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Polygenic prediction via Bayesian regression and continuous shrinkage priors.

Tian GeChia-Yen ChenYang NiYen-Chen Anne FengJordan W Smoller
Published in: Nature communications (2019)
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.
Keyphrases
  • genome wide
  • healthcare
  • genome wide association
  • dna methylation
  • endothelial cells
  • copy number
  • big data
  • gene expression
  • machine learning
  • virtual reality
  • data analysis
  • human immunodeficiency virus
  • health insurance