Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data

TLDR

Recent heritability analyses indicate that GWAS can enhance genetic risk prediction for complex diseases using polygenic risk scores derived from summary‑level data. The study proposes modifications to improve PRS performance. The authors introduce winner’s‑curse adjustments for SNP weights and variable thresholds that incorporate functional annotation, applying these methods to GWAS summary data for 14 diseases. Winner’s‑curse correction consistently improved PRS performance across all diseases, and when combined with functional‑annotation‑based variable thresholds, yielded a 25–50 % increase in prediction R² for five diseases (e.g., T2D R² rose from 2.29 % to 3.53 %), though simulations show that enrichment of functional SNPs does not always translate into proportional gains due to linkage‑disequilibrium heterogeneity.

Abstract

Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25–50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10−5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.