Additive Multilocus Burden and Epistatic Interactions Improves Genetic Risk Predictions for Complex Diseases

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 cumulative burden (G+G; summed allele counts), statistical epistasis (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 comprise the inclusion of multiple interaction-driven biological components rather than a single signal. A composite score improved case detection beyond clinical predictors, including individuals within clinically normal ranges. These findings were generalized to celiac disease, with similar complementarity across models, with potential for clinical use pending prospective validation.