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dc.contributor.authorShi, Jianxin
dc.contributor.authorPark, Ju-Hyun
dc.contributor.authorDuan, Jubao
dc.contributor.authorBerndt, Sonja T
dc.contributor.authorMoy, Winton
dc.contributor.authorYu, Kai
dc.contributor.authorSong, Lei
dc.contributor.authorWheeler, William
dc.contributor.authorHua, Xing
dc.contributor.authorSilverman, Debra
dc.contributor.authorGarcia-Closas, Montserrat
dc.contributor.authorHsiung, Chao Agnes
dc.contributor.authorFigueroa, Jonine D
dc.contributor.authorCortessis, Victoria K
dc.contributor.authorMalats, Nuria 
dc.contributor.authorKaragas, Margaret R
dc.contributor.authorVineis, Paolo
dc.contributor.authorChang, I-Shou
dc.contributor.authorLin, Dongxin
dc.contributor.authorZhou, Baosen
dc.contributor.authorSeow, Adeline
dc.contributor.authorMatsuo, Keitaro
dc.contributor.authorHong, Yun-Chul
dc.contributor.authorCaporaso, Neil E
dc.contributor.authorWolpin, Brian
dc.contributor.authorJacobs, Eric
dc.contributor.authorPetersen, Gloria M
dc.contributor.authorKlein, Alison P
dc.contributor.authorLi, Donghui
dc.contributor.authorRisch, Harvey
dc.contributor.authorSanders, Alan R
dc.contributor.authorHsu, Li
dc.contributor.authorSchoen, Robert E
dc.contributor.authorBrenner, Hermann
dc.contributor.authorStolzenberg-Solomon, Rachael
dc.contributor.authorGejman, Pablo
dc.contributor.authorLan, Qing
dc.contributor.authorRothman, Nathaniel
dc.contributor.authorAmundadottir, Laufey T
dc.contributor.authorLandi, Maria Teresa
dc.contributor.authorLevinson, Douglas F
dc.contributor.authorChanock, Stephen J
dc.contributor.authorChatterjee, Nilanjan
dc.date.accessioned2019-07-12T11:19:32Z
dc.date.available2019-07-12T11:19:32Z
dc.date.issued2016-12
dc.identifier.citationPLoS Genet. 2016;12(12):e1006493.es_ES
dc.identifier.issn1553-7404es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/7901
dc.description.abstractRecent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10-5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.es_ES
dc.language.isoenges_ES
dc.publisherPUBLIC LIBRARY SCIENCEes_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subject.meshAlgorithms es_ES
dc.subject.meshComputer Simulation es_ES
dc.subject.meshGenome-Wide Association Study es_ES
dc.subject.meshHumans es_ES
dc.subject.meshLinkage Disequilibrium es_ES
dc.subject.meshMultifactorial Inheritance es_ES
dc.subject.meshPolymorphism, Single Nucleotidees_ES
dc.subject.meshRisk Factors es_ES
dc.subject.meshGenetic Predisposition to Disease es_ES
dc.subject.meshModels, Genetices_ES
dc.titleWinner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Dataes_ES
dc.typeArtículoes_ES
dc.rights.licenseAtribución-NoComercial-CompartirIgual 4.0 Internacional*
dc.identifier.pubmedID28036406es_ES
dc.format.volume12es_ES
dc.format.number12es_ES
dc.format.pagee1006493es_ES
dc.identifier.doi10.1371/journal.pgen.1006493es_ES
dc.contributor.funderNCI/NIHes_ES
dc.contributor.funderGerman Research Council (Deutsche Forschungsgemeinschaft)es_ES
dc.contributor.funderGerman Federal Ministry of Education and Researches_ES
dc.contributor.funderNational Heart, Lung, and Blood Institute, National Institutes of Health, U.S.es_ES
dc.description.peerreviewedes_ES
dc.identifier.e-issn1553-7404es_ES
dc.identifier.journalPLoS geneticses_ES
dc.repisalud.institucionCNIOes_ES
dc.repisalud.orgCNIOCNIO::Grupos de investigación::Grupo de Epidemiología Genética y Moleculares_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Atribución-NoComercial-CompartirIgual 4.0 Internacional
This item is licensed under a: Atribución-NoComercial-CompartirIgual 4.0 Internacional