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

Abstract

High throughput technologies 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, we applied a systematic approach to combine microarray gene expression data, QTL data and pathway analysis resources in order to identify functional candidate genes underlying tolerance of Trypanosoma congolense infection in cattle (see Agaba et al poster at this conference). We automated much of the analysis using Taverna workflows previously developed for the study of trypanotolerance in the mouse model. We identified pathways represented by genes within the QTL regions, and subsequently ranked this list 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 strong candidates for experimental confirmation.