Polygenic prediction via Bayesian regression and continuous shrinkage priors

• Published in: Nature communications Vol. 10; no. 1; p. 1776
• Main Authors: Ge, Tian, Chen, Chia-Yen, Ni, Yang, Feng, Yen-Chen Anne, Smoller, Jordan W.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16.04.2019; Nature Publishing Group; Nature Portfolio
• Subjects: 45/43; 631/114/2415; 631/208/205; 631/208/205/2138; 639/705/531; Arthritis, Rheumatoid - diagnosis; Arthritis, Rheumatoid - genetics; Bayes Theorem; Bayesian analysis; Biobanks; Breast Neoplasms - diagnosis; Breast Neoplasms - genetics; Computer applications; Computer Simulation; Coronary Artery Disease - diagnosis; Coronary Artery Disease - genetics; Databases, Genetic - statistics & numerical data; Depression - diagnosis; Depression - genetics; Diabetes Mellitus, Type 2 - diagnosis; Diabetes Mellitus, Type 2 - genetics; Female; Genetic Predisposition to Disease; Genome-Wide Association Study; Genomes; Health care; Humanities and Social Sciences; Humans; Inflammatory Bowel Diseases - diagnosis; Inflammatory Bowel Diseases - genetics; Linkage disequilibrium; Linkage Disequilibrium - genetics; Male; Models, Genetic; multidisciplinary; Multifactorial Inheritance - genetics; Polygenic inheritance; Polymorphism, Single Nucleotide - genetics; Predictions; Quantitative Trait, Heritable; Risk Factors; Science; Science (multidisciplinary); Shrinkage; Single-nucleotide polymorphism
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-09718-5

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. Polygenic risk scores (PRS) have the potential to predict complex diseases and traits from genetic data. Here, Ge et al. develop PRS-CS which uses a Bayesian regression framework, continuous shrinkage (CS) priors and an external LD reference panel for polygenic prediction of binary and quantitative traits from GWAS summary statistics.