Polygenic prediction via Bayesian regression and continuous shrinkage priors

TLDR

Polygenic risk scores have shown promise in predicting human complex traits and diseases. The study presents PRS‑CS, a polygenic prediction method that infers SNP effect sizes from GWAS summary statistics and an external LD reference panel. PRS‑CS employs a high‑dimensional Bayesian regression with a continuous shrinkage prior on SNP effect sizes, enabling robust modeling of local linkage disequilibrium patterns and computational efficiency. Simulation and real‑world analyses demonstrate that PRS‑CS outperforms existing methods in predicting complex diseases and quantitative traits, especially with large training samples.

Abstract

Abstract Polygenic risk scores (PRS) have shown promise in predicting human complex traits and diseases. Here, we present PRS-CS, a polygenic prediction method that infers posterior effect sizes of single nucleotide polymorphisms (SNPs) using genome-wide association summary statistics and an external linkage disequilibrium (LD) reference panel. PRS-CS utilizes a high-dimensional Bayesian regression framework, and is distinct from previous work by placing a continuous shrinkage (CS) prior on SNP effect sizes, which is robust to varying genetic architectures, provides substantial computational advantages, and enables multivariate modeling of local LD patterns. Simulation studies using data from the UK Biobank show that PRS-CS outperforms existing methods across a wide range of genetic architectures, especially when the training sample size is large. We apply PRS-CS to predict six common complex diseases and six quantitative traits in the Partners HealthCare Biobank, and further demonstrate the improvement of PRS-CS in prediction accuracy over alternative methods.