Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers
Kenneth E. Westerman, Timothy D. Majarian, Franco Giulianini, Dongkeun Jang, Jenkai Miao, José C. Florez, Han Chen, Daniel I. Chasman, Miriam S. Udler, Alisa K. Manning, Joanne B. Cole
Abstract
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 −9 ). 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 −7 ), 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.