02787nas a2200649 4500008004100000022001400041245009900055210006900154260001600223300000900239490000800248520092400256653002201180653001201202653004001214653002501254653001001279653001101289653001901300653003201319653003801351653002201389653001801411653004201429653001101471653000901482653003601491653002201527653002301549100002401572700002301596700002501619700001601644700002101660700001801681700001601699700001201715700001801727700001301745700002001758700001901778700002101797700002001818700002001838700002201858700002101880700002601901700002101927700002001948700002601968700002401994700002002018710001302038710001502051710003502066856003602101 2012 eng d a1095-920300aEvolution and functional impact of rare coding variation from deep sequencing of human exomes.0 aEvolution and functional impact of rare coding variation from de c2012 Jul 06 a64-90 v3373 a
As a first step toward understanding how rare variants contribute to risk for complex diseases, we sequenced 15,585 human protein-coding genes to an average median depth of 111× in 2440 individuals of European (n = 1351) and African (n = 1088) ancestry. We identified over 500,000 single-nucleotide variants (SNVs), the majority of which were rare (86% with a minor allele frequency less than 0.5%), previously unknown (82%), and population-specific (82%). On average, 2.3% of the 13,595 SNVs each person carried were predicted to affect protein function of ~313 genes per genome, and ~95.7% of SNVs predicted to be functionally important were rare. This excess of rare functional variants is due to the combined effects of explosive, recent accelerated population growth and weak purifying selection. Furthermore, we show that large sample sizes will be required to associate rare variants with complex traits.
10aAfrican Americans10aDisease10aEuropean Continental Ancestry Group10aEvolution, Molecular10aExome10aFemale10aGene Frequency10aGenetic Association Studies10aGenetic Predisposition to Disease10aGenetic Variation10aGenome, Human10aHigh-Throughput Nucleotide Sequencing10aHumans10aMale10aPolymorphism, Single Nucleotide10aPopulation Growth10aSelection, Genetic1 aTennessen, Jacob, A1 aBigham, Abigail, W1 aO'Connor, Timothy, D1 aFu, Wenqing1 aKenny, Eimear, E1 aGravel, Simon1 aMcGee, Sean1 aDo, Ron1 aLiu, Xiaoming1 aJun, Goo1 aKang, Hyun, Min1 aJordan, Daniel1 aLeal, Suzanne, M1 aGabriel, Stacey1 aRieder, Mark, J1 aAbecasis, Goncalo1 aAltshuler, David1 aNickerson, Deborah, A1 aBoerwinkle, Eric1 aSunyaev, Shamil1 aBustamante, Carlos, D1 aBamshad, Michael, J1 aAkey, Joshua, M1 aBroad GO1 aSeattle GO1 aNHLBI Exome Sequencing Project uhttps://chs-nhlbi.org/node/138702102nas a2200469 4500008004100000022001400041245008200055210006900137260001300206300001200219490000700231520073300238653002100971653002600992653002301018653002201041653001801063653003401081653001301115653001701128653001101145653002401156100002401180700001901204700002301223700001801246700001201264700001801276700001701294700001301311700001801324700001901342700001601361700001901377700003001396700002001426700002501446700001901471700002101490710008501511856003601596 2013 eng d a1546-171800aWhole-genome sequence-based analysis of high-density lipoprotein cholesterol.0 aWholegenome sequencebased analysis of highdensity lipoprotein ch c2013 Aug a899-9010 v453 aWe describe initial steps for interrogating whole-genome sequence data to characterize the genetic architecture of a complex trait, levels of high-density lipoprotein cholesterol (HDL-C). We report whole-genome sequencing and analysis of 962 individuals from the Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) studies. From this analysis, we estimate that common variation contributes more to heritability of HDL-C levels than rare variation, and screening for mendelian variants for dyslipidemia identified individuals with extreme HDL-C levels. Whole-genome sequencing analyses highlight the value of regulatory and non-protein-coding regions of the genome in addition to protein-coding regions.
