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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 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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Lloyd-Jones LR,Zeng J,Sidorenko J,Yengo L,Moser G,Kemper KE,Wang H,Zheng Z,Magi R,Esko T,Metspalu A,Wray NR,Goddard ME,Yang J,Visscher PM

doi

10.1038/s41467-019-12653-0

subject

Has Abstract

pub_date

2019-11-08 00:00:00

pages

5086

issue

1

issn

2041-1723

pii

10.1038/s41467-019-12653-0

journal_volume

10

pub_type

杂志文章

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