Identification of genetic components of gene co-regulation is important because there is compelling evidence that aberrant gene co-regulation participates in human disease [6,7]. Investigations into the genetic basis of gene co-regulation have used Bayesian networks to identify cis- and trans-acting factors controlling modules of co-regulated genes [8-10] or gene clusters. A key result here was the identification of associations that would have been missed when genes were tested individually, as in traditional eQTL. Biologically, this is very compelling because genes typically do not perform their functions in isolation but rather in coordinated groups. The existing Bayesian methods have focused primarily on the identification of yeast regulatory programs where other sources of information, such as sequence conservation, transcription factor binding site (TFBS) data, or protein interaction data are readily available and serve as prior information [8,10]. Extension of these methods to human genetics with data from HapMap subjects has shown that sequence conservation and cis-regulatory information were the most useful prior data [8].