10aCholesterol, HDL10aComputational Biology10aDatabases, Genetic10aGenetic Variation10aGenome, Human10aGenome-Wide Association Study10aGenomics10aHeterozygote10aHumans10aOpen Reading Frames1 aMorrison, Alanna, C1 aVoorman, Arend1 aJohnson, Andrew, D1 aLiu, Xiaoming1 aYu, Jin1 aLi, Alexander1 aMuzny, Donna1 aYu, Fuli1 aRice, Kenneth1 aZhu, Chengsong1 aBis, Joshua1 aHeiss, Gerardo1 aO'Donnell, Christopher, J1 aPsaty, Bruce, M1 aCupples, Adrienne, L1 aGibbs, Richard1 aBoerwinkle, Eric1 aCohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) Consortium uhttps://chs-nhlbi.org/node/628303656nas a2200589 4500008004100000022001400041245015400055210006900209260000900278300001100287490000600298520189800304653003802202653004002240653001102280653003202291653002602323653001102349653001202360653001302372653000902385653002602394653003602420653002402456653002702480100001902507700002102526700001802547700002302565700002202588700002902610700002302639700002002662700001702682700002302699700002602722700001702748700002302765700002002788700001902808700001902827700001902846700002002865700002002885700002202905700002202927700002002949700002102969700002002990700002003010856003603030 2014 eng d a1932-620300aAssociations of NINJ2 sequence variants with incident ischemic stroke in the Cohorts for Heart and Aging in Genomic Epidemiology (CHARGE) consortium.0 aAssociations of NINJ2 sequence variants with incident ischemic s c2014 ae997980 v93 aBACKGROUND: Stroke, the leading neurologic cause of death and disability, has a substantial genetic component. We previously conducted a genome-wide association study (GWAS) in four prospective studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and demonstrated that sequence variants near the NINJ2 gene are associated with incident ischemic stroke. Here, we sought to fine-map functional variants in the region and evaluate the contribution of rare variants to ischemic stroke risk.
METHODS AND RESULTS: We sequenced 196 kb around NINJ2 on chromosome 12p13 among 3,986 European ancestry participants, including 475 ischemic stroke cases, from the Atherosclerosis Risk in Communities Study, Cardiovascular Health Study, and Framingham Heart Study. Meta-analyses of single-variant tests for 425 common variants (minor allele frequency [MAF] ≥ 1%) confirmed the original GWAS results and identified an independent intronic variant, rs34166160 (MAF = 0.012), most significantly associated with incident ischemic stroke (HR = 1.80, p = 0.0003). Aggregating 278 putatively-functional variants with MAF≤ 1% using count statistics, we observed a nominally statistically significant association, with the burden of rare NINJ2 variants contributing to decreased ischemic stroke incidence (HR = 0.81; p = 0.026).
CONCLUSION: Common and rare variants in the NINJ2 region were nominally associated with incident ischemic stroke among a subset of CHARGE participants. Allelic heterogeneity at this locus, caused by multiple rare, low frequency, and common variants with disparate effects on risk, may explain the difficulties in replicating the original GWAS results. Additional studies that take into account the complex allelic architecture at this locus are needed to confirm these findings.
