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Polygenic prediction via Bayesian regression and continuous shrinkage priors

2019· article· en· 1,990 citations· W2951553270 on OpenAlex· 10.1038/s41467-019-09718-5

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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.

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The record

Venue
Nature Communications
Topic
Genetic Associations and Epidemiology
Field
Biochemistry, Genetics and Molecular Biology
Canadian institutions
Funders
National Institute on AgingHorizon 2020 Framework ProgrammeEuropean CommissionNational Cancer InstituteNational Institutes of HealthCancer Research UKGovernment of CanadaCanadian Institutes of Health ResearchGenome CanadaNational Human Genome Research InstituteGenomic Health
Keywords
BiobankLinkage disequilibriumComputer scienceBayesian probabilityRegressionPrior probabilitySingle-nucleotide polymorphismMultivariate statisticsGenome-wide association studySample size determinationGenetic associationStatisticsArtificial intelligenceMachine learningBioinformaticsBiologyMathematicsGeneticsGenotype
Has abstract in OpenAlex
yes