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

Rennie, C.; Hulme, H.; Fisher, P.; Hall, L.; Agaba, M.; Noyes, H.A.; Kemp, S.J. and Brass, A. (2007) A systematic, data-driven approach to the combined analysis of microarray and QTL data. In: International Symposium on Animal Genomics for Animal Health., OIE HQ, World Organisation for Animal Health, 12 Rue de Prony, Paris France. (Unpublished)

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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.

Item Type:Conference or Workshop Item (Other)
Uncontrolled Keywords:analysis; Animal; microarray
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QH Natural history > QH301 Biology
Q Science > QH Natural history > QH426 Genetics
Q Science > QA Mathematics > QA76 Computer software
Departments, Research Centres and Related Units:Academic Faculties, Institutes and Research Centres > Faculty of Science > Department of Biological Sciences
Refereed:No
Status:Unpublished
ID Code:315
Deposited On:20 Apr 2010 14:51
Last Modified:05 Mar 2012 11:21

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