10aCell Adhesion Molecules, Neuronal10aEuropean Continental Ancestry Group10aFemale10aGenetic Association Studies10aGenetic Heterogeneity10aHumans10aIntrons10aIschemia10aMale10aMyocardial Infarction10aPolymorphism, Single Nucleotide10aProspective Studies10aSequence Analysis, DNA1 aBis, Joshua, C1 aDeStefano, Anita1 aLiu, Xiaoming1 aBrody, Jennifer, A1 aChoi, Seung, Hoan1 aVerhaaren, Benjamin, F J1 aDebette, Stephanie1 aIkram, Arfan, M1 aShahar, Eyal1 aButler, Kenneth, R1 aGottesman, Rebecca, F1 aMuzny, Donna1 aKovar, Christie, L1 aPsaty, Bruce, M1 aHofman, Albert1 aLumley, Thomas1 aGupta, Mayetri1 aWolf, Philip, A1 aDuijn, Cornelia1 aGibbs, Richard, A1 aMosley, Thomas, H1 aLongstreth, W T1 aBoerwinkle, Eric1 aSeshadri, Sudha1 aFornage, Myriam uhttps://chs-nhlbi.org/node/654803673nas a2200529 4500008004100000022001400041245019500055210006900250260001300319300001100332490000600343520199700349653000902346653002202355653001002377653002002387653004302407653001902450653004002469653001102509653002202520653003402542653001302576653001102589653000902600653001602609653003602625653002702661653005202688100001902740700002202759700002302781700002002804700002002824700001702844700002202861700001902883700001802902700001902920700002102939700002002960700001902980700002502999700003003024710005303054856003603107 2014 eng d a1942-326800aSequencing of 2 subclinical atherosclerosis candidate regions in 3669 individuals: Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study.0 aSequencing of 2 subclinical atherosclerosis candidate regions in c2014 Jun a359-640 v73 aBACKGROUND: Atherosclerosis, the precursor to coronary heart disease and stroke, is characterized by an accumulation of fatty cells in the arterial intimal-medial layers. Common carotid intima media thickness (cIMT) and plaque are subclinical atherosclerosis measures that predict cardiovascular disease events. Previously, genome-wide association studies demonstrated evidence for association with cIMT (SLC17A4) and plaque (PIK3CG).
METHODS AND RESULTS: We sequenced 120 kb around SLC17A4 (6p22.2) and 251 kb around PIK3CG (7q22.3) among 3669 European ancestry participants from the Atherosclerosis Risk in Communities (ARIC) study, Cardiovascular Health Study (CHS), and Framingham Heart Study (FHS) in Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Primary analyses focused on 438 common variants (minor allele frequency ≥1%), which were independently meta-analyzed. A 3' untranslated region CCDC71L variant (rs2286149), upstream from PIK3CG, was the most significant finding in cIMT (P=0.00033) and plaque (P=0.0004) analyses. A SLC17A4 intronic variant was also associated with cIMT (P=0.008). Both were in low linkage disequilibrium with the genome-wide association study single nucleotide polymorphisms. Gene-based tests including T1 count and sequence kernel association test for rare variants (minor allele frequency <1%) did not yield statistically significant associations. However, we observed nominal associations for rare variants in CCDC71L and SLC17A3 with cIMT and of the entire 7q22 region with plaque (P=0.05).
CONCLUSIONS: Common and rare variants in PIK3CG and SLC17A4 regions demonstrated modest association with subclinical atherosclerosis traits. Although not conclusive, these findings may help to understand the genetic architecture of regions previously implicated by genome-wide association studies and identify variants within these regions for further investigation in larger samples.
10aAged10aAged, 80 and over10aAging10aAtherosclerosis10aClass Ib Phosphatidylinositol 3-Kinase10aCohort Studies10aEuropean Continental Ancestry Group10aFemale10aGenetic Variation10aGenome-Wide Association Study10aGenomics10aHumans10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide10aSequence Analysis, DNA10aSodium-Phosphate Cotransporter Proteins, Type I1 aBis, Joshua, C1 aWhite, Charles, C1 aFranceschini, Nora1 aBrody, Jennifer1 aZhang, Xiaoling1 aMuzny, Donna1 aSantibanez, Jireh1 aGibbs, Richard1 aLiu, Xiaoming1 aLin, Honghuang1 aBoerwinkle, Eric1 aPsaty, Bruce, M1 aNorth, Kari, E1 aCupples, Adrienne, L1 aO'Donnell, Christopher, J1 aCHARGE Subclinical Atherosclerosis Working Group uhttps://chs-nhlbi.org/node/654703990nas 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/657802458nas a2200457 4500008004100000022001400041245012100055210006900176260000900245300001300254490000700267520116800274653002001442653001701462653001101479653001701490653002401507653003601531100001301567700001301580700001801593700001901611700001701630700002001647700002401667700001801691700002101709700001901730700002101749700002301770700001601793700002501809700002001834700001701854700001201871700002201883700001701905700002101922700002101943856003601964 2015 eng d a1932-620300aPopulation genomic analysis of 962 whole genome sequences of humans reveals natural selection in non-coding regions.0 aPopulation genomic analysis of 962 whole genome sequences of hum c2015 ae01216440 v103 aWhole genome analysis in large samples from a single population is needed to provide adequate power to assess relative strengths of natural selection across different functional components of the genome. In this study, we analyzed next-generation sequencing data from 962 European Americans, and found that as expected approximately 60% of the top 1% of positive selection signals lie in intergenic regions, 33% in intronic regions, and slightly over 1% in coding regions. Several detailed functional annotation categories in intergenic regions showed statistically significant enrichment in positively selected loci when compared to the null distribution of the genomic span of ENCODE categories. There was a significant enrichment of purifying selection signals detected in enhancers, transcription factor binding sites, microRNAs and target sites, but not on lincRNA or piRNAs, suggesting different evolutionary constraints for these domains. Loci in "repressed or low activity regions" and loci near or overlapping the transcription start site were the most significantly over-represented annotations among the top 1% of signals for positive selection.
