Joint High-Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis
Type
ArticleAuthors
Bhadra, AnindyaMallick, Bani K.
KAUST Grant Number
KUS-C1-016-04Date
2013-04-22Online Publication Date
2013-04-22Print Publication Date
2013-06Permanent link to this record
http://hdl.handle.net/10754/598680
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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.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).Publisher
WileyJournal
BiometricsPubMed ID
23607608ae974a485f413a2113503eed53cd6c53
10.1111/biom.12021
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