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Effects of trait definition on genetic parameter estimates and sire evaluation for clinical mastitis with threshold models

Published online by Cambridge University Press:  18 August 2016

Y. M. Chang ,
D. Gianola ,
B. Heringstad  and
G. Klemetsdal
Y. M. Chang*
Affiliation:
Department of Dairy Science, University of Wisconsin, 1675 Observatory Drive Madison, WI53706, USA
D. Gianola
Affiliation:
Department of Dairy Science, University of Wisconsin, 1675 Observatory Drive Madison, WI53706, USA department of Animal and Aquacultural Sciences, Agricultural University of Norway, PO Box 5025, N-1432, As, Norway
B. Heringstad
Affiliation:
department of Animal and Aquacultural Sciences, Agricultural University of Norway, PO Box 5025, N-1432, As, Norway
G. Klemetsdal
Affiliation:
department of Animal and Aquacultural Sciences, Agricultural University of Norway, PO Box 5025, N-1432, As, Norway
*
† E-mail: chang@calshp.cals.wisc.edu
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Abstract

Clinical mastitis records on 36 178 first-lactation Norwegian dairy cattle (NRF) cows, daughters of 245 sires from 5286 herds, were analysed to study the impact of trait definition on estimates of genetic parameters and sire evaluations for clinical mastitis. The opportunity interval for infection, going from 30 days pre-calving to 300 days post partum, was divided into either 11 periods (each 30 days long); four periods ((-30, 0), (1, 30), (31, 120), (121, 300)); a single period (-30, 300) or defined as the interval currently used for sire evaluation in Norway (-15,120). Within each period, clinical mastitis was scored as 1 if it occurred at least once and 0 otherwise. Analysis was with Bayesian threshold models, assuming that mastitis (presence v. absence) was a different trait in each period. By use of multivariate or univariate normal link functions, unobserved liabilities to disease were modelled as a linear function of year of calving, age-season of calving, herd, sire of cow and residual effects. Estimates of heritability of liability to clinical mastitis ranged from 0-06 to 0-14, depending on the model and stage of lactation. In multi-period models, estimates of genetic correlations between periods were positive and ranged from 0-13 to 0-55. This suggests that clinical mastitis resistance is not the same trait in different periods of the first lactation, which is not captured by the single-interval models. The single-interval (-30, 300) model gave slightly smaller sire-specific posterior probabilities of clinical mastitis during the first lactation than the multi-period models. Furthermore, the interval used in current Norwegian sire evaluation understated the posterior probabilities of clinical mastitis, relative to the multi-period specifications. This led to some differences in sire rankings between the four models, although there was agreement between the four- and 11-period models. In conclusion, the multi-period models captured more genetic variation than the single-interval models, but the four-period model gave sire rankings that differed little from those obtained with an 11-period definition of clinical mastitis.

Keywords

clinical mastitisgenetic correlationgenetic evaluationthreshold model

Information

Type
Research Article
Information
Animal Science , Volume 79 , Issue 3 , December 2004 , pp. 355 - 363
Copyright
Copyright © British Society of Animal Science 2004

