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dc.contributor.authorChagnon, Miguel
dc.contributor.authorO’Loughlin, Jennifer
dc.contributor.authorEngert, James C.
dc.contributor.authorKarp, Igor
dc.contributor.authorSylvestre, Marie-Pierre
dc.date.accessioned2022-10-17T14:18:55Z
dc.date.availableNO_RESTRICTIONfr
dc.date.available2022-10-17T14:18:55Z
dc.date.issued2018-07-19
dc.identifier.urihttp://hdl.handle.net/1866/26785
dc.publisherPublic Library of Sciencefr
dc.rightsCe document est mis à disposition selon les termes de la Licence Creative Commons Paternité 4.0 International. / This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.fr
dc.titleMissing single nucleotide polymorphisms in Genetic Risk Scores : a simulation studyfr
dc.typeArticlefr
dc.contributor.affiliationUniversité de Montréal. École de santé publique. Département de médecine sociale et préventivefr
dc.identifier.doi10.1371/journal.pone.0200630
dcterms.abstractUsing a genetic risk score (GRS) to predict a phenotype in a target sample can be complicated by missing data on the single nucleotide polymorphisms (SNPs) that comprise the GRS. This is usually addressed by imputation, omission of the SNPs or by replacing the missing SNPs with proxy SNPs. To assess the impact of the omission and proxy approaches on effect size estimation and predictive ability of weighted and unweighted GRS with small numbers of SNPs, we simulated a dichotomous phenotype conditional on real genotype data. We considered scenarios in which the proportion of missing SNPs ranged from 20–70%. We assessed the impact of omitting or replacing missing SNPs on the association between the GRS and phenotype, the corresponding statistical power and the area under the receiver operating curve. Omission resulted in a larger bias towards the null value of the effect size, a smaller predictive ability and greater loss of statistical power than proxy approaches. The predictive ability of a weighted GRS that includes SNPs with large weights depends of the availability of these large-weight SNPs.fr
dcterms.isPartOfurn:ISSN:1932-6203fr
dcterms.languageengfr
UdeM.ReferenceFournieParDeposantChagnon M, O'Loughlin J, Engert JC, Karp I, and Sylvestre MP, Missing Single Nucleotide Polymorphisms in Genetic Risk Scores: A Simulation Study. PLoS One, 2018. 13(7): p. e0200630.fr
UdeM.VersionRioxxVersion publiée / Version of Recordfr
oaire.citationTitlePLoS ONEfr
oaire.citationVolume13fr
oaire.citationIssue7fr


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Ce document est mis à disposition selon les termes de la Licence Creative Commons Paternité 4.0 International. / This work is licensed under a Creative Commons Attribution 4.0 International License.
Usage rights : Ce document est mis à disposition selon les termes de la Licence Creative Commons Paternité 4.0 International. / This work is licensed under a Creative Commons Attribution 4.0 International License.