammatory and autoimmune diseases.
1 aTajuddin, Salman, M1 aSchick, Ursula, M1 aEicher, John, D1 aChami, Nathalie1 aGiri, Ayush1 aBrody, Jennifer, A1 aHill, David1 aKacprowski, Tim1 aLi, Jin1 aLyytikäinen, Leo-Pekka1 aManichaikul, Ani1 aMihailov, Evelin1 aO'Donoghue, Michelle, L1 aPankratz, Nathan1 aPazoki, Raha1 aPolfus, Linda, M1 aSmith, Albert, Vernon1 aSchurmann, Claudia1 aVacchi-Suzzi, Caterina1 aWaterworth, Dawn, M1 aEvangelou, Evangelos1 aYanek, Lisa, R1 aBurt, Amber1 aChen, Ming-Huei1 avan Rooij, Frank, J A1 aFloyd, James, S1 aGreinacher, Andreas1 aHarris, Tamara, B1 aHighland, Heather, M1 aLange, Leslie, A1 aLiu, Yongmei1 aMägi, Reedik1 aNalls, Mike, A1 aMathias, Rasika, A1 aNickerson, Deborah, A1 aNikus, Kjell1 aStarr, John, M1 aTardif, Jean-Claude1 aTzoulaki, Ioanna1 aEdwards, Digna, R Velez1 aWallentin, Lars1 aBartz, Traci, M1 aBecker, Lewis, C1 aDenny, Joshua, C1 aRaffield, Laura, M1 aRioux, John, D1 aFriedrich, Nele1 aFornage, Myriam1 aGao, He1 aHirschhorn, Joel, N1 aLiewald, David, C M1 aRich, Stephen, S1 aUitterlinden, Andre1 aBastarache, Lisa1 aBecker, Diane, M1 aBoerwinkle, Eric1 ade Denus, Simon1 aBottinger, Erwin, P1 aHayward, Caroline1 aHofman, Albert1 aHomuth, Georg1 aLange, Ethan1 aLauner, Lenore, J1 aLehtimäki, Terho1 aLu, Yingchang1 aMetspalu, Andres1 aO'Donnell, Chris, J1 aQuarells, Rakale, C1 aRichard, Melissa1 aTorstenson, Eric, S1 aTaylor, Kent, D1 aVergnaud, Anne-Claire1 aZonderman, Alan, B1 aCrosslin, David, R1 aDeary, Ian, J1 aDörr, Marcus1 aElliott, Paul1 aEvans, Michele, K1 aGudnason, Vilmundur1 aKähönen, Mika1 aPsaty, Bruce, M1 aRotter, Jerome, I1 aSlater, Andrew, J1 aDehghan, Abbas1 aWhite, Harvey, D1 aGanesh, Santhi, K1 aLoos, Ruth, J F1 aEsko, Tõnu1 aFaraday, Nauder1 aWilson, James, G1 aCushman, Mary1 aJohnson, Andrew, D1 aEdwards, Todd, L1 aZakai, Neil, A1 aLettre, Guillaume1 aReiner, Alex, P1 aAuer, Paul, L uhttps://chs-nhlbi.org/node/714604981nas a2201333 4500008004100000022001400041245015700055210006900212260001300281300001000294490000700304520112400311100003001435700001601465700001901481700002501500700002101525700002101546700002101567700002301588700002001611700001501631700002101646700002401667700001701691700001901708700001801727700001801745700002001763700002001783700001801803700002501821700002801846700002201874700001801896700002701914700002801941700001901969700002101988700001902009700002102028700001902049700002602068700002302094700002802117700002602145700002102171700002202192700002502214700002302239700002102262700002202283700001902305700001802324700002002342700002102362700001602383700001802399700001702417700002202434700002002456700002102476700002202497700002002519700002202539700002002561700002002581700002102601700002702622700002302649700002302672700001402695700002302709700002802732700002302760700002202783700001702805700002402822700002302846700002502869700002602894700002302920700002002943700002902963700002002992700002303012700002003035700002203055700001803077700001503095700001903110700002903129700002303158700002203181700001903203700002003222700002603242700002203268700001903290700002203309700002103331700002103352700002403373700002103397700002003418700002303438700002103461700002203482700002503504700002303529710002703552710003203579856003603611 2016 eng d a1468-624400aMeta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels.0 aMetaanalysis of 49 549 individuals imputed with the 1000 Genomes c2016 Jul a441-90 v533 aBACKGROUND: So far, more than 170 loci have been associated with circulating lipid levels through genome-wide association studies (GWAS). These associations are largely driven by common variants, their function is often not known, and many are likely to be markers for the causal variants. In this study we aimed to identify more new rare and low-frequency functional variants associated with circulating lipid levels.
METHODS: We used the 1000 Genomes Project as a reference panel for the imputations of GWAS data from ∼60 000 individuals in the discovery stage and ∼90 000 samples in the replication stage.
RESULTS: Our study resulted in the identification of five new associations with circulating lipid levels at four loci. All four loci are within genes that can be linked biologically to lipid metabolism. One of the variants, rs116843064, is a damaging missense variant within the ANGPTL4 gene.
CONCLUSIONS: This study illustrates that GWAS with high-scale imputation may still help us unravel the biological mechanism behind circulating lipid levels.
1 avan Leeuwen, Elisabeth, M1 aSabo, Aniko1 aBis, Joshua, C1 aHuffman, Jennifer, E1 aManichaikul, Ani1 aSmith, Albert, V1 aFeitosa, Mary, F1 aDemissie, Serkalem1 aJoshi, Peter, K1 aDuan, Qing1 aMarten, Jonathan1 avan Klinken, Jan, B1 aSurakka, Ida1 aNolte, Ilja, M1 aZhang, Weihua1 aMbarek, Hamdi1 aLi-Gao, Ruifang1 aTrompet, Stella1 aVerweij, Niek1 aEvangelou, Evangelos1 aLyytikäinen, Leo-Pekka1 aTayo, Bamidele, O1 aDeelen, Joris1 avan der Most, Peter, J1 avan der Laan, Sander, W1 aArking, Dan, E1 aMorrison, Alanna1 aDehghan, Abbas1 aFranco, Oscar, H1 aHofman, Albert1 aRivadeneira, Fernando1 aSijbrands, Eric, J1 aUitterlinden, André, G1 aMychaleckyj, Josyf, C1 aCampbell, Archie1 aHocking, Lynne, J1 aPadmanabhan, Sandosh1 aBrody, Jennifer, A1 aRice, Kenneth, M1 aWhite, Charles, C1 aHarris, Tamara1 aIsaacs, Aaron1 aCampbell, Harry1 aLange, Leslie, A1 aRudan, Igor1 aKolcic, Ivana1 aNavarro, Pau1 aZemunik, Tatijana1 aSalomaa, Veikko1 aKooner, Angad, S1 aKooner, Jaspal, S1 aLehne, Benjamin1 aScott, William, R1 aTan, Sian-Tsung1 ade Geus, Eco, J1 aMilaneschi, Yuri1 aPenninx, Brenda, W J H1 aWillemsen, Gonneke1 ade Mutsert, Renée1 aFord, Ian1 aGansevoort, Ron, T1 aSegura-Lepe, Marcelo, P1 aRaitakari, Olli, T1 aViikari, Jorma, S1 aNikus, Kjell1 aForrester, Terrence1 aMcKenzie, Colin, A1 ade Craen, Anton, J M1 ade Ruijter, Hester, M1 aPasterkamp, Gerard1 aSnieder, Harold1 aOldehinkel, Albertine, J1 aSlagboom, Eline1 aCooper, Richard, S1 aKähönen, Mika1 aLehtimäki, Terho1 aElliott, Paul1 aHarst, Pim1 aJukema, Wouter1 aMook-Kanamori, Dennis, O1 aBoomsma, Dorret, I1 aChambers, John, C1 aSwertz, Morris1 aRipatti, Samuli1 avan Dijk, Ko, Willems1 aVitart, Veronique1 aPolasek, Ozren1 aHayward, Caroline1 aWilson, James, G1 aWilson, James, F1 aGudnason, Vilmundur1 aRich, Stephen, S1 aPsaty, Bruce, M1 aBorecki, Ingrid, B1 aBoerwinkle, Eric1 aRotter, Jerome, I1 aCupples, Adrienne, L1 aDuijn, Cornelia, M1 aLifeLines Cohort Study1 aCHARGE Lipids Working Group uhttps://chs-nhlbi.org/node/701104724nas a2201189 4500008004100000022001400041245009300055210006900148260001300217300001200230490000700242520135800249100001801607700002101625700002001646700002501666700002201691700001901713700002301732700002801755700002501783700002101808700001701829700001601846700002201862700002101884700001601905700002001921700002101941700002301962700002201985700002102007700002402028700002102052700002402073700001902097700002502116700002402141700002102165700002502186700002402211700002402235700001402259700001702273700002202290700002302312700001902335700001802354700002502372700002302397700001802420700001702438700001902455700002202474700002402496700001702520700002602537700002002563700001802583700003002601700001602631700001802647700002702665700001802692700002102710700002602731700001902757700001702776700002202793700002202815700002002837700002302857700002102880700002202901700001902923700002002942700002302962700001802985700002803003700001603031700002603047700002103073700002203094700002203116700002803138700002203166700002503188700002103213700002403234700001903258700002303277700002003300700002003320700002603340700002203366700002403388700002303412700001903435700002203454700002203476856003603498 2016 eng d a1468-624400aMeta-analysis of genome-wide association studies of HDL cholesterol response to statins.0 aMetaanalysis of genomewide association studies of HDL cholestero c2016 Dec a835-8450 v533 aBACKGROUND: In addition to lowering low density lipoprotein cholesterol (LDL-C), statin therapy also raises high density lipoprotein cholesterol (HDL-C) levels. Inter-individual variation in HDL-C response to statins may be partially explained by genetic variation.
METHODS AND RESULTS: We performed a meta-analysis of genome-wide association studies (GWAS) to identify variants with an effect on statin-induced high density lipoprotein cholesterol (HDL-C) changes. The 123 most promising signals with p<1×10(-4) from the 16 769 statin-treated participants in the first analysis stage were followed up in an independent group of 10 951 statin-treated individuals, providing a total sample size of 27 720 individuals. The only associations of genome-wide significance (p<5×10(-8)) were between minor alleles at the CETP locus and greater HDL-C response to statin treatment.
CONCLUSIONS: Based on results from this study that included a relatively large sample size, we suggest that CETP may be the only detectable locus with common genetic variants that influence HDL-C response to statins substantially in individuals of European descent. Although CETP is known to be associated with HDL-C, we provide evidence that this pharmacogenetic effect is independent of its association with baseline HDL-C levels.
1 aPostmus, Iris1 aWarren, Helen, R1 aTrompet, Stella1 aArsenault, Benoit, J1 aAvery, Christy, L1 aBis, Joshua, C1 aChasman, Daniel, I1 ade Keyser, Catherine, E1 aDeshmukh, Harshal, A1 aEvans, Daniel, S1 aFeng, QiPing1 aLi, Xiaohui1 aSmit, Roelof, A J1 aSmith, Albert, V1 aSun, Fangui1 aTaylor, Kent, D1 aArnold, Alice, M1 aBarnes, Michael, R1 aBarratt, Bryan, J1 aBetteridge, John1 aBoekholdt, Matthijs1 aBoerwinkle, Eric1 aBuckley, Brendan, M1 aChen, Y-D, Ida1 ade Craen, Anton, J M1 aCummings, Steven, R1 aDenny, Joshua, C1 aDubé, Marie, Pierre1 aDurrington, Paul, N1 aEiriksdottir, Gudny1 aFord, Ian1 aGuo, Xiuqing1 aHarris, Tamara, B1 aHeckbert, Susan, R1 aHofman, Albert1 aHovingh, Kees1 aKastelein, John, J P1 aLauner, Leonore, J1 aLiu, Ching-Ti1 aLiu, Yongmei1 aLumley, Thomas1 aMcKeigue, Paul, M1 aMunroe, Patricia, B1 aNeil, Andrew1 aNickerson, Deborah, A1 aNyberg, Fredrik1 aO'Brien, Eoin1 aO'Donnell, Christopher, J1 aPost, Wendy1 aPoulter, Neil1 aVasan, Ramachandran, S1 aRice, Kenneth1 aRich, Stephen, S1 aRivadeneira, Fernando1 aSattar, Naveed1 aSever, Peter1 aShaw-Hawkins, Sue1 aShields, Denis, C1 aSlagboom, Eline1 aSmith, Nicholas, L1 aSmith, Joshua, D1 aSotoodehnia, Nona1 aStanton, Alice1 aStott, David, J1 aStricker, Bruno, H1 aStürmer, Til1 aUitterlinden, André, G1 aWei, Wei-Qi1 aWestendorp, Rudi, G J1 aWhitsel, Eric, A1 aWiggins, Kerri, L1 aWilke, Russell, A1 aBallantyne, Christie, M1 aColhoun, Helen, M1 aCupples, Adrienne, L1 aFranco, Oscar, H1 aGudnason, Vilmundur1 aHitman, Graham1 aPalmer, Colin, N A1 aPsaty, Bruce, M1 aRidker, Paul, M1 aStafford, Jeanette, M1 aStein, Charles, M1 aTardif, Jean-Claude1 aCaulfield, Mark, J1 aJukema, Wouter1 aRotter, Jerome, I1 aKrauss, Ronald, M uhttps://chs-nhlbi.org/node/735806109nas a2201525 4500008004100000022001400041245009200055210006900147260001500216300001000231490000700241520179700248100002002045700002002065700002002085700002002105700002002125700001902145700002402164700002202188700002202210700002102232700001802253700002302271700001602294700002302310700002102333700002102354700001602375700001702391700001702408700002502425700002102450700001202471700002602483700002302509700002102532700002102553700001602574700002302590700002802613700001702641700002302658700002002681700002102701700001802722700002302740700002202763700001802785700001902803700002602822700002302848700002102871700002302892700002002915700002502935700002002960700002002980700002203000700002403022700002203046700002003068700002103088700002403109700001903133700002403152700002003176700001803196700002403214700002003238700002603258700002203284700002203306700002403328700002803352700002403380700001703404700002203421700002203443700002103465700002503486700002103511700001203532700001703544700002103561700002103582700001803603700002103621700002103642700001603663700002703679700001903706700002303725700002203748700002203770700002503792700001903817700002803836700002403864700002003888700002103908700002003929700002003949700002103969700002003990700001604010700002404026700002204050700001804072700002404090700001704114700001904131700001904150700002604169700002804195700002104223700001904244700002104263700002204284700002204306700002004328700001804348700002004366700002204386700002304408710003804431710003204469710004604501856003604547 2016 eng d a1537-660500aPlatelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals.0 aPlateletRelated Variants Identified by Exomechip Metaanalysis in c2016 Jul 7 a40-550 v993 aPlatelet production, maintenance, and clearance are tightly controlled processes indicative of platelets' important roles in hemostasis and thrombosis. Platelets are common targets for primary and secondary prevention of several conditions. They are monitored clinically by complete blood counts, specifically with measurements of platelet count (PLT) and mean platelet volume (MPV). Identifying genetic effects on PLT and MPV can provide mechanistic insights into platelet biology and their role in disease. Therefore, we formed the Blood Cell Consortium (BCX) to perform a large-scale meta-analysis of Exomechip association results for PLT and MPV in 157,293 and 57,617 individuals, respectively. Using the low-frequency/rare coding variant-enriched Exomechip genotyping array, we sought to identify genetic variants associated with PLT and MPV. In addition to confirming 47 known PLT and 20 known MPV associations, we identified 32 PLT and 18 MPV associations not previously observed in the literature across the allele frequency spectrum, including rare large effect (FCER1A), low-frequency (IQGAP2, MAP1A, LY75), and common (ZMIZ2, SMG6, PEAR1, ARFGAP3/PACSIN2) variants. Several variants associated with PLT/MPV (PEAR1, MRVI1, PTGES3) were also associated with platelet reactivity. In concurrent BCX analyses, there was overlap of platelet-associated variants with red (MAP1A, TMPRSS6, ZMIZ2) and white (PEAR1, ZMIZ2, LY75) blood cell traits, suggesting common regulatory pathways with shared genetic architecture among these hematopoietic lineages. Our large-scale Exomechip analyses identified previously undocumented associations with platelet traits and further indicate that several complex quantitative hematological, lipid, and cardiovascular traits share genetic factors.
1 aEicher, John, D1 aChami, Nathalie1 aKacprowski, Tim1 aNomura, Akihiro1 aChen, Ming-Huei1 aYanek, Lisa, R1 aTajuddin, Salman, M1 aSchick, Ursula, M1 aSlater, Andrew, J1 aPankratz, Nathan1 aPolfus, Linda1 aSchurmann, Claudia1 aGiri, Ayush1 aBrody, Jennifer, A1 aLange, Leslie, A1 aManichaikul, Ani1 aHill, David1 aPazoki, Raha1 aElliot, Paul1 aEvangelou, Evangelos1 aTzoulaki, Ioanna1 aGao, He1 aVergnaud, Anne-Claire1 aMathias, Rasika, A1 aBecker, Diane, M1 aBecker, Lewis, C1 aBurt, Amber1 aCrosslin, David, R1 aLyytikäinen, Leo-Pekka1 aNikus, Kjell1 aHernesniemi, Jussi1 aKähönen, Mika1 aRaitoharju, Emma1 aMononen, Nina1 aRaitakari, Olli, T1 aLehtimäki, Terho1 aCushman, Mary1 aZakai, Neil, A1 aNickerson, Deborah, A1 aRaffield, Laura, M1 aQuarells, Rakale1 aWiller, Cristen, J1 aPeloso, Gina, M1 aAbecasis, Goncalo, R1 aLiu, Dajiang, J1 aDeloukas, Panos1 aSamani, Nilesh, J1 aSchunkert, Heribert1 aErdmann, Jeanette1 aFornage, Myriam1 aRichard, Melissa1 aTardif, Jean-Claude1 aRioux, John, D1 aDubé, Marie-Pierre1 ade Denus, Simon1 aLu, Yingchang1 aBottinger, Erwin, P1 aLoos, Ruth, J F1 aSmith, Albert, Vernon1 aHarris, Tamara, B1 aLauner, Lenore, J1 aGudnason, Vilmundur1 aEdwards, Digna, R Velez1 aTorstenson, Eric, S1 aLiu, Yongmei1 aTracy, Russell, P1 aRotter, Jerome, I1 aRich, Stephen, S1 aHighland, Heather, M1 aBoerwinkle, Eric1 aLi, Jin1 aLange, Ethan1 aWilson, James, G1 aMihailov, Evelin1 aMägi, Reedik1 aHirschhorn, Joel1 aMetspalu, Andres1 aEsko, Tõnu1 aVacchi-Suzzi, Caterina1 aNalls, Mike, A1 aZonderman, Alan, B1 aEvans, Michele, K1 aEngström, Gunnar1 aOrho-Melander, Marju1 aMelander, Olle1 aO'Donoghue, Michelle, L1 aWaterworth, Dawn, M1 aWallentin, Lars1 aWhite, Harvey, D1 aFloyd, James, S1 aBartz, Traci, M1 aRice, Kenneth, M1 aPsaty, Bruce, M1 aStarr, J, M1 aLiewald, David, C M1 aHayward, Caroline1 aDeary, Ian, J1 aGreinacher, Andreas1 aVölker, Uwe1 aThiele, Thomas1 aVölzke, Henry1 avan Rooij, Frank, J A1 aUitterlinden, André, G1 aFranco, Oscar, H1 aDehghan, Abbas1 aEdwards, Todd, L1 aGanesh, Santhi, K1 aKathiresan, Sekar1 aFaraday, Nauder1 aAuer, Paul, L1 aReiner, Alex, P1 aLettre, Guillaume1 aJohnson, Andrew, D1 aGlobal Lipids Genetics Consortium1 aCARDIoGRAM Exome Consortium1 aMyocardial Infarction Genetics Consortium uhttps://chs-nhlbi.org/node/713905835nas a2201585 4500008004100000022001400041245012700055210006900182260001500251300001000266490000700276520141400283100001801697700002401715700001901739700002801758700001901786700001901805700002501824700001801849700001301867700002001880700002001900700002101920700001701941700002401958700002501982700001202007700002202019700002002041700002002061700002202081700002602103700001902129700001802148700001802166700002502184700002102209700002102230700002202251700002102273700001702294700001702311700001702328700001502345700002102360700002402381700003102405700002402436700002402460700002002484700001902504700001402523700002102537700002402558700001902582700002402601700002202625700001802647700001902665700002102684700002202705700001802727700002002745700002002765700002302785700001602808700002202824700002002846700001602866700002202882700002302904700002302927700001702950700002202967700002302989700001703012700002003029700002203049700002303071700001603094700002003110700001503130700002103145700002203166700001903188700002503207700002403232700002103256700002103277700002203298700001603320700002303336700002403359700002203383700001803405700001303423700001803436700002203454700002103476700002303497700002603520700002303546700002103569700002003590700002003610700002403630700001903654700002103673700002303694700002203717700002603739700002103765700002203786700002203808700002503830700002003855700002403875700002003899700002103919700002003940700001903960700002103979700002004000700002204020700002204042700002004064710002004084710002004104710002504124710002204149710002104171710002104192856003604213 2016 eng d a1537-660500aTrans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin.0 aTransethnic Metaanalysis and Functional Annotation Illuminates t c2016 Jul 7 a56-750 v993 aKnowledge of the genetic basis of the type 2 diabetes (T2D)-related quantitative traits fasting glucose (FG) and insulin (FI) in African ancestry (AA) individuals has been limited. In non-diabetic subjects of AA (n = 20,209) and European ancestry (EA; n = 57,292), we performed trans-ethnic (AA+EA) fine-mapping of 54 established EA FG or FI loci with detailed functional annotation, assessed their relevance in AA individuals, and sought previously undescribed loci through trans-ethnic (AA+EA) meta-analysis. We narrowed credible sets of variants driving association signals for 22/54 EA-associated loci; 18/22 credible sets overlapped with active islet-specific enhancers or transcription factor (TF) binding sites, and 21/22 contained at least one TF motif. Of the 54 EA-associated loci, 23 were shared between EA and AA. Replication with an additional 10,096 AA individuals identified two previously undescribed FI loci, chrX FAM133A (rs213676) and chr5 PELO (rs6450057). Trans-ethnic analyses with regulatory annotation illuminate the genetic architecture of glycemic traits and suggest gene regulation as a target to advance precision medicine for T2D. Our approach to utilize state-of-the-art functional annotation and implement trans-ethnic association analysis for discovery and fine-mapping offers a framework for further follow-up and characterization of GWAS signals of complex trait loci.
1 aLiu, Ching-Ti1 aRaghavan, Sridharan1 aMaruthur, Nisa1 aKabagambe, Edmond, Kato1 aHong, Jaeyoung1 aC Y Ng, Maggie1 aHivert, Marie-France1 aLu, Yingchang1 aAn, Ping1 aBentley, Amy, R1 aDrolet, Anne, M1 aGaulton, Kyle, J1 aGuo, Xiuqing1 aArmstrong, Loren, L1 aIrvin, Marguerite, R1 aLi, Man1 aLipovich, Leonard1 aRybin, Denis, V1 aTaylor, Kent, D1 aAgyemang, Charles1 aPalmer, Nicholette, D1 aCade, Brian, E1 aChen, Wei-Min1 aDauriz, Marco1 aDelaney, Joseph, A C1 aEdwards, Todd, L1 aEvans, Daniel, S1 aEvans, Michele, K1 aLange, Leslie, A1 aLeong, Aaron1 aLiu, Jingmin1 aLiu, Yongmei1 aNayak, Uma1 aPatel, Sanjay, R1 aPorneala, Bianca, C1 aRasmussen-Torvik, Laura, J1 aSnijder, Marieke, B1 aStallings, Sarah, C1 aTanaka, Toshiko1 aYanek, Lisa, R1 aZhao, Wei1 aBecker, Diane, M1 aBielak, Lawrence, F1 aBiggs, Mary, L1 aBottinger, Erwin, P1 aBowden, Donald, W1 aChen, Guanjie1 aCorrea, Adolfo1 aCouper, David, J1 aCrawford, Dana, C1 aCushman, Mary1 aEicher, John, D1 aFornage, Myriam1 aFranceschini, Nora1 aFu, Yi-Ping1 aGoodarzi, Mark, O1 aGottesman, Omri1 aHara, Kazuo1 aHarris, Tamara, B1 aJensen, Richard, A1 aJohnson, Andrew, D1 aJhun, Min, A1 aKarter, Andrew, J1 aKeller, Margaux, F1 aKho, Abel, N1 aKizer, Jorge, R1 aKrauss, Ronald, M1 aLangefeld, Carl, D1 aLi, Xiaohui1 aLiang, Jingling1 aLiu, Simin1 aLowe, William, L1 aMosley, Thomas, H1 aNorth, Kari, E1 aPacheco, Jennifer, A1 aPeyser, Patricia, A1 aPatrick, Alan, L1 aRice, Kenneth, M1 aSelvin, Elizabeth1 aSims, Mario1 aSmith, Jennifer, A1 aTajuddin, Salman, M1 aVaidya, Dhananjay1 aWren, Mary, P1 aYao, Jie1 aZhu, Xiaofeng1 aZiegler, Julie, T1 aZmuda, Joseph, M1 aZonderman, Alan, B1 aZwinderman, Aeilko, H1 aAdeyemo, Adebowale1 aBoerwinkle, Eric1 aFerrucci, Luigi1 aHayes, Geoffrey1 aKardia, Sharon, L R1 aMiljkovic, Iva1 aPankow, James, S1 aRotimi, Charles, N1 aSale, Michèle, M1 aWagenknecht, Lynne, E1 aArnett, Donna, K1 aChen, Yii-Der Ida1 aNalls, Michael, A1 aProvince, Michael, A1 aKao, Linda, W H1 aSiscovick, David, S1 aPsaty, Bruce, M1 aWilson, James, G1 aLoos, Ruth, J F1 aDupuis, Josée1 aRich, Stephen, S1 aFlorez, Jose, C1 aRotter, Jerome, I1 aMorris, Andrew, P1 aMeigs, James, B1 aAAAG Consortium1 aCARe Consortium1 aCOGENT-BP Consortium1 aeMERGE Consortium1 aMEDIA Consortium1 aMAGIC Consortium uhttps://chs-nhlbi.org/node/714102324nas a2200793 4500008004100000022001400041245015800055210006900213260001600282300000800298490000700306100002100313700002300334700002200357700002100379700001700400700002300417700002000440700001800460700002000478700001500498700001800513700002600531700002200557700002000579700001700599700002900616700002000645700001300665700001600678700002100694700002000715700002100735700001500756700002100771700001600792700001500808700002000823700002300843700002000866700002100886700002000907700002800927700002000955700002200975700002300997700002001020700002901040700002201069700002301091700002101114700002101135700002601156700001901182700002801201700002101229700002001250700002001270700001901290700002101309700002001330700002301350700003001373700002101403700002501424700002201449700002301471856003601494 2016 eng d a1537-660500aWhole-Exome Sequencing Identifies Loci Associated with Blood Cell Traits and Reveals a Role for Alternative GFI1B Splice Variants in Human Hematopoiesis.0 aWholeExome Sequencing Identifies Loci Associated with Blood Cell c2016 Sep 01 a7850 v991 aPolfus, Linda, M1 aKhajuria, Rajiv, K1 aSchick, Ursula, M1 aPankratz, Nathan1 aPazoki, Raha1 aBrody, Jennifer, A1 aChen, Ming-Huei1 aAuer, Paul, L1 aFloyd, James, S1 aHuang, Jie1 aLange, Leslie1 avan Rooij, Frank, J A1 aGibbs, Richard, A1 aMetcalf, Ginger1 aMuzny, Donna1 aVeeraraghavan, Narayanan1 aWalter, Klaudia1 aChen, Lu1 aYanek, Lisa1 aBecker, Lewis, C1 aPeloso, Gina, M1 aWakabayashi, Aoi1 aKals, Mart1 aMetspalu, Andres1 aEsko, Tõnu1 aFox, Keolu1 aWallace, Robert1 aFranceschini, Nora1 aMatijevic, Nena1 aRice, Kenneth, M1 aBartz, Traci, M1 aLyytikäinen, Leo-Pekka1 aKähönen, Mika1 aLehtimäki, Terho1 aRaitakari, Olli, T1 aLi-Gao, Ruifang1 aMook-Kanamori, Dennis, O1 aLettre, Guillaume1 aDuijn, Cornelia, M1 aFranco, Oscar, H1 aRich, Stephen, S1 aRivadeneira, Fernando1 aHofman, Albert1 aUitterlinden, André, G1 aWilson, James, G1 aPsaty, Bruce, M1 aSoranzo, Nicole1 aDehghan, Abbas1 aBoerwinkle, Eric1 aZhang, Xiaoling1 aJohnson, Andrew, D1 aO'Donnell, Christopher, J1 aJohnsen, Jill, M1 aReiner, Alexander, P1 aGanesh, Santhi, K1 aSankaran, Vijay, G uhttps://chs-nhlbi.org/node/726301904nas a2200577 4500008004100000022001400041245010300055210006900158260001600227300001400243490000700257100002300264700002400287700001900311700002600330700002200356700002500378700002000403700002000423700002400443700002000467700002200487700002700509700001900536700002400555700002300579700002100602700001800623700002200641700002800663700003000691700002100721700002100742700002400763700002300787700002300810700002100833700002500854700002700879700002100906700002200927700002100949700002100970700002000991700002501011710006501036710008501101710005101186710005301237856003601290 2017 eng d a1546-171800aAnalysis commons, a team approach to discovery in a big-data environment for genetic epidemiology.0 aAnalysis commons a team approach to discovery in a bigdata envir c2017 Oct 27 a1560-15630 v491 aBrody, Jennifer, A1 aMorrison, Alanna, C1 aBis, Joshua, C1 aO'Connell, Jeffrey, R1 aBrown, Michael, R1 aHuffman, Jennifer, E1 aAmes, Darren, C1 aCarroll, Andrew1 aConomos, Matthew, P1 aGabriel, Stacey1 aGibbs, Richard, A1 aGogarten, Stephanie, M1 aGupta, Namrata1 aJaquish, Cashell, E1 aJohnson, Andrew, D1 aLewis, Joshua, P1 aLiu, Xiaoming1 aManning, Alisa, K1 aPapanicolaou, George, J1 aPitsillides, Achilleas, N1 aRice, Kenneth, M1 aSalerno, William1 aSitlani, Colleen, M1 aSmith, Nicholas, L1 aHeckbert, Susan, R1 aLaurie, Cathy, C1 aMitchell, Braxton, D1 aVasan, Ramachandran, S1 aRich, Stephen, S1 aRotter, Jerome, I1 aWilson, James, G1 aBoerwinkle, Eric1 aPsaty, Bruce, M1 aCupples, Adrienne, L1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aCohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium1 aTOPMed Hematology and Hemostasis Working Group1 aCHARGE Analysis and Bioinformatics Working Group uhttps://chs-nhlbi.org/node/755303246nas a2200505 4500008004100000022001400041245015500055210006900210260001600279520174700295100001202042700002002054700001702074700001702091700001802108700002002126700002202146700001902168700001202187700002102199700002102220700002802241700002502269700002102294700002102315700002002336700002502356700002102381700002102402700001802423700002402441700002102465700002102486700002002507700001702527700001802544700002202562700002402584700002502608700002002633700002202653700001702675700001202692856003602704 2017 eng d a1539-726200aDiscovery and fine-mapping of loci associated with monounsaturated fatty acids through trans-ethnic meta-analysis in Chinese and European populations.0 aDiscovery and finemapping of loci associated with monounsaturate c2017 Mar 153 aMonounsaturated fatty acids (MUFAs) are unsaturated fatty acids with one double bond and are derived from endogenous synthesis and dietary intake. Accumulating evidence has suggested that plasma and erythrocyte MUFA levels were associated with cardiometabolic disorders including cardiovascular disease (CVD), type 2 diabetes (T2D) and metabolic syndrome (MS). Previous genome-wide association studies (GWAS) have identified seven loci for plasma and erythrocyte palmitoleic acid and oleic acid levels in populations of European origin. To identify additional MUFA-associated loci and the potential causal variant at each locus, we performed ethnic-specific GWAS meta-analyses and trans-ethnic meta-analyses in over 15,000 participants of Chinese- and European-ancestry. We identified novel genome-wide significant associations for vaccenic acid at FADS1/2 and PKD2L1 [log10(Bayes factor)>=8.07] and for gondoic acid at FADS1/2 and GCKR [log10(Bayes factor)>=61619;6.22], and also observed improved fine-mapping resolutions at FADS1/2 and GCKR loci. The greatest improvement was observed at GCKR, where the number of variants in the 99% credible set was reduced from 16 (covering ~95kb) to five (covering ~20kb, including a missense variant rs1260326) after trans-ethnic meta-analysis. We also confirmed the previously reported associations of PKD2L1, FADS1/2, GCKR and HIF1AN with palmitoleic acid and of FADS1/2 and LPCAT3 with oleic acid in the Chinese-specific GWAS and trans-ethnic meta-analyses. Pathway-based analyses suggested that the identified loci were enriched in unsaturated fatty acids metabolism and signaling pathways. Our findings provided novel insight into the genetic basis relevant to MUFA metabolism and biology.
1 aHu, Yao1 aTanaka, Toshiko1 aZhu, Jingwen1 aGuan, Weihua1 aH Y Wu, Jason1 aPsaty, Bruce, M1 aMcKnight, Barbara1 aKing, Irena, B1 aSun, Qi1 aRichard, Melissa1 aManichaikul, Ani1 aFrazier-Wood, Alexis, C1 aKabagambe, Edmond, K1 aHopkins, Paul, N1 aOrdovas, Jose, M1 aFerrucci, Luigi1 aBandinelli, Stefania1 aArnett, Donna, K1 aChen, Yii-der, I1 aLiang, Shuang1 aSiscovick, David, S1 aTsai, Michael, Y1 aRich, Stephen, S1 aFornage, Myriam1 aHu, Frank, B1 aRimm, Eric, B1 aJensen, Majken, K1 aLemaitre, Rozenn, N1 aMozaffarian, Dariush1 aSteffen, Lyn, M1 aMorris, Andrew, P1 aLi, Huaixing1 aLin, Xu uhttps://chs-nhlbi.org/node/734609668nas a2203061 4500008004100000022001400041245007500055210006900130260001300199300001400212490000700226520116500233653002801398653003001426653001001456653003201466653003801498653002201536653001301558653001101571653001101582653002501593653001401618653001701632100002001649700002001669700001501689700002501704700001501729700002001744700002101764700001801785700001601803700003101819700002201850700003001872700002401902700002901926700001801955700001701973700002801990700001702018700001902035700001902054700002102073700002102094700002302115700002402138700002102162700001802183700002002201700002302221700002202244700002302266700001702289700002202306700001902328700002402347700001902371700002102390700002102411700002502432700002302457700001702480700001802497700002202515700002102537700002102558700002402579700002002603700002402623700001602647700002502663700002102688700002002709700002002729700001402749700002002763700002002783700002502803700002502828700002202853700002302875700002102898700002202919700001302941700002302954700002202977700002302999700002203022700002103044700001803065700001603083700002003099700002403119700001903143700002203162700002203184700002403206700002303230700002203253700001403275700002003289700002003309700002103329700002603350700002703376700002003403700002503423700001903448700002503467700002103492700002203513700001903535700001503554700001903569700001803588700002503606700002203631700002403653700002003677700002203697700001903719700001603738700002403754700001903778700002203797700002303819700002403842700001603866700002103882700002003903700001803923700001803941700001803959700002303977700002104000700002204021700002404043700002004067700002504087700002004112700002004132700001904152700002104171700002204192700002404214700002104238700003004259700002404289700002104313700002204334700002104356700002804377700002104405700001904426700002904445700002504474700002204499700002204521700002504543700002304568700001804591700002304609700001904632700001904651700002004670700002404690700002004714700001904734700001804753700002004771700002104791700001804812700002104830700002204851700002004873700002004893700002104913700002004934700001904954700002304973700001804996700002205014700001605036700002005052700002205072700001805094700001905112700002205131700002105153700001705174700002305191700002605214700001705240700002805257700002105285700002105306700002005327700002605347700002205373700002405395700002805419700001905447700002605466700002105492700002405513700002605537700002305563700001705586700001805603700001405621700002405635700002005659700002005679700002005699700002305719700002605742700002705768700001805795700002005813700001905833700002605852700001405878700002105892700002105913700002005934700002205954700002105976700002105997700002306018700001306041700002306054700001706077700002406094700001406118700001906132700001806151700001506169700001406184700001706198700002806215700002406243700001706267700002206284700001906306700002206325700002006347700002006367700002306387700002206410710003406432710002906466710002406495710002006519710003106539856003606570 2017 eng d a1546-171800aExome-wide association study of plasma lipids in >300,000 individuals.0 aExomewide association study of plasma lipids in 300000 individua c2017 Dec a1758-17660 v493 aWe screened variants on an exome-focused genotyping array in >300,000 participants (replication in >280,000 participants) and identified 444 independent variants in 250 loci significantly associated with total cholesterol (TC), high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), and/or triglycerides (TG). At two loci (JAK2 and A1CF), experimental analysis in mice showed lipid changes consistent with the human data. We also found that: (i) beta-thalassemia trait carriers displayed lower TC and were protected from coronary artery disease (CAD); (ii) excluding the CETP locus, there was not a predictable relationship between plasma HDL-C and risk for age-related macular degeneration; (iii) only some mechanisms of lowering LDL-C appeared to increase risk for type 2 diabetes (T2D); and (iv) TG-lowering alleles involved in hepatic production of TG-rich lipoproteins (TM6SF2 and PNPLA3) tracked with higher liver fat, higher risk for T2D, and lower risk for CAD, whereas TG-lowering alleles involved in peripheral lipolysis (LPL and ANGPTL4) had no effect on liver fat but decreased risks for both T2D and CAD.
10aCoronary Artery Disease10aDiabetes Mellitus, Type 210aExome10aGenetic Association Studies10aGenetic Predisposition to Disease10aGenetic Variation10aGenotype10aHumans10aLipids10aMacular Degeneration10aPhenotype10aRisk Factors1 aLiu, Dajiang, J1 aPeloso, Gina, M1 aYu, Haojie1 aButterworth, Adam, S1 aWang, Xiao1 aMahajan, Anubha1 aSaleheen, Danish1 aEmdin, Connor1 aAlam, Dewan1 aAlves, Alexessander, Couto1 aAmouyel, Philippe1 aDi Angelantonio, Emanuele1 aArveiler, Dominique1 aAssimes, Themistocles, L1 aAuer, Paul, L1 aBaber, Usman1 aBallantyne, Christie, M1 aBang, Lia, E1 aBenn, Marianne1 aBis, Joshua, C1 aBoehnke, Michael1 aBoerwinkle, Eric1 aBork-Jensen, Jette1 aBottinger, Erwin, P1 aBrandslund, Ivan1 aBrown, Morris1 aBusonero, Fabio1 aCaulfield, Mark, J1 aChambers, John, C1 aChasman, Daniel, I1 aChen, Eugene1 aChen, Yii-Der Ida1 aChowdhury, Raj1 aChristensen, Cramer1 aChu, Audrey, Y1 aConnell, John, M1 aCucca, Francesco1 aCupples, Adrienne, L1 aDamrauer, Scott, M1 aDavies, Gail1 aDeary, Ian, J1 aDedoussis, George1 aDenny, Joshua, C1 aDominiczak, Anna1 aDubé, Marie-Pierre1 aEbeling, Tapani1 aEiriksdottir, Gudny1 aEsko, Tõnu1 aFarmaki, Aliki-Eleni1 aFeitosa, Mary, F1 aFerrario, Marco1 aFerrieres, Jean1 aFord, Ian1 aFornage, Myriam1 aFranks, Paul, W1 aFrayling, Timothy, M1 aFrikke-Schmidt, Ruth1 aFritsche, Lars, G1 aFrossard, Philippe1 aFuster, Valentin1 aGanesh, Santhi, K1 aGao, Wei1 aGarcia, Melissa, E1 aGieger, Christian1 aGiulianini, Franco1 aGoodarzi, Mark, O1 aGrallert, Harald1 aGrarup, Niels1 aGroop, Leif1 aGrove, Megan, L1 aGudnason, Vilmundur1 aHansen, Torben1 aHarris, Tamara, B1 aHayward, Caroline1 aHirschhorn, Joel, N1 aHolmen, Oddgeir, L1 aHuffman, Jennifer1 aHuo, Yong1 aHveem, Kristian1 aJabeen, Sehrish1 aJackson, Anne, U1 aJakobsdottir, Johanna1 aJarvelin, Marjo-Riitta1 aJensen, Gorm, B1 aJørgensen, Marit, E1 aJukema, Wouter1 aJustesen, Johanne, M1 aKamstrup, Pia, R1 aKanoni, Stavroula1 aKarpe, Fredrik1 aKee, Frank1 aKhera, Amit, V1 aKlarin, Derek1 aKoistinen, Heikki, A1 aKooner, Jaspal, S1 aKooperberg, Charles1 aKuulasmaa, Kari1 aKuusisto, Johanna1 aLaakso, Markku1 aLakka, Timo1 aLangenberg, Claudia1 aLangsted, Anne1 aLauner, Lenore, J1 aLauritzen, Torsten1 aLiewald, David, C M1 aLin, Li, An1 aLinneberg, Allan1 aLoos, Ruth, J F1 aLu, Yingchang1 aLu, Xiangfeng1 aMägi, Reedik1 aMälarstig, Anders1 aManichaikul, Ani1 aManning, Alisa, K1 aMäntyselkä, Pekka1 aMarouli, Eirini1 aMasca, Nicholas, G D1 aMaschio, Andrea1 aMeigs, James, B1 aMelander, Olle1 aMetspalu, Andres1 aMorris, Andrew, P1 aMorrison, Alanna, C1 aMulas, Antonella1 aMüller-Nurasyid, Martina1 aMunroe, Patricia, B1 aNeville, Matt, J1 aNielsen, Jonas, B1 aNielsen, Sune, F1 aNordestgaard, Børge, G1 aOrdovas, Jose, M1 aMehran, Roxana1 aO'Donnell, Christoper, J1 aOrho-Melander, Marju1 aMolony, Cliona, M1 aMuntendam, Pieter1 aPadmanabhan, Sandosh1 aPalmer, Colin, N A1 aPasko, Dorota1 aPatel, Aniruddh, P1 aPedersen, Oluf1 aPerola, Markus1 aPeters, Annette1 aPisinger, Charlotta1 aPistis, Giorgio1 aPolasek, Ozren1 aPoulter, Neil1 aPsaty, Bruce, M1 aRader, Daniel, J1 aRasheed, Asif1 aRauramaa, Rainer1 aReilly, Dermot, F1 aReiner, Alex, P1 aRenstrom, Frida1 aRich, Stephen, S1 aRidker, Paul, M1 aRioux, John, D1 aRobertson, Neil, R1 aRoden, Dan, M1 aRotter, Jerome, I1 aRudan, Igor1 aSalomaa, Veikko1 aSamani, Nilesh, J1 aSanna, Serena1 aSattar, Naveed1 aSchmidt, Ellen, M1 aScott, Robert, A1 aSever, Peter1 aSevilla, Raquel, S1 aShaffer, Christian, M1 aSim, Xueling1 aSivapalaratnam, Suthesh1 aSmall, Kerrin, S1 aSmith, Albert, V1 aSmith, Blair, H1 aSomayajula, Sangeetha1 aSoutham, Lorraine1 aSpector, Timothy, D1 aSpeliotes, Elizabeth, K1 aStarr, John, M1 aStirrups, Kathleen, E1 aStitziel, Nathan1 aStrauch, Konstantin1 aStringham, Heather, M1 aSurendran, Praveen1 aTada, Hayato1 aTall, Alan, R1 aTang, Hua1 aTardif, Jean-Claude1 aTaylor, Kent, D1 aTrompet, Stella1 aTsao, Philip, S1 aTuomilehto, Jaakko1 aTybjaerg-Hansen, Anne1 avan Zuydam, Natalie, R1 aVarbo, Anette1 aVarga, Tibor, V1 aVirtamo, Jarmo1 aWaldenberger, Melanie1 aWang, Nan1 aWareham, Nick, J1 aWarren, Helen, R1 aWeeke, Peter, E1 aWeinstock, Joshua1 aWessel, Jennifer1 aWilson, James, G1 aWilson, Peter, W F1 aXu, Ming1 aYaghootkar, Hanieh1 aYoung, Robin1 aZeggini, Eleftheria1 aZhang, He1 aZheng, Neil, S1 aZhang, Weihua1 aZhang, Yan1 aZhou, Wei1 aZhou, Yanhua1 aZoledziewska, Magdalena1 aHowson, Joanna, M M1 aDanesh, John1 aMcCarthy, Mark, I1 aCowan, Chad, A1 aAbecasis, Goncalo1 aDeloukas, Panos1 aMusunuru, Kiran1 aWiller, Cristen, J1 aKathiresan, Sekar1 aCharge Diabetes Working Group1 aEPIC-InterAct Consortium1 aEPIC-CVD Consortium1 aGOLD Consortium1 aVA Million Veteran Program uhttps://chs-nhlbi.org/node/757303442nas a2200469 4500008004100000022001400041245018400055210006900239260001300308300001200321490000700333520199400340100002202334700002502356700002402381700002502405700002002430700001702450700002402467700002002491700002602511700001302537700002302550700002002573700002702593700001902620700002202639700001902661700002102680700002302701700002002724700002102744700002202765700001702787700002102804700001902825700002702844700002202871700002402893700001902917856003602936 2017 eng d a1556-387100aFine mapping of QT interval regions in global populations refines previously identified QT interval loci and identifies signals unique to African and Hispanic descent populations.0 aFine mapping of QT interval regions in global populations refine c2017 Apr a572-5800 v143 aBACKGROUND: The electrocardiographically measured QT interval (QT) is heritable and its prolongation is an established risk factor for several cardiovascular diseases. Yet, most QT genetic studies have been performed in European ancestral populations, possibly reducing their global relevance.
OBJECTIVE: To leverage diversity and improve biological insight, we fine mapped 16 of the 35 previously identified QT loci (46%) in populations of African American (n = 12,410) and Hispanic/Latino (n = 14,837) ancestry.
METHODS: Racial/ethnic-specific multiple linear regression analyses adjusted for heart rate and clinical covariates were examined separately and in combination after inverse-variance weighted trans-ethnic meta-analysis.
RESULTS: The 16 fine-mapped QT loci included on the Illumina Metabochip represented 21 independent signals, of which 16 (76%) were significantly (P-value≤9.1×10(-5)) associated with QT. Through sequential conditional analysis we also identified three trans-ethnic novel SNPs at ATP1B1, SCN5A-SCN10A, and KCNQ1 and three Hispanic/Latino-specific novel SNPs at NOS1AP and SCN5A-SCN10A (two novel SNPs) with evidence of associations with QT independent of previous identified GWAS lead SNPs. Linkage disequilibrium patterns helped to narrow the region likely to contain the functional variants at several loci, including NOS1AP, USP50-TRPM7, and PRKCA, although intervals surrounding SLC35F1-PLN and CNOT1 remained broad in size (>100 kb). Finally, bioinformatics-based functional characterization suggested a regulatory function in cardiac tissues for the majority of independent signals that generalized and the novel SNPs.
CONCLUSION: Our findings suggest that a majority of identified SNPs implicate gene regulatory dysfunction in QT prolongation, that the same loci influence variation in QT across global populations, and that additional, novel, population-specific QT signals exist.
1 aAvery, Christy, L1 aWassel, Christina, L1 aRichard, Melissa, A1 aHighland, Heather, M1 aBien, Stephanie1 aZubair, Niha1 aSoliman, Elsayed, Z1 aFornage, Myriam1 aBielinski, Suzette, J1 aTao, Ran1 aSeyerle, Amanda, A1 aShah, Sanjiv, J1 aLloyd-Jones, Donald, M1 aBuyske, Steven1 aRotter, Jerome, I1 aPost, Wendy, S1 aRich, Stephen, S1 aHindorff, Lucia, A1 aJeff, Janina, M1 aShohet, Ralph, V1 aSotoodehnia, Nona1 aLin, Dan, Yu1 aWhitsel, Eric, A1 aPeters, Ulrike1 aHaiman, Christopher, A1 aCrawford, Dana, C1 aKooperberg, Charles1 aNorth, Kari, E uhttps://chs-nhlbi.org/node/746304391nas a2201129 4500008004100000022001400041245013100055210006900186260001300255300001200268490000700280520110800287100002001395700001701415700002301432700001801455700001701473700002501490700002001515700001701535700002501552700001901577700002501596700001901621700002301640700002301663700002001686700002201706700002001728700002301748700002101771700002001792700002301812700002001835700002001855700001801875700002001893700002201913700001601935700002401951700002101975700002401996700002002020700001602040700002002056700002602076700001902102700002402121700001902145700001802164700002002182700001702202700002002219700002402239700001902263700002402282700002402306700002102330700001802351700002102369700001702390700001902407700002502426700001502451700001902466700001902485700002202504700002302526700002802549700002802577700002402605700002602629700002302655700001602678700002002694700001802714700002102732700002502753700002102778700001802799700002402817700002302841700002202864700002002886700002102906700002402927700001902951700002002970710002702990710002603017710002803043710002903071710005403100710002803154710004303182856003603225 2017 eng d a1546-171800aGenetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis.0 aGenetic loci associated with chronic obstructive pulmonary disea c2017 Mar a426-4320 v493 aChronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide. We performed a genetic association study in 15,256 cases and 47,936 controls, with replication of select top results (P < 5 × 10(-6)) in 9,498 cases and 9,748 controls. In the combined meta-analysis, we identified 22 loci associated at genome-wide significance, including 13 new associations with COPD. Nine of these 13 loci have been associated with lung function in general population samples, while 4 (EEFSEC, DSP, MTCL1, and SFTPD) are new. We noted two loci shared with pulmonary fibrosis (FAM13A and DSP) but that had opposite risk alleles for COPD. None of our loci overlapped with genome-wide associations for asthma, although one locus has been implicated in joint susceptibility to asthma and obesity. We also identified genetic correlation between COPD and asthma. Our findings highlight new loci associated with COPD, demonstrate the importance of specific loci associated with lung function to COPD, and identify potential regions of genetic overlap between COPD and other respiratory diseases.
1 aHobbs, Brian, D1 ade Jong, Kim1 aLamontagne, Maxime1 aBossé, Yohan1 aShrine, Nick1 aArtigas, Maria Soler1 aWain, Louise, V1 aHall, Ian, P1 aJackson, Victoria, E1 aWyss, Annah, B1 aLondon, Stephanie, J1 aNorth, Kari, E1 aFranceschini, Nora1 aStrachan, David, P1 aBeaty, Terri, H1 aHokanson, John, E1 aCrapo, James, D1 aCastaldi, Peter, J1 aChase, Robert, P1 aBartz, Traci, M1 aHeckbert, Susan, R1 aPsaty, Bruce, M1 aGharib, Sina, A1 aZanen, Pieter1 aLammers, Jan, W1 aOudkerk, Matthijs1 aGroen, H, J1 aLocantore, Nicholas1 aTal-Singer, Ruth1 aRennard, Stephen, I1 aVestbo, Jørgen1 aTimens, Wim1 aParé, Peter, D1 aLatourelle, Jeanne, C1 aDupuis, Josée1 aO'Connor, George, T1 aWilk, Jemma, B1 aKim, Woo, Jin1 aLee, Mi, Kyeong1 aOh, Yeon-Mok1 aVonk, Judith, M1 ade Koning, Harry, J1 aLeng, Shuguang1 aBelinsky, Steven, A1 aTesfaigzi, Yohannes1 aManichaikul, Ani1 aWang, Xin-Qun1 aRich, Stephen, S1 aBarr, Graham1 aSparrow, David1 aLitonjua, Augusto, A1 aBakke, Per1 aGulsvik, Amund1 aLahousse, Lies1 aBrusselle, Guy, G1 aStricker, Bruno, H1 aUitterlinden, André, G1 aAmpleford, Elizabeth, J1 aBleecker, Eugene, R1 aWoodruff, Prescott, G1 aMeyers, Deborah, A1 aQiao, Dandi1 aLomas, David, A1 aYim, Jae-Joon1 aKim, Deog, Kyeom1 aHawrylkiewicz, Iwona1 aSliwinski, Pawel1 aHardin, Megan1 aFingerlin, Tasha, E1 aSchwartz, David, A1 aPostma, Dirkje, S1 aMacNee, William1 aTobin, Martin, D1 aSilverman, Edwin, K1 aBoezen, Marike1 aCho, Michael, H1 aCOPDGene Investigators1 aECLIPSE Investigators1 aLifeLines Investigators1 aSPIROMICS Research Group1 aInternational COPD Genetics Network Investigators1 aUK BiLEVE Investigators1 aInternational COPD Genetics Consortium uhttps://chs-nhlbi.org/node/734504016nas a2200865 4500008004100000022001400041245009800055210006900153260001600222520147500238100002001713700002001733700002201753700002001775700002201795700002201817700002801839700002401867700002101891700001901912700003201931700001701963700002801980700002402008700001602032700002202048700001902070700001902089700002002108700001902128700002102147700002002168700002302188700001902211700002002230700001402250700002302264700001902287700002502306700002102331700002102352700002402373700002902397700002502426700002502451700002302476700002502499700002502524700002102549700002402570700002202594700003202616700002002648700002202668700001902690700002302709700002202732700002902754700002502783700002302808700001902831700001702850700002102867700001902888700002202907700002202929700002802951700002602979700002103005700001803026700002403044700002503068700002103093856003603114 2017 eng d a1613-413300aGenome-Wide Interactions with Dairy Intake for Body Mass Index in Adults of European Descent.0 aGenomeWide Interactions with Dairy Intake for Body Mass Index in c2017 Sep 213 aSCOPE: Body weight responds variably to the intake of dairy foods. Genetic variation may contribute to inter-individual variability in associations between body weight and dairy consumption.
METHODS AND RESULTS: A genome-wide interaction study to discover genetic variants that account for variation in BMI in the context of low-fat, high-fat and total dairy intake in cross-sectional analysis was conducted. Data from nine discovery studies (up to 25 513 European descent individuals) were meta-analyzed. Twenty-six genetic variants reached the selected significance threshold (p-interaction <10-7) , and six independent variants (LINC01512-rs7751666, PALM2/AKAP2-rs914359, ACTA2-rs1388, PPP1R12A-rs7961195, LINC00333-rs9635058, AC098847.1-rs1791355) were evaluated meta-analytically for replication of interaction in up to 17 675 individuals. Variant rs9635058 (128 kb 3' of LINC00333) was replicated (p-interaction = 0.004). In the discovery cohorts, rs9635058 interacted with dairy (p-interaction = 7.36 × 10-8) such that each serving of low-fat dairy was associated with 0.225 kg m-2 lower BMI per each additional copy of the effect allele (A). A second genetic variant (ACTA2-rs1388) approached interaction replication significance for low-fat dairy exposure.
CONCLUSION: Body weight responses to dairy intake may be modified by genotype, in that greater dairy intake may protect a genetic subgroup from higher body weight.
1 aSmith, Caren, E1 aFollis, Jack, L1 aDashti, Hassan, S1 aTanaka, Toshiko1 aGraff, Mariaelisa1 aFretts, Amanda, M1 aKilpeläinen, Tuomas, O1 aWojczynski, Mary, K1 aRichardson, Kris1 aNalls, Mike, A1 aSchulz, Christina-Alexandra1 aLiu, Yongmei1 aFrazier-Wood, Alexis, C1 avan Eekelen, Esther1 aWang, Carol1 ade Vries, Paul, S1 aMikkilä, Vera1 aRohde, Rebecca1 aPsaty, Bruce, M1 aHansen, Torben1 aFeitosa, Mary, F1 aLai, Chao-Qiang1 aHouston, Denise, K1 aFerruci, Luigi1 aEricson, Ulrika1 aWang, Zhe1 ade Mutsert, Renée1 aOddy, Wendy, H1 ade Jonge, Ester, A L1 aSeppälä, Ilkka1 aJustice, Anne, E1 aLemaitre, Rozenn, N1 aSørensen, Thorkild, I A1 aProvince, Michael, A1 aParnell, Laurence, D1 aGarcia, Melissa, E1 aBandinelli, Stefania1 aOrho-Melander, Marju1 aRich, Stephen, S1 aRosendaal, Frits, R1 aPennell, Craig, E1 ade Jong, Jessica, C Kiefte-1 aKähönen, Mika1 aYoung, Kristin, L1 aPedersen, Oluf1 aAslibekyan, Stella1 aRotter, Jerome, I1 aMook-Kanamori, Dennis, O1 aZillikens, Carola, M1 aRaitakari, Olli, T1 aNorth, Kari, E1 aOvervad, Kim1 aArnett, Donna, K1 aHofman, Albert1 aLehtimäki, Terho1 aTjønneland, Anne1 aUitterlinden, André, G1 aRivadeneira, Fernando1 aFranco, Oscar, H1 aGerman, Bruce1 aSiscovick, David, S1 aCupples, Adrienne, L1 aOrdovas, Jose, M uhttps://chs-nhlbi.org/node/758811080nas a2202833 4500008004100000022001400041245017500055210006900230260001300299300001300312490000700325520325900332653003003591653002203621653003403643653002603677653001103703653001403714653000903728100002103737700001703758700001803775700002503793700002503818700001903843700001203862700001303874700001703887700001903904700001903923700001803942700001303960700001503973700002203988700002404010700002204034700001704056700002504073700002004098700001804118700001904136700002104155700001404176700002304190700001604213700002404229700001204253700002104265700002204286700002004308700002204328700002104350700002104371700001804392700001804410700002004428700001804448700001904466700001704485700001904502700001904521700002004540700001804560700001404578700002104592700002304613700001804636700002704654700003504681700001704716700001604733700001804749700001504767700003004782700001804812700001704830700002004847700002604867700002204893700001604915700002504931700002204956700002304978700002205001700002405023700002005047700002105067700002205088700001905110700002005129700002205149700002205171700002105193700002005214700002605234700002205260700002405282700001505306700001905321700001805340700002205358700002205380700001905402700002205421700001805443700001705461700001805478700002005496700001705516700001705533700002205550700001805572700002305590700002205613700001805635700001905653700001905672700002105691700002205712700003005734700002205764700002005786700002105806700001905827700002105846700002005867700002805887700002305915700002105938700002305959700001905982700001906001700001606020700002106036700002206057700002006079700002406099700002406123700001506147700001906162700002406181700002006205700001806225700002106243700001806264700002506282700002306307700001906330700002406349700002006373700002006393700001806413700001606431700001906447700001806466700002106484700002306505700002706528700002206555700001806577700001406595700002206609700002106631700002306652700002506675700002206700700002306722700002206745700002206767700002006789700002106809700001906830700002006849700002206869700002006891700002506911700002206936700001606958700002006974700002206994700002207016700002007038700002007058700001907078700001707097700001907114700002207133700001907155700001507174700002207189700001907211700001207230700001707242700002007259700002707279700002007306700002207326700002907348700001607377700002307393700002107416700002007437700002007457700002807477700001807505700002107523700001807544700002307562700002107585700002007606700002407626700002107650700002307671700002007694700002107714700002107735700003007756700002007786700001807806700001907824700002307843700002007866700001707886700002207903700002007925700001907945700001907964700002207983700001808005700002208023700002208045700002408067700001908091700002008110710002408130710002908154710002708183856003608210 2017 eng d a1549-167600aImpact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis.0 aImpact of common genetic determinants of Hemoglobin A1c on type c2017 Sep ae10023830 v143 aBACKGROUND: Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes.
