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

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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@article{Ghazalpour2006IntegratingGA, title={Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight}, author={Anatole Ghazalpour and Sudheer Doss and Bin Zhang and Susanna S. Wang and Christopher Plaisier and Ruth Castellanos and Alec J Brozell and Eric E. Schadt and Thomas Drake and Aldons J. Lusis and Steve Horvath}, journal={PLoS Genetics}, year={2006}, volume={2}, pages={S103 - S109} }