Improved polygenic prediction by Bayesian multiple regression on summary statistics

被引：296

作者：

Lloyd-Jones, Luke R. ^{[1
]}

Zeng, Jian ^{[1
,8
]}

Sidorenko, Julia ^{[1
,2
]}

Yengo, Loic ^{[1
]}

Moser, Gerhard ^{[3
,4
]}

Kemper, Kathryn E. ^{[1
]}

Wang, Huanwei ^{[1
]}

Zheng, Zhili ^{[1
]}

Magi, Reedik ^{[2
]}

Esko, Tonu ^{[2
]}

Metspalu, Andres ^{[2
,5
]}

Wray, Naomi R. ^{[1
,6
]}

Goddard, Michael E. ^{[7
]}

Yang, Jian ^{[1
]}

Visscher, Peter M. ^{[1
]}

机构：

[1] Univ Queensland, Inst Mol Biosci, Brisbane, Qld 4072, Australia

[2] Univ Tartu, Inst Genom, Estonian Genome Ctr, Riia 23b, EE-51010 Tartu, Estonia

[3] Cent Queensland Univ, Sch Engn & Technol, Rockhampton, Qld 4702, Australia

[4] Australian Agr Co Ltd, Brisbane, Qld 4006, Australia

[5] Univ Tartu, Inst Mol & Cell Biol, EE-51010 Tartu, Estonia

[6] Univ Queensland, Queensland Brain Inst, Brisbane, Qld 4072, Australia

[7] Univ Melbourne, Fac Vet & Agr Sci, Melbourne, Vic 3052, Australia

[8] Wenzhou Med Univ, Inst Adv Res, Wenzhou 325027, Zhejiang, Peoples R China

来源：

NATURE COMMUNICATIONS | 2019年 / 10卷 / 1期

基金：

英国医学研究理事会; 澳大利亚研究理事会;

关键词：

GENOME-WIDE ASSOCIATION; MODELING LINKAGE DISEQUILIBRIUM; COMPLEX TRAITS; MIXED-MODEL; MISSING HERITABILITY; RISK; VARIANTS; IMPUTATION; ACCURACY; POPULATION;

D O I：

10.1038/s41467-019-12653-0

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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 approximate to 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.

引用

页数：11

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