04013nas a2200685 4500008004100000022001400041245016200055210006900217260001600286300001200302490000800314520192200322100002102244700001702265700002302282700001502305700002202320700002002342700001702362700001602379700001702395700001802412700001202430700002302442700002202465700002302487700002502510700001702535700001802552700001902570700002602589700002002615700002402635700002202659700002102681700002002702700002202722700002102744700001902765700002402784700002002808700002302828700002502851700002502876700002502901700002702926700001902953700002402972700002102996700002203017700002103039700002203060700001903082700002003101710003403121710003603155710003603191710006403227856003603291 2019 eng d a1537-660500aImpact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program.0 aImpact of Rare and Common Genetic Variants on Diabetes Diagnosis c2019 Oct 03 a706-7180 v1053 a
Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in individuals with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics, and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K and/or FN3KRP in Europeans, and G6PD in African-Americans and Hispanics) and we identified an African-ancestry-specific low-frequency variant (rs1039215 in HBG2 and HBE1, minor allele frequency (MAF) = 0.03). The most associated G6PD variant (rs1050828-T, p.Val98Met, MAF = 12% in African-Americans, MAF = 2% in Hispanics) lowered HbA1c (-0.88% in hemizygous males, -0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693, p.Leu353Pro, MAF = 0.5%; -0.98% in hemizygous males, -0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African American cohorts, and we replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.
1 aSarnowski, Chloe1 aLeong, Aaron1 aRaffield, Laura, M1 aWu, Peitao1 ade Vries, Paul, S1 aDiCorpo, Daniel1 aGuo, Xiuqing1 aXu, Huichun1 aLiu, Yongmei1 aZheng, Xiuwen1 aHu, Yao1 aBrody, Jennifer, A1 aGoodarzi, Mark, O1 aHidalgo, Bertha, A1 aHighland, Heather, M1 aJain, Deepti1 aLiu, Ching-Ti1 aNaik, Rakhi, P1 aO'Connell, Jeffrey, R1 aPerry, James, A1 aPorneala, Bianca, C1 aSelvin, Elizabeth1 aWessel, Jennifer1 aPsaty, Bruce, M1 aCurran, Joanne, E1 aPeralta, Juan, M1 aBlangero, John1 aKooperberg, Charles1 aMathias, Rasika1 aJohnson, Andrew, D1 aReiner, Alexander, P1 aMitchell, Braxton, D1 aCupples, Adrienne, L1 aVasan, Ramachandran, S1 aCorrea, Adolfo1 aMorrison, Alanna, C1 aBoerwinkle, Eric1 aRotter, Jerome, I1 aRich, Stephen, S1 aManning, Alisa, K1 aDupuis, Josée1 aMeigs, James, B1 aTOPMed Diabetes Working Group1 aTOPMed Hematology Working Group1 aTOPMed Hemostasis Working Group1 aNational Heart, Lung, and Blood Institute TOPMed Consortium uhttps://chs-nhlbi.org/node/820504776nas a2201261 4500008004100000022001400041245010300055210006900158260001500227300000900242490000700251520104900258653001001307653002201317653000901339653002201348653005001370653002901420653002401449653001101473653002201484653001701506653003801523653003401561653001101595653005001606653000901656653000901665653001601674653003601690653004101726653004301767653004001810653004601850653002801896100001701924700001601941700001801957700001801975700001501993700001502008700001702023700001902040700001702059700003002076700002902106700002202135700002302157700001302180700002102193700001902214700001902233700001502252700002002267700001902287700002302306700002002329700002502349700002002374700002002394700002402414700002002438700002702458700002102485700002002506700001702526700001902543700002002562700001702582700001702599700002202616700002302638700002002661700002502681700002802706700002202734700002402756700001702780700002602797700002002823700002302843700003102866700002502897700001902922700002002941700002102961700002402982700001903006700002003025700002103045700002403066700002503090700002403115700002203139700002003161700002103181700002503202700002303227700002603250700002503276700002403301700001703325700002003342700002103362710006503383710003003448856003603478 2020 eng d a2041-172300aWhole genome sequence analysis of pulmonary function and COPD in 19,996 multi-ethnic participants.0 aWhole genome sequence analysis of pulmonary function and COPD in c2020 10 14 a51820 v113 aChronic obstructive pulmonary disease (COPD), diagnosed by reduced lung function, is a leading cause of morbidity and mortality. We performed whole genome sequence (WGS) analysis of lung function and COPD in a multi-ethnic sample of 11,497 participants from population- and family-based studies, and 8499 individuals from COPD-enriched studies in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. We identify at genome-wide significance 10 known GWAS loci and 22 distinct, previously unreported loci, including two common variant signals from stratified analysis of African Americans. Four novel common variants within the regions of PIAS1, RGN (two variants) and FTO show evidence of replication in the UK Biobank (European ancestry n ~ 320,000), while colocalization analyses leveraging multi-omic data from GTEx and TOPMed identify potential molecular mechanisms underlying four of the 22 novel loci. Our study demonstrates the value of performing WGS analyses and multi-omic follow-up in cohorts of diverse ancestry.
