title: Fast and robust group-wise eQTL mapping using sparse graphical models.
abstract: BACKGROUND
  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 correspond to hidden biological pathways.
  
  
  RESULTS
  We introduce a new approach to identify novel group-wise associations between sets of SNPs and sets of genes. Such associations are captured by hidden variables connecting SNPs and genes. Our model is a linear-Gaussian model and uses two types of hidden variables. One captures the set associations between SNPs and genes, and the other captures confounders. We develop an efficient optimization procedure which makes this approach suitable for large scale studies. Extensive experimental evaluations on both simulated and real datasets demonstrate that the proposed methods can effectively capture both individual and group-wise signals that cannot be identified by the state-of-the-art eQTL mapping methods.
  
  
  CONCLUSIONS
  Considering group-wise associations significantly improves the accuracy of eQTL mapping, and the successful multi-layer regression model opens a new approach to understand how multiple SNPs interact with each other to jointly affect the expression level of a group of genes.
authors.affiliation: Department of Computer Science, UNC at Chapel Hill, 201 S Columbia St., Chapel Hill, 27599, NC, USA. weicheng@cs.unc.edu.
authors.forename: Wei
authors.initials: W
authors.lastname: Cheng
authors.affiliation: Computer Science at the University of Illinois at Urbana-Champaign, 201 North Goodwin Avenue, Urbana, 61801, IL, USA. shiyu7500@gmail.com.
authors.forename: Yu
authors.initials: Y
authors.lastname: Shi
authors.affiliation: Department of Elect. Eng. and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, 44106, OH, USA. xiang.zhang@case.edu.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Computer Science, University of California, Los Angeles, 3531-G Boelter Hall, Los Angeles, 90095, CA, USA. weiwang@cs.ucla.edu.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D019295:Computational Biology; D003196:Computer Graphics; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D016681:Genome, Fungal; D008957:Models, Genetic; D040641:Quantitative Trait Loci; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics
chemical_list: D029701:Saccharomyces cerevisiae Proteins