METHODS & FINDINGS: Using genome-wide association meta-analyses in up to 159,940 individuals from 82 cohorts of European, African, East Asian, and South Asian ancestry, we identified 60 common genetic variants associated with HbA1c. We classified variants as implicated in glycemic, erythrocytic, or unclassified biology and tested whether additive genetic scores of erythrocytic variants (GS-E) or glycemic variants (GS-G) were associated with higher T2D incidence in multiethnic longitudinal cohorts (N = 33,241). Nineteen glycemic and 22 erythrocytic variants were associated with HbA1c at genome-wide significance. GS-G was associated with higher T2D risk (incidence OR = 1.05, 95% CI 1.04-1.06, per HbA1c-raising allele, p = 3 × 10-29); whereas GS-E was not (OR = 1.00, 95% CI 0.99-1.01, p = 0.60). In Europeans and Asians, erythrocytic variants in aggregate had only modest effects on the diagnostic accuracy of HbA1c. Yet, in African Americans, the X-linked G6PD G202A variant (T-allele frequency 11%) was associated with an absolute decrease in HbA1c of 0.81%-units (95% CI 0.66-0.96) per allele in hemizygous men, and 0.68%-units (95% CI 0.38-0.97) in homozygous women. The G6PD variant may cause approximately 2% (N = 0.65 million, 95% CI 0.55-0.74) of African American adults with T2D to remain undiagnosed when screened with HbA1c. Limitations include the smaller sample sizes for non-European ancestries and the inability to classify approximately one-third of the variants. Further studies in large multiethnic cohorts with HbA1c, glycemic, and erythrocytic traits are required to better determine the biological action of the unclassified variants.
CONCLUSIONS: As G6PD deficiency can be clinically silent until illness strikes, we recommend investigation of the possible benefits of screening for the G6PD genotype along with using HbA1c to diagnose T2D in populations of African ancestry or groups where G6PD deficiency is common. Screening with direct glucose measurements, or genetically-informed HbA1c diagnostic thresholds in people with G6PD deficiency, may be required to avoid missed or delayed diagnoses.
10aDiabetes Mellitus, Type 210aGenetic Variation10aGenome-Wide Association Study10aGlycated Hemoglobin A10aHumans10aPhenotype10aRisk1 aWheeler, Eleanor1 aLeong, Aaron1 aLiu, Ching-Ti1 aHivert, Marie-France1 aStrawbridge, Rona, J1 aPodmore, Clara1 aLi, Man1 aYao, Jie1 aSim, Xueling1 aHong, Jaeyoung1 aChu, Audrey, Y1 aZhang, Weihua1 aWang, Xu1 aChen, Peng1 aMaruthur, Nisa, M1 aPorneala, Bianca, C1 aSharp, Stephen, J1 aJia, Yucheng1 aKabagambe, Edmond, K1 aChang, Li-Ching1 aChen, Wei-Min1 aElks, Cathy, E1 aEvans, Daniel, S1 aFan, Qiao1 aGiulianini, Franco1 aGo, Min Jin1 aHottenga, Jouke-Jan1 aHu, Yao1 aJackson, Anne, U1 aKanoni, Stavroula1 aKim, Young, Jin1 aKleber, Marcus, E1 aLadenvall, Claes1 aLecoeur, Cécile1 aLim, Sing-Hui1 aLu, Yingchang1 aMahajan, Anubha1 aMarzi, Carola1 aNalls, Mike, A1 aNavarro, Pau1 aNolte, Ilja, M1 aRose, Lynda, M1 aRybin, Denis, V1 aSanna, Serena1 aShi, Yuan1 aStram, Daniel, O1 aTakeuchi, Fumihiko1 aTan, Shu, Pei1 avan der Most, Peter, J1 avan Vliet-Ostaptchouk, Jana, V1 aWong, Andrew1 aYengo, Loic1 aZhao, Wanting1 aGoel, Anuj1 aLarrad, Maria, Teresa Mar1 aRadke, Dörte1 aSalo, Perttu1 aTanaka, Toshiko1 avan Iperen, Erik, P A1 aAbecasis, Goncalo1 aAfaq, Saima1 aAlizadeh, Behrooz, Z1 aBertoni, Alain, G1 aBonnefond, Amélie1 aBöttcher, Yvonne1 aBottinger, Erwin, P1 aCampbell, Harry1 aCarlson, Olga, D1 aChen, Chien-Hsiun1 aCho, Yoon Shin1 aGarvey, Timothy1 aGieger, Christian1 aGoodarzi, Mark, O1 aGrallert, Harald1 aHamsten, Anders1 aHartman, Catharina, A1 aHerder, Christian1 aHsiung, Chao, Agnes1 aHuang, Jie1 aIgase, Michiya1 aIsono, Masato1 aKatsuya, Tomohiro1 aKhor, Chiea-Chuen1 aKiess, Wieland1 aKohara, Katsuhiko1 aKovacs, Peter1 aLee, Juyoung1 aLee, Wen-Jane1 aLehne, Benjamin1 aLi, Huaixing1 aLiu, Jianjun1 aLobbens, Stephane1 aLuan, Jian'an1 aLyssenko, Valeriya1 aMeitinger, Thomas1 aMiki, Tetsuro1 aMiljkovic, Iva1 aMoon, Sanghoon1 aMulas, Antonella1 aMüller, Gabriele1 aMüller-Nurasyid, Martina1 aNagaraja, Ramaiah1 aNauck, Matthias1 aPankow, James, S1 aPolasek, Ozren1 aProkopenko, Inga1 aRamos, Paula, S1 aRasmussen-Torvik, Laura1 aRathmann, Wolfgang1 aRich, Stephen, S1 aRobertson, Neil, R1 aRoden, Michael1 aRoussel, Ronan1 aRudan, Igor1 aScott, Robert, A1 aScott, William, R1 aSennblad, Bengt1 aSiscovick, David, S1 aStrauch, Konstantin1 aSun, Liang1 aSwertz, Morris1 aTajuddin, Salman, M1 aTaylor, Kent, D1 aTeo, Yik-Ying1 aTham, Yih, Chung1 aTönjes, Anke1 aWareham, Nicholas, J1 aWillemsen, Gonneke1 aWilsgaard, Tom1 aHingorani, Aroon, D1 aEgan, Josephine1 aFerrucci, Luigi1 aHovingh, Kees1 aJula, Antti1 aKivimaki, Mika1 aKumari, Meena1 aNjølstad, Inger1 aPalmer, Colin, N A1 aRíos, Manuel, Serrano1 aStumvoll, Michael1 aWatkins, Hugh1 aAung, Tin1 aBlüher, Matthias1 aBoehnke, Michael1 aBoomsma, Dorret, I1 aBornstein, Stefan, R1 aChambers, John, C1 aChasman, Daniel, I1 aChen, Yii-Der Ida1 aChen, Yduan-Tsong1 aCheng, Ching-Yu1 aCucca, Francesco1 aGeus, Eco, J C1 aDeloukas, Panos1 aEvans, Michele, K1 aFornage, Myriam1 aFriedlander, Yechiel1 aFroguel, Philippe1 aGroop, Leif1 aGross, Myron, D1 aHarris, Tamara, B1 aHayward, Caroline1 aHeng, Chew-Kiat1 aIngelsson, Erik1 aKato, Norihiro1 aKim, Bong-Jo1 aKoh, Woon-Puay1 aKooner, Jaspal, S1 aKörner, Antje1 aKuh, Diana1 aKuusisto, Johanna1 aLaakso, Markku1 aLin, Xu1 aLiu, Yongmei1 aLoos, Ruth, J F1 aMagnusson, Patrik, K E1 aMärz, Winfried1 aMcCarthy, Mark, I1 aOldehinkel, Albertine, J1 aOng, Ken, K1 aPedersen, Nancy, L1 aPereira, Mark, A1 aPeters, Annette1 aRidker, Paul, M1 aSabanayagam, Charumathi1 aSale, Michele1 aSaleheen, Danish1 aSaltevo, Juha1 aSchwarz, Peter, Eh1 aSheu, Wayne, H H1 aSnieder, Harold1 aSpector, Timothy, D1 aTabara, Yasuharu1 aTuomilehto, Jaakko1 avan Dam, Rob, M1 aWilson, James, G1 aWilson, James, F1 aWolffenbuttel, Bruce, H R1 aWong, Tien, Yin1 aWu, Jer-Yuarn1 aYuan, Jian-Min1 aZonderman, Alan, B1 aSoranzo, Nicole1 aGuo, Xiuqing1 aRoberts, David, J1 aFlorez, Jose, C1 aSladek, Robert1 aDupuis, Josée1 aMorris, Andrew, P1 aTai, E-Shyong1 aSelvin, Elizabeth1 aRotter, Jerome, I1 aLangenberg, Claudia1 aBarroso, Inês1 aMeigs, James, B1 aEPIC-CVD Consortium1 aEPIC-InterAct Consortium1 aLifeLines Cohort Study uhttps://chs-nhlbi.org/node/759603950nas a2200685 4500008004100000022001400041245009900055210006900154260001300223300001200236490000700248520197400255100002002229700002302249700001902272700002302291700001702314700001902331700002102350700002202371700002202393700001802415700002402433700001902457700001302476700002202489700002102511700001902532700002302551700002402574700001902598700002402617700002502641700001902666700002902685700001802714700002302732700002602755700001902781700002102800700003002821700002202851700002602873700002302899700002302922700002402945700001702969700002202986700002403008700002203032700002503054700002103079700002003100700002003120700002103140700002103161700002203182700002403204856003603228 2018 eng d a2574-830000aCommon Coding Variants in Are Associated With the Nav1.8 Late Current and Cardiac Conduction.0 aCommon Coding Variants in Are Associated With the Nav18 Late Cur c2018 May ae0016630 v113 aBACKGROUND: Genetic variants at the / locus are strongly associated with electrocardiographic PR and QRS intervals. While is the canonical cardiac sodium channel gene, the role of in cardiac conduction is less well characterized.
METHODS: We sequenced the locus in 3699 European-ancestry individuals to identify variants associated with cardiac conduction, and replicated our findings in 21,000 individuals of European ancestry. We examined association with expression in human atrial tissue. We explored the biophysical effect of variation on channel function using cellular electrophysiology.
RESULTS: We identified 2 intronic single nucleotide polymorphisms in high linkage disequilibrium ( =0.86) with each other to be the strongest signals for PR (rs10428132, β=-4.74, =1.52×10) and QRS intervals (rs6599251, QRS β=-0.73; =1.2×10), respectively. Although these variants were not associated with or expression in human atrial tissue (n=490), they were in high linkage disequilibrium ( ≥0.72) with a common missense variant, rs6795970 (V1073A). In total, we identified 7 missense variants, 4 of which (I962V, P1045T, V1073A, and L1092P) were associated with cardiac conduction. These 4 missense variants cluster in the cytoplasmic linker of the second and third domains of the SCN10A protein and together form 6 common haplotypes. Using cellular electrophysiology, we found that haplotypes associated with shorter PR intervals had a significantly larger percentage of late current compared with wild-type (I962V+V1073A+L1092P, 20.2±3.3%, =0.03, and I962V+V1073A, 22.4±0.8%, =0.0004 versus wild-type 11.7±1.6%), and the haplotype associated with the longest PR interval had a significantly smaller late current percentage (P1045T, 6.4±1.2%, =0.03).
CONCLUSIONS: Our findings suggest an association between genetic variation in , the late sodium current, and alterations in cardiac conduction.
1 aMacri, Vincenzo1 aBrody, Jennifer, A1 aArking, Dan, E1 aHucker, William, J1 aYin, Xiaoyan1 aLin, Honghuang1 aMills, Robert, W1 aSinner, Moritz, F1 aLubitz, Steven, A1 aLiu, Ching-Ti1 aMorrison, Alanna, C1 aAlonso, Alvaro1 aLi, Ning1 aFedorov, Vadim, V1 aJanssen, Paul, M1 aBis, Joshua, C1 aHeckbert, Susan, R1 aDolmatova, Elena, V1 aLumley, Thomas1 aSitlani, Colleen, M1 aCupples, Adrienne, L1 aPulit, Sara, L1 aNewton-Cheh, Christopher1 aBarnard, John1 aSmith, Jonathan, D1 aVan Wagoner, David, R1 aChung, Mina, K1 aVlahakes, Gus, J1 aO'Donnell, Christopher, J1 aRotter, Jerome, I1 aMargulies, Kenneth, B1 aMorley, Michael, P1 aCappola, Thomas, P1 aBenjamin, Emelia, J1 aMuzny, Donna1 aGibbs, Richard, A1 aJackson, Rebecca, D1 aMagnani, Jared, W1 aHerndon, Caroline, N1 aRich, Stephen, S1 aPsaty, Bruce, M1 aMilan, David, J1 aBoerwinkle, Eric1 aMohler, Peter, J1 aSotoodehnia, Nona1 aEllinor, Patrick, T uhttps://chs-nhlbi.org/node/780203876nas a2200565 4500008004100000022001400041245013500055210006900190260000900259300001300268490000700281520223900288653001602527653003402543653001102577653001202588653001202600653001202612653003602624653001902660653003402679653003002713100002902743700002402772700001202796700001902808700001802827700002102845700002102866700002102887700001502908700002002923700001702943700002302960700002502983700002503008700002103033700002203054700002203076700002003098700002003118700002003138700001703158700001503175700001703190700001803207700002403225700002503249856003603274 2018 eng d a1932-620300aGenome-wide association meta-analysis of circulating odd-numbered chain saturated fatty acids: Results from the CHARGE Consortium.0 aGenomewide association metaanalysis of circulating oddnumbered c c2018 ae01969510 v133 aBACKGROUND: Odd-numbered chain saturated fatty acids (OCSFA) have been associated with potential health benefits. Although some OCSFA (e.g., C15:0 and C17:0) are found in meats and dairy products, sources and metabolism of C19:0 and C23:0 are relatively unknown, and the influence of non-dietary determinants, including genetic factors, on circulating levels of OCSFA is not established.
OBJECTIVE: To elucidate the biological processes that influence circulating levels of OCSFA by investigating associations between genetic variation and OCSFA.
DESIGN: We performed a meta-analysis of genome-wide association studies (GWAS) of plasma phospholipid/erythrocyte levels of C15:0, C17:0, C19:0, and C23:0 among 11,494 individuals of European descent. We also investigated relationships between specific single nucleotide polymorphisms (SNPs) in the lactase (LCT) gene, associated with adult-onset lactase intolerance, with circulating levels of dairy-derived OCSFA, and evaluated associations of candidate sphingolipid genes with C23:0 levels.
RESULTS: We found no genome-wide significant evidence that common genetic variation is associated with circulating levels of C15:0 or C23:0. In two cohorts with available data, we identified one intronic SNP (rs13361131) in myosin X gene (MYO10) associated with C17:0 level (P = 1.37×10-8), and two intronic SNP (rs12874278 and rs17363566) in deleted in lymphocytic leukemia 1 (DLEU1) region associated with C19:0 level (P = 7.07×10-9). In contrast, when using a candidate-gene approach, we found evidence that three SNPs in LCT (rs11884924, rs16832067, and rs3816088) are associated with circulating C17:0 level (adjusted P = 4×10-2). In addition, nine SNPs in the ceramide synthase 4 (CERS4) region were associated with circulating C23:0 levels (adjusted P<5×10-2).
CONCLUSIONS: Our findings suggest that circulating levels of OCSFA may be predominantly influenced by non-genetic factors. SNPs associated with C17:0 level in the LCT gene may reflect genetic influence in dairy consumption or in metabolism of dairy foods. SNPs associated with C23:0 may reflect a role of genetic factors in the synthesis of sphingomyelin.
10aFatty Acids10aGenome-Wide Association Study10aHumans10aIntrons10aLactase10aMyosins10aPolymorphism, Single Nucleotide10aSphingomyelins10aSphingosine N-Acyltransferase10aTumor Suppressor Proteins1 aOtto, Marcia, C de Olive1 aLemaitre, Rozenn, N1 aSun, Qi1 aKing, Irena, B1 aH Y Wu, Jason1 aManichaikul, Ani1 aRich, Stephen, S1 aTsai, Michael, Y1 aChen, Y, D1 aFornage, Myriam1 aWeihua, Guan1 aAslibekyan, Stella1 aIrvin, Marguerite, R1 aKabagambe, Edmond, K1 aArnett, Donna, K1 aJensen, Majken, K1 aMcKnight, Barbara1 aPsaty, Bruce, M1 aSteffen, Lyn, M1 aSmith, Caren, E1 aRiserus, Ulf1 aLind, Lars1 aHu, Frank, B1 aRimm, Eric, B1 aSiscovick, David, S1 aMozaffarian, Dariush uhttps://chs-nhlbi.org/node/779105033nas a2201381 4500008004100000022001400041245013800055210006900193260001600262300000800278490000600286520112300292100001501415700002201430700002001452700001901472700002001491700001901511700002001530700001901550700001701569700001701586700002201603700001901625700001701644700002501661700002701686700002301713700002201736700001201758700001801770700002201788700002501810700001901835700001401854700001701868700002201885700002001907700002101927700002801948700002201976700001801998700002702016700002802043700003102071700001802102700002502120700002002145700002702165700001802192700001702210700002002227700001902247700002402266700002302290700002302313700001502336700002302351700002802374700001702402700002002419700002002439700001902459700002202478700002002500700002002520700001802540700002202558700002202580700002002602700002002622700002002642700002202662700002102684700003002705700002402735700002702759700002002786700002002806700002102826700002202847700001502869700002302884700002002907700002502927700002802952700002602980700001703006700001603023700002003039700002403059700002503083700002103108700002303129700001903152700001903171700002203190700002803212700002003240700002303260700001903283700002003302700001903322700001903341700002503360700002403385700002103409700002503430700001803455700002503473700001703498700002103515700002003536700002103556700001703577700002103594856003603615 2018 eng d a2041-172300aGenome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels.0 aGenomewide association study in 79366 Europeanancestry individua c2018 Jan 17 a2600 v93 aVitamin D is a steroid hormone precursor that is associated with a range of human traits and diseases. Previous GWAS of serum 25-hydroxyvitamin D concentrations have identified four genome-wide significant loci (GC, NADSYN1/DHCR7, CYP2R1, CYP24A1). In this study, we expand the previous SUNLIGHT Consortium GWAS discovery sample size from 16,125 to 79,366 (all European descent). This larger GWAS yields two additional loci harboring genome-wide significant variants (P = 4.7×10 at rs8018720 in SEC23A, and P = 1.9×10 at rs10745742 in AMDHD1). The overall estimate of heritability of 25-hydroxyvitamin D serum concentrations attributable to GWAS common SNPs is 7.5%, with statistically significant loci explaining 38% of this total. Further investigation identifies signal enrichment in immune and hematopoietic tissues, and clustering with autoimmune diseases in cell-type-specific analysis. Larger studies are required to identify additional common SNPs, and to explore the role of rare or structural variants and gene-gene interactions in the heritability of circulating 25-hydroxyvitamin D levels.
1 aJiang, Xia1 aO'Reilly, Paul, F1 aAschard, Hugues1 aHsu, Yi-Hsiang1 aRichards, Brent1 aDupuis, Josée1 aIngelsson, Erik1 aKarasik, David1 aPilz, Stefan1 aBerry, Diane1 aKestenbaum, Bryan1 aZheng, Jusheng1 aLuan, Jianan1 aSofianopoulou, Eleni1 aStreeten, Elizabeth, A1 aAlbanes, Demetrius1 aLutsey, Pamela, L1 aYao, Lu1 aTang, Weihong1 aEcons, Michael, J1 aWallaschofski, Henri1 aVölzke, Henry1 aZhou, Ang1 aPower, Chris1 aMcCarthy, Mark, I1 aMichos, Erin, D1 aBoerwinkle, Eric1 aWeinstein, Stephanie, J1 aFreedman, Neal, D1 aHuang, Wen-Yi1 avan Schoor, Natasja, M1 avan der Velde, Nathalie1 ade Groot, Lisette, C P G M1 aEnneman, Anke1 aCupples, Adrienne, L1 aBooth, Sarah, L1 aVasan, Ramachandran, S1 aLiu, Ching-Ti1 aZhou, Yanhua1 aRipatti, Samuli1 aOhlsson, Claes1 aVandenput, Liesbeth1 aLorentzon, Mattias1 aEriksson, Johan, G1 aShea, Kyla1 aHouston, Denise, K1 aKritchevsky, Stephen, B1 aLiu, Yongmei1 aLohman, Kurt, K1 aFerrucci, Luigi1 aPeacock, Munro1 aGieger, Christian1 aBeekman, Marian1 aSlagboom, Eline1 aDeelen, Joris1 avan Heemst, Diana1 aKleber, Marcus, E1 aMärz, Winfried1 ade Boer, Ian, H1 aWood, Alexis, C1 aRotter, Jerome, I1 aRich, Stephen, S1 aRobinson-Cohen, Cassianne1 aHeijer, Martin, den1 aJarvelin, Marjo-Riitta1 aCavadino, Alana1 aJoshi, Peter, K1 aWilson, James, F1 aHayward, Caroline1 aLind, Lars1 aMichaëlsson, Karl1 aTrompet, Stella1 aZillikens, Carola, M1 aUitterlinden, André, G1 aRivadeneira, Fernando1 aBroer, Linda1 aZgaga, Lina1 aCampbell, Harry1 aTheodoratou, Evropi1 aFarrington, Susan, M1 aTimofeeva, Maria1 aDunlop, Malcolm, G1 aValdes, Ana, M1 aTikkanen, Emmi1 aLehtimäki, Terho1 aLyytikäinen, Leo-Pekka1 aKähönen, Mika1 aRaitakari, Olli, T1 aMikkilä, Vera1 aIkram, Arfan, M1 aSattar, Naveed1 aJukema, Wouter1 aWareham, Nicholas, J1 aLangenberg, Claudia1 aForouhi, Nita, G1 aGundersen, Thomas, E1 aKhaw, Kay-Tee1 aButterworth, Adam, S1 aDanesh, John1 aSpector, Timothy1 aWang, Thomas, J1 aHyppönen, Elina1 aKraft, Peter1 aKiel, Douglas, P uhttps://chs-nhlbi.org/node/766703699nas a2200673 4500008004100000022001400041245018700055210006900242260001600311300000900327520167400336100001402010700002002024700002102044700002402065700002402089700001602113700002302129700001402152700002002166700002202186700002002208700002002228700002802248700002202276700002402298700002202322700001902344700001602363700002302379700002102402700002802423700002302451700002402474700002202498700002102520700002602541700002102567700002002588700002202608700002102630700002202651700002002673700002302693700002202716700002002738700002502758700001702783700001902800700002002819700001902839700002502858700001902883700002102902700002002923700002102943700002502964856003602989 2018 eng d a1475-266200aMeta-analysis across Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium provides evidence for an association of serum vitamin D with pulmonary function.0 aMetaanalysis across Cohorts for Heart and Aging Research in Geno c2018 Sep 12 a1-123 aThe role that vitamin D plays in pulmonary function remains uncertain. Epidemiological studies reported mixed findings for serum 25-hydroxyvitamin D (25(OH)D)-pulmonary function association. We conducted the largest cross-sectional meta-analysis of the 25(OH)D-pulmonary function association to date, based on nine European ancestry (EA) cohorts (n 22 838) and five African ancestry (AA) cohorts (n 4290) in the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium. Data were analysed using linear models by cohort and ancestry. Effect modification by smoking status (current/former/never) was tested. Results were combined using fixed-effects meta-analysis. Mean serum 25(OH)D was 68 (sd 29) nmol/l for EA and 49 (sd 21) nmol/l for AA. For each 1 nmol/l higher 25(OH)D, forced expiratory volume in the 1st second (FEV1) was higher by 1·1 ml in EA (95 % CI 0·9, 1·3; P<0·0001) and 1·8 ml (95 % CI 1·1, 2·5; P<0·0001) in AA (P race difference=0·06), and forced vital capacity (FVC) was higher by 1·3 ml in EA (95 % CI 1·0, 1·6; P<0·0001) and 1·5 ml (95 % CI 0·8, 2·3; P=0·0001) in AA (P race difference=0·56). Among EA, the 25(OH)D-FVC association was stronger in smokers: per 1 nmol/l higher 25(OH)D, FVC was higher by 1·7 ml (95 % CI 1·1, 2·3) for current smokers and 1·7 ml (95 % CI 1·2, 2·1) for former smokers, compared with 0·8 ml (95 % CI 0·4, 1·2) for never smokers. In summary, the 25(OH)D associations with FEV1 and FVC were positive in both ancestries. In EA, a stronger association was observed for smokers compared with never smokers, which supports the importance of vitamin D in vulnerable populations.
1 aXu, Jiayi1 aBartz, Traci, M1 aChittoor, Geetha1 aEiriksdottir, Gudny1 aManichaikul, Ani, W1 aSun, Fangui1 aTerzikhan, Natalie1 aZhou, Xia1 aBooth, Sarah, L1 aBrusselle, Guy, G1 ade Boer, Ian, H1 aFornage, Myriam1 aFrazier-Wood, Alexis, C1 aGraff, Mariaelisa1 aGudnason, Vilmundur1 aHarris, Tamara, B1 aHofman, Albert1 aHou, Ruixue1 aHouston, Denise, K1 aJacobs, David, R1 aKritchevsky, Stephen, B1 aLatourelle, Jeanne1 aLemaitre, Rozenn, N1 aLutsey, Pamela, L1 aO'Connor, George1 aOelsner, Elizabeth, C1 aPankow, James, S1 aPsaty, Bruce, M1 aRohde, Rebecca, R1 aRich, Stephen, S1 aRotter, Jerome, I1 aSmith, Lewis, J1 aStricker, Bruno, H1 aVoruganti, Saroja1 aWang, Thomas, J1 aZillikens, Carola, M1 aBarr, Graham1 aDupuis, Josée1 aGharib, Sina, A1 aLahousse, Lies1 aLondon, Stephanie, J1 aNorth, Kari, E1 aSmith, Albert, V1 aSteffen, Lyn, M1 aHancock, Dana, B1 aCassano, Patricia, A uhttps://chs-nhlbi.org/node/777505089nas a2201429 4500008004100000022001400041245009100055210006900146260000900215300000600224490000600230520111900236100002501355700002601380700002001406700002101426700002001447700002001467700001901487700002501506700001301531700002001544700002301564700002501587700002801612700001801640700001201658700002401670700002101694700002101715700002101736700002301757700002001780700002401800700002001824700001901844700001901863700002201882700001601904700002801920700002401948700002301972700002401995700002302019700001802042700001602060700001602076700001702092700001602109700001702125700002302142700002502165700002102190700002102211700001802232700002102250700002502271700002002296700001702316700002402333700002602357700002202383700001902405700001502424700002202439700001802461700001802479700002002497700001902517700002002536700002302556700001202579700002002591700002802611700001702639700001602656700002402672700001902696700001902715700001902734700002102753700001902774700002102793700002902814700001902843700002002862700002802882700001802910700002302928700002402951700001902975700002002994700001603014700002003030700002403050700001903074700002203093700001903115700002403134700002103158700002403179700002203203700001603225700001803241700001703259700002303276700002003299700002203319700002003341700002603361700001803387700002403405700002203429700002303451700001903474700002403493700001703517700002103534700002503555710004303580856003603623 2018 eng d a2398-502X00aMeta-analysis of exome array data identifies six novel genetic loci for lung function.0 aMetaanalysis of exome array data identifies six novel genetic lo c2018 a40 v33 aOver 90 regions of the genome have been associated with lung function to date, many of which have also been implicated in chronic obstructive pulmonary disease. We carried out meta-analyses of exome array data and three lung function measures: forced expiratory volume in one second (FEV ), forced vital capacity (FVC) and the ratio of FEV to FVC (FEV /FVC). These analyses by the SpiroMeta and CHARGE consortia included 60,749 individuals of European ancestry from 23 studies, and 7,721 individuals of African Ancestry from 5 studies in the discovery stage, with follow-up in up to 111,556 independent individuals. We identified significant (P<2·8x10 ) associations with six SNPs: a nonsynonymous variant in , which is predicted to be damaging, three intronic SNPs ( and ) and two intergenic SNPs near to and Expression quantitative trait loci analyses found evidence for regulation of gene expression at three signals and implicated several genes, including and . Further interrogation of these loci could provide greater understanding of the determinants of lung function and pulmonary disease.
1 aJackson, Victoria, E1 aLatourelle, Jeanne, C1 aWain, Louise, V1 aSmith, Albert, V1 aGrove, Megan, L1 aBartz, Traci, M1 aObeidat, Ma'en1 aProvince, Michael, A1 aGao, Wei1 aQaiser, Beenish1 aPorteous, David, J1 aCassano, Patricia, A1 aAhluwalia, Tarunveer, S1 aGrarup, Niels1 aLi, Jin1 aAltmaier, Elisabeth1 aMarten, Jonathan1 aHarris, Sarah, E1 aManichaikul, Ani1 aPottinger, Tess, D1 aLi-Gao, Ruifang1 aLind-Thomsen, Allan1 aMahajan, Anubha1 aLahousse, Lies1 aImboden, Medea1 aTeumer, Alexander1 aPrins, Bram1 aLyytikäinen, Leo-Pekka1 aEiriksdottir, Gudny1 aFranceschini, Nora1 aSitlani, Colleen, M1 aBrody, Jennifer, A1 aBossé, Yohan1 aTimens, Wim1 aKraja, Aldi1 aLoukola, Anu1 aTang, Wenbo1 aLiu, Yongmei1 aBork-Jensen, Jette1 aJustesen, Johanne, M1 aLinneberg, Allan1 aLange, Leslie, A1 aRawal, Rajesh1 aKarrasch, Stefan1 aHuffman, Jennifer, E1 aSmith, Blair, H1 aDavies, Gail1 aBurkart, Kristin, M1 aMychaleckyj, Josyf, C1 aBonten, Tobias, N1 aEnroth, Stefan1 aLind, Lars1 aBrusselle, Guy, G1 aKumar, Ashish1 aStubbe, Beate1 aKähönen, Mika1 aWyss, Annah, B1 aPsaty, Bruce, M1 aHeckbert, Susan, R1 aHao, Ke1 aRantanen, Taina1 aKritchevsky, Stephen, B1 aLohman, Kurt1 aSkaaby, Tea1 aPisinger, Charlotta1 aHansen, Torben1 aSchulz, Holger1 aPolasek, Ozren1 aCampbell, Archie1 aStarr, John, M1 aRich, Stephen, S1 aMook-Kanamori, Dennis, O1 aJohansson, Asa1 aIngelsson, Erik1 aUitterlinden, André, G1 aWeiss, Stefan1 aRaitakari, Olli, T1 aGudnason, Vilmundur1 aNorth, Kari, E1 aGharib, Sina, A1 aSin, Don, D1 aTaylor, Kent, D1 aO'Connor, George, T1 aKaprio, Jaakko1 aHarris, Tamara, B1 aPederson, Oluf1 aVestergaard, Henrik1 aWilson, James, G1 aStrauch, Konstantin1 aHayward, Caroline1 aKerr, Shona1 aDeary, Ian, J1 aBarr, Graham1 ade Mutsert, Renée1 aGyllensten, Ulf1 aMorris, Andrew, P1 aIkram, Arfan, M1 aProbst-Hensch, Nicole1 aGläser, Sven1 aZeggini, Eleftheria1 aLehtimäki, Terho1 aStrachan, David, P1 aDupuis, Josée1 aMorrison, Alanna, C1 aHall, Ian, P1 aTobin, Martin, D1 aLondon, Stephanie, J1 aUnderstanding Society Scientific Group uhttps://chs-nhlbi.org/node/779507136nas a2202245 4500008004100000022001400041245011700055210006900172260001300241300001000254490000700264520074900271100002301020700003301043700002401076700002601100700002301126700001301149700001701162700002001179700002101199700001701220700001901237700003201256700002101288700002301309700002301332700002501355700003101380700002101411700002201432700002201454700002401476700001701500700002201517700002101539700003101560700002001591700002001611700001801631700002501649700003101674700002501705700002101730700001701751700002301768700002001791700002101811700002101832700002401853700002201877700001801899700001901917700002001936700001801956700001801974700002001992700002102012700002002033700002402053700002802077700001902105700002302124700001902147700002202166700002402188700002002212700002102232700002002253700002402273700001902297700001802316700002002334700002202354700001602376700001902392700002102411700002502432700001602457700002102473700002702494700002002521700001802541700002502559700002202584700002102606700002002627700002102647700002602668700002402694700002502718700002402743700001902767700001902786700001802805700001802823700002102841700001902862700002002881700002302901700001802924700001702942700001802959700002202977700002302999700002103022700001203043700001803055700002003073700002503093700001803118700002103136700002003157700002603177700002503203700002303228700002103251700001803272700002203290700001703312700001703329700002003346700001703366700002003383700002003403700002103423700003003444700001903474700002403493700002603517700002303543700001903566700001903585700002203604700002203626700002203648700002203670700002603692700002303718700002203741700002303763700002603786700002103812700002403833700002103857700002303878700002003901700002203921700001803943700002603961700002203987700002004009700002104029700002004050700002204070700001804092700002104110700002304131700002304154700002304177700002404200700002504224700002804249700002404277700002304301700002304324700002804347700002804375700001904403700002204422700002104444700002204465700002004487700002004507700002104527700002004548700002004568700002004588700002104608700002304629700002004652700002104672700001904693700001904712700002304731700001704754700002004771710006304791856003604854 2018 eng d a1546-171800aMultiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks.0 aMultiancestry association study identifies new asthma risk loci c2018 Jan a42-530 v503 aWe examined common variation in asthma risk by conducting a meta-analysis of worldwide asthma genome-wide association studies (23,948 asthma cases, 118,538 controls) of individuals from ethnically diverse populations. We identified five new asthma loci, found two new associations at two known asthma loci, established asthma associations at two loci previously implicated in the comorbidity of asthma plus hay fever, and confirmed nine known loci. Investigation of pleiotropy showed large overlaps in genetic variants with autoimmune and inflammatory diseases. The enrichment in enhancer marks at asthma risk loci, especially in immune cells, suggested a major role of these loci in the regulation of immunologically related mechanisms.
1 aDemenais, Florence1 aMargaritte-Jeannin, Patricia1 aBarnes, Kathleen, C1 aCookson, William, O C1 aAltmüller, Janine1 aAng, Wei1 aBarr, Graham1 aBeaty, Terri, H1 aBecker, Allan, B1 aBeilby, John1 aBisgaard, Hans1 aBjornsdottir, Unnur, Steina1 aBleecker, Eugene1 aBønnelykke, Klaus1 aBoomsma, Dorret, I1 aBouzigon, Emmanuelle1 aBrightling, Christopher, E1 aBrossard, Myriam1 aBrusselle, Guy, G1 aBurchard, Esteban1 aBurkart, Kristin, M1 aBush, Andrew1 aChan-Yeung, Moira1 aChung, Kian, Fan1 aAlves, Alexessander, Couto1 aCurtin, John, A1 aCustovic, Adnan1 aDaley, Denise1 ade Jongste, Johan, C1 aDel-Rio-Navarro, Blanca, E1 aDonohue, Kathleen, M1 aDuijts, Liesbeth1 aEng, Celeste1 aEriksson, Johan, G1 aFarrall, Martin1 aFedorova, Yuliya1 aFeenstra, Bjarke1 aFerreira, Manuel, A1 aFreidin, Maxim, B1 aGajdos, Zofia1 aGauderman, Jim1 aGehring, Ulrike1 aGeller, Frank1 aGenuneit, Jon1 aGharib, Sina, A1 aGilliland, Frank1 aGranell, Raquel1 aGraves, Penelope, E1 aGudbjartsson, Daniel, F1 aHaahtela, Tari1 aHeckbert, Susan, R1 aHeederik, Dick1 aHeinrich, Joachim1 aHeliövaara, Markku1 aHenderson, John1 aHimes, Blanca, E1 aHirose, Hiroshi1 aHirschhorn, Joel, N1 aHofman, Albert1 aHolt, Patrick1 aHottenga, Jouke1 aHudson, Thomas, J1 aHui, Jennie1 aImboden, Medea1 aIvanov, Vladimir1 aJaddoe, Vincent, W V1 aJames, Alan1 aJanson, Christer1 aJarvelin, Marjo-Riitta1 aJarvis, Deborah1 aJones, Graham1 aJonsdottir, Ingileif1 aJousilahti, Pekka1 aKabesch, Michael1 aKähönen, Mika1 aKantor, David, B1 aKarunas, Alexandra, S1 aKhusnutdinova, Elza1 aKoppelman, Gerard, H1 aKozyrskyj, Anita, L1 aKreiner, Eskil1 aKubo, Michiaki1 aKumar, Rajesh1 aKumar, Ashish1 aKuokkanen, Mikko1 aLahousse, Lies1 aLaitinen, Tarja1 aLaprise, Catherine1 aLathrop, Mark1 aLau, Susanne1 aLee, Young-Ae1 aLehtimäki, Terho1 aLetort, Sébastien1 aLevin, Albert, M1 aLi, Guo1 aLiang, Liming1 aLoehr, Laura, R1 aLondon, Stephanie, J1 aLoth, Daan, W1 aManichaikul, Ani1 aMarenholz, Ingo1 aMartinez, Fernando, J1 aMatheson, Melanie, C1 aMathias, Rasika, A1 aMatsumoto, Kenji1 aMbarek, Hamdi1 aMcArdle, Wendy, L1 aMelbye, Mads1 aMelén, Erik1 aMeyers, Deborah1 aMichel, Sven1 aMohamdi, Hamida1 aMusk, Arthur, W1 aMyers, Rachel, A1 aNieuwenhuis, Maartje, A E1 aNoguchi, Emiko1 aO'Connor, George, T1 aOgorodova, Ludmila, M1 aPalmer, Cameron, D1 aPalotie, Aarno1 aPark, Julie, E1 aPennell, Craig, E1 aPershagen, Göran1 aPolonikov, Alexey1 aPostma, Dirkje, S1 aProbst-Hensch, Nicole1 aPuzyrev, Valery, P1 aRaby, Benjamin, A1 aRaitakari, Olli, T1 aRamasamy, Adaikalavan1 aRich, Stephen, S1 aRobertson, Colin, F1 aRomieu, Isabelle1 aSalam, Muhammad, T1 aSalomaa, Veikko1 aSchlünssen, Vivi1 aScott, Robert1 aSelivanova, Polina, A1 aSigsgaard, Torben1 aSimpson, Angela1 aSiroux, Valérie1 aSmith, Lewis, J1 aSolodilova, Maria1 aStandl, Marie1 aStefansson, Kari1 aStrachan, David, P1 aStricker, Bruno, H1 aTakahashi, Atsushi1 aThompson, Philip, J1 aThorleifsson, Gudmar1 aThorsteinsdottir, Unnur1 aTiesler, Carla, M T1 aTorgerson, Dara, G1 aTsunoda, Tatsuhiko1 aUitterlinden, André, G1 avan der Valk, Ralf, J P1 aVaysse, Amaury1 aVedantam, Sailaja1 avon Berg, Andrea1 avon Mutius, Erika1 aVonk, Judith, M1 aWaage, Johannes1 aWareham, Nick, J1 aWeiss, Scott, T1 aWhite, Wendy, B1 aWickman, Magnus1 aWiden, Elisabeth1 aWillemsen, Gonneke1 aWilliams, Keoki1 aWouters, Inge, M1 aYang, James, J1 aZhao, Jing Hua1 aMoffatt, Miriam, F1 aOber, Carole1 aNicolae, Dan, L1 aAustralian Asthma Genetics Consortium (AAGC) collaborators uhttps://chs-nhlbi.org/node/755816401nas a2205425 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2018 eng d a1546-171800aMultiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes.0 aMultiancestry genomewide association study of 520000 subjects id c2018 Apr a524-5370 v503 aStroke has multiple etiologies, but the underlying genes and pathways are largely unknown. We conducted a multiancestry genome-wide-association meta-analysis in 521,612 individuals (67,162 cases and 454,450 controls) and discovered 22 new stroke risk loci, bringing the total to 32. We further found shared genetic variation with related vascular traits, including blood pressure, cardiac traits, and venous thromboembolism, at individual loci (n = 18), and using genetic risk scores and linkage-disequilibrium-score regression. Several loci exhibited distinct association and pleiotropy patterns for etiological stroke subtypes. Eleven new susceptibility loci indicate mechanisms not previously implicated in stroke pathophysiology, with prioritization of risk variants and genes accomplished through bioinformatics analyses using extensive functional datasets. Stroke risk loci were significantly enriched in drug targets for antithrombotic therapy.
1 aMalik, Rainer1 aChauhan, Ganesh1 aTraylor, Matthew1 aSargurupremraj, Muralidharan1 aOkada, Yukinori1 aMishra, Aniket1 aRutten-Jacobs, Loes1 aGiese, Anne-Katrin1 avan der Laan, Sander, W1 aGretarsdottir, Solveig1 aAnderson, Christopher, D1 aChong, Michael1 aAdams, Hieab, H H1 aAgo, Tetsuro1 aAlmgren, Peter1 aAmouyel, Philippe1 aAy, Hakan1 aBartz, Traci, M1 aBenavente, Oscar, R1 aBevan, Steve1 aBoncoraglio, Giorgio, B1 aBrown, Robert, D1 aButterworth, Adam, S1 aCarrera, Caty1 aCarty, Cara, L1 aChasman, Daniel, I1 aChen, Wei-Min1 aCole, John, W1 aCorrea, Adolfo1 aCotlarciuc, Ioana1 aCruchaga, Carlos1 aDanesh, John1 ade Bakker, Paul, I W1 aDeStefano, Anita, L1 aHoed, Marcel, den1 aDuan, Qing1 aEngelter, Stefan, T1 aFalcone, Guido, J1 aGottesman, Rebecca, F1 aGrewal, Raji, P1 aGudnason, Vilmundur1 aGustafsson, Stefan1 aHaessler, Jeffrey1 aHarris, Tamara, B1 aHassan, Ahamad1 aHavulinna, Aki, S1 aHeckbert, Susan, R1 aHolliday, Elizabeth, G1 aHoward, George1 aHsu, Fang-Chi1 aHyacinth, Hyacinth, I1 aIkram, Arfan, M1 aIngelsson, Erik1 aIrvin, Marguerite, R1 aJian, Xueqiu1 aJimenez-Conde, Jordi1 aJohnson, Julie, A1 aJukema, Wouter1 aKanai, Masahiro1 aKeene, Keith, L1 aKissela, Brett, M1 aKleindorfer, Dawn, O1 aKooperberg, Charles1 aKubo, Michiaki1 aLange, Leslie, A1 aLangefeld, Carl, D1 aLangenberg, Claudia1 aLauner, Lenore, J1 aLee, Jin-Moo1 aLemmens, Robin1 aLeys, Didier1 aLewis, Cathryn, M1 aLin, Wei-Yu1 aLindgren, Arne, G1 aLorentzen, Erik1 aMagnusson, Patrik, K1 aMaguire, Jane1 aManichaikul, Ani1 aMcArdle, Patrick, F1 aMeschia, James, F1 aMitchell, Braxton, D1 aMosley, Thomas, H1 aNalls, Michael, A1 aNinomiya, Toshiharu1 aO'Donnell, Martin, J1 aPsaty, Bruce, M1 aPulit, Sara, L1 aRannikmae, Kristiina1 aReiner, Alexander, P1 aRexrode, Kathryn, M1 aRice, Kenneth1 aRich, Stephen, S1 aRidker, Paul, M1 aRost, Natalia, S1 aRothwell, Peter, M1 aRotter, Jerome, I1 aRundek, Tatjana1 aSacco, Ralph, L1 aSakaue, Saori1 aSale, Michèle, M1 aSalomaa, Veikko1 aSapkota, Bishwa, R1 aSchmidt, Reinhold1 aSchmidt, Carsten, O1 aSchminke, Ulf1 aSharma, Pankaj1 aSlowik, Agnieszka1 aSudlow, Cathie, L M1 aTanislav, Christian1 aTatlisumak, Turgut1 aTaylor, Kent, D1 aThijs, Vincent, N S1 aThorleifsson, Gudmar1 aThorsteinsdottir, Unnur1 aTiedt, Steffen1 aTrompet, Stella1 aTzourio, Christophe1 aDuijn, Cornelia, M1 aWalters, Matthew1 aWareham, Nicholas, J1 aWassertheil-Smoller, Sylvia1 aWilson, James, G1 aWiggins, Kerri, L1 aYang, Qiong1 aYusuf, Salim1 aBis, Joshua, C1 aPastinen, Tomi1 aRuusalepp, Arno1 aSchadt, Eric, E1 aKoplev, Simon1 aBjörkegren, Johan, L M1 aCodoni, Veronica1 aCivelek, Mete1 aSmith, Nicholas, L1 aTrégouët, David, A1 aChristophersen, Ingrid, E1 aRoselli, Carolina1 aLubitz, Steven, A1 aEllinor, Patrick, T1 aTai, Shyong, E1 aKooner, Jaspal, S1 aKato, Norihiro1 aHe, Jiang1 aHarst, Pim1 aElliott, Paul1 aChambers, John, C1 aTakeuchi, Fumihiko1 aJohnson, Andrew, D1 aSanghera, Dharambir, K1 aMelander, Olle1 aJern, Christina1 aStrbian, Daniel1 aFernandez-Cadenas, Israel1 aLongstreth, W T1 aRolfs, Arndt1 aHata, Jun1 aWoo, Daniel1 aRosand, Jonathan1 aParé, Guillaume1 aHopewell, Jemma, C1 aSaleheen, Danish1 aStefansson, Kari1 aWorrall, Bradford, B1 aKittner, Steven, J1 aSeshadri, Sudha1 aFornage, Myriam1 aMarkus, Hugh, S1 aHowson, Joanna, M M1 aKamatani, Yoichiro1 aDebette, Stephanie1 aDichgans, Martin1 aMalik, Rainer1 aChauhan, Ganesh1 aTraylor, Matthew1 aSargurupremraj, Muralidharan1 aOkada, Yukinori1 aMishra, Aniket1 aRutten-Jacobs, Loes1 aGiese, Anne-Katrin1 avan der Laan, Sander, W1 aGretarsdottir, Solveig1 aAnderson, Christopher, D1 aChong, Michael1 aAdams, Hieab, H H1 aAgo, Tetsuro1 aAlmgren, Peter1 aAmouyel, Philippe1 aAy, Hakan1 aBartz, Traci, M1 aBenavente, Oscar, R1 aBevan, Steve1 aBoncoraglio, Giorgio, B1 aBrown, Robert, D1 aButterworth, Adam, S1 aCarrera, Caty1 aCarty, Cara, L1 aChasman, Daniel, I1 aChen, Wei-Min1 aCole, John, W1 aCorrea, Adolfo1 aCotlarciuc, Ioana1 aCruchaga, Carlos1 aDanesh, John1 ade Bakker, Paul, I W1 aDeStefano, Anita, L1 aHoed, Marcel, den1 aDuan, Qing1 aEngelter, Stefan, T1 aFalcone, Guido, J1 aGottesman, Rebecca, F1 aGrewal, Raji, P1 aGudnason, Vilmundur1 aGustafsson, Stefan1 aHaessler, Jeffrey1 aHarris, Tamara, B1 aHassan, Ahamad1 aHavulinna, Aki, S1 aHeckbert, Susan, R1 aHolliday, Elizabeth, G1 aHoward, George1 aHsu, Fang-Chi1 aHyacinth, Hyacinth, I1 aIkram, Arfan, M1 aIngelsson, Erik1 aIrvin, Marguerite, R1 aJian, Xueqiu1 aJimenez-Conde, Jordi1 aJohnson, Julie, A1 aJukema, Wouter1 aKanai, Masahiro1 aKeene, Keith, L1 aKissela, Brett, M1 aKleindorfer, Dawn, O1 aKooperberg, Charles1 aKubo, Michiaki1 aLange, Leslie, A1 aLangefeld, Carl, D1 aLangenberg, Claudia1 aLauner, Lenore, J1 aLee, Jin-Moo1 aLemmens, Robin1 aLeys, Didier1 aLewis, Cathryn, M1 aLin, Wei-Yu1 aLindgren, Arne, G1 aLorentzen, Erik1 aMagnusson, Patrik, K1 aMaguire, Jane1 aManichaikul, Ani1 aMcArdle, Patrick, F1 aMeschia, James, F1 aMitchell, Braxton, D1 aMosley, Thomas, H1 aNalls, Michael, A1 aNinomiya, Toshiharu1 aO'Donnell, Martin, J1 aPsaty, Bruce, M1 aPulit, Sara, L1 aRannikmae, Kristiina1 aReiner, Alexander, P1 aRexrode, Kathryn, M1 aRice, Kenneth1 aRich, Stephen, S1 aRidker, Paul, M1 aRost, Natalia, S1 aRothwell, Peter, M1 aRotter, Jerome, I1 aRundek, Tatjana1 aSacco, Ralph, L1 aSakaue, Saori1 aSale, Michèle, M1 aSalomaa, Veikko1 aSapkota, Bishwa, R1 aSchmidt, Reinhold1 aSchmidt, Carsten, O1 aSchminke, Ulf1 aSharma, Pankaj1 aSlowik, Agnieszka1 aSudlow, Cathie, L M1 aTanislav, Christian1 aTatlisumak, Turgut1 aTaylor, Kent, D1 aThijs, Vincent, N S1 aThorleifsson, Gudmar1 aThorsteinsdottir, Unnur1 aTiedt, Steffen1 aTrompet, Stella1 aTzourio, Christophe1 aDuijn, Cornelia, M1 aWalters, Matthew1 aWareham, Nicholas, J1 aWassertheil-Smoller, Sylvia1 aWilson, James, G1 aWiggins, Kerri, L1 aYang, Qiong1 aYusuf, Salim1 aAmin, Najaf1 aAparicio, Hugo, S1 aArnett, Donna, K1 aAttia, John1 aBeiser, Alexa, S1 aBerr, Claudine1 aBuring, Julie, E1 aBustamante, Mariana1 aCaso, Valeria1 aCheng, Yu-Ching1 aChoi, Seung, Hoan1 aChowhan, Ayesha1 aCullell, Natalia1 aDartigues, Jean-François1 aDelavaran, Hossein1 aDelgado, Pilar1 aDörr, Marcus1 aEngström, Gunnar1 aFord, Ian1 aGurpreet, Wander, S1 aHamsten, Anders1 aHeitsch, Laura1 aHozawa, Atsushi1 aIbanez, Laura1 aIlinca, Andreea1 aIngelsson, Martin1 aIwasaki, Motoki1 aJackson, Rebecca, D1 aJood, Katarina1 aJousilahti, Pekka1 aKaffashian, Sara1 aKalra, Lalit1 aKamouchi, Masahiro1 aKitazono, Takanari1 aKjartansson, Olafur1 aKloss, Manja1 aKoudstaal, Peter, J1 aKrupinski, Jerzy1 aLabovitz, Daniel, L1 aLaurie, Cathy, C1 aLevi, Christopher, R1 aLi, Linxin1 aLind, Lars1 aLindgren, Cecilia, M1 aLioutas, Vasileios1 aLiu, Yong, Mei1 aLopez, Oscar, L1 aMakoto, Hirata1 aMartinez-Majander, Nicolas1 aMatsuda, Koichi1 aMinegishi, Naoko1 aMontaner, Joan1 aMorris, Andrew, P1 aMuiño, Elena1 aMüller-Nurasyid, Martina1 aNorrving, Bo1 aOgishima, Soichi1 aParati, Eugenio, A1 aPeddareddygari, Leema, Reddy1 aPedersen, Nancy, L1 aPera, Joanna1 aPerola, Markus1 aPezzini, Alessandro1 aPileggi, Silvana1 aRabionet, Raquel1 aRiba-Llena, Iolanda1 aRibasés, Marta1 aRomero, Jose, R1 aRoquer, Jaume1 aRudd, Anthony, G1 aSarin, Antti-Pekka1 aSarju, Ralhan1 aSarnowski, Chloe1 aSasaki, Makoto1 aSatizabal, Claudia, L1 aSatoh, Mamoru1 aSattar, Naveed1 aSawada, Norie1 aSibolt, Gerli1 aSigurdsson, Ásgeir1 aSmith, Albert1 aSobue, Kenji1 aSoriano-Tárraga, Carolina1 aStanne, Tara1 aStine, Colin1 aStott, David, J1 aStrauch, Konstantin1 aTakai, Takako1 aTanaka, Hideo1 aTanno, Kozo1 aTeumer, Alexander1 aTomppo, Liisa1 aTorres-Aguila, Nuria, P1 aTouze, Emmanuel1 aTsugane, Shoichiro1 aUitterlinden, André, G1 aValdimarsson, Einar, M1 avan der Lee, Sven, J1 aVölzke, Henry1 aWakai, Kenji1 aWeir, David1 aWilliams, Stephen, R1 aWolfe, Charles, D A1 aWong, Quenna1 aXu, Huichun1 aYamaji, Taiki1 aSanghera, Dharambir, K1 aMelander, Olle1 aJern, Christina1 aStrbian, Daniel1 aFernandez-Cadenas, Israel1 aLongstreth, W T1 aRolfs, Arndt1 aHata, Jun1 aWoo, Daniel1 aRosand, Jonathan1 aParé, Guillaume1 aHopewell, Jemma, C1 aSaleheen, Danish1 aStefansson, Kari1 aWorrall, Bradford, B1 aKittner, Steven, J1 aSeshadri, Sudha1 aFornage, Myriam1 aMarkus, Hugh, S1 aHowson, Joanna, M M1 aKamatani, Yoichiro1 aDebette, Stephanie1 aDichgans, Martin1 aAFGen Consortium1 aCohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium1 aInternational Genomics of Blood Pressure (iGEN-BP) Consortium1 aINVENT Consortium1 aSTARNET1 aBioBank Japan Cooperative Hospital Group1 aCOMPASS Consortium1 aEPIC-CVD Consortium1 aEPIC-InterAct Consortium1 aInternational Stroke Genetics Consortium (ISGC)1 aMETASTROKE Consortium1 aNeurology Working Group of the CHARGE Consortium1 aNINDS Stroke Genetics Network (SiGN)1 aUK Young Lacunar DNA Study1 aMEGASTROKE Consortium1 aMEGASTROKE Consortium: uhttps://chs-nhlbi.org/node/768304963nas a2201357 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2018 eng d a2041-172300aMultiethnic meta-analysis identifies ancestry-specific and cross-ancestry loci for pulmonary function.0 aMultiethnic metaanalysis identifies ancestryspecific and crossan c2018 Jul 30 a29760 v93 aNearly 100 loci have been identified for pulmonary function, almost exclusively in studies of European ancestry populations. We extend previous research by meta-analyzing genome-wide association studies of 1000 Genomes imputed variants in relation to pulmonary function in a multiethnic population of 90,715 individuals of European (N = 60,552), African (N = 8429), Asian (N = 9959), and Hispanic/Latino (N = 11,775) ethnicities. We identify over 50 additional loci at genome-wide significance in ancestry-specific or multiethnic meta-analyses. Using recent fine-mapping methods incorporating functional annotation, gene expression, and differences in linkage disequilibrium between ethnicities, we further shed light on potential causal variants and genes at known and newly identified loci. Several of the novel genes encode proteins with predicted or established drug targets, including KCNK2 and CDK12. Our study highlights the utility of multiethnic and integrative genomics approaches to extend existing knowledge of the genetics of lung function and clinical relevance of implicated loci.
1 aWyss, Annah, B1 aSofer, Tamar1 aLee, Mi, Kyeong1 aTerzikhan, Natalie1 aNguyen, Jennifer, N1 aLahousse, Lies1 aLatourelle, Jeanne, C1 aSmith, Albert, Vernon1 aBartz, Traci, M1 aFeitosa, Mary, F1 aGao, Wei1 aAhluwalia, Tarunveer, S1 aTang, Wenbo1 aOldmeadow, Christopher1 aDuan, Qing1 ade Jong, Kim1 aWojczynski, Mary, K1 aWang, Xin-Qun1 aNoordam, Raymond1 aHartwig, Fernando, Pires1 aJackson, Victoria, E1 aWang, Tianyuan1 aObeidat, Ma'en1 aHobbs, Brian, D1 aHuan, Tianxiao1 aGui, Hongsheng1 aParker, Margaret, M1 aHu, Donglei1 aMogil, Lauren, S1 aKichaev, Gleb1 aJin, Jianping1 aGraff, Mariaelisa1 aHarris, Tamara, B1 aKalhan, Ravi1 aHeckbert, Susan, R1 aPaternoster, Lavinia1 aBurkart, Kristin, M1 aLiu, Yongmei1 aHolliday, Elizabeth, G1 aWilson, James, G1 aVonk, Judith, M1 aSanders, Jason, L1 aBarr, Graham1 ade Mutsert, Renée1 aMenezes, Ana, Maria Bapt1 aAdams, Hieab, H H1 avan den Berge, Maarten1 aJoehanes, Roby1 aLevin, Albert, M1 aLiberto, Jennifer1 aLauner, Lenore, J1 aMorrison, Alanna, C1 aSitlani, Colleen, M1 aCeledón, Juan, C1 aKritchevsky, Stephen, B1 aScott, Rodney, J1 aChristensen, Kaare1 aRotter, Jerome, I1 aBonten, Tobias, N1 aWehrmeister, Fernando, César1 aBossé, Yohan1 aXiao, Shujie1 aOh, Sam1 aFranceschini, Nora1 aBrody, Jennifer, A1 aKaplan, Robert, C1 aLohman, Kurt1 aMcEvoy, Mark1 aProvince, Michael, A1 aRosendaal, Frits, R1 aTaylor, Kent, D1 aNickle, David, C1 aWilliams, Keoki1 aBurchard, Esteban, G1 aWheeler, Heather, E1 aSin, Don, D1 aGudnason, Vilmundur1 aNorth, Kari, E1 aFornage, Myriam1 aPsaty, Bruce, M1 aMyers, Richard, H1 aO'Connor, George1 aHansen, Torben1 aLaurie, Cathy, C1 aCassano, Patricia, A1 aSung, Joohon1 aKim, Woo, Jin1 aAttia, John, R1 aLange, Leslie1 aBoezen, Marike1 aThyagarajan, Bharat1 aRich, Stephen, S1 aMook-Kanamori, Dennis, O1 aHorta, Bernardo, Lessa1 aUitterlinden, André, G1 aIm, Hae, Kyung1 aCho, Michael, H1 aBrusselle, Guy, G1 aGharib, Sina, A1 aDupuis, Josée1 aManichaikul, Ani1 aLondon, Stephanie, J uhttps://chs-nhlbi.org/node/781904010nas a2200709 4500008004100000022001400041245010600055210006900161260001600230520196000246100001402206700002202220700002002242700001602262700002402278700002102302700002102323700001602344700002302360700002702383700002302410700001802433700002002451700002202471700002402493700001402517700002002531700002202551700002202573700002002595700002102615700002202636700002402658700002102682700002602703700002002729700002702749700002202776700002102798700002202819700002002841700002002861700002202881700002102903700002802924700002202952700001502974700002102989700002003010700002503030700002503055700001703080700001903097700002003116700002403136700001903160700001903179700002003198700002503218700002103243856003603264 2018 eng d a1535-497000aOmega-3 Fatty Acids and Genome-wide Interaction Analyses Reveal DPP10-Pulmonary Function Association.0 aOmega3 Fatty Acids and Genomewide Interaction Analyses Reveal DP c2018 Sep 103 aRATIONALE: Omega-3 poly-unsaturated fatty acids (n-3 PUFAs) have anti-inflammatory properties that could benefit adults with comprised pulmonary health.
OBJECTIVE: To investigate n-3 PUFA associations with spirometric measures of pulmonary function tests (PFTs) and determine underlying genetic susceptibility.
METHODS: Associations of n-3 PUFA biomarkers (alpha-linolenic acid, eicosapentaenoic acid, docosapentaenoic acid [DPA], and docosahexaenoic acid [DHA]) were evaluated with PFTs (forced expiratory volume in the first second [FEV], forced vital capacity [FVC], and [FEV/FVC]) in meta-analyses across seven cohorts from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (N=16,134 of European or African ancestry). PFT-associated n-3 PUFAs were carried forward to genome-wide interaction analyses in the four largest cohorts (N=11,962) and replicated in one cohort (N=1,687). Cohort-specific results were combined using joint 2 degree-of-freedom (2df) meta-analyses of single nucleotide polymorphism (SNP) associations and their interactions with n-3 PUFAs.