10aAdult10aAfrican Americans10aAged10aAged, 80 and over10aAlpha-Ketoglutarate-Dependent Dioxygenase FTO10aCalcium-Binding Proteins10aFeasibility Studies10aFemale10aFollow-Up Studies10aGenetic Loci10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aIntracellular Signaling Peptides and Proteins10aLung10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide10aProtein Inhibitors of Activated STAT10aPulmonary Disease, Chronic Obstructive10aRespiratory Physiological Phenomena10aSmall Ubiquitin-Related Modifier Proteins10aWhole Genome Sequencing1 aZhao, Xutong1 aQiao, Dandi1 aYang, Chaojie1 aKasela, Silva1 aKim, Wonji1 aMa, Yanlin1 aShrine, Nick1 aBatini, Chiara1 aSofer, Tamar1 aTaliun, Sarah, A Gagliano1 aSakornsakolpat, Phuwanat1 aBalte, Pallavi, P1 aProkopenko, Dmitry1 aYu, Bing1 aLange, Leslie, A1 aDupuis, Josée1 aCade, Brian, E1 aLee, Jiwon1 aGharib, Sina, A1 aDaya, Michelle1 aLaurie, Cecelia, A1 aRuczinski, Ingo1 aCupples, Adrienne, L1 aLoehr, Laura, R1 aBartz, Traci, M1 aMorrison, Alanna, C1 aPsaty, Bruce, M1 aVasan, Ramachandran, S1 aWilson, James, G1 aTaylor, Kent, D1 aDurda, Peter1 aJohnson, Craig1 aCornell, Elaine1 aGuo, Xiuqing1 aLiu, Yongmei1 aTracy, Russell, P1 aArdlie, Kristin, G1 aAguet, Francois1 aVanDenBerg, David, J1 aPapanicolaou, George, J1 aRotter, Jerome, I1 aBarnes, Kathleen, C1 aJain, Deepti1 aNickerson, Deborah, A1 aMuzny, Donna, M1 aMetcalf, Ginger, A1 aDoddapaneni, Harshavardhan1 aDugan-Perez, Shannon1 aGupta, Namrata1 aGabriel, Stacey1 aRich, Stephen, S1 aO'Connor, George, T1 aRedline, Susan1 aReed, Robert, M1 aLaurie, Cathy, C1 aDaviglus, Martha, L1 aPreudhomme, Liana, K1 aBurkart, Kristin, M1 aKaplan, Robert, C1 aWain, Louise, V1 aTobin, Martin, D1 aLondon, Stephanie, J1 aLappalainen, Tuuli1 aOelsner, Elizabeth, C1 aAbecasis, Goncalo, R1 aSilverman, Edwin, K1 aBarr, Graham1 aCho, Michael, H1 aManichaikul, Ani1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aTOPMed Lung Working Group uhttps://chs-nhlbi.org/node/863903306nas 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/883604364nas a2200997 4500008004100000022001400041245016200055210006900217260001300286300001100299490000700310520151100317100002001828700002201848700002301870700002301893700002301916700002601939700002501965700002001990700002102010700002602031700001702057700002502074700002202099700001702121700001702138700002002155700002002175700002302195700001702218700002102235700001802256700001802274700001902292700001202311700001802323700001502341700002302356700002802379700001802407700002002425700002002445700002102465700002302486700002002509700002102529700002302550700001802573700002202591700002302613700002202636700001802658700002002676700002302696700002302719700001902742700002002761700001402781700002002795700002202815700002002837700002102857700002102878700001702899700002402916700001902940700002502959700001402984700002302998700002403021700002303045700002503068700002603093700002103119700002703140700002203167700001703189700002103206700001903227700002103246700001603267700002403283700002303307856003603330 2021 eng d a2352-396400aWhole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium.0 aWhole genome sequence analyses of eGFR in 23732 people represent c2021 Jan a1031570 v633 aBACKGROUND: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants.
METHODS: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity.
FINDINGS: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants.
INTERPRETATION: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.
1 aLin, Bridget, M1 aGrinde, Kelsey, E1 aBrody, Jennifer, A1 aBreeze, Charles, E1 aRaffield, Laura, M1 aMychaleckyj, Josyf, C1 aThornton, Timothy, A1 aPerry, James, A1 aBaier, Leslie, J1 aFuentes, Lisa, de Las1 aGuo, Xiuqing1 aHeavner, Benjamin, D1 aHanson, Robert, L1 aHung, Yi-Jen1 aQian, Huijun1 aHsiung, Chao, A1 aHwang, Shih-Jen1 aIrvin, Margaret, R1 aJain, Deepti1 aKelly, Tanika, N1 aKobes, Sayuko1 aLange, Leslie1 aLash, James, P1 aLi, Yun1 aLiu, Xiaoming1 aMi, Xuenan1 aMusani, Solomon, K1 aPapanicolaou, George, J1 aParsa, Afshin1 aReiner, Alex, P1 aSalimi, Shabnam1 aSheu, Wayne, H-H1 aShuldiner, Alan, R1 aTaylor, Kent, D1 aSmith, Albert, V1 aSmith, Jennifer, A1 aTin, Adrienne1 aVaidya, Dhananjay1 aWallace, Robert, B1 aYamamoto, Kenichi1 aSakaue, Saori1 aMatsuda, Koichi1 aKamatani, Yoichiro1 aMomozawa, Yukihide1 aYanek, Lisa, R1 aYoung, Betsi, A1 aZhao, Wei1 aOkada, Yukinori1 aAbecasis, Gonzalo1 aPsaty, Bruce, M1 aArnett, Donna, K1 aBoerwinkle, Eric1 aCai, Jianwen1 aDer Chen, Ida, Yii-1 aCorrea, Adolfo1 aCupples, Adrienne, L1 aHe, Jiang1 aKardia, Sharon, Lr1 aKooperberg, Charles1 aMathias, Rasika, A1 aMitchell, Braxton, D1 aNickerson, Deborah, A1 aTurner, Steve, T1 aVasan, Ramachandran, S1 aRotter, Jerome, I1 aLevy, Daniel1 aKramer, Holly, J1 aKöttgen, Anna1 aRich, Stephen, S1 aLin, Dan-Yu1 aBrowning, Sharon, R1 aFranceschini, Nora uhttps://chs-nhlbi.org/node/866404419nas a2200925 4500008004100000022001400041245011400055210006900169260001600238520175700254100002002011700001202031700001402043700001702057700001602074700002002090700002102110700002002131700002302151700002502174700002302199700001902222700002002241700001902261700002002280700002302300700002002323700002102343700002002364700002302384700001902407700001402426700002002440700001902460700002502479700001802504700002002522700002102542700001902563700002102582700002502603700002002628700001902648700002202667700002002689700002002709700002002729700001602749700002002765700002402785700002102809700002702830700002002857700001502877700002502892700002402917700002202941700001902963700002602982700002103008700002003029700002703049700002103076700002203097700002103119700002303140700001403163700002203177700002403199700002303223700002303246700001203269700001803281700002203299700002503321700002303346700002303369710006503392856003603457 2021 eng d a1460-208300aWhole genome sequence analysis of platelet traits in the NHLBI trans-omics for precision medicine initiative.0 aWhole genome sequence analysis of platelet traits in the NHLBI t c2021 Sep 063 aPlatelets play a key role in thrombosis and hemostasis. Platelet count (PLT) and mean platelet volume (MPV) are highly heritable quantitative traits, with hundreds of genetic signals previously identified, mostly in European ancestry populations. We here utilize whole genome sequencing from NHLBI's Trans-Omics for Precision Medicine Initiative (TOPMed) in a large multi-ethnic sample to further explore common and rare variation contributing to PLT (n = 61 200) and MPV (n = 23 485). We identified and replicated secondary signals at MPL (rs532784633) and PECAM1 (rs73345162), both more common in African ancestry populations. We also observed rare variation in Mendelian platelet related disorder genes influencing variation in platelet traits in TOPMed cohorts (not enriched for blood disorders). For example, association of GP9 with lower PLT and higher MPV was partly driven by a pathogenic Bernard-Soulier syndrome variant (rs5030764, p.Asn61Ser), and the signals at TUBB1 and CD36 were partly driven by loss of function variants not annotated as pathogenic in ClinVar (rs199948010 and rs571975065). However, residual signal remained for these gene-based signals after adjusting for lead variants, suggesting that additional variants in Mendelian genes with impacts in general population cohorts remain to be identified. Gene-based signals were also identified at several GWAS identified loci for genes not annotated for Mendelian platelet disorders (PTPRH, TET2, CHEK2), with somatic variation driving the result at TET2. These results highlight the value of whole genome sequencing in populations of diverse genetic ancestry to identify novel regulatory and coding signals, even for well-studied traits like platelet traits.
