biostudies-literatureHe ZU.S. Department of Health & Human Services | NIH | National Institute on AgingU.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)7209https://www.ebi.ac.uk/biostudies/studies/S-EPMC9684164biostudies-literatureUnknown13(1)Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance uNature communicationsGhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.PMC9684164AG060747AG066515AG066206Le Guen YMa SLiu LWyss-Coray TTang HBelloy MELiu XIonita-Laza IGyawali PKHe ZQi XCandes ESossin ASabatti CGreicius MDfalseGhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance u2022-01-01T00:00:00Z2022 Nov2025-04-25T21:00:33.296Z2025-04-06T08:37:23.486ZS-EPMC96841643641833810.1038/s41467-022-34932-z