RESULTS: DPA and DHA were positively associated with FEV1 and FVC (P<0.025), with evidence for effect modification by smoking and by sex. Genome-wide analyses identified a novel association of rs11693320-an intronic DPP10 SNP-with FVC when incorporating an interaction with DHA, and the finding was replicated (P=9.4×10 across discovery and replication cohorts). The rs11693320-A allele (frequency~80%) was associated with lower FVC (P=2.1×10; β= -161.0mL), and the association was attenuated by higher DHA levels (P=2.1×10; β=36.2mL).
CONCLUSIONS: We corroborated beneficial effects of n-3 PUFAs on pulmonary function. By modeling genome-wide n-3 PUFA interactions, we identified a novel DPP10 SNP association with FVC that was not detectable in much larger studies ignoring this interaction.
1 aXu, Jiayi1 aGaddis, Nathan, C1 aBartz, Traci, M1 aHou, Ruixue1 aManichaikul, Ani, W1 aPankratz, Nathan1 aSmith, Albert, V1 aSun, Fangui1 aTerzikhan, Natalie1 aMarkunas, Christina, A1 aPatchen, Bonnie, K1 aSchu, Matthew1 aBeydoun, May, A1 aBrusselle, Guy, G1 aEiriksdottir, Gudny1 aZhou, Xia1 aWood, Alexis, C1 aGraff, Mariaelisa1 aHarris, Tamara, B1 aIkram, Arfan, M1 aJacobs, David, R1 aLauner, Lenore, J1 aLemaitre, Rozenn, N1 aO'Connor, George1 aOelsner, Elizabeth, C1 aPsaty, Bruce, M1 aRamachandran, Vasan, S1 aRohde, Rebecca, R1 aRich, Stephen, S1 aRotter, Jerome, I1 aSeshadri, Sudha1 aSmith, Lewis, J1 aTiemeier, Henning1 aTsai, Michael, Y1 aUitterlinden, André, G1 aVoruganti, Saroja1 aXu, Hanfei1 aZilhão, Nuno, R1 aFornage, Myriam1 aZillikens, Carola, M1 aLondon, Stephanie, J1 aBarr, Graham1 aDupuis, Josée1 aGharib, Sina, A1 aGudnason, Vilmundur1 aLahousse, Lies1 aNorth, Kari, E1 aSteffen, Lyn, M1 aCassano, Patricia, A1 aHancock, Dana, B uhttps://chs-nhlbi.org/node/777609584nas a2203049 4500008004100000022001400041245011700055210006900172260001300241300001200254490000700266520112500273100002001398700002101418700002101439700001401460700002301474700001901497700001301516700002401529700001901553700002001572700001701592700001801609700001201627700002301639700001201662700001801674700002001692700001801712700001901730700002301749700002001772700001801792700003601810700002001846700002001866700002001886700002301906700003001929700002101959700002001980700002002000700002102020700001202041700002602053700001302079700002002092700002302112700002202135700001902157700002702176700003202203700002102235700002102256700002002277700002402297700002102321700002502342700002102367700002002388700002002408700002402428700002102452700001602473700001802489700001902507700001902526700001602545700001702561700002202578700002302600700002302623700002102646700002302667700001902690700002202709700002202731700001902753700002402772700002402796700002102820700002002841700002202861700002602883700002002909700002502929700002202954700001402976700001502990700002603005700002503031700001503056700002003071700002503091700002303116700002903139700001703168700001903185700002503204700001803229700002203247700002403269700001803293700002103311700001803332700002003350700001703370700001903387700002103406700001803427700001303445700001503458700001803473700002203491700002703513700002103540700001803561700002103579700001903600700002403619700002303643700001803666700002103684700001503705700002103720700002603741700002203767700002103789700002503810700002303835700002003858700002403878700002103902700002503923700001803948700001803966700001803984700002104002700002004023700001904043700001304062700001604075700001704091700002204108700001904130700002104149700002404170700001904194700002404213700002204237700001904259700002004278700002304298700002004321700002004341700002304361700002404384700002204408700002304430700002404453700002204477700002204499700001504521700002204536700002404558700002204582700002504604700002104629700002204650700001904672700002104691700001804712700002204730700002204752700002304774700002504797700002304822700001904845700002004864700002504884700001804909700002004927700002604947700002404973700002504997700002005022700002105042700001605063700002005079700002405099700002805123700001705151700002305168700002205191700002505213700002905238700002305267700001705290700002005307700001905327700002105346700002005367700002205387700002105409700001605430700001805446700001905464700002605483700002205509700002005531700002405551700001905575700002605594700001905620700002005639700001905659700001205678700001505690700001705705700002005722700002305742700002105765700002605786700002805812700002305840700002105863700001905884700002005903700002105923700001905944700002505963700002005988700002406008700002106032700002206053700002806075700002206103700002306125700002506148700002106173700002406194700002106218700001906239700002506258700002206283700002006305700002106325700002106346700002206367700002206389700002206411710002306433710002106456710002106477856003606498 2018 eng d a1546-171800aRefining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.0 aRefining the accuracy of validated target identification through c2018 Apr a559-5710 v503 aWe aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.
1 aMahajan, Anubha1 aWessel, Jennifer1 aWillems, Sara, M1 aZhao, Wei1 aRobertson, Neil, R1 aChu, Audrey, Y1 aGan, Wei1 aKitajima, Hidetoshi1 aTaliun, Daniel1 aRayner, William1 aGuo, Xiuqing1 aLu, Yingchang1 aLi, Man1 aJensen, Richard, A1 aHu, Yao1 aHuo, Shaofeng1 aLohman, Kurt, K1 aZhang, Weihua1 aCook, James, P1 aPrins, Bram, Peter1 aFlannick, Jason1 aGrarup, Niels1 aTrubetskoy, Vassily, Vladimirov1 aKravic, Jasmina1 aKim, Young, Jin1 aRybin, Denis, V1 aYaghootkar, Hanieh1 aMüller-Nurasyid, Martina1 aMeidtner, Karina1 aLi-Gao, Ruifang1 aVarga, Tibor, V1 aMarten, Jonathan1 aLi, Jin1 aSmith, Albert, Vernon1 aAn, Ping1 aLigthart, Symen1 aGustafsson, Stefan1 aMalerba, Giovanni1 aDemirkan, Ayse1 aTajes, Juan, Fernandez1 aSteinthorsdottir, Valgerdur1 aWuttke, Matthias1 aLecoeur, Cécile1 aPreuss, Michael1 aBielak, Lawrence, F1 aGraff, Marielisa1 aHighland, Heather, M1 aJustice, Anne, E1 aLiu, Dajiang, J1 aMarouli, Eirini1 aPeloso, Gina, Marie1 aWarren, Helen, R1 aAfaq, Saima1 aAfzal, Shoaib1 aAhlqvist, Emma1 aAlmgren, Peter1 aAmin, Najaf1 aBang, Lia, B1 aBertoni, Alain, G1 aBombieri, Cristina1 aBork-Jensen, Jette1 aBrandslund, Ivan1 aBrody, Jennifer, A1 aBurtt, Noel, P1 aCanouil, Mickaël1 aChen, Yii-Der Ida1 aCho, Yoon Shin1 aChristensen, Cramer1 aEastwood, Sophie, V1 aEckardt, Kai-Uwe1 aFischer, Krista1 aGambaro, Giovanni1 aGiedraitis, Vilmantas1 aGrove, Megan, L1 ade Haan, Hugoline, G1 aHackinger, Sophie1 aHai, Yang1 aHan, Sohee1 aTybjærg-Hansen, Anne1 aHivert, Marie-France1 aIsomaa, Bo1 aJäger, Susanne1 aJørgensen, Marit, E1 aJørgensen, Torben1 aKäräjämäki, AnneMari1 aKim, Bong-Jo1 aKim, Sung, Soo1 aKoistinen, Heikki, A1 aKovacs, Peter1 aKriebel, Jennifer1 aKronenberg, Florian1 aLäll, Kristi1 aLange, Leslie, A1 aLee, Jung-Jin1 aLehne, Benjamin1 aLi, Huaixing1 aLin, Keng-Hung1 aLinneberg, Allan1 aLiu, Ching-Ti1 aLiu, Jun1 aLoh, Marie1 aMägi, Reedik1 aMamakou, Vasiliki1 aMcKean-Cowdin, Roberta1 aNadkarni, Girish1 aNeville, Matt1 aNielsen, Sune, F1 aNtalla, Ioanna1 aPeyser, Patricia, A1 aRathmann, Wolfgang1 aRice, Kenneth1 aRich, Stephen, S1 aRode, Line1 aRolandsson, Olov1 aSchönherr, Sebastian1 aSelvin, Elizabeth1 aSmall, Kerrin, S1 aStančáková, Alena1 aSurendran, Praveen1 aTaylor, Kent, D1 aTeslovich, Tanya, M1 aThorand, Barbara1 aThorleifsson, Gudmar1 aTin, Adrienne1 aTönjes, Anke1 aVarbo, Anette1 aWitte, Daniel, R1 aWood, Andrew, R1 aYajnik, Pranav1 aYao, Jie1 aYengo, Loic1 aYoung, Robin1 aAmouyel, Philippe1 aBoeing, Heiner1 aBoerwinkle, Eric1 aBottinger, Erwin, P1 aChowdhury, Raj1 aCollins, Francis, S1 aDedoussis, George1 aDehghan, Abbas1 aDeloukas, Panos1 aFerrario, Marco, M1 aFerrieres, Jean1 aFlorez, Jose, C1 aFrossard, Philippe1 aGudnason, Vilmundur1 aHarris, Tamara, B1 aHeckbert, Susan, R1 aHowson, Joanna, M M1 aIngelsson, Martin1 aKathiresan, Sekar1 aKee, Frank1 aKuusisto, Johanna1 aLangenberg, Claudia1 aLauner, Lenore, J1 aLindgren, Cecilia, M1 aMännistö, Satu1 aMeitinger, Thomas1 aMelander, Olle1 aMohlke, Karen, L1 aMoitry, Marie1 aMorris, Andrew, D1 aMurray, Alison, D1 ade Mutsert, Renée1 aOrho-Melander, Marju1 aOwen, Katharine, R1 aPerola, Markus1 aPeters, Annette1 aProvince, Michael, A1 aRasheed, Asif1 aRidker, Paul, M1 aRivadineira, Fernando1 aRosendaal, Frits, R1 aRosengren, Anders, H1 aSalomaa, Veikko1 aSheu, Wayne, H-H1 aSladek, Rob1 aSmith, Blair, H1 aStrauch, Konstantin1 aUitterlinden, André, G1 aVarma, Rohit1 aWiller, Cristen, J1 aBlüher, Matthias1 aButterworth, Adam, S1 aChambers, John, Campbell1 aChasman, Daniel, I1 aDanesh, John1 aDuijn, Cornelia1 aDupuis, Josée1 aFranco, Oscar, H1 aFranks, Paul, W1 aFroguel, Philippe1 aGrallert, Harald1 aGroop, Leif1 aHan, Bok-Ghee1 aHansen, Torben1 aHattersley, Andrew, T1 aHayward, Caroline1 aIngelsson, Erik1 aKardia, Sharon, L R1 aKarpe, Fredrik1 aKooner, Jaspal, Singh1 aKöttgen, Anna1 aKuulasmaa, Kari1 aLaakso, Markku1 aLin, Xu1 aLind, Lars1 aLiu, Yongmei1 aLoos, Ruth, J F1 aMarchini, Jonathan1 aMetspalu, Andres1 aMook-Kanamori, Dennis1 aNordestgaard, Børge, G1 aPalmer, Colin, N A1 aPankow, James, S1 aPedersen, Oluf1 aPsaty, Bruce, M1 aRauramaa, Rainer1 aSattar, Naveed1 aSchulze, Matthias, B1 aSoranzo, Nicole1 aSpector, Timothy, D1 aStefansson, Kari1 aStumvoll, Michael1 aThorsteinsdottir, Unnur1 aTuomi, Tiinamaija1 aTuomilehto, Jaakko1 aWareham, Nicholas, J1 aWilson, James, G1 aZeggini, Eleftheria1 aScott, Robert, A1 aBarroso, Inês1 aFrayling, Timothy, M1 aGoodarzi, Mark, O1 aMeigs, James, B1 aBoehnke, Michael1 aSaleheen, Danish1 aMorris, Andrew, P1 aRotter, Jerome, I1 aMcCarthy, Mark, I1 aExomeBP Consortium1 aMAGIC Consortium1 aGIANT Consortium uhttps://chs-nhlbi.org/node/766804864nas a2200733 4500008004100000022001400041245018700055210006900242260001300311300001200324490000700336520267100343100002303014700002203037700001603059700002403075700003203099700002003131700002003151700002203171700002403193700001703217700002103234700002803255700002903283700001603312700001903328700003103347700001903378700002203397700002403419700002503443700003203468700001703500700002003517700001503537700001903552700002003571700001803591700002003609700002003629700002003649700002403669700001903693700002003712700002003732700002203752700002803774700001903802700002403821700002103845700001903866700002503885700001903910700002503929700002103954700002303975700001103998700002204009700002104031700002204052700002004074856003604094 2018 eng d a1432-042800aSugar-sweetened beverage intake associations with fasting glucose and insulin concentrations are not modified by selected genetic variants in a ChREBP-FGF21 pathway: a meta-analysis.0 aSugarsweetened beverage intake associations with fasting glucose c2018 Feb a317-3300 v613 aAIMS/HYPOTHESIS: Sugar-sweetened beverages (SSBs) are a major dietary contributor to fructose intake. A molecular pathway involving the carbohydrate responsive element-binding protein (ChREBP) and the metabolic hormone fibroblast growth factor 21 (FGF21) may influence sugar metabolism and, thereby, contribute to fructose-induced metabolic disease. We hypothesise that common variants in 11 genes involved in fructose metabolism and the ChREBP-FGF21 pathway may interact with SSB intake to exacerbate positive associations between higher SSB intake and glycaemic traits.
METHODS: Data from 11 cohorts (six discovery and five replication) in the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium provided association and interaction results from 34,748 adults of European descent. SSB intake (soft drinks, fruit punches, lemonades or other fruit drinks) was derived from food-frequency questionnaires and food diaries. In fixed-effects meta-analyses, we quantified: (1) the associations between SSBs and glycaemic traits (fasting glucose and fasting insulin); and (2) the interactions between SSBs and 18 independent SNPs related to the ChREBP-FGF21 pathway.
RESULTS: In our combined meta-analyses of discovery and replication cohorts, after adjustment for age, sex, energy intake, BMI and other dietary covariates, each additional serving of SSB intake was associated with higher fasting glucose (β ± SE 0.014 ± 0.004 [mmol/l], p = 1.5 × 10-3) and higher fasting insulin (0.030 ± 0.005 [log e pmol/l], p = 2.0 × 10-10). No significant interactions on glycaemic traits were observed between SSB intake and selected SNPs. While a suggestive interaction was observed in the discovery cohorts with a SNP (rs1542423) in the β-Klotho (KLB) locus on fasting insulin (0.030 ± 0.011 log e pmol/l, uncorrected p = 0.006), results in the replication cohorts and combined meta-analyses were non-significant.
CONCLUSIONS/INTERPRETATION: In this large meta-analysis, we observed that SSB intake was associated with higher fasting glucose and insulin. Although a suggestive interaction with a genetic variant in the ChREBP-FGF21 pathway was observed in the discovery cohorts, this observation was not confirmed in the replication analysis.
TRIAL REGISTRATION: Trials related to this study were registered at clinicaltrials.gov as NCT00005131 (Atherosclerosis Risk in Communities), NCT00005133 (Cardiovascular Health Study), NCT00005121 (Framingham Offspring Study), NCT00005487 (Multi-Ethnic Study of Atherosclerosis) and NCT00005152 (Nurses' Health Study).
1 aMcKeown, Nicola, M1 aDashti, Hassan, S1 aMa, Jiantao1 aHaslam, Danielle, E1 ade Jong, Jessica, C Kiefte-1 aSmith, Caren, E1 aTanaka, Toshiko1 aGraff, Mariaelisa1 aLemaitre, Rozenn, N1 aRybin, Denis1 aSonestedt, Emily1 aFrazier-Wood, Alexis, C1 aMook-Kanamori, Dennis, O1 aLi, Yanping1 aWang, Carol, A1 aLeermakers, Elisabeth, T M1 aMikkilä, Vera1 aYoung, Kristin, L1 aMukamal, Kenneth, J1 aCupples, Adrienne, L1 aSchulz, Christina-Alexandra1 aChen, Tzu-An1 aLi-Gao, Ruifang1 aHuang, Tao1 aOddy, Wendy, H1 aRaitakari, Olli1 aRice, Kenneth1 aMeigs, James, B1 aEricson, Ulrika1 aSteffen, Lyn, M1 aRosendaal, Frits, R1 aHofman, Albert1 aKähönen, Mika1 aPsaty, Bruce, M1 aBrunkwall, Louise1 aUitterlinden, André, G1 aViikari, Jorma1 aSiscovick, David, S1 aSeppälä, Ilkka1 aNorth, Kari, E1 aMozaffarian, Dariush1 aDupuis, Josée1 aOrho-Melander, Marju1 aRich, Stephen, S1 ade Mutsert, Renée1 aQi, Lu1 aPennell, Craig, E1 aFranco, Oscar, H1 aLehtimäki, Terho1 aHerman, Mark, A uhttps://chs-nhlbi.org/node/757615950nas a2205365 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2019 eng d a2041-172300aAssociations of autozygosity with a broad range of human phenotypes.0 aAssociations of autozygosity with a broad range of human phenoty c2019 Oct 31 a49570 v103 aIn many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (F) for >1.4 million individuals, we show that F is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of F are confirmed within full-sibling pairs, where the variation in F is independent of all environmental confounding.
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aNutile, Teresa1 aPalviainen, Teemu1 aPrasad, Gauri1 aPrasad, Rashmi, B1 aPriyanka, Tallapragada, Divya Sri1 aRizzi, Federica1 aSalvi, Erika1 aSapkota, Bishwa, R1 aShriner, Daniel1 aSkotte, Line1 aSmart, Melissa, C1 aSmith, Albert, Vernon1 avan der Spek, Ashley1 aSpracklen, Cassandra, N1 aStrawbridge, Rona, J1 aTajuddin, Salman, M1 aTrompet, Stella1 aTurman, Constance1 aVerweij, Niek1 aViberti, Clara1 aWang, Lihua1 aWarren, Helen, R1 aWootton, Robyn, E1 aYanek, Lisa, R1 aYao, Jie1 aYousri, Noha, A1 aZhao, Wei1 aAdeyemo, Adebowale, A1 aAfaq, Saima1 aAguilar-Salinas, Carlos, Alberto1 aAkiyama, Masato1 aAlbert, Matthew, L1 aAllison, Matthew, A1 aAlver, Maris1 aAung, Tin1 aAzizi, Fereidoun1 aBentley, Amy, R1 aBoeing, Heiner1 aBoerwinkle, Eric1 aBorja, Judith, B1 ade Borst, Gert, J1 aBottinger, Erwin, P1 aBroer, Linda1 aCampbell, Harry1 aChanock, Stephen1 aChee, Miao-Li1 aChen, Guanjie1 aChen, Yii-der, I1 aChen, Zhengming1 aChiu, Yen-Feng1 aCocca, Massimiliano1 aCollins, Francis, S1 aConcas, Maria, Pina1 aCorley, Janie1 aCugliari, Giovanni1 avan Dam, Rob, M1 aDamulina, Anna1 aDaneshpour, Maryam, S1 aDay, Felix, R1 aDelgado, Graciela, E1 aDhana, Klodian1 aDoney, Alexander, S F1 aDörr, Marcus1 aDoumatey, Ayo, P1 aDzimiri, Nduna1 aEbenesersdóttir, Sunna1 aElliott, Joshua1 aElliott, Paul1 aEwert, Ralf1 aFelix, Janine, F1 aFischer, Krista1 aFreedman, Barry, I1 aGirotto, Giorgia1 aGoel, Anuj1 aGögele, Martin1 aGoodarzi, Mark, O1 aGraff, Mariaelisa1 aGranot-Hershkovitz, Einat1 aGrodstein, Francine1 aGuarrera, Simonetta1 aGudbjartsson, Daniel, F1 aGuity, Kamran1 aGunnarsson, Bjarni1 aGuo, Yu1 aHagenaars, Saskia, P1 aHaiman, Christopher, A1 aHalevy, Avner1 aHarris, Tamara, B1 aHedayati, Mehdi1 avan Heel, David, A1 aHirata, Makoto1 aHöfer, Imo1 aHsiung, Chao, Agnes1 aHuang, Jinyan1 aHung, Yi-Jen1 aIkram, Arfan, M1 aJagadeesan, Anuradha1 aJousilahti, Pekka1 aKamatani, Yoichiro1 aKanai, Masahiro1 aKerrison, Nicola, D1 aKessler, Thorsten1 aKhaw, Kay-Tee1 aKhor, Chiea, Chuen1 ade Kleijn, Dominique, P V1 aKoh, Woon-Puay1 aKolcic, Ivana1 aKraft, Peter1 aKrämer, Bernhard, K1 aKutalik, Zoltán1 aKuusisto, Johanna1 aLangenberg, Claudia1 aLauner, Lenore, J1 aLawlor, Deborah, A1 aLee, I-Te1 aLee, Wen-Jane1 aLerch, Markus, M1 aLi, Liming1 aLiu, Jianjun1 aLoh, Marie1 aLondon, Stephanie, J1 aLoomis, Stephanie1 aLu, Yingchang1 aLuan, Jian'an1 aMägi, Reedik1 aManichaikul, Ani, W1 aManunta, Paolo1 aMásson, Gísli1 aMatoba, Nana1 aMei, Xue, W1 aMeisinger, Christa1 aMeitinger, Thomas1 aMezzavilla, Massimo1 aMilani, Lili1 aMillwood, Iona, Y1 aMomozawa, Yukihide1 aMoore, Amy1 aMorange, Pierre-Emmanuel1 aMoreno-Macias, Hortensia1 aMori, Trevor, A1 aMorrison, Alanna, C1 aMuka, Taulant1 aMurakami, Yoshinori1 aMurray, Alison, D1 ade Mutsert, Renée1 aMychaleckyj, Josyf, C1 aNalls, Mike, A1 aNauck, Matthias1 aNeville, Matt, J1 aNolte, Ilja, M1 aOng, Ken, K1 aOrozco, Lorena1 aPadmanabhan, Sandosh1 aPálsson, Gunnar1 aPankow, James, S1 aPattaro, Cristian1 aPattie, Alison1 aPolasek, 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Russell, P1 aTusié-Luna, Teresa1 aTzoulaki, Ioanna1 aVaccargiu, Simona1 aVangipurapu, Jagadish1 aVeldink, Jan, H1 aVitart, Veronique1 aVölker, Uwe1 aVuoksimaa, Eero1 aWakil, Salma, M1 aWaldenberger, Melanie1 aWander, Gurpreet, S1 aWang, Ya, Xing1 aWareham, Nicholas, J1 aWild, Sarah1 aYajnik, Chittaranjan, S1 aYuan, Jian-Min1 aZeng, Lingyao1 aZhang, Liang1 aZhou, Jie1 aAmin, Najaf1 aAsselbergs, Folkert, W1 aBakker, Stephan, J L1 aBecker, Diane, M1 aLehne, Benjamin1 aBennett, David, A1 avan den Berg, Leonard, H1 aBerndt, Sonja, I1 aBharadwaj, Dwaipayan1 aBielak, Lawrence, F1 aBochud, Murielle1 aBoehnke, Mike1 aBouchard, Claude1 aBradfield, Jonathan, P1 aBrody, Jennifer, A1 aCampbell, Archie1 aCarmi, Shai1 aCaulfield, Mark, J1 aCesarini, David1 aChambers, John, C1 aChandak, Giriraj, Ratan1 aCheng, Ching-Yu1 aCiullo, Marina1 aCornelis, Marilyn1 aCusi, Daniele1 aSmith, George Davey1 aDeary, Ian, J1 aDorajoo, Rajkumar1 aDuijn, Cornelia, M1 aEllinghaus, David1 aErdmann, Jeanette1 aEriksson, Johan, G1 aEvangelou, Evangelos1 aEvans, Michele, K1 aFaul, Jessica, D1 aFeenstra, Bjarke1 aFeitosa, Mary1 aFoisy, Sylvain1 aFranke, Andre1 aFriedlander, Yechiel1 aGasparini, Paolo1 aGieger, Christian1 aGonzalez, Clicerio1 aGoyette, Philippe1 aGrant, Struan, F A1 aGriffiths, Lyn, R1 aGroop, Leif1 aGudnason, Vilmundur1 aGyllensten, Ulf1 aHakonarson, Hakon1 aHamsten, Anders1 aHarst, Pim1 aHeng, Chew-Kiat1 aHicks, Andrew, A1 aHochner, Hagit1 aHuikuri, Heikki1 aHunt, Steven, C1 aJaddoe, Vincent, W V1 aDe Jager, Philip, L1 aJohannesson, Magnus1 aJohansson, Asa1 aJonas, Jost, B1 aJukema, Wouter1 aJunttila, Juhani1 aKaprio, Jaakko1 aKardia, Sharon, L R1 aKarpe, Fredrik1 aKumari, Meena1 aLaakso, Markku1 avan der Laan, Sander, W1 aLahti, Jari1 aLaudes, Matthias1 aLea, Rodney, A1 aLieb, Wolfgang1 aLumley, Thomas1 aMartin, Nicholas, G1 aMärz, Winfried1 aMatullo, Giuseppe1 aMcCarthy, Mark, I1 aMedland, Sarah, E1 aMerriman, Tony, R1 aMetspalu, Andres1 aMeyer, Brian, F1 aMohlke, Karen, L1 aMontgomery, Grant, W1 aMook-Kanamori, Dennis1 aMunroe, Patricia, B1 aNorth, Kari, E1 aNyholt, Dale, R1 aO'Connell, Jeffery, R1 aOber, Carole1 aOldehinkel, Albertine, J1 aPalmas, Walter1 aPalmer, Colin1 aPasterkamp, Gerard, G1 aPatin, Etienne1 aPennell, Craig, E1 aPerusse, Louis1 aPeyser, Patricia, A1 aPirastu, Mario1 aPolderman, Tinca, J C1 aPorteous, David, J1 aPosthuma, Danielle1 aPsaty, Bruce, M1 aRioux, John, D1 aRivadeneira, Fernando1 aRotimi, Charles1 aRotter, Jerome, I1 aRudan, Igor1 aRuijter, Hester, M den1 aSanghera, Dharambir, K1 aSattar, Naveed1 aSchmidt, Reinhold1 aSchulze, Matthias, B1 aSchunkert, Heribert1 aScott, Robert, A1 aShuldiner, Alan, R1 aSim, Xueling1 aSmall, Neil1 aSmith, Jennifer, A1 aSotoodehnia, Nona1 aTai, E-Shyong1 aTeumer, Alexander1 aTimpson, Nicholas, J1 aToniolo, Daniela1 aTrégouët, David-Alexandre1 aTuomi, Tiinamaija1 aVollenweider, Peter1 aWang, Carol, A1 aWeir, David, R1 aWhitfield, John, B1 aWijmenga, Cisca1 aWong, Tien-Yin1 aWright, John1 aYang, Jingyun1 aYu, Lei1 aZemel, Babette, S1 aZonderman, Alan, B1 aPerola, Markus1 aMagnusson, Patrik, K E1 aUitterlinden, André, G1 aKooner, Jaspal, S1 aChasman, Daniel, I1 aLoos, Ruth, J F1 aFranceschini, Nora1 aFranke, Lude1 aHaley, Chris, S1 aHayward, Caroline1 aWalters, Robin, G1 aPerry, John, R B1 aEsko, Tõnu1 aHelgason, Agnar1 aStefansson, Kari1 aJoshi, Peter, K1 aKubo, Michiaki1 aWilson, James, F uhttps://chs-nhlbi.org/node/819804013nas a2200685 4500008004100000022001400041245016200055210006900217260001600286300001200302490000800314520192200322100002102244700001702265700002302282700001502305700002202320700002002342700001702362700001602379700001702395700001802412700001202430700002302442700002202465700002302487700002502510700001702535700001802552700001902570700002602589700002002615700002402635700002202659700002102681700002002702700002202722700002102744700001902765700002402784700002002808700002302828700002502851700002502876700002502901700002702926700001902953700002402972700002102996700002203017700002103039700002203060700001903082700002003101710003403121710003603155710003603191710006403227856003603291 2019 eng d a1537-660500aImpact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program.0 aImpact of Rare and Common Genetic Variants on Diabetes Diagnosis c2019 Oct 03 a706-7180 v1053 aHemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in individuals with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics, and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K and/or FN3KRP in Europeans, and G6PD in African-Americans and Hispanics) and we identified an African-ancestry-specific low-frequency variant (rs1039215 in HBG2 and HBE1, minor allele frequency (MAF) = 0.03). The most associated G6PD variant (rs1050828-T, p.Val98Met, MAF = 12% in African-Americans, MAF = 2% in Hispanics) lowered HbA1c (-0.88% in hemizygous males, -0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693, p.Leu353Pro, MAF = 0.5%; -0.98% in hemizygous males, -0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African American cohorts, and we replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.
1 aSarnowski, Chloe1 aLeong, Aaron1 aRaffield, Laura, M1 aWu, Peitao1 ade Vries, Paul, S1 aDiCorpo, Daniel1 aGuo, Xiuqing1 aXu, Huichun1 aLiu, Yongmei1 aZheng, Xiuwen1 aHu, Yao1 aBrody, Jennifer, A1 aGoodarzi, Mark, O1 aHidalgo, Bertha, A1 aHighland, Heather, M1 aJain, Deepti1 aLiu, Ching-Ti1 aNaik, Rakhi, P1 aO'Connell, Jeffrey, R1 aPerry, James, A1 aPorneala, Bianca, C1 aSelvin, Elizabeth1 aWessel, Jennifer1 aPsaty, Bruce, M1 aCurran, Joanne, E1 aPeralta, Juan, M1 aBlangero, John1 aKooperberg, Charles1 aMathias, Rasika1 aJohnson, Andrew, D1 aReiner, Alexander, P1 aMitchell, Braxton, D1 aCupples, Adrienne, L1 aVasan, Ramachandran, S1 aCorrea, Adolfo1 aMorrison, Alanna, C1 aBoerwinkle, Eric1 aRotter, Jerome, I1 aRich, Stephen, S1 aManning, Alisa, K1 aDupuis, Josée1 aMeigs, James, B1 aTOPMed Diabetes Working Group1 aTOPMed Hematology Working Group1 aTOPMed Hemostasis Working Group1 aNational Heart, Lung, and Blood Institute TOPMed Consortium uhttps://chs-nhlbi.org/node/820510550nas a2203325 4500008004100000022001400041245010600055210006900161260001600230520134200246100002201588700002201610700002001632700001701652700002301669700001901692700002101711700001901732700001701751700002301768700002001791700001701811700002001828700002501848700002301873700001701896700002101913700002401934700002101958700002001979700002001999700002402019700001502043700002202058700002002080700002202100700002002122700002102142700002402163700002502187700002202212700001702234700002202251700002002273700001302293700001902306700001902325700001502344700001502359700002302374700002102397700002502418700001402443700002802457700001802485700002102503700002902524700002202553700002102575700002202596700001902618700001802637700002802655700001702683700001902700700001902719700001902738700001902757700002102776700001702797700002502814700002302839700002202862700002702884700002002911700001702931700001802948700001702966700001902983700001803002700001903020700001603039700001603055700001903071700001903090700001403109700002503123700002103148700002103169700002103190700002203211700002803233700002203261700002103283700001803304700002603322700002303348700002103371700002003392700002403412700001503436700002203451700002203473700002103495700002103516700002403537700002103561700002503582700002103607700002303628700001903651700002003670700001603690700002203706700002003728700002003748700001903768700002103787700002303808700002003831700002103851700001903872700002303891700002503914700002203939700002303961700002803984700001904012700002504031700001804056700002404074700002104098700002604119700001904145700002204164700001904186700002304205700002404228700002204252700002004274700002404294700001304318700002004331700001704351700001504368700001504383700001504398700001804413700002404431700002304455700002204478700002104500700002104521700001704542700002104559700002204580700002404602700001904626700002004645700002704665700002204692700002304714700002604737700001704763700002304780700002004803700002404823700001904847700001804866700002204884700002304906700002004929700001904949700002104968700002304989700002605012700001905038700001605057700002405073700002505097700002205122700001705144700001405161700002005175700001605195700002005211700002305231700002005254700001905274700002405293700001805317700002005335700001905355700002105374700002805395700002205423700002205445700002605467700001605493700001605509700001405525700001705539700002405556700002005580700002405600700001305624700001305637700001705650700001905667700001405686700002305700700002105723700002105744700002205765700002205787700001805809700001605827700002005843700002005863700002305883700002205906700002105928700002205949700002305971700002305994700001906017700002206036700001906058700001906077700002206096700002706118700002006145700002606165700002106191700002206212700001706234700001706251700001506268700002606283700001906309700002506328700001806353700001906371700003006390700001506420700001806435700001906453700002106472700002206493700002406515700001806539700001706557700002006574700002006594700002006614700002406634700001806658700002306676700002906699700002006728700002106748700001806769700002206787700002306809700001906832700002006851700001706871700002206888700002106910700002506931700002406956700001806980700002306998700002507021700002007046700002407066710002407090710007407114856003607188 2019 eng d a1476-625600aMulti-Ancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions.0 aMultiAncestry GenomeWide Association Study of Lipid Levels Incor c2019 Jan 293 aAn individual's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multi-ancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in Stage 1 (genome-wide discovery) and 66 studies in Stage 2 (focused follow-up), for a total of 394,584 individuals from five ancestry groups. Genetic main and interaction effects were jointly assessed by a 2 degrees of freedom (DF) test, and a 1 DF test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10-6) with lipid levels in Stage 1 and were evaluated in Stage 2, followed by combined analyses of Stage 1 and Stage 2. In the combined analysis of Stage 1 and Stage 2, 147 independent loci were associated with lipid levels at P < 5 × 10-8 using 2 DF tests, of which 18 were novel. No genome-wide significant associations were found testing the interaction effect alone. The novel loci included several genes (PCSK5, VEGFB, and A1CF) with a putative role in lipid metabolism based on existing evidence from cellular and experimental models.
1 ade Vries, Paul, S1 aBrown, Michael, R1 aBentley, Amy, R1 aSung, Yun, J1 aWinkler, Thomas, W1 aNtalla, Ioanna1 aSchwander, Karen1 aKraja, Aldi, T1 aGuo, Xiuqing1 aFranceschini, Nora1 aCheng, Ching-Yu1 aSim, Xueling1 aVojinovic, Dina1 aHuffman, Jennifer, E1 aMusani, Solomon, K1 aLi, Changwei1 aFeitosa, Mary, F1 aRichard, Melissa, A1 aNoordam, Raymond1 aAschard, Hugues1 aBartz, Traci, M1 aBielak, Lawrence, F1 aDeng, Xuan1 aDorajoo, Rajkumar1 aLohman, Kurt, K1 aManning, Alisa, K1 aRankinen, Tuomo1 aSmith, Albert, V1 aTajuddin, Salman, M1 aEvangelou, Evangelos1 aGraff, Mariaelisa1 aAlver, Maris1 aBoissel, Mathilde1 aChai, Jin, Fang1 aChen, Xu1 aDivers, Jasmin1 aGandin, Ilaria1 aGao, Chuan1 aGoel, Anuj1 aHagemeijer, Yanick1 aHarris, Sarah, E1 aHartwig, Fernando, P1 aHe, Meian1 aHorimoto, Andrea, R V R1 aHsu, Fang-Chi1 aJackson, Anne, U1 aKasturiratne, Anuradhani1 aKomulainen, Pirjo1 aKuhnel, Brigitte1 aLaguzzi, Federica1 aLee, Joseph, H1 aLuan, Jian'an1 aLyytikäinen, Leo-Pekka1 aMatoba, Nana1 aNolte, Ilja, M1 aPietzner, Maik1 aRiaz, Muhammad1 aSaid, Abdullah1 aScott, Robert, A1 aSofer, Tamar1 aStančáková, Alena1 aTakeuchi, Fumihiko1 aTayo, Bamidele, O1 avan der Most, Peter, J1 aVarga, Tibor, V1 aWang, Yajuan1 aWare, Erin, B1 aWen, Wanqing1 aYanek, Lisa, R1 aZhang, Weihua1 aZhao, Jing Hua1 aAfaq, Saima1 aAmin, Najaf1 aAmini, Marzyeh1 aArking, Dan, E1 aAung, Tin1 aBallantyne, Christie1 aBoerwinkle, Eric1 aBroeckel, Ulrich1 aCampbell, Archie1 aCanouil, Mickaël1 aCharumathi, Sabanayagam1 aChen, Yii-Der Ida1 aConnell, John, M1 ade Faire, Ulf1 aFuentes, Lisa, de Las1 ade Mutsert, Renée1 ade Silva, Janaka1 aDing, Jingzhong1 aDominiczak, Anna, F1 aDuan, Qing1 aEaton, Charles, B1 aEppinga, Ruben, N1 aFaul, Jessica, D1 aFisher, Virginia1 aForrester, Terrence1 aFranco, Oscar, H1 aFriedlander, Yechiel1 aGhanbari, Mohsen1 aGiulianini, Franco1 aGrabe, Hans, J1 aGrove, Megan, L1 aGu, Charles1 aHarris, Tamara, B1 aHeikkinen, Sami1 aHeng, Chew-Kiat1 aHirata, Makoto1 aHixson, James, E1 aHoward, Barbara, V1 aIkram, Arfan, M1 aJacobs, David, R1 aJohnson, Craig1 aJonas, Jost, Bruno1 aKammerer, Candace, M1 aKatsuya, Tomohiro1 aKhor, Chiea, Chuen1 aKilpeläinen, Tuomas, O1 aKoh, Woon-Puay1 aKoistinen, Heikki, A1 aKolcic, Ivana1 aKooperberg, Charles1 aKrieger, Jose, E1 aKritchevsky, Steve, B1 aKubo, Michiaki1 aKuusisto, Johanna1 aLakka, Timo, A1 aLangefeld, Carl, D1 aLangenberg, Claudia1 aLauner, Lenore, J1 aLehne, Benjamin1 aLemaitre, Rozenn, N1 aLi, Yize1 aLiang, Jingjing1 aLiu, Jianjun1 aLiu, Kiang1 aLoh, Marie1 aLouie, Tin1 aMägi, Reedik1 aManichaikul, Ani, W1 aMcKenzie, Colin, A1 aMeitinger, Thomas1 aMetspalu, Andres1 aMilaneschi, Yuri1 aMilani, Lili1 aMohlke, Karen, L1 aMosley, Thomas, H1 aMukamal, Kenneth, J1 aNalls, Mike, A1 aNauck, Matthias1 aNelson, Christopher, P1 aSotoodehnia, Nona1 aO'Connell, Jeff, R1 aPalmer, Nicholette, D1 aPazoki, Raha1 aPedersen, Nancy, L1 aPeters, Annette1 aPeyser, Patricia, A1 aPolasek, Ozren1 aPoulter, Neil1 aRaffel, Leslie, J1 aRaitakari, Olli, T1 aReiner, Alex, P1 aRice, Treva, K1 aRich, Stephen, S1 aRobino, Antonietta1 aRobinson, Jennifer, G1 aRose, Lynda, M1 aRudan, Igor1 aSchmidt, Carsten, O1 aSchreiner, Pamela, J1 aScott, William, R1 aSever, Peter1 aShi, Yuan1 aSidney, Stephen1 aSims, Mario1 aSmith, Blair, H1 aSmith, Jennifer, A1 aSnieder, Harold1 aStarr, John, M1 aStrauch, Konstantin1 aTan, Nicholas1 aTaylor, Kent, D1 aTeo, Yik, Ying1 aTham, Yih, Chung1 aUitterlinden, André, G1 avan Heemst, Diana1 aVuckovic, Dragana1 aWaldenberger, Melanie1 aWang, Lihua1 aWang, Yujie1 aWang, Zhe1 aBin Wei, Wen1 aWilliams, Christine1 aWilson, Gregory1 aWojczynski, Mary, K1 aYao, Jie1 aYu, Bing1 aYu, Caizheng1 aYuan, Jian-Min1 aZhao, Wei1 aZonderman, Alan, B1 aBecker, Diane, M1 aBoehnke, Michael1 aBowden, Donald, W1 aChambers, John, C1 aDeary, Ian, J1 aEsko, Tõnu1 aFarrall, Martin1 aFranks, Paul, W1 aFreedman, Barry, I1 aFroguel, Philippe1 aGasparini, Paolo1 aGieger, Christian1 aHorta, Bernardo, L1 aKamatani, Yoichiro1 aKato, Norihiro1 aKooner, Jaspal, S1 aLaakso, Markku1 aLeander, Karin1 aLehtimäki, Terho1 aMagnusson, Patrik, K E1 aPenninx, Brenda1 aPereira, Alexandre, C1 aRauramaa, Rainer1 aSamani, Nilesh, J1 aScott, James1 aShu, Xiao-Ou1 aHarst, Pim1 aWagenknecht, Lynne, E1 aWang, Ya, Xing1 aWareham, Nicholas, J1 aWatkins, Hugh1 aWeir, David, R1 aWickremasinghe, Ananda, R1 aZheng, Wei1 aElliott, Paul1 aNorth, Kari, E1 aBouchard, Claude1 aEvans, Michele, K1 aGudnason, Vilmundur1 aLiu, Ching-Ti1 aLiu, Yongmei1 aPsaty, Bruce, M1 aRidker, Paul, M1 avan Dam, Rob, M1 aKardia, Sharon, L R1 aZhu, Xiaofeng1 aRotimi, Charles, N1 aMook-Kanamori, Dennis, O1 aFornage, Myriam1 aKelly, Tanika, N1 aFox, Ervin, R1 aHayward, Caroline1 aDuijn, Cornelia, M1 aTai, Shyong, E1 aWong, Tien, Yin1 aLiu, Jingmin1 aRotter, Jerome, I1 aGauderman, James1 aProvince, Michael, A1 aMunroe, Patricia, B1 aRice, Kenneth1 aChasman, Daniel, I1 aCupples, Adrienne, L1 aRao, Dabeeru, C1 aMorrison, Alanna, C1 aInterAct Consortium1 aLifelines Cohort, Groningen, The Netherlands (Lifelines Cohort Study) uhttps://chs-nhlbi.org/node/797011178nas a2203793 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2019 eng d a1546-171800aMulti-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids.0 aMultiancestry genomewide genesmoking interaction study of 387272 c2019 Apr a636-6480 v513 aThe concentrations of high- and low-density-lipoprotein cholesterol and triglycerides are influenced by smoking, but it is unknown whether genetic associations with lipids may be modified by smoking. We conducted a multi-ancestry genome-wide gene-smoking interaction study in 133,805 individuals with follow-up in an additional 253,467 individuals. Combined meta-analyses identified 13 new loci associated with lipids, some of which were detected only because association differed by smoking status. Additionally, we demonstrate the importance of including diverse populations, particularly in studies of interactions with lifestyle factors, where genomic and lifestyle differences by ancestry may contribute to novel findings.
1 aBentley, Amy, R1 aSung, Yun, J1 aBrown, Michael, R1 aWinkler, Thomas, W1 aKraja, Aldi, T1 aNtalla, Ioanna1 aSchwander, Karen1 aChasman, Daniel, I1 aLim, Elise1 aDeng, Xuan1 aGuo, Xiuqing1 aLiu, Jingmin1 aLu, Yingchang1 aCheng, Ching-Yu1 aSim, Xueling1 aVojinovic, Dina1 aHuffman, Jennifer, E1 aMusani, Solomon, K1 aLi, Changwei1 aFeitosa, Mary, F1 aRichard, Melissa, A1 aNoordam, Raymond1 aBaker, Jenna1 aChen, Guanjie1 aAschard, Hugues1 aBartz, Traci, M1 aDing, Jingzhong1 aDorajoo, Rajkumar1 aManning, Alisa, K1 aRankinen, Tuomo1 aSmith, Albert, V1 aTajuddin, Salman, M1 aZhao, Wei1 aGraff, Mariaelisa1 aAlver, Maris1 aBoissel, Mathilde1 aChai, Jin, Fang1 aChen, Xu1 aDivers, Jasmin1 aEvangelou, Evangelos1 aGao, Chuan1 aGoel, Anuj1 aHagemeijer, Yanick1 aHarris, Sarah, E1 aHartwig, Fernando, P1 aHe, Meian1 aHorimoto, Andrea, R V R1 aHsu, Fang-Chi1 aHung, Yi-Jen1 aJackson, Anne, U1 aKasturiratne, Anuradhani1 aKomulainen, Pirjo1 aKuhnel, Brigitte1 aLeander, Karin1 aLin, Keng-Hung1 aLuan, Jian'an1 aLyytikäinen, Leo-Pekka1 aMatoba, Nana1 aNolte, Ilja, M1 aPietzner, Maik1 aPrins, Bram1 aRiaz, Muhammad1 aRobino, Antonietta1 aSaid, Abdullah1 aSchupf, Nicole1 aScott, Robert, A1 aSofer, Tamar1 aStančáková, Alena1 aTakeuchi, Fumihiko1 aTayo, Bamidele, O1 avan der Most, Peter, J1 aVarga, Tibor, V1 aWang, Tzung-Dau1 aWang, Yajuan1 aWare, Erin, B1 aWen, Wanqing1 aXiang, Yong-Bing1 aYanek, Lisa, R1 aZhang, Weihua1 aZhao, Jing Hua1 aAdeyemo, Adebowale1 aAfaq, Saima1 aAmin, Najaf1 aAmini, Marzyeh1 aArking, Dan, E1 aArzumanyan, Zorayr1 aAung, Tin1 aBallantyne, Christie1 aBarr, Graham1 aBielak, Lawrence, F1 aBoerwinkle, Eric1 aBottinger, Erwin, P1 aBroeckel, Ulrich1 aBrown, Morris1 aCade, Brian, E1 aCampbell, Archie1 aCanouil, Mickaël1 aCharumathi, Sabanayagam1 aChen, Yii-Der Ida1 aChristensen, Kaare1 aConcas, Maria, Pina1 aConnell, John, M1 aFuentes, Lisa, de Las1 ade Silva, Janaka1 ade Vries, Paul, S1 aDoumatey, Ayo1 aDuan, Qing1 aEaton, Charles, B1 aEppinga, Ruben, N1 aFaul, Jessica, D1 aFloyd, James, S1 aForouhi, Nita, G1 aForrester, Terrence1 aFriedlander, Yechiel1 aGandin, Ilaria1 aGao, He1 aGhanbari, Mohsen1 aGharib, Sina, A1 aGigante, Bruna1 aGiulianini, Franco1 aGrabe, Hans, J1 aGu, Charles1 aHarris, Tamara, B1 aHeikkinen, Sami1 aHeng, Chew-Kiat1 aHirata, Makoto1 aHixson, James, E1 aIkram, Arfan, M1 aJia, Yucheng1 aJoehanes, Roby1 aJohnson, Craig1 aJonas, Jost, Bruno1 aJustice, Anne, E1 aKatsuya, Tomohiro1 aKhor, Chiea, Chuen1 aKilpeläinen, Tuomas, O1 aKoh, Woon-Puay1 aKolcic, Ivana1 aKooperberg, Charles1 aKrieger, Jose, E1 aKritchevsky, Stephen, B1 aKubo, Michiaki1 aKuusisto, Johanna1 aLakka, Timo, A1 aLangefeld, Carl, D1 aLangenberg, Claudia1 aLauner, Lenore, J1 aLehne, Benjamin1 aLewis, Cora, E1 aLi, Yize1 aLiang, Jingjing1 aLin, Shiow1 aLiu, Ching-Ti1 aLiu, Jianjun1 aLiu, Kiang1 aLoh, Marie1 aLohman, Kurt, K1 aLouie, Tin1 aLuzzi, Anna1 aMägi, Reedik1 aMahajan, Anubha1 aManichaikul, Ani, W1 aMcKenzie, Colin, A1 aMeitinger, Thomas1 aMetspalu, Andres1 aMilaneschi, Yuri1 aMilani, Lili1 aMohlke, Karen, L1 aMomozawa, Yukihide1 aMorris, Andrew, P1 aMurray, Alison, D1 aNalls, Mike, A1 aNauck, Matthias1 aNelson, Christopher, P1 aNorth, Kari, E1 aO'Connell, Jeffrey, R1 aPalmer, Nicholette, D1 aPapanicolau, George, J1 aPedersen, Nancy, L1 aPeters, Annette1 aPeyser, Patricia, A1 aPolasek, Ozren1 aPoulter, Neil1 aRaitakari, Olli, T1 aReiner, Alex, P1 aRenstrom, Frida1 aRice, Treva, K1 aRich, Stephen, S1 aRobinson, Jennifer, G1 aRose, Lynda, M1 aRosendaal, Frits, R1 aRudan, Igor1 aSchmidt, Carsten, O1 aSchreiner, Pamela, J1 aScott, William, R1 aSever, Peter1 aShi, Yuan1 aSidney, Stephen1 aSims, Mario1 aSmith, Jennifer, A1 aSnieder, Harold1 aStarr, John, M1 aStrauch, Konstantin1 aStringham, Heather, M1 aTan, Nicholas, Y Q1 aTang, Hua1 aTaylor, Kent, D1 aTeo, Yik, Ying1 aTham, Yih, Chung1 aTiemeier, Henning1 aTurner, Stephen, T1 aUitterlinden, André, G1 avan Heemst, Diana1 aWaldenberger, Melanie1 aWang, Heming1 aWang, Lan1 aWang, Lihua1 aBin Wei, Wen1 aWilliams, Christine, A1 aWilson, Gregory1 aWojczynski, Mary, K1 aYao, Jie1 aYoung, Kristin1 aYu, Caizheng1 aYuan, Jian-Min1 aZhou, Jie1 aZonderman, Alan, B1 aBecker, Diane, M1 aBoehnke, Michael1 aBowden, Donald, W1 aChambers, John, C1 aCooper, Richard, S1 ade Faire, Ulf1 aDeary, Ian, J1 aElliott, Paul1 aEsko, Tõnu1 aFarrall, Martin1 aFranks, Paul, W1 aFreedman, Barry, I1 aFroguel, Philippe1 aGasparini, Paolo1 aGieger, Christian1 aHorta, Bernardo, L1 aJuang, Jyh-Ming, Jimmy1 aKamatani, Yoichiro1 aKammerer, Candace, M1 aKato, Norihiro1 aKooner, Jaspal, S1 aLaakso, Markku1 aLaurie, Cathy, C1 aLee, I-Te1 aLehtimäki, Terho1 aMagnusson, Patrik, K E1 aOldehinkel, Albertine, J1 aPenninx, Brenda, W J H1 aPereira, Alexandre, C1 aRauramaa, Rainer1 aRedline, Susan1 aSamani, Nilesh, J1 aScott, James1 aShu, Xiao-Ou1 aHarst, Pim1 aWagenknecht, Lynne, E1 aWang, Jun-Sing1 aWang, Ya, Xing1 aWareham, Nicholas, J1 aWatkins, Hugh1 aWeir, David, R1 aWickremasinghe, Ananda, R1 aWu, Tangchun1 aZeggini, Eleftheria1 aZheng, Wei1 aBouchard, Claude1 aEvans, Michele, K1 aGudnason, Vilmundur1 aKardia, Sharon, L R1 aLiu, Yongmei1 aPsaty, Bruce, M1 aRidker, Paul, M1 avan Dam, Rob, M1 aMook-Kanamori, Dennis, O1 aFornage, Myriam1 aProvince, Michael, A1 aKelly, Tanika, N1 aFox, Ervin, R1 aHayward, Caroline1 aDuijn, Cornelia, M1 aTai, Shyong, E1 aWong, Tien, Yin1 aLoos, Ruth, J F1 aFranceschini, Nora1 aRotter, Jerome, I1 aZhu, Xiaofeng1 aBierut, Laura, J1 aGauderman, James1 aRice, Kenneth1 aMunroe, Patricia, B1 aMorrison, Alanna, C1 aRao, Dabeeru, C1 aRotimi, Charles, N1 aCupples, Adrienne, L1 aCOGENT-Kidney Consortium1 aEPIC-InterAct Consortium1 aUnderstanding Society Scientific Group1 aLifelines Cohort uhttps://chs-nhlbi.org/node/800506391nas a2201909 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2019 eng d a2041-172300aMulti-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.0 aMultiancestry sleepbySNP interaction analysis in 126926 individu c2019 Nov 12 a51210 v103 aBoth short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.
1 aNoordam, Raymond1 aBos, Maxime, M1 aWang, Heming1 aWinkler, Thomas, W1 aBentley, Amy, R1 aKilpeläinen, Tuomas, O1 ade Vries, Paul, S1 aSung, Yun, Ju1 aSchwander, Karen1 aCade, Brian, E1 aManning, Alisa1 aAschard, Hugues1 aBrown, Michael, R1 aChen, Han1 aFranceschini, Nora1 aMusani, Solomon, K1 aRichard, Melissa1 aVojinovic, Dina1 aAslibekyan, Stella1 aBartz, Traci, M1 aFuentes, Lisa, de Las1 aFeitosa, Mary1 aHorimoto, Andrea, R1 aIlkov, Marjan1 aKho, Minjung1 aKraja, Aldi1 aLi, Changwei1 aLim, Elise1 aLiu, Yongmei1 aMook-Kanamori, Dennis, O1 aRankinen, Tuomo1 aTajuddin, Salman, M1 avan der Spek, Ashley1 aWang, Zhe1 aMarten, Jonathan1 aLaville, Vincent1 aAlver, Maris1 aEvangelou, Evangelos1 aGraff, Maria, E1 aHe, Meian1 aKuhnel, Brigitte1 aLyytikäinen, Leo-Pekka1 aMarques-Vidal, Pedro1 aNolte, Ilja, M1 aPalmer, Nicholette, D1 aRauramaa, Rainer1 aShu, Xiao-Ou1 aSnieder, Harold1 aWeiss, Stefan1 aWen, Wanqing1 aYanek, Lisa, R1 aAdolfo, Correa1 aBallantyne, Christie1 aBielak, Larry1 aBiermasz, Nienke, R1 aBoerwinkle, Eric1 aDimou, Niki1 aEiriksdottir, Gudny1 aGao, Chuan1 aGharib, Sina, A1 aGottlieb, Daniel, J1 aHaba-Rubio, José1 aHarris, Tamara, B1 aHeikkinen, Sami1 aHeinzer, Raphael1 aHixson, James, E1 aHomuth, Georg1 aIkram, Arfan, M1 aKomulainen, Pirjo1 aKrieger, Jose, E1 aLee, Jiwon1 aLiu, Jingmin1 aLohman, Kurt, K1 aLuik, Annemarie, I1 aMägi, Reedik1 aMartin, Lisa, W1 aMeitinger, Thomas1 aMetspalu, Andres1 aMilaneschi, Yuri1 aNalls, Mike, A1 aO'Connell, Jeff1 aPeters, Annette1 aPeyser, Patricia1 aRaitakari, Olli, T1 aReiner, Alex, P1 aRensen, Patrick, C N1 aRice, Treva, K1 aRich, Stephen, S1 aRoenneberg, Till1 aRotter, Jerome, I1 aSchreiner, Pamela, J1 aShikany, James1 aSidney, Stephen, S1 aSims, Mario1 aSitlani, Colleen, M1 aSofer, Tamar1 aStrauch, Konstantin1 aSwertz, Morris, A1 aTaylor, Kent, D1 aUitterlinden, André, G1 aDuijn, Cornelia, M1 aVölzke, Henry1 aWaldenberger, Melanie1 aWallance, Robert, B1 aDijk, Ko Willems1 aYu, Caizheng1 aZonderman, Alan, B1 aBecker, Diane, M1 aElliott, Paul1 aEsko, Tõnu1 aGieger, Christian1 aGrabe, Hans, J1 aLakka, Timo, A1 aLehtimäki, Terho1 aNorth, Kari, E1 aPenninx, Brenda, W J H1 aVollenweider, Peter1 aWagenknecht, Lynne, E1 aWu, Tangchun1 aXiang, Yong-Bing1 aZheng, Wei1 aArnett, Donna, K1 aBouchard, Claude1 aEvans, Michele, K1 aGudnason, Vilmundur1 aKardia, Sharon1 aKelly, Tanika, N1 aKritchevsky, Stephen, B1 aLoos, Ruth, J F1 aPereira, Alexandre, C1 aProvince, Mike1 aPsaty, Bruce, M1 aRotimi, Charles1 aZhu, Xiaofeng1 aAmin, Najaf1 aCupples, Adrienne, L1 aFornage, Myriam1 aFox, Ervin, F1 aGuo, Xiuqing1 aGauderman, James1 aRice, Kenneth1 aKooperberg, Charles1 aMunroe, Patricia, B1 aLiu, Ching-Ti1 aMorrison, Alanna, C1 aRao, Dabeeru, C1 avan Heemst, Diana1 aRedline, Susan uhttps://chs-nhlbi.org/node/820210097nas a2203265 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2019 eng d a2041-172300aMulti-ancestry study of blood lipid levels identifies four loci interacting with physical activity.0 aMultiancestry study of blood lipid levels identifies four loci i c2019 01 22 a3760 v103 aMany genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels.
