Genomic selection for carrier-state resistance in chicken commercial lines
© Calenge et al; licensee BioMed Central Ltd. 2011
Published: 3 June 2011
Salmonella propagation by apparently healthy chicken and subsequent food security concerns could be decreased by the selection and use of chicken lines more resistant to carrier-state. In the present study we applied the first steps of the genomic selection methodology to assess the interest of including genetic markers for the genetic evaluation of hen lines infected with Salmonella Enteritidis.
We studied commercial laying hen lines divergently selected for resistance to Salmonella carrier-state at two different ages. A total of 600 animals were typed with 1536 SNP markers and artificially infected with S. Enteritidis. Phenotypes were collected four weeks (young animals) or five weeks (adults) later. Two types of variance component analyses, including or not including SNP data, were performed and compared. All variance components were estimated by Bayesian methods and Gibbs sampling.
The comparison of both genetic analyses shows that SNP are efficient in capturing genetic variation, although none of them captures a large affect on the traits studied. Average accuracies do not change between analyses, showing that using SNP data does not really increase information.
These preliminary results show that genomic selection for Salmonella carrier-state resistance in laying hens is promising, although a denser SNP coverage of the genome on a higher number of animals is needed to assess its feasibility and efficiency compared to classical pedigree evaluation.
Like most disease resistance related traits, resistance to Salmonella carrier-state has a rather weak heritability ; in addition selection has to be conducted on siblings of infected animals. Therefore selection efficiency for this trait could probably be improved by genomic selection . Resistance to carrier state is the animal’s ability to rapidly eliminate Salmonella, once infected. The use of chicken more resistant to Salmonella carrier-state could be a way to decrease the propagation of Salmonella in poultry stocks, which could have a direct impact on food safety. The feasibility of selection for an improved resistance to carrier state has been demonstrated by a divergent selection experiment from a laying hen commercial line . In the present study we applied the first steps of the genomic selection methodology  to assess the interest of including genetic markers for the genetic evaluation of the aforementioned lines infected with Salmonella Enteritidis..
The S. Enteritidis (SE) nalidixic acid and streptomycin resistant PT 4 strain 1009 was used for all challenges. Chicks and adult hen resistance was assessed as described previously [4, 5]. Three hundreds and eighty nine chicks were orally inoculated. Caeca bacterial counts were expressed in log (cfu) per gram of caeca (trait YoungCaeca). Two hundreds and eight hens were inoculated at the peak of lay The presence/absence of Salmonella in spleen (AdultSpleen), liver (AdultLiver) and caeca (AdultCaeca) and the global contamination rate Adult0-1 (0 for no organ contaminated/ 1 for one or more organ contaminated) were considered for further analyses.
SNP markers obtention
SNP marker genotypes were obtained by the Illumina Golden Gate technology on a BeadExpress station (KOS Genetic, Italy); 194 markers were chosen to specifically cover three previously identified QTL regions on chromosomes 1, 2 and 5 [6, 7], while the remaining 1342 SNP markers were chosen to cover homogeneously the entire genome.
For each of the 5 traits, two preliminary analyses were performed. Approach PEDVC (pedigree-variance components): regular estimation of genetic parameters using the usual pedigree-based relationship matrix A. Approach COMVC (combined variance components): two random effects, one with covariance matrix A (thus based on pedigree) and the other using a combined pedigree-genomic relationship matrix H. After variance component estimation, and using a point estimate of variance components, BLUP  estimates of genetic values were computed using either the matrix A only (PEDBLUP), or including the pedigree-genomic relationship matrix H (COMBLUP) as well. To obtain only one EBV a new relationship matrix was created weighting each matrix by its associated variance component . The theoretical accuracy r was computed in both cases from the diagonal elements (PEV) of the inverse of the mixed model equations, as , where is the genetic variance. In both variance components and BLUP estimates, we used the BLUPF90 series of programs http://nce.ads.uga.edu/~ignacy/newprograms.html, with the modifications included to account for genomic relationship matrices .
SNP markers obtention
From 1536 original SNP, only 831 turned out to be polymorphic; the rest were discarded from statistical analyses. A set of 141 SNP were not in Hardy-Weinberg equilibrium (p<10-6), but they were retained in the analysis, because a selected population is not expected to behave in Hardy-Weinberg equilibrium .
