08958nas a2202881 4500008004100000022001400041245014400055210006900199260001300268300001300281490000700294520091900301653001001220653001201230653001801242653001201260653001101272653001901283653003401302653001101336653001201347653000901359653003601368653000901404653002601413653002801439100002101467700002001488700001901508700002101527700002201548700001801570700001801588700002501606700001901631700002301650700002201673700003101695700001601726700002101742700002001763700001801783700001801801700002301819700001901842700001801861700002001879700001801899700002501917700002401942700001601966700002101982700002002003700002202023700001702045700001902062700002502081700002002106700002302126700001302149700002502162700001602187700002402203700001402227700002202241700001702263700001802280700002602298700002302324700002202347700002202369700001602391700002102407700002002428700001502448700002402463700002502487700001402512700002502526700002202551700002102573700002002594700001702614700001802631700002502649700002102674700002102695700002102716700002002737700002602757700001802783700002002801700001902821700001502840700002102855700001302876700003002889700002802919700002102947700002102968700002002989700002903009700002403038700002103062700001903083700002103102700002003123700001803143700002003161700002103181700001703202700002103219700002203240700002003262700001403282700002503296700002003321700001803341700001903359700002203378700002403400700002003424700001603444700002103460700001803481700001903499700001703518700002003535700001903555700001903574700002603593700002803619700002303647700002103670700002303691700002203714700002003736700001903756700002503775700002003800700002103820700002403841700002203865700001803887700002203905700002203927700002503949700001903974700002303993700002304016700002104039700002004060700002204080700002304102700002004125700002604145700002504171700002304196700002404219700001604243700002004259700001804279700002004297700002204317700002004339700002404359700002404383700002404407700002304431700002504454700001904479700002304498700001904521700001904540700002004559700002404579700002104603700002004624700001804644700002104662700002004683700002704703700002204730700001904752700002104771700002504792700002004817700002004837700001504857700002104872700002304893700002304916700002204939700001804961700002404979700002005003700001905023700002205042700001905064700002505083700002405108700001905132700002105151700002005172700002405192700002005216700001805236700001805254700002005272700002405292700001905316700002105335700003705356700002205393700002305415700002105438700001505459700001905474700002305493700001905516700002205535700002105557700002005578700002005598700002105618700002105639700002205660700001805682700002705700700002505727700002505752700002205777700002005799700002005819700002405839700002005863700002405883700002005907700002105927700001905948710007305967856003606040 2012 eng d a1546-171800aLarge-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.0 aLargescale association analyses identify new loci influencing gl c2012 Sep a991-10050 v443 a
Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.
10aAdult10aAnimals10aBlood Glucose10aFasting10aFemale10aGene Frequency10aGenome-Wide Association Study10aHumans10aInsulin10aMale10aMetabolic Networks and Pathways10aMice10aOsmolar Concentration10aQuantitative Trait Loci1 aScott, Robert, A1 aLagou, Vasiliki1 aWelch, Ryan, P1 aWheeler, Eleanor1 aMontasser, May, E1 aLuan, Jian'an1 aMägi, Reedik1 aStrawbridge, Rona, J1 aRehnberg, Emil1 aGustafsson, Stefan1 aKanoni, Stavroula1 aRasmussen-Torvik, Laura, J1 aYengo, Loic1 aLecoeur, Cécile1 aShungin, Dmitry1 aSanna, Serena1 aSidore, Carlo1 aJohnson, Paul, C D1 aJukema, Wouter1 aJohnson, Toby1 aMahajan, Anubha1 aVerweij, Niek1 aThorleifsson, Gudmar1 aHottenga, Jouke-Jan1 aShah, Sonia1 aSmith, Albert, V1 aSennblad, Bengt1 aGieger, Christian1 aSalo, Perttu1 aPerola, Markus1 aTimpson, Nicholas, J1 aEvans, David, M1 aSt Pourcain, Beate1 aWu, Ying1 aAndrews, Jeanette, S1 aHui, Jennie1 aBielak, Lawrence, F1 aZhao, Wei1 aHorikoshi, Momoko1 aNavarro, Pau1 aIsaacs, Aaron1 aO'Connell, Jeffrey, R1 aStirrups, Kathleen1 aVitart, Veronique1 aHayward, Caroline1 aEsko, Tõnu1 aMihailov, Evelin1 aFraser, Ross, M1 aFall, Tove1 aVoight, Benjamin, F1 aRaychaudhuri, Soumya1 aChen, Han1 aLindgren, Cecilia, M1 aMorris, Andrew, P1 aRayner, Nigel, W1 aRobertson, Neil1 aRybin, Denis1 aLiu, Ching-Ti1 aBeckmann, Jacques, S1 aWillems, Sara, M1 aChines, Peter, S1 aJackson, Anne, U1 aKang, Hyun, Min1 aStringham, Heather, M1 aSong, Kijoung1 aTanaka, Toshiko1 aPeden, John, F1 aGoel, Anuj1 aHicks, Andrew, A1 aAn, Ping1 aMüller-Nurasyid, Martina1 aFranco-Cereceda, Anders1 aFolkersen, Lasse1 aMarullo, Letizia1 aJansen, Hanneke1 aOldehinkel, Albertine, J1 aBruinenberg, Marcel1 aPankow, James, S1 aNorth, Kari, E1 aForouhi, Nita, G1 aLoos, Ruth, J F1 aEdkins, Sarah1 aVarga, Tibor, V1 aHallmans, Göran1 aOksa, Heikki1 aAntonella, Mulas1 aNagaraja, Ramaiah1 aTrompet, Stella1 aFord, Ian1 aBakker, Stephan, J L1 aKong, Augustine1 aKumari, Meena1 aGigante, Bruna1 aHerder, Christian1 aMunroe, Patricia, B1 