10aAdolescent10aAdult10aAfrican Continental Ancestry Group10aAged10aAged, 80 and over10aAsian Continental Ancestry Group10aBrazil10aCalcium-Binding Proteins10aCholesterol10aCholesterol, HDL10aCholesterol, LDL10aEuropean Continental Ancestry Group10aExercise10aFemale10aGenetic Loci10aGenome-Wide Association Study10aGenotype10aHispanic Americans10aHumans10aLIM-Homeodomain Proteins10aLipid Metabolism10aLipids10aMale10aMembrane Proteins10aMicrotubule-Associated Proteins10aMiddle Aged10aMuscle Proteins10aNerve Tissue Proteins10aTranscription Factors10aTriglycerides10aYoung Adult1 aKilpeläinen, Tuomas, O1 aBentley, Amy, R1 aNoordam, Raymond1 aSung, Yun, Ju1 aSchwander, Karen1 aWinkler, Thomas, W1 aJakupović, Hermina1 aChasman, Daniel, I1 aManning, Alisa1 aNtalla, Ioanna1 aAschard, Hugues1 aBrown, Michael, R1 aFuentes, Lisa, de Las1 aFranceschini, Nora1 aGuo, Xiuqing1 aVojinovic, Dina1 aAslibekyan, Stella1 aFeitosa, Mary, F1 aKho, Minjung1 aMusani, Solomon, K1 aRichard, Melissa1 aWang, Heming1 aWang, Zhe1 aBartz, Traci, M1 aBielak, Lawrence, F1 aCampbell, Archie1 aDorajoo, Rajkumar1 aFisher, Virginia1 aHartwig, Fernando, P1 aHorimoto, Andrea, R V R1 aLi, Changwei1 aLohman, Kurt, K1 aMarten, Jonathan1 aSim, Xueling1 aSmith, Albert, V1 aTajuddin, Salman, M1 aAlver, Maris1 aAmini, Marzyeh1 aBoissel, Mathilde1 aChai, Jin, Fang1 aChen, Xu1 aDivers, Jasmin1 aEvangelou, Evangelos1 aGao, Chuan1 aGraff, Mariaelisa1 aHarris, Sarah, E1 aHe, Meian1 aHsu, Fang-Chi1 aJackson, Anne, U1 aZhao, Jing Hua1 aKraja, Aldi, T1 aKuhnel, Brigitte1 aLaguzzi, Federica1 aLyytikäinen, Leo-Pekka1 aNolte, Ilja, M1 aRauramaa, Rainer1 aRiaz, Muhammad1 aRobino, Antonietta1 aRueedi, Rico1 aStringham, Heather, M1 aTakeuchi, Fumihiko1 avan der Most, Peter, J1 aVarga, Tibor, V1 aVerweij, Niek1 aWare, Erin, B1 aWen, Wanqing1 aLi, Xiaoyin1 aYanek, Lisa, R1 aAmin, Najaf1 aArnett, Donna, K1 aBoerwinkle, Eric1 aBrumat, Marco1 aCade, Brian1 aCanouil, Mickaël1 aChen, Yii-Der Ida1 aConcas, Maria, Pina1 aConnell, John1 ade Mutsert, Renée1 ade Silva, Janaka1 ade Vries, Paul, S1 aDemirkan, Ayse1 aDing, Jingzhong1 aEaton, Charles, B1 aFaul, Jessica, D1 aFriedlander, Yechiel1 aGabriel, Kelley, P1 aGhanbari, Mohsen1 aGiulianini, Franco1 aGu, Chi, Charles1 aGu, Dongfeng1 aHarris, Tamara, B1 aHe, Jiang1 aHeikkinen, Sami1 aHeng, Chew-Kiat1 aHunt, Steven, C1 aIkram, Arfan, M1 aJonas, Jost, B1 aKoh, Woon-Puay1 aKomulainen, Pirjo1 aKrieger, Jose, E1 aKritchevsky, Stephen, B1 aKutalik, Zoltán1 aKuusisto, Johanna1 aLangefeld, Carl, D1 aLangenberg, Claudia1 aLauner, Lenore, J1 aLeander, Karin1 aLemaitre, Rozenn, N1 aLewis, Cora, E1 aLiang, Jingjing1 aLiu, Jianjun1 aMägi, Reedik1 aManichaikul, Ani1 aMeitinger, Thomas1 aMetspalu, Andres1 aMilaneschi, Yuri1 aMohlke, Karen, L1 aMosley, Thomas, H1 aMurray, Alison, D1 aNalls, Mike, A1 aNang, Ei-Ei, Khaing1 aNelson, Christopher, P1 aNona, Sotoodehnia1 aNorris, Jill, M1 aNwuba, Chiamaka, Vivian1 aO'Connell, Jeff1 aPalmer, Nicholette, D1 aPapanicolau, George, J1 aPazoki, Raha1 aPedersen, Nancy, L1 aPeters, Annette1 aPeyser, Patricia, A1 aPolasek, Ozren1 aPorteous, David, J1 aPoveda, Alaitz1 aRaitakari, Olli, T1 aRich, Stephen, S1 aRisch, Neil1 aRobinson, Jennifer, G1 aRose, Lynda, M1 aRudan, Igor1 aSchreiner, Pamela, J1 aScott, Robert, A1 aSidney, Stephen, S1 aSims, Mario1 aSmith, Jennifer, A1 aSnieder, Harold1 aSofer, Tamar1 aStarr, John, M1 aSternfeld, Barbara1 aStrauch, Konstantin1 aTang, Hua1 aTaylor, Kent, D1 aTsai, Michael, Y1 aTuomilehto, Jaakko1 aUitterlinden, André, G1 avan der Ende, Yldau1 avan Heemst, Diana1 aVoortman, Trudy1 aWaldenberger, Melanie1 aWennberg, Patrik1 aWilson, Gregory1 aXiang, Yong-Bing1 aYao, Jie1 aYu, Caizheng1 aYuan, Jian-Min1 aZhao, Wei1 aZonderman, Alan, B1 aBecker, Diane, M1 aBoehnke, Michael1 aBowden, Donald, W1 ade Faire, Ulf1 aDeary, Ian, J1 aElliott, Paul1 aEsko, Tõnu1 aFreedman, Barry, I1 aFroguel, Philippe1 aGasparini, Paolo1 aGieger, Christian1 aKato, Norihiro1 aLaakso, Markku1 aLakka, Timo, A1 aLehtimäki, Terho1 aMagnusson, Patrik, K E1 aOldehinkel, Albertine, J1 aPenninx, Brenda, W J H1 aSamani, Nilesh, J1 aShu, Xiao-Ou1 aHarst, Pim1 avan Vliet-Ostaptchouk, Jana, V1 aVollenweider, Peter1 aWagenknecht, Lynne, E1 aWang, Ya, X1 aWareham, Nicholas, J1 aWeir, David, R1 aWu, Tangchun1 aZheng, Wei1 aZhu, Xiaofeng1 aEvans, Michele, K1 aFranks, Paul, W1 aGudnason, Vilmundur1 aHayward, Caroline1 aHorta, Bernardo, L1 aKelly, Tanika, N1 aLiu, Yongmei1 aNorth, Kari, E1 aPereira, Alexandre, C1 aRidker, Paul, M1 aTai, Shyong, E1 avan Dam, Rob, M1 aFox, Ervin, R1 aKardia, Sharon, L R1 aLiu, Ching-Ti1 aMook-Kanamori, Dennis, O1 aProvince, Michael, A1 aRedline, Susan1 aDuijn, Cornelia, M1 aRotter, Jerome, I1 aKooperberg, Charles, B1 aGauderman, James1 aPsaty, Bruce, M1 aRice, Kenneth1 aMunroe, Patricia, B1 aFornage, Myriam1 aCupples, Adrienne, L1 aRotimi, Charles, N1 aMorrison, Alanna, C1 aRao, Dabeeru, C1 aLoos, Ruth, J F1 aLifeLines Cohort Study uhttps://chs-nhlbi.org/node/797603253nas a2200637 4500008004100000022001400041245013600055210006900191260001600260300001400276490000800290520140100298100002001699700001901719700001701738700001701755700001501772700001701787700002401804700001601828700001401844700002401858700002101882700001701903700002001920700001701940700002201957700002201979700002202001700002802023700002002051700002002071700001902091700002102110700001802131700002002149700001802169700002302187700002102210700001602231700001702247700002002264700002702284700002002311700002102331700002402352700001702376700002102393700002202414700002102436700001902457700001802476710005402494710003102548856003602579 2019 eng d a1537-660500aSequencing Analysis at 8p23 Identifies Multiple Rare Variants in DLC1 Associated with Sleep-Related Oxyhemoglobin Saturation Level.0 aSequencing Analysis at 8p23 Identifies Multiple Rare Variants in c2019 Nov 07 a1057-10680 v1053 aAverage arterial oxyhemoglobin saturation during sleep (AvSpOS) is a clinically relevant measure of physiological stress associated with sleep-disordered breathing, and this measure predicts incident cardiovascular disease and mortality. Using high-depth whole-genome sequencing data from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) project and focusing on genes with linkage evidence on chromosome 8p23, we observed that six coding and 51 noncoding variants in a gene that encodes the GTPase-activating protein (DLC1) are significantly associated with AvSpOS and replicated in independent subjects. The combined DLC1 association evidence of discovery and replication cohorts reaches genome-wide significance in European Americans (p = 7.9 × 10). A risk score for these variants, built on an independent dataset, explains 0.97% of the AvSpOS variation and contributes to the linkage evidence. The 51 noncoding variants are enriched in regulatory features in a human lung fibroblast cell line and contribute to DLC1 expression variation. Mendelian randomization analysis using these variants indicates a significant causal effect of DLC1 expression in fibroblasts on AvSpOS. Multiple sources of information, including genetic variants, gene expression, and methylation, consistently suggest that DLC1 is a gene associated with AvSpOS.
1 aLiang, Jingjing1 aCade, Brian, E1 aHe, Karen, Y1 aWang, Heming1 aLee, Jiwon1 aSofer, Tamar1 aWilliams, Stephanie1 aLi, Ruitong1 aChen, Han1 aGottlieb, Daniel, J1 aEvans, Daniel, S1 aGuo, Xiuqing1 aGharib, Sina, A1 aHale, Lauren1 aHillman, David, R1 aLutsey, Pamela, L1 aMukherjee, Sutapa1 aOchs-Balcom, Heather, M1 aPalmer, Lyle, J1 aRhodes, Jessica1 aPurcell, Shaun1 aPatel, Sanjay, R1 aSaxena, Richa1 aStone, Katie, L1 aTang, Weihong1 aTranah, Gregory, J1 aBoerwinkle, Eric1 aLin, Xihong1 aLiu, Yongmei1 aPsaty, Bruce, M1 aVasan, Ramachandran, S1 aCho, Michael, H1 aManichaikul, Ani1 aSilverman, Edwin, K1 aBarr, Graham1 aRich, Stephen, S1 aRotter, Jerome, I1 aWilson, James, G1 aRedline, Susan1 aZhu, Xiaofeng1 aNHLBI Trans-Omics for Precision Medicine (TOPMed)1 aTOPMed Sleep Working Group uhttps://chs-nhlbi.org/node/819904660nas a2200793 4500008004100000022001400041245013400055210006900189260001200258300001200270490000600282520241900288653001002707653002502717653001902742653001102761653002902772653003402801653001102835653000902846653001602855653001402871653004302885653001702928653001902945100001802964700002902982700001703011700002003028700001903048700002003067700002003087700002603107700002003133700001903153700001903172700002303191700001903214700002303233700002403256700001603280700002103296700002103317700002103338700001603359700001803375700001503393700002203408700001703430700002003447700002003467700002103487700002103508700002203529700002003551700001703571700001503588700001903603700001703622700001703639700002003656700002403676700002103700700002103721700002003742710004303762710002503805856003603830 2020 eng d a2213-261900aChronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts.0 aChronic obstructive pulmonary disease and related phenotypes pol c2020 07 a696-7080 v83 aBACKGROUND: Genetic factors influence chronic obstructive pulmonary disease (COPD) risk, but the individual variants that have been identified have small effects. We hypothesised that a polygenic risk score using additional variants would predict COPD and associated phenotypes.
METHODS: We constructed a polygenic risk score using a genome-wide association study of lung function (FEV and FEV/forced vital capacity [FVC]) from the UK Biobank and SpiroMeta. We tested this polygenic risk score in nine cohorts of multiple ethnicities for an association with moderate-to-severe COPD (defined as FEV/FVC <0·7 and FEV <80% of predicted). Associations were tested using logistic regression models, adjusting for age, sex, height, smoking pack-years, and principal components of genetic ancestry. We assessed predictive performance of models by area under the curve. In a subset of studies, we also studied quantitative and qualitative CT imaging phenotypes that reflect parenchymal and airway pathology, and patterns of reduced lung growth.
FINDINGS: The polygenic risk score was associated with COPD in European (odds ratio [OR] per SD 1·81 [95% CI 1·74-1·88] and non-European (1·42 [1·34-1·51]) populations. Compared with the first decile, the tenth decile of the polygenic risk score was associated with COPD, with an OR of 7·99 (6·56-9·72) in European ancestry and 4·83 (3·45-6·77) in non-European ancestry cohorts. The polygenic risk score was superior to previously described genetic risk scores and, when combined with clinical risk factors (ie, age, sex, and smoking pack-years), showed improved prediction for COPD compared with a model comprising clinical risk factors alone (AUC 0·80 [0·79-0·81] vs 0·76 [0·75-0·76]). The polygenic risk score was associated with CT imaging phenotypes, including wall area percent, quantitative and qualitative measures of emphysema, local histogram emphysema patterns, and destructive emphysema subtypes. The polygenic risk score was associated with a reduced lung growth pattern.
INTERPRETATION: A risk score comprised of genetic variants can identify a small subset of individuals at markedly increased risk for moderate-to-severe COPD, emphysema subtypes associated with cigarette smoking, and patterns of reduced lung growth.
FUNDING: US National Institutes of Health, Wellcome Trust.
10aAdult10aCase-Control Studies10aCohort Studies10aFemale10aForced Expiratory Volume10aGenome-Wide Association Study10aHumans10aMale10aMiddle Aged10aPhenotype10aPulmonary Disease, Chronic Obstructive10aRisk Factors10aVital Capacity1 aMoll, Matthew1 aSakornsakolpat, Phuwanat1 aShrine, Nick1 aHobbs, Brian, D1 aDeMeo, Dawn, L1 aJohn, Catherine1 aGuyatt, Anna, L1 aMcGeachie, Michael, J1 aGharib, Sina, A1 aObeidat, Ma'en1 aLahousse, Lies1 aWijnant, Sara, R A1 aBrusselle, Guy1 aMeyers, Deborah, A1 aBleecker, Eugene, R1 aLi, Xingnan1 aTal-Singer, Ruth1 aManichaikul, Ani1 aRich, Stephen, S1 aWon, Sungho1 aKim, Woo, Jin1 aDo, Ah, Ra1 aWashko, George, R1 aBarr, Graham1 aPsaty, Bruce, M1 aBartz, Traci, M1 aHansel, Nadia, N1 aBarnes, Kathleen1 aHokanson, John, E1 aCrapo, James, D1 aLynch, David1 aBakke, Per1 aGulsvik, Amund1 aHall, Ian, P1 aWain, Louise1 aWeiss, Scott, T1 aSilverman, Edwin, K1 aDudbridge, Frank1 aTobin, Martin, D1 aCho, Michael, H1 aInternational COPD Genetics Consortium1 aSpiroMeta Consortium uhttps://chs-nhlbi.org/node/840004618nas a2200805 4500008004100000022001400041245019400055210006900249260000900318300001300327490000700340520222500347653001002572653002802582653002802610653001102638653004002649653001702689653003402706653001102740653002602751653003602777653002402813100001602837700001602853700002202869700001902891700002202910700002102932700001902953700001802972700002302990700002603013700001703039700002003056700002103076700002103097700002103118700002303139700002203162700002103184700001703205700002203222700001903244700002003263700001903283700002203302700002603324700002103350700002103371700002003392700002603412700002203438700002203460700002003482700002803502700001703530700001903547700001303566700002303579700001803602700002403620700002303644700001603667700002003683700001903703700003003722700002403752856003603776 2020 eng d a1932-620300aGenetic loci associated with prevalent and incident myocardial infarction and coronary heart disease in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium.0 aGenetic loci associated with prevalent and incident myocardial i c2020 ae02300350 v153 aBACKGROUND: Genome-wide association studies have identified multiple genomic loci associated with coronary artery disease, but most are common variants in non-coding regions that provide limited information on causal genes and etiology of the disease. To overcome the limited scope that common variants provide, we focused our investigation on low-frequency and rare sequence variations primarily residing in coding regions of the genome.
METHODS AND RESULTS: Using samples of individuals of European ancestry from ten cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, both cross-sectional and prospective analyses were conducted to examine associations between genetic variants and myocardial infarction (MI), coronary heart disease (CHD), and all-cause mortality following these events. For prevalent events, a total of 27,349 participants of European ancestry, including 1831 prevalent MI cases and 2518 prevalent CHD cases were used. For incident cases, a total of 55,736 participants of European ancestry were included (3,031 incident MI cases and 5,425 incident CHD cases). There were 1,860 all-cause deaths among the 3,751 MI and CHD cases from six cohorts that contributed to the analysis of all-cause mortality. Single variant and gene-based analyses were performed separately in each cohort and then meta-analyzed for each outcome. A low-frequency intronic variant (rs988583) in PLCL1 was significantly associated with prevalent MI (OR = 1.80, 95% confidence interval: 1.43, 2.27; P = 7.12 × 10-7). We conducted gene-based burden tests for genes with a cumulative minor allele count (cMAC) ≥ 5 and variants with minor allele frequency (MAF) < 5%. TMPRSS5 and LDLRAD1 were significantly associated with prevalent MI and CHD, respectively, and RC3H2 and ANGPTL4 were significantly associated with incident MI and CHD, respectively. No loci were significantly associated with all-cause mortality following a MI or CHD event.
CONCLUSION: This study identified one known locus (ANGPTL4) and four new loci (PLCL1, RC3H2, TMPRSS5, and LDLRAD1) associated with cardiovascular disease risk that warrant further investigation.
10aAging10aCoronary Artery Disease10aCross-Sectional Studies10aEurope10aEuropean Continental Ancestry Group10aGenetic Loci10aGenome-Wide Association Study10aHumans10aMyocardial Infarction10aPolymorphism, Single Nucleotide10aProspective Studies1 aHahn, Julie1 aFu, Yi-Ping1 aBrown, Michael, R1 aBis, Joshua, C1 ade Vries, Paul, S1 aFeitosa, Mary, F1 aYanek, Lisa, R1 aWeiss, Stefan1 aGiulianini, Franco1 aSmith, Albert, Vernon1 aGuo, Xiuqing1 aBartz, Traci, M1 aBecker, Diane, M1 aBecker, Lewis, C1 aBoerwinkle, Eric1 aBrody, Jennifer, A1 aChen, Yii-Der Ida1 aFranco, Oscar, H1 aGrove, Megan1 aHarris, Tamara, B1 aHofman, Albert1 aHwang, Shih-Jen1 aKral, Brian, G1 aLauner, Lenore, J1 aMarkus, Marcello, R P1 aRice, Kenneth, M1 aRich, Stephen, S1 aRidker, Paul, M1 aRivadeneira, Fernando1 aRotter, Jerome, I1 aSotoodehnia, Nona1 aTaylor, Kent, D1 aUitterlinden, André, G1 aVölker, Uwe1 aVölzke, Henry1 aYao, Jie1 aChasman, Daniel, I1 aDörr, Marcus1 aGudnason, Vilmundur1 aMathias, Rasika, A1 aPost, Wendy1 aPsaty, Bruce, M1 aDehghan, Abbas1 aO'Donnell, Christopher, J1 aMorrison, Alanna, C uhttps://chs-nhlbi.org/node/862506570nas a2201753 4500008004100000022001400041245007100055210006900126260001200195300001200207490000800219520164100227100002301868700002501891700002601916700002201942700001701964700002401981700002002005700001902025700002602044700001902070700001602089700002002105700002902125700002702154700001602181700002202197700002002219700002002239700002502259700002402284700001902308700002002327700001902347700002002366700002402386700002002410700002602430700002702456700002502483700002402508700001402532700002202546700002302568700002102591700002302612700001902635700002302654700001702677700002302694700001802717700002102735700001902756700002302775700002502798700002202823700002402845700002402869700001902893700002702912700001902939700001702958700002202975700001902997700002103016700001903037700002203056700001903078700001903097700002003116700002203136700002203158700002503180700001903205700002103224700002303245700002103268700002303289700002303312700002203335700001903357700001803376700002203394700002103416700002203437700002103459700002103480700002003501700001903521700002003540700002203560700001803582700002203600700002003622700001703642700001703659700001603676700001503692700002703707700002303734700001703757700002403774700002103798700002003819700002103839700002403860700002503884700002403909700002303933700002503956700002303981700002104004700001904025700002004044700001904064700002804083700001804111700002104129700002104150700002304171700001804194700002004212700002304232700001804255700002404273700002104297700002504318700002104343700001704364700001704381700001804398700002104416700002204437700002104459700001704480700002204497700002004519700002304539700002304562700002504585700002404610700002204634700002304656700002204679700002304701710005604724856003604780 2020 eng d a1476-468700aInherited causes of clonal haematopoiesis in 97,691 whole genomes.0 aInherited causes of clonal haematopoiesis in 97691 whole genomes c2020 10 a763-7680 v5863 aAge is the dominant risk factor for most chronic human diseases, but the mechanisms through which ageing confers this risk are largely unknown. The age-related acquisition of somatic mutations that lead to clonal expansion in regenerating haematopoietic stem cell populations has recently been associated with both haematological cancer and coronary heart disease-this phenomenon is termed clonal haematopoiesis of indeterminate potential (CHIP). Simultaneous analyses of germline and somatic whole-genome sequences provide the opportunity to identify root causes of CHIP. Here we analyse high-coverage whole-genome sequences from 97,691 participants of diverse ancestries in the National Heart, Lung, and Blood Institute Trans-omics for Precision Medicine (TOPMed) programme, and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid and inflammatory traits that are specific to different CHIP driver genes. Association of a genome-wide set of germline genetic variants enabled the identification of three genetic loci associated with CHIP status, including one locus at TET2 that was specific to individuals of African ancestry. In silico-informed in vitro evaluation of the TET2 germline locus enabled the identification of a causal variant that disrupts a TET2 distal enhancer, resulting in increased self-renewal of haematopoietic stem cells. Overall, we observe that germline genetic variation shapes haematopoietic stem cell function, leading to CHIP through mechanisms that are specific to clonal haematopoiesis as well as shared mechanisms that lead to somatic mutations across tissues.
1 aBick, Alexander, G1 aWeinstock, Joshua, S1 aNandakumar, Satish, K1 aFulco, Charles, P1 aBao, Erik, L1 aZekavat, Seyedeh, M1 aSzeto, Mindy, D1 aLiao, Xiaotian1 aLeventhal, Matthew, J1 aNasser, Joseph1 aChang, Kyle1 aLaurie, Cecelia1 aBurugula, Bala, Bharathi1 aGibson, Christopher, J1 aLin, Amy, E1 aTaub, Margaret, A1 aAguet, Francois1 aArdlie, Kristin1 aMitchell, Braxton, D1 aBarnes, Kathleen, C1 aMoscati, Arden1 aFornage, Myriam1 aRedline, Susan1 aPsaty, Bruce, M1 aSilverman, Edwin, K1 aWeiss, Scott, T1 aPalmer, Nicholette, D1 aVasan, Ramachandran, S1 aBurchard, Esteban, G1 aKardia, Sharon, L R1 aHe, Jiang1 aKaplan, Robert, C1 aSmith, Nicholas, L1 aArnett, Donna, K1 aSchwartz, David, A1 aCorrea, Adolfo1 ade Andrade, Mariza1 aGuo, Xiuqing1 aKonkle, Barbara, A1 aCuster, Brian1 aPeralta, Juan, M1 aGui, Hongsheng1 aMeyers, Deborah, A1 aMcGarvey, Stephen, T1 aChen, Ida Yii-Der1 aShoemaker, Benjamin1 aPeyser, Patricia, A1 aBroome, Jai, G1 aGogarten, Stephanie, M1 aWang, Fei, Fei1 aWong, Quenna1 aMontasser, May, E1 aDaya, Michelle1 aKenny, Eimear, E1 aNorth, Kari, E1 aLauner, Lenore, J1 aCade, Brian, E1 aBis, Joshua, C1 aCho, Michael, H1 aLasky-Su, Jessica1 aBowden, Donald, W1 aCupples, Adrienne, L1 aC Y Mak, Angel1 aBecker, Lewis, C1 aSmith, Jennifer, A1 aKelly, Tanika, N1 aAslibekyan, Stella1 aHeckbert, Susan, R1 aTiwari, Hemant, K1 aYang, Ivana, V1 aHeit, John, A1 aLubitz, Steven, A1 aJohnsen, Jill, M1 aCurran, Joanne, E1 aWenzel, Sally, E1 aWeeks, Daniel, E1 aRao, Dabeeru, C1 aDarbar, Dawood1 aMoon, Jee-Young1 aTracy, Russell, P1 aButh, Erin, J1 aRafaels, Nicholas1 aLoos, Ruth, J F1 aDurda, Peter1 aLiu, Yongmei1 aHou, Lifang1 aLee, Jiwon1 aKachroo, Priyadarshini1 aFreedman, Barry, I1 aLevy, Daniel1 aBielak, Lawrence, F1 aHixson, James, E1 aFloyd, James, S1 aWhitsel, Eric, A1 aEllinor, Patrick, T1 aIrvin, Marguerite, R1 aFingerlin, Tasha, E1 aRaffield, Laura, M1 aArmasu, Sebastian, M1 aWheeler, Marsha, M1 aSabino, Ester, C1 aBlangero, John1 aWilliams, Keoki1 aLevy, Bruce, D1 aSheu, Wayne, Huey-Herng1 aRoden, Dan, M1 aBoerwinkle, Eric1 aManson, JoAnn, E1 aMathias, Rasika, A1 aDesai, Pinkal1 aTaylor, Kent, D1 aJohnson, Andrew, D1 aAuer, Paul, L1 aKooperberg, Charles1 aLaurie, Cathy, C1 aBlackwell, Thomas, W1 aSmith, Albert, V1 aZhao, Hongyu1 aLange, Ethan1 aLange, Leslie1 aRich, Stephen, S1 aRotter, Jerome, I1 aWilson, James, G1 aScheet, Paul1 aKitzman, Jacob, O1 aLander, Eric, S1 aEngreitz, Jesse, M1 aEbert, Benjamin, L1 aReiner, Alexander, P1 aJaiswal, Siddhartha1 aAbecasis, Goncalo1 aSankaran, Vijay, G1 aKathiresan, Sekar1 aNatarajan, Pradeep1 aNHLBI Trans-Omics for Precision Medicine Consortium uhttps://chs-nhlbi.org/node/862105243nas a2201489 4500008004100000022001400041245006800055210006300123260001600186300001800202490000800220520113000228100002201358700001701380700001801397700002101415700001801436700001501454700002001469700002301489700002301512700002501535700002201560700001601582700002201598700002801620700002301648700002201671700001801693700001901711700002201730700001701752700001701769700002001786700002001806700002101826700002001847700002301867700002401890700002301914700002601937700002901963700002301992700002002015700001702035700003002052700001602082700002002098700001802118700001602136700002202152700002202174700002002196700001702216700001802233700002002251700001202271700002402283700001902307700001902326700002402345700001502369700002002384700002002404700002002424700002302444700002002467700002402487700002302511700002102534700002202555700002102577700001702598700002802615700002102643700002002664700002102684700001802705700002402723700002402747700001702771700002102788700001902809700002002828700002002848700002302868700002102891700002902912700002302941700002202964700002002986700002803006700002303034700001403057700002403071700002103095700001703116700001903133700002503152700001803177700001203195700001803207700002103225700002003246700002303266700001503289700001203304700002003316700002203336700002003358700001903378700002503397700002003422700002203442700002003464700002303484700001803507700002203525700002503547700002503572700002403597700002203621700002303643700002003666710003103686856003603717 2020 eng d a1097-417200aThe Polygenic and Monogenic Basis of Blood Traits and Diseases.0 aPolygenic and Monogenic Basis of Blood Traits and Diseases c2020 Sep 03 a1214-1231.e110 v1823 aBlood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.
1 aVuckovic, Dragana1 aBao, Erik, L1 aAkbari, Parsa1 aLareau, Caleb, A1 aMousas, Abdou1 aJiang, Tao1 aChen, Ming-Huei1 aRaffield, Laura, M1 aTardaguila, Manuel1 aHuffman, Jennifer, E1 aRitchie, Scott, C1 aMegy, Karyn1 aPonstingl, Hannes1 aPenkett, Christopher, J1 aAlbers, Patrick, K1 aWigdor, Emilie, M1 aSakaue, Saori1 aMoscati, Arden1 aManansala, Regina1 aLo, Ken, Sin1 aQian, Huijun1 aAkiyama, Masato1 aBartz, Traci, M1 aBen-Shlomo, Yoav1 aBeswick, Andrew1 aBork-Jensen, Jette1 aBottinger, Erwin, P1 aBrody, Jennifer, A1 avan Rooij, Frank, J A1 aChitrala, Kumaraswamy, N1 aWilson, Peter, W F1 aChoquet, Helene1 aDanesh, John1 aDi Angelantonio, Emanuele1 aDimou, Niki1 aDing, Jingzhong1 aElliott, Paul1 aEsko, Tõnu1 aEvans, Michele, K1 aFelix, Stephan, B1 aFloyd, James, S1 aBroer, Linda1 aGrarup, Niels1 aGuo, Michael, H1 aGuo, Qi1 aGreinacher, Andreas1 aHaessler, Jeff1 aHansen, Torben1 aHowson, Joanna, M M1 aHuang, Wei1 aJorgenson, Eric1 aKacprowski, Tim1 aKähönen, Mika1 aKamatani, Yoichiro1 aKanai, Masahiro1 aKarthikeyan, Savita1 aKoskeridis, Fotios1 aLange, Leslie, A1 aLehtimäki, Terho1 aLinneberg, Allan1 aLiu, Yongmei1 aLyytikäinen, Leo-Pekka1 aManichaikul, Ani1 aMatsuda, Koichi1 aMohlke, Karen, L1 aMononen, Nina1 aMurakami, Yoshinori1 aNadkarni, Girish, N1 aNikus, Kjell1 aPankratz, Nathan1 aPedersen, Oluf1 aPreuss, Michael1 aPsaty, Bruce, M1 aRaitakari, Olli, T1 aRich, Stephen, S1 aRodriguez, Benjamin, A T1 aRosen, Jonathan, D1 aRotter, Jerome, I1 aSchubert, Petra1 aSpracklen, Cassandra, N1 aSurendran, Praveen1 aTang, Hua1 aTardif, Jean-Claude1 aGhanbari, Mohsen1 aVölker, Uwe1 aVölzke, Henry1 aWatkins, Nicholas, A1 aWeiss, Stefan1 aCai, Na1 aKundu, Kousik1 aWatt, Stephen, B1 aWalter, Klaudia1 aZonderman, Alan, B1 aCho, Kelly1 aLi, Yun1 aLoos, Ruth, J F1 aKnight, Julian, C1 aGeorges, Michel1 aStegle, Oliver1 aEvangelou, Evangelos1 aOkada, Yukinori1 aRoberts, David, J1 aInouye, Michael1 aJohnson, Andrew, D1 aAuer, Paul, L1 aAstle, William, J1 aReiner, Alexander, P1 aButterworth, Adam, S1 aOuwehand, Willem, H1 aLettre, Guillaume1 aSankaran, Vijay, G1 aSoranzo, Nicole1 aVA Million Veteran Program uhttps://chs-nhlbi.org/node/849003741nas a2200625 4500008004100000022001400041245009700055210006900152260001300221300001200234490000700246520194400253100001402197700001402211700002002225700002402245700002302269700002102292700002302313700001702336700001502353700002102368700002402389700001702413700001602430700002502446700002202471700002202493700001502515700002402530700002002554700002002574700002102594700002502615700002002640700002402660700002302684700002602707700001302733700001402746700002002760700002402780700002402804700001802828700002902846700002502875700002002900700001902920700002002939700002202959700002102981700002403002710005303026856003603079 2020 eng d a2574-830000aRole of Rare and Low-Frequency Variants in Gene-Alcohol Interactions on Plasma Lipid Levels.0 aRole of Rare and LowFrequency Variants in GeneAlcohol Interactio c2020 Aug ae0027720 v133 aBACKGROUND: Alcohol intake influences plasma lipid levels, and such effects may be moderated by genetic variants. We aimed to characterize the role of aggregated rare and low-frequency protein-coding variants in gene by alcohol consumption interactions associated with fasting plasma lipid levels.
METHODS: In the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, fasting plasma triglycerides and high- and low-density lipoprotein cholesterol were measured in 34 153 individuals with European ancestry from 5 discovery studies and 32 277 individuals from 6 replication studies. Rare and low-frequency functional protein-coding variants (minor allele frequency, ≤5%) measured by an exome array were aggregated by genes and evaluated by a gene-environment interaction test and a joint test of genetic main and gene-environment interaction effects. Two dichotomous self-reported alcohol consumption variables, current drinker, defined as any recurrent drinking behavior, and regular drinker, defined as the subset of current drinkers who consume at least 2 drinks per week, were considered.
RESULTS: We discovered and replicated 21 gene-lipid associations at 13 known lipid loci through the joint test. Eight loci (, , , , , , , and ) remained significant after conditioning on the common index single-nucleotide polymorphism identified by previous genome-wide association studies, suggesting an independent role for rare and low-frequency variants at these loci. One significant gene-alcohol interaction on triglycerides in a novel locus was significantly discovered (=6.65×10 for the interaction test) and replicated at nominal significance level (=0.013) in .
CONCLUSIONS: In conclusion, this study applied new gene-based statistical approaches and suggested that rare and low-frequency genetic variants interacted with alcohol consumption on lipid levels.
1 aWang, Zhe1 aChen, Han1 aBartz, Traci, M1 aBielak, Lawrence, F1 aChasman, Daniel, I1 aFeitosa, Mary, F1 aFranceschini, Nora1 aGuo, Xiuqing1 aLim, Elise1 aNoordam, Raymond1 aRichard, Melissa, A1 aWang, Heming1 aCade, Brian1 aCupples, Adrienne, L1 ade Vries, Paul, S1 aGiulanini, Franco1 aLee, Jiwon1 aLemaitre, Rozenn, N1 aMartin, Lisa, W1 aReiner, Alex, P1 aRich, Stephen, S1 aSchreiner, Pamela, J1 aSidney, Stephen1 aSitlani, Colleen, M1 aSmith, Jennifer, A1 avan Dijk, Ko, Willems1 aYao, Jie1 aZhao, Wei1 aFornage, Myriam1 aKardia, Sharon, L R1 aKooperberg, Charles1 aLiu, Ching-Ti1 aMook-Kanamori, Dennis, O1 aProvince, Michael, A1 aPsaty, Bruce, M1 aRedline, Susan1 aRidker, Paul, M1 aRotter, Jerome, I1 aBoerwinkle, Eric1 aMorrison, Alanna, C1 aCHARGE Gene-Lifestyle Interactions Working Group uhttps://chs-nhlbi.org/node/840705226nas a2201465 4500008004100000022001400041245010900055210006900164260001600233300001800249490000800267520110800275100002001383700002301403700001801426700001801444700002501462700001901487700001901506700001501525700001801540700002201558700001701580700001501597700002201612700002501634700001701659700001701676700001701693700002101710700002301731700001901754700002001773700002001793700002101813700002001834700002301854700002401877700002301901700002601924700003101950700001501981700002001996700001902016700001702035700003002052700001602082700002002098700001802118700001602136700002202152700002002174700001702194700001802211700002002229700002402249700001902273700001902292700002402311700002002335700001502355700002002370700002002390700002002410700002302430700002002453700002402473700002202497700002102519700002202540700002102562700002102583700001702604700002802621700002102649700002202670700002002692700002102712700001802733700002402751700002402775700002002799700001702819700002402836700002102860700001902881700002002900700002002920700002302940700002202963700002102985700002903006700002303035700002203058700002003080700002803100700002303128700001403151700002403165700002503189700002103214700001703235700001903252700002503271700002303296700002303319700001203342700002503354700002803379700002803407700001703435700002003452700002203472700002503494700002303519700002303542700002003565700002003585700002303605700002503628700001803653700002203671710003103693856003603724 2020 eng d a1097-417200aTrans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations.0 aTransethnic and AncestrySpecific BloodCell Genetics in 746667 In c2020 Sep 03 a1198-1213.e140 v1823 aMost loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.
1 aChen, Ming-Huei1 aRaffield, Laura, M1 aMousas, Abdou1 aSakaue, Saori1 aHuffman, Jennifer, E1 aMoscati, Arden1 aTrivedi, Bhavi1 aJiang, Tao1 aAkbari, Parsa1 aVuckovic, Dragana1 aBao, Erik, L1 aZhong, Xue1 aManansala, Regina1 aLaplante, Véronique1 aChen, Minhui1 aLo, Ken, Sin1 aQian, Huijun1 aLareau, Caleb, A1 aBeaudoin, Mélissa1 aHunt, Karen, A1 aAkiyama, Masato1 aBartz, Traci, M1 aBen-Shlomo, Yoav1 aBeswick, Andrew1 aBork-Jensen, Jette1 aBottinger, Erwin, P1 aBrody, Jennifer, A1 avan Rooij, Frank, J A1 aChitrala, Kumaraswamynaidu1 aCho, Kelly1 aChoquet, Helene1 aCorrea, Adolfo1 aDanesh, John1 aDi Angelantonio, Emanuele1 aDimou, Niki1 aDing, Jingzhong1 aElliott, Paul1 aEsko, Tõnu1 aEvans, Michele, K1 aFloyd, James, S1 aBroer, Linda1 aGrarup, Niels1 aGuo, Michael, H1 aGreinacher, Andreas1 aHaessler, Jeff1 aHansen, Torben1 aHowson, Joanna, M M1 aHuang, Qin, Qin1 aHuang, Wei1 aJorgenson, Eric1 aKacprowski, Tim1 aKähönen, Mika1 aKamatani, Yoichiro1 aKanai, Masahiro1 aKarthikeyan, Savita1 aKoskeridis, Fotis1 aLange, Leslie, A1 aLehtimäki, Terho1 aLerch, Markus, M1 aLinneberg, Allan1 aLiu, Yongmei1 aLyytikäinen, Leo-Pekka1 aManichaikul, Ani1 aMartin, Hilary, C1 aMatsuda, Koichi1 aMohlke, Karen, L1 aMononen, Nina1 aMurakami, Yoshinori1 aNadkarni, Girish, N1 aNauck, Matthias1 aNikus, Kjell1 aOuwehand, Willem, H1 aPankratz, Nathan1 aPedersen, Oluf1 aPreuss, Michael1 aPsaty, Bruce, M1 aRaitakari, Olli, T1 aRoberts, David, J1 aRich, Stephen, S1 aRodriguez, Benjamin, A T1 aRosen, Jonathan, D1 aRotter, Jerome, I1 aSchubert, Petra1 aSpracklen, Cassandra, N1 aSurendran, Praveen1 aTang, Hua1 aTardif, Jean-Claude1 aTrembath, Richard, C1 aGhanbari, Mohsen1 aVölker, Uwe1 aVölzke, Henry1 aWatkins, Nicholas, A1 aZonderman, Alan, B1 aWilson, Peter, W F1 aLi, Yun1 aButterworth, Adam, S1 aGauchat, Jean-François1 aChiang, Charleston, W K1 aLi, Bingshan1 aLoos, Ruth, J F1 aAstle, William, J1 aEvangelou, Evangelos1 avan Heel, David, A1 aSankaran, Vijay, G1 aOkada, Yukinori1 aSoranzo, Nicole1 aJohnson, Andrew, D1 aReiner, Alexander, P1 aAuer, Paul, L1 aLettre, Guillaume1 aVA Million Veteran Program uhttps://chs-nhlbi.org/node/848104776nas a2201261 4500008004100000022001400041245010300055210006900158260001500227300000900242490000700251520104900258653001001307653002201317653000901339653002201348653005001370653002901420653002401449653001101473653002201484653001701506653003801523653003401561653001101595653005001606653000901656653000901665653001601674653003601690653004101726653004301767653004001810653004601850653002801896100001701924700001601941700001801957700001801975700001501993700001502008700001702023700001902040700001702059700003002076700002902106700002202135700002302157700001302180700002102193700001902214700001902233700001502252700002002267700001902287700002302306700002002329700002502349700002002374700002002394700002402414700002002438700002702458700002102485700002002506700001702526700001902543700002002562700001702582700001702599700002202616700002302638700002002661700002502681700002802706700002202734700002402756700001702780700002602797700002002823700002302843700003102866700002502897700001902922700002002941700002102961700002402982700001903006700002003025700002103045700002403066700002503090700002403115700002203139700002003161700002103181700002503202700002303227700002603250700002503276700002403301700001703325700002003342700002103362710006503383710003003448856003603478 2020 eng d a2041-172300aWhole genome sequence analysis of pulmonary function and COPD in 19,996 multi-ethnic participants.0 aWhole genome sequence analysis of pulmonary function and COPD in c2020 10 14 a51820 v113 aChronic obstructive pulmonary disease (COPD), diagnosed by reduced lung function, is a leading cause of morbidity and mortality. We performed whole genome sequence (WGS) analysis of lung function and COPD in a multi-ethnic sample of 11,497 participants from population- and family-based studies, and 8499 individuals from COPD-enriched studies in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. We identify at genome-wide significance 10 known GWAS loci and 22 distinct, previously unreported loci, including two common variant signals from stratified analysis of African Americans. Four novel common variants within the regions of PIAS1, RGN (two variants) and FTO show evidence of replication in the UK Biobank (European ancestry n ~ 320,000), while colocalization analyses leveraging multi-omic data from GTEx and TOPMed identify potential molecular mechanisms underlying four of the 22 novel loci. Our study demonstrates the value of performing WGS analyses and multi-omic follow-up in cohorts of diverse ancestry.
10aAdult10aAfrican Americans10aAged10aAged, 80 and over10aAlpha-Ketoglutarate-Dependent Dioxygenase FTO10aCalcium-Binding Proteins10aFeasibility Studies10aFemale10aFollow-Up Studies10aGenetic Loci10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aIntracellular Signaling Peptides and Proteins10aLung10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide10aProtein Inhibitors of Activated STAT10aPulmonary Disease, Chronic Obstructive10aRespiratory Physiological Phenomena10aSmall Ubiquitin-Related Modifier Proteins10aWhole Genome Sequencing1 aZhao, Xutong1 aQiao, Dandi1 aYang, Chaojie1 aKasela, Silva1 aKim, Wonji1 aMa, Yanlin1 aShrine, Nick1 aBatini, Chiara1 aSofer, Tamar1 aTaliun, Sarah, A Gagliano1 aSakornsakolpat, Phuwanat1 aBalte, Pallavi, P1 aProkopenko, Dmitry1 aYu, Bing1 aLange, Leslie, A1 aDupuis, Josée1 aCade, Brian, E1 aLee, Jiwon1 aGharib, Sina, A1 aDaya, Michelle1 aLaurie, Cecelia, A1 aRuczinski, Ingo1 aCupples, Adrienne, L1 aLoehr, Laura, R1 aBartz, Traci, M1 aMorrison, Alanna, C1 aPsaty, Bruce, M1 aVasan, Ramachandran, S1 aWilson, James, G1 aTaylor, Kent, D1 aDurda, Peter1 aJohnson, Craig1 aCornell, Elaine1 aGuo, Xiuqing1 aLiu, Yongmei1 aTracy, Russell, P1 aArdlie, Kristin, G1 aAguet, Francois1 aVanDenBerg, David, J1 aPapanicolaou, George, J1 aRotter, Jerome, I1 aBarnes, Kathleen, C1 aJain, Deepti1 aNickerson, Deborah, A1 aMuzny, Donna, M1 aMetcalf, Ginger, A1 aDoddapaneni, Harshavardhan1 aDugan-Perez, Shannon1 aGupta, Namrata1 aGabriel, Stacey1 aRich, Stephen, S1 aO'Connor, George, T1 aRedline, Susan1 aReed, Robert, M1 aLaurie, Cathy, C1 aDaviglus, Martha, L1 aPreudhomme, Liana, K1 aBurkart, Kristin, M1 aKaplan, Robert, C1 aWain, Louise, V1 aTobin, Martin, D1 aLondon, Stephanie, J1 aLappalainen, Tuuli1 aOelsner, Elizabeth, C1 aAbecasis, Goncalo, R1 aSilverman, Edwin, K1 aBarr, Graham1 aCho, Michael, H1 aManichaikul, Ani1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aTOPMed Lung Working Group uhttps://chs-nhlbi.org/node/863902650nas a2200589 4500008004100000022001400041245008000055210006900135260001600204490000600220520090300226100001301129700002401142700002201166700002301188700002401211700001401235700002701249700002501276700001301301700001701314700002501331700002401356700002101380700002101401700002001422700002401442700002301466700002001489700002501509700002101534700002001555700002401575700002301599700002701622700001701649700002801666700002001694700001401714700001901728700002201747700002001769700002101789700001701810700002201827700001901849700002601868700001901894700001601913710009501929856003602024 2021 eng d a2666-979X00aAssociation of mitochondrial DNA copy number with cardiometabolic diseases.0 aAssociation of mitochondrial DNA copy number with cardiometaboli c2021 Oct 130 v13 aMitochondrial DNA (mtDNA) is present in multiple copies in human cells. We evaluated cross-sectional associations of whole blood mtDNA copy number (CN) with several cardiometabolic disease traits in 408,361 participants of multiple ancestries in TOPMed and UK Biobank. Age showed a threshold association with mtDNA CN: among younger participants (<65 years of age), each additional 10 years of age was associated with 0.03 standard deviation (s.d.) higher level of mtDNA CN ( = 0.0014) versus a 0.14 s.d. lower level of mtDNA CN ( = 1.82 × 10) among older participants (≥65 years). At lower mtDNA CN levels, we found age-independent associations with increased odds of obesity ( = 5.6 × 10), hypertension ( = 2.8 × 10), diabetes ( = 3.6 × 10), and hyperlipidemia ( = 6.3 × 10). The observed decline in mtDNA CN after 65 years of age may be a key to understanding age-related diseases.
1 aLiu, Xue1 aLongchamps, Ryan, J1 aWiggins, Kerri, L1 aRaffield, Laura, M1 aBielak, Lawrence, F1 aZhao, Wei1 aPitsillides, Achilleas1 aBlackwell, Thomas, W1 aYao, Jie1 aGuo, Xiuqing1 aKurniansyah, Nuzulul1 aThyagarajan, Bharat1 aPankratz, Nathan1 aRich, Stephen, S1 aTaylor, Kent, D1 aPeyser, Patricia, A1 aHeckbert, Susan, R1 aSeshadri, Sudha1 aCupples, Adrienne, L1 aBoerwinkle, Eric1 aGrove, Megan, L1 aLarson, Nicholas, B1 aSmith, Jennifer, A1 aVasan, Ramachandran, S1 aSofer, Tamar1 aFitzpatrick, Annette, L1 aFornage, Myriam1 aDing, Jun1 aCorrea, Adolfo1 aAbecasis, Goncalo1 aPsaty, Bruce, M1 aWilson, James, G1 aLevy, Daniel1 aRotter, Jerome, I1 aBis, Joshua, C1 aSatizabal, Claudia, L1 aArking, Dan, E1 aLiu, Chunyu1 aTOPMed mtDNA Working Group in NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/899703306nas a2200565 4500008004100000022001400041245014400055210006900199260001600268490000600284520159100290100001701881700001501898700002501913700001701938700002301955700002701978700002402005700001702029700001202046700002402058700002202082700002702104700002502131700002402156700002002180700002002200700001402220700002602234700001502260700002402275700002002299700002002319700002202339700002002361700002102381700001702402700002102419700002402440700002102464700002302485700002102508700002002529700001902549700002302568700002102591700002702612710006502639856003602704 2021 eng d a2666-247700aBinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion.0 aBinomiRare A robust test for association of a rare genetic varia c2021 Jul 080 v23 aWhole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.
1 aSofer, Tamar1 aLee, Jiwon1 aKurniansyah, Nuzulul1 aJain, Deepti1 aLaurie, Cecelia, A1 aGogarten, Stephanie, M1 aConomos, Matthew, P1 aHeavner, Ben1 aHu, Yao1 aKooperberg, Charles1 aHaessler, Jeffrey1 aVasan, Ramachandran, S1 aCupples, Adrienne, L1 aCoombes, Brandon, J1 aSeyerle, Amanda1 aGharib, Sina, A1 aChen, Han1 aO'Connell, Jeffrey, R1 aZhang, Man1 aGottlieb, Daniel, J1 aPsaty, Bruce, M1 aLongstreth, W T1 aRotter, Jerome, I1 aTaylor, Kent, D1 aRich, Stephen, S1 aGuo, Xiuqing1 aBoerwinkle, Eric1 aMorrison, Alanna, C1 aPankow, James, S1 aJohnson, Andrew, D1 aPankratz, Nathan1 aReiner, Alex, P1 aRedline, Susan1 aSmith, Nicholas, L1 aRice, Kenneth, M1 aSchifano, Elizabeth, D1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/883805153nas a2201369 4500008004100000022001400041245011700055210006900172260001500241300000900256490000700265520124400272100002301516700001901539700002101558700002701579700002001606700002201626700001901648700002601667700002601693700002001719700002001739700002301759700003001782700001901812700002301831700002901854700002801883700002401911700001901935700001901954700001201973700002401985700002102009700001702030700002502047700001902072700001802091700001502109700002402124700002002148700002002168700002202188700002002210700001702230700002602247700002602273700002102299700002402320700002302344700001802367700001902385700002102404700001902425700002102444700002202465700002102487700002102508700001902529700002102548700002202569700002002591700002102611700002002632700001902652700002202671700001802693700002202711700002402733700002002757700002302777700002002800700001802820700002202838700001402860700002002874700002002894700002202914700002402936700001802960700001702978700002402995700002103019700001803040700002403058700002003082700002203102700002303124700003003147700002503177700002503202700002603227700001903253700001903272700002103291700002003312700001403332700001903346700002503365700001703390700001903407700002103426700002203447700002703469700002203496700002503518700002203543700002403565700002103589700002003610700002003630700002003650710006503670710001203735856003603747 2021 eng d a2041-172300aChromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices.0 aChromosome Xq23 is associated with lower atherogenic lipid conce c2021 04 12 a21820 v123 aAutosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids.
1 aNatarajan, Pradeep1 aPampana, Akhil1 aGraham, Sarah, E1 aRuotsalainen, Sanni, E1 aPerry, James, A1 ade Vries, Paul, S1 aBroome, Jai, G1 aPirruccello, James, P1 aHonigberg, Michael, C1 aAragam, Krishna1 aWolford, Brooke1 aBrody, Jennifer, A1 aAntonacci-Fulton, Lucinda1 aArden, Moscati1 aAslibekyan, Stella1 aAssimes, Themistocles, L1 aBallantyne, Christie, M1 aBielak, Lawrence, F1 aBis, Joshua, C1 aCade, Brian, E1 aDo, Ron1 aDoddapaneni, Harsha1 aEmery, Leslie, S1 aHung, Yi-Jen1 aIrvin, Marguerite, R1 aKhan, Alyna, T1 aLange, Leslie1 aLee, Jiwon1 aLemaitre, Rozenn, N1 aMartin, Lisa, W1 aMetcalf, Ginger1 aMontasser, May, E1 aMoon, Jee-Young1 aMuzny, Donna1 aO'Connell, Jeffrey, R1 aPalmer, Nicholette, D1 aPeralta, Juan, M1 aPeyser, Patricia, A1 aStilp, Adrienne, M1 aTsai, Michael1 aWang, Fei, Fei1 aWeeks, Daniel, E1 aYanek, Lisa, R1 aWilson, James, G1 aAbecasis, Goncalo1 aArnett, Donna, K1 aBecker, Lewis, C1 aBlangero, John1 aBoerwinkle, Eric1 aBowden, Donald, W1 aChang, Yi-Cheng1 aChen, Yii-der, I1 aChoi, Won, Jung1 aCorrea, Adolfo1 aCurran, Joanne, E1 aDaly, Mark, J1 aDutcher, Susan, K1 aEllinor, Patrick, T1 aFornage, Myriam1 aFreedman, Barry, I1 aGabriel, Stacey1 aGermer, Soren1 aGibbs, Richard, A1 aHe, Jiang1 aHveem, Kristian1 aJarvik, Gail, P1 aKaplan, Robert, C1 aKardia, Sharon, L R1 aKenny, Eimear1 aKim, Ryan, W1 aKooperberg, Charles1 aLaurie, Cathy, C1 aLee, Seonwook1 aLloyd-Jones, Don, M1 aLoos, Ruth, J F1 aLubitz, Steven, A1 aMathias, Rasika, A1 aMartinez, Karine, A Viaud1 aMcGarvey, Stephen, T1 aMitchell, Braxton, D1 aNickerson, Deborah, A1 aNorth, Kari, E1 aPalotie, Aarno1 aPark, Cheol, Joo1 aPsaty, Bruce, M1 aRao, D, C1 aRedline, Susan1 aReiner, Alexander, P1 aSeo, Daekwan1 aSeo, Jeong-Sun1 aSmith, Albert, V1 aTracy, Russell, P1 aVasan, Ramachandran, S1 aKathiresan, Sekar1 aCupples, Adrienne, L1 aRotter, Jerome, I1 aMorrison, Alanna, C1 aRich, Stephen, S1 aRipatti, Samuli1 aWiller, Cristen1 aPeloso, Gina, M1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aFinnGen uhttps://chs-nhlbi.org/node/871106377nas a2201849 4500008004100000022001400041245011300055210006900168260001500237300000900252490000700261520114200268653001001410653003701420653001501457653003001472653001801502653001001520653003801530653001301568653001101581653003101592653001501623653002001638100002301658700002401681700001601705700002001721700001701741700002001758700002001778700001801798700001601816700001901832700001901851700002001870700002101890700001901911700002401930700001501954700003101969700001602000700002902016700001802045700001902063700002102082700002502103700002102128700002202149700002702171700002202198700002002220700001702240700001802257700002002275700001902295700002902314700002102343700001902364700002302383700002802406700001902434700003102453700002802484700002202512700002102534700003602555700002402591700001802615700001602633700001702649700002402666700001502690700002002705700001902725700002602744700002402770700003202794700002102826700002602847700002502873700002002898700001702918700002002935700002502955700002602980700002203006700001903028700001903047700001803066700002003084700001703104700002103121700002103142700001703163700002003180700002303200700002003223700003603243700002003279700002203299700002603321700002103347700002203368700002403390700001903414700002203433700003003455700001903485700002303504700002103527700001903548700001903567700002003586700002003606700002503626700003103651700002103682700002203703700002103725700002303746700001703769700001603786700002103802700001803823700002403841700002403865700001803889700001903907700002203926700001903948700002003967700002103987700002204008700002204030700002304052700002404075700002004099700002704119700002104146700002104167700001904188700001904207700001804226700002204244700002104266700002004287700002004307700002304327700002604350700002104376700002004397700002504417700002104442710002804463856003604491 2021 eng d a2041-172300aDeterminants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes.0 aDeterminants of penetrance and variable expressivity in monogeni c2021 06 09 a35050 v123 aHundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias.
10aAdult10aBiological Variation, Population10aBiomarkers10aDiabetes Mellitus, Type 210aDyslipidemias10aExome10aGenetic Predisposition to Disease10aGenotype10aHumans10aMultifactorial Inheritance10aPenetrance10aRisk Assessment1 aGoodrich, Julia, K1 aSinger-Berk, Moriel1 aSon, Rachel1 aSveden, Abigail1 aWood, Jordan1 aEngland, Eleina1 aCole, Joanne, B1 aWeisburd, Ben1 aWatts, Nick1 aCaulkins, Lizz1 aDornbos, Peter1 aKoesterer, Ryan1 aZappala, Zachary1 aZhang, Haichen1 aMaloney, Kristin, A1 aDahl, Andy1 aAguilar-Salinas, Carlos, A1 aAtzmon, Gil1 aBarajas-Olmos, Francisco1 aBarzilai, Nir1 aBlangero, John1 aBoerwinkle, Eric1 aBonnycastle, Lori, L1 aBottinger, Erwin1 aBowden, Donald, W1 aCenteno-Cruz, Federico1 aChambers, John, C1 aChami, Nathalie1 aChan, Edmund1 aChan, Juliana1 aCheng, Ching-Yu1 aCho, Yoon Shin1 aContreras-Cubas, Cecilia1 aCórdova, Emilio1 aCorrea, Adolfo1 aDeFronzo, Ralph, A1 aDuggirala, Ravindranath1 aDupuis, Josée1 aGaray-Sevilla, Ma, Eugenia1 aGarcía-Ortiz, Humberto1 aGieger, Christian1 aGlaser, Benjamin1 aGonzález-Villalpando, Clicerio1 aGonzalez, Ma, Elena1 aGrarup, Niels1 aGroop, Leif1 aGross, Myron1 aHaiman, Christopher1 aHan, Sohee1 aHanis, Craig, L1 aHansen, Torben1 aHeard-Costa, Nancy, L1 aHenderson, Brian, E1 aHernandez, Juan, Manuel Mal1 aHwang, Mi, Yeong1 aIslas-Andrade, Sergio1 aJørgensen, Marit, E1 aKang, Hyun, Min1 aKim, Bong-Jo1 aKim, Young, Jin1 aKoistinen, Heikki, A1 aKooner, Jaspal, Singh1 aKuusisto, Johanna1 aKwak, Soo-Heon1 aLaakso, Markku1 aLange, Leslie1 aLee, Jong-Young1 aLee, Juyoung1 aLehman, Donna, M1 aLinneberg, Allan1 aLiu, Jianjun1 aLoos, Ruth, J F1 aLyssenko, Valeriya1 aMa, Ronald, C W1 aMartínez-Hernández, Angélica1 aMeigs, James, B1 aMeitinger, Thomas1 aMendoza-Caamal, Elvia1 aMohlke, Karen, L1 aMorris, Andrew, D1 aMorrison, Alanna, C1 aC Y Ng, Maggie1 aNilsson, Peter, M1 aO'Donnell, Christopher, J1 aOrozco, Lorena1 aPalmer, Colin, N A1 aPark, Kyong, Soo1 aPost, Wendy, S1 aPedersen, Oluf1 aPreuss, Michael1 aPsaty, Bruce, M1 aReiner, Alexander, P1 aRevilla-Monsalve, Cristina1 aRich, Stephen, S1 aRotter, Jerome, I1 aSaleheen, Danish1 aSchurmann, Claudia1 aSim, Xueling1 aSladek, Rob1 aSmall, Kerrin, S1 aSo, Wing, Yee1 aSpector, Timothy, D1 aStrauch, Konstantin1 aStrom, Tim, M1 aTai, Shyong, E1 aTam, Claudia, H T1 aTeo, Yik, Ying1 aThameem, Farook1 aTomlinson, Brian1 aTracy, Russell, P1 aTuomi, Tiinamaija1 aTuomilehto, Jaakko1 aTusié-Luna, Teresa1 avan Dam, Rob, M1 aVasan, Ramachandran, S1 aWilson, James, G1 aWitte, Daniel, R1 aWong, Tien-Yin1 aBurtt, Noel, P1 aZaitlen, Noah1 aMcCarthy, Mark, I1 aBoehnke, Michael1 aPollin, Toni, I1 aFlannick, Jason1 aMercader, Josep, M1 aO'Donnell-Luria, Anne1 aBaxter, Samantha1 aFlorez, Jose, C1 aMacArthur, Daniel, G1 aUdler, Miriam, S1 aAMP-T2D-GENES Consortia uhttps://chs-nhlbi.org/node/877406233nas a2201609 4500008004100000022001400041245009500055210006900150260001600219520178500235100001702020700002102037700001902058700002102077700002302098700001502121700001802136700002002154700002202174700002002196700002802216700001802244700002202262700002402284700002102308700002002329700002002349700001502369700002502384700001702409700003102426700002002457700002302477700002302500700002102523700001702544700002002561700002102581700002702602700001902629700002402648700002602672700002102698700001802719700001702737700001902754700002802773700002002801700002402821700002202845700002202867700001902889700001402908700002102922700002102943700002502964700002002989700002203009700001803031700002203049700002003071700001903091700002303110700002003133700002803153700001603181700001503197700001703212700001403229700002003243700002303263700001903286700001603305700002003321700001703341700002103358700001903379700001303398700001703411700002203428700002403450700002903474700002003503700001503523700002303538700002403561700002203585700002003607700001803627700001703645700002103662700001903683700002103702700001603723700001503739700002603754700002003780700002403800700002203824700002103846700001703867700001803884700001703902700002203919700002203941700002003963700002603983700002204009700001904031700001504050700002004065700002204085700002404107700002404131700002604155700002004181700002004201700002304221700001804244700002104262700002204283700002104305700001804326700002404344700001804368700001904386700001604405700001904421700001804440700002004458700002704478700002104505700002004526700001904546700002204565856003604587 2021 eng d a1476-557800aMulti-ancestry genome-wide gene-sleep interactions identify novel loci for blood pressure.0 aMultiancestry genomewide genesleep interactions identify novel l c2021 Apr 153 aLong and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups in two stages using 2 degree of freedom (df) joint test followed by 1df test of interaction effects. Primary multi-ancestry analysis in 62,969 individuals in stage 1 identified three novel gene by sleep interactions that were replicated in an additional 59,296 individuals in stage 2 (stage 1 + 2 P < 5 × 10), including rs7955964 (FIGNL2/ANKRD33) that increases BP among long sleepers, and rs73493041 (SNORA26/C9orf170) and rs10406644 (KCTD15/LSM14A) that increase BP among short sleepers (P < 5 × 10). Secondary ancestry-specific analysis identified another novel gene by long sleep interaction at rs111887471 (TRPC3/KIAA1109) in individuals of African ancestry (P = 2 × 10). Combined stage 1 and 2 analyses additionally identified significant gene by long sleep interactions at 10 loci including MKLN1 and RGL3/ELAVL3 previously associated with BP, and significant gene by short sleep interactions at 10 loci including C2orf43 previously associated with BP (P < 10). 2df test also identified novel loci for BP after modeling sleep that has known functions in sleep-wake regulation, nervous and cardiometabolic systems. This study indicates that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.
