23: Comparative Evaluation of Statistical Learning Methods for Polygenic Prediction in UK Biobank

Music City Center 

Accurate prediction of complex traits and diseases, is crucial for advancing personalized medicine and preventive healthcare. However, guidelines for selecting optimal PRS models under diverse conditions remain limited, often leaving researchers to rely on generalized assumptions rather than tailored methodologies. In this study, we systematically evaluated PRS prediction models using both simulation-based experiments and real-world datasets, including height, BMI, T2D, and glaucoma. We compared various PRS models across key factors: (1) trait heritability, (2) number of SNPs, (3) proportion of causal SNPs, and (4) trait prevalence. Our results highlight key distinctions between infinitesimal models, which assume all SNPs contribute to traits, and non-infinitesimal models, which consider only a subset of SNPs as causal. Specifically, we demonstrate that non-infinitesimal models, such as LDpred and PRScs, outperform infinitesimal models when the proportion of causal SNPs is low-a characteristic common to many phenotypes. Additionally, sample size was a critical determinant of performance, with LDpred excelling in smaller datasets and PRScs outperforming LDpred in larger datasets.

Polygenic risk score (PRS)

PRS models

Computational tools

UK Biobank 

Korean International Statistical Society