Transforming polygenic risk prediction: functional annotation and digital twin modeling with whole-exome sequencing

Background Polygenic risk scores (PRSs) are widely used to assess genetic predisposition, but genotyping arrays typically target non-coding variants with limited functional annotation. In contrast, whole-exome sequencing (WES) maps variants to protein-coding regions, providing functional insights that can enrich PRS interpretation and support novel computational frameworks to infer individual genetic predisposition. Results We evaluated WES for polygenic risk modeling and functional interpretation using common exonic variants across 27 clinical biomarkers and 17 disease outcomes in the UK Biobank (N = 105,506) and applied the approach to the VITO IAM Frontier cohort (N = 30). WES achieved a 70.63% mapping rate of single-nucleotide polymorphisms (SNPs) to functional genomic information, compared to 11.64% for genotyping arrays, with most associations observed for lipid, hepatic, and renal biomarkers. PRS performance was comparable to that derived from imputed array data and linked to 11 disease outcomes, including cardiovascular conditions. The best-performing PRS in the target cohort was used to develop a digital twin model that integrates biological pathways, gene tissue expression signatures, and disease associations, validated by existing clinical and metabolomic data. Conclusions Our study demonstrates that WES-derived PRSs can effectively capture clinically relevant disease associations. However, through functional characterization of associated exonic variants, we show that a PRS, as a digital twin model, could potentially explain individual-level variation and provide biological information on how genetic variants mediate genetic risk.