TY - GEN
T1 - Inferring novel associations between SNP sets and gene sets in eQTL study using sparse graphical model
AU - Cheng, Wei
AU - Zhang, Xiang
AU - Wu, Yubao
AU - Yin, Xiaolin
AU - Li, Jing
AU - Heckerman, David
AU - Wang, Wei
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84869461927&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84869461927&partnerID=8YFLogxK
U2 - 10.1145/2382936.2382996
DO - 10.1145/2382936.2382996
M3 - Conference contribution
AN - SCOPUS:84869461927
SN - 9781450316705
T3 - 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012
SP - 466
EP - 473
BT - 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012
T2 - 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012
Y2 - 7 October 2012 through 10 October 2012
ER -