aCaulfield, Mark1 aAntti, Jula1 aMangino, Massimo1 aSmall, Kerrin1 aMiljkovic, Iva1 aLiu, Yongmei1 aAtalay, Mustafa1 aKiess, Wieland1 aJames, Alan, L1 aRivadeneira, Fernando1 aUitterlinden, André, G1 aPalmer, Colin, N A1 aDoney, Alex, S F1 aWillemsen, Gonneke1 aSmit, Johannes, H1 aCampbell, Susan1 aPolasek, Ozren1 aBonnycastle, Lori, L1 aHercberg, Serge1 aDimitriou, Maria1 aBolton, Jennifer, L1 aFowkes, Gerard, R1 aKovacs, Peter1 aLindström, Jaana1 aZemunik, Tatijana1 aBandinelli, Stefania1 aWild, Sarah, H1 aBasart, Hanneke, V1 aRathmann, Wolfgang1 aGrallert, Harald1 aMaerz, Winfried1 aKleber, Marcus, E1 aBoehm, Bernhard, O1 aPeters, Annette1 aPramstaller, Peter, P1 aProvince, Michael, A1 aBorecki, Ingrid, B1 aHastie, Nicholas, D1 aRudan, Igor1 aCampbell, Harry1 aWatkins, Hugh1 aFarrall, Martin1 aStumvoll, Michael1 aFerrucci, Luigi1 aWaterworth, Dawn, M1 aBergman, Richard, N1 aCollins, Francis, S1 aTuomilehto, Jaakko1 aWatanabe, Richard, M1 aGeus, Eco, J C1 aPenninx, Brenda, W1 aHofman, Albert1 aOostra, Ben, A1 aPsaty, Bruce, M1 aVollenweider, Peter1 aWilson, James, F1 aWright, Alan, F1 aHovingh, Kees1 aMetspalu, Andres1 aUusitupa, Matti1 aMagnusson, Patrik, K E1 aKyvik, Kirsten, O1 aKaprio, Jaakko1 aPrice, Jackie, F1 aDedoussis, George, V1 aDeloukas, Panos1 aMeneton, Pierre1 aLind, Lars1 aBoehnke, Michael1 aShuldiner, Alan, R1 aDuijn, Cornelia, M1 aMorris, Andrew, D1 aToenjes, Anke1 aPeyser, Patricia, A1 aBeilby, John, P1 aKörner, Antje1 aKuusisto, Johanna1 aLaakso, Markku1 aBornstein, Stefan, R1 aSchwarz, Peter, E H1 aLakka, Timo, A1 aRauramaa, Rainer1 aAdair, Linda, S1 aSmith, George Davey1 aSpector, Tim, D1 aIllig, Thomas1 ade Faire, Ulf1 aHamsten, Anders1 aGudnason, Vilmundur1 aKivimaki, Mika1 aHingorani, Aroon1 aKeinanen-Kiukaanniemi, Sirkka, M1 aSaaristo, Timo, E1 aBoomsma, Dorret, I1 aStefansson, Kari1 aHarst, Pim1 aDupuis, Josée1 aPedersen, Nancy, L1 aSattar, Naveed1 aHarris, Tamara, B1 aCucca, Francesco1 aRipatti, Samuli1 aSalomaa, Veikko1 aMohlke, Karen, L1 aBalkau, Beverley1 aFroguel, Philippe1 aPouta, Anneli1 aJarvelin, Marjo-Riitta1 aWareham, Nicholas, J1 aBouatia-Naji, Nabila1 aMcCarthy, Mark, I1 aFranks, Paul, W1 aMeigs, James, B1 aTeslovich, Tanya, M1 aFlorez, Jose, C1 aLangenberg, Claudia1 aIngelsson, Erik1 aProkopenko, Inga1 aBarroso, Inês1 aDIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium uhttps://chs-nhlbi.org/node/609103990nas a2200757 4500008004100000022001400041245017100055210006900226260001300295300001100308490000600319520184400325653001002169653000902179653002202188653001002210653001902220653001102239653002202250653003402272653001302306653001902319653001102338653000902349653001602358653003602374653002002410653002702430100001902457700001402476700002302490700001902513700001902532700001902551700002202570700002102592700002402613700002102637700001802658700001802676700002002694700002302714700003002737700002302767700001602790700001702806700001902823700001402842700001602856700002402872700001902896700002402915700001802939700002202957700001902979700001702998700002403015700002403039700002303063700002003086700002003106700002503126700002403151700002103175856003603196 2014 eng d a1942-326800aStrategies to design and analyze targeted sequencing data: cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study.0 aStrategies to design and analyze targeted sequencing data cohort c2014 Jun a335-430 v73 aBACKGROUND: Genome-wide association studies have identified thousands of genetic variants that influence a variety of diseases and health-related quantitative traits. However, the causal variants underlying the majority of genetic associations remain unknown. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study aims to follow up genome-wide association study signals and identify novel associations of the allelic spectrum of identified variants with cardiovascular-related traits.
METHODS AND RESULTS: The study included 4231 participants from 3 CHARGE cohorts: the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, and the Framingham Heart Study. We used a case-cohort design in which we selected both a random sample of participants and participants with extreme phenotypes for each of 14 traits. We sequenced and analyzed 77 genomic loci, which had previously been associated with ≥1 of 14 phenotypes. A total of 52 736 variants were characterized by sequencing and passed our stringent quality control criteria. For common variants (minor allele frequency ≥1%), we performed unweighted regression analyses to obtain P values for associations and weighted regression analyses to obtain effect estimates that accounted for the sampling design. For rare variants, we applied 2 approaches: collapsed aggregate statistics and joint analysis of variants using the sequence kernel association test.
CONCLUSIONS: We sequenced 77 genomic loci in participants from 3 cohorts. We established a set of filters to identify high-quality variants and implemented statistical and bioinformatics strategies to analyze the sequence data and identify potentially functional variants within genome-wide association study loci.
