Accounting for gene action improves the accuracy of polygenic scores for some complex traits

Polygenic scores (PGS) predict an individual’s genetic value for a complex trait of interest by aggregating effect size estimates from genome-wide association studies (GWAS). While powerful, this approach assumes that genetic variants affect the phenotype in an additive way, which might be problematic when dominance is part of the genetic architecture. To address this limitation, we developed PGS _a,d , a simple polygenic scoring framework, rooted in quantitative genetics theory, that incorporates both additive ( a ) and dominance effects ( d ) directly. We evaluated PGS _a,d against the standard GWAS-based PGS (PGS _β ) in simulations. The results showed that PGS _a,d consistently achieved lower root mean squared error (RMSE) and progressively higher R ² as dominance contributed more to the genetic architecture. We then applied the method to 16 blood cell traits, height, and body mass index (BMI) from UK Biobank. PGS _a,d showed comparable R ² to PGS _β for all traits (including height and BMI whose architecture is predominantly additive) and significantly lower RMSE for four traits after strict Bonferroni correction. Across traits, RMSE improvement from PGS _a,d was positively associated with the magnitude of the estimated dominance effects, whereby traits with larger dominance effects displayed lower RMSE from PGS _a,d . As a byproduct of our analysis, we also identified 141 variants in 39 genomic regions with significant dominance effects after Bonferroni correction across traits, recapitulating known associations and identifying novel ones. Together, these results show that explicitly modeling additive and dominance effects is a robust strategy that can improve individual-level predictions, especially when dominance contributes to complex trait architecture.