Incorporating additive genetic effects and linkage disequilibrium information to discover gene-environment interactions using BV-LDER-GE

Uncovering environmental factors interacting with genetic factors to influence complex traits is important in genetic epidemiology and disease etiology. We introduce BiVariate Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (BV-LDER-GE), a statistical method that detects the overall contributions of G × E interactions in the genome using summary statistics of complex traits. In comparison to existing methods which either ignore correlations with additive effects or use partial information of linkage disequilibrium (LD), BV-LDER-GE harnesses correlations with additive genetic effects and full LD information to enhance the statistical power to detect genome-scale G × E interactions.