10aDNA, Intergenic10aGenetic Loci10aHumans10aMetagenomics10aOpen Reading Frames10aPolymorphism, Single Nucleotide1 aYu, Fuli1 aLu, Jian1 aLiu, Xiaoming1 aGazave, Elodie1 aChang, Diana1 aRaj, Srilakshmi1 aHunter-Zinck, Haley1 aBlekhman, Ran1 aArbiza, Leonardo1 aVan Hout, Cris1 aMorrison, Alanna1 aJohnson, Andrew, D1 aBis, Joshua1 aCupples, Adrienne, L1 aPsaty, Bruce, M1 aMuzny, Donna1 aYu, Jin1 aGibbs, Richard, A1 aKeinan, Alon1 aClark, Andrew, G1 aBoerwinkle, Eric uhttps://chs-nhlbi.org/node/681401904nas a2200577 4500008004100000022001400041245010300055210006900158260001600227300001400243490000700257100002300264700002400287700001900311700002600330700002200356700002500378700002000403700002000423700002400443700002000467700002200487700002700509700001900536700002400555700002300579700002100602700001800623700002200641700002800663700003000691700002100721700002100742700002400763700002300787700002300810700002100833700002500854700002700879700002100906700002200927700002100949700002100970700002000991700002501011710006501036710008501101710005101186710005301237856003601290 2017 eng d a1546-171800aAnalysis commons, a team approach to discovery in a big-data environment for genetic epidemiology.0 aAnalysis commons a team approach to discovery in a bigdata envir c2017 Oct 27 a1560-15630 v491 aBrody, Jennifer, A1 aMorrison, Alanna, C1 aBis, Joshua, C1 aO'Connell, Jeffrey, R1 aBrown, Michael, R1 aHuffman, Jennifer, E1 aAmes, Darren, C1 aCarroll, Andrew1 aConomos, Matthew, P1 aGabriel, Stacey1 aGibbs, Richard, A1 aGogarten, Stephanie, M1 aGupta, Namrata1 aJaquish, Cashell, E1 aJohnson, Andrew, D1 aLewis, Joshua, P1 aLiu, Xiaoming1 aManning, Alisa, K1 aPapanicolaou, George, J1 aPitsillides, Achilleas, N1 aRice, Kenneth, M1 aSalerno, William1 aSitlani, Colleen, M1 aSmith, Nicholas, L1 aHeckbert, Susan, R1 aLaurie, Cathy, C1 aMitchell, Braxton, D1 aVasan, Ramachandran, S1 aRich, Stephen, S1 aRotter, Jerome, I1 aWilson, James, G1 aBoerwinkle, Eric1 aPsaty, Bruce, M1 aCupples, Adrienne, L1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aCohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium1 aTOPMed Hematology and Hemostasis Working Group1 aCHARGE Analysis and Bioinformatics Working Group uhttps://chs-nhlbi.org/node/755303488nas a2200637 4500008004100000022001400041245010900055210006900164260000900233300001300242490000700255520161700262100002001879700002401899700002301923700002301946700002501969700002101994700002002015700002102035700001802056700001902074700001702093700001402110700001902124700001802143700002702161700001902188700002202207700002402229700002002253700001902273700002302292700002702315700002302342700002502365700001902390700002202409700003102431700001902462700002102481700001902502700002002521700002102541700002402562700002102586700003902607700001802646700002202664700002202686700002302708700002002731700001902751710004402770856003602814 2019 eng d a1932-620300aPharmacogenomics of statin-related myopathy: Meta-analysis of rare variants from whole-exome sequencing.0 aPharmacogenomics