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Genetic parameters for pathogen-specific mastitis resistance in Danish Holstein Cattle

Published online by Cambridge University Press:  01 May 2009

L. P. Sørensen ,
P. Madsen ,
T. Mark  and
M. S. Lund
L. P. Sørensen*
Affiliation:
Department of Large Animal Sciences, Faculty of Life Sciences, University of Copenhagen, Grønnegårdsvej 8, DK-1870 Frederiksberg C, Denmark Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, Research Centre Foulum, PO Box 50, DK-8830 Tjele, Denmark
P. Madsen
Affiliation:
Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, Research Centre Foulum, PO Box 50, DK-8830 Tjele, Denmark
T. Mark
Affiliation:
Department of Large Animal Sciences, Faculty of Life Sciences, University of Copenhagen, Grønnegårdsvej 8, DK-1870 Frederiksberg C, Denmark
M. S. Lund
Affiliation:
Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, Research Centre Foulum, PO Box 50, DK-8830 Tjele, Denmark
*
E-mail: [email protected]
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Abstract

The objective of this study was to estimate heritabilities for and genetic correlations among different pathogen-specific mastitis traits. The traits were unspecific mastitis, which is all mastitis treatments regardless of the causative pathogen as well as mastitis caused by Streptococcus dysgalactiae, Escherichia coli, coagulase-negative staphylococci (CNS), Staphylococcus aureus and Streptococcus uberis. Also groups of pathogens were investigated, Gram-negative v. Gram-positive and contagious v. environmental pathogens. Data from 168 158 Danish Holstein cows calving first time between 1998 and 2006 were used in the analyses. Variances and covariances were estimated using uni- and bivariate threshold models via Gibbs sampling. Posterior means of heritabilities of pathogen-specific mastitis were lower than the heritability of unspecific mastitis, ranging from 0.035 to 0.076 for S. aureus and S. uberis, respectively. The heritabilities of groups of pathogen ranged from 0.053 to 0.087. Genetic correlations among the pathogen-specific mastitis traits ranged from 0.45 to 0.77. These estimates tended to be lowest for bacteria eliciting very different immune responses, which can be considered as the overall pleiotropic effect of genes affecting resistance to a specific pathogen, and highest for bacteria sharing characteristics regarding immune response. The genetic correlations between the groups of pathogens were high, 0.73 and 0.83. Results showed that the pathogen-specific traits used in this study should be considered as different traits. Genetic evaluation for pathogen-specific mastitis resistance may be beneficial despite lower heritabilities than unspecific mastitis because a pathogen-specific mastitis trait is a direct measure of an udder infection, and because the cost of a mastitis case caused by different pathogens has been shown to differ greatly. Sampling bias may be present because there were not pathogen information on all mastitis treatments and because some farms do not record pathogen information. Therefore, improved recording of pathogen information and mastitis treatments in general is critical for a successful genetic evaluation of udder health. Also, economic values have to be specified for each pathogen-specific trait separately.

Keywords

dairy cattlemastitispathogen-specificthreshold model
Type
Full Paper
Information
animal , Volume 3 , Issue 5 , May 2009 , pp. 647 - 656
Copyright
Copyright © The Animal Consortium 2008