1 aWang, Heming1 aNoordam, Raymond1 aCade, Brian, E1 aSchwander, Karen1 aWinkler, Thomas, W1 aLee, Jiwon1 aSung, Yun, Ju1 aBentley, Amy, R1 aManning, Alisa, K1 aAschard, Hugues1 aKilpeläinen, Tuomas, O1 aIlkov, Marjan1 aBrown, Michael, R1 aHorimoto, Andrea, R1 aRichard, Melissa1 aBartz, Traci, M1 aVojinovic, Dina1 aLim, Elise1 aNierenberg, Jovia, L1 aLiu, Yongmei1 aChitrala, Kumaraswamynaidu1 aRankinen, Tuomo1 aMusani, Solomon, K1 aFranceschini, Nora1 aRauramaa, Rainer1 aAlver, Maris1 aZee, Phyllis, C1 aHarris, Sarah, E1 avan der Most, Peter, J1 aNolte, Ilja, M1 aMunroe, Patricia, B1 aPalmer, Nicholette, D1 aKuhnel, Brigitte1 aWeiss, Stefan1 aWen, Wanqing1 aHall, Kelly, A1 aLyytikäinen, Leo-Pekka1 aO'Connell, Jeff1 aEiriksdottir, Gudny1 aLauner, Lenore, J1 ade Vries, Paul, S1 aArking, Dan, E1 aChen, Han1 aBoerwinkle, Eric1 aKrieger, Jose, E1 aSchreiner, Pamela, J1 aSidney, Stephen1 aShikany, James, M1 aRice, Kenneth1 aChen, Yii-Der Ida1 aGharib, Sina, A1 aBis, Joshua, C1 aLuik, Annemarie, I1 aIkram, Arfan, M1 aUitterlinden, André, G1 aAmin, Najaf1 aXu, Hanfei1 aLevy, Daniel1 aHe, Jiang1 aLohman, Kurt, K1 aZonderman, Alan, B1 aRice, Treva, K1 aSims, Mario1 aWilson, Gregory1 aSofer, Tamar1 aRich, Stephen, S1 aPalmas, Walter1 aYao, Jie1 aGuo, Xiuqing1 aRotter, Jerome, I1 aBiermasz, Nienke, R1 aMook-Kanamori, Dennis, O1 aMartin, Lisa, W1 aBarac, Ana1 aWallace, Robert, B1 aGottlieb, Daniel, J1 aKomulainen, Pirjo1 aHeikkinen, Sami1 aMägi, Reedik1 aMilani, Lili1 aMetspalu, Andres1 aStarr, John, M1 aMilaneschi, Yuri1 aWaken, R, J1 aGao, Chuan1 aWaldenberger, Melanie1 aPeters, Annette1 aStrauch, Konstantin1 aMeitinger, Thomas1 aRoenneberg, Till1 aVölker, Uwe1 aDörr, Marcus1 aShu, Xiao-Ou1 aMukherjee, Sutapa1 aHillman, David, R1 aKähönen, Mika1 aWagenknecht, Lynne, E1 aGieger, Christian1 aGrabe, Hans, J1 aZheng, Wei1 aPalmer, Lyle, J1 aLehtimäki, Terho1 aGudnason, Vilmundur1 aMorrison, Alanna, C1 aPereira, Alexandre, C1 aFornage, Myriam1 aPsaty, Bruce, M1 aDuijn, Cornelia, M1 aLiu, Ching-Ti1 aKelly, Tanika, N1 aEvans, Michele, K1 aBouchard, Claude1 aFox, Ervin, R1 aKooperberg, Charles1 aZhu, Xiaofeng1 aLakka, Timo, A1 aEsko, Tõnu1 aNorth, Kari, E1 aDeary, Ian, J1 aSnieder, Harold1 aPenninx, Brenda, W J H1 aGauderman, James1 aRao, Dabeeru, C1 aRedline, Susan1 avan Heemst, Diana uhttps://chs-nhlbi.org/node/871408483nas a2202413 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2021 eng d a1476-468700aSequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.0 aSequencing of 53831 diverse genomes from the NHLBI TOPMed Progra c2021 02 a290-2990 v5903 aThe Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes). In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
1 aTaliun, Daniel1 aHarris, Daniel, N1 aKessler, Michael, D1 aCarlson, Jedidiah1 aSzpiech, Zachary, A1 aTorres, Raul1 aTaliun, Sarah, A Gagliano1 aCorvelo, André1 aGogarten, Stephanie, M1 aKang, Hyun, Min1 aPitsillides, Achilleas, N1 aLeFaive, Jonathon1 aLee, Seung-Been1 aTian, Xiaowen1 aBrowning, Brian, L1 aDas, Sayantan1 aEmde, Anne-Katrin1 aClarke, Wayne, E1 aLoesch, Douglas, P1 aShetty, Amol, C1 aBlackwell, Thomas, W1 aSmith, Albert, V1 aWong, Quenna1 aLiu, Xiaoming1 aConomos, Matthew, P1 aBobo, Dean, M1 aAguet, Francois1 aAlbert, Christine1 aAlonso, Alvaro1 aArdlie, Kristin, G1 aArking, Dan, E1 aAslibekyan, Stella1 aAuer, Paul, L1 aBarnard, John1 aBarr, Graham1 aBarwick, Lucas1 aBecker, Lewis, C1 aBeer, Rebecca, L1 aBenjamin, Emelia, J1 aBielak, Lawrence, F1 aBlangero, John1 aBoehnke, Michael1 aBowden, Donald, W1 aBrody, Jennifer, A1 aBurchard, Esteban, G1 aCade, Brian, E1 aCasella, James, F1 aChalazan, Brandon1 aChasman, Daniel, I1 aChen, Yii-Der Ida1 aCho, Michael, H1 aChoi, Seung, Hoan1 aChung, Mina, K1 aClish, Clary, B1 aCorrea, Adolfo1 aCurran, Joanne, E1 aCuster, Brian1 aDarbar, Dawood1 aDaya, Michelle1 ade Andrade, Mariza1 aDeMeo, Dawn, L1 aDutcher, Susan, K1 aEllinor, Patrick, T1 aEmery, Leslie, S1 aEng, Celeste1 aFatkin, Diane1 aFingerlin, Tasha1 aForer, Lukas1 aFornage, Myriam1 aFranceschini, Nora1 aFuchsberger, Christian1 aFullerton, Stephanie, M1 aGermer, Soren1 aGladwin, Mark, T1 aGottlieb, Daniel, J1 aGuo, Xiuqing1 aHall, Michael, E1 aHe, Jiang1 aHeard-Costa, Nancy, L1 aHeckbert, Susan, R1 aIrvin, Marguerite, R1 aJohnsen, Jill, M1 aJohnson, Andrew, D1 aKaplan, Robert1 aKardia, Sharon, L R1 aKelly, Tanika1 aKelly, Shannon1 aKenny, Eimear, E1 aKiel, Douglas, P1 aKlemmer, Robert1 aKonkle, Barbara, A1 aKooperberg, Charles1 aKöttgen, Anna1 aLange, Leslie, A1 aLasky-Su, Jessica1 aLevy, Daniel1 aLin, Xihong1 aLin, Keng-Han1 aLiu, Chunyu1 aLoos, Ruth, J F1 aGarman, Lori1 aGerszten, Robert1 aLubitz, Steven, A1 aLunetta, Kathryn, L1 aC Y Mak, Angel1 aManichaikul, Ani1 aManning, Alisa, K1 aMathias, Rasika, A1 aMcManus, David, D1 aMcGarvey, Stephen, T1 aMeigs, James, B1 aMeyers, Deborah, A1 aMikulla, Julie, L1 aMinear, Mollie, A1 aMitchell, Braxton, D1 aMohanty, Sanghamitra1 aMontasser, May, E1 aMontgomery, Courtney1 aMorrison, Alanna, C1 aMurabito, Joanne, M1 aNatale, Andrea1 aNatarajan, Pradeep1 aNelson, Sarah, C1 aNorth, Kari, E1 aO'Connell, Jeffrey, R1 aPalmer, Nicholette, D1 aPankratz, Nathan1 aPeloso, Gina, M1 aPeyser, Patricia, A1 aPleiness, Jacob1 aPost, Wendy, S1 aPsaty, Bruce, M1 aRao, D, C1 aRedline, Susan1 aReiner, Alexander, P1 aRoden, Dan1 aRotter, Jerome, I1 aRuczinski, Ingo1 aSarnowski, Chloe1 aSchoenherr, Sebastian1 aSchwartz, David, A1 aSeo, Jeong-Sun1 aSeshadri, Sudha1 aSheehan, Vivien, A1 aSheu, Wayne, H1 aShoemaker, Benjamin1 aSmith, Nicholas, L1 aSmith, Jennifer, A1 aSotoodehnia, Nona1 aStilp, Adrienne, M1 aTang, Weihong1 aTaylor, Kent, D1 aTelen, Marilyn1 aThornton, Timothy, A1 aTracy, Russell, P1 aVan Den Berg, David, J1 aVasan, Ramachandran, S1 aViaud-Martinez, Karine, A1 aVrieze, Scott1 aWeeks, Daniel, E1 aWeir, Bruce, S1 aWeiss, Scott, T1 aWeng, Lu-Chen1 aWiller, Cristen, J1 aZhang, Yingze1 aZhao, Xutong1 aArnett, Donna, K1 aAshley-Koch, Allison, E1 aBarnes, Kathleen, C1 aBoerwinkle, Eric1 aGabriel, Stacey1 aGibbs, Richard1 aRice, Kenneth, M1 aRich, Stephen, S1 aSilverman, Edwin, K1 aQasba, Pankaj1 aGan, Weiniu1 aPapanicolaou, George, J1 aNickerson, Deborah, A1 aBrowning, Sharon, R1 aZody, Michael, C1 aZöllner, Sebastian1 aWilson, James, G1 aCupples, Adrienne, L1 aLaurie, Cathy, C1 aJaquish, Cashell, E1 aHernandez, Ryan, D1 aO'Connor, Timothy, D1 aAbecasis, Goncalo, R1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/866604296nas a2200973 4500008004100000022001400041245010700055210006900162260001600231520147300247100002301720700002101743700001901764700001801783700001901801700002301820700001901843700001701862700001901879700003001898700002601928700002801954700002801982700002102010700001702031700002202048700002302070700002202093700002202115700002102137700002402158700002302182700002002205700002402225700002002249700002102269700002302290700002402313700002402337700001902361700001902380700002002399700001902419700002502438700002302463700002402486700002002510700002302530700001602553700002202569700002002591700002002611700001702631700002202648700001802670700002402688700002402712700002302736700002402759700001902783700001502802700002302817700002502840700002502865700002202890700002402912700001902936700002602955700002602981700002103007700002103028700002203049700002303071700002003094700002703114700002103141700002003162700002103182700001903203700002003222700002303242700002103265856003603286 2021 eng d a1476-625600aA System for Phenotype Harmonization in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program.0 aSystem for Phenotype Harmonization in the NHLBI TransOmics for P c2021 Apr 163 aGenotype-phenotype association studies often combine phenotype data from multiple studies to increase power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data sharing mechanisms. This system was developed for the National Heart, Lung and Blood Institute's Trans-Omics for Precision Medicine program, which is generating genomic and other omics data for >80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants from up to 17 studies per phenotype (participants recruited 1948-2012). We discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled-access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include (1) the code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify or extend these harmonizations to additional studies; and (2) results of labeling thousands of phenotype variables with controlled vocabulary terms.
1 aStilp, Adrienne, M1 aEmery, Leslie, S1 aBroome, Jai, G1 aButh, Erin, J1 aKhan, Alyna, T1 aLaurie, Cecelia, A1 aWang, Fei, Fei1 aWong, Quenna1 aChen, Dongquan1 aD'Augustine, Catherine, M1 aHeard-Costa, Nancy, L1 aHohensee, Chancellor, R1 aJohnson, William, Craig1 aJuarez, Lucia, D1 aLiu, Jingmin1 aMutalik, Karen, M1 aRaffield, Laura, M1 aWiggins, Kerri, L1 ade Vries, Paul, S1 aKelly, Tanika, N1 aKooperberg, Charles1 aNatarajan, Pradeep1 aPeloso, Gina, M1 aPeyser, Patricia, A1 aReiner, Alex, P1 aArnett, Donna, K1 aAslibekyan, Stella1 aBarnes, Kathleen, C1 aBielak, Lawrence, F1 aBis, Joshua, C1 aCade, Brian, E1 aChen, Ming-Huei1 aCorrea, Adolfo1 aCupples, Adrienne, L1 ade Andrade, Mariza1 aEllinor, Patrick, T1 aFornage, Myriam1 aFranceschini, Nora1 aGan, Weiniu1 aGanesh, Santhi, K1 aGraffelman, Jan1 aGrove, Megan, L1 aGuo, Xiuqing1 aHawley, Nicola, L1 aHsu, Wan-Ling1 aJackson, Rebecca, D1 aJaquish, Cashell, E1 aJohnson, Andrew, D1 aKardia, Sharon, L R1 aKelly, Shannon1 aLee, Jiwon1 aMathias, Rasika, A1 aMcGarvey, Stephen, T1 aMitchell, Braxton, D1 aMontasser, May, E1 aMorrison, Alanna, C1 aNorth, Kari, E1 aNouraie, Seyed, Mehdi1 aOelsner, Elizabeth, C1 aPankratz, Nathan1 aRich, Stephen, S1 aRotter, Jerome, I1 aSmith, Jennifer, A1 aTaylor, Kent, D1 aVasan, Ramachandran, S1 aWeeks, Daniel, E1 aWeiss, Scott, T1 aWilson, Carla, G1 aYanek, Lisa, R1 aPsaty, Bruce, M1 aHeckbert, Susan, R1 aLaurie, Cathy, C uhttps://chs-nhlbi.org/node/871304364nas a2200997 4500008004100000022001400041245016200055210006900217260001300286300001100299490000700310520151100317100002001828700002201848700002301870700002301893700002301916700002601939700002501965700002001990700002102010700002602031700001702057700002502074700002202099700001702121700001702138700002002155700002002175700002302195700001702218700002102235700001802256700001802274700001902292700001202311700001802323700001502341700002302356700002802379700001802407700002002425700002002445700002102465700002302486700002002509700002102529700002302550700001802573700002202591700002302613700002202636700001802658700002002676700002302696700002302719700001902742700002002761700001402781700002002795700002202815700002002837700002102857700002102878700001702899700002402916700001902940700002502959700001402984700002302998700002403021700002303045700002503068700002603093700002103119700002703140700002203167700001703189700002103206700001903227700002103246700001603267700002403283700002303307856003603330 2021 eng d a2352-396400aWhole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium.0 aWhole genome sequence analyses of eGFR in 23732 people represent c2021 Jan a1031570 v633 aBACKGROUND: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants.
METHODS: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity.
FINDINGS: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants.
INTERPRETATION: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.
1 aLin, Bridget, M1 aGrinde, Kelsey, E1 aBrody, Jennifer, A1 aBreeze, Charles, E1 aRaffield, Laura, M1 aMychaleckyj, Josyf, C1 aThornton, Timothy, A1 aPerry, James, A1 aBaier, Leslie, J1 aFuentes, Lisa, de Las1 aGuo, Xiuqing1 aHeavner, Benjamin, D1 aHanson, Robert, L1 aHung, Yi-Jen1 aQian, Huijun1 aHsiung, Chao, A1 aHwang, Shih-Jen1 aIrvin, Margaret, R1 aJain, Deepti1 aKelly, Tanika, N1 aKobes, Sayuko1 aLange, Leslie1 aLash, James, P1 aLi, Yun1 aLiu, Xiaoming1 aMi, Xuenan1 aMusani, Solomon, K1 aPapanicolaou, George, J1 aParsa, Afshin1 aReiner, Alex, P1 aSalimi, Shabnam1 aSheu, Wayne, H-H1 aShuldiner, Alan, R1 aTaylor, Kent, D1 aSmith, Albert, V1 aSmith, Jennifer, A1 aTin, Adrienne1 aVaidya, Dhananjay1 aWallace, Robert, B1 aYamamoto, Kenichi1 aSakaue, Saori1 aMatsuda, Koichi1 aKamatani, Yoichiro1 aMomozawa, Yukihide1 aYanek, Lisa, R1 aYoung, Betsi, A1 aZhao, Wei1 aOkada, Yukinori1 aAbecasis, Gonzalo1 aPsaty, Bruce, M1 aArnett, Donna, K1 aBoerwinkle, Eric1 aCai, Jianwen1 aDer Chen, Ida, Yii-1 aCorrea, Adolfo1 aCupples, Adrienne, L1 aHe, Jiang1 aKardia, Sharon, Lr1 aKooperberg, Charles1 aMathias, Rasika, A1 aMitchell, Braxton, D1 aNickerson, Deborah, A1 aTurner, Steve, T1 aVasan, Ramachandran, S1 aRotter, Jerome, I1 aLevy, Daniel1 aKramer, Holly, J1 aKöttgen, Anna1 aRich, Stephen, S1 aLin, Dan-Yu1 aBrowning, Sharon, R1 aFranceschini, Nora uhttps://chs-nhlbi.org/node/866404419nas a2200925 4500008004100000022001400041245011400055210006900169260001600238520175700254100002002011700001202031700001402043700001702057700001602074700002002090700002102110700002002131700002302151700002502174700002302199700001902222700002002241700001902261700002002280700002302300700002002323700002102343700002002364700002302384700001902407700001402426700002002440700001902460700002502479700001802504700002002522700002102542700001902563700002102582700002502603700002002628700001902648700002202667700002002689700002002709700002002729700001602749700002002765700002402785700002102809700002702830700002002857700001502877700002502892700002402917700002202941700001902963700002602982700002103008700002003029700002703049700002103076700002203097700002103119700002303140700001403163700002203177700002403199700002303223700002303246700001203269700001803281700002203299700002503321700002303346700002303369710006503392856003603457 2021 eng d a1460-208300aWhole genome sequence analysis of platelet traits in the NHLBI trans-omics for precision medicine initiative.0 aWhole genome sequence analysis of platelet traits in the NHLBI t c2021 Sep 063 aPlatelets play a key role in thrombosis and hemostasis. Platelet count (PLT) and mean platelet volume (MPV) are highly heritable quantitative traits, with hundreds of genetic signals previously identified, mostly in European ancestry populations. We here utilize whole genome sequencing from NHLBI's Trans-Omics for Precision Medicine Initiative (TOPMed) in a large multi-ethnic sample to further explore common and rare variation contributing to PLT (n = 61 200) and MPV (n = 23 485). We identified and replicated secondary signals at MPL (rs532784633) and PECAM1 (rs73345162), both more common in African ancestry populations. We also observed rare variation in Mendelian platelet related disorder genes influencing variation in platelet traits in TOPMed cohorts (not enriched for blood disorders). For example, association of GP9 with lower PLT and higher MPV was partly driven by a pathogenic Bernard-Soulier syndrome variant (rs5030764, p.Asn61Ser), and the signals at TUBB1 and CD36 were partly driven by loss of function variants not annotated as pathogenic in ClinVar (rs199948010 and rs571975065). However, residual signal remained for these gene-based signals after adjusting for lead variants, suggesting that additional variants in Mendelian genes with impacts in general population cohorts remain to be identified. Gene-based signals were also identified at several GWAS identified loci for genes not annotated for Mendelian platelet disorders (PTPRH, TET2, CHEK2), with somatic variation driving the result at TET2. These results highlight the value of whole genome sequencing in populations of diverse genetic ancestry to identify novel regulatory and coding signals, even for well-studied traits like platelet traits.
1 aLittle, Amarise1 aHu, Yao1 aSun, Quan1 aJain, Deepti1 aBroome, Jai1 aChen, Ming-Huei1 aThibord, Florian1 aMcHugh, Caitlin1 aSurendran, Praveen1 aBlackwell, Thomas, W1 aBrody, Jennifer, A1 aBhan, Arunoday1 aChami, Nathalie1 aVries, Paul, S1 aEkunwe, Lynette1 aHeard-Costa, Nancy1 aHobbs, Brian, D1 aManichaikul, Ani1 aMoon, Jee-Young1 aPreuss, Michael, H1 aRyan, Kathleen1 aWang, Zhe1 aWheeler, Marsha1 aYanek, Lisa, R1 aAbecasis, Goncalo, R1 aAlmasy, Laura1 aBeaty, Terri, H1 aBecker, Lewis, C1 aBlangero, John1 aBoerwinkle, Eric1 aButterworth, Adam, S1 aChoquet, Helene1 aCorrea, Adolfo1 aCurran, Joanne, E1 aFaraday, Nauder1 aFornage, Myriam1 aGlahn, David, C1 aHou, Lifang1 aJorgenson, Eric1 aKooperberg, Charles1 aLewis, Joshua, P1 aLloyd-Jones, Donald, M1 aLoos, Ruth, J F1 aMin, Nancy1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aNickerson, Debbie1 aNorth, Kari, E1 aO'Connell, Jeffrey, R1 aPankratz, Nathan1 aPsaty, Bruce, M1 aVasan, Ramachandran, S1 aRich, Stephen, S1 aRotter, Jerome, I1 aSmith, Albert, V1 aSmith, Nicholas, L1 aTang, Hua1 aTracy, Russell, P1 aConomos, Matthew, P1 aLaurie, Cecelia, A1 aMathias, Rasika, A1 aLi, Yun1 aAuer, Paul, L1 aThornton, Timothy1 aReiner, Alexander, P1 aJohnson, Andrew, D1 aRaffield, Laura, M1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/891303828nas a2200625 4500008004100000022001400041245010800055210006900163260001500232300000800247490000700255520201900262100001902281700001502300700001702315700001702332700001502349700001402364700002002378700002402398700001702422700002402439700002002463700001602483700001302499700002102512700002202533700001802555700001902573700002102592700002002613700002202633700002202655700002002677700002002697700002302717700002502740700002402765700001902789700002502808700002202833700002602855700001902881700002002900700002202920700002102942700002202963700002702985700002103012700001803033700001903051710006503070710003103135856003603166 2021 eng d a1756-994X00aWhole-genome association analyses of sleep-disordered breathing phenotypes in the NHLBI TOPMed program.0 aWholegenome association analyses of sleepdisordered breathing ph c2021 08 26 a1360 v133 aBACKGROUND: Sleep-disordered breathing is a common disorder associated with significant morbidity. The genetic architecture of sleep-disordered breathing remains poorly understood. Through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, we performed the first whole-genome sequence analysis of sleep-disordered breathing.
METHODS: The study sample was comprised of 7988 individuals of diverse ancestry. Common-variant and pathway analyses included an additional 13,257 individuals. We examined five complementary traits describing different aspects of sleep-disordered breathing: the apnea-hypopnea index, average oxyhemoglobin desaturation per event, average and minimum oxyhemoglobin saturation across the sleep episode, and the percentage of sleep with oxyhemoglobin saturation < 90%. We adjusted for age, sex, BMI, study, and family structure using MMSKAT and EMMAX mixed linear model approaches. Additional bioinformatics analyses were performed with MetaXcan, GIGSEA, and ReMap.
RESULTS: We identified a multi-ethnic set-based rare-variant association (p = 3.48 × 10) on chromosome X with ARMCX3. Additional rare-variant associations include ARMCX3-AS1, MRPS33, and C16orf90. Novel common-variant loci were identified in the NRG1 and SLC45A2 regions, and previously associated loci in the IL18RAP and ATP2B4 regions were associated with novel phenotypes. Transcription factor binding site enrichment identified associations with genes implicated with respiratory and craniofacial traits. Additional analyses identified significantly associated pathways.
CONCLUSIONS: We have identified the first gene-based rare-variant associations with objectively measured sleep-disordered breathing traits. Our results increase the understanding of the genetic architecture of sleep-disordered breathing and highlight associations in genes that modulate lung development, inflammation, respiratory rhythmogenesis, and HIF1A-mediated hypoxic response.
1 aCade, Brian, E1 aLee, Jiwon1 aSofer, Tamar1 aWang, Heming1 aZhang, Man1 aChen, Han1 aGharib, Sina, A1 aGottlieb, Daniel, J1 aGuo, Xiuqing1 aLane, Jacqueline, M1 aLiang, Jingjing1 aLin, Xihong1 aMei, Hao1 aPatel, Sanjay, R1 aPurcell, Shaun, M1 aSaxena, Richa1 aShah, Neomi, A1 aEvans, Daniel, S1 aHanis, Craig, L1 aHillman, David, R1 aMukherjee, Sutapa1 aPalmer, Lyle, J1 aStone, Katie, L1 aTranah, Gregory, J1 aAbecasis, Goncalo, R1 aBoerwinkle, Eric, A1 aCorrea, Adolfo1 aCupples, Adrienne, L1 aKaplan, Robert, C1 aNickerson, Deborah, A1 aNorth, Kari, E1 aPsaty, Bruce, M1 aRotter, Jerome, I1 aRich, Stephen, S1 aTracy, Russell, P1 aVasan, Ramachandran, S1 aWilson, James, G1 aZhu, Xiaofeng1 aRedline, Susan1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aTOPMed Sleep Working Group uhttps://chs-nhlbi.org/node/892005743nas a2201309 4500008004100000022001400041245011800055210006900173260001500242300001200257490000800269520205100277653001002328653000902338653003202347653002202379653001702401653001102418653001702429653002202446653003402468653001702502653001102519653000902530653001602539653005302555653001402608653002002622653003102642653001802673100001202691700002302703700002302726700001702749700001702766700001802783700001502801700003602816700002302852700002002875700001902895700002002914700001302934700001702947700001602964700002002980700002203000700002003022700001403042700002303056700002303079700002503102700002003127700001903147700002803166700002303194700001403217700002003231700002303251700001503274700002003289700002103309700001803330700002003348700002203368700001803390700001803408700002103426700002003447700002203467700002003489700002703509700002703536700002303563700001903586700002103605700002103626700002103647700002503668700001603693700002603709700002403735700002003759700001903779700001903798700001903817700002003836700002003856700002403876700002303900700002903923700001403952700002003966700002003986700002504006700002204031700002104053700002504074700002304099700001804122700001204140700002304152700002204175700002104197700002104218700002304239700002104262700002404283700002504307710006504332856003604397 2021 eng d a1537-660500aWhole-genome sequencing association analysis of quantitative red blood cell phenotypes: The NHLBI TOPMed program.0 aWholegenome sequencing association analysis of quantitative red c2021 05 06 a874-8930 v1083 aWhole-genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci, which have not been reported previously. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, and G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3 bp indel p.Lys2169del (g.88717175_88717177TCT[4]) (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis (MIM: 194380), associated with higher mean corpuscular hemoglobin concentration (MCHC). In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.
10aAdult10aAged10aChromosomes, Human, Pair 1610aDatasets as Topic10aErythrocytes10aFemale10aGene Editing10aGenetic Variation10aGenome-Wide Association Study10aHEK293 Cells10aHumans10aMale10aMiddle Aged10aNational Heart, Lung, and Blood Institute (U.S.)10aPhenotype10aQuality Control10aReproducibility of Results10aUnited States1 aHu, Yao1 aStilp, Adrienne, M1 aMcHugh, Caitlin, P1 aRao, Shuquan1 aJain, Deepti1 aZheng, Xiuwen1 aLane, John1 ade Bellefon, Sébastian, Méric1 aRaffield, Laura, M1 aChen, Ming-Huei1 aYanek, Lisa, R1 aWheeler, Marsha1 aYao, Yao1 aRen, Chunyan1 aBroome, Jai1 aMoon, Jee-Young1 ade Vries, Paul, S1 aHobbs, Brian, D1 aSun, Quan1 aSurendran, Praveen1 aBrody, Jennifer, A1 aBlackwell, Thomas, W1 aChoquet, Helene1 aRyan, Kathleen1 aDuggirala, Ravindranath1 aHeard-Costa, Nancy1 aWang, Zhe1 aChami, Nathalie1 aPreuss, Michael, H1 aMin, Nancy1 aEkunwe, Lynette1 aLange, Leslie, A1 aCushman, Mary1 aFaraday, Nauder1 aCurran, Joanne, E1 aAlmasy, Laura1 aKundu, Kousik1 aSmith, Albert, V1 aGabriel, Stacey1 aRotter, Jerome, I1 aFornage, Myriam1 aLloyd-Jones, Donald, M1 aVasan, Ramachandran, S1 aSmith, Nicholas, L1 aNorth, Kari, E1 aBoerwinkle, Eric1 aBecker, Lewis, C1 aLewis, Joshua, P1 aAbecasis, Goncalo, R1 aHou, Lifang1 aO'Connell, Jeffrey, R1 aMorrison, Alanna, C1 aBeaty, Terri, H1 aKaplan, Robert1 aCorrea, Adolfo1 aBlangero, John1 aJorgenson, Eric1 aPsaty, Bruce, M1 aKooperberg, Charles1 aWalton, Russell, T1 aKleinstiver, Benjamin, P1 aTang, Hua1 aLoos, Ruth, J F1 aSoranzo, Nicole1 aButterworth, Adam, S1 aNickerson, Debbie1 aRich, Stephen, S1 aMitchell, Braxton, D1 aJohnson, Andrew, D1 aAuer, Paul, L1 aLi, Yun1 aMathias, Rasika, A1 aLettre, Guillaume1 aPankratz, Nathan1 aLaurie, Cathy, C1 aLaurie, Cecelia, A1 aBauer, Daniel, E1 aConomos, Matthew, P1 aReiner, Alexander, P1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/877905763nas a2201477 4500008004100000022001400041245012500055210006900180260001500249300001400264490000800278520153200286653001101818653001501829653002301844653003801867653001801905653003401923653001101957653001501968653005301983653001402036653003602050653001402086653001302100653004302113653002802156653001902184653001802203653002802221100002402249700002302273700002102296700002302317700002902340700002502369700002302394700001602417700002002433700002002453700002402473700001502497700002202512700001902534700002002553700002002573700002302593700002502616700002002641700001802661700001702679700002102696700002802717700001502745700002002760700002302780700002002803700001902823700002102842700001402863700002302877700002202900700002002922700001402942700002002956700001902976700001502995700002503010700001803035700002403053700002003077700002103097700001903118700002103137700002503158700001903183700002003202700002003222700001903242700001503261700001903276700002003295700002003315700002403335700001603359700002303375700002003398700001903418700002403437700001803461700002303479700002203502700002103524700001703545700001203562700002703574700002003601700002103621700002303642700002503665700002403690700001503714700002603729700002303755700001903778700002603797700002203823700002103845700002003866700002003886700002103906700002003927700002203947700002403969700002303993700001404016700002204030700002504052700002704077700001404104700002304118700002504141700001804166710006504184856003604249 2021 eng d a1537-660500aWhole-genome sequencing in diverse subjects identifies genetic correlates of leukocyte traits: The NHLBI TOPMed program.0 aWholegenome sequencing in diverse subjects identifies genetic co c2021 10 07 a1836-18510 v1083 aMany common and rare variants associated with hematologic traits have been discovered through imputation on large-scale reference panels. However, the majority of genome-wide association studies (GWASs) have been conducted in Europeans, and determining causal variants has proved challenging. We performed a GWAS of total leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts generated from 109,563,748 variants in the autosomes and the X chromosome in the Trans-Omics for Precision Medicine (TOPMed) program, which included data from 61,802 individuals of diverse ancestry. We discovered and replicated 7 leukocyte trait associations, including (1) the association between a chromosome X, pseudo-autosomal region (PAR), noncoding variant located between cytokine receptor genes (CSF2RA and CLRF2) and lower eosinophil count; and (2) associations between single variants found predominantly among African Americans at the S1PR3 (9q22.1) and HBB (11p15.4) loci and monocyte and lymphocyte counts, respectively. We further provide evidence indicating that the newly discovered eosinophil-lowering chromosome X PAR variant might be associated with reduced susceptibility to common allergic diseases such as atopic dermatitis and asthma. Additionally, we found a burden of very rare FLT3 (13q12.2) variants associated with monocyte counts. Together, these results emphasize the utility of whole-genome sequencing in diverse samples in identifying associations missed by European-ancestry-driven GWASs.
10aAsthma10aBiomarkers10aDermatitis, Atopic10aGenetic Predisposition to Disease10aGenome, Human10aGenome-Wide Association Study10aHumans10aLeukocytes10aNational Heart, Lung, and Blood Institute (U.S.)10aPhenotype10aPolymorphism, Single Nucleotide10aPrognosis10aProteome10aPulmonary Disease, Chronic Obstructive10aQuantitative Trait Loci10aUnited Kingdom10aUnited States10aWhole Genome Sequencing1 aMikhaylova, Anna, V1 aMcHugh, Caitlin, P1 aPolfus, Linda, M1 aRaffield, Laura, M1 aBoorgula, Meher, Preethi1 aBlackwell, Thomas, W1 aBrody, Jennifer, A1 aBroome, Jai1 aChami, Nathalie1 aChen, Ming-Huei1 aConomos, Matthew, P1 aCox, Corey1 aCurran, Joanne, E1 aDaya, Michelle1 aEkunwe, Lynette1 aGlahn, David, C1 aHeard-Costa, Nancy1 aHighland, Heather, M1 aHobbs, Brian, D1 aIlboudo, Yann1 aJain, Deepti1 aLange, Leslie, A1 aMiller-Fleming, Tyne, W1 aMin, Nancy1 aMoon, Jee-Young1 aPreuss, Michael, H1 aRosen, Jonathon1 aRyan, Kathleen1 aSmith, Albert, V1 aSun, Quan1 aSurendran, Praveen1 ade Vries, Paul, S1 aWalter, Klaudia1 aWang, Zhe1 aWheeler, Marsha1 aYanek, Lisa, R1 aZhong, Xue1 aAbecasis, Goncalo, R1 aAlmasy, Laura1 aBarnes, Kathleen, C1 aBeaty, Terri, H1 aBecker, Lewis, C1 aBlangero, John1 aBoerwinkle, Eric1 aButterworth, Adam, S1 aChavan, Sameer1 aCho, Michael, H1 aChoquet, Helene1 aCorrea, Adolfo1 aCox, Nancy1 aDeMeo, Dawn, L1 aFaraday, Nauder1 aFornage, Myriam1 aGerszten, Robert, E1 aHou, Lifang1 aJohnson, Andrew, D1 aJorgenson, Eric1 aKaplan, Robert1 aKooperberg, Charles1 aKundu, Kousik1 aLaurie, Cecelia, A1 aLettre, Guillaume1 aLewis, Joshua, P1 aLi, Bingshan1 aLi, Yun1 aLloyd-Jones, Donald, M1 aLoos, Ruth, J F1 aManichaikul, Ani1 aMeyers, Deborah, A1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aNgo, Debby1 aNickerson, Deborah, A1 aNongmaithem, Suraj1 aNorth, Kari, E1 aO'Connell, Jeffrey, R1 aOrtega, Victor, E1 aPankratz, Nathan1 aPerry, James, A1 aPsaty, Bruce, M1 aRich, Stephen, S1 aSoranzo, Nicole1 aRotter, Jerome, I1 aSilverman, Edwin, K1 aSmith, Nicholas, L1 aTang, Hua1 aTracy, Russell, P1 aThornton, Timothy, A1 aVasan, Ramachandran, S1 aZein, Joe1 aMathias, Rasika, A1 aReiner, Alexander, P1 aAuer, Paul, L1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/891403404nas a2200757 4500008004100000022001400041245011100055210006900166260001300235300001200248490000700260520115000267100002601417700001701443700001701460700002501477700002401502700002201526700002701548700002501575700002001600700002401620700001801644700002501662700001501687700002301702700001901725700002701744700002101771700002401792700002101816700002201837700002601859700001901885700002301904700002401927700002801951700001901979700002001998700002002018700002302038700002402061700002702085700002302112700002002135700002102155700002202176700002302198700001902221700001702240700002002257700002302277700002302300700002502323700002402348700002102372700001902393700002102412700001902433700001602452700001502468700002302483710003902506710006502545856003602610 2022 eng d a1546-171800aAssessing the contribution of rare variants to complex trait heritability from whole-genome sequence data.0 aAssessing the contribution of rare variants to complex trait her c2022 Mar a263-2730 v543 aAnalyses of data from genome-wide association studies on unrelated individuals have shown that, for human traits and diseases, approximately one-third to two-thirds of heritability is captured by common SNPs. However, it is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular whether the causal variants are rare, or whether it is overestimated due to bias in inference from pedigree data. Here we estimated heritability for height and body mass index (BMI) from whole-genome sequence data on 25,465 unrelated individuals of European ancestry. The estimated heritability was 0.68 (standard error 0.10) for height and 0.30 (standard error 0.10) for body mass index. Low minor allele frequency variants in low linkage disequilibrium (LD) with neighboring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection. Our results imply that rare variants, in particular those in regions of low linkage disequilibrium, are a major source of the still missing heritability of complex traits and disease.
1 aWainschtein, Pierrick1 aJain, Deepti1 aZheng, Zhili1 aCupples, Adrienne, L1 aShadyab, Aladdin, H1 aMcKnight, Barbara1 aShoemaker, Benjamin, M1 aMitchell, Braxton, D1 aPsaty, Bruce, M1 aKooperberg, Charles1 aLiu, Ching-Ti1 aAlbert, Christine, M1 aRoden, Dan1 aChasman, Daniel, I1 aDarbar, Dawood1 aLloyd-Jones, Donald, M1 aArnett, Donna, K1 aRegan, Elizabeth, A1 aBoerwinkle, Eric1 aRotter, Jerome, I1 aO'Connell, Jeffrey, R1 aYanek, Lisa, R1 ade Andrade, Mariza1 aAllison, Matthew, A1 aMcDonald, Merry-Lynn, N1 aChung, Mina, K1 aFornage, Myriam1 aChami, Nathalie1 aSmith, Nicholas, L1 aEllinor, Patrick, T1 aVasan, Ramachandran, S1 aMathias, Rasika, A1 aLoos, Ruth, J F1 aRich, Stephen, S1 aLubitz, Steven, A1 aHeckbert, Susan, R1 aRedline, Susan1 aGuo, Xiuqing1 aChen, Y, -D Ida1 aLaurie, Cecelia, A1 aHernandez, Ryan, D1 aMcGarvey, Stephen, T1 aGoddard, Michael, E1 aLaurie, Cathy, C1 aNorth, Kari, E1 aLange, Leslie, A1 aWeir, Bruce, S1 aYengo, Loic1 aYang, Jian1 aVisscher, Peter, M1 aTOPMed Anthropometry Working Group1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/904203135nas a2200529 4500008004100000022001400041245006700055210006600122260001300188300001200201490000700213520159100220100002401811700002401835700002201859700002001881700002001901700002201921700002301943700002201966700001301988700002402001700002102025700002402046700002002070700002702090700001902117700002102136700002202157700002202179700001902201700002002220700002002240700002202260700002402282700002102306700002802327700002402355700002302379700002402402700002102426700002102447700002302468700002502491710005302516856003602569 2022 eng d a1524-462800aClonal Hematopoiesis Is Associated With Higher Risk of Stroke.0 aClonal Hematopoiesis Is Associated With Higher Risk of Stroke c2022 Mar a788-7970 v533 aBACKGROUND AND PURPOSE: Clonal hematopoiesis of indeterminate potential (CHIP) is a novel age-related risk factor for cardiovascular disease-related morbidity and mortality. The association of CHIP with risk of incident ischemic stroke was reported previously in an exploratory analysis including a small number of incident stroke cases without replication and lack of stroke subphenotyping. The purpose of this study was to discover whether CHIP is a risk factor for ischemic or hemorrhagic stroke.
METHODS: We utilized plasma genome sequence data of blood DNA to identify CHIP in 78 752 individuals from 8 prospective cohorts and biobanks. We then assessed the association of CHIP and commonly mutated individual CHIP driver genes (, , and ) with any stroke, ischemic stroke, and hemorrhagic stroke.
RESULTS: CHIP was associated with an increased risk of total stroke (hazard ratio, 1.14 [95% CI, 1.03-1.27]; =0.01) after adjustment for age, sex, and race. We observed associations with CHIP with risk of hemorrhagic stroke (hazard ratio, 1.24 [95% CI, 1.01-1.51]; =0.04) and with small vessel ischemic stroke subtypes. In gene-specific association results, showed the strongest association with total stroke and ischemic stroke, whereas and were each associated with increased risk of hemorrhagic stroke.
CONCLUSIONS: CHIP is associated with an increased risk of stroke, particularly with hemorrhagic and small vessel ischemic stroke. Future studies clarifying the relationship between CHIP and subtypes of stroke are needed.
1 aBhattacharya, Romit1 aZekavat, Seyedeh, M1 aHaessler, Jeffrey1 aFornage, Myriam1 aRaffield, Laura1 aUddin, Md, Mesbah1 aBick, Alexander, G1 aNiroula, Abhishek1 aYu, Bing1 aGibson, Christopher1 aGriffin, Gabriel1 aMorrison, Alanna, C1 aPsaty, Bruce, M1 aLongstreth, William, T1 aBis, Joshua, C1 aRich, Stephen, S1 aRotter, Jerome, I1 aTracy, Russell, P1 aCorrea, Adolfo1 aSeshadri, Sudha1 aJohnson, Andrew1 aCollins, Jason, M1 aHayden, Kathleen, M1 aMadsen, Tracy, E1 aBallantyne, Christie, M1 aJaiswal, Siddhartha1 aEbert, Benjamin, L1 aKooperberg, Charles1 aManson, JoAnn, E1 aWhitsel, Eric, A1 aNatarajan, Pradeep1 aReiner, Alexander, P1 aNHLBI Trans-Omics for Precision Medicine Program uhttps://chs-nhlbi.org/node/898602548nas a2200469 4500008004100000022001400041245010300055210006900158260001600227300001100243490000600254520119300260653001101453653002301464653001101487653000901498653001401507100002001521700002401541700001801565700001401583700002401597700002201621700001601643700002401659700001701683700002301700700002001723700002101743700002201764700002701786700002001813700001901833700002601852700002701878700002001905700002201925700001901947700001701966710005901983856003602042 2022 eng d a2666-379100aCorrelations between complex human phenotypes vary by genetic background, gender, and environment.0 aCorrelations between complex human phenotypes vary by genetic ba c2022 Dec 20 a1008440 v33 aWe develop a closed-form Haseman-Elston estimator for genetic and environmental correlation coefficients between complex phenotypes, which we term HEc, that is as precise as GCTA yet ∼20× faster. We estimate genetic and environmental correlations between over 7,000 phenotype pairs in subgroups from the Trans-Omics in Precision Medicine (TOPMed) program. We demonstrate substantial differences in both heritabilities and genetic correlations for multiple phenotypes and phenotype pairs between individuals of self-reported Black, Hispanic/Latino, and White backgrounds. We similarly observe differences in many of the genetic and environmental correlations between genders. To estimate the contribution of genetics to the observed phenotypic correlation, we introduce "fractional genetic correlation" as the fraction of phenotypic correlation explained by genetics. Finally, we quantify the enrichment of correlations between phenotypic domains, each of which is comprised of multiple phenotypes. Altogether, we demonstrate that the observed correlations between complex human phenotypes depend on the genetic background of the individuals, their gender, and their environment.
10aFemale10aGenetic Background10aHumans10aMale10aPhenotype1 aElgart, Michael1 aGoodman, Matthew, O1 aIsasi, Carmen1 aChen, Han1 aMorrison, Alanna, C1 ade Vries, Paul, S1 aXu, Huichun1 aManichaikul, Ani, W1 aGuo, Xiuqing1 aFranceschini, Nora1 aPsaty, Bruce, M1 aRich, Stephen, S1 aRotter, Jerome, I1 aLloyd-Jones, Donald, M1 aFornage, Myriam1 aCorrea, Adolfo1 aHeard-Costa, Nancy, L1 aVasan, Ramachandran, S1 aHernandez, Ryan1 aKaplan, Robert, C1 aRedline, Susan1 aSofer, Tamar1 aTrans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/924606322nas a2201285 4500008004100000022001400041245007900055210006900134260001600203300001400219490000800233520251800241653003802759653003402797653001302831653001102844653003602855653002802891653001502919653002702934100002102961700001802982700002303000700002003023700002203043700002303065700002003088700002003108700002403128700002603152700001803178700002503196700001503221700002303236700002203259700002403281700002203305700002903327700002303356700002003379700002403399700002403423700002003447700001803467700001903485700002003504700001903524700002203543700001403565700003003579700002003609700002303629700002303652700002203675700001703697700002603714700002003740700002203760700001203782700002503794700001403819700001403833700002403847700002403871700002103895700002203916700002303938700002403961700002203985700001804007700002204025700002004047700002004067700002304087700002504110700001804135700002104153700001904174700001804193700002604211700002004237700002304257700002504280700002104305700002504326700002204351700002004373700003004393700002704423700001704450700002104467700002004488700002004508700002604528700002104554700001904575700002104594700001704615700001804632700002404650700002404674700002904698700002504727700003204752700002304784700002304807700002304830710014704853856003605000 2022 eng d a1524-453900aCross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors.0 aCrossAncestry Investigation of Venous Thromboembolism Genomic Pr c2022 Oct 18 a1225-12420 v1463 aBACKGROUND: Venous thromboembolism (VTE) is a life-threatening vascular event with environmental and genetic determinants. Recent VTE genome-wide association studies (GWAS) meta-analyses involved nearly 30 000 VTE cases and identified up to 40 genetic loci associated with VTE risk, including loci not previously suspected to play a role in hemostasis. The aim of our research was to expand discovery of new genetic loci associated with VTE by using cross-ancestry genomic resources.
METHODS: We present new cross-ancestry meta-analyzed GWAS results involving up to 81 669 VTE cases from 30 studies, with replication of novel loci in independent populations and loci characterization through in silico genomic interrogations.
RESULTS: In our genetic discovery effort that included 55 330 participants with VTE (47 822 European, 6320 African, and 1188 Hispanic ancestry), we identified 48 novel associations, of which 34 were replicated after correction for multiple testing. In our combined discovery-replication analysis (81 669 VTE participants) and ancestry-stratified meta-analyses (European, African, and Hispanic), we identified another 44 novel associations, which are new candidate VTE-associated loci requiring replication. In total, across all GWAS meta-analyses, we identified 135 independent genomic loci significantly associated with VTE risk. A genetic risk score of the significantly associated loci in Europeans identified a 6-fold increase in risk for those in the top 1% of scores compared with those with average scores. We also identified 31 novel transcript associations in transcriptome-wide association studies and 8 novel candidate genes with protein quantitative-trait locus Mendelian randomization analyses. In silico interrogations of hemostasis and hematology traits and a large phenome-wide association analysis of the 135 GWAS loci provided insights to biological pathways contributing to VTE, with some loci contributing to VTE through well-characterized coagulation pathways and others providing new data on the role of hematology traits, particularly platelet function. Many of the replicated loci are outside of known or currently hypothesized pathways to thrombosis.
CONCLUSIONS: Our cross-ancestry GWAS meta-analyses identified new loci associated with VTE. These findings highlight new pathways to thrombosis and provide novel molecules that may be useful in the development of improved antithrombosis treatments.
10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aGenomics10aHumans10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci10aThrombosis10aVenous Thromboembolism1 aThibord, Florian1 aKlarin, Derek1 aBrody, Jennifer, A1 aChen, Ming-Huei1 aLevin, Michael, G1 aChasman, Daniel, I1 aGoode, Ellen, L1 aHveem, Kristian1 aTeder-Laving, Maris1 aMartinez-Perez, Angel1 aAïssi, Dylan1 aDaian-Bacq, Delphine1 aIto, Kaoru1 aNatarajan, Pradeep1 aLutsey, Pamela, L1 aNadkarni, Girish, N1 ade Vries, Paul, S1 aCuellar-Partida, Gabriel1 aWolford, Brooke, N1 aPattee, Jack, W1 aKooperberg, Charles1 aBraekkan, Sigrid, K1 aLi-Gao, Ruifang1 aSaut, Noémie1 aSept, Corriene1 aGermain, Marine1 aJudy, Renae, L1 aWiggins, Kerri, L1 aKo, Darae1 aO'Donnell, Christopher, J1 aTaylor, Kent, D1 aGiulianini, Franco1 ade Andrade, Mariza1 aNøst, Therese, H1 aBoland, Anne1 aEmpana, Jean-Philippe1 aKoyama, Satoshi1 aGilliland, Thomas1 aDo, Ron1 aHuffman, Jennifer, E1 aWang, Xin1 aZhou, Wei1 aSoria, Jose, Manuel1 aSouto, Juan, Carlos1 aPankratz, Nathan1 aHaessler, Jeffery1 aHindberg, Kristian1 aRosendaal, Frits, R1 aTurman, Constance1 aOlaso, Robert1 aKember, Rachel, L1 aBartz, Traci, M1 aLynch, Julie, A1 aHeckbert, Susan, R1 aArmasu, Sebastian, M1 aBrumpton, Ben1 aSmadja, David, M1 aJouven, Xavier1 aKomuro, Issei1 aClapham, Katharine, R1 aLoos, Ruth, J F1 aWiller, Cristen, J1 aSabater-Lleal, Maria1 aPankow, James, S1 aReiner, Alexander, P1 aMorelli, Vania, M1 aRidker, Paul, M1 aVlieg, Astrid, van Hylcka1 aDeleuze, Jean-Francois1 aKraft, Peter1 aRader, Daniel, J1 aLee, Kyung, Min1 aPsaty, Bruce, M1 aSkogholt, Anne, Heidi1 aEmmerich, Joseph1 aSuchon, Pierre1 aRich, Stephen, S1 aVy, Ha, My T1 aTang, Weihong1 aJackson, Rebecca, D1 aHansen, John-Bjarne1 aMorange, Pierre-Emmanuel1 aKabrhel, Christopher1 aTrégouët, David-Alexandre1 aDamrauer, Scott, M1 aJohnson, Andrew, D1 aSmith, Nicholas, L1 aGlobal Biobank Meta-Analysis Initiative; Estonian Biobank Research Team; 23andMe Research Team; Biobank Japan; CHARGE Hemostasis Working Group uhttps://chs-nhlbi.org/node/919403248nas a2200733 4500008004100000022001400041245010400055210006900159260001500228300000900243490000700252520109600259653002701355653001901382653001101401653002101412653001401433100002501447700002401472700002201496700002101518700002101539700002501560700001801585700002001603700002001623700001701643700002901660700002601689700002001715700001901735700002401754700002101778700002301799700001901822700002501841700001901866700002901885700002201914700002301936700002101959700002401980700001802004700002002022700002502042700002402067700001602091700002002107700001902127700002102146700002102167700002202188700002202210700002402232700002202256700002002278700002202298700002302320700002402343700002402367700002202391710006502413856003602478 2022 eng d a2041-172300aEndophenotype effect sizes support variant pathogenicity in monogenic disease susceptibility genes.0 aEndophenotype effect sizes support variant pathogenicity in mono c2022 08 30 a51060 v133 aAccurate and efficient classification of variant pathogenicity is critical for research and clinical care. Using data from three large studies, we demonstrate that population-based associations between rare variants and quantitative endophenotypes for three monogenic diseases (low-density-lipoprotein cholesterol for familial hypercholesterolemia, electrocardiographic QTc interval for long QT syndrome, and glycosylated hemoglobin for maturity-onset diabetes of the young) provide evidence for variant pathogenicity. Effect sizes are associated with pathogenic ClinVar assertions (P < 0.001 for each trait) and discriminate pathogenic from non-pathogenic variants (area under the curve 0.82-0.84 across endophenotypes). An effect size threshold of ≥ 0.5 times the endophenotype standard deviation nominates up to 35% of rare variants of uncertain significance or not in ClinVar in disease susceptibility genes with pathogenic potential. We propose that variant associations with quantitative endophenotypes for monogenic diseases can provide evidence supporting pathogenicity.
10aDisease Susceptibility10aEndophenotypes10aHumans10aLong QT Syndrome10aVirulence1 aHalford, Jennifer, L1 aMorrill, Valerie, N1 aChoi, Seung, Hoan1 aJurgens, Sean, J1 aMelloni, Giorgio1 aMarston, Nicholas, A1 aWeng, Lu-Chen1 aNauffal, Victor1 aHall, Amelia, W1 aGunn, Sophia1 aAustin-Tse, Christina, A1 aPirruccello, James, P1 aKhurshid, Shaan1 aRehm, Heidi, L1 aBenjamin, Emelia, J1 aBoerwinkle, Eric1 aBrody, Jennifer, A1 aCorrea, Adolfo1 aFornwalt, Brandon, K1 aGupta, Namrata1 aHaggerty, Christopher, M1 aHarris, Stephanie1 aHeckbert, Susan, R1 aHong, Charles, C1 aKooperberg, Charles1 aLin, Henry, J1 aLoos, Ruth, J F1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aPost, Wendy1 aPsaty, Bruce, M1 aRedline, Susan1 aRice, Kenneth, M1 aRich, Stephen, S1 aRotter, Jerome, I1 aSchnatz, Peter, F1 aSoliman, Elsayed, Z1 aSotoodehnia, Nona1 aWong, Eugene, K1 aSabatine, Marc, S1 aRuff, Christian, T1 aLunetta, Kathryn, L1 aEllinor, Patrick, T1 aLubitz, Steven, A1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/916704304nas a2201045 4500008004100000022001400041245011400055210006900169260001300238300001400251490000700265520128500272653002201557653001101579653003401590653001101624653001401635653002801649100001401677700001401691700001701705700002201722700003201744700002401776700001801800700001501818700001401833700001401847700001601861700002101877700001801898700002401916700001901940700002501959700001901984700002102003700002202024700002302046700001902069700002402088700001902112700002502131700002202156700002202178700002802200700002302228700002302251700002502274700001702299700002102316700002402337700001902361700002102380700002002401700002102421700002202442700002002464700002302484700002002507700002502527700002202552700002402574700001702598700002602615700002602641700002402667700002002691700002302711700001902734700002502753700003402778700002102812700002102833700002302854700002002877700002202897700002702919700002102946700002102967700001902988700001403007700002203021700002303043700002303066700002003089700001603109710006503125710003203190856003603222 2022 eng d a1548-710500aA framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.0 aframework for detecting noncoding rarevariant associations of la c2022 Dec a1599-16110 v193 aLarge-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.
10aGenetic Variation10aGenome10aGenome-Wide Association Study10aHumans10aPhenotype10aWhole Genome Sequencing1 aLi, Zilin1 aLi, Xihao1 aZhou, Hufeng1 aGaynor, Sheila, M1 aSelvaraj, Margaret, Sunitha1 aArapoglou, Theodore1 aQuick, Corbin1 aLiu, Yaowu1 aChen, Han1 aSun, Ryan1 aDey, Rounak1 aArnett, Donna, K1 aAuer, Paul, L1 aBielak, Lawrence, F1 aBis, Joshua, C1 aBlackwell, Thomas, W1 aBlangero, John1 aBoerwinkle, Eric1 aBowden, Donald, W1 aBrody, Jennifer, A1 aCade, Brian, E1 aConomos, Matthew, P1 aCorrea, Adolfo1 aCupples, Adrienne, L1 aCurran, Joanne, E1 ade Vries, Paul, S1 aDuggirala, Ravindranath1 aFranceschini, Nora1 aFreedman, Barry, I1 aGöring, Harald, H H1 aGuo, Xiuqing1 aKalyani, Rita, R1 aKooperberg, Charles1 aKral, Brian, G1 aLange, Leslie, A1 aLin, Bridget, M1 aManichaikul, Ani1 aManning, Alisa, K1 aMartin, Lisa, W1 aMathias, Rasika, A1 aMeigs, James, B1 aMitchell, Braxton, D1 aMontasser, May, E1 aMorrison, Alanna, C1 aNaseri, Take1 aO'Connell, Jeffrey, R1 aPalmer, Nicholette, D1 aPeyser, Patricia, A1 aPsaty, Bruce, M1 aRaffield, Laura, M1 aRedline, Susan1 aReiner, Alexander, P1 aReupena, Muagututi'a, Sefuiva1 aRice, Kenneth, M1 aRich, Stephen, S1 aSmith, Jennifer, A1 aTaylor, Kent, D1 aTaub, Margaret, A1 aVasan, Ramachandran, S1 aWeeks, Daniel, E1 aWilson, James, G1 aYanek, Lisa, R1 aZhao, Wei1 aRotter, Jerome, I1 aWiller, Cristen, J1 aNatarajan, Pradeep1 aPeloso, Gina, M1 aLin, Xihong1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aTOPMed Lipids Working Group uhttps://chs-nhlbi.org/node/925305976nas a2201393 4500008004100000022001400041245013400055210006900189260001600258300003400274520189200308100002102200700001402221700001702235700002202252700003002274700002502304700002502329700001502354700002302369700002302392700001702415700002002432700002202452700001302474700001902487700002402506700001902530700001802549700002302567700001902590700002602609700001602635700002302651700002402674700002102698700002902719700002202748700001602770700001902786700001802805700002102823700001802844700002602862700002202888700002102910700001802931700001602949700002002965700001902985700001803004700002403022700002503046700002203071700002103093700002203114700001703136700001703153700002003170700002103190700003303211700002403244700001703268700002203285700003403307700002503341700002203366700002103388700002103409700001903430700002203449700002003471700001903491700001403510700002503524700002103549700002503570700001703595700001403612700002303626700001803649700001703667700002203684700002103706700002203727700002403749700002103773700001903794700002203813700002103835700001903856700002603875700002103901700002003922700001903942700001903961700002503980700002004005700002304025700002404048700002204072700002304094700001404117700002004131700002004151700002004171700001904191700002104210700002204231700002404253700002104277700002504298700001804323700001704341700002104358700002404379710014304403856003604546 2022 eng d a1524-456300aInsights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension.0 aInsights From a LargeScale WholeGenome Sequencing Study of Systo c2022 Jun 02 a101161HYPERTENSIONAHA122193243 aBACKGROUND: The availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure.
METHODS: We conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants.
RESULTS: Two blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (<5×10). Among them, a rare intergenic variant at novel locus, , was associated with lower systolic blood pressure in stage-1 (beta [SE]=-32.6 [6.0]; =4.99×10) but not stage-2 analysis (=0.11). Furthermore, a novel common variant at the known locus was suggestively associated with diastolic blood pressure in stage-1 (beta [SE]=-0.36 [0.07]; =4.18×10) and attained genome-wide significance in stage-2 (beta [SE]=-0.29 [0.03]; =7.28×10). Nineteen additional signals suggestively associated with blood pressure in meta-analysis of single and aggregate rare variant findings (<1×10 and <1×10, respectively).
DISCUSSION: We report one promising but unconfirmed rare variant for blood pressure and, more importantly, contribute insights for future blood pressure sequencing studies. Our findings suggest promise of aggregate analyses to complement single variant analysis strategies and the need for larger, diverse samples, and family studies to enable robust rare variant identification.
