Fast and robust group-wise eQTL mapping using sparse graphical models Public Deposited

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Creator
  • Cheng, Wei
    • Affiliation: College of Arts and Sciences, Department of Computer Science
  • Zhang, Xiang
    • Other Affiliation: Department of Elect. Eng. and Computer Science, Case Western Reserve University, Cleveland, USA
  • Shi, Yu
    • Other Affiliation: Computer Science at the University of Illinois at Urbana-Champaign, Urbana, USA
  • Wang, Wei
    • Other Affiliation: Department of Computer Science, University of California, Los Angeles, Los Angeles, USA
Abstract
  • 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.
Date of publication
Identifier
  • doi:10.1186/s12859-014-0421-z
Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • Cheng et al.; licensee BioMed Central.
Language
  • English
Bibliographic citation
  • BMC Bioinformatics. 2015 Jan 16;16(1):2
Publisher
  • BioMed Central
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