Integrating genetic and network analysis to characterize genes related to mouse weight.
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.
Ghazalpour
Anatole
A
Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, California, USA.
Doss
Sudheer
S
Zhang
Bin
B
Wang
Susanna
S
Plaisier
Christopher
C
Castellanos
Ruth
R
Brozell
Alec
A
Schadt
Eric E
EE
Drake
Thomas A
TA
Lusis
Aldons J
AJ
Horvath
Steve
S
D000818:Animals; D001835:Body Weight / Q000235:genetics*; D002874:Chromosome Mapping / Q000379:methods*; D016000:Cluster Analysis; D003433:Crosses, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D008099:Liver / Q000378:metabolism; D051379:Mice; D008809:Mice, Inbred C3H; D008810:Mice, Inbred C57BL; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci