biostudies-literatureLi XU.S. Department of Health & Human Services | NIH | National Center for Advancing Translational SciencesNCATS NIH HHSNIA NIH HHSNIEHS NIH HHSNHLBI NIH HHSNIMH NIH HHSU.S. Department of Health & Human Services | NIH | National Cancer InstituteNINDS NIH HHSThe Evans Medical Foundation and the Jay and Louis Coffman Endowment from the Department of Medicine, Boston University School of MedicineNIAMS NIH HHSNIH HHSU.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney DiseasesNCRR NIH HHSU.S. Department of Health & Human Services | NIH | National Institute on AgingU.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood InstituteU.S. Department of Health & Human Services | NIH | National Institute of Mental HealthAmerican Heart AssociationU.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and StrokeNIDDK NIH HHSU.S. Department of Health & Human Services | NIH | National Center for Research ResourcesMedical Research CouncilNIMHD NIH HHSU.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin DiseasesNHGRI NIH HHSNCI NIH HHSThe American Diabetes AssociationU.S. Department of Health & Human Services | NIH | National Human Genome Research InstituteU.S. Department of Health & Human Services | NIH | National Institute of Nursing ResearchU.S. Department of Health & Human Services | National Institutes of Health154-164https://www.ebi.ac.uk/biostudies/studies/S-EPMC10084891biostudies-literatureUnknown55(1)Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision MedicNature geneticsPowerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.PMC10084891R01 HL071205R01-AR48797R01 HL113323R01 HL092301R01 HL059684HL436801R01-MH078111HHSN268201600003CR01 HL067348HHSN268201800010IF32-HL085989R01-HL67348R01-HL153805R35-HL135824S10 OD030463U01-HL054509U01-HG009088R01 HL120393U01 DK085524R01 HL071051P01 HL132825R01 MH078143R01-DK071891U01-HL054473R01 HL163560U01-HL137162U01 HL072524R01-AG058921U01-HL054472NO1-HC-25195R01 HL112064BioData Catalyst FellowshipKL2 TR002490P30 DK063491NR0224103HHSN268201600004CN01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166U01-DK085524UL1 TR001420U01-HG012064R01 HL093093R01 HL117626UL1 RR033176R03 HL154284R35-HL135818R01 HL153805P60-AG10484R01 AR048797U01 HL120393N01 HC025195R35-CA197449M01-RR000052R01 DK071891R01-HL113338P01 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HL141601HHSN268201800001CR01-HL104135-04S1HHSN268201800001IHHSN268201700003CHHSN268201800013IR01-MH078143R01 HL125005KL2TR002490U01 HL054472DK063491U01 HL054473HHSN268201500001IUL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1-TR-001881M01 RR000052R01 HL071025R01-HL045522R01-HL093093HHSN268201700002IU01 HL07251875N92020D00007HHSN268201800012CHHSN268201800012IHHSN268201700002C75N92019D0003175N92020D0000175N92020D00002R03-HL154284HHSN268201600001CR35 HL13582475N92020D00005R01 HL142711U01 HG01206475N92020D00006R01 HL08769875N92020D00003M01 RR00712275N92020D00004R35 CA197449HHSN268201500003CR01 NS058700R01 HL127564R01HL071251, R01HL071258, R01HL071259S10 OD026880UL1 TR001079R01-HL071051, R01-HL071205, R01HL071250R01-MH083824N01 HC095159R01 HL113338U01 HL137181HHSN268201700001IR35 HL135818HHSN268201800011CHHSN268201700001CHHSN268201800011IR01-HL055673-18S1HHSN268201600002CR01 HL133040U01 HL054495R01 HL049762U19 CA203654HHSN268201500003IP50 HL118006Ekunwe LBowden DWLaurie CManning AGlahn DSalzberg Sde las Fuentes LNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working GroupSylvia JSandow KReupena MSNekhai SWalker TXu HChaffin MRussell PGass MChung