Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight

Anatole Ghazalpour(University of California, Los Angeles), Sudheer Doss(University of California, Los Angeles), Bin Zhang(University of California, Los Angeles), Susanna Wang(University of California, Los Angeles), Christopher Plaisier(University of California, Los Angeles), Ruth Castellanos(University of California, Los Angeles), Alec Brozell(University of California, Los Angeles), Eric E. Schadt(Rosetta Stone (United States)), Thomas A. Drake(University of California, Los Angeles), Aldons J. Lusis(University of California, Los Angeles), Steve Horvath(University of California, Los Angeles)
PLoS Genetics
August 15, 2006
10.1371/journal.pgen.0020130
Cited by 498Open Access
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Abstract

Systems biology approaches that are based on the genetics of gene expression have been fruitful in identifying genetic regulatory loci related to complex traits. We use microarray and genetic marker data from an F2 mouse intercross to examine the large-scale organization of the gene co-expression network in liver, and annotate several gene modules in terms of 22 physiological traits. We identify chromosomal loci (referred to as module quantitative trait loci, mQTL) that perturb the modules and describe a novel approach that integrates network properties with genetic marker information to model gene/trait relationships. Specifically, using the mQTL and the intramodular connectivity of a body weight-related module, we describe which factors determine the relationship between gene expression profiles and weight. Our approach results in the identification of genetic targets that influence gene modules (pathways) that are related to the clinical phenotypes of interest.

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