1 aKelly, Tanika, N1 aSun, Xiao1 aHe, Karen, Y1 aBrown, Michael, R1 aTaliun, Sarah, A Gagliano1 aHellwege, Jacklyn, N1 aIrvin, Marguerite, R1 aMi, Xuenan1 aBrody, Jennifer, A1 aFranceschini, Nora1 aGuo, Xiuqing1 aHwang, Shih-Jen1 ade Vries, Paul, S1 aGao, Yan1 aMoscati, Arden1 aNadkarni, Girish, N1 aYanek, Lisa, R1 aElfassy, Tali1 aSmith, Jennifer, A1 aChung, Ren-Hua1 aBeitelshees, Amber, L1 aPatki, Amit1 aAslibekyan, Stella1 aBlobner, Brandon, M1 aPeralta, Juan, M1 aAssimes, Themistocles, L1 aPalmas, Walter, R1 aLiu, Chunyu1 aBress, Adam, P1 aHuang, Zhijie1 aBecker, Lewis, C1 aHwa, Chii-Min1 aO'Connell, Jeffrey, R1 aCarlson, Jenna, C1 aWarren, Helen, R1 aDas, Sayantan1 aGiri, Ayush1 aMartin, Lisa, W1 aJohnson, Craig1 aFox, Ervin, R1 aBottinger, Erwin, P1 aRazavi, Alexander, C1 aVaidya, Dhananjay1 aChuang, Lee-Ming1 aChang, Yen-Pei, C1 aNaseri, Take1 aJain, Deepti1 aKang, Hyun, Min1 aHung, Adriana, M1 aSrinivasasainagendra, Vinodh1 aSnively, Beverly, M1 aGu, Dongfeng1 aMontasser, May, E1 aReupena, Muagututi'a, Sefuiva1 aHeavner, Benjamin, D1 aLeFaive, Jonathon1 aHixson, James, E1 aRice, Kenneth, M1 aWang, Fei, Fei1 aNielsen, Jonas, B1 aHuang, Jianfeng1 aKhan, Alyna, T1 aZhou, Wei1 aNierenberg, Jovia, L1 aLaurie, Cathy, C1 aArmstrong, Nicole, D1 aShi, Mengyao1 aPan, Yang1 aStilp, Adrienne, M1 aEmery, Leslie1 aWong, Quenna1 aHawley, Nicola, L1 aMinster, Ryan, L1 aCurran, Joanne, E1 aMunroe, Patricia, B1 aWeeks, Daniel, E1 aNorth, Kari, E1 aTracy, Russell, P1 aKenny, Eimear, E1 aShimbo, Daichi1 aChakravarti, Aravinda1 aRich, Stephen, S1 aReiner, Alex, P1 aBlangero, John1 aRedline, Susan1 aMitchell, Braxton, D1 aRao, Dabeeru, C1 aChen, Yii-Der, Ida1 aKardia, Sharon, L R1 aKaplan, Robert, C1 aMathias, Rasika, A1 aHe, Jiang1 aPsaty, Bruce, M1 aFornage, Myriam1 aLoos, Ruth, J F1 aCorrea, Adolfo1 aBoerwinkle, Eric1 aRotter, Jerome, I1 aKooperberg, Charles1 aEdwards, Todd, L1 aAbecasis, Goncalo, R1 aZhu, Xiaofeng1 aLevy, Daniel1 aArnett, Donna, K1 aMorrison, Alanna, C1 aNHLBI Trans-Omics for Precision Medicine TOPMed) Consortium, The Samoan Obesity, Lifestyle, and Genetic Adaptations Study (OLaGA) Group† uhttps://chs-nhlbi.org/node/909903474nas a2200565 4500008004100000022001400041245008100055210006900136260001600205520182300221100002002044700002402064700002102088700002202109700002002131700001802151700002502169700002602194700002902220700002502249700002002274700001902294700001902313700002402332700002102356700001602377700001902393700002502412700002302437700002402460700001802484700002002502700002102522700001902543700002502562700002502587700002402612700002002636700001902656700001902675700001902694700002102713700002202734700002402756700002202780700002402802700002402826700002202850856003602872 2022 eng d a1524-453900aMonogenic and Polygenic Contributions to QTc Prolongation in the Population.0 aMonogenic and Polygenic Contributions to QTc Prolongation in the c2022 Apr 073 aRare sequence variation in genes underlying cardiac repolarization and common polygenic variation influence QT interval duration. However, current clinical genetic testing of individuals with unexplained QT prolongation is restricted to examination of monogenic rare variants. The recent emergence of large-scale biorepositories with sequence data enables examination of the joint contribution of rare and common variation to the QT interval in the population. We performed a genome wide association study (GWAS) of the QTc in 84,630 United Kingdom Biobank (UKB) participants and created a polygenic risk score (PRS). Among 26,976 participants with whole genome sequencing and electrocardiogram data in the Trans-Omics for Precision Medicine (TOPMed) program, we identified 160 carriers of putative pathogenic rare variants in 10 genes known to be associated with the QT interval. We examined QTc associations with the PRS and with rare variants in TOPMed. Fifty-four independent loci were identified by GWAS in the UKB. Twenty-one loci were novel, of which 12 were replicated in TOPMed. The PRS comprising 1,110,494 common variants was significantly associated with the QTc in TOPMed (ΔQTc/ = 1.4 ms, 95% CI 1.3 -1.5; p-value=1.1×10). Carriers of putative pathogenic rare variants had longer QTc than non-carriers (ΔQTc=10.9 ms [7.4-14.4]). 23.7% of individuals with QTc>480 ms carried either a monogenic rare variant or had a PRS in the top decile (3.4% monogenic, 21% top decile of PRS). QTc duration in the population is influenced by both rare variants in genes underlying cardiac repolarization and polygenic risk, with a sizeable contribution from polygenic risk. Comprehensive assessment of the genetic determinants of QTc prolongation includes incorporation of both polygenic and monogenic risk.
1 aNauffal, Victor1 aMorrill, Valerie, N1 aJurgens, Sean, J1 aChoi, Seung, Hoan1 aHall, Amelia, W1 aWeng, Lu-Chen1 aHalford, Jennifer, L1 aAustin-Tse, Christina1 aHaggerty, Christopher, M1 aHarris, Stephanie, L1 aWong, Eugene, K1 aAlonso, Alvaro1 aArking, Dan, E1 aBenjamin, Emelia, J1 aBoerwinkle, Eric1 aMin, Yuan-I1 aCorrea, Adolfo1 aFornwalt, Brandon, K1 aHeckbert, Susan, R1 aKooperberg, Charles1 aLin, Henry, J1 aLoos, Ruth, J F1 aRice, Kenneth, M1 aGupta, Namrata1 aBlackwell, Thomas, W1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aPsaty, Bruce, M1 aPost, Wendy, S1 aRedline, Susan1 aRehm, Heidi, L1 aRich, Stephen, S1 aRotter, Jerome, I1 aSoliman, Elsayed, Z1 aSotoodehnia, Nona1 aLunetta, Kathryn, L1 aEllinor, Patrick, T1 aLubitz, Steven, A uhttps://chs-nhlbi.org/node/903813363nas a2204429 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2022 eng d a1546-171800aMulti-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.0 aMultiancestry genetic study of type 2 diabetes highlights the po c2022 May a560-5720 v543 aWe assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.
10aDiabetes Mellitus, Type 210aEthnicity10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aPolymorphism, Single Nucleotide10aRisk Factors1 aMahajan, Anubha1 aSpracklen, Cassandra, N1 aZhang, Weihua1 aC Y Ng, Maggie1 aPetty, Lauren, E1 aKitajima, Hidetoshi1 aYu, Grace, Z1 aRüeger, Sina1 aSpeidel, Leo1 aKim, Young, Jin1 aHorikoshi, Momoko1 aMercader, Josep, M1 aTaliun, Daniel1 aMoon, Sanghoon1 aKwak, Soo-Heon1 aRobertson, Neil, R1 aRayner, Nigel, W1 aLoh, Marie1 aKim, Bong-Jo1 aChiou, Joshua1 aMiguel-Escalada, Irene1 aParolo, Pietro, Della Brio1 aLin, Kuang1 aBragg, Fiona1 aPreuss, Michael, H1 aTakeuchi, Fumihiko1 aNano, Jana1 aGuo, Xiuqing1 aLamri, Amel1 aNakatochi, Masahiro1 aScott, Robert, A1 aLee, Jung-Jin1 aHuerta-Chagoya, Alicia1 aGraff, Mariaelisa1 aChai, Jin-Fang1 aParra, Esteban, J1 aYao, Jie1 aBielak, Lawrence, F1 aTabara, Yasuharu1 aHai, Yang1 aSteinthorsdottir, Valgerdur1 aCook, James, P1 aKals, Mart1 aGrarup, Niels1 aSchmidt, Ellen, M1 aPan, Ian1 aSofer, Tamar1 aWuttke, Matthias1 aSarnowski, Chloe1 aGieger, Christian1 aNousome, Darryl1 aTrompet, Stella1 aLong, Jirong1 aSun, Meng1 aTong, Lin1 aChen, Wei-Min1 aAhmad, Meraj1 aNoordam, Raymond1 aJ Y Lim, Victor1 aTam, Claudia, H T1 aJoo, Yoonjung, Yoonie1 aChen, Chien-Hsiun1 aRaffield, Laura, M1 aLecoeur, Cécile1 aPrins, Bram, Peter1 aNicolas, Aude1 aYanek, Lisa, R1 aChen, Guanjie1 aJensen, Richard, A1 aTajuddin, Salman1 aKabagambe, Edmond, K1 aAn, Ping1 aXiang, Anny, H1 aChoi, Hyeok, Sun1 aCade, Brian, E1 aTan, Jingyi1 aFlanagan, Jack1 aAbaitua, Fernando1 aAdair, Linda, S1 aAdeyemo, Adebowale1 aAguilar-Salinas, Carlos, A1 aAkiyama, Masato1 aAnand, Sonia, S1 aBertoni, Alain1 aBian, Zheng1 aBork-Jensen, Jette1 aBrandslund, Ivan1 aBrody, Jennifer, A1 aBrummett, Chad, M1 aBuchanan, Thomas, A1 aCanouil, Mickaël1 aChan, Juliana, C N1 aChang, Li-Ching1 aChee, Miao-Li1 aChen, Ji1 aChen, Shyh-Huei1 aChen, Yuan-Tsong1 aChen, Zhengming1 aChuang, Lee-Ming1 aCushman, Mary1 aDas, Swapan, K1 ade Silva, Janaka1 aDedoussis, George1 aDimitrov, Latchezar1 aDoumatey, Ayo, P1 aDu, Shufa1 aDuan, Qing1 aEckardt, Kai-Uwe1 aEmery, Leslie, S1 aEvans, Daniel, S1 aEvans, Michele, K1 aFischer, Krista1 aFloyd, James, S1 aFord, Ian1 aFornage, Myriam1 aFranco, Oscar, H1 aFrayling, Timothy, M1 aFreedman, Barry, I1 aFuchsberger, Christian1 aGenter, Pauline1 aGerstein, Hertzel, C1 aGiedraitis, Vilmantas1 aGonzález-Villalpando, Clicerio1 aGonzalez-Villalpando, Maria, Elena1 aGoodarzi, Mark, O1 aGordon-Larsen, Penny1 aGorkin, David1 aGross, Myron1 aGuo, Yu1 aHackinger, Sophie1 aHan, Sohee1 aHattersley, Andrew, T1 aHerder, Christian1 aHoward, Annie-Green1 aHsueh, Willa1 aHuang, Mengna1 aHuang, Wei1 aHung, Yi-Jen1 aHwang, Mi, Yeong1 aHwu, Chii-Min1 aIchihara, Sahoko1 aIkram, Mohammad, Arfan1 aIngelsson, Martin1 aIslam, Md, Tariqul1 aIsono, Masato1 aJang, Hye-Mi1 aJasmine, Farzana1 aJiang, Guozhi1 aJonas, Jost, B1 aJørgensen, Marit, E1 aJørgensen, Torben1 aKamatani, Yoichiro1 aKandeel, Fouad, R1 aKasturiratne, Anuradhani1 aKatsuya, Tomohiro1 aKaur, Varinderpal1 aKawaguchi, Takahisa1 aKeaton, Jacob, M1 aKho, Abel, N1 aKhor, Chiea-Chuen1 aKibriya, Muhammad, G1 aKim, Duk-Hwan1 aKohara, Katsuhiko1 aKriebel, Jennifer1 aKronenberg, Florian1 aKuusisto, Johanna1 aLäll, Kristi1 aLange, Leslie, A1 aLee, Myung-Shik1 aLee, Nanette, R1 aLeong, Aaron1 aLi, Liming1 aLi, Yun1 aLi-Gao, Ruifang1 aLigthart, Symen1 aLindgren, Cecilia, M1 aLinneberg, Allan1 aLiu, Ching-Ti1 aLiu, Jianjun1 aLocke, Adam, E1 aLouie, Tin1 aLuan, Jian'an1 aLuk, Andrea, O1 aLuo, Xi1 aLv, Jun1 aLyssenko, Valeriya1 aMamakou, Vasiliki1 aMani, Radha, K1 aMeitinger, Thomas1 aMetspalu, Andres1 aMorris, Andrew, D1 aNadkarni, Girish, N1 aNadler, Jerry, L1 aNalls, Michael, A1 aNayak, Uma1 aNongmaithem, Suraj, S1 aNtalla, Ioanna1 aOkada, Yukinori1 aOrozco, Lorena1 aPatel, Sanjay, R1 aPereira, Mark, A1 aPeters, Annette1 aPirie, Fraser, J1 aPorneala, Bianca1 aPrasad, Gauri1 aPreissl, Sebastian1 aRasmussen-Torvik, Laura, J1 aReiner, Alexander, P1 aRoden, Michael1 aRohde, Rebecca1 aRoll, Kathryn1 aSabanayagam, Charumathi1 aSander, Maike1 aSandow, Kevin1 aSattar, Naveed1 aSchönherr, Sebastian1 aSchurmann, Claudia1 aShahriar, Mohammad1 aShi, Jinxiu1 aShin, Dong, Mun1 aShriner, Daniel1 aSmith, Jennifer, A1 aSo, Wing, Yee1 aStančáková, Alena1 aStilp, Adrienne, M1 aStrauch, Konstantin1 aSuzuki, Ken1 aTakahashi, Atsushi1 aTaylor, Kent, D1 aThorand, Barbara1 aThorleifsson, Gudmar1 aThorsteinsdottir, Unnur1 aTomlinson, Brian1 aTorres, Jason, M1 aTsai, Fuu-Jen1 aTuomilehto, Jaakko1 aTusié-Luna, Teresa1 aUdler, Miriam, S1 aValladares-Salgado, Adan1 avan Dam, Rob, M1 avan Klinken, Jan, B1 aVarma, Rohit1 aVujkovic, Marijana1 aWacher-Rodarte, Niels1 aWheeler, Eleanor1 aWhitsel, Eric, A1 aWickremasinghe, Ananda, R1 aDijk, Ko Willems1 aWitte, Daniel, R1 aYajnik, Chittaranjan, S1 aYamamoto, Ken1 aYamauchi, Toshimasa1 aYengo, Loic1 aYoon, Kyungheon1 aYu, Canqing1 aYuan, Jian-Min1 aYusuf, Salim1 aZhang, Liang1 aZheng, Wei1 aRaffel, Leslie, J1 aIgase, Michiya1 aIpp, Eli1 aRedline, Susan1 aCho, Yoon Shin1 aLind, Lars1 aProvince, Michael, A1 aHanis, Craig, L1 aPeyser, Patricia, A1 aIngelsson, Erik1 aZonderman, Alan, B1 aPsaty, Bruce, M1 aWang, Ya-Xing1 aRotimi, Charles, N1 aBecker, Diane, M1 aMatsuda, Fumihiko1 aLiu, Yongmei1 aZeggini, Eleftheria1 aYokota, Mitsuhiro1 aRich, Stephen, S1 aKooperberg, Charles1 aPankow, James, S1 aEngert, James, C1 aChen, Yii-Der Ida1 aFroguel, Philippe1 aWilson, James, G1 aSheu, Wayne, H H1 aKardia, Sharon, L R1 aWu, Jer-Yuarn1 aHayes, Geoffrey1 aMa, Ronald, C W1 aWong, Tien-Yin1 aGroop, Leif1 aMook-Kanamori, Dennis, O1 aChandak, Giriraj, R1 aCollins, Francis, S1 aBharadwaj, Dwaipayan1 aParé, Guillaume1 aSale, Michèle, M1 aAhsan, Habibul1 aMotala, Ayesha, A1 aShu, Xiao-Ou1 aPark, Kyong-Soo1 aJukema, Wouter1 aCruz, Miguel1 aMcKean-Cowdin, Roberta1 aGrallert, Harald1 aCheng, Ching-Yu1 aBottinger, Erwin, P1 aDehghan, Abbas1 aTai, E-Shyong1 aDupuis, Josée1 aKato, Norihiro1 aLaakso, Markku1 aKöttgen, Anna1 aKoh, Woon-Puay1 aPalmer, Colin, N A1 aLiu, Simin1 aAbecasis, Goncalo1 aKooner, Jaspal, S1 aLoos, Ruth, J F1 aNorth, Kari, E1 aHaiman, Christopher, A1 aFlorez, Jose, C1 aSaleheen, Danish1 aHansen, Torben1 aPedersen, Oluf1 aMägi, Reedik1 aLangenberg, Claudia1 aWareham, Nicholas, J1 aMaeda, Shiro1 aKadowaki, Takashi1 aLee, Juyoung1 aMillwood, Iona, Y1 aWalters, Robin, G1 aStefansson, Kari1 aMyers, Simon, R1 aFerrer, Jorge1 aGaulton, Kyle, J1 aMeigs, James, B1 aMohlke, Karen, L1 aGloyn, Anna, L1 aBowden, Donald, W1 aBelow, Jennifer, E1 aChambers, John, C1 aSim, Xueling1 aBoehnke, Michael1 aRotter, Jerome, I1 aMcCarthy, Mark, I1 aMorris, Andrew, P1 aFinnGen1 aeMERGE Consortium uhttps://chs-nhlbi.org/node/910403010nas a2200697 4500008004100000022001400041245012100055210006900176260001600245300000900261490000700270520097500277653001001252653003001262653003801292653003401330653001101364653001701375653003101392653001501423653001701438100002501455700002401480700002101504700001801525700002201543700001901565700001701584700001901601700002001620700001801640700002201658700001301680700001901693700002301712700001301735700002401748700002201772700001401794700002001808700001501828700003201843700002101875700002701896700002101923700002001944700001901964700001901983700002402002700002002026700002202046700002002068700002202088700002102110700002402131700002302155700001702178700001702195710006402212856003602276 2022 eng d a2041-172300aA multi-ethnic polygenic risk score is associated with hypertension prevalence and progression throughout adulthood.0 amultiethnic polygenic risk score is associated with hypertension c2022 Jun 21 a35490 v133 aIn a multi-stage analysis of 52,436 individuals aged 17-90 across diverse cohorts and biobanks, we train, test, and evaluate a polygenic risk score (PRS) for hypertension risk and progression. The PRS is trained using genome-wide association studies (GWAS) for systolic, diastolic blood pressure, and hypertension, respectively. For each trait, PRS is selected by optimizing the coefficient of variation (CV) across estimated effect sizes from multiple potential PRS using the same GWAS, after which the 3 trait-specific PRSs are combined via an unweighted sum called "PRSsum", forming the HTN-PRS. The HTN-PRS is associated with both prevalent and incident hypertension at 4-6 years of follow up. This association is further confirmed in age-stratified analysis. In an independent biobank of 40,201 individuals, the HTN-PRS is confirmed to be predictive of increased risk for coronary artery disease, ischemic stroke, type 2 diabetes, and chronic kidney disease.
10aAdult10aDiabetes Mellitus, Type 210aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aHypertension10aMultifactorial Inheritance10aPrevalence10aRisk Factors1 aKurniansyah, Nuzulul1 aGoodman, Matthew, O1 aKelly, Tanika, N1 aElfassy, Tali1 aWiggins, Kerri, L1 aBis, Joshua, C1 aGuo, Xiuqing1 aPalmas, Walter1 aTaylor, Kent, D1 aLin, Henry, J1 aHaessler, Jeffrey1 aGao, Yan1 aShimbo, Daichi1 aSmith, Jennifer, A1 aYu, Bing1 aFeofanova, Elena, V1 aSmit, Roelof, A J1 aWang, Zhe1 aHwang, Shih-Jen1 aLiu, Simin1 aWassertheil-Smoller, Sylvia1 aManson, JoAnn, E1 aLloyd-Jones, Donald, M1 aRich, Stephen, S1 aLoos, Ruth, J F1 aRedline, Susan1 aCorrea, Adolfo1 aKooperberg, Charles1 aFornage, Myriam1 aKaplan, Robert, C1 aPsaty, Bruce, M1 aRotter, Jerome, I1 aArnett, Donna, K1 aMorrison, Alanna, C1 aFranceschini, Nora1 aLevy, Daniel1 aSofer, Tamar1 aNHLBI Trans-Omics in Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/910002763nas a2200481 4500008004100000022001400041245012500055210006900180260001500249300000800264490000600272520128200278653003801560653003401598653001101632653002101643653003101664653003601695100002001731700002101751700002801772700002501800700002301825700001701848700001801865700002001883700001301903700001401916700001901930700002701949700002101976700002001997700002002017700002202037700002102059700002402080700002002104700001702124700001902141700001702160710006802177856003602245 2022 eng d a2399-364200aNon-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations.0 aNonlinear machine learning models incorporating SNPs and PRS imp c2022 08 22 a8560 v53 aPolygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22% for height, 27% for HDL cholesterol, 43% for body mass index, 50% for sleep duration, 58% for systolic blood pressure, 64% for total cholesterol, 66% for triglycerides, 77% for LDL cholesterol, and 100% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.
10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aMachine Learning10aMultifactorial Inheritance10aPolymorphism, Single Nucleotide1 aElgart, Michael1 aLyons, Genevieve1 aRomero-Brufau, Santiago1 aKurniansyah, Nuzulul1 aBrody, Jennifer, A1 aGuo, Xiuqing1 aLin, Henry, J1 aRaffield, Laura1 aGao, Yan1 aChen, Han1 ade Vries, Paul1 aLloyd-Jones, Donald, M1 aLange, Leslie, A1 aPeloso, Gina, M1 aFornage, Myriam1 aRotter, Jerome, I1 aRich, Stephen, S1 aMorrison, Alanna, C1 aPsaty, Bruce, M1 aLevy, Daniel1 aRedline, Susan1 aSofer, Tamar1 aNHLBI’s Trans-Omics in Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/916103962nas a2200733 4500008004100000022001400041245014300055210006900198260001600267520182100283100001602104700001602120700001502136700001802151700002202169700002102191700002002212700001802232700001602250700001302266700001902279700002302298700001702321700002002338700002602358700002002384700002302404700003102427700001902458700001902477700002502496700002502521700002002546700001702566700002202583700002102605700002002626700002002646700002402666700002702690700001902717700002102736700002402757700002102781700002202802700001702824700001902841700002002860700002002880700001902900700001902919700002502938700002302963700002602986700002403012700001703036700002503053700002403078700002003102700001903122700002103141710003003162856003603192 2022 eng d a1537-660500aPolygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program.0 aPolygenic transcriptome risk scores for COPD and lung function i c2022 Mar 313 aWhile polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1 s [FEV] and its ratio to forced vital capacity [FEV/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV and FEV/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR] = 1.24 [95% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with ≥ 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p < 2.2 × 10 with portability gains ranging from 5% to 28%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.
1 aHu, Xiaowei1 aQiao, Dandi1 aKim, Wonji1 aMoll, Matthew1 aBalte, Pallavi, P1 aLange, Leslie, A1 aBartz, Traci, M1 aKumar, Rajesh1 aLi, Xingnan1 aYu, Bing1 aCade, Brian, E1 aLaurie, Cecelia, A1 aSofer, Tamar1 aRuczinski, Ingo1 aNickerson, Deborah, A1 aMuzny, Donna, M1 aMetcalf, Ginger, A1 aDoddapaneni, Harshavardhan1 aGabriel, Stacy1 aGupta, Namrata1 aDugan-Perez, Shannon1 aCupples, Adrienne, L1 aLoehr, Laura, R1 aJain, Deepti1 aRotter, Jerome, I1 aWilson, James, G1 aPsaty, Bruce, M1 aFornage, Myriam1 aMorrison, Alanna, C1 aVasan, Ramachandran, S1 aWashko, George1 aRich, Stephen, S1 aO'Connor, George, T1 aBleecker, Eugene1 aKaplan, Robert, C1 aKalhan, Ravi1 aRedline, Susan1 aGharib, Sina, A1 aMeyers, Deborah1 aOrtega, Victor1 aDupuis, Josée1 aLondon, Stephanie, J1 aLappalainen, Tuuli1 aOelsner, Elizabeth, C1 aSilverman, Edwin, K1 aBarr, Graham1 aThornton, Timothy, A1 aWheeler, Heather, E1 aCho, Michael, H1 aIm, Hae, Kyung1 aManichaikul, Ani1 aTOPMed Lung Working Group uhttps://chs-nhlbi.org/node/903708881nas a2202605 4500008004100000022001400041245012200055210006900177260001500246300001000261490000800271520141300279653001201692653001801704653002501722653002601747653002301773653003001796653001001826653003801836653002201874653002501896653003401921653001101955653002101966653001101987653001001998653003402008653003102042653002402073653001402097653003602111100001802147700001902165700002102184700002002205700001802225700003202243700001702275700001702292700003102309700003002340700002102370700002102391700002302412700001602435700002802451700002902479700001802508700002102526700002402547700001902571700001902590700002102609700002502630700002102655700002202676700002102698700002302719700001902742700001902761700001902780700002702799700001702826700002002843700002102863700002002884700002002904700001902924700002902943700002402972700001902996700002503015700002203040700001703062700002203079700002303101700002403124700002803148700002203176700002403198700002103222700002003243700002003263700002303283700002103306700003103327700002803358700001803386700002203404700002203426700002103448700002303469700003903492700002203531700002103553700001803574700001903592700001703611700001903628700001503647700002003662700001903682700001403701700002603715700001703741700002103758700002503779700002603804700002003830700002003850700002403870700001803894700002103912700001903933700001703952700001703969700002003986700002504006700002404031700002204055700002004077700001904097700002104116700001504137700001704152700001804169700002104187700002404208700002104232700001704253700002004270700002204290700002304312700002004335700002804355700003604383700002304419700002504442700002004467700002004487700002204507700001904529700002604548700002304574700001504597700002104612700002204633700002004655700002904675700002404704700002004728700002104748700002204769700002604791700002504817700001904842700002304861700002604884700002104910700002104931700001904952700002104971700002404992700001905016700002005035700002005055700001405075700001405089700001905103700002505122700003105147700002105178700001905199700002405218700002305242700001705265700001905282700001705301700001605318700002105334700001805355700002305373700001905396700002205415700002005437700001905457700002005476700002105496700002105517700002205538700002305560700002405583700002105607700002005628700002705648700003005675700001905705700001605724700001805740700002105758700002105779700002105800700001905821700001905840700002205859700002105881700002205902700002105924700002205945700002305967700002305990700002306013700001906036700002006055710006106075710006506136710003806201856003606239 2022 eng d a1537-660500aRare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes.0 aRare coding variants in 35 genes associate with circulating lipi c2022 01 06 a81-960 v1093 aLarge-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.
10aAlleles10aBlood Glucose10aCase-Control Studies10aComputational Biology10aDatabases, Genetic10aDiabetes Mellitus, Type 210aExome10aGenetic Predisposition to Disease10aGenetic Variation10aGenetics, Population10aGenome-Wide Association Study10aHumans10aLipid Metabolism10aLipids10aLiver10aMolecular Sequence Annotation10aMultifactorial Inheritance10aOpen Reading Frames10aPhenotype10aPolymorphism, Single Nucleotide1 aHindy, George1 aDornbos, Peter1 aChaffin, Mark, D1 aLiu, Dajiang, J1 aWang, Minxian1 aSelvaraj, Margaret, Sunitha1 aZhang, David1 aPark, Joseph1 aAguilar-Salinas, Carlos, A1 aAntonacci-Fulton, Lucinda1 aArdissino, Diego1 aArnett, Donna, K1 aAslibekyan, Stella1 aAtzmon, Gil1 aBallantyne, Christie, M1 aBarajas-Olmos, Francisco1 aBarzilai, Nir1 aBecker, Lewis, C1 aBielak, Lawrence, F1 aBis, Joshua, C1 aBlangero, John1 aBoerwinkle, Eric1 aBonnycastle, Lori, L1 aBottinger, Erwin1 aBowden, Donald, W1 aBown, Matthew, J1 aBrody, Jennifer, A1 aBroome, Jai, G1 aBurtt, Noel, P1 aCade, Brian, E1 aCenteno-Cruz, Federico1 aChan, Edmund1 aChang, Yi-Cheng1 aChen, Yii-der, I1 aCheng, Ching-Yu1 aChoi, Won, Jung1 aChowdhury, Raj1 aContreras-Cubas, Cecilia1 aCórdova, Emilio, J1 aCorrea, Adolfo1 aCupples, Adrienne, L1 aCurran, Joanne, E1 aDanesh, John1 ade Vries, Paul, S1 aDeFronzo, Ralph, A1 aDoddapaneni, Harsha1 aDuggirala, Ravindranath1 aDutcher, Susan, K1 aEllinor, Patrick, T1 aEmery, Leslie, S1 aFlorez, Jose, C1 aFornage, Myriam1 aFreedman, Barry, I1 aFuster, Valentin1 aGaray-Sevilla, Ma, Eugenia1 aGarcía-Ortiz, Humberto1 aGermer, Soren1 aGibbs, Richard, A1 aGieger, Christian1 aGlaser, Benjamin1 aGonzalez, Clicerio1 aGonzalez-Villalpando, Maria, Elena1 aGraff, Mariaelisa1 aGraham, Sarah, E1 aGrarup, Niels1 aGroop, Leif, C1 aGuo, Xiuqing1 aGupta, Namrata1 aHan, Sohee1 aHanis, Craig, L1 aHansen, Torben1 aHe, Jiang1 aHeard-Costa, Nancy, L1 aHung, Yi-Jen1 aHwang, Mi, Yeong1 aIrvin, Marguerite, R1 aIslas-Andrade, Sergio1 aJarvik, Gail, P1 aKang, Hyun, Min1 aKardia, Sharon, L R1 aKelly, Tanika1 aKenny, Eimear, E1 aKhan, Alyna, T1 aKim, Bong-Jo1 aKim, Ryan, W1 aKim, Young, Jin1 aKoistinen, Heikki, A1 aKooperberg, Charles1 aKuusisto, Johanna1 aKwak, Soo, Heon1 aLaakso, Markku1 aLange, Leslie, A1 aLee, Jiwon1 aLee, Juyoung1 aLee, Seonwook1 aLehman, Donna, M1 aLemaitre, Rozenn, N1 aLinneberg, Allan1 aLiu, Jianjun1 aLoos, Ruth, J F1 aLubitz, Steven, A1 aLyssenko, Valeriya1 aMa, Ronald, C W1 aMartin, Lisa, Warsinger1 aMartínez-Hernández, Angélica1 aMathias, Rasika, A1 aMcGarvey, Stephen, T1 aMcPherson, Ruth1 aMeigs, James, B1 aMeitinger, Thomas1 aMelander, Olle1 aMendoza-Caamal, Elvia1 aMetcalf, Ginger, A1 aMi, Xuenan1 aMohlke, Karen, L1 aMontasser, May, E1 aMoon, Jee-Young1 aMoreno-Macias, Hortensia1 aMorrison, Alanna, C1 aMuzny, Donna, M1 aNelson, Sarah, C1 aNilsson, Peter, M1 aO'Connell, Jeffrey, R1 aOrho-Melander, Marju1 aOrozco, Lorena1 aPalmer, Colin, N A1 aPalmer, Nicholette, D1 aPark, Cheol, Joo1 aPark, Kyong, Soo1 aPedersen, Oluf1 aPeralta, Juan, M1 aPeyser, Patricia, A1 aPost, Wendy, S1 aPreuss, Michael1 aPsaty, Bruce, M1 aQi, Qibin1 aRao, D, C1 aRedline, Susan1 aReiner, Alexander, P1 aRevilla-Monsalve, Cristina1 aRich, Stephen, S1 aSamani, Nilesh1 aSchunkert, Heribert1 aSchurmann, Claudia1 aSeo, Daekwan1 aSeo, Jeong-Sun1 aSim, Xueling1 aSladek, Rob1 aSmall, Kerrin, S1 aSo, Wing, Yee1 aStilp, Adrienne, M1 aTai, Shyong, E1 aTam, Claudia, H T1 aTaylor, Kent, D1 aTeo, Yik, Ying1 aThameem, Farook1 aTomlinson, Brian1 aTsai, Michael, Y1 aTuomi, Tiinamaija1 aTuomilehto, Jaakko1 aTusié-Luna, Teresa1 aUdler, Miriam, S1 avan Dam, Rob, M1 aVasan, Ramachandran, S1 aMartinez, Karine, A Viaud1 aWang, Fei, Fei1 aWang, Xuzhi1 aWatkins, Hugh1 aWeeks, Daniel, E1 aWilson, James, G1 aWitte, Daniel, R1 aWong, Tien-Yin1 aYanek, Lisa, R1 aKathiresan, Sekar1 aRader, Daniel, J1 aRotter, Jerome, I1 aBoehnke, Michael1 aMcCarthy, Mark, I1 aWiller, Cristen, J1 aNatarajan, Pradeep1 aFlannick, Jason, A1 aKhera, Amit, V1 aPeloso, Gina, M1 aAMP-T2D-GENES, Myocardial Infarction Genetics Consortium1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aNHLBI TOPMed Lipids Working Group uhttps://chs-nhlbi.org/node/897504507nas a2201189 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2022 eng d a2397-337400aRare genetic variants explain missing heritability in smoking.0 aRare genetic variants explain missing heritability in smoking c2022 Aug 043 aCommon genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.
1 aJang, Seon-Kyeong1 aEvans, Luke1 aFialkowski, Allison1 aArnett, Donna, K1 aAshley-Koch, Allison, E1 aBarnes, Kathleen, C1 aBecker, Diane, M1 aBis, Joshua, C1 aBlangero, John1 aBleecker, Eugene, R1 aBoorgula, Meher, Preethi1 aBowden, Donald, W1 aBrody, Jennifer, A1 aCade, Brian, E1 aJenkins, Brenda, W Campbell1 aCarson, April, P1 aChavan, Sameer1 aCupples, Adrienne, L1 aCuster, Brian1 aDamrauer, Scott, M1 aDavid, Sean, P1 ade Andrade, Mariza1 aDinardo, Carla, L1 aFingerlin, Tasha, E1 aFornage, Myriam1 aFreedman, Barry, I1 aGarrett, Melanie, E1 aGharib, Sina, A1 aGlahn, David, C1 aHaessler, Jeffrey1 aHeckbert, Susan, R1 aHokanson, John, E1 aHou, Lifang1 aHwang, Shih-Jen1 aHyman, Matthew, C1 aJudy, Renae1 aJustice, Anne, E1 aKaplan, Robert, C1 aKardia, Sharon, L R1 aKelly, Shannon1 aKim, Wonji1 aKooperberg, Charles1 aLevy, Daniel1 aLloyd-Jones, Donald, M1 aLoos, Ruth, J F1 aManichaikul, Ani, W1 aGladwin, Mark, T1 aMartin, Lisa, Warsinger1 aNouraie, Mehdi1 aMelander, Olle1 aMeyers, Deborah, A1 aMontgomery, Courtney, G1 aNorth, Kari, E1 aOelsner, Elizabeth, C1 aPalmer, Nicholette, D1 aPayton, Marinelle1 aPeljto, Anna, L1 aPeyser, Patricia, A1 aPreuss, Michael1 aPsaty, Bruce, M1 aQiao, Dandi1 aRader, Daniel, J1 aRafaels, Nicholas1 aRedline, Susan1 aReed, Robert, M1 aReiner, Alexander, P1 aRich, Stephen, S1 aRotter, Jerome, I1 aSchwartz, David, A1 aShadyab, Aladdin, H1 aSilverman, Edwin, K1 aSmith, Nicholas, L1 aSmith, Gustav1 aSmith, Albert, V1 aSmith, Jennifer, A1 aTang, Weihong1 aTaylor, Kent, D1 aTelen, Marilyn, J1 aVasan, Ramachandran, S1 aGordeuk, Victor, R1 aWang, Zhe1 aWiggins, Kerri, L1 aYanek, Lisa, R1 aYang, Ivana, V1 aYoung, Kendra, A1 aYoung, Kristin, L1 aZhang, Yingze1 aLiu, Dajiang, J1 aKeller, Matthew, C1 aVrieze, Scott uhttps://chs-nhlbi.org/node/916809141nas a2202533 4500008004100000022001400041245008200055210006900137260001600206520188300222100001902105700001802124700002202142700002202164700002002186700002202206700002502228700001802253700002202271700001802293700001402311700002502325700001602350700001802366700001902384700002002403700001502423700003202438700003302470700001802503700001802521700002002539700002202559700001802581700002002599700002802619700001702647700001702664700002902681700001602710700002702726700002202753700002402775700001802799700002802817700001902845700001902864700001902883700001702902700002202919700002502941700002002966700002002986700002203006700001903028700002203047700001903069700001603088700002003104700001503124700002003139700002103159700001803180700002203198700001903220700002103239700002403260700002503284700002203309700002403331700001403355700001303369700002103382700001603403700002203419700002303441700002603464700002003490700001903510700002303529700002003552700002303572700002003595700002303615700002303638700002103661700001603682700001703698700002203715700001803737700001703755700001403772700001503786700002103801700002003822700002503842700002203867700002503889700002603914700002303940700002203963700001903985700002804004700002504032700002004057700003104077700002104108700002004129700002004149700002104169700002104190700002204211700002004233700002104253700002904274700002004303700003304323700001904356700002604375700002404401700002104425700002304446700002104469700001804490700001604508700001604524700001904540700002304559700002804582700002204610700003204632700002004664700002404684700002604708700002104734700001604755700002204771700002204793700002104815700001604836700002304852700001604875700001604891700002004907700002204927700002204949700002404971700001904995700001905014700002105033700002005054700002005074700001905094700002205113700002405135700002505159700001705184700003205201700002305233700002305256700002005279700002505299700002105324700001605345700002605361700001805387700002405405700001605429700002205445700002505467700002505492700001805517700002605535700002105561700003005582700002005612700001905632700002105651700002105672700002305693700002305716700002205739700002905761700002205790700002305812700002005835700001805855700002005873700003005893700002205923700002305945700002305968700002505991700001706016700002306033700002106056700002306077710002306100710002206123710007406145710002106219710002406240710002306264710004106287710002606328710002106354710004706375710003106422710005206453710001806505710002206523710002606545856003606571 2022 eng d a1476-468700aStroke genetics informs drug discovery and risk prediction across ancestries.0 aStroke genetics informs drug discovery and risk prediction acros c2022 Sep 303 aPrevious genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.
1 aMishra, Aniket1 aMalik, Rainer1 aHachiya, Tsuyoshi1 aJürgenson, Tuuli1 aNamba, Shinichi1 aPosner, Daniel, C1 aKamanu, Frederick, K1 aKoido, Masaru1 aLe Grand, Quentin1 aShi, Mingyang1 aHe, Yunye1 aGeorgakis, Marios, K1 aCaro, Ilana1 aKrebs, Kristi1 aLiaw, Yi-Ching1 aVaura, Felix, C1 aLin, Kuang1 aWinsvold, Bendik, Slagsvold1 aSrinivasasainagendra, Vinodh1 aParodi, Livia1 aBae, Hee-Joon1 aChauhan, Ganesh1 aChong, Michael, R1 aTomppo, Liisa1 aAkinyemi, Rufus1 aRoshchupkin, Gennady, V1 aHabib, Naomi1 aJee, Yon, Ho1 aThomassen, Jesper, Qvist1 aAbedi, Vida1 aCárcel-Márquez, Jara1 aNygaard, Marianne1 aLeonard, Hampton, L1 aYang, Chaojie1 aYonova-Doing, Ekaterina1 aKnol, Maria, J1 aLewis, Adam, J1 aJudy, Renae, L1 aAgo, Tetsuro1 aAmouyel, Philippe1 aArmstrong, Nicole, D1 aBakker, Mark, K1 aBartz, Traci, M1 aBennett, David, A1 aBis, Joshua, C1 aBordes, Constance1 aBørte, Sigrid1 aCain, Anael1 aRidker, Paul, M1 aCho, Kelly1 aChen, Zhengming1 aCruchaga, Carlos1 aCole, John, W1 aDe Jager, Phil, L1 ade Cid, Rafael1 aEndres, Matthias1 aFerreira, Leslie, E1 aGeerlings, Mirjam, I1 aGasca, Natalie, C1 aGudnason, Vilmundur1 aHata, Jun1 aHe, Jing1 aHeath, Alicia, K1 aHo, Yuk-Lam1 aHavulinna, Aki, S1 aHopewell, Jemma, C1 aHyacinth, Hyacinth, I1 aInouye, Michael1 aJacob, Mina, A1 aJeon, Christina, E1 aJern, Christina1 aKamouchi, Masahiro1 aKeene, Keith, L1 aKitazono, Takanari1 aKittner, Steven, J1 aKonuma, Takahiro1 aKumar, Amit1 aLacaze, Paul1 aLauner, Lenore, J1 aLee, Keon-Joo1 aLepik, Kaido1 aLi, Jiang1 aLi, Liming1 aManichaikul, Ani1 aMarkus, Hugh, S1 aMarston, Nicholas, A1 aMeitinger, Thomas1 aMitchell, Braxton, D1 aMontellano, Felipe, A1 aMorisaki, Takayuki1 aMosley, Thomas, H1 aNalls, Mike, A1 aNordestgaard, Børge, G1 aO'Donnell, Martin, J1 aOkada, Yukinori1 aOnland-Moret, Charlotte, N1 aOvbiagele, Bruce1 aPeters, Annette1 aPsaty, Bruce, M1 aRich, Stephen, S1 aRosand, Jonathan1 aSabatine, Marc, S1 aSacco, Ralph, L1 aSaleheen, Danish1 aSandset, Else, Charlotte1 aSalomaa, Veikko1 aSargurupremraj, Muralidharan1 aSasaki, Makoto1 aSatizabal, Claudia, L1 aSchmidt, Carsten, O1 aShimizu, Atsushi1 aSmith, Nicholas, L1 aSloane, Kelly, L1 aSutoh, Yoichi1 aSun, Yan, V1 aTanno, Kozo1 aTiedt, Steffen1 aTatlisumak, Turgut1 aTorres-Aguila, Nuria, P1 aTiwari, Hemant, K1 aTrégouët, David-Alexandre1 aTrompet, Stella1 aTuladhar, Anil, Man1 aTybjærg-Hansen, Anne1 avan Vugt, Marion1 aVibo, Riina1 aVerma, Shefali, S1 aWiggins, Kerri, L1 aWennberg, Patrik1 aWoo, Daniel1 aWilson, Peter, W F1 aXu, Huichun1 aYang, Qiong1 aYoon, Kyungheon1 aMillwood, Iona, Y1 aGieger, Christian1 aNinomiya, Toshiharu1 aGrabe, Hans, J1 aJukema, Wouter1 aRissanen, Ina, L1 aStrbian, Daniel1 aKim, Young, Jin1 aChen, Pei-Hsin1 aMayerhofer, Ernst1 aHowson, Joanna, M M1 aIrvin, Marguerite, R1 aAdams, Hieab1 aWassertheil-Smoller, Sylvia1 aChristensen, Kaare1 aIkram, Mohammad, A1 aRundek, Tatjana1 aWorrall, Bradford, B1 aLathrop, Mark, G1 aRiaz, Moeen1 aSimonsick, Eleanor, M1 aKõrv, Janika1 aFrança, Paulo, H C1 aZand, Ramin1 aPrasad, Kameshwar1 aFrikke-Schmidt, Ruth1 ade Leeuw, Frank-Erik1 aLiman, Thomas1 aHaeusler, Karl, Georg1 aRuigrok, Ynte, M1 aHeuschmann, Peter, Ulrich1 aLongstreth, W T1 aJung, Keum, Ji1 aBastarache, Lisa1 aParé, Guillaume1 aDamrauer, Scott, M1 aChasman, Daniel, I1 aRotter, Jerome, I1 aAnderson, Christopher, D1 aZwart, John-Anker1 aNiiranen, Teemu, J1 aFornage, Myriam1 aLiaw, Yung-Po1 aSeshadri, Sudha1 aFernandez-Cadenas, Israel1 aWalters, Robin, G1 aRuff, Christian, T1 aOwolabi, Mayowa, O1 aHuffman, Jennifer, E1 aMilani, Lili1 aKamatani, Yoichiro1 aDichgans, Martin1 aDebette, Stephanie1 aCOMPASS Consortium1 aINVENT Consortium1 aDutch Parelsnoer Initiative (PSI) Cerebrovascular Disease Study Group1 aEstonian Biobank1 aPRECISEQ Consortium1 aFinnGen Consortium1 aNINDS Stroke Genetics Network (SiGN)1 aMEGASTROKE Consortium1 aSIREN Consortium1 aChina Kadoorie Biobank Collaborative Group1 aVA Million Veteran Program1 aInternational Stroke Genetics Consortium (ISGC)1 aBiobank Japan1 aCHARGE Consortium1 aGIGASTROKE Consortium uhttps://chs-nhlbi.org/node/917203348nas a2200541 4500008004100000022001400041245012000055210006900175260001600244520177900260100002002039700001702059700001902076700002502095700001702120700001502137700002002152700002002172700001402192700002402206700002102230700001702251700002002268700001702288700002202305700002202327700002202349700002802371700002002399700001902419700001802438700002102456700002002477700002302497700002102520700001602541700001702557700002002574700002702594700002102621700002102642700002202663700001702685700001902702700001802721710003102739856003602770 2022 eng d a1535-497000aTargeted Genome Sequencing Identifies Multiple Rare Variants in Caveolin-1 Associated with Obstructive Sleep Apnea.0 aTargeted Genome Sequencing Identifies Multiple Rare Variants in c2022 Jul 133 aINTRODUCTION: Obstructive sleep apnea (OSA) is a common disorder associated with increased risk for cardiovascular disease, diabetes, and premature mortality. There is strong clinical and epi-demiologic evidence supporting the importance of genetic factors influencing OSA, but limited data implicating specific genes.
METHODS: Leveraging high depth genomic sequencing data from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program and imputed genotype data from multiple population-based studies, we performed linkage analysis in the Cleve-land Family Study (CFS) followed by multi-stage gene-based association analyses in independent cohorts to search for rare variants contributing to OSA severity as assessed by the apnea-hypopnea index (AHI) in a total of 7,708 individuals of European ancestry.
RESULTS: Linkage analysis in CFS identified a suggestive linkage peak on chromosome 7q31 (LOD=2.31). Gene-based analysis identified 21 non-coding rare variants in Caveolin-1 (CAV1) associated with lower AHI after accounting for multiple comparisons (p=7.4×10-8). These non-coding variants together significantly contributed to the linkage evidence (p<10-3). Follow-up anal-ysis revealed significant associations between these variants and increased CAV1 expression, and increased CAV1 expression in peripheral monocytes was associated with lower AHI (p=0.024) and higher minimum overnight oxygen saturation (p=0.007).
CONCLUSION: Rare variants in CAV1, a membrane scaffolding protein essential in multiple cellular and metabolic functions, are associated with higher CAV1 gene expression and lower OSA severity, suggesting a novel target for modulating OSA severity.
1 aLiang, Jingjing1 aWang, Heming1 aCade, Brian, E1 aKurniansyah, Nuzulul1 aHe, Karen, Y1 aLee, Jiwon1 aSands, Scott, A1 aBrody, Jennifer1 aChen, Han1 aGottlieb, Daniel, J1 aEvans, Daniel, S1 aGuo, Xiuqing1 aGharib, Sina, A1 aHale, Lauren1 aHillman, David, R1 aLutsey, Pamela, L1 aMukherjee, Sutapa1 aOchs-Balcom, Heather, M1 aPalmer, Lyle, J1 aPurcell, Shaun1 aSaxena, Richa1 aPatel, Sanjay, R1 aStone, Katie, L1 aTranah, Gregory, J1 aBoerwinkle, Eric1 aLin, Xihong1 aLiu, Yongmei1 aPsaty, Bruce, M1 aVasan, Ramachandran, S1 aManichaikul, Ani1 aRich, Stephen, S1 aRotter, Jerome, I1 aSofer, Tamar1 aRedline, Susan1 aZhu, Xiaofeng1 aTOPMed Sleep Working Group uhttps://chs-nhlbi.org/node/910104203nas a2200697 4500008004100000022001400041245011000055210006900165260000900234300001100243490000700254520220700261653001902468653002202487653003402509653001102543653001202554653002802566100001402594700002102608700002002629700002102649700002002670700001902690700001902709700002302728700002002751700001502771700002802786700002402814700002402838700001802862700002702880700002102907700002302928700001902951700002102970700002102991700001903012700001903031700002303050700002303073700001703096700002103113700002103134700002203155700002403177700002203201700001903223700002403242700001903266700002103285700002503306700002303331700002403354700002403378700002503402700002203427700002003449856003603469 2022 eng d a1664-239200aThe Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations.0 aValue of Rare Genetic Variation in the Prediction of Common Obes c2022 a8638930 v133 aPolygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) ≥1%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRS) with a rare variant PRS (PRS) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m), obesity (BMI ≥ 30 kg/m), and extreme obesity (BMI ≥ 40 kg/m). We built PRSs and PRSs using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRS explained 10.1% of variation in BMI, and 18.3% and 22.5% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRS explained 1.49%, and 2.97% and 3.68%, respectively. The PRS was associated with an increased risk of obesity and extreme obesity (OR = 1.37 per SD, = 1.7x10; OR = 1.55 per SD, = 3.8x10), which was attenuated, after adjusting for PRS (OR = 1.08 per SD, = 9.8x10; OR= 1.09 per SD, = 0.02). When PRS and PRS are combined, the increase in explained variance attributed to PRS was small (incremental Nagelkerke R = 0.24% for obesity and 0.51% for extreme obesity). Consistently, combining PRS to PRS provided little improvement to the prediction of obesity (PRS AUC = 0.591; PRS AUC = 0.708; PRS AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRS provides limited improvement over PRS in the prediction of obesity risk, based on these large populations.
10aGene Frequency10aGenetic Variation10aGenome-Wide Association Study10aHumans10aObesity10aWhole Genome Sequencing1 aWang, Zhe1 aChoi, Shing, Wan1 aChami, Nathalie1 aBoerwinkle, Eric1 aFornage, Myriam1 aRedline, Susan1 aBis, Joshua, C1 aBrody, Jennifer, A1 aPsaty, Bruce, M1 aKim, Wonji1 aMcDonald, Merry-Lynn, N1 aRegan, Elizabeth, A1 aSilverman, Edwin, K1 aLiu, Ching-Ti1 aVasan, Ramachandran, S1 aKalyani, Rita, R1 aMathias, Rasika, A1 aYanek, Lisa, R1 aArnett, Donna, K1 aJustice, Anne, E1 aNorth, Kari, E1 aKaplan, Robert1 aHeckbert, Susan, R1 ade Andrade, Mariza1 aGuo, Xiuqing1 aLange, Leslie, A1 aRich, Stephen, S1 aRotter, Jerome, I1 aEllinor, Patrick, T1 aLubitz, Steven, A1 aBlangero, John1 aShoemaker, Benjamin1 aDarbar, Dawood1 aGladwin, Mark, T1 aAlbert, Christine, M1 aChasman, Daniel, I1 aJackson, Rebecca, D1 aKooperberg, Charles1 aReiner, Alexander, P1 aO'Reilly, Paul, F1 aLoos, Ruth, J F uhttps://chs-nhlbi.org/node/910905141nas a2201381 4500008004100000022001400041245014300055210006900198260001500267300000800282490000600290520117000296653003001466653001201496653001201508653001101520653001201531653005301543653002601596653003601622653002301658653002701681653001801708100002001726700002201746700002201768700002601790700002301816700002501839700001501864700002101879700002501900700001801925700002401943700002201967700002301989700002002012700001702032700002002049700002602069700001902095700002202114700001902136700001702155700001702172700003302189700002102222700001902243700001802262700002602280700002002306700002102326700002302347700002602370700002202396700002402418700002302442700002202465700002002487700002202507700002002529700002502549700002102574700002102595700001802616700001802634700002002652700002102672700002102693700001702714700002102731700003102752700002502783700003402808700002202842700002302864700002102887700001602908700001302924700001402937700002002951700001902971700002102990700001903011700002103030700001903051700002503070700002203095700002803117700001403145700002303159700002403182700001703206700002403223700001503247700002303262700002503285700002503310700002403335700002403359700002003383700001903403700002303422700002003445700002703465700003003492700002003522700002103542700001903563700002103582700002203603700001603625700001903641700002003660700002103680700002203701856003603723 2022 eng d a2399-364200aWhole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program.0 aWhole genome sequence association analysis of fasting glucose an c2022 07 28 a7560 v53 aThe genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI's Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.
10aDiabetes Mellitus, Type 210aFasting10aGlucose10aHumans10aInsulin10aNational Heart, Lung, and Blood Institute (U.S.)10aNerve Tissue Proteins10aPolymorphism, Single Nucleotide10aPrecision Medicine10aReceptors, Immunologic10aUnited States1 aDiCorpo, Daniel1 aGaynor, Sheila, M1 aRussell, Emily, M1 aWesterman, Kenneth, E1 aRaffield, Laura, M1 aMajarian, Timothy, D1 aWu, Peitao1 aSarnowski, Chloe1 aHighland, Heather, M1 aJackson, Anne1 aHasbani, Natalie, R1 ade Vries, Paul, S1 aBrody, Jennifer, A1 aHidalgo, Bertha1 aGuo, Xiuqing1 aPerry, James, A1 aO'Connell, Jeffrey, R1 aLent, Samantha1 aMontasser, May, E1 aCade, Brian, E1 aJain, Deepti1 aWang, Heming1 aAlbanus, Ricardo, D'Oliveira1 aVarshney, Arushi1 aYanek, Lisa, R1 aLange, Leslie1 aPalmer, Nicholette, D1 aAlmeida, Marcio1 aPeralta, Juan, M1 aAslibekyan, Stella1 aBaldridge, Abigail, S1 aBertoni, Alain, G1 aBielak, Lawrence, F1 aChen, Chung-Shiuan1 aChen, Yii-Der Ida1 aChoi, Won, Jung1 aGoodarzi, Mark, O1 aFloyd, James, S1 aIrvin, Marguerite, R1 aKalyani, Rita, R1 aKelly, Tanika, N1 aLee, Seonwook1 aLiu, Ching-Ti1 aLoesch, Douglas1 aManson, JoAnn, E1 aMinster, Ryan, L1 aNaseri, Take1 aPankow, James, S1 aRasmussen-Torvik, Laura, J1 aReiner, Alexander, P1 aReupena, Muagututi'a, Sefuiva1 aSelvin, Elizabeth1 aSmith, Jennifer, A1 aWeeks, Daniel, E1 aXu, Huichun1 aYao, Jie1 aZhao, Wei1 aParker, Stephen1 aAlonso, Alvaro1 aArnett, Donna, K1 aBlangero, John1 aBoerwinkle, Eric1 aCorrea, Adolfo1 aCupples, Adrienne, L1 aCurran, Joanne, E1 aDuggirala, Ravindranath1 aHe, Jiang1 aHeckbert, Susan, R1 aKardia, Sharon, L R1 aKim, Ryan, W1 aKooperberg, Charles1 aLiu, Simin1 aMathias, Rasika, A1 aMcGarvey, Stephen, T1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aPeyser, Patricia, A1 aPsaty, Bruce, M1 aRedline, Susan1 aShuldiner, Alan, R1 aTaylor, Kent, D1 aVasan, Ramachandran, S1 aViaud-Martinez, Karine, A1 aFlorez, Jose, C1 aWilson, James, G1 aSladek, Robert1 aRich, Stephen, S1 aRotter, Jerome, I1 aLin, Xihong1 aDupuis, Josée1 aMeigs, James, B1 aWessel, Jennifer1 aManning, Alisa, K uhttps://chs-nhlbi.org/node/915803611nas a2200889 4500008004100000022001400041245012300055210006900178260001600247300000900263490000700272520111000279653001601389653003401405653001101439653002801450100002301478700002301501700001701524700002701541700001801568700001301586700002401599700002301623700001501646700001901661700002301680700001301703700002401716700002001740700001301760700001901773700002001792700002201812700001801834700002001852700001301872700001701885700001501902700002001917700001901937700001901956700001801975700002101993700001902014700002002033700002202053700002002075700002202095700002102117700002002138700002502158700002402183700002002207700002002227700002102247700002202268700001402290700002202304700002102326700002502347700002502372700002102397700002302418700002302441700002602464700002402490700001202514700002802526700002702554700002102581700002402602700002102626700001802647700002002665856003602685 2022 eng d a2041-172300aWhole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program.0 aWhole genome sequencing identifies structural variants contribut c2022 Dec 08 a75920 v133 aGenome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.
10aBlood Cells10aGenome-Wide Association Study10aHumans10aWhole Genome Sequencing1 aWheeler, Marsha, M1 aStilp, Adrienne, M1 aRao, Shuquan1 aHalldorsson, Bjarni, V1 aBeyter, Doruk1 aWen, Jia1 aMihkaylova, Anna, V1 aMcHugh, Caitlin, P1 aLane, John1 aJiang, Min-Zhi1 aRaffield, Laura, M1 aJun, Goo1 aSedlazeck, Fritz, J1 aMetcalf, Ginger1 aYao, Yao1 aBis, Joshua, B1 aChami, Nathalie1 ade Vries, Paul, S1 aDesai, Pinkal1 aFloyd, James, S1 aGao, Yan1 aKammers, Kai1 aKim, Wonji1 aMoon, Jee-Young1 aRatan, Aakrosh1 aYanek, Lisa, R1 aAlmasy, Laura1 aBecker, Lewis, C1 aBlangero, John1 aCho, Michael, H1 aCurran, Joanne, E1 aFornage, Myriam1 aKaplan, Robert, C1 aLewis, Joshua, P1 aLoos, Ruth, J F1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aPreuss, Michael1 aPsaty, Bruce, M1 aRich, Stephen, S1 aRotter, Jerome, I1 aTang, Hua1 aTracy, Russell, P1 aBoerwinkle, Eric1 aAbecasis, Goncalo, R1 aBlackwell, Thomas, W1 aSmith, Albert, V1 aJohnson, Andrew, D1 aMathias, Rasika, A1 aNickerson, Deborah, A1 aConomos, Matthew, P1 aLi, Yun1 aÞorsteinsdottir, Unnur1 aMagnússon, Magnús, K1 aStefansson, Kari1 aPankratz, Nathan, D1 aBauer, Daniel, E1 aAuer, Paul, L1 aReiner, Alex, P uhttps://chs-nhlbi.org/node/926106073nas a2201597 4500008004100000022001400041245008800055210006900143260001300212300001200225490000800237520158400245653001201829653001201841653002501853653003401878653001801912653002901930653001101959653000901970653001301979653003001992100002502022700002802047700002902075700002202104700001902126700001902145700001702164700001802181700001702199700001302216700002202229700001902251700002602270700002602296700002302322700001902345700002002364700001702384700002202401700003102423700001902454700002302473700002602496700002102522700002102543700002402564700002002588700002102608700002002629700002002649700001602669700002702685700001902712700001902731700002002750700001902770700002302789700002402812700001802836700001602854700002602870700002302896700002202919700002002941700001902961700002702980700001903007700002103026700002403047700002403071700001403095700002203109700001503131700001903146700002303165700002303188700002203211700002103233700002503254700001903279700002303298700001903321700002203340700001903362700002303381700001303404700002303417700002203440700002103462700002203483700002303505700002103528700002303549700001803572700001903590700002203609700002103631700002803652700002203680700001903702700002203721700002003743700001703763700001403780700001603794700002203810700001803832700002303850700001803873700002403891700002403915700001903939700001803958700002203976700002403998700001504022700002104037700001804058700002304076700002304099700002504122700002504147700002104172700001804193700002504211700002304236700002204259700002104281700002504302700002304327700002404350710006504374856003604439 2023 eng d a1476-468700aAberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis.0 aAberrant activation of TCL1A promotes stem cell expansion in clo c2023 Apr a755-7630 v6163 aMutations in a diverse set of driver genes increase the fitness of haematopoietic stem cells (HSCs), leading to clonal haematopoiesis. These lesions are precursors for blood cancers, but the basis of their fitness advantage remains largely unknown, partly owing to a paucity of large cohorts in which the clonal expansion rate has been assessed by longitudinal sampling. Here, to circumvent this limitation, we developed a method to infer the expansion rate from data from a single time point. We applied this method to 5,071 people with clonal haematopoiesis. A genome-wide association study revealed that a common inherited polymorphism in the TCL1A promoter was associated with a slower expansion rate in clonal haematopoiesis overall, but the effect varied by driver gene. Those carrying this protective allele exhibited markedly reduced growth rates or prevalence of clones with driver mutations in TET2, ASXL1, SF3B1 and SRSF2, but this effect was not seen in clones with driver mutations in DNMT3A. TCL1A was not expressed in normal or DNMT3A-mutated HSCs, but the introduction of mutations in TET2 or ASXL1 led to the expression of TCL1A protein and the expansion of HSCs in vitro. The protective allele restricted TCL1A expression and expansion of mutant HSCs, as did experimental knockdown of TCL1A expression. Forced expression of TCL1A promoted the expansion of human HSCs in vitro and mouse HSCs in vivo. Our results indicate that the fitness advantage of several commonly mutated driver genes in clonal haematopoiesis may be mediated by TCL1A activation.
