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dc.contributor.authorChen, Sui-Pien_US
dc.contributor.authorHuang, Guan-Huaen_US
dc.date.accessioned2014-12-08T15:36:21Z-
dc.date.available2014-12-08T15:36:21Z-
dc.date.issued2014-06-01en_US
dc.identifier.issn2194-6302en_US
dc.identifier.urihttp://dx.doi.org/10.1515/sagmb-2012-0074en_US
dc.identifier.urihttp://hdl.handle.net/11536/24695-
dc.description.abstractThis paper uses a Bayesian formulation of a clustering procedure to identify gene-gene interactions under case-control studies, called the Algorithm via Bayesian Clustering to Detect Epistasis (ABCDE). The ABCDE uses Dirichlet process mixtures to model SNP marker partitions, and uses the Gibbs weighted Chinese restaurant sampling to simulate posterior distributions of these partitions. Unlike the representative Bayesian epistasis detection algorithm BEAM, which partitions markers into three groups, the ABCDE can be evaluated at any given partition, regardless of the number of groups. This study also develops permutation tests to validate the disease association for SNP subsets identified by the ABCDE, which can yield results that are more robust to model specification and prior assumptions. This study examines the performance of the ABCDE and compares it with the BEAM using various simulated data and a schizophrenia SNP dataset.en_US
dc.language.isoen_USen_US
dc.subjectDirichlet process mixturesen_US
dc.subjectepistasisen_US
dc.subjectpermutation testen_US
dc.subjectstochastic searchen_US
dc.titleA Bayesian clustering approach for detecting gene-gene interactions in high-dimensional genotype dataen_US
dc.typeArticleen_US
dc.identifier.doi10.1515/sagmb-2012-0074en_US
dc.identifier.journalSTATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGYen_US
dc.citation.volume13en_US
dc.citation.issue3en_US
dc.citation.spage275en_US
dc.citation.epage297en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:000337155900002-
dc.citation.woscount0-
Appears in Collections:Articles