Uncovering Regulatory Pathways with Expression Quantitative Trait Loci

The automated inference or prediction of protein-protein interaction networks from large-scale measurements and other genomic data has become a standard technique in systems biology. However, typically these networks only represent undirected interactions between proteins, without classifying the type and directionality of interactions. Regulatory interactions transmit signals and are activating or repressing. As a step towards more detailed understanding of such regulatory networks, we present a novel approach for the integration of expression quantitative trait loci (eQTL) data with protein-protein interaction (PPI) data. Application of this approach to a new yeast interaction network with 3,491 proteins and 16,438 interactions (covering PPI and transcriptional interactions) allows us to infer the directionality of interactions and also to identify pathways that regulate the expression of individual genes. Inferred pathways contain chains of PPI as well as transcription factor - DNA interactions. We discuss the regulation of the DNA damage related transcription factor Rpn4p as an example. This new approach facilitates eQTL as a rich data source for the unbiased inference of regulatory pathways.