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dc.contributor.authorLi, Huaen_US
dc.contributor.authorGao, Guiminen_US
dc.contributor.authorLi, Jianen_US
dc.contributor.authorPage, Grier Pen_US
dc.contributor.authorZhang, Kuien_US
dc.date.accessioned2012-08-24T12:07:34Z
dc.date.available2012-08-24T12:07:34Z
dc.date.issued2007-12-18
dc.identifier.citationBMC Proceedings. 2007 Dec 18;1(Suppl 1):S67en_US
dc.identifier.urihttp://hdl.handle.net/10919/18910
dc.description.abstractIt is believed that epistatic interactions among loci contribute to variations in quantitative traits. Several methods are available to detect epistasis using population-based data. However, methods to characterize epistasis for quantitative traits in family-based association analysis are not well developed, especially for studying thousands of gene expression traits. Here, we proposed a linear mixed-model approach to detect epistasis for quantitative traits using family data. The proposed method was implemented in a widely used software program SOLAR. We evaluated the power of the method by simulation studies and applied this method to the analysis of the Centre d'Etude du Polymorphisme Humain family gene expression data provided by Genetics Analysis Workshop 15 (GAW15).en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.rightsCreative Commons Attribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.titleDetecting epistatic interactions contributing to human gene expression using the CEPH family dataen_US
dc.typeArticle - Refereed
dc.date.updated2012-08-24T12:07:34Z
dc.description.versionPeer Reviewed
dc.rights.holderHua Li et al.; licensee BioMed Central Ltd.en_US
dc.title.serialBMC Proceedings
dc.identifier.doihttps://doi.org/10.1186/1753-6561-1-S1-S67
dc.type.dcmitypeText


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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International