Improved polygenic prediction by Bayesian multiple regression on summary statistics

Abstract Accurate prediction of an individual’s phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. We extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies (GWAS), SBayesR. In simulation and cross-validation using 12 real traits and 1.1 million variants on 350,000 individuals from the UK Biobank, SBayesR improves prediction accuracy relative to commonly used state-of-the-art summary statistics methods at a fraction of the computational resources. Furthermore, using summary statistics for variants from the largest GWAS meta-analysis ( n ≈ 700, 000) on height…

• MR
  Medical Research Council

  Awards: 1113400, 1078037, 1078901, MC_PC_17228
• AR
  Australian Research Council

  Awards: DP160102400, 1113400, FT180100186
• NH
  National Health and Medical Research Council

  Awards: 1080157, 1113400, 1078901, 1078037