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Open Access Research article

A maximum likelihood QTL analysis reveals common genome regions controlling resistance to Salmonella colonization and carrier-state

Tran Thanh-Son1, Beaumont Catherine1, Salmon Nigel2, Fife Mark2, Kaiser Pete3, Le Bihan-Duval Elisabeth1, Vignal Alain4, Velge Philippe5 and Calenge Fanny1*

Author Affiliations

1 INRA, UR83 Recherches Avicoles, F-37083, Nouzilly, France

2 Institute for Animal Health, Compton Berkshire, RG20 7NN, UK

3 The Roslin Institute & R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK

4 INRA UR 0444 Laboratoire de Génétique Cellulaire, F-31326, Auzeville, France

5 INRA, UR 1282 IASP Infectiologie Animale et Santé Publique, F-37083, Nouzilly, France

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BMC Genomics 2012, 13:198  doi:10.1186/1471-2164-13-198

Published: 21 May 2012

Abstract

Background

The serovars Enteritidis and Typhimurium of the Gram-negative bacterium Salmonella enterica are significant causes of human food poisoning. Fowl carrying these bacteria often show no clinical disease, with detection only established post-mortem. Increased resistance to the carrier state in commercial poultry could be a way to improve food safety by reducing the spread of these bacteria in poultry flocks. Previous studies identified QTLs for both resistance to carrier state and resistance to Salmonella colonization in the same White Leghorn inbred lines. Until now, none of the QTLs identified was common to the two types of resistance. All these analyses were performed using the F2 inbred or backcross option of the QTLExpress software based on linear regression. In the present study, QTL analysis was achieved using Maximum Likelihood with QTLMap software, in order to test the effect of the QTL analysis method on QTL detection. We analyzed the same phenotypic and genotypic data as those used in previous studies, which were collected on 378 animals genotyped with 480 genome-wide SNP markers. To enrich these data, we added eleven SNP markers located within QTLs controlling resistance to colonization and we looked for potential candidate genes co-localizing with QTLs.

Results

In our case the QTL analysis method had an important impact on QTL detection. We were able to identify new genomic regions controlling resistance to carrier-state, in particular by testing the existence of two segregating QTLs. But some of the previously identified QTLs were not confirmed. Interestingly, two QTLs were detected on chromosomes 2 and 3, close to the locations of the major QTLs controlling resistance to colonization and to candidate genes involved in the immune response identified in other, independent studies.

Conclusions

Due to the lack of stability of the QTLs detected, we suggest that interesting regions for further studies are those that were identified in several independent studies, which is the case of the QTL regions on chromosomes 2 and 3, involved in resistance to both Salmonella colonization and carrier state. These observations provide evidence of common genes controlling S. Typhimurium colonization and S. Enteritidis carrier-state in chickens.