GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.

ABSTRACT: 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 u

SUBMITTER: He Z 

PROVIDER: S-EPMC9684164 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature