Jin X, Shi G. Variance-component-based meta-analysis of gene-environment interactions for rare variants.
G3-GENES GENOMES GENETICS 2021;
11:6298593. [PMID:
34544119 PMCID:
PMC8661424 DOI:
10.1093/g3journal/jkab203]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 06/07/2021] [Indexed: 11/13/2022]
Abstract
Complex diseases are often caused by interplay between genetic and environmental factors. Existing gene-environment interaction (G × E) tests for rare variants largely focus on detecting gene-based G × E effects in a single study; thus, their statistical power is limited by the sample size of the study. Meta-analysis methods that synthesize summary statistics of G × E effects from multiple studies for rare variants are still limited. Based on variance component models, we propose four meta-analysis methods of testing G × E effects for rare variants: HOM-INT-FIX, HET-INT-FIX, HOM-INT-RAN, and HET-INT-RAN. Our methods consider homogeneous or heterogeneous G × E effects across studies and treat the main genetic effect as either fixed or random. Through simulations, we show that the empirical distributions of the four meta-statistics under the null hypothesis align with their expected theoretical distributions. When the interaction effect is homogeneous across studies, HOM-INT-FIX and HOM-INT-RAN have as much statistical power as a pooled analysis conducted on a single interaction test with individual-level data from all studies. When the interaction effect is heterogeneous across studies, HET-INT-FIX and HET-INT-RAN provide higher power than pooled analysis. Our methods are further validated via testing 12 candidate gene-age interactions in blood pressure traits using whole-exome sequencing data from UK Biobank.
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