Gene-gene and Gene-Environment Interactions Improve Genetic Risk Predictions for Complex Diseases

Abstract

Polygenic risk scores (PRS) assume additive SNP effects, yet genetic risk also arises from interactions between loci and environmental factors that contribute to broad-sense heritability. We developed an extended PRS (ePRS) framework for type 2 diabetes (T2D) that incorporates locus-by-locus non-additive effects beyond those captured by additive single-locus PRS or linkage disequilibrium (LD) tagging. These were modelled as interaction-informed cumulative burden (G+G; summed allele counts), epistasis-screened (GxG; allele count products), and gene-environment effects derived from cardiometabolic variables in electronic health records. Across 235,000 UK Biobank participants, five complementary ePRS models captured largely non-overlapping high-risk individuals, suggesting that a key to individual risk predictions comprises the inclusion of multiple interaction-driven biological components rather than a single signal. A composite score was predictive over traditional additive PRS and predictive of T2D among individuals whose clinical symptoms fall within normal ranges.