Co-regulatory expression quantitative trait loci mapping: method and application to endometrial cancer

We describe a method to identify the trans-acting drivers of multiple gene co-expression, which reflects the action of regulatory molecules. This method-termed co-regulatory expression quantitative trait locus (creQTL) mapping-allows for evaluation of a more focused set of phenotypes within a clear biological context than conventional eQTL mapping.Applying this method to a study of endometrial cancer revealed regulatory mechanisms supported by the literature: a creQTL between a locus upstream of STARD13/DLC2 and a group of seven IFNβ-induced genes. This suggests that the Rho-GTPase encoded by STARD13 regulates IFNβ-induced genes and the DNA damage response.Because of the importance of IFNβ in cancer, our results suggest that creQTL may provide a finer picture of gene regulation and may reveal additional molecular targets for intervention. An open source R implementation of the method is available at http://sites.google.com/site/kenkompass/ webcite.Expression Quantitative Trait Locus (eQTL) mapping searches across the genome for markers associated with individual transcripts to identify loci containing regulatory elements [1,2]. Although cis regulators are easily interpreted, assigning function to trans regulators is more difficult. At the transcriptional level, genes in trans are often co-regulated by transcription factors binding the same regulatory elements in the noncoding sequences of multiple genes [3]. When looking genome-wide, however, genes across many ontologies acting upstream of transcription factors participate in the co-regulation of genes [4,5]. Because of the difficulty in predicting the trans-regulators, the majority of transcriptional regulatory proteins remain unknown.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- a