A systematic, data-driven approach to the combined analysis of microarray and QTL data

High-throughputtechnologies inevitably produce vast quantities of data. This presents challenges in terms of developing effective analysis methods, particularly where the analysis involves combining data derived from different experimental technologies. In this investigation, a systematic approach was applied to combine microarray gene expression data, quantitative trait loci (QTL) data and pathway analysis resources in order to identify functional candidate genes underlying tolerance to Trypanosoma congolense infection in cattle. We automated much of the analysis using Taverna workflows previously developed for the study of trypanotolerance in the mouse model. Pathways represented by genes within the QTL regions were identified, and this list was subsequently ranked according to which pathways were over-represented in the set of genes that were differentially expressed (over time or between tolerant N'dama and susceptible Boran breeds) at various timepoints after T. congolense infection. The genes within the QTL that played a role in the highest ranked pathways were flagged as good targets for further investigation and experimental confirmation.