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

Anindya Bhadra, Bani K. Mallick

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)447-457
Number of pages11
JournalBiometrics
Volume69
Issue number2
DOIs
StatePublished - Apr 22 2013
Externally publishedYes

Fingerprint

Dive into the research topics of 'Joint High-Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis'. Together they form a unique fingerprint.

Cite this