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

Wei Cheng, Yu Shi, Xiang Zhang, Wei Wang

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageEnglish (US)
Article number2
JournalBMC bioinformatics
Volume16
Issue number1
DOIs
StatePublished - Jan 16 2015

Fingerprint

Quantitative Trait Loci
Single nucleotide Polymorphism
Graphical Models
Nucleotides
Polymorphism
Single Nucleotide Polymorphism
Genes
Gene
Hidden Variables
Gene expression
Gene Expression
Gaussian Model
Experimental Evaluation
Multilayer
Linear Models
Linear Model
Pathway
Regression Model
Genome
Testing

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

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Fast and robust group-wise eQTL mapping using sparse graphical models. / Cheng, Wei; Shi, Yu; Zhang, Xiang; Wang, Wei.

In: BMC bioinformatics, Vol. 16, No. 1, 2, 16.01.2015.

Research output: Contribution to journalArticle

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