Portal de Periódicos da CAPES

Improved polygenic prediction by Bayesian multiple regression on summary statistics

2019; Nature Portfolio; Volume: 10; Issue: 1 Linguagem: Inglês

10.1038/s41467-019-12653-0

2041-1723

Luke R. Lloyd‐Jones, Jian Zeng, Julia Sidorenko, Loïc Yengo, G. Möser, Kathryn E. Kemper, Huanwei Wang, Zhili Zheng, Reedik Mägi, Tõnu Esko, Andres Metspalu, Naomi R. Wray, Michael E. Goddard, Jian Yang, Peter M. Visscher,

Genetic Mapping and Diversity in Plants and Animals

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 R 2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding.