HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Ødegård, J. Articles by Heringstad, B. Search for Related Content PubMed PubMed Citation Articles by Ødegård, J. Articles by Heringstad, B.

Detection of Mastitis in Dairy Cattle by Use of Mixture Models for Repeated Somatic Cell Scores: A Bayesian Approach via Gibbs Sampling

J. Ødegård^*, J. Jensen, P. Madsen, D. Gianola^,*, G. Klemetsdal^* and B. Heringstad^*

^* Department of Animal Science, Agricultural University of Norway,N-1432 Ås, Norway
Department of Animal Breeding and Genetics,Danish Institute of Agricultural Sciences,Research Centre Foulum, DK-8830 Tjele, Denmark
Department of Animal Sciences, University of Wisconsin-Madison, Madison 53706

Corresponding author: J. Ødegård; e-mail: jorgen.odegard{at}ihf.nlh.no.

The distribution of somatic cell scores could be regarded as a mixture of at least two components depending on a cow’s udder health status. A heteroscedastic two-component Bayesian normal mixture model with random effects was developed and implemented via Gibbs sampling. The model was evaluated using datasets consisting of simulated somatic cell score records. Somatic cell score was simulated as a mixture representing two alternative udder health statuses ("healthy" or "diseased"). Animals were assigned randomly to the two components according to the probability of group membership (P_m). Random effects (additive genetic and permanent environment), when included, had identical distributions across mixture components. Posterior probabilities of putative mastitis were estimated for all observations, and model adequacy was evaluated using measures of sensitivity, specificity, and posterior probability of misclassification. Fitting different residual variances in the two mixture components caused some bias in estimation of parameters. When the components were difficult to disentangle, so were their residual variances, causing bias in estimation of P_m and of location parameters of the two underlying distributions. When all variance components were identical across mixture components, the mixture model analyses returned parameter estimates essentially without bias and with a high degree of precision. Including random effects in the model increased the probability of correct classification substantially. No sizable differences in probability of correct classification were found between models in which a single cow effect (ignoring relationships) was fitted and models where this effect was split into genetic and permanent environmental components, utilizing relationship information. When genetic and permanent environmental effects were fitted, the between-replicate variance of estimates of posterior means was smaller because the model accounted for random genetic drift.

Key Words: Bayesian method • mixture model • somatic cell score • udder health

Abbreviation key: CM = clinical mastitis, IMI- = intramammary infection absent, IMI+ = intramammary infection present, PE = permanent environment, PPM = posterior probability of putative mastitis, PMC = probability of misclassification

This article has been cited by other articles:

				P. Madsen, M. M. Shariati, and J. Odegard Genetic Analysis of Somatic Cell Score in Danish Holsteins Using a Liability-Normal Mixture Model J Dairy Sci, November 1, 2008; 91(11): 4355 - 4364. [Abstract] [Full Text] [PDF]

				A. B. Samore, A. F. Groen, P. J. Boettcher, J. Jamrozik, F. Canavesi, and A. Bagnato Genetic Correlation Patterns Between Somatic Cell Score and Protein Yield in the Italian Holstein-Friesian Population J Dairy Sci, October 1, 2008; 91(10): 4013 - 4021. [Abstract] [Full Text] [PDF]

				P. J. Boettcher, D. Caraviello, and D. Gianola Genetic Analysis of Somatic Cell Scores in US Holsteins with a Bayesian Mixture Model J Dairy Sci, January 1, 2007; 90(1): 435 - 434. [Abstract] [Full Text] [PDF]

				D. Gianola, B. Heringstad, and J. Odegaard On the Quantitative Genetics of Mixture Characters Genetics, August 1, 2006; 173(4): 2247 - 2255. [Abstract] [Full Text] [PDF]

				B. Heringstad, D. Gianola, Y. M. Chang, J. Odegard, and G. Klemetsdal Genetic associations between clinical mastitis and somatic cell score in early first-lactation cows. J Dairy Sci, June 1, 2006; 89(6): 2236 - 2244. [Abstract] [Full Text] [PDF]

				J. Odegard, P. Madsen, D. Gianola, G. Klemetsdal, J. Jensen, B. Heringstad, and I. R. Korsgaard A Bayesian Threshold-Normal Mixture Model for Analysis of a Continuous Mastitis-Related Trait J Dairy Sci, July 1, 2005; 88(7): 2652 - 2659. [Abstract] [Full Text] [PDF]

				P. J. Boettcher, P. Moroni, G. Pisoni, and D. Gianola Application of a Finite Mixture Model to Somatic Cell Scores of Italian Goats J Dairy Sci, June 1, 2005; 88(6): 2209 - 2216. [Abstract] [Full Text] [PDF]

				J. Odegard, B. Heringstad, and G. Klemetsdal Short Communication: Bivariate Genetic Analysis of Clinical Mastitis and Somatic Cell Count in Norwegian Dairy Cattle J Dairy Sci, October 1, 2004; 87(10): 3515 - 3517. [Abstract] [Full Text] [PDF]