1 aLittle, Amarise1 aHu, Yao1 aSun, Quan1 aJain, Deepti1 aBroome, Jai1 aChen, Ming-Huei1 aThibord, Florian1 aMcHugh, Caitlin1 aSurendran, Praveen1 aBlackwell, Thomas, W1 aBrody, Jennifer, A1 aBhan, Arunoday1 aChami, Nathalie1 aVries, Paul, S1 aEkunwe, Lynette1 aHeard-Costa, Nancy1 aHobbs, Brian, D1 aManichaikul, Ani1 aMoon, Jee-Young1 aPreuss, Michael, H1 aRyan, Kathleen1 aWang, Zhe1 aWheeler, Marsha1 aYanek, Lisa, R1 aAbecasis, Goncalo, R1 aAlmasy, Laura1 aBeaty, Terri, H1 aBecker, Lewis, C1 aBlangero, John1 aBoerwinkle, Eric1 aButterworth, Adam, S1 aChoquet, Helene1 aCorrea, Adolfo1 aCurran, Joanne, E1 aFaraday, Nauder1 aFornage, Myriam1 aGlahn, David, C1 aHou, Lifang1 aJorgenson, Eric1 aKooperberg, Charles1 aLewis, Joshua, P1 aLloyd-Jones, Donald, M1 aLoos, Ruth, J F1 aMin, Nancy1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aNickerson, Debbie1 aNorth, Kari, E1 aO'Connell, Jeffrey, R1 aPankratz, Nathan1 aPsaty, Bruce, M1 aVasan, Ramachandran, S1 aRich, Stephen, S1 aRotter, Jerome, I1 aSmith, Albert, V1 aSmith, Nicholas, L1 aTang, Hua1 aTracy, Russell, P1 aConomos, Matthew, P1 aLaurie, Cecelia, A1 aMathias, Rasika, A1 aLi, Yun1 aAuer, Paul, L1 aThornton, Timothy1 aReiner, Alexander, P1 aJohnson, Andrew, D1 aRaffield, Laura, M1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/891305743nas a2201309 4500008004100000022001400041245011800055210006900173260001500242300001200257490000800269520205100277653001002328653000902338653003202347653002202379653001702401653001102418653001702429653002202446653003402468653001702502653001102519653000902530653001602539653005302555653001402608653002002622653003102642653001802673100001202691700002302703700002302726700001702749700001702766700001802783700001502801700003602816700002302852700002002875700001902895700002002914700001302934700001702947700001602964700002002980700002203000700002003022700001403042700002303056700002303079700002503102700002003127700001903147700002803166700002303194700001403217700002003231700002303251700001503274700002003289700002103309700001803330700002003348700002203368700001803390700001803408700002103426700002003447700002203467700002003489700002703509700002703536700002303563700001903586700002103605700002103626700002103647700002503668700001603693700002603709700002403735700002003759700001903779700001903798700001903817700002003836700002003856700002403876700002303900700002903923700001403952700002003966700002003986700002504006700002204031700002104053700002504074700002304099700001804122700001204140700002304152700002204175700002104197700002104218700002304239700002104262700002404283700002504307710006504332856003604397 2021 eng d a1537-660500aWhole-genome sequencing association analysis of quantitative red blood cell phenotypes: The NHLBI TOPMed program.0 aWholegenome sequencing association analysis of quantitative red c2021 05 06 a874-8930 v1083 aWhole-genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci, which have not been reported previously. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, and G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3 bp indel p.Lys2169del (g.88717175_88717177TCT[4]) (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis (MIM: 194380), associated with higher mean corpuscular hemoglobin concentration (MCHC). In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.
10aAdult10aAged10aChromosomes, Human, Pair 1610aDatasets as Topic10aErythrocytes10aFemale10aGene Editing10aGenetic Variation10aGenome-Wide Association Study10aHEK293 Cells10aHumans10aMale10aMiddle Aged10aNational Heart, Lung, and Blood Institute (U.S.)10aPhenotype10aQuality Control10aReproducibility of Results10aUnited States1 aHu, Yao1 aStilp, Adrienne, M1 aMcHugh, Caitlin, P1 aRao, Shuquan1 aJain, Deepti1 aZheng, Xiuwen1 aLane, John1 ade Bellefon, Sébastian, Méric1 aRaffield, Laura, M1 aChen, Ming-Huei1 aYanek, Lisa, R1 aWheeler, Marsha1 aYao, Yao1 aRen, Chunyan1 aBroome, Jai1 aMoon, Jee-Young1 ade Vries, Paul, S1 aHobbs, Brian, D1 aSun, Quan1 aSurendran, Praveen1 aBrody, Jennifer, A1 aBlackwell, Thomas, W1 aChoquet, Helene1 aRyan, Kathleen1 aDuggirala, Ravindranath1 aHeard-Costa, Nancy1 aWang, Zhe1 aChami, Nathalie1 aPreuss, Michael, H1 aMin, Nancy1 aEkunwe, Lynette1 aLange, Leslie, A1 aCushman, Mary1 aFaraday, Nauder1 aCurran, Joanne, E1 aAlmasy, Laura1 aKundu, Kousik1 aSmith, Albert, V1 aGabriel, Stacey1 aRotter, Jerome, I1 aFornage, Myriam1 aLloyd-Jones, Donald, M1 aVasan, Ramachandran, S1 aSmith, Nicholas, L1 aNorth, Kari, E1 aBoerwinkle, Eric1 aBecker, Lewis, C1 aLewis, Joshua, P1 aAbecasis, Goncalo, R1 aHou, Lifang1 aO'Connell, Jeffrey, R1 aMorrison, Alanna, C1 aBeaty, Terri, H1 aKaplan, Robert1 aCorrea, Adolfo1 aBlangero, John1 aJorgenson, Eric1 aPsaty, Bruce, M1 aKooperberg, Charles1 aWalton, Russell, T1 aKleinstiver, Benjamin, P1 aTang, Hua1 aLoos, Ruth, J F1 aSoranzo, Nicole1 aButterworth, Adam, S1 aNickerson, Debbie1 aRich, Stephen, S1 aMitchell, Braxton, D1 aJohnson, Andrew, D1 aAuer, Paul, L1 aLi, Yun1 aMathias, Rasika, A1 aLettre, Guillaume1 aPankratz, Nathan1 aLaurie, Cathy, C1 aLaurie, Cecelia, A1 aBauer, Daniel, E1 aConomos, Matthew, P1 aReiner, Alexander, P1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/877905763nas a2201477 