of statinrelated myopathy Metaanalysis of rare c2019 ae02181150 v143 aAIMS: Statin-related myopathy (SRM), which includes rhabdomyolysis, is an uncommon but important adverse drug reaction because the number of people prescribed statins world-wide is large. Previous association studies of common genetic variants have had limited success in identifying a genetic basis for this adverse drug reaction. We conducted a multi-site whole-exome sequencing study to investigate whether rare coding variants confer an increased risk of SRM.
METHODS AND RESULTS: SRM 3-5 cases (N = 505) and statin treatment-tolerant controls (N = 2047) were recruited from multiple sites in North America and Europe. SRM 3-5 was defined as symptoms consistent with muscle injury and an elevated creatine phosphokinase level >4 times upper limit of normal without another likely cause of muscle injury. Whole-exome sequencing and variant calling was coordinated from two analysis centres, and results of single-variant and gene-based burden tests were meta-analysed. No genome-wide significant associations were identified. Given the large number of cases, we had 80% power to identify a variant with minor allele frequency of 0.01 that increases the risk of SRM 6-fold at genome-wide significance.
CONCLUSIONS: In this large whole-exome sequencing study of severe statin-related muscle injury conducted to date, we did not find evidence that rare coding variants are responsible for this adverse drug reaction. Larger sample sizes would be required to identify rare variants with small effects, but it is unclear whether such findings would be clinically actionable.
1 aFloyd, James, S1 aBloch, Katarzyna, M1 aBrody, Jennifer, A1 aMaroteau, Cyrielle1 aSiddiqui, Moneeza, K1 aGregory, Richard1 aCarr, Daniel, F1 aMolokhia, Mariam1 aLiu, Xiaoming1 aBis, Joshua, C1 aAhmed, Ammar1 aLiu, Xuan1 aHallberg, Pär1 aYue, Qun-Ying1 aMagnusson, Patrik, K E1 aBrisson, Diane1 aWiggins, Kerri, L1 aMorrison, Alanna, C1 aKhoury, Etienne1 aMcKeigue, Paul1 aStricker, Bruno, H1 aLapeyre-Mestre, Maryse1 aHeckbert, Susan, R1 aGallagher, Arlene, M1 aChinoy, Hector1 aGibbs, Richard, A1 aBondon-Guitton, Emmanuelle1 aTracy, Russell1 aBoerwinkle, Eric1 aGaudet, Daniel1 aConforti, Anita1 avan Staa, Tjeerd1 aSitlani, Colleen, M1 aRice, Kenneth, M1 avan der Zee, Anke-Hilse, Maitland-1 aWadelius, Mia1 aMorris, Andrew, P1 aPirmohamed, Munir1 aPalmer, Colin, A N1 aPsaty, Bruce, M1 aAlfirevic, Ana1 aPREDICTION-ADR Consortium and EUDRAGENE uhttps://chs-nhlbi.org/node/810208483nas a2202413 4500008004100000022001400041245007200055210006900127260001200196300001200208490000800220520169100228100001901919700002201938700002401960700002201984700002402006700001702030700003002047700002002077700002702097700002002124700003002144700002202174700002002196700001802216700002302234700001802257700002202275700002102297700002302318700002002341700002502361700002102386700001702407700001802424700002402442700001802466700002002484700002202504700001902526700002302545700001902568700002302587700