10aAdult10aAged10aAged, 80 and over10aAging10aCohort Studies10aFemale10aGenetic Variation10aGenome-Wide Association Study10aGenomics10aHeart Diseases10aHumans10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide10aResearch Design10aSequence Analysis, DNA1 aLin, Honghuang1 aWang, Min1 aBrody, Jennifer, A1 aBis, Joshua, C1 aDupuis, Josée1 aLumley, Thomas1 aMcKnight, Barbara1 aRice, Kenneth, M1 aSitlani, Colleen, M1 aReid, Jeffrey, G1 aBressler, Jan1 aLiu, Xiaoming1 aDavis, Brian, C1 aJohnson, Andrew, D1 aO'Donnell, Christopher, J1 aKovar, Christie, L1 aDinh, Huyen1 aWu, Yuanqing1 aNewsham, Irene1 aChen, Han1 aBroka, Andi1 aDeStefano, Anita, L1 aGupta, Mayetri1 aLunetta, Kathryn, L1 aLiu, Ching-Ti1 aWhite, Charles, C1 aXing, Chuanhua1 aZhou, Yanhua1 aBenjamin, Emelia, J1 aSchnabel, Renate, B1 aHeckbert, Susan, R1 aPsaty, Bruce, M1 aMuzny, Donna, M1 aCupples, Adrienne, L1 aMorrison, Alanna, C1 aBoerwinkle, Eric uhttps://chs-nhlbi.org/node/657804555nas a2201081 4500008004100000022001400041245014800055210006900203260001300272300001200285490000700297520142500304100002501729700002301754700001701777700002501794700001401819700001801833700001901851700002301870700001401893700002201907700001801929700001801947700002101965700002201986700002202008700002602030700002102056700001902077700001802096700002102114700002202135700002202157700002002179700001802199700002502217700002302242700001602265700002302281700003602304700001902340700002202359700003002381700002102411700003502432700002202467700002002489700002502509700001702534700001502551700002202566700002102588700002202609700003602631700002602667700002202693700002402715700002102739700002102760700002602781700001502807700002302822700001902845700002602864700002202890700001902912700001702931700002402948700002102972700002502993700002203018700002503040700002003065700001603085700002303101700002103124700002403145700002403169700002103193700002303214700002003237700001803257700002203275700002003297700002203317700001903339700002003358700001903378700002003397700002003417856003603437 2016 eng d a1939-327X00aGenome-Wide Association Study of the Modified Stumvoll Insulin Sensitivity Index Identifies BCL2 and FAM19A2 as Novel Insulin Sensitivity Loci.0 aGenomeWide Association Study of the Modified Stumvoll Insulin Se c2016 Oct a3200-110 v653 aGenome-wide association studies (GWAS) have found few common variants that influence fasting measures of insulin sensitivity. We hypothesized that a GWAS of an integrated assessment of fasting and dynamic measures of insulin sensitivity would detect novel common variants. We performed a GWAS of the modified Stumvoll Insulin Sensitivity Index (ISI) within the Meta-Analyses of Glucose and Insulin-Related Traits Consortium. Discovery for genetic association was performed in 16,753 individuals, and replication was attempted for the 23 most significant novel loci in 13,354 independent individuals. Association with ISI was tested in models adjusted for age, sex, and BMI and in a model analyzing the combined influence of the genotype effect adjusted for BMI and the interaction effect between the genotype and BMI on ISI (model 3). In model 3, three variants reached genome-wide significance: rs13422522 (NYAP2; P = 8.87 × 10(-11)), rs12454712 (BCL2; P = 2.7 × 10(-8)), and rs10506418 (FAM19A2; P = 1.9 × 10(-8)). The association at NYAP2 was eliminated by conditioning on the known IRS1 insulin sensitivity locus; the BCL2 and FAM19A2 associations were independent of known cardiometabolic loci. In conclusion, we identified two novel loci and replicated known variants associated with insulin sensitivity. Further studies are needed to clarify the causal variant and function at the BCL2 and FAM19A2 loci.
1 aWalford, Geoffrey, A1 aGustafsson, Stefan1 aRybin, Denis1 aStančáková, Alena1 aChen, Han1 aLiu, Ching-Ti1 aHong, Jaeyoung1 aJensen, Richard, A1 aRice, Ken1 aMorris, Andrew, P1 aMägi, Reedik1 aTönjes, Anke1 aProkopenko, Inga1 aKleber, Marcus, E1 aDelgado, Graciela1 aSilbernagel, Günther1 aJackson, Anne, U1 aAppel, Emil, V1 aGrarup, Niels1 aLewis, Joshua, P1 aMontasser, May, E1 aLandenvall, Claes1 aStaiger, Harald1 aLuan, Jian'an1 aFrayling, Timothy, M1 aWeedon, Michael, N1 aXie, Weijia1 aMorcillo, Sonsoles1 aMartínez-Larrad, María Teresa1 aBiggs, Mary, L1 aChen, Yii-Der Ida1 aCorbaton-Anchuelo, Arturo1 aFærch, Kristine1 aGómez-Zumaquero, Juan, Miguel1 aGoodarzi, Mark, O1 aKizer, Jorge, R1 aKoistinen, Heikki, A1 aLeong, Aaron1 aLind, Lars1 aLindgren, Cecilia1 aMachicao, Fausto1 aManning, Alisa, K1 aMartín-Núñez, Gracia, María1 aRojo-Martínez, Gemma1 aRotter, Jerome, I1 aSiscovick, David, S1 aZmuda, Joseph, M1 aZhang, Zhongyang1 aSerrano-Ríos, Manuel1 aSmith, Ulf1 aSoriguer, Federico1 aHansen, Torben1 aJørgensen, Torben, J1 aLinnenberg, Allan1 aPedersen, Oluf1 aWalker, Mark1 aLangenberg, Claudia1 aScott, Robert, A1 aWareham, Nicholas, J1 aFritsche, Andreas1 aHäring, Hans-Ulrich1 aStefan, Norbert1 aGroop, Leif1 aO'Connell, Jeff, R1 aBoehnke, Michael1 aBergman, Richard, N1 aCollins, Francis, S1 aMohlke, Karen, L1 aTuomilehto, Jaakko1 aMärz, Winfried1 aKovacs, Peter1 aStumvoll, Michael1 aPsaty, Bruce, M1 aKuusisto, Johanna1 aLaakso, Markku1 aMeigs, James, B1 aDupuis, Josée1 aIngelsson, Erik1 aFlorez, Jose, C uhttps://chs-nhlbi.org/node/716704937nas a2201345 4500008004100000022001400041245012400055210006900179260001300248300001200261490000700273520113500280100001601415700001901431700002301450700002301473700002301496700001901519700001801538700002401556700001801580700001801598700001701616700001901633700002001652700002201672700002301694700001901717700001901736700001801755700001901773700001601792700001401808700002601822700001501848700002601863700001601889700001301905700001301918700001901931700002201950700002001972700002101992700002002013700001402033700001902047700001802066700002402084700002202108700001902130700002402149700002002173700001202193700002702205700002202232700002002254700002402274700002802298700002402326700002102350700002402371700002102395700002202416700002802438700002102466700002702487700003002514700002002544700001502564700002402579700002002603700001702623700002302640700001902663700001902682700002202701700002202723700001802745700002202763700001902785700001902804700001402823700001802837700001402855700002102869700002302890700002802913700001702941700001902958700001902977700001702996700002003013700002103033700002403054700002503078700002303103700002303126700002103149700002603170700002203196700002003218700002003238700002003258700002003278700002903298700001703327700002303344710002603367710002303393710002603416710002503442710006603467710002203533856003603555 2016 eng d a1546-171800aMeta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci.0 aMetaanalysis identifies common and rare variants influencing blo c2016 Oct a1162-700 v483 aMeta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure-associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT1. In addition, blood pressure associations at 39 previously reported loci were confirmed. The identified variants implicate biological pathways related to cardiometabolic traits, vascular function, and development. Several new variants are inferred to have roles in transcription or as hubs in protein-protein interaction networks. Genetic risk scores constructed from the identified variants were strongly associated with coronary disease and myocardial infarction. This large collection of blood pressure-associated loci suggests new therapeutic strategies for hypertension, emphasizing a link with cardiometabolic risk.