4500008004100000022001400041245012500055210006900180260001500249300001400264490000800278520153200286653001101818653001501829653002301844653003801867653001801905653003401923653001101957653001501968653005301983653001402036653003602050653001402086653001302100653004302113653002802156653001902184653001802203653002802221100002402249700002302273700002102296700002302317700002902340700002502369700002302394700001602417700002002433700002002453700002402473700001502497700002202512700001902534700002002553700002002573700002302593700002502616700002002641700001802661700001702679700002102696700002802717700001502745700002002760700002302780700002002803700001902823700002102842700001402863700002302877700002202900700002002922700001402942700002002956700001902976700001502995700002503010700001803035700002403053700002003077700002103097700001903118700002103137700002503158700001903183700002003202700002003222700001903242700001503261700001903276700002003295700002003315700002403335700001603359700002303375700002003398700001903418700002403437700001803461700002303479700002203502700002103524700001703545700001203562700002703574700002003601700002103621700002303642700002503665700002403690700001503714700002603729700002303755700001903778700002603797700002203823700002103845700002003866700002003886700002103906700002003927700002203947700002403969700002303993700001404016700002204030700002504052700002704077700001404104700002304118700002504141700001804166710006504184856003604249 2021 eng d a1537-660500aWhole-genome sequencing in diverse subjects identifies genetic correlates of leukocyte traits: The NHLBI TOPMed program.0 aWholegenome sequencing in diverse subjects identifies genetic co c2021 10 07 a1836-18510 v1083 aMany common and rare variants associated with hematologic traits have been discovered through imputation on large-scale reference panels. However, the majority of genome-wide association studies (GWASs) have been conducted in Europeans, and determining causal variants has proved challenging. We performed a GWAS of total leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts generated from 109,563,748 variants in the autosomes and the X chromosome in the Trans-Omics for Precision Medicine (TOPMed) program, which included data from 61,802 individuals of diverse ancestry. We discovered and replicated 7 leukocyte trait associations, including (1) the association between a chromosome X, pseudo-autosomal region (PAR), noncoding variant located between cytokine receptor genes (CSF2RA and CLRF2) and lower eosinophil count; and (2) associations between single variants found predominantly among African Americans at the S1PR3 (9q22.1) and HBB (11p15.4) loci and monocyte and lymphocyte counts, respectively. We further provide evidence indicating that the newly discovered eosinophil-lowering chromosome X PAR variant might be associated with reduced susceptibility to common allergic diseases such as atopic dermatitis and asthma. Additionally, we found a burden of very rare FLT3 (13q12.2) variants associated with monocyte counts. Together, these results emphasize the utility of whole-genome sequencing in diverse samples in identifying associations missed by European-ancestry-driven GWASs.
10aAsthma10aBiomarkers10aDermatitis, Atopic10aGenetic Predisposition to Disease10aGenome, Human10aGenome-Wide Association Study10aHumans10aLeukocytes10aNational Heart, Lung, and Blood Institute (U.S.)10aPhenotype10aPolymorphism, Single Nucleotide10aPrognosis10aProteome10aPulmonary Disease, Chronic Obstructive10aQuantitative Trait Loci10aUnited Kingdom10aUnited States10aWhole Genome Sequencing1 aMikhaylova, Anna, V1 aMcHugh, Caitlin, P1 aPolfus, Linda, M1 aRaffield, Laura, M1 aBoorgula, Meher, Preethi1 aBlackwell, Thomas, W1 aBrody, Jennifer, A1 aBroome, Jai1 aChami, Nathalie1 aChen, Ming-Huei1 aConomos, Matthew, P1 aCox, Corey1 aCurran, Joanne, E1 aDaya, Michelle1 aEkunwe, Lynette1 aGlahn, David, C1 aHeard-Costa, Nancy1 aHighland, Heather, M1 aHobbs, Brian, D1 aIlboudo, Yann1 aJain, Deepti1 aLange, Leslie, A1 aMiller-Fleming, Tyne, W1 aMin, Nancy1 aMoon, Jee-Young1 aPreuss, Michael, H1 aRosen, Jonathon1 aRyan, Kathleen1 aSmith, Albert, V1 aSun, Quan1 aSurendran, Praveen1 ade Vries, Paul, S1 aWalter, Klaudia1 aWang, Zhe1 aWheeler, Marsha1 aYanek, Lisa, R1 aZhong, Xue1 aAbecasis, Goncalo, R1 aAlmasy, Laura1 aBarnes, Kathleen, C1 aBeaty, Terri, H1 aBecker, Lewis, C1 aBlangero, John1 aBoerwinkle, Eric1 aButterworth, Adam, S1 aChavan, Sameer1 aCho, Michael, H1 aChoquet, Helene1 aCorrea, Adolfo1 aCox, Nancy1 aDeMeo, Dawn, L1 aFaraday, Nauder1 aFornage, Myriam1 aGerszten, Robert, E1 aHou, Lifang1 aJohnson, Andrew, D1 aJorgenson, Eric1 aKaplan, Robert1 aKooperberg, Charles1 aKundu, Kousik1 aLaurie, Cecelia, A1 aLettre, Guillaume1 aLewis, Joshua, P1 aLi, Bingshan1 aLi, Yun1 aLloyd-Jones, Donald, M1 aLoos, Ruth, J F1 aManichaikul, Ani1 aMeyers, Deborah, A1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aNgo, Debby1 aNickerson, Deborah, A1 aNongmaithem, Suraj1 aNorth, Kari, E1 aO'Connell, Jeffrey, R1 aOrtega, Victor, E1 aPankratz, Nathan1 aPerry, James, A1 aPsaty, Bruce, M1 aRich, Stephen, S1 aSoranzo, Nicole1 aRotter, Jerome, I1 aSilverman, Edwin, K1 aSmith, Nicholas, L1 aTang, Hua1 aTracy, Russell, P1 aThornton, Timothy, A1 aVasan, Ramachandran, S1 aZein, Joe1 aMathias, Rasika, A1 aReiner, Alexander, P1 aAuer, Paul, L1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/891403404nas a2200757 