001802610700001802628700001702646700001902663700002102682700002102703700002402724700002402748700001902772700002102791700002202812700002302834700002502857700001902882700002202901700002202923700002302945700002202968700002002990700002203010700001903032700002003051700001903071700002203090700001803112700001903130700001903149700002303168700001903191700002203210700002403232700002103256700001703277700001803294700002103312700001703333700002003350700002303370700002703393700002803420700001803448700002103466700002403487700001703511700002103528700001403549700002603563700002303589700002503612700002103637700002303658700001903681700002403700700001803724700001903742700002103761700002103782700002003803700002303823700002403846700001903870700002103889700002203910700001703932700001603949700001803965700001603983700002003999700001704019700002104036700002204057700002404079700001904103700002104122700002204143700002304165700002204188700002504210700002004235700002304255700002204278700002204300700002504322700002504347700002204372700002504394700002404419700002404443700001904467700002304486700002104509700001904530700002604549700002604575700002104601700002004622700002404642700002004666700001904686700002004705700001404725700001904739700002504758700001504783700002204798700002004820700002104840700002604861700002304887700001904910700002004929700002304949700001904972700002404991700002305015700002305038700002205061700002305083700001805106700002005124700001905144700002505163700002205188700002705210700002705237700003005264700001805294700002105312700001905333700002005352700001805372700002305390700001805413700001705431700002105448700002805469700002405497700002105521700002005542700001905562700002105581700002105602700002405623700001805647700001605665700002805681700002605709700002405735700002105759700002405780700002105804700002505825700002105850700002405871700002305895700002505918700002505943710006505968856003606033 2021 eng d a1476-468700aSequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.0 aSequencing of 53831 diverse genomes from the NHLBI TOPMed Progra c2021 02 a290-2990 v5903 aThe Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes). In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
1 aTaliun, Daniel1 aHarris, Daniel, N1 aKessler, Michael, D1 aCarlson, Jedidiah1 aSzpiech, Zachary, A1 aTorres, Raul1 aTaliun, Sarah, A Gagliano1 aCorvelo, André1 aGogarten, Stephanie, M1 aKang, Hyun, Min1 aPitsillides, Achilleas, N1 aLeFaive, Jonathon1 aLee, Seung-Been1 aTian, Xiaowen1 aBrowning, Brian, L1 aDas, Sayantan1 aEmde, Anne-Katrin1 aClarke, Wayne, E1 aLoesch, Douglas, P1 aShetty, Amol, C1 aBlackwell, Thomas, W1 aSmith, Albert, V1 aWong, Quenna1 aLiu, Xiaoming1 aConomos, Matthew, P1 aBobo, Dean, M1 aAguet, Francois1 aAlbert, Christine1 aAlonso, Alvaro1 aArdlie, Kristin, G1 aArking, Dan, E1 aAslibekyan, Stella1 aAuer, Paul, L1 aBarnard, John1 aBarr, Graham1 aBarwick, Lucas1 aBecker, Lewis, C1 aBeer, Rebecca, L1 aBenjamin, Emelia, J1 aBielak, Lawrence, F1 aBlangero, John1 aBoehnke, Michael1 aBowden, Donald, W1 