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- Publisher Website: 10.1093/bioinformatics/btu783
- Scopus: eid_2-s2.0-84929144067
- PMID: 25431328
- WOS: WOS:000352269500005
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Article: MGAS: a powerful tool for multivariate gene-based genome-wide association analysis
Title | MGAS: a powerful tool for multivariate gene-based genome-wide association analysis |
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Authors | |
Issue Date | 2015 |
Publisher | Oxford 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? |
Abstract | Motivation: 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 Identifier | http://hdl.handle.net/10722/215087 |
ISSN | 2023 Impact Factor: 4.4 2023 SCImago Journal Rankings: 2.574 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Van der Sluis, S | - |
dc.contributor.author | Dolan, CV | - |
dc.contributor.author | Li, J | - |
dc.contributor.author | Song, Y | - |
dc.contributor.author | Sham, PC | - |
dc.contributor.author | Posthuma, D | - |
dc.contributor.author | Li, MX | - |
dc.date.accessioned | 2015-08-21T12:25:52Z | - |
dc.date.available | 2015-08-21T12:25:52Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Bioinformatics, 2015, v. 31 n. 7, p. 1007-1015 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | http://hdl.handle.net/10722/215087 | - |
dc.description.abstract | Motivation: 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.language | eng | - |
dc.publisher | Oxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/ | - |
dc.relation.ispartof | Bioinformatics | - |
dc.rights | Pre-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.title | MGAS: a powerful tool for multivariate gene-based genome-wide association analysis | - |
dc.type | Article | - |
dc.identifier.email | Song, Y: songy@hku.hk | - |
dc.identifier.email | Sham, PC: pcsham@hku.hk | - |
dc.identifier.email | Li, MX: mxli@hku.hk | - |
dc.identifier.authority | Song, Y=rp00488 | - |
dc.identifier.authority | Sham, PC=rp00459 | - |
dc.identifier.authority | Li, MX=rp01722 | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1093/bioinformatics/btu783 | - |
dc.identifier.pmid | 25431328 | - |
dc.identifier.pmcid | PMC4382905 | - |
dc.identifier.scopus | eid_2-s2.0-84929144067 | - |
dc.identifier.hkuros | 246408 | - |
dc.identifier.volume | 31 | - |
dc.identifier.issue | 7 | - |
dc.identifier.spage | 1007 | - |
dc.identifier.epage | 1015 | - |
dc.identifier.isi | WOS:000352269500005 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 1367-4803 | - |