{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yang J"],"funding":["NCRR NIH HHS","NIDA NIH HHS","NHLBI NIH HHS","Medical Research Council","NIAAA NIH HHS","NHGRI NIH HHS","Wellcome Trust","PHS HHS"],"pagination":["369-75, S1-3"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC3593158"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["44(4)"],"pubmed_abstract":["We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium."],"journal":["Nature genetics"],"pubmed_title":["Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits."],"pmcid":["PMC3593158"],"funding_grant_id":["AA13320","U01 HG004402","HHSN268201100011I","R01 AA013326","R01 DA012854","AA014041","090532","WT081682/Z/06/Z","MC_U106188470","R01 HL059367","HHSN268201100009I","HHSN268201100005C","R01 AA013321","HHSN268201100007C","K05 AA017688","R01 AA013320","UL1 RR025005","HHSN268201100009C","AA13321","HHSN268201100011C","HHSN268201100005I","R01HL087641","HHSN268201100005G","R01HL086694","HHSN268201100007I","AA13326","R01 AA007535","DA12854","HHSN268200625226C","UL1RR025005","R01HL59367","HHSN268201100006C","R56 DA012854","HHSN268201100008C","U01HG004402","WT083270","HHSN268201100010C","HHSN268201100008I","R01 AA014041","AA10248","R01 HL086694","HHSN268201100012C","R01 HL087641"],"pubmed_authors":["Morris AP","McCarthy MI","Yang J","Genetic Investigation of ANthropometric Traits (GIANT) Consortium","Martin NG","Montgomery GW","Goddard ME","Visscher PM","Loos RJ","Madden PA","Heath AC","Ferreira T","DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium","Hirschhorn JN","Medland SE","Weedon MN","Frayling TM"],"additional_accession":[]},"is_claimable":false,"name":"Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits.","description":"We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.","dates":{"release":"2012-01-01T00:00:00Z","publication":"2012 Mar","modification":"2020-11-19T08:49:29Z","creation":"2019-03-27T01:05:50Z"},"accession":"S-EPMC3593158","cross_references":{"pubmed":["22426310"],"doi":["10.1038/ng.2213"]}}