4500008004100000022001400041245011100055210006900166260001300235300001200248490000700260520115000267100002601417700001701443700001701460700002501477700002401502700002201526700002701548700002501575700002001600700002401620700001801644700002501662700001501687700002301702700001901725700002701744700002101771700002401792700002101816700002201837700002601859700001901885700002301904700002401927700002801951700001901979700002001998700002002018700002302038700002402061700002702085700002302112700002002135700002102155700002202176700002302198700001902221700001702240700002002257700002302277700002302300700002502323700002402348700002102372700001902393700002102412700001902433700001602452700001502468700002302483710003902506710006502545856003602610 2022 eng d a1546-171800aAssessing the contribution of rare variants to complex trait heritability from whole-genome sequence data.0 aAssessing the contribution of rare variants to complex trait her c2022 Mar a263-2730 v543 aAnalyses of data from genome-wide association studies on unrelated individuals have shown that, for human traits and diseases, approximately one-third to two-thirds of heritability is captured by common SNPs. However, it is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular whether the causal variants are rare, or whether it is overestimated due to bias in inference from pedigree data. Here we estimated heritability for height and body mass index (BMI) from whole-genome sequence data on 25,465 unrelated individuals of European ancestry. The estimated heritability was 0.68 (standard error 0.10) for height and 0.30 (standard error 0.10) for body mass index. Low minor allele frequency variants in low linkage disequilibrium (LD) with neighboring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection. Our results imply that rare variants, in particular those in regions of low linkage disequilibrium, are a major source of the still missing heritability of complex traits and disease.
1 aWainschtein, Pierrick1 aJain, Deepti1 aZheng, Zhili1 aCupples, Adrienne, L1 aShadyab, Aladdin, H1 aMcKnight, Barbara1 aShoemaker, Benjamin, M1 aMitchell, Braxton, D1 aPsaty, Bruce, M1 aKooperberg, Charles1 aLiu, Ching-Ti1 aAlbert, Christine, M1 aRoden, Dan1 aChasman, Daniel, I1 aDarbar, Dawood1 aLloyd-Jones, Donald, M1 aArnett, Donna, K1 aRegan, Elizabeth, A1 aBoerwinkle, Eric1 aRotter, Jerome, I1 aO'Connell, Jeffrey, R1 aYanek, Lisa, R1 ade Andrade, Mariza1 aAllison, Matthew, A1 aMcDonald, Merry-Lynn, N1 aChung, Mina, K1 aFornage, Myriam1 aChami, Nathalie1 aSmith, Nicholas, L1 aEllinor, Patrick, T1 aVasan, Ramachandran, S1 aMathias, Rasika, A1 aLoos, Ruth, J F1 aRich, Stephen, S1 aLubitz, Steven, A1 aHeckbert, Susan, R1 aRedline, Susan1 aGuo, Xiuqing1 aChen, Y, -D Ida1 aLaurie, Cecelia, A1 aHernandez, Ryan, D1 aMcGarvey, Stephen, T1 aGoddard, Michael, E1 aLaurie, Cathy, C1 aNorth, Kari, E1 aLange, Leslie, A1 aWeir, Bruce, S1 aYengo, Loic1 aYang, Jian1 aVisscher, Peter, M1 aTOPMed Anthropometry Working Group1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/904205976nas a2201393 4500008004100000022001400041245013400055210006900189260001600258300003400274520189200308100002102200700001402221700001702235700002202252700003002274700002502304700002502329700001502354700002302369700002302392700001702415700002002432700002202452700001302474700001902487700002402506700001902530700001802549700002302567700001902590700002602609700001602635700002302651700002402674700002102698700002902719700002202748700001602770700001902786700001802805700002102823700001802844700002602862700002202888700002102910700001802931700001602949700002002965700001902985700001803004700002403022700002503046700002203071700002103093700002203114700001703136700001703153700002003170700002103190700003303211700002403244700001703268700002203285700003403307700002503341700002203366700002103388700002103409700001903430700002203449700002003471700001903491700001403510700002503524700002103549700002503570700001703595700001403612700002303626700001803649700001703667700002203684700002103706700002203727700002403749700002103773700001903794700002203813700002103835700001903856700002603875700002103901700002003922700001903942700001903961700002503980700002004005700002304025700002404048700002204072700002304094700001404117700002004131700002004151700002004171700001904191700002104210700002204231700002404253700002104277700002504298700001804323700001704341700002104358700002404379710014304403856003604546 2022 eng d a1524-456300aInsights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension.0 aInsights From a LargeScale WholeGenome Sequencing Study of Systo c2022 Jun 02 a101161HYPERTENSIONAHA122193243 aBACKGROUND: The availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure.
METHODS: We conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants.
RESULTS: Two blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (<5×10). Among them, a rare intergenic variant at novel locus, , was associated with lower systolic blood pressure in stage-1 (beta [SE]=-32.6 [6.0]; =4.99×10) but not stage-2 analysis (=0.11). Furthermore, a novel common variant at the known locus was suggestively associated with diastolic blood pressure in stage-1 (beta [SE]=-0.36 [0.07]; =4.18×10) and attained genome-wide significance in stage-2 (beta [SE]=-0.29 [0.03]; =7.28×10). Nineteen additional signals suggestively associated with blood pressure in meta-analysis of single and aggregate rare variant findings (<1×10 and <1×10, respectively).
DISCUSSION: We report one promising but unconfirmed rare variant for blood pressure and, more importantly, contribute insights for future blood pressure sequencing studies. Our findings suggest promise of aggregate analyses to complement single variant analysis strategies and the need for larger, diverse samples, and family studies to enable robust rare variant identification.
