Accounting for heterogeneity due to environmental sources in meta-analysis of genome-wide association studies

• Published in: Communications biology Vol. 7; no. 1; pp. 1512 - 12
• Main Authors: Wang, Siru, Ojewunmi, Oyesola O., Kamiza, Abram, Ramsay, Michele, Morris, Andrew P., Chikowore, Tinashe, Fatumo, Segun, Asimit, Jennifer L.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14-11-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 45/43; 631/208/205/2138; 631/208/212; Biomedical and Life Sciences; Cholesterol; Environmental effects; Environmental factors; Genetic diversity; Genome-wide association studies; Genomes; Genomic analysis; Life Sciences; Meta-analysis; Population genetics; Population studies
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-024-07236-9

Meta-analysis of genome-wide association studies (GWAS) across diverse populations offers power gains to identify loci associated with complex traits and diseases. Often heterogeneity in effect sizes across populations will be correlated with genetic ancestry and environmental exposures (e.g. lifestyle factors). We present an environment-adjusted meta-regression model (env-MR-MEGA) to detect genetic associations by adjusting for and quantifying environmental and ancestral heterogeneity between populations. In simulations, env-MR-MEGA has similar or greater association power than MR-MEGA, with notable gains when the environmental factor has a greater correlation with the trait than ancestry. In our analysis of low-density lipoprotein cholesterol in ~19,000 individuals across twelve sex-stratified GWAS from Africa, adjusting for sex, BMI, and urban status, we identify additional heterogeneity beyond ancestral effects for seven variants. Env-MR-MEGA provides an approach to account for environmental effects using summary-level data, making it a useful tool for meta-analyses without the need to share individual-level data.
Adjusting for and quantifying environmental heterogeneity in the meta-analysis of genomewide association studies of diverse populations identifies additional heterogeneity beyond ancestral effects.