Accounting for gene action improves the accuracy of polygenic scores for some complex traits
- 1Clemson University
Abstract
Polygenic scores (PGS) predict an individuals 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 PGSa,d, a simple polygenic scoring framework, rooted in quantitative genetics theory, that incorporates both additive (a) and dominance effects (d) directly. We evaluated PGSa,d against the standard GWAS-based PGS (PGS{beta}) in simulations. The results showed that PGSa,d consistently achieved lower root mean squared error (RMSE) and progressively higher R2 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. PGSa,d showed comparable R2 to PGS{beta} 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 PGSa,d was positively associated with the magnitude of the estimated dominance effects, whereby traits with larger dominance effects displayed lower RMSE from PGSa,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.
Related
- The landscape of polygenic score prediction accuracy and transportability across the phenome in ancestrally diverse populations
- Integration of hereditary thrombophilia data and polygenic risk scores into prognostic models improves differential diagnosis of CTEPH.
- One-shot synthesis of rare gastrointestinal lesions improves diagnostic accuracy and clinical training
- Disentangling phenotype scale, amplification, and effect heterogeneity underlying polygenic GxE architecture of complex traits
- Multiple action potentials and complex calcium dynamics following electroporation of S-HEK cells depend on gap junction coupling
BibTeX
@misc{sun2026accounting,
title = {{Accounting for gene action improves the accuracy of polygenic scores for some complex traits}},
author = {Y. Sun and F. Morgante and T. Mackay},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.15.26365665},
url = {https://www.medrxiv.org/content/10.64898/2026.10.15.26365665v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Accounting for gene action improves the accuracy of polygenic scores for some complex traits
AU - Y. Sun
AU - F. Morgante
AU - T. Mackay
PY - 2026
DA - 2026/10/15
PB - medRxiv
DO - 10.64898/2026.10.15.26365665
UR - https://www.medrxiv.org/content/10.64898/2026.10.15.26365665v1
AB - Polygenic scores (PGS) predict an individuals 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 PGSa,d, a simple polygenic scoring framework, rooted in quantitative genetics theory, that incorporates both additive (a) and dominance effects (d) directly. We evaluated PGSa,d against the standard GWAS-based PGS (PGS{beta}) in simulations. The results showed that PGSa,d consistently achieved lower root mean squared error (RMSE) and progressively higher R2 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. PGSa,d showed comparable R2 to PGS{beta} 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 PGSa,d was positively associated with the magnitude of the estimated dominance effects, whereby traits with larger dominance effects displayed lower RMSE from PGSa,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.
ER -