{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhu X"],"funding":["NHLBI NIH HHS","NIMHD NIH HHS","NHGRI NIH HHS","NCI NIH HHS"],"pagination":["21-36"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC4289691"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["96(1)"],"pubmed_abstract":["Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple-even distinct-traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also "],"journal":["American journal of human genetics"],"pubmed_title":["Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension."],"pmcid":["PMC4289691"],"funding_grant_id":["P20 MD006899","R35 CA197449","T32 HL007567","R21 HL123677","HG003054","R01 HL053353","R01 HL086718","R01 HL055673","HL123677","R01 HL113338","U01 HL107446","R21 HL121429","R35 HL135818","R01 HG003054","HL086718","HL053353","HL113338"],"pubmed_authors":["Psaty BM","Gottesman O","Cooper RS","Guo X","Rice K","Guo W","Morrison AC","Mosley TH","Williams SM","Liang J","Fox E","Sung YJ","Rotimi CN","Keating BJ","Margolis K","Risch N","Weir DR","Musani SK","Palmas W","Edwards TL","Redline S","Howard VJ","Chen WM","Burnier M","Adeyemo A","Levy D","McKnight B","Loos R","Feng T","Young J","Bovet P","Nalls MA","Yanek LR","Becker LC","Schork NJ","Arnett D","Velez Edwards DR","Kardia SL","Vasan R","Zhao W","Lotay V","Tang H","Bochud M","Eaton C","Carty C","Papanicolaou G","Smith JA","Murray SS","Garcia ME","Ridker PM","Taylor KD","Reiner AP","Heiss G","Young JH","Martin LW","Becker DM","COGENT BP Consortium","Faul JD","Dreisbach AW","Hunt SC","Zhu X","Wiggins K","Salako B","Keene KL","Ogunniyi A","Rotter JI","Johnson AD","Andrews JS","Shriner D","Boerwinkle E","Wang H","Zonderman AB","Chen W","Chakravati A","Keller MF","Chen G","Hirschhron JN","Franceschini N","Chen F","Broeckel U","Berenson G","Evans MK","Rotimi C","Li Y","Sale MM","Penman A","Fornage M","Nalls M","Liu K","Tayo BO","Lettre G","Ding J","Liu Y","Lanktree MB","Sun YV","Srinivasan S","Arnett DK","Jensen R","Polak JF","Chakravarti A","Singleton AB","Cushman M","Harris T","Duan Q","Rao DC","Bottinger E","Guangfa Z","Zhang Z","Sale M"],"additional_accession":[]},"is_claimable":false,"name":"Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension.","description":"Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple-even distinct-traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also ","dates":{"release":"2015-01-01T00:00:00Z","publication":"2015 Jan","modification":"2026-05-02T21:50:45.139Z","creation":"2019-03-27T01:43:13Z"},"accession":"S-EPMC4289691","cross_references":{"pubmed":["25500260"],"doi":["10.1016/j.ajhg.2014.11.011"]}}