aBrody, Jennifer, A1 aBurchard, Esteban, G1 aCade, Brian, E1 aCasella, James, F1 aChalazan, Brandon1 aChasman, Daniel, I1 aChen, Yii-Der Ida1 aCho, Michael, H1 aChoi, Seung, Hoan1 aChung, Mina, K1 aClish, Clary, B1 aCorrea, Adolfo1 aCurran, Joanne, E1 aCuster, Brian1 aDarbar, Dawood1 aDaya, Michelle1 ade Andrade, Mariza1 aDeMeo, Dawn, L1 aDutcher, Susan, K1 aEllinor, Patrick, T1 aEmery, Leslie, S1 aEng, Celeste1 aFatkin, Diane1 aFingerlin, Tasha1 aForer, Lukas1 aFornage, Myriam1 aFranceschini, Nora1 aFuchsberger, Christian1 aFullerton, Stephanie, M1 aGermer, Soren1 aGladwin, Mark, T1 aGottlieb, Daniel, J1 aGuo, Xiuqing1 aHall, Michael, E1 aHe, Jiang1 aHeard-Costa, Nancy, L1 aHeckbert, Susan, R1 aIrvin, Marguerite, R1 aJohnsen, Jill, M1 aJohnson, Andrew, D1 aKaplan, Robert1 aKardia, Sharon, L R1 aKelly, Tanika1 aKelly, Shannon1 aKenny, Eimear, E1 aKiel, Douglas, P1 aKlemmer, Robert1 aKonkle, Barbara, A1 aKooperberg, Charles1 aKöttgen, Anna1 aLange, Leslie, A1 aLasky-Su, Jessica1 aLevy, Daniel1 aLin, Xihong1 aLin, Keng-Han1 aLiu, Chunyu1 aLoos, Ruth, J F1 aGarman, Lori1 aGerszten, Robert1 aLubitz, Steven, A1 aLunetta, Kathryn, L1 aC Y Mak, Angel1 aManichaikul, Ani1 aManning, Alisa, K1 aMathias, Rasika, A1 aMcManus, David, D1 aMcGarvey, Stephen, T1 aMeigs, James, B1 aMeyers, Deborah, A1 aMikulla, Julie, L1 aMinear, Mollie, A1 aMitchell, Braxton, D1 aMohanty, Sanghamitra1 aMontasser, May, E1 aMontgomery, Courtney1 aMorrison, Alanna, C1 aMurabito, Joanne, M1 aNatale, Andrea1 aNatarajan, Pradeep1 aNelson, Sarah, C1 aNorth, Kari, E1 aO'Connell, Jeffrey, R1 aPalmer, Nicholette, D1 aPankratz, Nathan1 aPeloso, Gina, M1 aPeyser, Patricia, A1 aPleiness, Jacob1 aPost, Wendy, S1 aPsaty, Bruce, M1 aRao, D, C1 aRedline, Susan1 aReiner, Alexander, P1 aRoden, Dan1 aRotter, Jerome, I1 aRuczinski, Ingo1 aSarnowski, Chloe1 aSchoenherr, Sebastian1 aSchwartz, David, A1 aSeo, Jeong-Sun1 aSeshadri, Sudha1 aSheehan, Vivien, A1 aSheu, Wayne, H1 aShoemaker, Benjamin1 aSmith, Nicholas, L1 aSmith, Jennifer, A1 aSotoodehnia, Nona1 aStilp, Adrienne, M1 aTang, Weihong1 aTaylor, Kent, D1 aTelen, Marilyn1 aThornton, Timothy, A1 aTracy, Russell, P1 aVan Den Berg, David, J1 aVasan, Ramachandran, S1 aViaud-Martinez, Karine, A1 aVrieze, Scott1 aWeeks, Daniel, E1 aWeir, Bruce, S1 aWeiss, Scott, T1 aWeng, Lu-Chen1 aWiller, Cristen, J1 aZhang, Yingze1 aZhao, Xutong1 aArnett, Donna, K1 aAshley-Koch, Allison, E1 aBarnes, Kathleen, C1 aBoerwinkle, Eric1 aGabriel, Stacey1 aGibbs, Richard1 aRice, Kenneth, M1 aRich, Stephen, S1 aSilverman, Edwin, K1 aQasba, Pankaj1 aGan, Weiniu1 aPapanicolaou, George, J1 aNickerson, Deborah, A1 aBrowning, Sharon, R1 aZody, Michael, C1 aZöllner, Sebastian1 aWilson, James, G1 aCupples, Adrienne, L1 aLaurie, Cathy, C1 aJaquish, Cashell, E1 aHernandez, Ryan, D1 aO'Connor, Timothy, D1 aAbecasis, Goncalo, R1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/866604364nas a2200997 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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/8664