Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers

Kenneth E. Westerman (), Timothy D. Majarian, Franco Giulianini, Dong-Keun Jang, Jenkai Miao, Jose C. Florez, Han Chen, Daniel I. Chasman, Miriam S. Udler, Alisa K. Manning and Joanne B. Cole ()
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Kenneth E. Westerman: Massachusetts General Hospital
Timothy D. Majarian: Broad Institute of Harvard and MIT
Franco Giulianini: Brigham and Women’s Hospital
Dong-Keun Jang: Broad Institute of Harvard and MIT
Jenkai Miao: Boston Children’s Hospital
Jose C. Florez: Broad Institute of Harvard and MIT
Han Chen: The University of Texas Health Science Center at Houston
Daniel I. Chasman: Brigham and Women’s Hospital
Miriam S. Udler: Broad Institute of Harvard and MIT
Alisa K. Manning: Massachusetts General Hospital
Joanne B. Cole: Broad Institute of Harvard and MIT

Nature Communications, 2022, vol. 13, issue 1, 1-11

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

Date: 2022
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DOI: 10.1038/s41467-022-31625-5

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