1 aLiu, Chunyu1 aKraja, Aldi, T1 aSmith, Jennifer, A1 aBrody, Jennifer, A1 aFranceschini, Nora1 aBis, Joshua, C1 aRice, Kenneth1 aMorrison, Alanna, C1 aLu, Yingchang1 aWeiss, Stefan1 aGuo, Xiuqing1 aPalmas, Walter1 aMartin, Lisa, W1 aChen, Yii-Der Ida1 aSurendran, Praveen1 aDrenos, Fotios1 aCook, James, P1 aAuer, Paul, L1 aChu, Audrey, Y1 aGiri, Ayush1 aZhao, Wei1 aJakobsdottir, Johanna1 aLin, Li-An1 aStafford, Jeanette, M1 aAmin, Najaf1 aMei, Hao1 aYao, Jie1 aVoorman, Arend1 aLarson, Martin, G1 aGrove, Megan, L1 aSmith, Albert, V1 aHwang, Shih-Jen1 aChen, Han1 aHuan, Tianxiao1 aKosova, Gulum1 aStitziel, Nathan, O1 aKathiresan, Sekar1 aSamani, Nilesh1 aSchunkert, Heribert1 aDeloukas, Panos1 aLi, Man1 aFuchsberger, Christian1 aPattaro, Cristian1 aGorski, Mathias1 aKooperberg, Charles1 aPapanicolaou, George, J1 aRossouw, Jacques, E1 aFaul, Jessica, D1 aKardia, Sharon, L R1 aBouchard, Claude1 aRaffel, Leslie, J1 aUitterlinden, André, G1 aFranco, Oscar, H1 aVasan, Ramachandran, S1 aO'Donnell, Christopher, J1 aTaylor, Kent, D1 aLiu, Kiang1 aBottinger, Erwin, P1 aGottesman, Omri1 aDaw, Warwick1 aGiulianini, Franco1 aGanesh, Santhi1 aSalfati, Elias1 aHarris, Tamara, B1 aLauner, Lenore, J1 aDörr, Marcus1 aFelix, Stephan, B1 aRettig, Rainer1 aVölzke, Henry1 aKim, Eric1 aLee, Wen-Jane1 aLee, I-Te1 aSheu, Wayne, H-H1 aTsosie, Krystal, S1 aEdwards, Digna, R Velez1 aLiu, Yongmei1 aCorrea, Adolfo1 aWeir, David, R1 aVölker, Uwe1 aRidker, Paul, M1 aBoerwinkle, Eric1 aGudnason, Vilmundur1 aReiner, Alexander, P1 aDuijn, Cornelia, M1 aBorecki, Ingrid, B1 aEdwards, Todd, L1 aChakravarti, Aravinda1 aRotter, Jerome, I1 aPsaty, Bruce, M1 aLoos, Ruth, J F1 aFornage, Myriam1 aEhret, Georg, B1 aNewton-Cheh, Christopher1 aLevy, Daniel1 aChasman, Daniel, I1 aCHD Exome+ Consortium1 aExomeBP Consortium1 aGoT2DGenes Consortium1 aT2D-GENES Consortium1 aMyocardial Infarction Genetics and CARDIoGRAM Exome Consortia1 aCKDGen Consortium uhttps://chs-nhlbi.org/node/726404205nas a2200877 4500008004100000022001400041245012500055210006900180260001300249300001200262490000700274520175300281100001402034700001902048700002202067700002302089700002302112700001702135700002402152700002502176700001802201700002002219700002002239700002302259700001502282700002102297700001602318700002802334700002002362700002202382700002002404700002102424700002202445700001902467700002102486700002402507700002002531700001902551700002002570700002002590700002202610700002002632700002802652700001902680700002102699700001902720700002202739700001402761700002202775700002002797700001902817700001502836700002002851700001802871700001902889700001702908700002002925700002202945700002502967700002302992700001903015700001703034700002003051700002003071700002003091700002003111700002103131700002403152700002103176700001803197700002303215700001803238700001603256700001903272856003603291 2018 eng d a1535-498900aMultiethnic Meta-Analysis Identifies RAI1 as a Possible Obstructive Sleep Apnea-related Quantitative Trait Locus in Men.0 aMultiethnic MetaAnalysis Identifies RAI1 as a Possible Obstructi c2018 Mar a391-4010 v583 aObstructive sleep apnea (OSA) is a common heritable disorder displaying marked sexual dimorphism in disease prevalence and progression. Previous genetic association studies have identified a few genetic loci associated with OSA and related quantitative traits, but they have only focused on single ethnic groups, and a large proportion of the heritability remains unexplained. The apnea-hypopnea index (AHI) is a commonly used quantitative measure characterizing OSA severity. Because OSA differs by sex, and the pathophysiology of obstructive events differ in rapid eye movement (REM) and non-REM (NREM) sleep, we hypothesized that additional genetic association signals would be identified by analyzing the NREM/REM-specific AHI and by conducting sex-specific analyses in multiethnic samples. We performed genome-wide association tests for up to 19,733 participants of African, Asian, European, and Hispanic/Latino American ancestry in 7 studies. We identified rs12936587 on chromosome 17 as a possible quantitative trait locus for NREM AHI in men (N = 6,737; P = 1.7 × 10) but not in women (P = 0.77). The association with NREM AHI was replicated in a physiological research study (N = 67; P = 0.047). This locus overlapping the RAI1 gene and encompassing genes PEMT1, SREBF1, and RASD1 was previously reported to be associated with coronary artery disease, lipid metabolism, and implicated in Potocki-Lupski syndrome and Smith-Magenis syndrome, which are characterized by abnormal sleep phenotypes. We also identified gene-by-sex interactions in suggestive association regions, suggesting that genetic variants for AHI appear to vary by sex, consistent with the clinical observations of strong sexual dimorphism.
