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Open Access Research article

Using transcriptome profiling to characterize QTL regions on chicken chromosome 5

Guillaume Le Mignon123, Colette Désert12, Frédérique Pitel4, Sophie Leroux4, Olivier Demeure12, Gregory Guernec5, Behnam Abasht12, Madeleine Douaire12, Pascale Le Roy12 and Sandrine Lagarrigue12*

Author Affiliations

1 INRA, UMR598, Génétique Animale, IFR140 GFAS, F-35000 Rennes, France

2 Agrocampus Ouest, UMR598, Génétique Animale, IFR140 GFAS, F-35000 Rennes, France

3 ITAVI, F-75008, Paris, France

4 INRA, UR444, Laboratoire de Génétique Cellulaire, F-31326 Auzeville, France

5 INRA, UR1012 SCRIBE, IFR140, GenOuest, 35000 Rennes, France

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BMC Genomics 2009, 10:575  doi:10.1186/1471-2164-10-575

Published: 2 December 2009

Abstract

Background

Although many QTL for various traits have been mapped in livestock, location confidence intervals remain wide that makes difficult the identification of causative mutations. The aim of this study was to test the contribution of microarray data to QTL detection in livestock species. Three different but complementary approaches are proposed to improve characterization of a chicken QTL region for abdominal fatness (AF) previously detected on chromosome 5 (GGA5).

Results

Hepatic transcriptome profiles for 45 offspring of a sire known to be heterozygous for the distal GGA5 AF QTL were obtained using a 20 K chicken oligochip. mRNA levels of 660 genes were correlated with the AF trait. The first approach was to dissect the AF phenotype by identifying animal subgroups according to their 660 transcript profiles. Linkage analysis using some of these subgroups revealed another QTL in the middle of GGA5 and increased the significance of the distal GGA5 AF QTL, thereby refining its localization. The second approach targeted the genes correlated with the AF trait and regulated by the GGA5 AF QTL region. Five of the 660 genes were considered as being controlled either by the AF QTL mutation itself or by a mutation close to it; one having a function related to lipid metabolism (HMGCS1). In addition, a QTL analysis with a multiple trait model combining this 5 gene-set and AF allowed us to refine the QTL region. The third approach was to use these 5 transcriptome profiles to predict the paternal Q versus q AF QTL mutation for each recombinant offspring and then refine the localization of the QTL from 31 cM (100 genes) at a most probable location confidence interval of 7 cM (12 genes) after determining the recombination breakpoints, an interval consistent with the reductions obtained by the two other approaches.

Conclusion

The results showed the feasibility and efficacy of the three strategies used, the first revealing a QTL undetected using the whole population, the second providing functional information about a QTL region through genes related to the trait and controlled by this region (HMGCS1), the third could drastically refine a QTL region.