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Nat Commun. 2019 Apr 16;10(1):1776. doi: 10.1038/s41467-019-09718-5.

Polygenic prediction via Bayesian regression and continuous shrinkage priors.

Ge T1,2,3, Chen CY4,5,6,7, Ni Y8, Feng YA4,5,6,7, Smoller JW4,5,6.

Author information

1
Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA. tge1@mgh.harvard.edu.
2
Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA. tge1@mgh.harvard.edu.
3
Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. tge1@mgh.harvard.edu.
4
Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
5
Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
6
Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
7
Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
8
Department of Statistics, Texas A&M University, College Station, TX, 77843, USA.

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.

PMID:
30992449
PMCID:
PMC6467998
DOI:
10.1038/s41467-019-09718-5
[Indexed for MEDLINE]
Free PMC Article

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