Estimates of genetic parameters of analysis with pedigree only (PEDVC) or pedigree and SNP markers (COMVC)
Mean ± standard deviation of theoretical accuracies from mixed model equations using either pedigree only (PEDVC) or pedigree and SNP markers (COMVC)
Overall, the use of SNP does not change the picture of genetic evaluation. Genetic parameters and theoretical accuracies are similar. This is probably partly due to an insufficient SNP density and to a lack of phenotypic data. A denser SNP coverage and more phenotypic data are thus needed to perform a more efficient evaluation of the interest of including SNP markers for the genetic evaluation of hens infected with S. Enteritidis, which is the first step toward genomic selection.
This study was funded by the Technology Transfer project of the EADGENE (European Animal Disease Genomics Network of Excellence for Animal Health and Food safety). We warmly thank Dr Paola Mariani (Parco Tecnologico Padano, Italy) for her contribution to obtain SNP genotypes.
This article has been published as part of BMC Proceedings Volume 5 Supplement 4, 2011: Proceedings of the International Symposium on Animal Genomics for Animal Health (AGAH 2010). The full contents of the supplement are available online at http://www.biomedcentral.com/1753-6561/5?issue=S4.
- Beaumont C, Chapuis H, Protais J, Sellier N, Menanteau P, Fravalo P, Velge P: Resistance to Salmonella carrier state: selection may be efficient but response depends on animal's age. Genetics Research. 2009, 91: 161-169. 10.1017/S0016672309000135.View ArticlePubMedGoogle Scholar
- Goddard M, Hayes B: Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat Rev Genet. 2009, 10: 381-391. 10.1038/nrg2575.View ArticlePubMedGoogle Scholar
- Meuwissen T, Hayes B, Goddard M: Prediction of total genetic value using genome wide dense marker maps. Genetics. 2001, 157: 1819-1829.PubMed CentralPubMedGoogle Scholar
- Duchet-Suchaux M, Léchopier P, Marly J, Bernardet P, Delaunay R, Pardon P: Quantification of experimental Salmonella enteritidis carrier state in B13 leghorn chicks. Avian Diseases. 1995, 39: 796-803. 10.2307/1592416.View ArticlePubMedGoogle Scholar
- Protais J, Colin P, Beaumont C, Guillot J, Lantier F, Pardon P, Bennejean G: Line differences in resistance to Salmonella enteritidis PT4 infection. British Poultry Science. 1996, 37: 329-339. 10.1080/00071669608417864.View ArticlePubMedGoogle Scholar
- Calenge F, Lecerf F, Demars J, Feve K, Vignoles F, Pitel F, Vignal A, Velge P, Sellier N, Beaumont C: QTL for resistance to salmonella carrier state confirmed in both experimental and commercial chicken lines. Animal Genetics. 2009, 40: 590-597. 10.1111/j.1365-2052.2009.01884.x.View ArticlePubMedGoogle Scholar
- Tilquin P, Barrow P, Marly J, Pitel F, Plisson-Petit F, Velge P, Vignal A, Baret P, Bumstead N, Beaumont C: A genome scan for quantitative trait loci affecting the Salmonella carrier-state in the chicken. Genet Sel Evol. 2005, 37: 539-561. 10.1186/1297-9686-37-6-539.PubMed CentralView ArticlePubMedGoogle Scholar
- Legarra A, Aguilar I, Misztal I: A relationship matrix including full pedigree and genomic information. Journal of Dairy Science. 2009, 92: 4656-4663. 10.3168/jds.2009-2061.View ArticlePubMedGoogle Scholar
- Henderson C: Sire evaluation and genetic trends. Animal Breeding and Genetic Symposium in Honor of Dr Jay L Lush. 1972, Blacksburg, Va American Society of Animal Science, 10-41.Google Scholar
- Christensen O, Lund M: Genomic prediction when some animals are not genotyped. Genet Sel Evol. 2010, 42: 2-10.1186/1297-9686-42-2.PubMed CentralView ArticlePubMedGoogle Scholar
- Aguilar I, Misztal I, Johnson D, Legarra A, Tsuruta S, Lawlor T: Hot topic: a unified approach to utilize phenotypic, full pedigree and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science. 2010, 93: 743-752. 10.3168/jds.2009-2730.View ArticlePubMedGoogle Scholar
- Wiggans G, VanRaden P, Bacheller L, Tooker M, Hutchinson J, Cooper T, Sonstegard T: Selection and management of DNA markers for use in genomic evaluation. Journal of Dairy Science. 2010, 93: 2287-2292. 10.3168/jds.2009-2773.View ArticlePubMedGoogle Scholar
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