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dc.contributor.authorBhadra, Anindya
dc.contributor.authorMallick, Bani K.
dc.date.accessioned2016-02-25T13:34:20Z
dc.date.available2016-02-25T13:34:20Z
dc.date.issued2013-04-22
dc.identifier.citationBhadra A, Mallick BK (2013) Joint High-Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis. Biom 69: 447–457. Available: http://dx.doi.org/10.1111/biom.12021.
dc.identifier.issn0006-341X
dc.identifier.pmid23607608
dc.identifier.doi10.1111/biom.12021
dc.identifier.urihttp://hdl.handle.net/10754/598680
dc.description.abstractWe describe a Bayesian technique to (a) perform a sparse joint selection of significant predictor variables and significant inverse covariance matrix elements of the response variables in a high-dimensional linear Gaussian sparse seemingly unrelated regression (SSUR) setting and (b) perform an association analysis between the high-dimensional sets of predictors and responses in such a setting. To search the high-dimensional model space, where both the number of predictors and the number of possibly correlated responses can be larger than the sample size, we demonstrate that a marginalization-based collapsed Gibbs sampler, in combination with spike and slab type of priors, offers a computationally feasible and efficient solution. As an example, we apply our method to an expression quantitative trait loci (eQTL) analysis on publicly available single nucleotide polymorphism (SNP) and gene expression data for humans where the primary interest lies in finding the significant associations between the sets of SNPs and possibly correlated genetic transcripts. Our method also allows for inference on the sparse interaction network of the transcripts (response variables) after accounting for the effect of the SNPs (predictor variables). We exploit properties of Gaussian graphical models to make statements concerning conditional independence of the responses. Our method compares favorably to existing Bayesian approaches developed for this purpose. © 2013, The International Biometric Society.
dc.description.sponsorshipWe thank Biometrics co-editor Thomas A. Louis, the associate editor, and two anonymous referees for valuable suggestions. This publication is based on work supported by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
dc.publisherWiley
dc.subjectEQTL Analysis
dc.subjectGaussian graphical model
dc.subjectHyper-inverse Wishart distribution
dc.subjectJoint variable and covariance selection
dc.subjectSparse seemingly unrelated regression
dc.titleJoint High-Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis
dc.typeArticle
dc.identifier.journalBiometrics
dc.contributor.institutionDepartment of Statistics; Purdue University, West Lafayette; Indiana 47907-2066, U.S.A.
dc.contributor.institutionDepartment of Statistics; Texas A&M University, College Station; Texas 77843-3143, U.S.A.
kaust.grant.numberKUS-C1-016-04
dc.date.published-online2013-04-22
dc.date.published-print2013-06


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