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Article: Exploring the predictive power of polygenic scores derived from genome-wide association studies: a study of 10 complex traits

TitleExploring the predictive power of polygenic scores derived from genome-wide association studies: a study of 10 complex traits
Authors
Issue Date2017
PublisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
Citation
Bioinformatics, 2017, v. 33, p. 886-892 How to Cite?
AbstractMOTIVATION: It is hoped that advances in our knowledge in disease genomics will contribute to personalized medicine such as individualized preventive strategies or early diagnoses of diseases. With the growth of genome-wide association studies (GWAS) in the past decade, how far have we reached this goal? In this study we explored the predictive ability of polygenic risk scores (PRSs) derived from GWAS for a range of complex disease and traits. RESULTS: We first proposed a new approach to evaluate predictive performances of PRS at arbitrary P -value thresholds. The method was based on corrected estimates of effect sizes, accounting for possible false positives and selection bias. This approach requires no distributional assumptions and only requires summary statistics as input. The validity of the approach was verified in simulations. We explored the predictive power of PRS for ten complex traits, including type 2 diabetes (DM), coronary artery disease (CAD), triglycerides, high- and low-density lipoprotein, total cholesterol, schizophrenia (SCZ), bipolar disorder (BD), major depressive disorder and anxiety disorders. We found that the predictive ability of PRS for CAD and DM were modest (best AUC = 0.608 and 0.607) while for lipid traits the prediction R-squared ranged from 16.1 to 29.8%. For psychiatric disorders, the predictive power for SCZ was estimated to be the highest (best AUC 0.820), followed by BD. Predictive performance of other psychiatric disorders ranged from 0.543 to 0.585. Psychiatric traits tend to have more gradual rise in AUC when significance thresholds increase and achieve the best predictive power at higher P -values than cardiometabolic traits.
Persistent Identifierhttp://hdl.handle.net/10722/248610
ISSN
2023 Impact Factor: 4.4
2023 SCImago Journal Rankings: 2.574
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSo, HC-
dc.contributor.authorSham, PC-
dc.date.accessioned2017-10-18T08:45:51Z-
dc.date.available2017-10-18T08:45:51Z-
dc.date.issued2017-
dc.identifier.citationBioinformatics, 2017, v. 33, p. 886-892-
dc.identifier.issn1367-4803-
dc.identifier.urihttp://hdl.handle.net/10722/248610-
dc.description.abstractMOTIVATION: It is hoped that advances in our knowledge in disease genomics will contribute to personalized medicine such as individualized preventive strategies or early diagnoses of diseases. With the growth of genome-wide association studies (GWAS) in the past decade, how far have we reached this goal? In this study we explored the predictive ability of polygenic risk scores (PRSs) derived from GWAS for a range of complex disease and traits. RESULTS: We first proposed a new approach to evaluate predictive performances of PRS at arbitrary P -value thresholds. The method was based on corrected estimates of effect sizes, accounting for possible false positives and selection bias. This approach requires no distributional assumptions and only requires summary statistics as input. The validity of the approach was verified in simulations. We explored the predictive power of PRS for ten complex traits, including type 2 diabetes (DM), coronary artery disease (CAD), triglycerides, high- and low-density lipoprotein, total cholesterol, schizophrenia (SCZ), bipolar disorder (BD), major depressive disorder and anxiety disorders. We found that the predictive ability of PRS for CAD and DM were modest (best AUC = 0.608 and 0.607) while for lipid traits the prediction R-squared ranged from 16.1 to 29.8%. For psychiatric disorders, the predictive power for SCZ was estimated to be the highest (best AUC 0.820), followed by BD. Predictive performance of other psychiatric disorders ranged from 0.543 to 0.585. Psychiatric traits tend to have more gradual rise in AUC when significance thresholds increase and achieve the best predictive power at higher P -values than cardiometabolic traits.-
dc.languageeng-
dc.publisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/-
dc.relation.ispartofBioinformatics-
dc.rightsPre-print: Journal Title] ©: [year] [owner as specified on the article] Published by Oxford University Press [on behalf of xxxxxx]. All rights reserved. Pre-print (Once an article is published, preprint notice should be amended to): This is an electronic version of an article published in [include the complete citation information for the final version of the Article as published in the print edition of the Journal.] Post-print: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in [insert journal title] following peer review. The definitive publisher-authenticated version [insert complete citation information here] is available online at: xxxxxxx [insert URL that the author will receive upon publication here]. -
dc.titleExploring the predictive power of polygenic scores derived from genome-wide association studies: a study of 10 complex traits-
dc.typeArticle-
dc.identifier.emailSham, PC: pcsham@hku.hk-
dc.identifier.authoritySham, PC=rp00459-
dc.identifier.doi10.1093/bioinformatics/btw745-
dc.identifier.scopuseid_2-s2.0-85016902841-
dc.identifier.hkuros281948-
dc.identifier.volume33-
dc.identifier.spage886-
dc.identifier.epage892-
dc.identifier.isiWOS:000397986500012-
dc.publisher.placeUnited Kingdom-
dc.identifier.f1000727188351-
dc.identifier.issnl1367-4803-

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