A0566
Title: GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies
Authors: Zihuai He - Stanford University (United States) [presenting]
Abstract: Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to study the contribution of genetic variants to complex phenotypes comprehensively. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. An efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) 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 a meta-analysis of multiple GWAS, allowing for arbitrary sample overlap. Its performance is demonstrated using empirical simulations and two applications: (1) a meta-analysis for Alzheimer's disease comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies and (2) an analysis of 1403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that conventional association tests miss.
