Joint High-Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis

Handle URI:
http://hdl.handle.net/10754/598680
Title:
Joint High-Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis
Authors:
Bhadra, Anindya; Mallick, Bani K.
Abstract:
We 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.
Citation:
Bhadra 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.
Publisher:
Wiley-Blackwell
Journal:
Biometrics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
22-Apr-2013
DOI:
10.1111/biom.12021
PubMed ID:
23607608
Type:
Article
ISSN:
0006-341X
Sponsors:
We 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).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorBhadra, Anindyaen
dc.contributor.authorMallick, Bani K.en
dc.date.accessioned2016-02-25T13:34:20Zen
dc.date.available2016-02-25T13:34:20Zen
dc.date.issued2013-04-22en
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.en
dc.identifier.issn0006-341Xen
dc.identifier.pmid23607608en
dc.identifier.doi10.1111/biom.12021en
dc.identifier.urihttp://hdl.handle.net/10754/598680en
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.en
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).en
dc.publisherWiley-Blackwellen
dc.subjectEQTL Analysisen
dc.subjectGaussian graphical modelen
dc.subjectHyper-inverse Wishart distributionen
dc.subjectJoint variable and covariance selectionen
dc.subjectSparse seemingly unrelated regressionen
dc.titleJoint High-Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysisen
dc.typeArticleen
dc.identifier.journalBiometricsen
dc.contributor.institutionDepartment of Statistics; Purdue University, West Lafayette; Indiana 47907-2066, U.S.A.en
dc.contributor.institutionDepartment of Statistics; Texas A&M University, College Station; Texas 77843-3143, U.S.A.en
kaust.grant.numberKUS-C1-016-04en

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