1 aChen, Han1 aCade, Brian, E1 aGleason, Kevin, J1 aBjonnes, Andrew, C1 aStilp, Adrienne, M1 aSofer, Tamar1 aConomos, Matthew, P1 aAncoli-Israel, Sonia1 aArens, Raanan1 aAzarbarzin, Ali1 aBell, Graeme, I1 aBelow, Jennifer, E1 aChun, Sung1 aEvans, Daniel, S1 aEwert, Ralf1 aFrazier-Wood, Alexis, C1 aGharib, Sina, A1 aHaba-Rubio, José1 aHagen, Erika, W1 aHeinzer, Raphael1 aHillman, David, R1 aJohnson, Craig1 aKutalik, Zoltán1 aLane, Jacqueline, M1 aLarkin, Emma, K1 aLee, Seung, Ku1 aLiang, Jingjing1 aLoredo, Jose, S1 aMukherjee, Sutapa1 aPalmer, Lyle, J1 aPapanicolaou, George, J1 aPenzel, Thomas1 aPeppard, Paul, E1 aPost, Wendy, S1 aRamos, Alberto, R1 aRice, Ken1 aRotter, Jerome, I1 aSands, Scott, A1 aShah, Neomi, A1 aShin, Chol1 aStone, Katie, L1 aStubbe, Beate1 aSul, Jae, Hoon1 aTafti, Mehdi1 aTaylor, Kent, D1 aTeumer, Alexander1 aThornton, Timothy, A1 aTranah, Gregory, J1 aWang, Chaolong1 aWang, Heming1 aWarby, Simon, C1 aWellman, Andrew1 aZee, Phyllis, C1 aHanis, Craig, L1 aLaurie, Cathy, C1 aGottlieb, Daniel, J1 aPatel, Sanjay, R1 aZhu, Xiaofeng1 aSunyaev, Shamil, R1 aSaxena, Richa1 aLin, Xihong1 aRedline, Susan uhttps://chs-nhlbi.org/node/767503584nas a2200649 4500008004100000022001400041245010200055210006900157260001500226300001200241490000700253520177200260100001702032700001902049700001702068700002002085700001402105700002402119700002302143700001802166700002502184700001902209700002302228700002402251700002102275700002002296700001702316700002002333700001302353700002002366700002002386700002202406700003002428700002502458700002002483700001802503700001902521700002002540700002302560700002002583700002002603700002102623700002402644700002102668700001402689700001902703700002202722700002302744700001702767700001602784700002202800700002102822700001802843700001902861700001802880856003602898 2019 eng d a1460-208300aAdmixture mapping identifies novel loci for obstructive sleep apnea in Hispanic/Latino Americans.0 aAdmixture mapping identifies novel loci for obstructive sleep ap c2019 02 15 a675-6870 v283 aObstructive sleep apnea (OSA) is a common disorder associated with increased risk of cardiovascular disease and mortality. Its prevalence and severity vary across ancestral background. Although OSA traits are heritable, few genetic associations have been identified. To identify genetic regions associated with OSA and improve statistical power, we applied admixture mapping on three primary OSA traits [the apnea hypopnea index (AHI), overnight average oxyhemoglobin saturation (SaO2) and percentage time SaO2 < 90%] and a secondary trait (respiratory event duration) in a Hispanic/Latino American population study of 11 575 individuals with significant variation in ancestral background. Linear mixed models were performed using previously inferred African, European and Amerindian local genetic ancestry markers. Global African ancestry was associated with a lower AHI, higher SaO2 and shorter event duration. Admixture mapping analysis of the primary OSA traits identified local African ancestry at the chromosomal region 2q37 as genome-wide significantly associated with AHI (P < 5.7 × 10-5), and European and Amerindian ancestries at 18q21 suggestively associated with both AHI and percentage time SaO2 < 90% (P < 10-3). Follow-up joint ancestry-SNP association analyses identified novel variants in ferrochelatase (FECH), significantly associated with AHI and percentage time SaO2 < 90% after adjusting for multiple tests (P < 8 × 10-6). These signals contributed to the admixture mapping associations and were replicated in independent cohorts. In this first admixture mapping study of OSA, novel associations with variants in the iron/heme metabolism pathway suggest a role for iron in influencing respiratory traits underlying OSA.