1 aKelly, Tanika, N1 aSun, Xiao1 aHe, Karen, Y1 aBrown, Michael, R1 aTaliun, Sarah, A Gagliano1 aHellwege, Jacklyn, N1 aIrvin, Marguerite, R1 aMi, Xuenan1 aBrody, Jennifer, A1 aFranceschini, Nora1 aGuo, Xiuqing1 aHwang, Shih-Jen1 ade Vries, Paul, S1 aGao, Yan1 aMoscati, Arden1 aNadkarni, Girish, N1 aYanek, Lisa, R1 aElfassy, Tali1 aSmith, Jennifer, A1 aChung, Ren-Hua1 aBeitelshees, Amber, L1 aPatki, Amit1 aAslibekyan, Stella1 aBlobner, Brandon, M1 aPeralta, Juan, M1 aAssimes, Themistocles, L1 aPalmas, Walter, R1 aLiu, Chunyu1 aBress, Adam, P1 aHuang, Zhijie1 aBecker, Lewis, C1 aHwa, Chii-Min1 aO'Connell, Jeffrey, R1 aCarlson, Jenna, C1 aWarren, Helen, R1 aDas, Sayantan1 aGiri, Ayush1 aMartin, Lisa, W1 aJohnson, Craig1 aFox, Ervin, R1 aBottinger, Erwin, P1 aRazavi, Alexander, C1 aVaidya, Dhananjay1 aChuang, Lee-Ming1 aChang, Yen-Pei, C1 aNaseri, Take1 aJain, Deepti1 aKang, Hyun, Min1 aHung, Adriana, M1 aSrinivasasainagendra, Vinodh1 aSnively, Beverly, M1 aGu, Dongfeng1 aMontasser, May, E1 aReupena, Muagututi'a, Sefuiva1 aHeavner, Benjamin, D1 aLeFaive, Jonathon1 aHixson, James, E1 aRice, Kenneth, M1 aWang, Fei, Fei1 aNielsen, Jonas, B1 aHuang, Jianfeng1 aKhan, Alyna, T1 aZhou, Wei1 aNierenberg, Jovia, L1 aLaurie, Cathy, C1 aArmstrong, Nicole, D1 aShi, Mengyao1 aPan, Yang1 aStilp, Adrienne, M1 aEmery, Leslie1 aWong, Quenna1 aHawley, Nicola, L1 aMinster, Ryan, L1 aCurran, Joanne, E1 aMunroe, Patricia, B1 aWeeks, Daniel, E1 aNorth, Kari, E1 aTracy, Russell, P1 aKenny, Eimear, E1 aShimbo, Daichi1 aChakravarti, Aravinda1 aRich, Stephen, S1 aReiner, Alex, P1 aBlangero, John1 aRedline, Susan1 aMitchell, Braxton, D1 aRao, Dabeeru, C1 aChen, Yii-Der, Ida1 aKardia, Sharon, L R1 aKaplan, Robert, C1 aMathias, Rasika, A1 aHe, Jiang1 aPsaty, Bruce, M1 aFornage, Myriam1 aLoos, Ruth, J F1 aCorrea, Adolfo1 aBoerwinkle, Eric1 aRotter, Jerome, I1 aKooperberg, Charles1 aEdwards, Todd, L1 aAbecasis, Goncalo, R1 aZhu, Xiaofeng1 aLevy, Daniel1 aArnett, Donna, K1 aMorrison, Alanna, C1 aNHLBI Trans-Omics for Precision Medicine TOPMed) Consortium, The Samoan Obesity, Lifestyle, and Genetic Adaptations Study (OLaGA) Group† uhttps://chs-nhlbi.org/node/909903962nas a2200733 4500008004100000022001400041245014300055210006900198260001600267520182100283100001602104700001602120700001502136700001802151700002202169700002102191700002002212700001802232700001602250700001302266700001902279700002302298700001702321700002002338700002602358700002002384700002302404700003102427700001902458700001902477700002502496700002502521700002002546700001702566700002202583700002102605700002002626700002002646700002402666700002702690700001902717700002102736700002402757700002102781700002202802700001702824700001902841700002002860700002002880700001902900700001902919700002502938700002302963700002602986700002403012700001703036700002503053700002403078700002003102700001903122700002103141710003003162856003603192 2022 eng d a1537-660500aPolygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program.0 aPolygenic transcriptome risk scores for COPD and lung function i c2022 Mar 313 aWhile polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1 s [FEV] and its ratio to forced vital capacity [FEV/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV and FEV/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR] = 1.24 [95% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with ≥ 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p < 2.2 × 10 with portability gains ranging from 5% to 28%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.
1 aHu, Xiaowei1 aQiao, Dandi1 aKim, Wonji1 aMoll, Matthew1 aBalte, Pallavi, P1 aLange, Leslie, A1 aBartz, Traci, M1 aKumar, Rajesh1 aLi, Xingnan1 aYu, Bing1 aCade, Brian, E1 aLaurie, Cecelia, A1 aSofer, Tamar1 aRuczinski, Ingo1 aNickerson, Deborah, A1 aMuzny, Donna, M1 aMetcalf, Ginger, A1 aDoddapaneni, Harshavardhan1 aGabriel, Stacy1 aGupta, Namrata1 aDugan-Perez, Shannon1 aCupples, Adrienne, L1 aLoehr, Laura, R1 aJain, Deepti1 aRotter, Jerome, I1 aWilson, James, G1 aPsaty, Bruce, M1 aFornage, Myriam1 aMorrison, Alanna, C1 aVasan, Ramachandran, S1 aWashko, George1 aRich, Stephen, S1 aO'Connor, George, T1 aBleecker, Eugene1 aKaplan, Robert, C1 aKalhan, Ravi1 aRedline, Susan1 aGharib, Sina, A1 aMeyers, Deborah1 aOrtega, Victor1 aDupuis, Josée1 aLondon, Stephanie, J1 aLappalainen, Tuuli1 aOelsner, Elizabeth, C1 aSilverman, Edwin, K1 aBarr, Graham1 aThornton, Timothy, A1 aWheeler, Heather, E1 aCho, Michael, H1 aIm, Hae, Kyung1 aManichaikul, Ani1 aTOPMed Lung Working Group uhttps://chs-nhlbi.org/node/903705141nas a2201381 4500008004100000022001400041245014300055210006900198260001500267300000800282490000600290520117000296653003001466653001201496653001201508653001101520653001201531653005301543653002601596653003601622653002301658653002701681653001801708100002001726700002201746700002201768700002601790700002301816700002501839700001501864700002101879700002501900700001801925700002401943700002201967700002301989700002002012700001702032700002002049700002602069700001902095700002202114700001902136700001702155700001702172700003302189700002102222700001902243700001802262700002602280700002002306700002102326700002302347700002602370700002202396700002402418700002302442700002202465700002002487700002202507700002002529700002502549700002102574700002102595700001802616700001802634700002002652700002102672700002102693700001702714700002102731700003102752700002502783700003402808700002202842700002302864700002102887700001602908700001302924700001402937700002002951700001902971700002102990700001903011700002103030700001903051700002503070700002203095700002803117700001403145700002303159700002403182700001703206700002403223700001503247700002303262700002503285700002503310700002403335700002403359700002003383700001903403700002303422700002003445700002703465700003003492700002003522700002103542700001903563700002103582700002203603700001603625700001903641700002003660700002103680700002203701856003603723 2022 eng d a2399-364200aWhole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program.0 aWhole genome sequence association analysis of fasting glucose an c2022 07 28 a7560 v53 aThe genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI's Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.
