Palette polygenic risk score framework improves risk prediction by capturing clinical heterogeneity of type 2 diabetes

Abstract

Polygenic risk scores (PRSs) are typically constructed under the assumption of a single, homogeneous disease phenotype. However, many common diseases exhibit considerable clinical heterogeneity and encompass multiple subtypes with distinct etiologies and clinical characteristics. As a result, conventional PRSs often overlook differences in underlying biological pathways among disease subtypes, consequently limiting predictive accuracy and cross-ancestry transferability. To address this challenge, we propose the "palette PRS," a framework that integrates a set of partitioned polygenic scores (pPSs) for biologically interpretable pathways with subtype-specific weights. This approach can flexibly capture the relative contributions of multiple pathways within each individual and provides a unified risk score. We applied this framework to type 2 diabetes (T2D), a clinically highly heterogeneous disease. For T2D, previous machine learning-based studies have identified four distinct subtypes and 12 biologically interpretable pathways derived from 650 genome-wide significant variants. Building on these established findings, we employed an elastic net model incorporating subtype membership probabilities to derive subtype-optimized palette PRS through the weighted integration of the pPSs of these 12 pathways. Our palette PRS showed superior predictive performance, with particularly high accuracy for the severe insulin-deficient diabetes (SIDD) subtype (AUC=0.744), compared with both conventional T2D PRS (AUC = 0.661) or subtype-stratified GWAS-based PRS (AUC = 0.547). Moreover, our palette PRS exhibited substantial cross-ancestry transferability between East Asian and European populations. This strategy represents a major step toward clinically actionable, subtype-optimized risk prediction and personalized prevention in T2D worldwide.