GhostKnockoff Inference Empowers Identification of Putative Causal Variants in Genome-wide Association Studies

Overview

Journal Nat Commun

Specialty Biology

Date 2022 Nov 23

PMID 36418338

Authors

Zihuai He

Linxi Liu

Michael E Belloy

Yann Le Guen

Aaron Sossin

XiaoXia Liu

Xinran Qi

Shiyang Ma

Prashnna K Gyawali

Tony Wyss-Coray

Hua Tang

Chiara Sabatti

Emmanuel Candes

Michael D Greicius

Iuliana Ionita-Laza

Affiliations

Soon will be listed here.

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 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) 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 are missed by conventional association tests.

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