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Article: MGAS: a powerful tool for multivariate gene-based genome-wide association analysis

TitleMGAS: a powerful tool for multivariate gene-based genome-wide association analysis
Authors
Issue Date2015
PublisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
Citation
Bioinformatics, 2015, v. 31 n. 7, p. 1007-1015 How to Cite?
AbstractMotivation: Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Results: Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. Conclusion: MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. © The Author 2014.
Persistent Identifierhttp://hdl.handle.net/10722/215087
ISSN
2015 Impact Factor: 5.766
2015 SCImago Journal Rankings: 4.643
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorVan der Sluis, S-
dc.contributor.authorDolan, CV-
dc.contributor.authorLi, J-
dc.contributor.authorSong, Y-
dc.contributor.authorSham, PC-
dc.contributor.authorPosthuma, D-
dc.contributor.authorLi, MX-
dc.date.accessioned2015-08-21T12:25:52Z-
dc.date.available2015-08-21T12:25:52Z-
dc.date.issued2015-
dc.identifier.citationBioinformatics, 2015, v. 31 n. 7, p. 1007-1015-
dc.identifier.issn1367-4803-
dc.identifier.urihttp://hdl.handle.net/10722/215087-
dc.description.abstractMotivation: Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Results: Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. Conclusion: MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. © The Author 2014.-
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.titleMGAS: a powerful tool for multivariate gene-based genome-wide association analysis-
dc.typeArticle-
dc.identifier.emailSong, Y: songy@hku.hk-
dc.identifier.emailSham, PC: pcsham@hku.hk-
dc.identifier.emailLi, MX: mxli@hku.hk-
dc.identifier.authoritySong, Y=rp00488-
dc.identifier.authoritySham, PC=rp00459-
dc.identifier.authorityLi, MX=rp01722-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1093/bioinformatics/btu783-
dc.identifier.pmid25431328-
dc.identifier.pmcidPMC4382905-
dc.identifier.scopuseid_2-s2.0-84929144067-
dc.identifier.hkuros246408-
dc.identifier.volume31-
dc.identifier.issue7-
dc.identifier.spage1007-
dc.identifier.epage1015-
dc.identifier.isiWOS:000352269500005-
dc.publisher.placeUnited Kingdom-

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