Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-11T03:23:23.225Z Has data issue: false hasContentIssue false
  • English
  • Français

Genetic parameters for clinical mastitis in Holstein-Friesians in the United Kingdom: a Bayesian analysis

Published online by Cambridge University Press:  18 August 2016

H. N. Kadarmideen ,
R. Rekaya  and
D. Gianola
H. N. Kadarmideen*
Affiliation:
Animal Breeding and Genetics Department, Animal Biology Division, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, United Kingdom
R. Rekaya
Affiliation:
Department of Animal Sciences, University of Wisconsin-Madison, 1675 Observatory Drive, Madison, WI 53706-1284, USA
D. Gianola
Affiliation:
Department of Animal Sciences, University of Wisconsin-Madison, 1675 Observatory Drive, Madison, WI 53706-1284, USA
*
†Email: h.kadarmideen@ed.sac.ac.uk
Get access

Abstract

A Bayesian threshold-liability model with Markov chain Monte Carlo techniques was used to infer genetic parameters for clinical mastitis records collected on Holstein-Friesian cows by one of the United Kingdom’s national recording schemes. Four data sets were created to investigate the effect of data sampling methods on genetic parameter estimates for first and multi-lactation cows, separately. The data sets were: (1) cows with complete first lactations only (8671 cows); (2) all cows, with first lactations whether complete or incomplete (10 967 cows); (3) cows with complete multi-lactations (32 948 records); and (4) all cows with multiple lactations whether complete or incomplete (44 268 records). A Gaussian mixed linear model with sire effects was adopted for liability. Explanatory variables included in the model varied for each data set. Analyses were conducted using Gibbs sampling and estimates were on the liability scale. Posterior means of heritability for clinical mastitis were higher for first lactations (0·11 and 0·10 for data sets 1 and 2, respectively) than for multiple lactations (0·09 and 0·07, for data sets 3 and 4, respectively). For multiple lactations, estimates of permanent environmental variance were higher for complete than incomplete lactations. Repeatability was 0·21 and 0·17 for data sets 3 and 4, respectively. This suggests the existence of effects, other than additive genetic effects, on susceptibility to mastitis that are common to all lactations. In first or multi-lactation data sets, heritability was proportionately 0·10 to 0·19 lower for data sets with all records (in which case the models had days in milk as a covariate) than for data with only complete lactation records (models without days in milk as a covariate). This suggests an effect of data sampling on genetic parameter estimates. The regression of liability on days in milk differed from zero, indicating that the probability of mastitis is higher for longer lactations, as expected. Results also indicated that a regression on days in milk should be included in a model for genetic evaluation of sires for mastitis resistance based on records in progress.

Keywords

dairy cattlegenetic parametersmastitisthreshold models
Type
Breeding and genetics
Information
Animal Science , Volume 73 , Issue 2 , October 2001 , pp. 229 - 240
Copyright
Copyright © British Society of Animal Science 2001

