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Article Abstract
Gene-environment interactions represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. These often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci can be prioritized in a two-stage interaction detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We perform genome-wide variance-quantitative trait locus analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs (p < 4.5×10). Most are concentrated in a small subset (4%) of loci with genome-wide significant main effects, and 44% replicate (p < 0.05) in the Women's Genome Health Study (N = 23,294). Next, we test each locus-biomarker pair for interaction across 2380 exposures, identifying 847 significant interactions (p < 2.4×10), of which 132 are independent (p < 0.05) after accounting for correlation between exposures. Specific examples demonstrate interaction of triglyceride-associated variants with distinct body mass- versus body fat-related exposures as well as genotype-specific associations between alcohol consumption and liver stress at the ADH1B gene. Our catalog of variance-quantitative trait loci and gene-environment interactions is publicly available in an online portal.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271055 | PMC |
| http://dx.doi.org/10.1038/s41467-022-31625-5 | DOI Listing |
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