1 aWang, Heming1 aCade, Brian, E1 aSofer, Tamar1 aSands, Scott, A1 aChen, Han1 aBrowning, Sharon, R1 aStilp, Adrienne, M1 aLouie, Tin, L1 aThornton, Timothy, A1 aJohnson, Craig1 aBelow, Jennifer, E1 aConomos, Matthew, P1 aEvans, Daniel, S1 aGharib, Sina, A1 aGuo, Xiuqing1 aWood, Alexis, C1 aMei, Hao1 aYaffe, Kristine1 aLoredo, Jose, S1 aRamos, Alberto, R1 aBarrett-Connor, Elizabeth1 aAncoli-Israel, Sonia1 aZee, Phyllis, C1 aArens, Raanan1 aShah, Neomi, A1 aTaylor, Kent, D1 aTranah, Gregory, J1 aStone, Katie, L1 aHanis, Craig, L1 aWilson, James, G1 aGottlieb, Daniel, J1 aPatel, Sanjay, R1 aRice, Ken1 aPost, Wendy, S1 aRotter, Jerome, I1 aSunyaev, Shamil, R1 aCai, Jianwen1 aLin, Xihong1 aPurcell, Shaun, M1 aLaurie, Cathy, C1 aSaxena, Richa1 aRedline, Susan1 aZhu, Xiaofeng uhttps://chs-nhlbi.org/node/804904619nas a2201021 4500008004100000022001400041245012900055210006900184260001200253300001300265490000700278520172700285653001502012653001002027653000902037653002202046653003802068653002602106653003402132653001102166653002902177653002202206653003402228653001502262653001102277653001202288653004202300653000902342653001602351653002602367653005002393653001102443653001902454653003602473653002802509653002602537653001002563653002602573653001602599100001902615700001402634700002302648700001502671700002502686700001802711700002202729700002302751700001702774700002402791700002102815700002802836700002002864700002202884700002402906700002202930700001902952700002202971700001502993700002003008700001303028700002203041700002103063700001903084700002203103700002203125700002103147700001403168700001903182700001703201700002003218700002503238700001703263700002003280700002003300700002003320700002003340700002203360700002003382700002303402700002103425700002303446700002103469700001803490700001803508700001603526700001903542856003603561 2019 eng d a1553-740400aAssociations of variants In the hexokinase 1 and interleukin 18 receptor regions with oxyhemoglobin saturation during sleep.0 aAssociations of variants In the hexokinase 1 and interleukin 18 c2019 04 ae10077390 v153 aSleep disordered breathing (SDB)-related overnight hypoxemia is associated with cardiometabolic disease and other comorbidities. Understanding the genetic bases for variations in nocturnal hypoxemia may help understand mechanisms influencing oxygenation and SDB-related mortality. We conducted genome-wide association tests across 10 cohorts and 4 populations to identify genetic variants associated with three correlated measures of overnight oxyhemoglobin saturation: average and minimum oxyhemoglobin saturation during sleep and the percent of sleep with oxyhemoglobin saturation under 90%. The discovery sample consisted of 8,326 individuals. Variants with p < 1 × 10(-6) were analyzed in a replication group of 14,410 individuals. We identified 3 significantly associated regions, including 2 regions in multi-ethnic analyses (2q12, 10q22). SNPs in the 2q12 region associated with minimum SpO2 (rs78136548 p = 2.70 × 10(-10)). SNPs at 10q22 were associated with all three traits including average SpO2 (rs72805692 p = 4.58 × 10(-8)). SNPs in both regions were associated in over 20,000 individuals and are supported by prior associations or functional evidence. Four additional significant regions were detected in secondary sex-stratified and combined discovery and replication analyses, including a region overlapping Reelin, a known marker of respiratory complex neurons.These are the first genome-wide significant findings reported for oxyhemoglobin saturation during sleep, a phenotype of high clinical interest. Our replicated associations with HK1 and IL18R1 suggest that variants in inflammatory pathways, such as the biologically-plausible NLRP3 inflammasome, may contribute to nocturnal hypoxemia.
10aAdolescent10aAdult10aAged10aAged, 80 and over10aCell Adhesion Molecules, Neuronal10aComputational Biology10aExtracellular Matrix Proteins10aFemale10aGene Regulatory Networks10aGenetic Variation10aGenome-Wide Association Study10aHexokinase10aHumans10aHypoxia10aInterleukin-18 Receptor alpha Subunit10aMale10aMiddle Aged10aNerve Tissue Proteins10aNLR Family, Pyrin Domain-Containing 3 Protein10aOxygen10aOxyhemoglobins10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci10aSerine Endopeptidases10aSleep10aSleep Apnea Syndromes10aYoung Adult1 aCade, Brian, E1 aChen, Han1 aStilp, Adrienne, M1 aLouie, Tin1 aAncoli-Israel, Sonia1 aArens, Raanan1 aBarfield, Richard1 aBelow, Jennifer, E1 aCai, Jianwen1 aConomos, Matthew, P1 aEvans, Daniel, S1 aFrazier-Wood, Alexis, C1 aGharib, Sina, A1 aGleason, Kevin, J1 aGottlieb, Daniel, J1 aHillman, David, R1 aJohnson, Craig1 aLederer, David, J1 aLee, Jiwon1 aLoredo, Jose, S1 aMei, Hao1 aMukherjee, Sutapa1 aPatel, Sanjay, R1 aPost, Wendy, S1 aPurcell, Shaun, M1 aRamos, Alberto, R1 aReid, Kathryn, J1 aRice, Ken1 aShah, Neomi, A1 aSofer, Tamar1 aTaylor, Kent, D1 aThornton, Timothy, A1 aWang, Heming1 aYaffe, Kristine1 aZee, Phyllis, C1 aHanis, Craig, L1 aPalmer, Lyle, J1 aRotter, Jerome, I1 aStone, Katie, L1 aTranah, Gregory, J1 aWilson, James, G1 aSunyaev, Shamil, R1 aLaurie, Cathy, C1 aZhu, Xiaofeng1 aSaxena, Richa1 aLin, Xihong1 aRedline, Susan uhttps://chs-nhlbi.org/node/804403011nas a2200361 4500008004100000022001400041245015500055210006900210260001600279520191700295100002202212700001702234700001702251700002302268700002102291700001602312700002002328700002202348700002102370700001902391700001402410700002102424700001802445700002002463700001902483700002202502700001802524700001902542700001602561700001902577700001702596856003602613 2019 eng d a1550-910900aEpigenome-wide association analysis of daytime sleepiness in the Multi-Ethnic Study of Atherosclerosis reveals African-American-specific associations.0 aEpigenomewide association analysis of daytime sleepiness in the c2019 May 293 aSTUDY OBJECTIVES: Daytime sleepiness is a consequence of inadequate sleep, sleep-wake control disorder, or other medical conditions. Population variability in prevalence of daytime sleepiness is likely due to genetic and biological factors as well as social and environmental influences. DNA methylation (DNAm) potentially influences multiple health outcomes. Here, we explored the association between DNAm and daytime sleepiness quantified by the Epworth Sleepiness Scale (ESS).
