Integrating Common and Rare Variants Improves Polygenic Risk Prediction Across Diverse Populations
Jacob Williams, Tony Chen, Xing Hua, Wendy S W Wong, Kai Yu, Peter Kraft, Xihao Li, Haoyu Zhang
Abstract
Abstract PRSs predict complex traits by aggregating genetic effects across the genome, yet most models focus on common variants, overlooking rare variants that may contribute to hidden heritability. Here, we develop RICE, a PRS framework integrating both common and rare variants to improve genetic risk prediction across diverse ancestries. RICE constructs separate PRSs: for common variants, it integrates methods using ensemble learning; for rare variants, it uses gene-level testing with functional annotations and penalized regression. We evaluate RICE using simulated datasets and sequencing data from UK Biobank and All of Us, involving up to 740 million genetic variants from 361,939 individuals across diverse ancestries and 11 complex traits. In real data analysis, RICE improves predictive accuracy compared to leading common variant methods for traits with distinct rare variant architectures, particularly lipids and height. For lipid traits, incorporating rare variants increased R 2 by up to ~11.2% in Europeans and ~60.7% in African ancestry compared to common variant PRS alone. Notably, for lipid traits, RICE captures substantial predictive signal beyond established high-penetrance genes, validating its ability to leverage the broader polygenic architecture of rare variation.