RHFarek JLevy DHwu CMPreuss MTishkoff SCarrier JChuang LMReed RZiv EAvramopoulos DBarr RGCornell ESotoodehnia NYu KMay SMathai SPapanicolaou GStorm Gde Andrade MSmith AVBroome JTaylor KDWong QHong EAlmasy LChung MRunnels AManson JRegan ESnyder MZhu XMcGarvey STNessner CMcFarland SWilson JGNadkarni GCurran JEDavis CBroeckel UFranceschini NChen HDan ArkingGu CCTsai MAbe NSalvi SClish CTracy RLunetta KVrieze SKral BGGladwin MEllinor PPeralta JMDevine SRajendran MQuick CConomos MPhillips LSLiu SWeir BLiu YSitlani CMMeigs JJohnson APollin TRuczinski IDavid SJohnson CArnett DKPerez MBlue NErzurum SWeinstock JStilp AMHernandez RFlickinger MGuan YChoi SHWinterkorn LHuston HHarris DAnderson PMontasser MEBeitelshees AGelb BMagalang UMathias RGuo XSzpiro ANelson SCJohnsen JTong CMychaleckyj JCArdlie KAlonso ASung YJZoellner SLevine DLeBoff MHerrington DHixson JKimoff JFrazar CRyan KKramer HJohnston RKalyani RRYanek LRVandeHaar PBlangero JHan YWalts ASeidman JMomin ZMarton MSeidman CSabino ECChang CCasella JGeiger HKaplan RReiner APSelvaraj MSMontgomery CKenny ECurtis JThrelkeld MHobbs BCuster BKooperberg CCastaldi PFu Mde Vries PSGrine DSilverman EKonkle BMinear MMin NWatson KHaessler JRafaels NDeBaun MGabriel SLubitz SCarey VCasaburi RComhair SFreedman BISeries FBowler RTinker LManichaikul AFingerlin TGao YLange LAAslibekyan SDuggirala RBurchard EGao SZekavat SMBecker LCarson AMignot EWiller CJKhan ZDey RCasas Romero JPTiwari HSchwartz DCho MNorth KWang FFDeka RMcHugh CPerry JGraw SRao DCKhan ALeFaive JSmith JMahaney MGross CSmith NCade BEBenos TSmith TMei HDeMeo DAyas NBecker DGan WRobillard RCupples LAPsaty BMMetcalf GDarbar DWilliams KBezerra MLin XTaylor STaylor MSnively BPack AGeraci MSheehan VHall MKang HMPost WHe JGignoux CJain DFulton LMuzny DWilliams LKChavan SShoemaker MBHu JLin HHersh CSalimi SKelly SZhao WNaseri TRuuska SGoring HHHLoos RJFMestroni LRasmussen-Torvik LTirschwell DMinster RLAguet FMake BGhosh AHokanson JLange ELange CReeves CHoth KMartin LWMeyers DABis JCAssimes TAuer PBarnard JMitchell BDRotter JICrandall CCardwell JGerszten RRich SSO'Connor TMcNeil BNickerson DVaidya DPeters UWatt JGupta NAbecasis GLewis JRobine NBarnes KParker CFarber CBecker JPLi XLi YLi ZJones KOkwuonu GFornage MNeltner BChen BJSherman SLWessel JRoselli CDaya MWallace RAshley-Koch AMenon VBeer RNatarajan POchs-Balcom HJackson RLee WJHidalgo BChasman DChang YCThornton TAHanly PLuo JZhang YSaleheen DZhao SXMcArdle PAlbert CEmery LBielak LFMorrison ACSheu WHPleiness JBalasubramanian AHawley NLTelen MJaquish CKardia SO'Connell JRBlackwell TSankaran VGQiao DTaliun DRedline SBeck GChen WMSciurba FDutcher SKShetty ABustamante CSchwander KIrvin MRLee SKessler MRatan ALee JCorrea ASantibanez JVan Den Berg DCrapo JDugan-Perez STang HEaton CBarron-Casella EQin ZHsiung CACarty CSmith JAZhou HSkomro RTaub MRice KMSilver BSu JLPalmer NDHung YJFullerton SMWilson CKim WGibbs RWu JNkechinyere ODinh HNaik RZody MBoerwinkle EWang HBrown DWang JMikulla JQasba PBeaty TSun RPeloso GMBarwick LDan RodenYang IVasan RSRaffield LMApplebaum-Bowden DAment SGaynor SMPaik DTAnugu PMcGoldrick DWeeks DEBen HeavnerBrody JADamcott CDurda JPPeyser PAGogarten SKinney GSmoller SWeng LCPankow JButh EHeckbert SGong DWBoorgula MPSofer TWeiss STGermer SMoll MBunting KMcDonald MLDoddapaneni HDuan QBenjamin EStreeten EGray KJChen YIEl Boueiz AfalsePowerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medic2023-01-01T00:00:00Z2023 Jan2026-05-29T14:02:47.948Z2026-04-08T04:51:50.395ZS-EPMC100848913656450510.1038/s41588-022-01225-6