10aDiabetes Mellitus, Type 210aFasting10aGlucose10aHumans10aInsulin10aNational Heart, Lung, and Blood Institute (U.S.)10aNerve Tissue Proteins10aPolymorphism, Single Nucleotide10aPrecision Medicine10aReceptors, Immunologic10aUnited States1 aDiCorpo, Daniel1 aGaynor, Sheila, M1 aRussell, Emily, M1 aWesterman, Kenneth, E1 aRaffield, Laura, M1 aMajarian, Timothy, D1 aWu, Peitao1 aSarnowski, Chloe1 aHighland, Heather, M1 aJackson, Anne1 aHasbani, Natalie, R1 ade Vries, Paul, S1 aBrody, Jennifer, A1 aHidalgo, Bertha1 aGuo, Xiuqing1 aPerry, James, A1 aO'Connell, Jeffrey, R1 aLent, Samantha1 aMontasser, May, E1 aCade, Brian, E1 aJain, Deepti1 aWang, Heming1 aAlbanus, Ricardo, D'Oliveira1 aVarshney, Arushi1 aYanek, Lisa, R1 aLange, Leslie1 aPalmer, Nicholette, D1 aAlmeida, Marcio1 aPeralta, Juan, M1 aAslibekyan, Stella1 aBaldridge, Abigail, S1 aBertoni, Alain, G1 aBielak, Lawrence, F1 aChen, Chung-Shiuan1 aChen, Yii-Der Ida1 aChoi, Won, Jung1 aGoodarzi, Mark, O1 aFloyd, James, S1 aIrvin, Marguerite, R1 aKalyani, Rita, R1 aKelly, Tanika, N1 aLee, Seonwook1 aLiu, Ching-Ti1 aLoesch, Douglas1 aManson, JoAnn, E1 aMinster, Ryan, L1 aNaseri, Take1 aPankow, James, S1 aRasmussen-Torvik, Laura, J1 aReiner, Alexander, P1 aReupena, Muagututi'a, Sefuiva1 aSelvin, Elizabeth1 aSmith, Jennifer, A1 aWeeks, Daniel, E1 aXu, Huichun1 aYao, Jie1 aZhao, Wei1 aParker, Stephen1 aAlonso, Alvaro1 aArnett, Donna, K1 aBlangero, John1 aBoerwinkle, Eric1 aCorrea, Adolfo1 aCupples, Adrienne, L1 aCurran, Joanne, E1 aDuggirala, Ravindranath1 aHe, Jiang1 aHeckbert, Susan, R1 aKardia, Sharon, L R1 aKim, Ryan, W1 aKooperberg, Charles1 aLiu, Simin1 aMathias, Rasika, A1 aMcGarvey, Stephen, T1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aPeyser, Patricia, A1 aPsaty, Bruce, M1 aRedline, Susan1 aShuldiner, Alan, R1 aTaylor, Kent, D1 aVasan, Ramachandran, S1 aViaud-Martinez, Karine, A1 aFlorez, Jose, C1 aWilson, James, G1 aSladek, Robert1 aRich, Stephen, S1 aRotter, Jerome, I1 aLin, Xihong1 aDupuis, Josée1 aMeigs, James, B1 aWessel, Jennifer1 aManning, Alisa, K uhttps://chs-nhlbi.org/node/915803549nas a2200541 4500008004100000022001400041245010100055210006900156260001600225300001200241520193400253100002302187700002302210700002402233700001702257700002402274700002002298700002002318700002702338700002402365700001902389700002302408700002402431700002302455700001402478700002102492700002202513700002302535700002402558700002502582700001702607700001802624700002202642700002102664700002302685700002302708700002202731700002102753700002002774700002702794700001402821700002402835700002102859700002302880700002102903710004702924856003602971 2023 eng d a2574-830000aWhole Genome Analysis of Venous Thromboembolism: the Trans-Omics for Precision Medicine Program.0 aWhole Genome Analysis of Venous Thromboembolism the TransOmics f c2023 Mar 24 ae0035323 aBackground Risk for venous thromboembolism has a strong genetic component. Whole genome sequencingfrom the Trans-Omics for Precision Medicine program allowed us to look for new associations, particularly rare variants missed by standard genome-wide association studies. Methods The 3793 cases and 7834 controls (11.6% of cases were Black, Hispanic/Latino, or Asian American) were analyzed using a single variant approach and an aggregate gene-based approach using our primary filter (included only loss-of-function and missense variants predicted to be deleterious) and our secondary filter (included all missense variants). Results Single variant analyses identified associations at 5 known loci. Aggregate gene-based analyses identified only (odds ratio, 6.2 for carriers of rare variants; =7.4×10) when using our primary filter. Employing our secondary variant filter led to a smaller effect size at (odds ratio, 3.8; =1.6×10), while excluding variants found only in rare isoforms led to a larger one (odds ratio, 7.5). Different filtering strategies improved the signal for 2 other known genes: became significant (minimum =1.8×10 with the secondary filter), while did not (minimum =4.4×10 with minor allele frequency <0.0005). Results were largely the same when restricting the analyses to include only unprovoked cases; however, one novel gene, , became significant (=4.4×10 using all missense variants with minor allele frequency <0.0005). Conclusions Here, we have demonstrated the importance of using multiple variant filtering strategies, as we detected additional genes when filtering variants based on their predicted deleteriousness, frequency, and presence on the most expressed isoforms. Our primary analyses did not identify new candidate loci; thus larger follow-up studies are needed to replicate the novel locus and to identify additional rare variation associated with venous thromboembolism.