10aAlleles10aAnimals10aClonal Hematopoiesis10aGenome-Wide Association Study10aHematopoiesis10aHematopoietic Stem Cells10aHumans10aMice10aMutation10aPromoter Regions, Genetic1 aWeinstock, Joshua, S1 aGopakumar, Jayakrishnan1 aBurugula, Bala, Bharathi1 aUddin, Md, Mesbah1 aJahn, Nikolaus1 aBelk, Julia, A1 aBouzid, Hind1 aDaniel, Bence1 aMiao, Zhuang1 aLy, Nghi1 aMack, Taralynn, M1 aLuna, Sofia, E1 aProthro, Katherine, P1 aMitchell, Shaneice, R1 aLaurie, Cecelia, A1 aBroome, Jai, G1 aTaylor, Kent, D1 aGuo, Xiuqing1 aSinner, Moritz, F1 avon Falkenhausen, Aenne, S1 aKääb, Stefan1 aShuldiner, Alan, R1 aO'Connell, Jeffrey, R1 aLewis, Joshua, P1 aBoerwinkle, Eric1 aBarnes, Kathleen, C1 aChami, Nathalie1 aKenny, Eimear, E1 aLoos, Ruth, J F1 aFornage, Myriam1 aHou, Lifang1 aLloyd-Jones, Donald, M1 aRedline, Susan1 aCade, Brian, E1 aPsaty, Bruce, M1 aBis, Joshua, C1 aBrody, Jennifer, A1 aSilverman, Edwin, K1 aYun, Jeong, H1 aQiao, Dandi1 aPalmer, Nicholette, D1 aFreedman, Barry, I1 aBowden, Donald, W1 aCho, Michael, H1 aDeMeo, Dawn, L1 aVasan, Ramachandran, S1 aYanek, Lisa, R1 aBecker, Lewis, C1 aKardia, Sharon, L R1 aPeyser, Patricia, A1 aHe, Jiang1 aRienstra, Michiel1 aHarst, Pim1 aKaplan, Robert1 aHeckbert, Susan, R1 aSmith, Nicholas, L1 aWiggins, Kerri, L1 aArnett, Donna, K1 aIrvin, Marguerite, R1 aTiwari, Hemant1 aCutler, Michael, J1 aKnight, Stacey1 aMuhlestein, Brent1 aCorrea, Adolfo1 aRaffield, Laura, M1 aGao, Yan1 ade Andrade, Mariza1 aRotter, Jerome, I1 aRich, Stephen, S1 aTracy, Russell, P1 aKonkle, Barbara, A1 aJohnsen, Jill, M1 aWheeler, Marsha, M1 aSmith, Gustav1 aMelander, Olle1 aNilsson, Peter, M1 aCuster, Brian, S1 aDuggirala, Ravindranath1 aCurran, Joanne, E1 aBlangero, John1 aMcGarvey, Stephen1 aWilliams, Keoki1 aXiao, Shujie1 aYang, Mao1 aGu, Charles1 aChen, Yii-Der Ida1 aLee, Wen-Jane1 aMarcus, Gregory, M1 aKane, John, P1 aPullinger, Clive, R1 aShoemaker, Benjamin1 aDarbar, Dawood1 aRoden, Dan, M1 aAlbert, Christine1 aKooperberg, Charles1 aZhou, Ying1 aManson, JoAnn, E1 aDesai, Pinkal1 aJohnson, Andrew, D1 aMathias, Rasika, A1 aBlackwell, Thomas, W1 aAbecasis, Goncalo, R1 aSmith, Albert, V1 aKang, Hyun, M1 aSatpathy, Ansuman, T1 aNatarajan, Pradeep1 aKitzman, Jacob, O1 aWhitsel, Eric, A1 aReiner, Alexander, P1 aBick, Alexander, G1 aJaiswal, Siddhartha1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/938703922nas a2200673 4500008004100000022001400041245014500055210006900200260001600269300001200285520190200297100001302199700001802212700001902230700001902249700001402268700002202282700002302304700002402327700001402351700002702365700002202392700001702414700002502431700001302456700001702469700001502486700002402501700002102525700002102546700002002567700002402587700002302611700002002634700002102654700002002675700002402695700002302719700002702742700002802769700002002797700001402817700002102831700002202852700001902874700002202893700002402915700001602939700002002955700002102975700001702996700002203013700001903035700002603054700001903080700001603099710009703115856003603212 2023 eng d a2047-998000aAssociation Between Whole Blood-Derived Mitochondrial DNA Copy Number, Low-Density Lipoprotein Cholesterol, and Cardiovascular Disease Risk.0 aAssociation Between Whole BloodDerived Mitochondrial DNA Copy Nu c2023 Oct 07 ae0290903 aBackground The relationship between mitochondrial DNA copy number (mtDNA CN) and cardiovascular disease remains elusive. Methods and Results We performed cross-sectional and prospective association analyses of blood-derived mtDNA CN and cardiovascular disease outcomes in 27 316 participants in 8 cohorts of multiple racial and ethnic groups with whole-genome sequencing. We also performed Mendelian randomization to explore causal relationships of mtDNA CN with coronary heart disease (CHD) and cardiometabolic risk factors (obesity, diabetes, hypertension, and hyperlipidemia). <0.01 was used for significance. We validated most of the previously reported associations between mtDNA CN and cardiovascular disease outcomes. For example, 1-SD unit lower level of mtDNA CN was associated with 1.08 (95% CI, 1.04-1.12; <0.001) times the hazard for developing incident CHD, adjusting for covariates. Mendelian randomization analyses showed no causal effect from a lower level of mtDNA CN to a higher CHD risk (β=0.091; =0.11) or in the reverse direction (β=-0.012; =0.076). Additional bidirectional Mendelian randomization analyses revealed that low-density lipoprotein cholesterol had a causal effect on mtDNA CN (β=-0.084; <0.001), but the reverse direction was not significant (=0.059). No causal associations were observed between mtDNA CN and obesity, diabetes, and hypertension, in either direction. Multivariable Mendelian randomization analyses showed no causal effect of CHD on mtDNA CN, controlling for low-density lipoprotein cholesterol level (=0.52), whereas there was a strong direct causal effect of higher low-density lipoprotein cholesterol on lower mtDNA CN, adjusting for CHD status (β=-0.092; <0.001). Conclusions Our findings indicate that high low-density lipoprotein cholesterol may underlie the complex relationships between mtDNA CN and vascular atherosclerosis.
1 aLiu, Xue1 aSun, Xianbang1 aZhang, Yuankai1 aJiang, Wenqing1 aLai, Meng1 aWiggins, Kerri, L1 aRaffield, Laura, M1 aBielak, Lawrence, F1 aZhao, Wei1 aPitsillides, Achilleas1 aHaessler, Jeffrey1 aZheng, Yinan1 aBlackwell, Thomas, W1 aYao, Jie1 aGuo, Xiuqing1 aQian, Yong1 aThyagarajan, Bharat1 aPankratz, Nathan1 aRich, Stephen, S1 aTaylor, Kent, D1 aPeyser, Patricia, A1 aHeckbert, Susan, R1 aSeshadri, Sudha1 aBoerwinkle, Eric1 aGrove, Megan, L1 aLarson, Nicholas, B1 aSmith, Jennifer, A1 aVasan, Ramachandran, S1 aFitzpatrick, Annette, L1 aFornage, Myriam1 aDing, Jun1 aCarson, April, P1 aAbecasis, Goncalo1 aDupuis, Josée1 aReiner, Alexander1 aKooperberg, Charles1 aHou, Lifang1 aPsaty, Bruce, M1 aWilson, James, G1 aLevy, Daniel1 aRotter, Jerome, I1 aBis, Joshua, C1 aSatizabal, Claudia, L1 aArking, Dan, E1 aLiu, Chunyu1 aTOPMed mtDNA Working Group in NHLBI Trans‐Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/950204492nas a2200649 4500008004100000022001400041245014000055210006900195260001600264520258200280100001902862700001302881700002202894700002502916700001702941700002402958700001402982700001902996700002203015700002703037700002903064700001703093700001903110700002203129700002103151700001903172700002103191700001803212700002203230700001603252700002303268700002203291700001903313700001703332700001903349700002303368700002103391700002203412700002003434700001903454700002403473700002403497700002003521700002103541700002003562700002403582700002303606700002003629700002503649700001903674700002003693700002303713700002803736700001603764700002603780856003603806 2023 eng d a1526-632X00aAssociation of Mitochondrial DNA Copy Number With Brain MRI Markers and Cognitive Function: A Meta-analysis of Community-Based Cohorts.0 aAssociation of Mitochondrial DNA Copy Number With Brain MRI Mark c2023 Mar 163 aBACKGROUND AND OBJECTIVES: Previous studies suggest lower mitochondrial DNA (mtDNA) copy number (CN) is associated with neurodegenerative diseases. However, whether mtDNA CN in whole blood is related to endophenotypes of Alzheimer's disease (AD) and AD related dementia (AD/ADRD) needs further investigation. We assessed the association of mtDNA CN with cognitive function and MRI measures in community-based samples of middle-aged to older adults.
METHODS: We included dementia-free participants from nine diverse community-based cohorts with whole-genome sequencing in the Trans-Omics for Precision Medicine (TOPMed) program. Circulating mtDNA CN was estimated as twice the ratio of the average coverage of mtDNA to nuclear DNA. Brain MRI markers included total brain, hippocampal, and white matter hyperintensity volumes. General cognitive function was derived from distinct cognitive domains. We performed cohort-specific association analyses of mtDNA CN with AD/ADRD endophenotypes assessed within ±5 years (i.e., cross-sectional analyses) or 5 to 20 years after blood draw (i.e., prospective analyses) adjusting for potential confounders. We further explored associations stratified by sex and age (<60 vs. ≥60 years). Fixed-effects or sample size-weighted meta-analyses were performed to combine results. Finally, we performed Mendelian randomization (MR) analyses to assess causality.
RESULTS: We included up to 19,152 participants (mean age 59 years, 57% women). Higher mtDNA CN was cross-sectionally associated with better general cognitive function (Beta=0.04; 95% CI 0.02, 0.06) independent of age, sex, batch effects, race/ethnicity, time between blood draw and cognitive evaluation, cohort-specific variables, and education. Additional adjustment for blood cell counts or cardiometabolic traits led to slightly attenuated results. We observed similar significant associations with cognition in prospective analyses, although of reduced magnitude. We found no significant associations between mtDNA CN and brain MRI measures in meta-analyses. MR analyses did not reveal a causal relation between mtDNA CN in blood and cognition.
DISCUSSION: Higher mtDNA CN in blood is associated with better current and future general cognitive function in large and diverse communities across the US. Although MR analyses did not support a causal role, additional research is needed to assess causality. Circulating mtDNA CN could serve nevertheless as a biomarker of current and future cognitive function in the community.
1 aZhang, Yuankai1 aLiu, Xue1 aWiggins, Kerri, L1 aKurniansyah, Nuzulul1 aGuo, Xiuqing1 aRodrigue, Amanda, L1 aZhao, Wei1 aYanek, Lisa, R1 aRatliff, Scott, M1 aPitsillides, Achilleas1 aPatiño, Juan, Sebastian1 aSofer, Tamar1 aArking, Dan, E1 aAustin, Thomas, R1 aBeiser, Alexa, S1 aBlangero, John1 aBoerwinkle, Eric1 aBressler, Jan1 aCurran, Joanne, E1 aHou, Lifang1 aHughes, Timothy, M1 aKardia, Sharon, L1 aLauner, Lenore1 aLevy, Daniel1 aMosley, Tom, H1 aNasrallah, Ilya, M1 aRich, Stephen, S1 aRotter, Jerome, I1 aSeshadri, Sudha1 aTarraf, Wassim1 aGonzález, Kevin, A1 aRamachandran, Vasan1 aYaffe, Kristine1 aNyquist, Paul, A1 aPsaty, Bruce, M1 aDeCarli, Charles, S1 aSmith, Jennifer, A1 aGlahn, David, C1 aGonzález, Hector, M1 aBis, Joshua, C1 aFornage, Myriam1 aHeckbert, Susan, R1 aFitzpatrick, Annette, L1 aLiu, Chunyu1 aSatizabal, Claudia, L uhttps://chs-nhlbi.org/node/932305367nas a2201033 4500008004100000022001400041245012400055210006900179260001600248300001200264490000800276520243900284653002502723653002502748653002902773653001102802653001602813653002402829653003302853653001702886100002102903700002002924700001802944700002002962700001802982700001903000700002203019700001603041700002003057700002703077700002603104700002403130700001703154700002103171700002203192700002403214700002103238700002103259700002503280700001803305700002003323700001803343700002503361700003103386700002303417700002003440700002203460700001903482700002303501700002203524700002303546700002403569700002003593700002003613700002503633700002303658700002103681700002303702700002603725700001703751700002203768700002603790700002303816700002103839700002103860700002003881700001903901700002003920700001803940700001503958700001703973700001203990700002104002700001904023700001704042700001504059700002204074700002104096700002204117700001604139700001804155700001804173700001904191700002004210700002404230700002504254700001804279856003604297 2023 eng d a1756-183300aAssociation of omega 3 polyunsaturated fatty acids with incident chronic kidney disease: pooled analysis of 19 cohorts.0 aAssociation of omega 3 polyunsaturated fatty acids with incident c2023 Jan 18 ae0729090 v3803 aOBJECTIVE: To assess the prospective associations of circulating levels of omega 3 polyunsaturated fatty acid (n-3 PUFA) biomarkers (including plant derived α linolenic acid and seafood derived eicosapentaenoic acid, docosapentaenoic acid, and docosahexaenoic acid) with incident chronic kidney disease (CKD).
DESIGN: Pooled analysis.
DATA SOURCES: A consortium of 19 studies from 12 countries identified up to May 2020.
STUDY SELECTION: Prospective studies with measured n-3 PUFA biomarker data and incident CKD based on estimated glomerular filtration rate.
DATA EXTRACTION AND SYNTHESIS: Each participating cohort conducted de novo analysis with prespecified and consistent exposures, outcomes, covariates, and models. The results were pooled across cohorts using inverse variance weighted meta-analysis.
MAIN OUTCOME MEASURES: Primary outcome of incident CKD was defined as new onset estimated glomerular filtration rate <60 mL/min/1.73 m. In a sensitivity analysis, incident CKD was defined as new onset estimated glomerular filtration rate <60 mL/min/1.73 m and <75% of baseline rate.
RESULTS: 25 570 participants were included in the primary outcome analysis and 4944 (19.3%) developed incident CKD during follow-up (weighted median 11.3 years). In multivariable adjusted models, higher levels of total seafood n-3 PUFAs were associated with a lower incident CKD risk (relative risk per interquintile range 0.92, 95% confidence interval 0.86 to 0.98; P=0.009, I=9.9%). In categorical analyses, participants with total seafood n-3 PUFA level in the highest fifth had 13% lower risk of incident CKD compared with those in the lowest fifth (0.87, 0.80 to 0.96; P=0.005, I=0.0%). Plant derived α linolenic acid levels were not associated with incident CKD (1.00, 0.94 to 1.06; P=0.94, I=5.8%). Similar results were obtained in the sensitivity analysis. The association appeared consistent across subgroups by age (≥60 <60 years), estimated glomerular filtration rate (60-89 ≥90 mL/min/1.73 m), hypertension, diabetes, and coronary heart disease at baseline.
CONCLUSIONS: Higher seafood derived n-3 PUFA levels were associated with lower risk of incident CKD, although this association was not found for plant derived n-3 PUFAs. These results support a favourable role for seafood derived n-3 PUFAs in preventing CKD.
10aalpha-Linolenic Acid10aFatty Acids, Omega-310aFatty Acids, Unsaturated10aHumans10aMiddle Aged10aProspective Studies10aRenal Insufficiency, Chronic10aRisk Factors1 aOng, Kwok, Leung1 aMarklund, Matti1 aHuang, Liping1 aRye, Kerry-Anne1 aHui, Nicholas1 aPan, Xiong-Fei1 aRebholz, Casey, M1 aKim, Hyunju1 aSteffen, Lyn, M1 avan Westing, Anniek, C1 aGeleijnse, Johanna, M1 aHoogeveen, Ellen, K1 aChen, Yun-Yu1 aChien, Kuo-Liong1 aFretts, Amanda, M1 aLemaitre, Rozenn, N1 aImamura, Fumiaki1 aForouhi, Nita, G1 aWareham, Nicholas, J1 aBirukov, Anna1 aJäger, Susanne1 aKuxhaus, Olga1 aSchulze, Matthias, B1 ade Mello, Vanessa, Derenji1 aTuomilehto, Jaakko1 aUusitupa, Matti1 aLindström, Jaana1 aTintle, Nathan1 aHarris, William, S1 aYamasaki, Keisuke1 aHirakawa, Yoichiro1 aNinomiya, Toshiharu1 aTanaka, Toshiko1 aFerrucci, Luigi1 aBandinelli, Stefania1 aVirtanen, Jyrki, K1 aVoutilainen, Ari1 aJayasena, Tharusha1 aThalamuthu, Anbupalam1 aPoljak, Anne1 aBustamante, Sonia1 aSachdev, Perminder, S1 aSenn, Mackenzie, K1 aRich, Stephen, S1 aTsai, Michael, Y1 aWood, Alexis, C1 aLaakso, Markku1 aLankinen, Maria1 aYang, Xiaowei1 aSun, Liang1 aLi, Huaixing1 aLin, Xu1 aNowak, Christoph1 aArnlöv, Johan1 aRiserus, Ulf1 aLind, Lars1 aLe Goff, Mélanie1 aSamieri, Cecilia1 aHelmer, Catherine1 aQian, Frank1 aMicha, Renata1 aTin, Adrienne1 aKöttgen, Anna1 ade Boer, Ian, H1 aSiscovick, David, S1 aMozaffarian, Dariush1 aWu, Jason, HY uhttps://chs-nhlbi.org/node/945603674nas a2200457 4500008004100000022001400041245015600055210006900211260001600280520226600296100002402562700002302586700001602609700002102625700002402646700001802670700002102688700002402709700002202733700002002755700001602775700002702791700002002818700002202838700001902860700002702879700001702906700002302923700002102946700001702967700002002984700002203004700002203026700002303048700002603071700002103097700002003118700002303138700001903161856003603180 2023 eng d a2380-659100aAssociation of Rare Protein-Truncating DNA Variants in APOB or PCSK9 With Low-density Lipoprotein Cholesterol Level and Risk of Coronary Heart Disease.0 aAssociation of Rare ProteinTruncating DNA Variants in APOB or PC c2023 Feb 013 aIMPORTANCE: Protein-truncating variants (PTVs) in apolipoprotein B (APOB) and proprotein convertase subtilisin/kexin type 9 (PCSK9) are associated with significantly lower low-density lipoprotein (LDL) cholesterol concentrations. The association of these PTVs with coronary heart disease (CHD) warrants further characterization in large, multiracial prospective cohort studies.
OBJECTIVE: To evaluate the association of PTVs in APOB and PCSK9 with LDL cholesterol concentrations and CHD risk.
DESIGN, SETTING, AND PARTICIPANTS: This studied included participants from 5 National Heart, Lung, and Blood Institute (NHLBI) studies and the UK Biobank. NHLBI study participants aged 5 to 84 years were recruited between 1971 and 2002 across the US and underwent whole-genome sequencing. UK Biobank participants aged 40 to 69 years were recruited between 2006 and 2010 in the UK and underwent whole-exome sequencing. Data were analyzed from June 2021 to October 2022.
EXPOSURES: PTVs in APOB and PCSK9.
MAIN OUTCOMES AND MEASURES: Estimated untreated LDL cholesterol levels and CHD.
RESULTS: Among 19 073 NHLBI participants (10 598 [55.6%] female; mean [SD] age, 52 [17] years), 139 (0.7%) carried an APOB or PCSK9 PTV, which was associated with 49 mg/dL (95% CI, 43-56) lower estimated untreated LDL cholesterol level. Over a median (IQR) follow-up of 21.5 (13.9-29.4) years, incident CHD was observed in 12 of 139 carriers (8.6%) vs 3029 of 18 934 noncarriers (16.0%), corresponding to an adjusted hazard ratio of 0.51 (95% CI, 0.28-0.89; P = .02). Among 190 464 UK Biobank participants (104 831 [55.0%] female; mean [SD] age, 57 [8] years), 662 (0.4%) carried a PTV, which was associated with 45 mg/dL (95% CI, 42-47) lower estimated untreated LDL cholesterol level. Estimated CHD risk by age 75 years was 3.7% (95% CI, 2.0-5.3) in carriers vs 7.0% (95% CI, 6.9-7.2) in noncarriers, corresponding to an adjusted hazard ratio of 0.51 (95% CI, 0.32-0.81; P = .004).
CONCLUSIONS AND RELEVANCE: Among 209 537 individuals in this study, 0.4% carried an APOB or PCSK9 PTV that was associated with less exposure to LDL cholesterol and a 49% lower risk of CHD.
1 aDron, Jacqueline, S1 aPatel, Aniruddh, P1 aZhang, Yiyi1 aJurgens, Sean, J1 aMaamari, Dimitri, J1 aWang, Minxian1 aBoerwinkle, Eric1 aMorrison, Alanna, C1 ade Vries, Paul, S1 aFornage, Myriam1 aHou, Lifang1 aLloyd-Jones, Donald, M1 aPsaty, Bruce, M1 aTracy, Russell, P1 aBis, Joshua, C1 aVasan, Ramachandran, S1 aLevy, Daniel1 aHeard-Costa, Nancy1 aRich, Stephen, S1 aGuo, Xiuqing1 aTaylor, Kent, D1 aGibbs, Richard, A1 aRotter, Jerome, I1 aWiller, Cristen, J1 aOelsner, Elizabeth, C1 aMoran, Andrew, E1 aPeloso, Gina, M1 aNatarajan, Pradeep1 aKhera, Amit, V uhttps://chs-nhlbi.org/node/928501675nas a2200565 4500008004100000022001400041245012700055210006900182260001600251300001400267490000800281653002100289653001300310653001100323653002500334653003300359100001600392700002400408700002400432700001900456700001600475700002200491700002600513700002100539700002100560700001500581700002000596700002300616700002200639700002000661700002100681700002800702700002200730700002000752700002700772700001600799700002000815700001900835700002000854700002700874700002600901700002100927700002100948700002100969700001700990700001901007700002601026700002101052856003601073 2023 eng d a1524-453900aAssociation of Severe Hypercholesterolemia and Familial Hypercholesterolemia Genotype With Risk of Coronary Heart Disease.0 aAssociation of Severe Hypercholesterolemia and Familial Hypercho c2023 May 16 a1556-15590 v14710aCoronary Disease10aGenotype10aHumans10aHypercholesterolemia10aHyperlipoproteinemia Type II1 aZhang, Yiyi1 aDron, Jacqueline, S1 aBellows, Brandon, K1 aKhera, Amit, V1 aLiu, Junxiu1 aBalte, Pallavi, P1 aOelsner, Elizabeth, C1 aAmr, Sami, Samir1 aLebo, Matthew, S1 aNagy, Anna1 aPeloso, Gina, M1 aNatarajan, Pradeep1 aRotter, Jerome, I1 aWiller, Cristen1 aBoerwinkle, Eric1 aBallantyne, Christie, M1 aLutsey, Pamela, L1 aFornage, Myriam1 aLloyd-Jones, Donald, M1 aHou, Lifang1 aPsaty, Bruce, M1 aBis, Joshua, C1 aFloyd, James, S1 aVasan, Ramachandran, S1 aHeard-Costa, Nancy, L1 aCarson, April, P1 aHall, Michael, E1 aRich, Stephen, S1 aGuo, Xiuqing1 aKazi, Dhruv, S1 ade Ferranti, Sarah, D1 aMoran, Andrew, E uhttps://chs-nhlbi.org/node/938804742nas a2200769 4500008004100000245009400041210006900135260001600204520254400220100002502764700001802789700002402807700002102831700002402852700001802876700001602894700002502910700001602935700002202951700001202973700002102985700002003006700002003026700002403046700001903070700002003089700002003109700002103129700002003150700002003170700001603190700001903206700001903225700002303244700002003267700002703287700002603314700002303340700002203363700001903385700001903404700002103423700002403444700002403468700001803492700002103510700002103531700002303552700002103575700001903596700002103615700002003636700001703656700002403673700002203697700001903719700002003738700002503758700001203783700002903795700002403824700002303848700002203871700002203893700002103915856003603936 2023 eng d00aCarriers of rare damaging genetic variants are at lower risk of atherosclerotic disease.0 aCarriers of rare damaging genetic variants are at lower risk of c2023 Aug 163 aBACKGROUND: The CCL2/CCR2 axis governs monocyte trafficking and recruitment to atherosclerotic lesions. Human genetic analyses and population-based studies support an association between circulating CCL2 levels and atherosclerosis. Still, it remains unknown whether pharmacological targeting of CCR2, the main CCL2 receptor, would provide protection against human atherosclerotic disease.
METHODS: In whole-exome sequencing data from 454,775 UK Biobank participants (40-69 years), we identified predicted loss-of-function (LoF) or damaging missense (REVEL score >0.5) variants within the gene. We prioritized variants associated with lower monocyte count (p<0.05) and tested associations with vascular risk factors and risk of atherosclerotic disease over a mean follow-up of 14 years. The results were replicated in a pooled cohort of three independent datasets (TOPMed, deCODE and Penn Medicine BioBank; total n=441,445) and the effect of the most frequent damaging variant was experimentally validated.
RESULTS: A total of 45 predicted LoF or damaging missense variants were identified in the gene, 4 of which were also significantly associated with lower monocyte count, but not with other white blood cell counts. Heterozygous carriers of these variants were at a lower risk of a combined atherosclerosis outcome, showed a lower burden of atherosclerosis across four vascular beds, and were at a lower lifetime risk of coronary artery disease and myocardial infarction. There was no evidence of association with vascular risk factors including LDL-cholesterol, blood pressure, glycemic status, or C-reactive protein. Using a cAMP assay, we found that cells transfected with the most frequent damaging variant (3:46358273:T:A, M249K, 547 carriers, frequency: 0.14%) show a decrease in signaling in response to CCL2. The associations of the M249K variant with myocardial infarction were consistent across cohorts (OR : 0.62 95%CI: 0.39-0.96; OR : 0.64 95%CI: 0.34-1.19; OR : 0.64 95%CI: 0.45-0.90). In a phenome-wide association study, we found no evidence for higher risk of common infections or mortality among carriers of damaging variants.
CONCLUSIONS: Heterozygous carriers of damaging variants have a lower burden of atherosclerosis and lower lifetime risk of myocardial infarction. In conjunction with previous evidence from experimental and epidemiological studies, our findings highlight the translational potential of CCR2-targeting as an atheroprotective approach.
1 aGeorgakis, Marios, K1 aMalik, Rainer1 aHasbani, Natalie, R1 aShakt, Gabrielle1 aMorrison, Alanna, C1 aTsao, Noah, L1 aJudy, Renae1 aMitchell, Braxton, D1 aXu, Huichun1 aMontasser, May, E1 aDo, Ron1 aKenny, Eimear, E1 aLoos, Ruth, J F1 aTerry, James, G1 aCarr, John, Jeffrey1 aBis, Joshua, C1 aPsaty, Bruce, M1 aLongstreth, W T1 aYoung, Kendra, A1 aLutz, Sharon, M1 aCho, Michael, H1 aBroome, Jai1 aKhan, Alyna, T1 aWang, Fei, Fei1 aHeard-Costa, Nancy1 aSeshadri, Sudha1 aVasan, Ramachandran, S1 aPalmer, Nicholette, D1 aFreedman, Barry, I1 aBowden, Donald, W1 aYanek, Lisa, R1 aKral, Brian, G1 aBecker, Lewis, C1 aPeyser, Patricia, A1 aBielak, Lawrence, F1 aAmmous, Farah1 aCarson, April, P1 aHall, Michael, E1 aRaffield, Laura, M1 aRich, Stephen, S1 aPost, Wendy, S1 aTracy, Russel, P1 aTaylor, Kent, D1 aGuo, Xiuqing1 aMahaney, Michael, C1 aCurran, Joanne, E1 aBlangero, John1 aClarke, Shoa, L1 aHaessler, Jeffrey, W1 aHu, Yao1 aAssimes, Themistocles, L1 aKooperberg, Charles1 aDamrauer, Scott, M1 aRotter, Jerome, I1 ade Vries, Paul, S1 aDichgans, Martin uhttps://chs-nhlbi.org/node/947903429nas a2200505 4500008004100000022001400041245009400055210006900149260001600218520189000234100002302124700002202147700002102169700002402190700002602214700002302240700002002263700002102283700001902304700001902323700002502342700001902367700002202386700001702408700002002425700002102445700001502466700002302481700002002504700002102524700002002545700001902565700002102584700001502605700002102620700002702641700002002668700002002688700002402708700002202732700001702754700002102771710009502792856003602887 2023 eng d a1935-554800aClonal Hematopoiesis of Indeterminate Potential (CHIP) and Incident Type 2 Diabetes Risk.0 aClonal Hematopoiesis of Indeterminate Potential CHIP and Inciden c2023 Sep 273 aOBJECTIVE: Clonal hematopoiesis of indeterminate potential (CHIP) is an aging-related accumulation of somatic mutations in hematopoietic stem cells, leading to clonal expansion. CHIP presence has been implicated in atherosclerotic coronary heart disease (CHD) and all-cause mortality, but its association with incident type 2 diabetes (T2D) is unknown. We hypothesized that CHIP is associated with elevated risk of T2D.
RESEARCH DESIGN AND METHODS: CHIP was derived from whole-genome sequencing of blood DNA in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) prospective cohorts. We performed analysis for 17,637 participants from six cohorts, without prior T2D, cardiovascular disease, or cancer. We evaluated baseline CHIP versus no CHIP prevalence with incident T2D, including associations with DNMT3A, TET2, ASXL1, JAK2, and TP53 variants. We estimated multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) with adjustment for age, sex, BMI, smoking, alcohol, education, self-reported race/ethnicity, and combined cohorts' estimates via fixed-effects meta-analysis.
RESULTS: Mean (SD) age was 63.4 (11.5) years, 76% were female, and CHIP prevalence was 6.0% (n = 1,055) at baseline. T2D was diagnosed in n = 2,467 over mean follow-up of 9.8 years. Participants with CHIP had 23% (CI = 1.04, 1.45) higher risk of T2D than those with no CHIP. Specifically, higher risk was for TET2 (HR 1.48; CI = 1.05, 2.08) and ASXL1 (HR 1.76; CI = 1.03, 2.99) mutations; DNMT3A was nonsignificant (HR 1.15; CI = 0.93, 1.43). Statistical power was limited for JAK2 and TP53 analyses.
CONCLUSIONS: CHIP was associated with higher incidence of T2D. CHIP mutations located on genes implicated in CHD and mortality were also related to T2D, suggesting shared aging-related pathology.
1 aTobias, Deirdre, K1 aManning, Alisa, K1 aWessel, Jennifer1 aRaghavan, Sridharan1 aWesterman, Kenneth, E1 aBick, Alexander, G1 aDiCorpo, Daniel1 aWhitsel, Eric, A1 aCollins, Jason1 aCorrea, Adolfo1 aCupples, Adrienne, L1 aDupuis, Josée1 aGoodarzi, Mark, O1 aGuo, Xiuqing1 aHoward, Barbara1 aLange, Leslie, A1 aLiu, Simin1 aRaffield, Laura, M1 aReiner, Alex, P1 aRich, Stephen, S1 aTaylor, Kent, D1 aTinker, Lesley1 aWilson, James, G1 aWu, Peitao1 aCarson, April, P1 aVasan, Ramachandran, S1 aFornage, Myriam1 aPsaty, Bruce, M1 aKooperberg, Charles1 aRotter, Jerome, I1 aMeigs, James1 aManson, JoAnn, E1 aTOPMed Diabetes Working Group; National Heart, Lung, and Blood Institute TOPMed Consortium uhttps://chs-nhlbi.org/node/950602868nas a2200625 4500008004100000245005900041210005800100260001600158520116400174100001801338700001901356700002001375700002501395700002301420700002501443700002101468700002101489700001801510700002001528700001401548700002101562700002101583700002001604700001901624700002001643700001901663700001701682700002401699700002001723700001501743700001601758700001701774700002001791700002001811700001901831700001801850700001601868700002301884700001801907700002301925700002101948700002201969700001701991700001502008700001802023700002302041700001802064700001702082700001802099700001702117700002402134700002502158700002302183856003602206 2023 eng d00aDeterminants of mosaic chromosomal alteration fitness.0 aDeterminants of mosaic chromosomal alteration fitness c2023 Oct 213 aClonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well-understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our estimates of mCA fitness were correlated (R = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using a theoretical probability distribution. Individuals with lymphoid-associated mCAs had a significantly higher white blood cell count and faster clonal expansion rate. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified , , and locus variants as modulators of mCA clonal expansion rate.
1 aPershad, Yash1 aMack, Taralynn1 aPoisner, Hannah1 aJakubek, Yasminka, A1 aStilp, Adrienne, M1 aMitchell, Braxton, D1 aLewis, Joshua, P1 aBoerwinkle, Eric1 aLoos, Ruth, J1 aChami, Nathalie1 aWang, Zhe1 aBarnes, Kathleen1 aPankratz, Nathan1 aFornage, Myriam1 aRedline, Susan1 aPsaty, Bruce, M1 aBis, Joshua, C1 aShojaie, Ali1 aSilverman, Edwin, K1 aCho, Michael, H1 aYun, Jeong1 aDeMeo, Dawn1 aLevy, Daniel1 aJohnson, Andrew1 aMathias, Rasika1 aTaub, Margaret1 aArnett, Donna1 aNorth, Kari1 aRaffield, Laura, M1 aCarson, April1 aDoyle, Margaret, F1 aRich, Stephen, S1 aRotter, Jerome, I1 aGuo, Xiuqing1 aCox, Nancy1 aRoden, Dan, M1 aFranceschini, Nora1 aDesai, Pinkal1 aReiner, Alex1 aAuer, Paul, L1 aScheet, Paul1 aJaiswal, Siddhartha1 aWeinstock, Joshua, S1 aBick, Alexander, G uhttps://chs-nhlbi.org/node/958803124nas a2200769 4500008004100000022001400041245010100055210006900156260001600225300000900241490000700250520097600257653001901233653001401252653001101266653003801277653003401315653001101349653000901360653003101369653002201400653001701422100002501439700002401464700001901488700002001507700002101527700001901548700002201567700002501589700001801614700001801632700001701650700001901667700001801686700002001704700001301724700001801737700002301755700002301778700001301801700001501814700003201829700002101861700001801882700002201900700001401922700001601936700002701952700002401979700002002003700002402023700001902047700002002066700002102086700002102107700002102128700002202149700001902171700002502190700002302215700001702238700002202255700002402277700001702301856003602318 2023 eng d a2041-172300aEvaluating the use of blood pressure polygenic risk scores across race/ethnic background groups.0 aEvaluating the use of blood pressure polygenic risk scores acros c2023 Jun 02 a32020 v143 aWe assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.
10aBlood Pressure10aEthnicity10aFemale10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aMale10aMultifactorial Inheritance10aPopulation Health10aRisk Factors1 aKurniansyah, Nuzulul1 aGoodman, Matthew, O1 aKhan, Alyna, T1 aWang, Jiongming1 aFeofanova, Elena1 aBis, Joshua, C1 aWiggins, Kerri, L1 aHuffman, Jennifer, E1 aKelly, Tanika1 aElfassy, Tali1 aGuo, Xiuqing1 aPalmas, Walter1 aLin, Henry, J1 aHwang, Shih-Jen1 aGao, Yan1 aYoung, Kendra1 aKinney, Gregory, L1 aSmith, Jennifer, A1 aYu, Bing1 aLiu, Simin1 aWassertheil-Smoller, Sylvia1 aManson, JoAnn, E1 aZhu, Xiaofeng1 aChen, Yii-Der Ida1 aLee, I-Te1 aGu, Charles1 aLloyd-Jones, Donald, M1 aZöllner, Sebastian1 aFornage, Myriam1 aKooperberg, Charles1 aCorrea, Adolfo1 aPsaty, Bruce, M1 aArnett, Donna, K1 aIsasi, Carmen, R1 aRich, Stephen, S1 aKaplan, Robert, C1 aRedline, Susan1 aMitchell, Braxton, D1 aFranceschini, Nora1 aLevy, Daniel1 aRotter, Jerome, I1 aMorrison, Alanna, C1 aSofer, Tamar uhttps://chs-nhlbi.org/node/937908759nas a2202509 4500008004100000022001400041245012000055210006900175260000900244300001200253490000700265520183300272100002602105700002402131700002202155700002002177700002302197700001802220700002402238700002102262700001802283700002302301700002002324700002402344700002002368700002002388700002202408700002102430700001702451700002502468700002102493700001802514700001502532700001702547700002202564700002102586700002302607700002102630700002502651700002002676700002402696700002002720700002102740700002002761700002302781700001702804700002202821700002302843700002302866700001502889700002302904700002102927700001402948700001802962700002102980700002203001700001603023700002803039700001903067700001903086700002103105700001903126700002303145700001703168700002303185700002203208700002703230700001803257700001803275700001803293700001703311700001903328700001803347700001603365700001903381700002503400700002103425700002303446700002103469700002103490700002203511700001803533700002203551700001303573700003303586700002403619700001803643700002303661700002103684700002203705700001203727700002103739700002103760700002003781700002403801700002503825700002103850700001603871700002103887700002003908700002003928700001803948700001703966700002003983700002104003700002004024700002304044700002804067700001904095700002204114700002304136700002004159700001504179700001704194700001704211700001804228700002404246700002304270700002204293700002104315700002004336700002704356700002604383700002604409700002604435700001804461700002004479700001904499700002304518700001804541700001904559700002104578700002804599700002504627700001704652700002004669700001604689700002304705700001904728700002404747700001904771700002004790700002104810700002804831700002204859700002604881700001804907700001804925700002004943700001504963700001304978700001704991700001905008700001405027700002105041700002305062700002205085700002305107700001805130700001905148700001605167700002005183700002205203700002205225700001905247700001905266700001905285700001905304700002205323700002705345700001905372700001905391700002005410700002205430700002005452700002405472700001505496700001905511700002605530700001905556700002405575700001705599700001505616700001805631700002105649700002305670700002205693700001805715700002405733700002205757700002305779700002405802700002705826700002905853700002405882700002505906700002005931700001605951700001705967700002005984700002006004700002306024700002006047700002106067700002006088700002106108700001806129700002406147700002206171700002006193856003606213 2023 eng d a1664-802100aGene-educational attainment interactions in a multi-population genome-wide meta-analysis identify novel lipid loci.0 aGeneeducational attainment interactions in a multipopulation gen c2023 a12353370 v143 aEducational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes. A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: "Some College" (yes/no, for any education beyond high school) and "Graduated College" (yes/no, for completing a 4-year college degree). Genome-wide significant ( < 5 × 10) and suggestive ( < 1 × 10) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals). In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (), brain (), and liver () biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue. Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.
1 aFuentes, Lisa, de Las1 aSchwander, Karen, L1 aBrown, Michael, R1 aBentley, Amy, R1 aWinkler, Thomas, W1 aSung, Yun, Ju1 aMunroe, Patricia, B1 aMiller, Clint, L1 aAschard, Hugo1 aAslibekyan, Stella1 aBartz, Traci, M1 aBielak, Lawrence, F1 aChai, Jin, Fang1 aCheng, Ching-Yu1 aDorajoo, Rajkumar1 aFeitosa, Mary, F1 aGuo, Xiuqing1 aHartwig, Fernando, P1 aHorimoto, Andrea1 aKolcic, Ivana1 aLim, Elise1 aLiu, Yongmei1 aManning, Alisa, K1 aMarten, Jonathan1 aMusani, Solomon, K1 aNoordam, Raymond1 aPadmanabhan, Sandosh1 aRankinen, Tuomo1 aRichard, Melissa, A1 aRidker, Paul, M1 aSmith, Albert, V1 aVojinovic, Dina1 aZonderman, Alan, B1 aAlver, Maris1 aBoissel, Mathilde1 aChristensen, Kaare1 aFreedman, Barry, I1 aGao, Chuan1 aGiulianini, Franco1 aHarris, Sarah, E1 aHe, Meian1 aHsu, Fang-Chi1 aKuhnel, Brigitte1 aLaguzzi, Federica1 aLi, Xiaoyin1 aLyytikäinen, Leo-Pekka1 aNolte, Ilja, M1 aPoveda, Alaitz1 aRauramaa, Rainer1 aRiaz, Muhammad1 aRobino, Antonietta1 aSofer, Tamar1 aTakeuchi, Fumihiko1 aTayo, Bamidele, O1 avan der Most, Peter, J1 aVerweij, Niek1 aWare, Erin, B1 aWeiss, Stefan1 aWen, Wanqing1 aYanek, Lisa, R1 aZhan, Yiqiang1 aAmin, Najaf1 aArking, Dan, E1 aBallantyne, Christie1 aBoerwinkle, Eric1 aBrody, Jennifer, A1 aBroeckel, Ulrich1 aCampbell, Archie1 aCanouil, Mickaël1 aChai, Xiaoran1 aChen, Yii-Der Ida1 aChen, Xu1 aChitrala, Kumaraswamy, Naidu1 aConcas, Maria, Pina1 ade Faire, Ulf1 ade Mutsert, Renée1 ade Silva, Janaka1 ade Vries, Paul, S1 aDo, Ahn1 aFaul, Jessica, D1 aFisher, Virginia1 aFloyd, James, S1 aForrester, Terrence1 aFriedlander, Yechiel1 aGirotto, Giorgia1 aGu, Charles1 aHallmans, Göran1 aHeikkinen, Sami1 aHeng, Chew-Kiat1 aHomuth, Georg1 aHunt, Steven1 aIkram, Arfan, M1 aJacobs, David, R1 aKavousi, Maryam1 aKhor, Chiea, Chuen1 aKilpeläinen, Tuomas, O1 aKoh, Woon-Puay1 aKomulainen, Pirjo1 aLangefeld, Carl, D1 aLiang, Jingjing1 aLiu, Kiang1 aLiu, Jianjun1 aLohman, Kurt1 aMägi, Reedik1 aManichaikul, Ani, W1 aMcKenzie, Colin, A1 aMeitinger, Thomas1 aMilaneschi, Yuri1 aNauck, Matthias1 aNelson, Christopher, P1 aO'Connell, Jeffrey, R1 aPalmer, Nicholette, D1 aPereira, Alexandre, C1 aPerls, Thomas1 aPeters, Annette1 aPolasek, Ozren1 aRaitakari, Olli, T1 aRice, Kenneth1 aRice, Treva, K1 aRich, Stephen, S1 aSabanayagam, Charumathi1 aSchreiner, Pamela, J1 aShu, Xiao-Ou1 aSidney, Stephen1 aSims, Mario1 aSmith, Jennifer, A1 aStarr, John, M1 aStrauch, Konstantin1 aTai, Shyong, E1 aTaylor, Kent, D1 aTsai, Michael, Y1 aUitterlinden, André, G1 avan Heemst, Diana1 aWaldenberger, Melanie1 aWang, Ya-Xing1 aBin Wei, Wen-1 aWilson, Gregory1 aXuan, Deng1 aYao, Jie1 aYu, Caizheng1 aYuan, Jian-Min1 aZhao, Wei1 aBecker, Diane, M1 aBonnefond, Amélie1 aBowden, Donald, W1 aCooper, Richard, S1 aDeary, Ian, J1 aDivers, Jasmin1 aEsko, Tõnu1 aFranks, Paul, W1 aFroguel, Philippe1 aGieger, Christian1 aJonas, Jost, B1 aKato, Norihiro1 aLakka, Timo, A1 aLeander, Karin1 aLehtimäki, Terho1 aMagnusson, Patrik, K E1 aNorth, Kari, E1 aNtalla, Ioanna1 aPenninx, Brenda1 aSamani, Nilesh, J1 aSnieder, Harold1 aSpedicati, Beatrice1 aHarst, Pim1 aVölzke, Henry1 aWagenknecht, Lynne, E1 aWeir, David, R1 aWojczynski, Mary, K1 aWu, Tangchun1 aZheng, Wei1 aZhu, Xiaofeng1 aBouchard, Claude1 aChasman, Daniel, I1 aEvans, Michele, K1 aFox, Ervin, R1 aGudnason, Vilmundur1 aHayward, Caroline1 aHorta, Bernardo, L1 aKardia, Sharon, L R1 aKrieger, Jose, Eduardo1 aMook-Kanamori, Dennis, O1 aPeyser, Patricia, A1 aProvince, Michael, M1 aPsaty, Bruce, M1 aRudan, Igor1 aSim, Xueling1 aSmith, Blair, H1 avan Dam, Rob, M1 aDuijn, Cornelia, M1 aWong, Tien, Yin1 aArnett, Donna, K1 aRao, Dabeeru, C1 aGauderman, James1 aLiu, Ching-Ti1 aMorrison, Alanna, C1 aRotter, Jerome, I1 aFornage, Myriam uhttps://chs-nhlbi.org/node/953502705nas a2200505 4500008004100000245011300041210006900154260001600223520116400239100001801403700001801421700002101439700002001460700001901480700002301499700002101522700002001543700002001563700001801583700001901601700002001620700001801640700001901658700002301677700002401700700001701724700002001741700002301761700002501784700001701809700002701826700002101853700002201874700002201896700002101918700002401939700002101963700002601984700002202010700002402032700001902056700002302075710006502098856003602163 2023 eng d00aGenetic control of mRNA splicing as a potential mechanism for incomplete penetrance of rare coding variants.0 aGenetic control of mRNA splicing as a potential mechanism for in c2023 Jan 313 aExonic variants present some of the strongest links between genotype and phenotype. However, these variants can have significant inter-individual pathogenicity differences, known as variable penetrance. In this study, we propose a model where genetically controlled mRNA splicing modulates the pathogenicity of exonic variants. By first cataloging exonic inclusion from RNA-seq data in GTEx v8, we find that pathogenic alleles are depleted on highly included exons. Using a large-scale phased WGS data from the TOPMed consortium, we observe that this effect may be driven by common splice-regulatory genetic variants, and that natural selection acts on haplotype configurations that reduce the transcript inclusion of putatively pathogenic variants, especially when limiting to haploinsufficient genes. Finally, we test if this effect may be relevant for autism risk using families from the Simons Simplex Collection, but find that splicing of pathogenic alleles has a penetrance reducing effect here as well. Overall, our results indicate that common splice-regulatory variants may play a role in reducing the damaging effects of rare exonic variants.
1 aEinson, Jonah1 aGlinos, Dafni1 aBoerwinkle, Eric1 aCastaldi, Peter1 aDarbar, Dawood1 ade Andrade, Mariza1 aEllinor, Patrick1 aFornage, Myriam1 aGabriel, Stacey1 aGermer, Soren1 aGibbs, Richard1 aHersh, Craig, P1 aJohnsen, Jill1 aKaplan, Robert1 aKonkle, Barbara, A1 aKooperberg, Charles1 aNassir, Rami1 aLoos, Ruth, J F1 aMeyers, Deborah, A1 aMitchell, Braxton, D1 aPsaty, Bruce1 aVasan, Ramachandran, S1 aRich, Stephen, S1 aRienstra, Michael1 aRotter, Jerome, I1 aSaferali, Aabida1 aShoemaker, Benjamin1 aSilverman, Edwin1 aSmith, Albert, Vernon1 aMohammadi, Pejman1 aCastel, Stephane, E1 aIossifov, Ivan1 aLappalainen, Tuuli1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/928604639nas a2201273 4500008004100000022001400041245008500055210006900140260001600209300001300225490000600238520107000244653002301314653001801337653001101355653001601366653001301382653002301395653001401418100002501432700002301457700001901480700002001499700001701519700002301536700002601559700002101585700002101606700002401627700002001651700002101671700002001692700002001712700001901732700001901751700002401770700001901794700002101813700001801834700002001852700002501872700002001897700001901917700002301936700002401959700001801983700001602001700002002017700002202037700001902059700002602078700002302104700002202127700002002149700002702169700002302196700001902219700002102238700001902259700001402278700001902292700002302311700002302334700002202357700002102379700002502400700001902425700001902444700002302463700001302486700002302499700002202522700002102544700002402565700002302589700002102612700002302633700002102656700002802677700002202705700001902727700001902746700001702765700002002782700002302802700001602825700001902841700002202860700001602882700002202898700001802920700002402938700001902962700001502981700002202996700002403018700001803042700002503060700002503085700002103110700001803131700002003149700002303169700002403192700002503216700002303241710006503264856003603329 2023 eng d a2375-254800aThe genetic determinants of recurrent somatic mutations in 43,693 blood genomes.0 agenetic determinants of recurrent somatic mutations in 43693 blo c2023 Apr 28 aeabm49450 v93 aNononcogenic somatic mutations are thought to be uncommon and inconsequential. To test this, we analyzed 43,693 National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine blood whole genomes from 37 cohorts and identified 7131 non-missense somatic mutations that are recurrently mutated in at least 50 individuals. These recurrent non-missense somatic mutations (RNMSMs) are not clearly explained by other clonal phenomena such as clonal hematopoiesis. RNMSM prevalence increased with age, with an average 50-year-old having 27 RNMSMs. Inherited germline variation associated with RNMSM acquisition. These variants were found in genes involved in adaptive immune function, proinflammatory cytokine production, and lymphoid lineage commitment. In addition, the presence of eight specific RNMSMs associated with blood cell traits at effect sizes comparable to Mendelian genetic mutations. Overall, we found that somatic mutations in blood are an unexpectedly common phenomenon with ancestry-specific determinants and human health consequences.
10aGerm-Line Mutation10aHematopoiesis10aHumans10aMiddle Aged10aMutation10aMutation, Missense10aPhenotype1 aWeinstock, Joshua, S1 aLaurie, Cecelia, A1 aBroome, Jai, G1 aTaylor, Kent, D1 aGuo, Xiuqing1 aShuldiner, Alan, R1 aO'Connell, Jeffrey, R1 aLewis, Joshua, P1 aBoerwinkle, Eric1 aBarnes, Kathleen, C1 aChami, Nathalie1 aKenny, Eimear, E1 aLoos, Ruth, J F1 aFornage, Myriam1 aRedline, Susan1 aCade, Brian, E1 aGilliland, Frank, D1 aChen, Zhanghua1 aGauderman, James1 aKumar, Rajesh1 aGrammer, Leslie1 aSchleimer, Robert, P1 aPsaty, Bruce, M1 aBis, Joshua, C1 aBrody, Jennifer, A1 aSilverman, Edwin, K1 aYun, Jeong, H1 aQiao, Dandi1 aWeiss, Scott, T1 aLasky-Su, Jessica1 aDeMeo, Dawn, L1 aPalmer, Nicholette, D1 aFreedman, Barry, I1 aBowden, Donald, W1 aCho, Michael, H1 aVasan, Ramachandran, S1 aJohnson, Andrew, D1 aYanek, Lisa, R1 aBecker, Lewis, C1 aKardia, Sharon1 aHe, Jiang1 aKaplan, Robert1 aHeckbert, Susan, R1 aSmith, Nicholas, L1 aWiggins, Kerri, L1 aArnett, Donna, K1 aIrvin, Marguerite, R1 aTiwari, Hemant1 aCorrea, Adolfo1 aRaffield, Laura, M1 aGao, Yan1 ade Andrade, Mariza1 aRotter, Jerome, I1 aRich, Stephen, S1 aManichaikul, Ani, W1 aKonkle, Barbara, A1 aJohnsen, Jill, M1 aWheeler, Marsha, M1 aCuster, Brian, S1 aDuggirala, Ravindranath1 aCurran, Joanne, E1 aBlangero, John1 aGui, Hongsheng1 aXiao, Shujie1 aWilliams, Keoki1 aMeyers, Deborah, A1 aLi, Xingnan1 aOrtega, Victor1 aMcGarvey, Stephen1 aGu, Charles1 aChen, Yii-Der Ida1 aLee, Wen-Jane1 aShoemaker, Benjamin1 aDarbar, Dawood1 aRoden, Dan1 aAlbert, Christine1 aKooperberg, Charles1 aDesai, Pinkal1 aBlackwell, Thomas, W1 aAbecasis, Goncalo, R1 aSmith, Albert, V1 aKang, Hyun, M1 aMathias, Rasika1 aNatarajan, Pradeep1 aJaiswal, Siddhartha1 aReiner, Alexander, P1 aBick, Alexander, G1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/941902914nas a2200553 4500008004100000245010000041210006900141260001600210520130500226100002001531700001901551700002001570700002501590700002101615700002401636700002101660700002101681700002401702700002001726700002101746700001601767700002401783700001701807700002401824700001701848700002301865700002301888700002201911700001801933700002001951700001601971700002001987700002002007700001902027700001902046700002002065700002402085700002102109700001902130700001802149700002202167700001402189700001502203700001702218700002302235700001702258710004902275856003602324 2023 eng d00aMachine learning models for blood pressure phenotypes combining multiple polygenic risk scores.0 aMachine learning models for blood pressure phenotypes combining c2023 Dec 143 aWe construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1% to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8% to 5.1% (SBP) and 4.7% to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.
1 aHrytsenko, Yana1 aShea, Benjamin1 aElgart, Michael1 aKurniansyah, Nuzulul1 aLyons, Genevieve1 aMorrison, Alanna, C1 aCarson, April, P1 aHaring, Bernhard1 aMitchel, Braxton, D1 aPsaty, Bruce, M1 aJaeger, Byron, C1 aGu, Charles1 aKooperberg, Charles1 aLevy, Daniel1 aLloyd-Jones, Donald1 aChoi, Eunhee1 aBrody, Jennifer, A1 aSmith, Jennifer, A1 aRotter, Jerome, I1 aMoll, Matthew1 aFornage, Myriam1 aSimon, Noah1 aCastaldi, Peter1 aCasanova, Ramon1 aChung, Ren-Hua1 aKaplan, Robert1 aLoos, Ruth, J F1 aKardia, Sharon, L R1 aRich, Stephen, S1 aRedline, Susan1 aKelly, Tanika1 aO'Connor, Timothy1 aZhao, Wei1 aKim, Wonji1 aGuo, Xiuqing1 aChen, Yii, Der Ida1 aSofer, Tamar1 aTrans-Omics in Precision Medicine Consortium uhttps://chs-nhlbi.org/node/958603311nas a2200829 4500008004100000022001400041245009300055210006900148260001300217300001400230490000700244520101000251653001701261653001801278653003401296653002301330653001101353653001401364653002301378100002501401700001501426700002001441700001701461700002001478700001701498700001801515700002101533700001901554700002101573700002301594700002101617700002301638700001701661700001701678700002401695700001501719700002301734700002001757700001701777700002401794700002101818700002001839700001701859700002601876700002201902700002301924700002501947700002601972700001601998700002102014700002402035700002302059700001702082700002302099700002702122700001902149700002102168700002202189700002402211700002302235700002002258700001902278700002002297700001502317700001202332700001802344700002302362700002502385700001702410700001802427856003602445 2023 eng d a1546-171800aMosaic chromosomal alterations in blood across ancestries using whole-genome sequencing.0 aMosaic chromosomal alterations in blood across ancestries using c2023 Nov a1912-19190 v553 aMegabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis.
10aBlack People10aGenome, Human10aGenome-Wide Association Study10aHispanic or Latino10aHumans10aMosaicism10aPrecision Medicine1 aJakubek, Yasminka, A1 aZhou, Ying1 aStilp, Adrienne1 aBacon, Jason1 aWong, Justin, W1 aOzcan, Zuhal1 aArnett, Donna1 aBarnes, Kathleen1 aBis, Joshua, C1 aBoerwinkle, Eric1 aBrody, Jennifer, A1 aCarson, April, P1 aChasman, Daniel, I1 aChen, Jiawen1 aCho, Michael1 aConomos, Matthew, P1 aCox, Nancy1 aDoyle, Margaret, F1 aFornage, Myriam1 aGuo, Xiuqing1 aKardia, Sharon, L R1 aLewis, Joshua, P1 aLoos, Ruth, J F1 aMa, Xiaolong1 aMachiela, Mitchell, J1 aMack, Taralynn, M1 aMathias, Rasika, A1 aMitchell, Braxton, D1 aMychaleckyj, Josyf, C1 aNorth, Kari1 aPankratz, Nathan1 aPeyser, Patricia, A1 aPreuss, Michael, H1 aPsaty, Bruce1 aRaffield, Laura, M1 aVasan, Ramachandran, S1 aRedline, Susan1 aRich, Stephen, S1 aRotter, Jerome, I1 aSilverman, Edwin, K1 aSmith, Jennifer, A1 aSmith, Aaron, P1 aTaub, Margaret1 aTaylor, Kent, D1 aYun, Jeong1 aLi, Yun1 aDesai, Pinkal1 aBick, Alexander, G1 aReiner, Alexander, P1 aScheet, Paul1 aAuer, Paul, L uhttps://chs-nhlbi.org/node/953813865nas a2204393 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2023 eng d00aMulti-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications.0 aMultiancestry genomewide study in 25 million individuals reveals c2023 Mar 313 aType 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P<5×10 ) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care.
