Mining microarray expression data for patterns of correlated gene expression (co-expression), has made it possible to identify novel gene/gene interactions [6] and construct non-parametric models of gene transcription networks. Extending these analyses to include genotypic data allows key regulators of a gene network to be implicated by scanning for associations between DNA variation and co-regulated groups of genes [7]. Such network-based genetical genomics approaches have previously been utilized to characterize the gene expression architectures of yeast [8], mouse liver [9] and the nervous system [10]. Investigators have also used this approach to dissect a variety of mouse models for complex traits, including alcohol preference [11], susceptibility to obesity [12], type 2 diabetes [13] and tumorigenesis [14].