METHODS: We performed multi-ethnic and ethnic-specific epigenome-wide association studies for DNAm and ESS in the Multi-Ethnic Study of Atherosclerosis (MESA; n = 619) and the Cardiovascular Health Study (n = 483), with cross-study replication and meta-analysis. Genetic variants near ESS-associated DNAm were analyzed for methylation quantitative trait loci and followed with replication of genotype-sleepiness associations in the UK Biobank.
RESULTS: In MESA only, we detected four DNAm-ESS associations: one across all race/ethnic groups; three in African-Americans (AA) only. Two of the MESA AA associations, in genes KCTD5 and RXRA, nominally replicated in CHS (p-value < 0.05). In the AA meta-analysis, we detected 14 DNAm-ESS associations (FDR q-value < 0.05, top association p-value = 4.26 × 10-8). Three DNAm sites mapped to genes (CPLX3, GFAP, and C7orf50) with biological relevance. We also found evidence for associations with DNAm sites in RAI1, a gene associated with sleep and circadian phenotypes. UK Biobank follow-up analyses detected SNPs in RAI1, RXRA, and CPLX3 with nominal sleepiness associations.
CONCLUSIONS: We identified methylation sites in multiple genes possibly implicated in daytime sleepiness. Most significant DNAm-ESS associations were specific to AA. Future work is needed to identify mechanisms driving ancestry-specific methylation effects.
1 aBarfield, Richard1 aWang, Heming1 aLiu, Yongmei1 aBrody, Jennifer, A1 aSwenson, Brenton1 aLi, Ruitong1 aBartz, Traci, M1 aSotoodehnia, Nona1 aChen, Yii-der, I1 aCade, Brian, E1 aChen, Han1 aPatel, Sanjay, R1 aZhu, Xiaofeng1 aGharib, Sina, A1 aJohnson, Craig1 aRotter, Jerome, I1 aSaxena, Richa1 aPurcell, Shaun1 aLin, Xihong1 aRedline, Susan1 aSofer, Tamar uhttps://chs-nhlbi.org/node/809606391nas a2201909 4500008004100000022001400041245012500055210006900180260001600249300000900265490000700274520109900281100002101380700001901401700001701420700002301437700002001460700002801480700002201508700001801530700002101548700001901569700001901588700002001607700002201627700001401649700002301663700002301686700002101709700002001730700002301750700002001773700002601793700001801819700002401837700001801861700001701879700001601896700001701912700001501929700001701944700002901961700002001990700002402010700002502034700001402059700002102073700002102094700001702115700002502132700002002157700001402177700002102191700002802212700002502240700001902265700002602284700002102310700001702331700002002348700001802368700001702386700001902403700001902422700002502441700001802466700002402484700002102508700001602529700002402545700001502569700002002584700002402604700002202628700002202650700002002672700002102692700002102713700001802734700002002752700002202772700002102794700001502815700001702830700002002847700002302867700001802890700002002908700002202928700002102950700002102971700001902992700002003011700002003031700002103051700002303072700002003095700002503115700001903140700002103159700002103180700002203201700002503223700001903248700002303267700001603290700002403306700001703330700002403347700002203371700002003393700002803413700002303441700001903464700002603483700002403509700002103533700001703554700002303571700002103594700001803615700001603633700002203649700001903671700001903690700002203709700001903731700002703750700002403777700002603801700001703827700002103844700001503865700002103880700002103901700002203922700002403944700001903968700002103987700002804008700002004036700002604056700001904082700002004101700002004121700001804141700001604159700002504175700002004200700001804220700001704238700002104255700001804276700002404294700002404318700001804342700002404360700002004384700002204404700001904426856003604445 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/820203253nas 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/819903741nas 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/840703306nas 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/883803417nas a2200493 4500008004100000022001400041245018000055210006900235260000900304300001300313490000700326520180900333100002102142700001402163700001902177700003202196700001802228700002502246700002202271700001902293700002102312700002502333700002002358700002702378700002502405700001702430700002002447700002402467700002102491700002502512700002402537700002602561700002002587700001802607700002102625700002702646700001802673700002102691700002402712700002402736710005302760710007402813856003602887 2021 eng d a1932-620300aIdentification of novel and rare variants associated with handgrip strength using whole genome sequence data from the NHLBI Trans-Omics in Precision Medicine (TOPMed) Program.0 aIdentification of novel and rare variants associated with handgr c2021 ae02536110 v163 aHandgrip strength is a widely used measure of muscle strength and a predictor of a range of morbidities including cardiovascular diseases and all-cause mortality. Previous genome-wide association studies of handgrip strength have focused on common variants primarily in persons of European descent. We aimed to identify rare and ancestry-specific genetic variants associated with handgrip strength by conducting whole-genome sequence association analyses using 13,552 participants from six studies representing diverse population groups from the Trans-Omics in Precision Medicine (TOPMed) Program. By leveraging multiple handgrip strength measures performed in study participants over time, we increased our effective sample size by 7-12%. Single-variant analyses identified ten handgrip strength loci among African-Americans: four rare variants, five low-frequency variants, and one common variant. One significant and four suggestive genes were identified associated with handgrip strength when aggregating rare and functional variants; all associations were ancestry-specific. We additionally leveraged the different ancestries available in the UK Biobank to further explore the ancestry-specific association signals from the single-variant association analyses. In conclusion, our study identified 11 new loci associated with handgrip strength with rare and/or ancestry-specific genetic variations, highlighting the added value of whole-genome sequencing in diverse samples. Several of the associations identified using single-variant or aggregate analyses lie in genes with a function relevant to the brain or muscle or were reported to be associated with muscle or age-related traits. Further studies in samples with sequence data and diverse ancestries are needed to confirm these findings.
