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Article: Improving polygenic risk prediction from summary statistics by an empirical Bayes approach

TitleImproving polygenic risk prediction from summary statistics by an empirical Bayes approach
Authors
Issue Date2017
PublisherNature Publishing Group: Open Access Journals - Option C. The Journal's web site is located at http://www.nature.com/srep/index.html
Citation
Scientific Reports, 2017, v. 7, p. 41262 How to Cite?
AbstractPolygenic 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 R2 from 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. Improving polygenic risk prediction from summary statistics by an empirical Bayes approach. Available from: https://www.researchgate.net/publication/313258278_Improving_polygenic_risk_prediction_from_summary_statistics_by_an_empirical_Bayes_approach [accessed Sep 29, 2017].
Persistent Identifierhttp://hdl.handle.net/10722/248612
ISSN
2017 Impact Factor: 4.122
2015 SCImago Journal Rankings: 2.073
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSo, HC-
dc.contributor.authorSham, PC-
dc.date.accessioned2017-10-18T08:45:53Z-
dc.date.available2017-10-18T08:45:53Z-
dc.date.issued2017-
dc.identifier.citationScientific Reports, 2017, v. 7, p. 41262-
dc.identifier.issn2045-2322-
dc.identifier.urihttp://hdl.handle.net/10722/248612-
dc.description.abstractPolygenic 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 R2 from 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. Improving polygenic risk prediction from summary statistics by an empirical Bayes approach. Available from: https://www.researchgate.net/publication/313258278_Improving_polygenic_risk_prediction_from_summary_statistics_by_an_empirical_Bayes_approach [accessed Sep 29, 2017].-
dc.languageeng-
dc.publisherNature Publishing Group: Open Access Journals - Option C. The Journal's web site is located at http://www.nature.com/srep/index.html-
dc.relation.ispartofScientific Reports-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleImproving polygenic risk prediction from summary statistics by an empirical Bayes approach-
dc.typeArticle-
dc.identifier.emailSham, PC: pcsham@hku.hk-
dc.identifier.authoritySham, PC=rp00459-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1038/srep41262-
dc.identifier.hkuros281963-
dc.identifier.volume7-
dc.identifier.spage41262-
dc.identifier.epage41262-
dc.identifier.isiWOS:000393299100001-
dc.publisher.placeUnited Kingdom-
dc.identifier.f1000727258959-

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