Gene-gene and Gene-Environment Interactions Improve Genetic Risk Predictions for Complex Diseases
- 1Victoria University of Wellington
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.
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BibTeX
@misc{multerer2026genegene,
title = {{Gene-gene and Gene-Environment Interactions Improve Genetic Risk Predictions for Complex Diseases}},
author = {K. Multerer and P. Atkinson and B. Nguyen and L. Woods and Y. Tanigawa and M. Kellis and A. Munkacsi},
howpublished = {medRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.12.26360291},
url = {https://www.medrxiv.org/content/10.64898/2026.08.12.26360291v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - Gene-gene and Gene-Environment Interactions Improve Genetic Risk Predictions for Complex Diseases
AU - K. Multerer
AU - P. Atkinson
AU - B. Nguyen
AU - L. Woods
AU - Y. Tanigawa
AU - M. Kellis
AU - A. Munkacsi
PY - 2026
DA - 2026/08/12
PB - medRxiv
DO - 10.64898/2026.08.12.26360291
UR - https://www.medrxiv.org/content/10.64898/2026.08.12.26360291v2
AB - 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.
ER -