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Albert, J. H. and Chib, C. 1993. Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association 88: 669–679.CrossRefGoogle Scholar
Boettcher, P. J., Jairath, L. K. and Dekkers, J. C. M. 1999. Comparison of methods for genetic evaluation of sires for survival of their daughters in the first three lactations. Journal of Dairy Science 82: 1034–1044.CrossRefGoogle ScholarPubMed
Foulley, J. L. 1992. Prediction of selection response for threshold dichotomous traits. Genetics 132: 1187–1194.CrossRefGoogle ScholarPubMed
Foulley, J. L. and Gianola, D. 1996. Statistical analysis of ordered categorical data via a structural heteroskedastic threshold model. Genetics, Selection, Evolution 28: 249–273.CrossRefGoogle Scholar
Gianola, D. 1980. Genetic evaluation of animals for traits with categorical responses. Journal of Animal Science 51: 1272–1276.CrossRefGoogle ScholarPubMed
Gianola, D. 1982. Theory and analysis of threshold characters. Journal of Animal Science 56: 1079–1096.CrossRefGoogle Scholar
Gianola, D. and Foulley, J. L. 1983. Sire evaluation for ordered categorical data with a threshold model. Genetics, Selection, Evolution 15: 201–224.CrossRefGoogle ScholarPubMed
Heringstad, B., Klemetsdal, G. and Ruane, J. 1999a. 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., Rekaya, R., Gianola, D., Klemetsdal, G. and Weigel, K. A. 1999b. Bayesian analysis of heritability of liability to clinical mastitis in Norwegian cattle with a threshold model. Proceedings of the 50th annual meeting of the European Association for Animal Production, Zurich, Switzerland, August 22–26.Google Scholar
Heuer, C., Schukken, Y. H. and Dobbelaar, P. 1999. Postpartum body condition score and results from the first test day milk as predictors of disease, fertility, yield and culling in commercial dairy herds. Journal of Dairy Science 82: 295–304.CrossRefGoogle ScholarPubMed
Janss, L. L. G. and Foulley, J. L. 1993. Bivariate analysis for one continuous and one threshold dichotomous trait with unequal design matrices and an application to birth weight and calving difficulty. Livestock Production Science 33: 183–198.CrossRefGoogle Scholar
Kadarmideen, H. N., Janss, L. L. G. and Dekkers, J. C. M. 2000a. Power of QTL mapping for polygenic binary traits using generalised and regression interval mapping in multi-family half-sib designs. Genetical Research 76: 305–317.CrossRefGoogle ScholarPubMed
Kadarmideen, H. N. and Pryce, J. E. 2001. Genetic and economic relationships between somatic cell count and clinical mastitis and their use in selection for mastitis resistance in dairy cattle. Animal Science 73: 19–28.CrossRefGoogle Scholar
Kadarmideen, H. N., Rekaya, R. and Gianola, D. 2000b. Genetic analysis of repeated mastitis episodes with Bayesian longitudinal threshold models. Proceedings of the 51st annual meeting of the European Association for Animal Production, August 21–24, 2000, The Hague, The Netherlands.Google Scholar
Kadarmideen, H. N., Thompson, R. and Simm, G. 2000c. Linear and threshold model genetic parameters for disease, fertility and milk production in dairy cattle. Animal Science 71: 411–419.CrossRefGoogle Scholar
Lund, M. S., Jensen, J. and Petersen, P. H. 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.CrossRefGoogle ScholarPubMed
Pryce, J. E., Veerkamp, R. F., Thompson, R., Hill, W. G. and Simm, G. 1997. Genetic aspects of common health disorders and measures of fertility in Holstein Friesian dairy cattle. Animal Science 65: 353–360.CrossRefGoogle Scholar
Quaas, R. L., Zhao, Y. and Pollak, E. J. 1988. Describing interactions in dystocia scores with a threshold model. Journal of Animal Science 66: 396–399.CrossRefGoogle ScholarPubMed
Raftery, A. L. and Lewis, S. 1992. How many iterations in the Gibbs sampler? In Bayesian statistics 4 (ed. Bernando, J. M., Berger, J. O., David, A. P. and Smith, A.F. M.), pp. 763–774. Oxford University Press. CrossRefGoogle Scholar
Robertson, A. and Lerner, I. M. 1949. The heritability of all-or-none traits: liability of poultry. Genetics 34: 395–411.CrossRefGoogle Scholar
Rodriguez-Zas, S.L, Gianola, D. and Shook, G. E. 1997. Factors affecting susceptibility to intramammary infection and mastitis: an approximate Bayesian analysis. Journal of Dairy Science 80: 75–85.CrossRefGoogle ScholarPubMed
Rodriguez-Zas, S.L, Gianola, D. and Shook, G. E. 1998. Bayesian analysis via Gibbs sampling of susceptibility to intramammary infection in Holstein cattle. Journal of Dairy Science 81: 2710–2722.CrossRefGoogle ScholarPubMed
Rupp, R. and Boichard, D. 1999. Genetic parameters for CM, somatic cell score, production, udder type traits, and milking ease in first lactation Holsteins. Journal of Dairy Science 82: 2198–2204.CrossRefGoogle Scholar
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: 4359–4365.CrossRefGoogle ScholarPubMed
Sorensen, D. A., Andersen, S., Gianola, D. and Korsgaard, I. 1995. Bayesian inference in threshold models using Gibbs sampling. Genetics, Selection, Evolution 27: 229–249.CrossRefGoogle Scholar
Thompson, R. 1979. Sire evaluation. Biometrics 35: 339–353.CrossRefGoogle Scholar
Uribe, H. A., Kennedy, B. W., Martin, S. W. and Kelton, D. F. 1995. Genetic parameters of common health disorders of Holsteins. Journal of Dairy Science 78: 421–430.CrossRefGoogle Scholar
Varona, L., Misztal, I. and Bertrand, J. K. 1999. Threshold-linear versus linear-linear analysis of birth weight and calving ease using an animal model. I. Variance component estimation. Journal of Animal Science 77: 1994–2002.CrossRefGoogle ScholarPubMed
Wang, C. S., Quaas, R. L. and Pollak, E. J. 1997. Bayesian analysis of calving ease scores and birth weights. Genetics, Selection, Evolution 29: 117–143.CrossRefGoogle Scholar
Wright, S. 1934. An analysis of variability in number of digits in an inbred strain of guinea pigs. Genetics 19: 506–536.CrossRefGoogle Scholar