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References

Almeida, RA, Matthews, KR, Cifrian, E, Guidry, AJ, Oliver, SP 1996. Staphylococcus aureus invasion of bovine mammary epithelial cells. Journal of Dairy Science 79, 1021–1026.CrossRefGoogle ScholarPubMed
Bannerman, DD, Paape, MJ, Lee, J-W, Zhao, X, Hope, JC, Rainard, P 2004a. E. coli and Staphylococcus aureus elicit differential innate immune response following intramammary infection. Clinical and Diagnostic Laboratory Immunology 11, 463–472.Google ScholarPubMed
Bannerman, DD, Paape, MJ, Goff, JP, Kimura, K, Lippolis, JD, Hope, JC 2004b. Innate immune response to intramammary infection with Serratia marcescens and Streptococcus uberis. Veterinary Research 35, 681–700.CrossRefGoogle ScholarPubMed
Calvinho, LF, Almeida, RA, Oliver, SP 1998. Potential virulence factors of Streptococcus dysgalactiae associated with bovine mastitis. Veterinary Microbiology 61, 93–110.CrossRefGoogle ScholarPubMed
Danish Agricultural Advisory Service 2003. Mastitishåndbogen. Retrieved September 12, 2008, from http://www.lr.dk/kvaeg/informationsserier/abonnement/mastitishandboghja.pdfGoogle Scholar
De Haas, Y, Barkema, HW, Veerkamp, RF 2002a. Genetic parameters of pathogen-specific incidence of clinical mastitis in dairy cows. Animal Science 74, 233–242.CrossRefGoogle Scholar
De Haas, Y, Barkema, HW, Veerkamp, RF 2002b. The effect of pathogen-specific clinical mastitis on the lactation curve for somatic cell count. Journal of Dairy Science 85, 1314–1323.CrossRefGoogle ScholarPubMed
Geyer, CJ 1992. Practical Markov chain Monte Carlo. Statistical Science 7, 473–511.Google Scholar
Glynn, PW, Iglehart, DL 1990. Simulation output analysis using standardized time series. Mathematics of Operations Research 15, 1–16.CrossRefGoogle Scholar
González, RL 2004. Data augmentation in the Bayesian multivariate probit model. Discussion Paper, University of Sheffield. Retrived January 23, 2008, from http://www.shef.ac.uk/content/1/c6/06/31/58/SERP2004001.pdfGoogle Scholar
Heringstad, B, Rekaya, R, Gianola, D, Klemetsdal, G, Weigel, KA 2001. Bayesian analysis of liability of clinical mastitis in Norwegian cattle with a threshold model: effects of data sampling method and model specification. Journal of Dairy Science 84, 2337–2346.CrossRefGoogle ScholarPubMed
Hillerton, JE, Berry, EA 2003. The management of environmental streptococcal mastitis. The Veterinary Clinics of North America. Food Animal Practice 19, 157–169.CrossRefGoogle Scholar
Johansson, K, Eriksson, S, Pôsô, J, Toivonen, M, Nielsen, U-S, Eriksson, J-Å, Aamand, GP 2006. Genetic evaluation of udder health traits for Denmark, Finland and Sweden. Proceedings of the 2006 Interbull Meeting, Bulletin no. 35, pp. 92–96. Interbull, Kuopio, Finland.Google Scholar
Kadarmideen, HN, Rekaya, R, Gianola, D 2001. Genetic parameters for clinical mastitis in Holstein–Frisians in the United Kingdom: a Bayesian analysis. Animal Science 73, 229–240.CrossRefGoogle Scholar
Korsgaard, IR, Lund, MS, Sorensen, D, Gianola, D, Madsen, P, Jensen, J 2003. Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling. Genetics Selection Evolution 35, 159–183.Google ScholarPubMed
Korsgaard, IR, Andersen, AH, Madsen, P, Ødegaard, J 2005. Another useful reparameterisation to obtain samples from conditional inverse Wishart distributions. In Proceedings of the European Association for Animal Production, Book of abstracts 11, Wageningen Peers, p. 193.Google Scholar
Lund, MS, Jensen, J, Petersen, PH 1999. Estimation of genetic and phenotypic parameters for clinical mastitis, somatic cell production deviance, and protein yield in dairy cattle using Gibbs sampling. Journal of Dairy Science 82, 1045–1051.Google ScholarPubMed
Madsen, P, Jensen, J 2006. A user’s guide to DMU. A package for analysing multivariate mixed models. Version 6, release 4.7. Department of Genetics and Biotechnology, Faculty of Life Sciences, University of Aarhus, Research Centre Foulum, Tjele, Denmark.Google Scholar
Martin, HL 2007. Yverbehandlinger. Retrieved January 14, 2008, from http://www.lr.dk/kvaeg/informationsserier/lk-meddelelser/yver-sdm-behand.htmGoogle Scholar
Moreno, C, Sorensen, D, García-Cortés, LA, Varona, L, Altarriba, J 1997. On biased inferences about variance components in the binary threshold model. Genetics Selection Evolution 29, 145–160.CrossRefGoogle Scholar
Nash, DL, Rogers, GW, Cooper, JB, Hargrove, GL, Keown, JF, Hansen, LB 2000. Heritability of clinical mastitis incidence and relationships with sire transmitting abilities for somatic cell score, udder type traits, productive life, and protein yield. Journal of Dairy Science 83, 2350–2360.CrossRefGoogle ScholarPubMed
Nielsen, US, Aamand, GP, Mark, T 2000. National genetic evaluation of udder health and other health traits in Denmark. In Proceedings of the 2000 Interbull Meeting. Bulletin no. 25, pp. 143–150. Interbull, Bled, Slovenia.Google Scholar
Østergaard, S, Chagunda, MGG, Friggens, NC, Bennedsgaard, TW, Klaas, IC 2005. A stochastic model simulating pathogen-specific mastitis control in a dairy herd. Journal of Dairy Science 88, 4243–4257.CrossRefGoogle Scholar
Riollet, C, Rainard, P, Poutrel, B 2000. Differential induction of complement fragment C5a and inflammatory cytokines during intramammary infections with E. coli and Staphylococcus aureus. Clinical and Diagnostic Laboratory Immunology 7, 161–167.CrossRefGoogle Scholar
Schafberg, R, Rosner, F, Swalve, HH 2006. Examinations on intramammary infections in dairy cows based on pathogen-specific data. In Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil, 4pp.Google Scholar
Sørensen, LP, Guldbrandtsen, B, Thomasen, JR, Lund, MS 2008. Pathogen-specific effects of QTL affecting clinical mastitis and somatic cell count in Danish Holstein cattle. Journal of Dairy Science 91, 2475–2480.CrossRefGoogle ScholarPubMed
Tanner, MA, Wong, WH 1987. The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association 82, 528–540.CrossRefGoogle Scholar
Wall, RJ, Powell, AM, Paape, M, Kerr, DE, Bannerman, DD, Pursel, VG, Wells, KD, Talbot, N, Hawk, HW 2005. Genetically enhanced cows resist intramammary Staphylococcus aureus infection. Nature Biotechnology 23, 445–451.CrossRefGoogle ScholarPubMed
Wright, S 1934. An analysis of variability in number of digits in an inbred strain of guinea pigs. Genetics 19, 506–536.CrossRefGoogle Scholar