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References

Chang, Y. M., Gianola, D., Heringstad, B. and Klemetsdal, G. 2002. Correlations between clinical mastitis in different periods of first-lactation Norwegian cattle using a multivariate threshold model. In Case studies in Bayesian statistics, volume 6 (ed. Gatsonis, C., Carriquiry, A., Gelman, A., Higdon, D., Kass, R., Pauler, D. and Verdinelli, I.), pp. 177–192. Springer-Verlag, New York.CrossRefGoogle Scholar
Chang, Y. M., Gianola, D., Heringstad, B. and Klemetsdal, G. 2004. Longitudinal analysis of clinical mastitis at different stages of lactation in Norwegian cattle. Livestock Production Science 88: 251–261.Google Scholar
Chib, S. and Greenberg, E 1998. Analysis of multivariate probit models. Biometrika 85: 347–361.Google Scholar
Emanuelson, U., Danell, B. and Philipsson, J. 1988. Genetic parameters for clinical mastitis, somatic cell counts, and milk production estimated by multiple-trait restricted maximum likelihood. Journal of Dairy Science 71: 467–476.Google Scholar
Falconer, D. S. and Mackay, T. F. C. 1996. Introduction to quantitative genetics, fourth edition. Longman Group Ltd, Essex.Google Scholar
Foulley, J. L. and Gianola, D. 1984. Estimation of genetic merit from bivariate ‘all-or-none’ responses. Génétique, Sélection, Évolution 16: 285–306.CrossRefGoogle ScholarPubMed
Foulley, J. L. and Gianola, D. 1986. Sire evaluation for multiple binary responses when information is missing on some traits. Journal of Dairy Science 69: 2681–2695.Google Scholar
Foulley, J. L., Im, S., Gianola, D. and Hoeschele, I. 1987. Empirical Bayes estimation of parameters for n polygenic binary traits. Génétique, Sélection, Évolution 19: 197–204.Google Scholar
García-Cortés, L. A. and Sorensen, D. 1996. On a multivariate implementation of the Gibbs sampler. Genetics, Selection, Evolution 28: 121–126.Google Scholar
Gianola, D. and Foulley, J. L. 1983. Sire evaluation for ordered categorical data with a threshold model. Génétique, Sélection, Évolution 15: 201–224.Google Scholar
Harville, D. A. and Mee, R. E. 1984. A mixed model procedure for analyzing ordered categorical data. Biometrics 40: 393–408.Google Scholar
Heringstad, B., Chang, Y. M., Gianola, D. and Klemetsdal, G. 2003c. Genetic analysis of longitudinal trajectory of clinical mastitis in first-lactation Norwegian cattle. Journal of Dairy Science 86: 2676–2683.CrossRefGoogle ScholarPubMed
Heringstad, B., Klemetsdal, G. and Ruane, J. 1999. Clinical mastitis in Norwegian cattle: frequency, variance components, and genetic correlation with protein yield. Journal of Dairy Science 82: 1325–1330.CrossRefGoogle ScholarPubMed
Heringstad, B., Klemetsdal, G. and Ruane, J. 2000. Selection for mastitis resistance in dairy cattle — a review with focus on the situation in the Nordic countries. Livestock Production Science 64: 95–106.CrossRefGoogle Scholar
Heringstad, B., Rekaya, R., Gianola, D., Klemetsdal, G. and Weigel, K. A. 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.Google Scholar
Heringstad, B., Rekaya, R., Gianola, D., Klemetsdal, G. and Weigel, K. A. 2003a. Genetic change for clinical mastitis in Norwegian Cattle: a threshold model analysis. Journal of Dairy Science 86: 369–375.Google Scholar
Heringstad, B., Rekaya, R., Gianola, D., Klemetsdal, G. and Weigel, K. A. 2003b. Bivariate analysis of liability to clinical mastitis and to culling in first-lactation cows. Journal of Dairy Science 86: 653–660.Google Scholar
Hoeschele, I., Foulley, J. L. and Gianola, D 1986. Genetic evaluation for multiple binary responses. Génétique, Sélection, Évolution 18: 299–321.Google Scholar
Kadarmideen, H. N., Rekaya, R. and Gianola, D. 2001. Genetic parameters for clinical mastitis in Holstein-Friesians in the United Kingdom: a Bayesian analysis. Animal Science 73: 229–240.Google Scholar
Lund, M. S., Jensen, J. and Petersen, P. H. 1999. Estimation of genetic and phenotypic parameters of clinical mastitis, somatic cell production deviance and protein yield in dairy cattle using Gibbs sampling. Journal of Dairy Science 82: 1045–1051.Google Scholar
Philipsson, J., Thafvelin, B. and Hedebro-Velander, I. 1980. Genetic studies on disease recordings in first lactation cows of Swedish dairy breeds. Acta Agriculturse Scandinavica, Section A, Animal Science 30: 327–335.Google Scholar
Raftery, A. E. and Lewis, S 1992. How many iterations in the Gibbs sampler? Technical report, Department of Statistics, University of Washington.Google Scholar
Rekaya, R., Weigel, K. A. and Gianola, D. 2000. Structure of the residual covariance matrix in the analysis of longitudinal binary data. Journal of Dairy Science 83: (suppl. 1)56.Google Scholar
Shook, G. E. 1989. Selection for disease resistance. Journal of Dairy Science 72: 1349–1362.CrossRefGoogle ScholarPubMed
Simianer, H., Solbu, H. and Schaeffer, L. R. 1991. Estimated genetic correlations between disease and yield traits in dairy cattle. Journal of Dairy Science 74: 4358–4365.Google Scholar
Sorensen, D., Andersen, S., Gianola, D. and Korsgaard, I. 1995. Bayesian inference in threshold models using Gibbs sampling. Genetics, Selection, Evolution 27: 229–249.Google Scholar
Weller, J., Saran, I. and Zeliger, Y. 1992. Genetic and environmental relationships among somatic cell count, bacteria infection, and clinical mastitis. Journal of Dairy Science 75: 2532–2540.Google Scholar