1 aSuzuki, Ken1 aHatzikotoulas, Konstantinos1 aSoutham, Lorraine1 aTaylor, Henry, J1 aYin, Xianyong1 aLorenz, Kim, M1 aMandla, Ravi1 aHuerta-Chagoya, Alicia1 aRayner, Nigel, W1 aBocher, Ozvan1 ade, S, V Arruda A1 aSonehara, Kyuto1 aNamba, Shinichi1 aLee, Simon, S K1 aPreuss, Michael, H1 aPetty, Lauren, E1 aSchroeder, Philip1 aVanderwerff, Brett1 aKals, Mart1 aBragg, Fiona1 aLin, Kuang1 aGuo, Xiuqing1 aZhang, Weihua1 aYao, Jie1 aKim, Young, Jin1 aGraff, Mariaelisa1 aTakeuchi, Fumihiko1 aNano, Jana1 aLamri, Amel1 aNakatochi, Masahiro1 aMoon, Sanghoon1 aScott, Robert, A1 aCook, James, P1 aLee, Jung-Jin1 aPan, Ian1 aTaliun, Daniel1 aParra, Esteban, J1 aChai, Jin-Fang1 aBielak, Lawrence, F1 aTabara, Yasuharu1 aHai, Yang1 aThorleifsson, Gudmar1 aGrarup, Niels1 aSofer, Tamar1 aWuttke, Matthias1 aSarnowski, Chloe1 aGieger, Christian1 aNousome, Darryl1 aTrompet, Stella1 aKwak, Soo-Heon1 aLong, Jirong1 aSun, Meng1 aTong, Lin1 aChen, Wei-Min1 aNongmaithem, Suraj, S1 aNoordam, Raymond1 aJ Y Lim, Victor1 aTam, Claudia, H T1 aJoo, Yoonjung, Yoonie1 aChen, Chien-Hsiun1 aRaffield, Laura, M1 aPrins, Bram, Peter1 aNicolas, Aude1 aYanek, Lisa, R1 aChen, Guanjie1 aBrody, Jennifer, A1 aKabagambe, Edmond1 aAn, Ping1 aXiang, Anny, H1 aChoi, Hyeok, Sun1 aCade, Brian, E1 aTan, Jingyi1 aBroadaway, Alaine1 aWilliamson, Alice1 aKamali, Zoha1 aCui, Jinrui1 aAdair, Linda, S1 aAdeyemo, Adebowale1 aAguilar-Salinas, Carlos, A1 aAhluwalia, Tarunveer, S1 aAnand, Sonia, S1 aBertoni, Alain1 aBork-Jensen, Jette1 aBrandslund, Ivan1 aBuchanan, Thomas, A1 aBurant, Charles, F1 aButterworth, Adam, S1 aCanouil, Mickaël1 aChan, Juliana, C N1 aChang, Li-Ching1 aChee, Miao-Li1 aChen, Ji1 aChen, Shyh-Huei1 aChen, Yuan-Tsong1 aChen, Zhengming1 aChuang, Lee-Ming1 aCushman, Mary1 aDanesh, John1 aDas, Swapan, K1 ade Silva, Janaka1 aDedoussis, George1 aDimitrov, Latchezar1 aDoumatey, Ayo, P1 aDu, Shufa1 aDuan, Qing1 aEckardt, Kai-Uwe1 aEmery, Leslie, S1 aEvans, Daniel, S1 aEvans, Michele, K1 aFischer, Krista1 aFloyd, James, S1 aFord, Ian1 aFranco, Oscar, H1 aFrayling, Timothy, M1 aFreedman, Barry, I1 aGenter, Pauline1 aGerstein, Hertzel, C1 aGiedraitis, Vilmantas1 aGonzález-Villalpando, Clicerio1 aGonzalez-Villalpando, Maria, Elena1 aGordon-Larsen, Penny1 aGross, Myron1 aGuare, Lindsay, A1 aHackinger, Sophie1 aHan, Sohee1 aHattersley, Andrew, T1 aHerder, Christian1 aHorikoshi, Momoko1 aHoward, Annie-Green1 aHsueh, Willa1 aHuang, Mengna1 aHuang, Wei1 aHung, Yi-Jen1 aHwang, Mi, Yeong1 aHwu, Chii-Min1 aIchihara, Sahoko1 aIkram, Mohammad, Arfan1 aIngelsson, Martin1 aIslam, Md, Tariqul1 aIsono, Masato1 aJang, Hye-Mi1 aJasmine, Farzana1 aJiang, Guozhi1 aJonas, Jost, B1 aJørgensen, Torben1 aKandeel, Fouad, R1 aKasturiratne, Anuradhani1 aKatsuya, Tomohiro1 aKaur, Varinderpal1 aKawaguchi, Takahisa1 aKeaton, Jacob, M1 aKho, Abel, N1 aKhor, Chiea-Chuen1 aKibriya, Muhammad, G1 aKim, Duk-Hwan1 aKronenberg, Florian1 aKuusisto, Johanna1 aLäll, Kristi1 aLange, Leslie, A1 aLee, Kyung, Min1 aLee, Myung-Shik1 aLee, Nanette, R1 aLeong, Aaron1 aLi, Liming1 aLi, Yun1 aLi-Gao, Ruifang1 aLithgart, Symen1 aLindgren, Cecilia, M1 aLinneberg, Allan1 aLiu, Ching-Ti1 aLiu, Jianjun1 aLocke, Adam, E1 aLouie, Tin1 aLuan, Jian'an1 aLuk, Andrea, O1 aLuo, Xi1 aLv, Jun1 aLynch, Julie, A1 aLyssenko, Valeriya1 aMaeda, Shiro1 aMamakou, Vasiliki1 aMansuri, Sohail, Rafik1 aMatsuda, Koichi1 aMeitinger, Thomas1 aMetspalu, Andres1 aMo, Huan1 aMorris, Andrew, D1 aNadler, Jerry, L1 aNalls, Michael, A1 aNayak, Uma1 aNtalla, Ioanna1 aOkada, Yukinori1 aOrozco, Lorena1 aPatel, Sanjay, R1 aPatil, Snehal1 aPei, Pei1 aPereira, Mark, A1 aPeters, Annette1 aPirie, Fraser, J1 aPolikowsky, Hannah, G1 aPorneala, Bianca1 aPrasad, Gauri1 aRasmussen-Torvik, Laura, J1 aReiner, Alexander, P1 aRoden, Michael1 aRohde, Rebecca1 aRoll, Katheryn1 aSabanayagam, Charumathi1 aSandow, Kevin1 aSankareswaran, Alagu1 aSattar, Naveed1 aSchönherr, Sebastian1 aShahriar, Mohammad1 aShen, Botong1 aShi, Jinxiu1 aShin, Dong, Mun1 aShojima, Nobuhiro1 aSmith, Jennifer, A1 aSo, Wing, Yee1 aStančáková, Alena1 aSteinthorsdottir, Valgerdur1 aStilp, Adrienne, M1 aStrauch, Konstantin1 aTaylor, Kent, D1 aThorand, Barbara1 aThorsteinsdottir, Unnur1 aTomlinson, Brian1 aTran, Tam, C1 aTsai, Fuu-Jen1 aTuomilehto, Jaakko1 aTusié-Luna, Teresa1 aUdler, Miriam, S1 aValladares-Salgado, Adan1 avan Dam, Rob, M1 avan Klinken, Jan, B1 aVarma, Rohit1 aWacher-Rodarte, Niels1 aWheeler, Eleanor1 aWickremasinghe, Ananda, R1 aDijk, Ko Willems1 aWitte, Daniel, R1 aYajnik, Chittaranjan, S1 aYamamoto, Ken1 aYamamoto, Kenichi1 aYoon, Kyungheon1 aYu, Canqing1 aYuan, Jian-Min1 aYusuf, Salim1 aZawistowski, Matthew1 aZhang, Liang1 aZheng, Wei1 aProject, Biobank, Japan1 aBioBank, Penn, Medicine1 aCenter, Regeneron, Genetics1 aConsortium, eMERGE1 aRaffel, Leslie, J1 aIgase, Michiya1 aIpp, Eli1 aRedline, Susan1 aCho, Yoon Shin1 aLind, Lars1 aProvince, Michael, A1 aFornage, Myriam1 aHanis, Craig, L1 aIngelsson, Erik1 aZonderman, Alan, B1 aPsaty, Bruce, M1 aWang, Ya-Xing1 aRotimi, Charles, N1 aBecker, Diane, M1 aMatsuda, Fumihiko1 aLiu, Yongmei1 aYokota, Mitsuhiro1 aKardia, Sharon, L R1 aPeyser, Patricia, A1 aPankow, James, S1 aEngert, James, C1 aBonnefond, Amélie1 aFroguel, Philippe1 aWilson, James, G1 aSheu, Wayne, H H1 aWu, Jer-Yuarn1 aHayes, Geoffrey1 aMa, Ronald, C W1 aWong, Tien-Yin1 aMook-Kanamori, Dennis, O1 aTuomi, Tiinamaija1 aChandak, Giriraj, R1 aCollins, Francis, S1 aBharadwaj, Dwaipayan1 aParé, Guillaume1 aSale, Michèle, M1 aAhsan, Habibul1 aMotala, Ayesha, A1 aShu, Xiao-Ou1 aPark, Kyong-Soo1 aJukema, Wouter1 aCruz, Miguel1 aChen, Yii-Der Ida1 aRich, Stephen, S1 aMcKean-Cowdin, Roberta1 aGrallert, Harald1 aCheng, Ching-Yu1 aGhanbari, Mohsen1 aTai, E-Shyong1 aDupuis, Josée1 aKato, Norihiro1 aLaakso, Markku1 aKöttgen, Anna1 aKoh, Woon-Puay1 aBowden, Donald, W1 aPalmer, Colin, N A1 aKooner, Jaspal, S1 aKooperberg, Charles1 aLiu, Simin1 aNorth, Kari, E1 aSaleheen, Danish1 aHansen, Torben1 aPedersen, Oluf1 aWareham, Nicholas, J1 aLee, Juyoung1 aKim, Bong-Jo1 aMillwood, Iona, Y1 aWalters, Robin, G1 aStefansson, Kari1 aGoodarzi, Mark, O1 aMohlke, Karen, L1 aLangenberg, Claudia1 aHaiman, Christopher, A1 aLoos, Ruth, J F1 aFlorez, Jose, C1 aRader, Daniel, J1 aRitchie, Marylyn, D1 aZöllner, Sebastian1 aMägi, Reedik1 aDenny, Joshua, C1 aYamauchi, Toshimasa1 aKadowaki, Takashi1 aChambers, John, C1 aC Y Ng, Maggie1 aSim, Xueling1 aBelow, Jennifer, E1 aTsao, Philip, S1 aChang, Kyong-Mi1 aMcCarthy, Mark, I1 aMeigs, James, B1 aMahajan, Anubha1 aSpracklen, Cassandra, N1 aMercader, Josep, M1 aBoehnke, Michael1 aRotter, Jerome, I1 aVujkovic, Marijana1 aVoight, Benjamin, F1 aMorris, Andrew, P1 aZeggini, Eleftheria1 aVA Million Veteran Program, AMED GRIFIN Diabetes Initiative Japan1 aInternational Consortium for Blood Pressure (ICBP)1 aMeta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC) uhttps://chs-nhlbi.org/node/938504817nas a2201309 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2023 eng d a1546-171800aMulti-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.0 aMultiancestry transcriptomewide association analyses yield insig c2023 Feb a291-3000 v553 aMost transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.
10aBiology10aDrug Repositioning10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aPolymorphism, Single Nucleotide10aTobacco Use10aTranscriptome1 aChen, Fang1 aWang, Xingyan1 aJang, Seon-Kyeong1 aQuach, Bryan, C1 aWeissenkampen, Dylan1 aKhunsriraksakul, Chachrit1 aYang, Lina1 aSauteraud, Renan1 aAlbert, Christine, M1 aAllred, Nicholette, D D1 aArnett, Donna, K1 aAshley-Koch, Allison, E1 aBarnes, Kathleen, C1 aBarr, Graham1 aBecker, Diane, M1 aBielak, Lawrence, F1 aBis, Joshua, C1 aBlangero, John1 aBoorgula, Meher, Preethi1 aChasman, Daniel, I1 aChavan, Sameer1 aChen, Yii-der, I1 aChuang, Lee-Ming1 aCorrea, Adolfo1 aCurran, Joanne, E1 aDavid, Sean, P1 aFuentes, Lisa, de Las1 aDeka, Ranjan1 aDuggirala, Ravindranath1 aFaul, Jessica, D1 aGarrett, Melanie, E1 aGharib, Sina, A1 aGuo, Xiuqing1 aHall, Michael, E1 aHawley, Nicola, L1 aHe, Jiang1 aHobbs, Brian, D1 aHokanson, John, E1 aHsiung, Chao, A1 aHwang, Shih-Jen1 aHyde, Thomas, M1 aIrvin, Marguerite, R1 aJaffe, Andrew, E1 aJohnson, Eric, O1 aKaplan, Robert1 aKardia, Sharon, L R1 aKaufman, Joel, D1 aKelly, Tanika, N1 aKleinman, Joel, E1 aKooperberg, Charles1 aLee, I-Te1 aLevy, Daniel1 aLutz, Sharon, M1 aManichaikul, Ani, W1 aMartin, Lisa, W1 aMarx, Olivia1 aMcGarvey, Stephen, T1 aMinster, Ryan, L1 aMoll, Matthew1 aMoussa, Karine, A1 aNaseri, Take1 aNorth, Kari, E1 aOelsner, Elizabeth, C1 aPeralta, Juan, M1 aPeyser, Patricia, A1 aPsaty, Bruce, M1 aRafaels, Nicholas1 aRaffield, Laura, M1 aReupena, Muagututi'a, Sefuiva1 aRich, Stephen, S1 aRotter, Jerome, I1 aSchwartz, David, A1 aShadyab, Aladdin, H1 aSheu, Wayne, H-H1 aSims, Mario1 aSmith, Jennifer, A1 aSun, Xiao1 aTaylor, Kent, D1 aTelen, Marilyn, J1 aWatson, Harold1 aWeeks, Daniel, E1 aWeir, David, R1 aYanek, Lisa, R1 aYoung, Kendra, A1 aYoung, Kristin, L1 aZhao, Wei1 aHancock, Dana, B1 aJiang, Bibo1 aVrieze, Scott1 aLiu, Dajiang, J uhttps://chs-nhlbi.org/node/941203951nas a2200925 4500008004100000022001400041245013100055210006900186260001300255300001200268490000700280520122300287653002101510653003401531653001101565653001401576653002801590100001401618700001801632700001701650700002201667700001501689700001401704700003201718700001401750700001601764700002101780700002401801700001901825700001901844700002101863700002201884700002301906700001901929700001901948700002501967700002201992700002202014700002802036700002302064700002502087700001702112700002202129700002102151700002402172700001902196700002102215700002102236700002002257700002502277700002502302700002202327700002402349700001702373700002602390700002602416700002402442700002002466700002302486700001902509700002502528700003402553700002102587700002102608700002402629700002302653700002002676700002702696700002302723700002102746700001902767700001402786700002202800700002302822700002002845700001402865700001602879710009402895856003602989 2023 eng d a1546-171800aPowerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.0 aPowerful scalable and resourceefficient metaanalysis of rare var c2023 Jan a154-1640 v553 aMeta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.
10aExome Sequencing10aGenome-Wide Association Study10aLipids10aPhenotype10aWhole Genome Sequencing1 aLi, Xihao1 aQuick, Corbin1 aZhou, Hufeng1 aGaynor, Sheila, M1 aLiu, Yaowu1 aChen, Han1 aSelvaraj, Margaret, Sunitha1 aSun, Ryan1 aDey, Rounak1 aArnett, Donna, K1 aBielak, Lawrence, F1 aBis, Joshua, C1 aBlangero, John1 aBoerwinkle, Eric1 aBowden, Donald, W1 aBrody, Jennifer, A1 aCade, Brian, E1 aCorrea, Adolfo1 aCupples, Adrienne, L1 aCurran, Joanne, E1 ade Vries, Paul, S1 aDuggirala, Ravindranath1 aFreedman, Barry, I1 aGöring, Harald, H H1 aGuo, Xiuqing1 aHaessler, Jeffrey1 aKalyani, Rita, R1 aKooperberg, Charles1 aKral, Brian, G1 aLange, Leslie, A1 aManichaikul, Ani1 aMartin, Lisa, W1 aMcGarvey, Stephen, T1 aMitchell, Braxton, D1 aMontasser, May, E1 aMorrison, Alanna, C1 aNaseri, Take1 aO'Connell, Jeffrey, R1 aPalmer, Nicholette, D1 aPeyser, Patricia, A1 aPsaty, Bruce, M1 aRaffield, Laura, M1 aRedline, Susan1 aReiner, Alexander, P1 aReupena, Muagututi'a, Sefuiva1 aRice, Kenneth, M1 aRich, Stephen, S1 aSitlani, Colleen, M1 aSmith, Jennifer, A1 aTaylor, Kent, D1 aVasan, Ramachandran, S1 aWiller, Cristen, J1 aWilson, James, G1 aYanek, Lisa, R1 aZhao, Wei1 aRotter, Jerome, I1 aNatarajan, Pradeep1 aPeloso, Gina, M1 aLi, Zilin1 aLin, Xihong1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group uhttps://chs-nhlbi.org/node/923904538nas a2200997 4500008004100000245012600041210006900167260001600236520164000252100001701892700003201909700001401941700001401955700002401969700002101993700001902014700001902033700002102052700002202073700001902095700002202114700002102136700002202157700002202179700002202201700002202223700002402245700002002269700002002289700002302309700002002332700001802352700002202370700001702392700001402409700002302423700002102446700001602467700002502483700001902508700002202527700002302549700002102572700001402593700002402607700001902631700001702650700001702667700001602684700002002700700001902720700002402739700002002763700002302783700002102806700002502827700002202852700002402874700002402898700001702922700002602939700002602965700002302991700002003014700002303034700002003057700001903077700002503096700002103121700003403142700002103176700002303197700001803220700002203238700002103260700003003281700001403311700001903325700001403344700002203358700001603380700002303396700002003419710006503439856003603504 2023 eng d00aRare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed Whole Genome Sequencing Study.0 aRare variants in long noncoding RNAs are associated with blood l c2023 Jun 293 aLong non-coding RNAs (lncRNAs) are known to perform important regulatory functions. Large-scale whole genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess the associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with blood lipid levels (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare variant aggregate association tests using the STAAR (variant-Set Test for Association using Annotation infoRmation) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare coding variants in nearby protein coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500 kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variations and rare protein coding variations at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNA, implicating new therapeutic opportunities.
1 aWang, Yuxuan1 aSelvaraj, Margaret, Sunitha1 aLi, Xihao1 aLi, Zilin1 aHoldcraft, Jacob, A1 aArnett, Donna, K1 aBis, Joshua, C1 aBlangero, John1 aBoerwinkle, Eric1 aBowden, Donald, W1 aCade, Brian, E1 aCarlson, Jenna, C1 aCarson, April, P1 aChen, Yii-Der Ida1 aCurran, Joanne, E1 ade Vries, Paul, S1 aDutcher, Susan, K1 aEllinor, Patrick, T1 aFloyd, James, S1 aFornage, Myriam1 aFreedman, Barry, I1 aGabriel, Stacey1 aGermer, Soren1 aGibbs, Richard, A1 aGuo, Xiuqing1 aHe, Jiang1 aHeard-Costa, Nancy1 aHildalgo, Bertha1 aHou, Lifang1 aIrvin, Marguerite, R1 aJoehanes, Roby1 aKaplan, Robert, C1 aKardia, Sharon, Lr1 aKelly, Tanika, N1 aKim, Ryan1 aKooperberg, Charles1 aKral, Brian, G1 aLevy, Daniel1 aLi, Changwei1 aLiu, Chunyu1 aLloyd-Jone, Don1 aLoos, Ruth, Jf1 aMahaney, Michael, C1 aMartin, Lisa, W1 aMathias, Rasika, A1 aMinster, Ryan, L1 aMitchell, Braxton, D1 aMontasser, May, E1 aMorrison, Alanna, C1 aMurabito, Joanne, M1 aNaseri, Take1 aO'Connell, Jeffrey, R1 aPalmer, Nicholette, D1 aPreuss, Michael, H1 aPsaty, Bruce, M1 aRaffield, Laura, M1 aRao, Dabeeru, C1 aRedline, Susan1 aReiner, Alexander, P1 aRich, Stephen, S1 aRuepena, Muagututi'a, Sefuiva1 aSheu, Wayne, H-H1 aSmith, Jennifer, A1 aSmith, Albert1 aTiwari, Hemant, K1 aTsai, Michael, Y1 aViaud-Martinez, Karine, A1 aWang, Zhe1 aYanek, Lisa, R1 aZhao, Wei1 aRotter, Jerome, I1 aLin, Xihong1 aNatarajan, Pradeep1 aPeloso, Gina, M1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/941803992nas a2200925 4500008004100000245012300041210006900164260001600233520132600249100001401575700001401589700003201603700002001635700001701655700001701672700001401689700002201703700001201725700002101737700001901758700001901777700002101796700002201817700002301839700001901862700002101881700002201902700002001924700002201944700002201966700002201988700002002010700002302030700002302053700001602076700002602092700001402118700001602132700001702148700002502165700002202190700002402212700001802236700002102254700002402275700001902299700001702318700002002335700002402355700002002379700002302399700002102422700002502443700002202468700002402490700002602514700002402540700002002564700002302584700001902607700002502626700002102651700002402672700002302696700002002719700001902739700002702758700001402785700001902799700001302818700002102831700002202852700002002874700002302894700001402917700001802931700001602949710006502965856003603030 2023 eng d00aA statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.0 astatistical framework for powerful multitrait rare variant analy c2023 Nov 023 aLarge-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of and an intergenic region on chromosome 1.
1 aLi, Xihao1 aChen, Han1 aSelvaraj, Margaret, Sunitha1 aVan Buren, Eric1 aZhou, Hufeng1 aWang, Yuxuan1 aSun, Ryan1 aMcCaw, Zachary, R1 aYu, Zhi1 aArnett, Donna, K1 aBis, Joshua, C1 aBlangero, John1 aBoerwinkle, Eric1 aBowden, Donald, W1 aBrody, Jennifer, A1 aCade, Brian, E1 aCarson, April, P1 aCarlson, Jenna, C1 aChami, Nathalie1 aChen, Yii-Der Ida1 aCurran, Joanne, E1 ade Vries, Paul, S1 aFornage, Myriam1 aFranceschini, Nora1 aFreedman, Barry, I1 aGu, Charles1 aHeard-Costa, Nancy, L1 aHe, Jiang1 aHou, Lifang1 aHung, Yi-Jen1 aIrvin, Marguerite, R1 aKaplan, Robert, C1 aKardia, Sharon, L R1 aKelly, Tanika1 aKonigsberg, Iain1 aKooperberg, Charles1 aKral, Brian, G1 aLi, Changwei1 aLoos, Ruth, J F1 aMahaney, Michael, C1 aMartin, Lisa, W1 aMathias, Rasika, A1 aMinster, Ryan, L1 aMitchell, Braxton, D1 aMontasser, May, E1 aMorrison, Alanna, C1 aPalmer, Nicholette, D1 aPeyser, Patricia, A1 aPsaty, Bruce, M1 aRaffield, Laura, M1 aRedline, Susan1 aReiner, Alexander, P1 aRich, Stephen, S1 aSitlani, Colleen, M1 aSmith, Jennifer, A1 aTaylor, Kent, D1 aTiwari, Hemant1 aVasan, Ramachandran, S1 aWang, Zhe1 aYanek, Lisa, R1 aYu, Bing1 aRice, Kenneth, M1 aRotter, Jerome, I1 aPeloso, Gina, M1 aNatarajan, Pradeep1 aLi, Zilin1 aLiu, Zhonghua1 aLin, Xihong1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/954305739nas a2200901 4500008004100000245012500041210006900166260001600235520316300251100002003414700003103434700002303465700002103488700001803509700001503527700002303542700001303565700001703578700002203595700001803617700001803635700002303653700001503676700002203691700001703713700001803730700002503748700001703773700001803790700002103808700001903829700002203848700002003870700001503890700002003905700001603925700002503941700002403966700001903990700002404009700002404033700002504057700002104082700002404103700002404127700002204151700002004173700002104193700002104214700003004235700002004265700002104285700001204306700002104318700001904339700002004358700001904378700002104397700002304418700002104441700001604462700002504478700002104503700002404524700001904548700002204567700002004589700002304609700002304632700002404655700001904679700002104698700002204719700001804741700002204759700002004781856003604801 2023 eng d00aTime-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus.0 aTimetoEvent GenomeWide Association Study for Incident Cardiovasc c2023 Jul 283 aBACKGROUND: Type 2 diabetes mellitus (T2D) confers a two- to three-fold increased risk of cardiovascular disease (CVD). However, the mechanisms underlying increased CVD risk among people with T2D are only partially understood. We hypothesized that a genetic association study among people with T2D at risk for developing incident cardiovascular complications could provide insights into molecular genetic aspects underlying CVD.
METHODS: From 16 studies of the Cohorts for Heart & Aging Research in Genomic Epidemiology (CHARGE) Consortium, we conducted a multi-ancestry time-to-event genome-wide association study (GWAS) for incident CVD among people with T2D using Cox proportional hazards models. Incident CVD was defined based on a composite of coronary artery disease (CAD), stroke, and cardiovascular death that occurred at least one year after the diagnosis of T2D. Cohort-level estimated effect sizes were combined using inverse variance weighted fixed effects meta-analysis. We also tested 204 known CAD variants for association with incident CVD among patients with T2D.
RESULTS: A total of 49,230 participants with T2D were included in the analyses (31,118 European ancestries and 18,112 non-European ancestries) which consisted of 8,956 incident CVD cases over a range of mean follow-up duration between 3.2 and 33.7 years (event rate 18.2%). We identified three novel, distinct genetic loci for incident CVD among individuals with T2D that reached the threshold for genome-wide significance ( <5.0×10 ): rs147138607 (intergenic variant between and ) with a hazard ratio (HR) 1.23, 95% confidence interval (CI) 1.15 - 1.32, =3.6×10 , rs11444867 (intergenic variant near ) with HR 1.89, 95% CI 1.52 - 2.35, =9.9×10 , and rs335407 (intergenic variant between and ) HR 1.25, 95% CI 1.16 - 1.35, =1.5×10 . Among 204 known CAD loci, 32 were associated with incident CVD in people with T2D with <0.05, and 5 were significant after Bonferroni correction ( <0.00024, 0.05/204). A polygenic score of these 204 variants was significantly associated with incident CVD with HR 1.14 (95% CI 1.12 - 1.16) per 1 standard deviation increase ( =1.0×10 ).
CONCLUSIONS: The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.
CLINICAL PERSPECTIVE: We conducted a large-scale multi-ancestry time-to-event GWAS to identify genetic variants associated with CVD among people with T2D. Three variants were significantly associated with incident CVD in people with T2D: rs147138607 (intergenic variant between and ), rs11444867 (intergenic variant near ), and rs335407 (intergenic variant between and ). A polygenic score composed of known CAD variants identified in the general population was significantly associated with the risk of CVD in people with T2D. There are genetic risk factors specific to T2D that could at least partially explain the excess risk of CVD in people with T2D.In addition, we show that people with T2D have enrichment of known CAD association signals which could also explain the excess risk of CVD.
1 aKwak, Soo, Heon1 aHernandez-Cancela, Ryan, B1 aDiCorpo, Daniel, A1 aCondon, David, E1 aMerino, Jordi1 aWu, Peitao1 aBrody, Jennifer, A1 aYao, Jie1 aGuo, Xiuqing1 aAhmadizar, Fariba1 aMeyer, Mariah1 aSincan, Murat1 aMercader, Josep, M1 aLee, Sujin1 aHaessler, Jeffrey1 aVy, Ha, My T1 aLin, Zhaotong1 aArmstrong, Nicole, D1 aGu, Shaopeng1 aTsao, Noah, L1 aLange, Leslie, A1 aWang, Ningyuan1 aWiggins, Kerri, L1 aTrompet, Stella1 aLiu, Simin1 aLoos, Ruth, J F1 aJudy, Renae1 aSchroeder, Philip, H1 aHasbani, Natalie, R1 aBos, Maxime, M1 aMorrison, Alanna, C1 aJackson, Rebecca, D1 aReiner, Alexander, P1 aManson, JoAnn, E1 aChaudhary, Ninad, S1 aCarmichael, Lynn, K1 aChen, Yii-Der Ida1 aTaylor, Kent, D1 aGhanbari, Mohsen1 avan Meurs, Joyce1 aPitsillides, Achilleas, N1 aPsaty, Bruce, M1 aNoordam, Raymond1 aDo, Ron1 aPark, Kyong, Soo1 aJukema, Wouter1 aKavousi, Maryam1 aCorrea, Adolfo1 aRich, Stephen, S1 aDamrauer, Scott, M1 aHajek, Catherine1 aCho, Nam, H1 aIrvin, Marguerite, R1 aPankow, James, S1 aNadkarni, Girish, N1 aSladek, Robert1 aGoodarzi, Mark, O1 aFlorez, Jose, C1 aChasman, Daniel, I1 aHeckbert, Susan, R1 aKooperberg, Charles1 aDupuis, Josée1 aMalhotra, Rajeev1 ade Vries, Paul, S1 aLiu, Ching-Ti1 aRotter, Jerome, I1 aMeigs, James, B uhttps://chs-nhlbi.org/node/945002774nas a2200397 4500008004100000022001400041245011600055210006900171260001600240300001200256490000600268520162400274100002401898700001501922700002301937700002101960700002401981700002002005700002002025700002102045700001702066700002202083700002402105700001302129700001902142700002002161700001802181700002202199700002002221700002002241700002202261700002102283700001902304700001702323856003602340 2023 eng d a2472-197200aA Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies.0 aType 1 Diabetes Polygenic Score Is Not Associated With Prevalent c2023 Oct 09 abvad1230 v73 aCONTEXT: Both type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight.
OBJECTIVE: We examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.
METHODS: We constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D.
RESULTS: The T1D PS was not associated with T2D both in CHARGE ( = .15) and in the MGB Biobank ( = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, = .03) in CHARGE T2D cases but not with other outcomes.
CONCLUSION: In large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping.
1 aSrinivasan, Shylaja1 aWu, Peitao1 aMercader, Josep, M1 aUdler, Miriam, S1 aPorneala, Bianca, C1 aBartz, Traci, M1 aFloyd, James, S1 aSitlani, Colleen1 aGuo, Xiquing1 aHaessler, Jeffrey1 aKooperberg, Charles1 aLiu, Jun1 aAhmad, Shahzad1 aDuijn, Cornelia1 aLiu, Ching-Ti1 aGoodarzi, Mark, O1 aFlorez, Jose, C1 aMeigs, James, B1 aRotter, Jerome, I1 aRich, Stephen, S1 aDupuis, Josée1 aLeong, Aaron uhttps://chs-nhlbi.org/node/954404412nas a2200865 4500008004100000022001400041245010800055210006900163260001600232300001200248520183100260100002402091700002602115700002002141700001402161700001402175700002002189700002102209700001902230700002102249700001502270700002402285700001702309700002302326700002002349700002402369700002202393700002602415700002302441700002002464700001902484700001702503700002202520700002302542700002802565700002102593700002102614700002102635700002502656700002302681700002002704700001802724700002202742700002002764700002302784700002002807700002202827700001902849700002402868700001902892700001902911700002002930700001902950700003002969700002102999700002103020700001903041700001903060700001803079700002203097700002003119700002103139700001803160700002103178700002403199700002403223700002703247700001603274700002203290700002003312700002203332700002203354710013403376856003603510 2023 eng d a2574-830000aType 2 Diabetes Modifies the Association of CAD Genomic Risk Variants With Subclinical Atherosclerosis.0 aType 2 Diabetes Modifies the Association of CAD Genomic Risk Var c2023 Nov 28 ae0041763 aBACKGROUND: Individuals with type 2 diabetes (T2D) have an increased risk of coronary artery disease (CAD), but questions remain about the underlying pathology. Identifying which CAD loci are modified by T2D in the development of subclinical atherosclerosis (coronary artery calcification [CAC], carotid intima-media thickness, or carotid plaque) may improve our understanding of the mechanisms leading to the increased CAD in T2D.
METHODS: We compared the common and rare variant associations of known CAD loci from the literature on CAC, carotid intima-media thickness, and carotid plaque in up to 29 670 participants, including up to 24 157 normoglycemic controls and 5513 T2D cases leveraging whole-genome sequencing data from the Trans-Omics for Precision Medicine program. We included first-order T2D interaction terms in each model to determine whether CAD loci were modified by T2D. The genetic main and interaction effects were assessed using a joint test to determine whether a CAD variant, or gene-based rare variant set, was associated with the respective subclinical atherosclerosis measures and then further determined whether these loci had a significant interaction test.
RESULTS: Using a Bonferroni-corrected significance threshold of <1.6×10, we identified 3 genes (, , and ) associated with CAC and 2 genes ( and ) associated with carotid intima-media thickness and carotid plaque, respectively, through gene-based rare variant set analysis. Both and also had significantly different associations for CAC in T2D cases versus controls. No significant interaction tests were identified through the candidate single-variant analysis.
CONCLUSIONS: These results highlight T2D as an important modifier of rare variant associations in CAD loci with CAC.
1 aHasbani, Natalie, R1 aWesterman, Kenneth, E1 aKwak, Soo, Heon1 aChen, Han1 aLi, Xihao1 aDiCorpo, Daniel1 aWessel, Jennifer1 aBis, Joshua, C1 aSarnowski, Chloe1 aWu, Peitao1 aBielak, Lawrence, F1 aGuo, Xiuqing1 aHeard-Costa, Nancy1 aKinney, Gregory1 aMahaney, Michael, C1 aMontasser, May, E1 aPalmer, Nicholette, D1 aRaffield, Laura, M1 aTerry, James, G1 aYanek, Lisa, R1 aBon, Jessica1 aBowden, Donald, W1 aBrody, Jennifer, A1 aDuggirala, Ravindranath1 aJacobs, David, R1 aKalyani, Rita, R1 aLange, Leslie, A1 aMitchell, Braxton, D1 aSmith, Jennifer, A1 aTaylor, Kent, D1 aCarson, April1 aCurran, Joanne, E1 aFornage, Myriam1 aFreedman, Barry, I1 aGabriel, Stacey1 aGibbs, Richard, A1 aGupta, Namrata1 aKardia, Sharon, L R1 aKral, Brian, G1 aMomin, Zeineen1 aNewman, Anne, B1 aPost, Wendy, S1 aViaud-Martinez, Karine, A1 aYoung, Kendra, A1 aBecker, Lewis, C1 aBertoni, Alain1 aBlangero, John1 aCarr, John, J1 aPratte, Katherine1 aPsaty, Bruce, M1 aRich, Stephen, S1 aWu, Joseph, C1 aMalhotra, Rajeev1 aPeyser, Patricia, A1 aMorrison, Alanna, C1 aVasan, Ramachandran, S1 aLin, Xihong1 aRotter, Jerome, I1 aMeigs, James, B1 aManning, Alisa, K1 ade Vries, Paul, S1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Atherosclerosis Working Group; TOPMed Diabetes Working Group uhttps://chs-nhlbi.org/node/953707212nas a2201753 4500008004100000245012700041210006900168260001600237520220800253100002502461700001902486700001602505700001902521700002302540700001902563700002302582700002102605700001802626700002002644700002402664700002302688700002902711700002302740700002002763700002102783700002102804700002102825700002402846700002202870700001902892700001502911700002102926700002002947700001802967700001902985700002103004700002503025700002603050700002103076700001903097700001903116700002803135700002203163700002203185700001903207700002003226700001803246700001703264700001503281700002003296700002803316700001703344700001703361700002703378700002503405700002003430700002003450700002003470700002403490700001203514700001603526700002003542700002803562700001603590700002103606700001903627700002103646700002703667700002103694700002403715700001603739700002203755700002403777700002503801700001703826700001403843700002103857700002003878700002203898700001903920700002003939700001503959700002003974700002203994700002404016700002004040700001804060700002004078700002704098700001804125700001704143700002104160700001604181700001804197700002404215700002004239700002004259700002604279700001904305700002004324700002304344700002304367700002504390700002004415700003004435700001804465700002304483700001804506700002104524700001904545700002004564700001804584700001904602700002304621700002204644700002004666700001904686700002204705700002004727700001904747700002304766700002104789700002004810700002304830700002504853700002904878700002904907700001904936700002604955700001904981700002205000700002005022700002405042700002005066700001705086700001805103700002105121700001305142700003205155700002305187700002205210700002205232700002505254700002405279700002305303710003105326710006505357856003605422 2023 eng d00aWhole genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles.0 aWhole genome analysis of plasma fibrinogen reveals populationdif c2023 Jun 123 aUNLABELLED: Genetic studies have identified numerous regions associated with plasma fibrinogen levels in Europeans, yet missing heritability and limited inclusion of non-Europeans necessitates further studies with improved power and sensitivity. Compared with array-based genotyping, whole genome sequencing (WGS) data provides better coverage of the genome and better representation of non-European variants. To better understand the genetic landscape regulating plasma fibrinogen levels, we meta-analyzed WGS data from the NHLBI's Trans-Omics for Precision Medicine (TOPMed) program (n=32,572), with array-based genotype data from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium (n=131,340) imputed to the TOPMed or Haplotype Reference Consortium panel. We identified 18 loci that have not been identified in prior genetic studies of fibrinogen. Of these, four are driven by common variants of small effect with reported MAF at least 10% higher in African populations. Three ( , and signals contain predicted deleterious missense variants. Two loci, and , each harbor two conditionally distinct, non-coding variants. The gene region encoding the protein chain subunits ( ), contains 7 distinct signals, including one novel signal driven by rs28577061, a variant common (MAF=0.180) in African reference panels but extremely rare (MAF=0.008) in Europeans. Through phenome-wide association studies in the VA Million Veteran Program, we found associations between fibrinogen polygenic risk scores and thrombotic and inflammatory disease phenotypes, including an association with gout. Our findings demonstrate the utility of WGS to augment genetic discovery in diverse populations and offer new insights for putative mechanisms of fibrinogen regulation.
KEY POINTS: Largest and most diverse genetic study of plasma fibrinogen identifies 54 regions (18 novel), housing 69 conditionally distinct variants (20 novel).Sufficient power achieved to identify signal driven by African population variant.Links to (1) liver enzyme, blood cell and lipid genetic signals, (2) liver regulatory elements, and (3) thrombotic and inflammatory disease.
1 aHuffman, Jennifer, E1 aNicolas, Jayna1 aHahn, Julie1 aHeath, Adam, S1 aRaffield, Laura, M1 aYanek, Lisa, R1 aBrody, Jennifer, A1 aThibord, Florian1 aAlmasy, Laura1 aBartz, Traci, M1 aBielak, Lawrence, F1 aBowler, Russell, P1 aCarrasquilla, Germán, D1 aChasman, Daniel, I1 aChen, Ming-Huei1 aEmmert, David, B1 aGhanbari, Mohsen1 aHaessle, Jeffery1 aHottenga, Jouke-Jan1 aKleber, Marcus, E1 aLe, Ngoc-Quynh1 aLee, Jiwon1 aLewis, Joshua, P1 aLi-Gao, Ruifang1 aLuan, Jian'an1 aMalmberg, Anni1 aMangino, Massimo1 aMarioni, Riccardo, E1 aMartinez-Perez, Angel1 aPankratz, Nathan1 aPolasek, Ozren1 aRichmond, Anne1 aRodriguez, Benjamin, At1 aRotter, Jerome, I1 aSteri, Maristella1 aSuchon, Pierre1 aTrompet, Stella1 aWeiss, Stefan1 aZare, Marjan1 aAuer, Paul1 aCho, Michael, H1 aChristofidou, Paraskevi1 aDavies, Gail1 ade Geus, Eco1 aDeleuze, Jean-Francois1 aDelgado, Graciela, E1 aEkunwe, Lynette1 aFaraday, Nauder1 aGögele, Martin1 aGreinacher, Andreas1 aHe, Gao1 aHoward, Tom1 aJoshi, Peter, K1 aKilpeläinen, Tuomas, O1 aLahti, Jari1 aLinneberg, Allan1 aNaitza, Silvia1 aNoordam, Raymond1 aPaüls-Vergés, Ferran1 aRich, Stephen, S1 aRosendaal, Frits, R1 aRudan, Igor1 aRyan, Kathleen, A1 aSouto, Juan, Carlos1 avan Rooij, Frank, Ja1 aWang, Heming1 aZhao, Wei1 aBecker, Lewis, C1 aBeswick, Andrew1 aBrown, Michael, R1 aCade, Brian, E1 aCampbell, Harry1 aCho, Kelly1 aCrapo, James, D1 aCurran, Joanne, E1 ade Maat, Moniek, Pm1 aDoyle, Margaret1 aElliott, Paul1 aFloyd, James, S1 aFuchsberger, Christian1 aGrarup, Niels1 aGuo, Xiuqing1 aHarris, Sarah, E1 aHou, Lifang1 aKolcic, Ivana1 aKooperberg, Charles1 aMenni, Cristina1 aNauck, Matthias1 aO'Connell, Jeffrey, R1 aOrrù, Valeria1 aPsaty, Bruce, M1 aRäikkönen, Katri1 aSmith, Jennifer, A1 aSoria, José, Manuel1 aStott, David, J1 aVlieg, Astrid, van Hylcka1 aWatkins, Hugh1 aWillemsen, Gonneke1 aWilson, Peter1 aBen-Shlomo, Yoav1 aBlangero, John1 aBoomsma, Dorret1 aCox, Simon, R1 aDehghan, Abbas1 aEriksson, Johan, G1 aFiorillo, Edoardo1 aFornage, Myriam1 aHansen, Torben1 aHayward, Caroline1 aIkram, Arfan, M1 aJukema, Wouter1 aKardia, Sharon, Lr1 aLange, Leslie, A1 aMärz, Winfried1 aMathias, Rasika, A1 aMitchell, Braxton, D1 aMook-Kanamori, Dennis, O1 aMorange, Pierre-Emmanuel1 aPedersen, Oluf1 aPramstaller, Peter, P1 aRedline, Susan1 aReiner, Alexander1 aRidker, Paul, M1 aSilverman, Edwin, K1 aSpector, Tim, D1 aVölker, Uwe1 aWareham, Nick1 aWilson, James, F1 aYao, Jie1 aTrégouët, David-Alexandre1 aJohnson, Andrew, D1 aWolberg, Alisa, S1 ade Vries, Paul, S1 aSabater-Lleal, Maria1 aMorrison, Alanna, C1 aSmith, Nicholas, L1 aVA Million Veteran Program1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/944903713nas a2200661 4500008004100000022001400041245015600055210006900211260000900280300001200289490000700301520176300308100002502071700003302096700001802129700002902147700002602176700002002202700001902222700002302241700001402264700002202278700002002300700002302320700001702343700002502360700001902385700001802404700002402422700002002446700002102466700002602487700002202513700002602535700002002561700001402581700002102595700001402616700001402630700002102644700002102665700002102686700002102707700002402728700002002752700002402772700002702796700002002823700002002843700001902863700002102882700002202903700002302925700002102948700002502969700002102994856003603015 2023 eng d a1664-802100aWhole genome sequence analysis of apparent treatment resistant hypertension status in participants from the Trans-Omics for Precision Medicine program.0 aWhole genome sequence analysis of apparent treatment resistant h c2023 a12782150 v143 aApparent treatment-resistant hypertension (aTRH) is characterized by the use of four or more antihypertensive (AHT) classes to achieve blood pressure (BP) control. In the current study, we conducted single-variant and gene-based analyses of aTRH among individuals from 12 Trans-Omics for Precision Medicine cohorts with whole-genome sequencing data. Cases were defined as individuals treated for hypertension (HTN) taking three different AHT classes, with average systolic BP ≥ 140 or diastolic BP ≥ 90 mmHg, or four or more medications regardless of BP ( = 1,705). A normotensive control group was defined as individuals with BP < 140/90 mmHg ( = 22,079), not on AHT medication. A second control group comprised individuals who were treatment responsive on one AHT medication with BP < 140/ 90 mmHg ( = 5,424). Logistic regression with kinship adjustment using the Scalable and Accurate Implementation of Generalized mixed models (SAIGE) was performed, adjusting for age, sex, and genetic ancestry. We assessed variants using SKAT-O in rare-variant analyses. Single-variant and gene-based tests were conducted in a pooled multi-ethnicity stratum, as well as self-reported ethnic/racial strata (European and African American). One variant in the known HTN locus, , was a top finding in the multi-ethnic analysis ( = 8.23E-07) for the normotensive control group [rs12476527, odds ratio (95% confidence interval) = 0.80 (0.74-0.88)]. This variant was replicated in the Vanderbilt University Medical Center's DNA repository data. Aggregate gene-based signals included the genes and . Additional work validating these loci in larger, more diverse populations, is warranted to determine whether these regions influence the pathobiology of aTRH.
1 aArmstrong, Nicole, D1 aSrinivasasainagendra, Vinodh1 aAmmous, Farah1 aAssimes, Themistocles, L1 aBeitelshees, Amber, L1 aBrody, Jennifer1 aCade, Brian, E1 aChen, Yii-Der, Ida1 aChen, Han1 ade Vries, Paul, S1 aFloyd, James, S1 aFranceschini, Nora1 aGuo, Xiuqing1 aHellwege, Jacklyn, N1 aHouse, John, S1 aHwu, Chii-Min1 aKardia, Sharon, L R1 aLange, Ethan, M1 aLange, Leslie, A1 aMcDonough, Caitrin, W1 aMontasser, May, E1 aO'Connell, Jeffrey, R1 aShuey, Megan, M1 aSun, Xiao1 aTanner, Rikki, M1 aWang, Zhe1 aZhao, Wei1 aCarson, April, P1 aEdwards, Todd, L1 aKelly, Tanika, N1 aKenny, Eimear, E1 aKooperberg, Charles1 aLoos, Ruth, J F1 aMorrison, Alanna, C1 aMotsinger-Reif, Alison1 aPsaty, Bruce, M1 aRao, Dabeeru, C1 aRedline, Susan1 aRich, Stephen, S1 aRotter, Jerome, I1 aSmith, Jennifer, A1 aSmith, Albert, V1 aIrvin, Marguerite, R1 aArnett, Donna, K uhttps://chs-nhlbi.org/node/958105493nas a2201573 4500008004100000245011200041210006900153260001600222520100600238100001801244700002301262700002201285700002501307700002001332700001501352700001401367700002001381700002101401700002201422700001401444700001401458700002401472700002101496700002401517700001901541700002901560700002201589700001901611700001801630700002801648700002001676700001901696700001901715700002101734700002401755700001901779700001701798700002801815700001701843700002201860700002301882700002401905700001701929700001801946700002501964700002001989700002102009700001602030700002002046700001602066700001302082700001802095700002402113700001702137700002102154700002402175700002102199700002002220700002002240700001702260700002202277700002802299700002302327700002402350700001702374700002302391700003002414700002602444700002102470700002002491700001902511700002302530700001402553700002402567700002402591700001802615700002802633700002402661700002302685700002202708700002702730700001702757700002102774700002102795700002202816700002202838700002302860700001902883700002302902700001402925700002402939700002102963700002802984700002403012700001903036700002103055700002503076700002103101700002303122700002203145700002203167700002003189700002303209700001403232700002303246700001603269700002503285700002403310700002103334700002503355700001903380700002003399700002303419700002503442700002103467700002203488700002403510700002303534700002003557700002203577700002003599700001803619700002503637700001603662700001603678700002503694700002103719700001803740700002003758700001903778700002103797710006503818856003603883 2023 eng d00aWHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES NOVEL AFRICAN ANCESTRY-SPECIFIC RISK ALLELE.0 aWHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES N c2023 Aug 223 aObesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals ( < 5 × 10 ). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the and loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.
1 aZhang, Xinruo1 aBrody, Jennifer, A1 aGraff, Mariaelisa1 aHighland, Heather, M1 aChami, Nathalie1 aXu, Hanfei1 aWang, Zhe1 aFerrier, Kendra1 aChittoor, Geetha1 aJosyula, Navya, S1 aLi, Xihao1 aLi, Zilin1 aAllison, Matthew, A1 aBecker, Diane, M1 aBielak, Lawrence, F1 aBis, Joshua, C1 aBoorgula, Meher, Preethi1 aBowden, Donald, W1 aBroome, Jai, G1 aButh, Erin, J1 aCarlson, Christopher, S1 aChang, Kyong-Mi1 aChavan, Sameer1 aChiu, Yen-Feng1 aChuang, Lee-Ming1 aConomos, Matthew, P1 aDeMeo, Dawn, L1 aDu, Margaret1 aDuggirala, Ravindranath1 aEng, Celeste1 aFohner, Alison, E1 aFreedman, Barry, I1 aGarrett, Melanie, E1 aGuo, Xiuqing1 aHaiman, Chris1 aHeavner, Benjamin, D1 aHidalgo, Bertha1 aHixson, James, E1 aHo, Yuk-Lam1 aHobbs, Brian, D1 aHu, Donglei1 aHui, Qin1 aHwu, Chii-Min1 aJackson, Rebecca, D1 aJain, Deepti1 aKalyani, Rita, R1 aKardia, Sharon, L R1 aKelly, Tanika, N1 aLange, Ethan, M1 aLeNoir, Michael1 aLi, Changwei1 aLe Marchand, Loic1 aMcDonald, Merry-Lynn, N1 aMcHugh, Caitlin, P1 aMorrison, Alanna, C1 aNaseri, Take1 aO'Connell, Jeffrey1 aO'Donnell, Christopher, J1 aPalmer, Nicholette, D1 aPankow, James, S1 aPerry, James, A1 aPeters, Ulrike1 aPreuss, Michael, H1 aRao, D, C1 aRegan, Elizabeth, A1 aReupena, Sefuiva, M1 aRoden, Dan, M1 aRodriguez-Santana, Jose1 aSitlani, Colleen, M1 aSmith, Jennifer, A1 aTiwari, Hemant, K1 aVasan, Ramachandran, S1 aWang, Zeyuan1 aWeeks, Daniel, E1 aWessel, Jennifer1 aWiggins, Kerri, L1 aWilkens, Lynne, R1 aWilson, Peter, W F1 aYanek, Lisa, R1 aYoneda, Zachary, T1 aZhao, Wei1 aZöllner, Sebastian1 aArnett, Donna, K1 aAshley-Koch, Allison, E1 aBarnes, Kathleen, C1 aBlangero, John1 aBoerwinkle, Eric1 aBurchard, Esteban, G1 aCarson, April, P1 aChasman, Daniel, I1 aChen, Yii-Der Ida1 aCurran, Joanne, E1 aFornage, Myriam1 aGordeuk, Victor, R1 aHe, Jiang1 aHeckbert, Susan, R1 aHou, Lifang1 aIrvin, Marguerite, R1 aKooperberg, Charles1 aMinster, Ryan, L1 aMitchell, Braxton, D1 aNouraie, Mehdi1 aPsaty, Bruce, M1 aRaffield, Laura, M1 aReiner, Alexander, P1 aRich, Stephen, S1 aRotter, Jerome, I1 aShoemaker, Benjamin1 aSmith, Nicholas, L1 aTaylor, Kent, D1 aTelen, Marilyn, J1 aWeiss, Scott, T1 aZhang, Yingze1 aCosta, Nancy, Heard-1 aSun, Yan, V1 aLin, Xihong1 aCupples, Adrienne, L1 aLange, Leslie, A1 aLiu, Ching-Ti1 aLoos, Ruth, J F1 aNorth, Kari, E1 aJustice, Anne, E1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/948403779nas a2200673 4500008004100000245013300041210006900174260001600243520177100259100001902030700002202049700001402071700002002085700002002105700001502125700001902140700002202159700002702181700001402208700002102222700002002243700001902263700002102282700001902303700001702322700002202339700002102361700002202382700002502404700002102429700002002450700002502470700002402495700002202519700001902541700001602560700002302576700002002599700001902619700001502638700002102653700002302674700002202697700002402719700002002743700001902763700001902782700001602801700002002817700002402837700002502861700002302886700001602909700001802925700002302943710006502966710003803031856003603069 2023 eng d00aWhole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium.0 aWhole Genome Sequencing Based Analysis of Inflammation Biomarker c2023 Sep 123 aInflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.
1 aJiang, Min-Zhi1 aGaynor, Sheila, M1 aLi, Xihao1 aVan Buren, Eric1 aStilp, Adrienne1 aButh, Erin1 aWang, Fei, Fei1 aManansala, Regina1 aGogarten, Stephanie, M1 aLi, Zilin1 aPolfus, Linda, M1 aSalimi, Shabnam1 aBis, Joshua, C1 aPankratz, Nathan1 aYanek, Lisa, R1 aDurda, Peter1 aTracy, Russell, P1 aRich, Stephen, S1 aRotter, Jerome, I1 aMitchell, Braxton, D1 aLewis, Joshua, P1 aPsaty, Bruce, M1 aPratte, Katherine, A1 aSilverman, Edwin, K1 aKaplan, Robert, C1 aAvery, Christy1 aNorth, Kari1 aMathias, Rasika, A1 aFaraday, Nauder1 aLin, Honghuang1 aWang, Biqi1 aCarson, April, P1 aNorwood, Arnita, F1 aGibbs, Richard, A1 aKooperberg, Charles1 aLundin, Jessica1 aPeters, Ulrike1 aDupuis, Josée1 aHou, Lifang1 aFornage, Myriam1 aBenjamin, Emelia, J1 aReiner, Alexander, P1 aBowler, Russell, P1 aLin, Xihong1 aAuer, Paul, L1 aRaffield, Laura, M1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aTOPMed Inflammation Working Group uhttps://chs-nhlbi.org/node/950002945nas a2200397 4500008004100000245009500041210006900136260001600205520178200221100001802003700002002021700001502041700001402056700003002070700001302100700001902113700001702132700001502149700002302164700002202187700002102209700002202230700002102252700002702273700001902300700002002319700002102339700002802360700002602388700001902414700001402433700001702447700001602464710003102480856003602511 2024 eng d00aAssociation analysis of mitochondrial DNA heteroplasmic variants: methods and application.0 aAssociation analysis of mitochondrial DNA heteroplasmic variants c2024 Jan 133 aWe rigorously assessed a comprehensive association testing framework for heteroplasmy, employing both simulated and real-world data. This framework employed a variant allele fraction (VAF) threshold and harnessed multiple gene-based tests for robust identification and association testing of heteroplasmy. Our simulation studies demonstrated that gene-based tests maintained an appropriate type I error rate at α=0.001. Notably, when 5% or more heteroplasmic variants within a target region were linked to an outcome, burden-extension tests (including the adaptive burden test, variable threshold burden test, and z-score weighting burden test) outperformed the sequence kernel association test (SKAT) and the original burden test. Applying this framework, we conducted association analyses on whole-blood derived heteroplasmy in 17,507 individuals of African and European ancestries (31% of African Ancestry, mean age of 62, with 58% women) with whole genome sequencing data. We performed both cohort- and ancestry-specific association analyses, followed by meta-analysis on bothpooled samples and within each ancestry group. Our results suggest that mtDNA-Enco ded genes/regions are likely to exhibit varying rates in somatic aging, with the notably strong associations observed between heteroplasmy in the and genes ( <0.001) and advance aging by the Original Burden test. In contrast, SKAT identified significant associations ( <0.001) between diabetes and the aggregated effects of heteroplasmy in several protein-coding genes. Further research is warranted to validate these findings. In summary, our proposed statistical framework represents a valuable tool for facilitating association testing of heteroplasmy with disease traits in large human populations.
1 aSun, Xianbang1 aBulekova, Katia1 aYang, Jian1 aLai, Meng1 aPitsillides, Achilleas, N1 aLiu, Xue1 aZhang, Yuankai1 aGuo, Xiuqing1 aYong, Qian1 aRaffield, Laura, M1 aRotter, Jerome, I1 aRich, Stephen, S1 aAbecasis, Goncalo1 aCarson, April, P1 aVasan, Ramachandran, S1 aBis, Joshua, C1 aPsaty, Bruce, M1 aBoerwinkle, Eric1 aFitzpatrick, Annette, L1 aSatizabal, Claudia, L1 aArking, Dan, E1 aDing, Jun1 aLevy, Daniel1 aLiu, Chunyu1 aTOPMed mtDNA working group uhttps://chs-nhlbi.org/node/958004426nas a2200493 4500008004100000022001400041245010800055210006900163260001600232520296700248100001603215700002403231700002403255700001903279700001603298700002203314700002603336700002103362700002103383700001503404700002003419700002303439700002203462700002003484700002103504700002803525700002203553700002003575700002703595700001603622700002003638700001903658700002003677700002703697700002603724700002103750700002103771700002103792700001703813700001903830700002603849700002103875856003603896 2024 eng d a2380-659100aFamilial Hypercholesterolemia Variant and Cardiovascular Risk in Individuals With Elevated Cholesterol.0 aFamilial Hypercholesterolemia Variant and Cardiovascular Risk in c2024 Jan 313 aIMPORTANCE: Familial hypercholesterolemia (FH) is a genetic disorder that often results in severely high low-density lipoprotein cholesterol (LDL-C) and high risk of premature coronary heart disease (CHD). However, the impact of FH variants on CHD risk among individuals with moderately elevated LDL-C is not well quantified.
OBJECTIVE: To assess CHD risk associated with FH variants among individuals with moderately (130-189 mg/dL) and severely (≥190 mg/dL) elevated LDL-C and to quantify excess CHD deaths attributable to FH variants in US adults.
DESIGN, SETTING, AND PARTICIPANTS: A total of 21 426 individuals without preexisting CHD from 6 US cohort studies (Atherosclerosis Risk in Communities study, Coronary Artery Risk Development in Young Adults study, Cardiovascular Health Study, Framingham Heart Study Offspring cohort, Jackson Heart Study, and Multi-Ethnic Study of Atherosclerosis) were included, 63 of whom had an FH variant. Data were collected from 1971 to 2018, and the median (IQR) follow-up was 18 (13-28) years. Data were analyzed from March to May 2023.
EXPOSURES: LDL-C, cumulative past LDL-C, FH variant status.
MAIN OUTCOMES AND MEASURES: Cox proportional hazards models estimated associations between FH variants and incident CHD. The Cardiovascular Disease Policy Model projected excess CHD deaths associated with FH variants in US adults.
RESULTS: Of the 21 426 individuals without preexisting CHD (mean [SD] age 52.1 [15.5] years; 12 041 [56.2%] female), an FH variant was found in 22 individuals with moderately elevated LDL-C (0.3%) and in 33 individuals with severely elevated LDL-C (2.5%). The adjusted hazard ratios for incident CHD comparing those with and without FH variants were 2.9 (95% CI, 1.4-6.0) and 2.6 (95% CI, 1.4-4.9) among individuals with moderately and severely elevated LDL-C, respectively. The association between FH variants and CHD was slightly attenuated when further adjusting for baseline LDL-C level, whereas the association was no longer statistically significant after adjusting for cumulative past LDL-C exposure. Among US adults 20 years and older with no history of CHD and LDL-C 130 mg/dL or higher, more than 417 000 carry an FH variant and were projected to experience more than 12 000 excess CHD deaths in those with moderately elevated LDL-C and 15 000 in those with severely elevated LDL-C compared with individuals without an FH variant.
CONCLUSIONS AND RELEVANCE: In this pooled cohort study, the presence of FH variants was associated with a 2-fold higher CHD risk, even when LDL-C was only moderately elevated. The increased CHD risk appeared to be largely explained by the higher cumulative LDL-C exposure in individuals with an FH variant compared to those without. Further research is needed to assess the value of adding genetic testing to traditional phenotypic FH screening.
1 aZhang, Yiyi1 aDron, Jacqueline, S1 aBellows, Brandon, K1 aKhera, Amit, V1 aLiu, Junxiu1 aBalte, Pallavi, P1 aOelsner, Elizabeth, C1 aAmr, Sami, Samir1 aLebo, Matthew, S1 aNagy, Anna1 aPeloso, Gina, M1 aNatarajan, Pradeep1 aRotter, Jerome, I1 aWiller, Cristen1 aBoerwinkle, Eric1 aBallantyne, Christie, M1 aLutsey, Pamela, L1 aFornage, Myriam1 aLloyd-Jones, Donald, M1 aHou, Lifang1 aPsaty, Bruce, M1 aBis, Joshua, C1 aFloyd, James, S1 aVasan, Ramachandran, S1 aHeard-Costa, Nancy, L1 aCarson, April, P1 aHall, Michael, E1 aRich, Stephen, S1 aGuo, Xiuqing1 aKazi, Dhruv, S1 ade Ferranti, Sarah, D1 aMoran, Andrew, E uhttps://chs-nhlbi.org/node/962014002nas a2204477 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2024 eng d a1476-468700aGenetic drivers of heterogeneity in type 2 diabetes pathophysiology.0 aGenetic drivers of heterogeneity in type 2 diabetes pathophysiol c2024 Feb 193 aType 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.
1 aSuzuki, Ken1 aHatzikotoulas, Konstantinos1 aSoutham, Lorraine1 aTaylor, Henry, J1 aYin, Xianyong1 aLorenz, Kim, M1 aMandla, Ravi1 aHuerta-Chagoya, Alicia1 aMelloni, Giorgio, E M1 aKanoni, Stavroula1 aRayner, Nigel, W1 aBocher, Ozvan1 aArruda, Ana, Luiza1 aSonehara, Kyuto1 aNamba, Shinichi1 aLee, Simon, S K1 aPreuss, Michael, H1 aPetty, Lauren, E1 aSchroeder, Philip1 aVanderwerff, Brett1 aKals, Mart1 aBragg, Fiona1 aLin, Kuang1 aGuo, Xiuqing1 aZhang, Weihua1 aYao, Jie1 aKim, Young, Jin1 aGraff, Mariaelisa1 aTakeuchi, Fumihiko1 aNano, Jana1 aLamri, Amel1 aNakatochi, Masahiro1 aMoon, Sanghoon1 aScott, Robert, A1 aCook, James, P1 aLee, Jung-Jin1 aPan, Ian1 aTaliun, Daniel1 aParra, Esteban, J1 aChai, Jin-Fang1 aBielak, Lawrence, F1 aTabara, Yasuharu1 aHai, Yang1 aThorleifsson, Gudmar1 aGrarup, Niels1 aSofer, Tamar1 aWuttke, Matthias1 aSarnowski, Chloe1 aGieger, Christian1 aNousome, Darryl1 aTrompet, Stella1 aKwak, Soo-Heon1 aLong, Jirong1 aSun, Meng1 aTong, Lin1 aChen, Wei-Min1 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