Improved polygenic prediction by Bayesian multiple regression on summary statistics

Luke R. Lloyd-Jones (), Jian Zeng (), Julia Sidorenko, Loïc Yengo, Gerhard Moser, Kathryn E. Kemper, Huanwei Wang, Zhili Zheng, Reedik Magi, Tõnu Esko, Andres Metspalu, Naomi R. Wray, Michael E. Goddard, Jian Yang () and Peter M. Visscher ()
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Luke R. Lloyd-Jones: University of Queensland, St Lucia
Jian Zeng: University of Queensland, St Lucia
Julia Sidorenko: University of Queensland, St Lucia
Loïc Yengo: University of Queensland, St Lucia
Gerhard Moser: Central Queensland University
Kathryn E. Kemper: University of Queensland, St Lucia
Huanwei Wang: University of Queensland, St Lucia
Zhili Zheng: University of Queensland, St Lucia
Reedik Magi: University of Tartu
Tõnu Esko: University of Tartu
Andres Metspalu: University of Tartu
Naomi R. Wray: University of Queensland, St Lucia
Michael E. Goddard: University of Melbourne
Jian Yang: University of Queensland, St Lucia
Peter M. Visscher: University of Queensland, St Lucia

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: 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 and BMI, we show that on average across traits and two independent data sets that SBayesR improves prediction R2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding.

Date: 2019
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DOI: 10.1038/s41467-019-12653-0

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