Improved polygenic prediction by Bayesian multiple regression on summary statistics
By Luke R. Lloyd-Jones, Jian Zeng, J. Sidorenko, L. Yengo, G. Moser, Kathryn E. Kemper, Huanwei Wang, Zhili Zheng, R. Magi, T. Esko and 5 more. Nature Communications, 2019
484 citations recorded by the source corpus.
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. Various approaches are being used for polygenic prediction including Bayesian multiple regression methods that require access to individual-level genotype data. Here, the authors extend BayesR to utilise GWAS summary statistics (SBayesR) and show that it outperforms other summary statistic-based methods.
Subfields: Genetics
View Paper: https://www.semanticscholar.org/paper/53e482709dcf7f3c6ef303045230cf50a235842d
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In Genetics
- Zhili Zheng
- Longda Jiang
- Hailing Fang
- Bjarni J. Vilhjálmsson
- Chia-Yen Chen
- Y. Feng
- Tian Ge
- Jordan W. Smoller
Citations in the corpus
- A generalized linear mixed model association tool for biobank-scale data (2021, methodology): “Overall, across five UKB traits, there was a difference in prediction accuracy between the polygenic risk score methods, with SBayesR performing consistently better than P + T, in line with prior work 24,25 , but the difference between SAIGE and fastGWA-GLMM was negligible (Supplementary Table 14).”
- LDpred2: better, faster, stronger (2020, background): “Yet, it has some instability issues that have been pointed out (Marquez-Luna et al. 2018; Lloyd-Jones et al. 2019) and likely contributed to discrepancies in reported prediction accuracies (Choi and O’Reilly 2019; Ge et al.”
- Polygenic prediction via Bayesian regression and continuous shrinkage priors (2018, background): “(6), Gibbs samplers with block update of the regression coefficients β (i.”