A Fast and Powerful Linear Mixed Model Approach for Genotype-Environment Interaction Tests in Large-Scale GWAS

• Activity Number: 415 - Recent advancements in the analysis of large-scale GWAS
• Type: Contributed
• Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Wujuan Zhong* and Judong Shen
• Companies: Merck & Co., Inc. and Merck & Co. Inc.
• Keywords: Genome-wide association studies; genotype-environment interaction; subject relatedness; population stratification; G+G*E; linear mixed model

Abstract

In genome-wide association studies (GWAS), test statistics could be inflated due to subject relatedness and population stratification. Linear mixed model (LMM) and principal components (PCs) are widely used to account for sample relatedness and population stratification for genotype (G) effect analysis. However, they have rarely been used to identify susceptibility loci that interact with environment variable (E) in G*E or G+G*E joint analysis. We propose a fast and powerful LMM-based approach, fastGWA-GE, to test G*E interaction effect and G+G*E joint effect, by utilizing PCs to account for population stratification and a sparse genetic relationship matrix for relatedness. Our extensive simulations showed that fastGWA-GE controlled genomic inflation better than alternative methods while achieving similar power. We performed fastGWA-GE analysis of ~7.3 million variants on 452,249 individuals of European ancestry for 13 quantitative traits and five environment variables in the UK Biobank GWAS data and identified 96 significant variants with G*E test P < 1E-09, with many of them with prior association evidence, highlighting the effectiveness of fastGWA-GE in signal discovery.