Inferring novel associations between SNP sets and gene sets in eQTL study using sparse graphical model

Authors:
Wei Cheng;Xiang Zhang;Yubao Wu;Xiaolin Yin;Jing Li;David Heckerman;Wei Wang
Affiliations:
University of North Carolina at Chapel Hill;Case Western Reserve University;Case Western Reserve University;Case Western Reserve University;Case Western Reserve University;Microsoft Research;University of California, Los Angeles
Venue:
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Year:
2012

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Abstract

Genome-wide expression quantitative trait loci (eQTL) studies have emerged as a powerful tool to understand the genetic basis of gene expression and complex traits. The traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may corresponds to biological pathways. In this paper, we propose a sparse (l1-regularized) graphical model, SET-eQTL, to identify novel associations between sets of SNPs and sets of genes. Such associations are captured by hidden variables connecting SNPs and genes. These hidden variables also naturally model the potential effect of unknown confounding factors. We compare three different methods on a yeast segregant dataset. Extensive experimental results demonstrate that the proposed graphical model SET-eQTL achieves better performance than the other two alternatives.