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Nucleic Acids Research Advance Access published online on August 20, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm623
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Computational Biology

A systematic strategy for large-scale analysis of genotype–phenotype correlations: identification of candidate genes involved in African trypanosomiasis

Paul Fisher1,*, Cornelia Hedeler1, Katherine Wolstencroft1, Helen Hulme1, Harry Noyes2, Stephen Kemp2, Robert Stevens1 and Andrew Brass1,3

1School of Computer Science, Kilburn Building, University of Manchester, Oxford Road, Manchester, M13 9PL, 2School of Biological Sciences, Biosciences Building, University of Liverpool, Crown Street, Liverpool, L69 7ZB and 3Faculty of Life Science, Michael Smith Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK

*To whom correspondence should be addressed: Tel: +01612 750646; Email: pfisher{at}cs.manchester.ac.uk

Received June 20, 2007. Accepted July 30, 2007.

It is increasingly common to combine Microarray and Quantitative Trait Loci data to aid the search for candidate genes responsible for phenotypic variation. Workflows provide a means of systematically processing these large datasets and also represent a framework for the re-use and the explicit declaration of experimental methods. In this article, we highlight the issues facing the manual analysis of microarray and QTL data for the discovery of candidate genes underlying complex phenotypes. We show how automated approaches provide a systematic means to investigate genotype–phenotype correlations. This methodology was applied to a use case of resistance to African trypanosomiasis in the mouse. Pathways represented in the results identified Daxx as one of the candidate genes within the Tir1 QTL region. Subsequent re-sequencing in Daxx identified a deletion of an amino acid, identified in susceptible mouse strains, in the Daxx–p53 protein-binding region. This supports recent experimental evidence that apoptosis could be playing a role in the trypanosomiasis resistance phenotype. Workflows developed in this investigation, including a guide to loading and executing them with example data, are available at http://workflows.mygrid.org.uk/repository/myGrid/PaulFisher/.


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