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.