title: Improving polygenic risk prediction from summary statistics by an empirical Bayes approach.
abstract: Polygenic risk scores (PRS) from genome-wide association studies (GWAS) are increasingly used to predict disease risks. However some included variants could be false positives and the raw estimates of effect sizes from them may be subject to selection bias. In addition, the standard PRS approach requires testing over a range of p-value thresholds, which are often chosen arbitrarily. The prediction error estimated from the optimized threshold may also be subject to an optimistic bias. To improve genomic risk prediction, we proposed new empirical Bayes approaches to recover the underlying effect sizes and used them as weights to construct PRS. We applied the new PRS to twelve cardio-metabolic traits in the Northern Finland Birth Cohort and demonstrated improvements in predictive power (in R2) when compared to standard PRS at the best p-value threshold. Importantly, for eleven out of the twelve traits studied, the predictive performance from the entire set of genome-wide markers outperformed the best R2from standard PRS at optimal p-value thresholds. Our proposed methodology essentially enables an automatic PRS weighting scheme without the need of choosing tuning parameters. The new method also performed satisfactorily in simulations. It is computationally simple and does not require assumptions on the effect size distributions.
authors.affiliation: School of Biomedical Sciences, Chinese University of Hong Kong, Shatin, Hong Kong.|KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and Chinese University of Hong Kong, China.
authors.forename: Hon-Cheong
authors.initials: HC
authors.lastname: So
authors.affiliation: Department of Psychiatry, University of Hong Kong, PokFuLam, Hong Kong.|Centre for Genomic Sciences, University of Hong Kong, PokFuLam, Hong Kong.|State Key Laboratory for Cognitive and Brain Sciences, University of Hong Kong, PokFuLam, Hong Kong.|Centre for Reproduction, Development and Growth, University of Hong Kong, PokFuLam, Hong Kong.
authors.forename: Pak C
authors.initials: PC
authors.lastname: Sham
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001499:Bayes Theorem; D015331:Cohort Studies; D003198:Computer Simulation; D005387:Finland; D020022:Genetic Predisposition to Disease; D006801:Humans; D020412:Multifactorial Inheritance / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012307:Risk Factors; D013223:Statistics as Topic