Polygenic prediction via Bayesian regression and continuous shrinkage priors

Tian Ge; Chia-Yen Chen; Yang Ni; Yen-Chen Anne Feng; Jordan W. Smoller

doi:10.1038/s41467-019-09718-5

Nature Communications (Apr 2019)

Polygenic prediction via Bayesian regression and continuous shrinkage priors

Tian Ge,
Chia-Yen Chen,
Yang Ni,
Yen-Chen Anne Feng,
Jordan W. Smoller

Affiliations

Tian Ge: Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital
Chia-Yen Chen: Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital
Yang Ni: Department of Statistics, Texas A&M University
Yen-Chen Anne Feng: Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital
Jordan W. Smoller: Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital

DOI: https://doi.org/10.1038/s41467-019-09718-5
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 10

Abstract

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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.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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