| Title: | Variance-component-based meta-analysis of gene-environment interactions for rare variants |
| Journal: | G3: Genes, Genomes, Genetics |
| Published: | 14 Jun 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34544119/ |
| DOI: | https://doi.org/10.1093/g3journal/jkab203 |
| URL: | https://academic.oup.com/g3journal/article-pdf/11/9/jkab203/40519808/jkab203.pdf |
| Citations: | 3 (2 in last 2 years) as of 8 Aug 2024 |
Publication 5981
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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.</p>
2 Authors
- Xiaoqin Jin
- Gang Shi
Enabling scientific discoveries that improve human health