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

Zihuai He (), Linxi Liu, Michael E. Belloy, Yann Guen, Aaron Sossin, Xiaoxia Liu, Xinran Qi, Shiyang Ma, Prashnna K. Gyawali, Tony Wyss-Coray, Hua Tang, Chiara Sabatti, Emmanuel Candès, Michael D. Greicius and Iuliana Ionita-Laza
Additional contact information
Zihuai He: Stanford University
Linxi Liu: University of Pittsburgh
Michael E. Belloy: Stanford University
Yann Guen: Stanford University
Aaron Sossin: Stanford University
Xiaoxia Liu: Stanford University
Xinran Qi: Stanford University
Shiyang Ma: Columbia University
Prashnna K. Gyawali: Stanford University
Tony Wyss-Coray: Stanford University
Hua Tang: Stanford University
Chiara Sabatti: Stanford University
Emmanuel Candès: Stanford University
Michael D. Greicius: Stanford University
Iuliana Ionita-Laza: Columbia University

Nature Communications, 2022, vol. 13, issue 1, 1-16

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-34932-z

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