1 aSeyerle, Amanda, A1 aLaurie, Cecelia, A1 aCoombes, Brandon, J1 aJain, Deepti1 aConomos, Matthew, P1 aBrody, Jennifer1 aChen, Ming-Huei1 aGogarten, Stephanie, M1 aBeutel, Kathleen, M1 aGupta, Namrata1 aHeckbert, Susan, R1 aJackson, Rebecca, D1 aJohnson, Andrew, D1 aKo, Darae1 aManson, JoAnn, E1 aMcKnight, Barbara1 aMetcalf, Ginger, A1 aMorrison, Alanna, C1 aReiner, Alexander, P1 aSofer, Tamar1 aTang, Weihong1 aWiggins, Kerri, L1 aBoerwinkle, Eric1 ade Andrade, Mariza1 aGabriel, Stacey, B1 aGibbs, Richard, A1 aLaurie, Cathy, C1 aPsaty, Bruce, M1 aVasan, Ramachandran, S1 aRice, Ken1 aKooperberg, Charles1 aPankow, James, S1 aSmith, Nicholas, L1 aPankratz, Nathan1 aTrans-Omics for Precision Medicine Program uhttps://chs-nhlbi.org/node/932105493nas a2201573 4500008004100000245011200041210006900153260001600222520100600238100001801244700002301262700002201285700002501307700002001332700001501352700001401367700002001381700002101401700002201422700001401444700001401458700002401472700002101496700002401517700001901541700002901560700002201589700001901611700001801630700002801648700002001676700001901696700001901715700002101734700002401755700001901779700001701798700002801815700001701843700002201860700002301882700002401905700001701929700001801946700002501964700002001989700002102009700001602030700002002046700001602066700001302082700001802095700002402113700001702137700002102154700002402175700002102199700002002220700002002240700001702260700002202277700002802299700002302327700002402350700001702374700002302391700003002414700002602444700002102470700002002491700001902511700002302530700001402553700002402567700002402591700001802615700002802633700002402661700002302685700002202708700002702730700001702757700002102774700002102795700002202816700002202838700002302860700001902883700002302902700001402925700002402939700002102963700002802984700002403012700001903036700002103055700002503076700002103101700002303122700002203145700002203167700002003189700002303209700001403232700002303246700001603269700002503285700002403310700002103334700002503355700001903380700002003399700002303419700002503442700002103467700002203488700002403510700002303534700002003557700002203577700002003599700001803619700002503637700001603662700001603678700002503694700002103719700001803740700002003758700001903778700002103797710006503818856003603883 2023 eng d00aWHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES NOVEL AFRICAN ANCESTRY-SPECIFIC RISK ALLELE.0 aWHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES N c2023 Aug 223 aObesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals ( < 5 × 10 ). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the and loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.
1 aZhang, Xinruo1 aBrody, Jennifer, A1 aGraff, Mariaelisa1 aHighland, Heather, M1 aChami, Nathalie1 aXu, Hanfei1 aWang, Zhe1 aFerrier, Kendra1 aChittoor, Geetha1 aJosyula, Navya, S1 aLi, Xihao1 aLi, Zilin1 aAllison, Matthew, A1 aBecker, Diane, M1 aBielak, Lawrence, F1 aBis, Joshua, C1 aBoorgula, Meher, Preethi1 aBowden, Donald, W1 aBroome, Jai, G1 aButh, Erin, J1 aCarlson, Christopher, S1 aChang, Kyong-Mi1 aChavan, Sameer1 aChiu, Yen-Feng1 aChuang, Lee-Ming1 aConomos, Matthew, P1 aDeMeo, Dawn, L1 aDu, Margaret1 aDuggirala, Ravindranath1 aEng, Celeste1 aFohner, Alison, E1 aFreedman, Barry, I1 aGarrett, Melanie, E1 aGuo, Xiuqing1 aHaiman, Chris1 aHeavner, Benjamin, D1 aHidalgo, Bertha1 aHixson, James, E1 aHo, Yuk-Lam1 aHobbs, Brian, D1 aHu, Donglei1 aHui, Qin1 aHwu, Chii-Min1 aJackson, Rebecca, D1 aJain, Deepti1 aKalyani, Rita, R1 aKardia, Sharon, L R1 aKelly, Tanika, N1 aLange, Ethan, M1 aLeNoir, Michael1 aLi, Changwei1 aLe Marchand, Loic1 aMcDonald, Merry-Lynn, N1 aMcHugh, Caitlin, P1 aMorrison, Alanna, C1 aNaseri, Take1 aO'Connell, Jeffrey1 aO'Donnell, Christopher, J1 aPalmer, Nicholette, D1 aPankow, James, S1 aPerry, James, A1 aPeters, Ulrike1 aPreuss, Michael, H1 aRao, D, C1 aRegan, Elizabeth, A1 aReupena, Sefuiva, M1 aRoden, Dan, M1 aRodriguez-Santana, Jose1 aSitlani, Colleen, M1 aSmith, Jennifer, A1 aTiwari, Hemant, K1 aVasan, Ramachandran, S1 aWang, Zeyuan1 aWeeks, Daniel, E1 aWessel, Jennifer1 aWiggins, Kerri, L1 aWilkens, Lynne, R1 aWilson, Peter, W F1 aYanek, Lisa, R1 aYoneda, Zachary, T1 aZhao, Wei1 aZöllner, Sebastian1 aArnett, Donna, K1 aAshley-Koch, Allison, E1 aBarnes, Kathleen, C1 aBlangero, John1 aBoerwinkle, Eric1 aBurchard, Esteban, G1 aCarson, April, P1 aChasman, Daniel, I1 aChen, Yii-Der Ida1 aCurran, Joanne, E1 aFornage, Myriam1 aGordeuk, Victor, R1 aHe, Jiang1 aHeckbert, Susan, R1 aHou, Lifang1 aIrvin, Marguerite, R1 aKooperberg, Charles1 aMinster, Ryan, L1 aMitchell, Braxton, D1 aNouraie, Mehdi1 aPsaty, Bruce, M1 aRaffield, Laura, M1 aReiner, Alexander, P1 aRich, Stephen, S1 aRotter, Jerome, I1 aShoemaker, Benjamin1 aSmith, Nicholas, L1 aTaylor, Kent, D1 aTelen, Marilyn, J1 aWeiss, Scott, T1 aZhang, Yingze1 aCosta, Nancy, Heard-1 aSun, Yan, V1 aLin, Xihong1 aCupples, Adrienne, L1 aLange, Leslie, A1 aLiu, Ching-Ti1 aLoos, Ruth, J F1 aNorth, Kari, E1 aJustice, Anne, E1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/9484