1 aSarnowski, Chloe1 aChen, Han1 aBiggs, Mary, L1 aWassertheil-Smoller, Sylvia1 aBressler, Jan1 aIrvin, Marguerite, R1 aRyan, Kathleen, A1 aKarasik, David1 aArnett, Donna, K1 aCupples, Adrienne, L1 aFardo, David, W1 aGogarten, Stephanie, M1 aHeavner, Benjamin, D1 aJain, Deepti1 aKang, Hyun, Min1 aKooperberg, Charles1 aMainous, Arch, G1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aO'Connell, Jeffrey, R1 aPsaty, Bruce, M1 aRice, Kenneth1 aSmith, Albert, V1 aVasan, Ramachandran, S1 aWindham, Gwen1 aKiel, Douglas, P1 aMurabito, Joanne, M1 aLunetta, Kathryn, L1 aTOPMed Longevity and Healthy Aging Working Group1 afrom the NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/883606233nas 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/871403828nas 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/892002548nas 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/924604304nas 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/925302763nas 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/916103348nas 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/910103534nas a2200649 4500008004100000245010900041210006900150260001600219520168900235100002201924700001601946700002001962700002201982700001402004700002202018700001602040700002202056700002402078700002002102700002202122700001702144700002402161700001802185700002102203700002302224700002402247700001902271700002102290700002002311700001902331700002202350700002002372700001902392700002002411700001702431700001902448700001702467700002502484700002402509700001902533700002302552700001902575700002302594700002002617700002102637700001802658700002002676700002202696700002402718700002102742700002002763700001802783700001402801700001702815700001602832856003602848 2023 eng d00aGenome-Wide Interaction Analysis with DASH Diet Score Identified Novel Loci for Systolic Blood Pressure.0 aGenomeWide Interaction Analysis with DASH Diet Score Identified c2023 Nov 113 aOBJECTIVE: We examined interactions between genotype and a Dietary Approaches to Stop Hypertension (DASH) diet score in relation to systolic blood pressure (SBP).
METHODS: We analyzed up to 9,420,585 biallelic imputed single nucleotide polymorphisms (SNPs) in up to 127,282 individuals of six population groups (91% of European population) from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (CHARGE; n=35,660) and UK Biobank (n=91,622) and performed European population-specific and cross-population meta-analyses.
RESULTS: We identified three loci in European-specific analyses and an additional four loci in cross-population analyses at P for interaction < 5e-8. We observed a consistent interaction between rs117878928 at 15q25.1 (minor allele frequency = 0.03) and the DASH diet score (P for interaction = 4e-8; P for heterogeneity = 0.35) in European population, where the interaction effect size was 0.42±0.09 mm Hg (P for interaction = 9.4e-7) and 0.20±0.06 mm Hg (P for interaction = 0.001) in CHARGE and the UK Biobank, respectively. The 1 Mb region surrounding rs117878928 was enriched with -expression quantitative trait loci (eQTL) variants (P = 4e-273) and -DNA methylation quantitative trait loci (mQTL) variants (P = 1e-300). While the closest gene for rs117878928 is , the highest narrow sense heritability accounted by SNPs potentially interacting with the DASH diet score in this locus was for gene at 15q25.1.
CONCLUSION: We demonstrated gene-DASH diet score interaction effects on SBP in several loci. Studies with larger diverse populations are needed to validate our findings.
1 aGuirette, Melanie1 aLan, Jessie1 aMcKeown, Nicola1 aBrown, Michael, R1 aChen, Han1 ade Vries, Paul, S1 aKim, Hyunju1 aRebholz, Casey, M1 aMorrison, Alanna, C1 aBartz, Traci, M1 aFretts, Amanda, M1 aGuo, Xiuqing1 aLemaitre, Rozenn, N1 aLiu, Ching-Ti1 aNoordam, Raymond1 ade Mutsert, Renée1 aRosendaal, Frits, R1 aWang, Carol, A1 aBeilin, Lawrence1 aMori, Trevor, A1 aOddy, Wendy, H1 aPennell, Craig, E1 aChai, Jin, Fang1 aWhitton, Clare1 avan Dam, Rob, M1 aLiu, Jianjun1 aTai, Shyong, E1 aSim, Xueling1 aNeuhouser, Marian, L1 aKooperberg, Charles1 aTinker, Lesley1 aFranceschini, Nora1 aHuan, Tianxiao1 aWinkler, Thomas, W1 aBentley, Amy, R1 aGauderman, James1 aHeerkens, Luc1 aTanaka, Toshiko1 avan Rooij, Jeroen1 aMunroe, Patricia, B1 aWarren, Helen, R1 aVoortman, Trudy1 aChen, Honglei1 aRao, D, C1 aLevy, Daniel1 aMa, Jiantao uhttps://chs-nhlbi.org/node/958303951nas 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/923903992nas 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/954304412nas 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/953703713nas 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/958102589nas a2200457 4500008004100000022001400041245008600055210006900141260001600210300000900226490000700235520126900242653001401511653003401525653001101559653001501570653003601585653002801621100002401649700002201673700001701695700002201712700001401734700002001748700001401768700002101782700002101803700001401824700001401838700002201852700002401874700002401898700002101922700001801943700002501961700002001986700002202006700001302028710005402041856003602095 2023 eng d a2041-172300aWhole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations.0 aWholeGenome Sequencing Analysis of Human Metabolome in MultiEthn c2023 May 30 a31110 v143 aCirculating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.
10aEthnicity10aGenome-Wide Association Study10aHumans10aMetabolome10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci1 aFeofanova, Elena, V1 aBrown, Michael, R1 aAlkis, Taryn1 aManuel, Astrid, M1 aLi, Xihao1 aTahir, Usman, A1 aLi, Zilin1 aMendez, Kevin, M1 aKelly, Rachel, S1 aQi, Qibin1 aChen, Han1 aLarson, Martin, G1 aLemaitre, Rozenn, N1 aMorrison, Alanna, C1 aGrieser, Charles1 aWong, Kari, E1 aGersztern, Robert, E1 aZhao, Zhongming1 aLasky-Su, Jessica1 aYu, Bing1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) uhttps://chs-nhlbi.org/node/9376