Complex biological systems are best modeled as highly modular, fluid systems exhibiting a plasticity that allows them to adapt to a vast array of changing conditions. Here we highlight several novel network-based approaches to elucidate genetic networks underlying complex traits. These integrative genomic approaches combine large-scale genotypic and gene expression results in segregating mouse populations to reconstruct reliable genetic networks underlying complex traits such as disease or drug response. We apply these novel approaches to one of the most extensive surveys of gene expression studies ever undertaken in whole brain in a segregating mouse population. More than 23,000 genes were monitored in whole brain samples from more than 300 mice derived from an F2 intercross population and genotyped at over 1200 SNP markers uniformly spread over the entire genome. We explore the topological properties of the brain transcriptional network and highlight different approaches to inferring causal associations among genes by integrating genotypic and expression data. We demonstrate the utility of these approaches by identifying and experimentally validating brain gene expression traits predicted to respond to a strong expression quantitative trait locus (eQTL) for the pituitary tumor-transforming 1 gene (Pttg1) that coincides with the physical location of this gene (a cis eQTL). We identify core functional modules making up the brain transcriptional network in mice that are coherent for core biological processes associated with metabolic disease traits including obesity and diabetes.