abstract
Characterizing co-expression networks in liver, adipose, and brain tissues in segregating mouse populations
Recent advances in gene expression microarray technology have provided
the ability to monitor transcriptional changes on a genome-wide scale,
allowing for the more general characterization of interaction networks
among gene expression traits. Here I discuss the correlation structure
among gene expression traits for a comprehensive set of genes (>20k)
in mouse liver, adipose and brain samples using oligonucleotide
microarrays. This analysis reveals interesting properties of the
gene-gene correlation structures that obtain in specific tissues in
segregating mouse populations, as well as highlighting differences
among the tissues and between the sexes. Specifically, there are a
moderate number of gene expression traits (hubs) that have a high
degree of connectivity to other gene expression traits, whereas the
majority of gene expression traits are found to have limited
connectivity to other gene expression traits. The degree of
connectivity in the context of expression quantitative trait loci
(eQTL) is also characterized, highlighting associations between highly
interconnected groups of gene and common genetic control, where the
shared genetic control enhances the ability to infer causal
associations among gene expression traits and between gene expression
and clinically relevant traits. In defining links (edges) between any
two genes if they are found to be strongly interacting (i.e., above
some absolute correlation threshold or p-value cutoff), the resulting
gene-gene interaction network is shown to exhibit scale-free network
properties and a hierarchical structure that tends to reflect common
functional properties of genes. The degree distributions of these
networks are shown to follow an inverse power law with exponents
between -1 to -1.3, while the cluster coefficient of some of the
networks decrease as an exponential function instead of a power
law. Both properties are distinct from most published biological
networks. Furthermore, we show that strongly correlated genes appear
to group into distinct sub-clusters enriched for a diversity of
functional pathways that are further enriched for eQTL hot spot
regions in the genome. Our analysis provides a simple and systematic
approach to study the functional organization of gene expression
interaction networks that can be used to functionally characterize
gene groups and identify key control points in the network associated
with complex traits such as common human diseases.