Gene-gene and Gene-Environment Interactions Improve Genetic Risk Predictions for Complex Diseases
Year: 2026
Type: preprint
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.
Source: medRxiv
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7203465809