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


CONCLUSIONS
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.
Jouffe
Vincent
V
Laboratoire PsyNuGen, INRA UMR1286, CNRS UMR5226, Université de Bordeaux 2, 146 rue Léo-Saignat, F-33076 Bordeaux, France.
Rowe
Suzanne
S
The Roslin Institute and R(D)SVS, University of Edinburgh, Roslin EH25 9PS, UK.
Liaubet
Laurence
L
Laboratoire de Génétique Cellulaire, INRA UMR444, F-31326 Castanet-Tolosan, France.
Buitenhuis
Bart
B
Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus University, DK-8830 Tjele, Denmark.
Hornshøj
Henrik
H
Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus University, DK-8830 Tjele, Denmark.
SanCristobal
Magali
M
Laboratoire de Génétique Cellulaire, INRA UMR444, F-31326 Castanet-Tolosan, France.
Mormède
Pierre
P
Laboratoire PsyNuGen, INRA UMR1286, CNRS UMR5226, Université de Bordeaux 2, 146 rue Léo-Saignat, F-33076 Bordeaux, France.
de Koning
D J
DJ
The Roslin Institute and R(D)SVS, University of Edinburgh, Roslin EH25 9PS, UK.