Journal article
Improved polygenic prediction by Bayesian multiple regression on summary statistics
LR Lloyd-Jones, J Zeng, J Sidorenko, L Yengo, G Moser, KE Kemper, H Wang, Z Zheng, R Magi, T Esko, A Metspalu, NR Wray, ME Goddard, J Yang, PM Visscher
Nature Communications | Published : 2019
Open access
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) o..
View full abstractGrants
Awarded by Sylvia and Charles Viertel Charitable Foundation
Funding Acknowledgements
We would like to thank the members of the Program in Complex Genetics for their insights and helpful discussion. We are grateful to Xiang Zhu for assistance and discussion concerning the Residual with Summary Statistics methodology. We would like to acknowledge the support from the Australian Research Council (DP160102400 and FT180100186), the Australian National Health and Medical Research Council (1113400, 1078037, 1078901 and 1080157), the National Institute of Health (R21 ES025052, R01MH100141 and R01 AG042568) and the Sylvia & Charles Viertel Charitable Foundation. We gratefully acknowledge CQU's eResearch support and the use of the high-performance computing facility (www.cqu.edu.au/hpc) in developing the updated BayesR software.