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In this study, we extended 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 improved 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 and BMI, we showed that on average across traits and two independent data sets that SBayesR improved prediction R2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding.