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This article is part of the supplement: EADGENE and SABRE Post-analyses Workshop

Open Access Research

Using microarrays to identify positional candidate genes for QTL: the case study of ACTH response in pigs

Vincent Jouffe1, Suzanne Rowe2, Laurence Liaubet3, Bart Buitenhuis4, Henrik Hornshøj4, Magali SanCristobal3, Pierre Mormède1 and DJ de Koning2*

Author Affiliations

1 Laboratoire PsyNuGen, INRA UMR1286, CNRS UMR5226, Université de Bordeaux 2, 146 rue Léo-Saignat, F-33076 Bordeaux, France

2 The Roslin Institute and R(D)SVS, University of Edinburgh, Roslin EH25 9PS, UK

3 Laboratoire de Génétique Cellulaire, INRA UMR444, F-31326 Castanet-Tolosan, France

4 Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus University, DK-8830 Tjele, Denmark

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BMC Proceedings 2009, 3(Suppl 4):S14  doi:10.1186/1753-6561-3-S4-S14

Published: 16 July 2009

Abstract

Background

Microarray studies can supplement QTL studies by suggesting potential candidate genes in the QTL regions, which by themselves are too large to provide a limited selection of candidate genes. Here we provide a case study where we explore ways to integrate QTL data and microarray data for the pig, which has only a partial genome sequence. We outline various procedures to localize differentially expressed genes on the pig genome and link this with information on published QTL. The starting point is a set of 237 differentially expressed cDNA clones in adrenal tissue from two pig breeds, before and after treatment with adrenocorticotropic hormone (ACTH).

Results

Different approaches to localize the differentially expressed (DE) genes to the pig genome showed different levels of success and a clear lack of concordance for some genes between the various approaches. For a focused analysis on 12 genes, overlapping QTL from the public domain were presented. Also, differentially expressed genes underlying QTL for ACTH response were described. Using the latest version of the draft sequence, the differentially expressed genes were mapped to the pig genome. This enabled co-location of DE genes and previously studied QTL regions, but the draft genome sequence is still incomplete and will contain many errors. A further step to explore links between DE genes and QTL at the pathway level was largely unsuccessful due to the lack of annotation of the pig genome. This could be improved by further comparative mapping analyses but this would be time consuming.

Conclusion

This paper provides a case study for the integration of QTL data and microarray data for a species with limited genome sequence information and annotation. The results illustrate the challenges that must be addressed but also provide a roadmap for future work that is applicable to other non-model species.