Genetic analysis of somatic cell scores in US Holsteins with a Bayesian mixture model.

The objective of this study was to apply finite mixture models to field data for somatic cell scores (SCS) for estimation of genetic parameters. Data were approximately 170,000 test-day records for SCS from first-parity Holstein cows in Wisconsin. Five different models of increasing level of complexity were fitted. Model 1 was the standard single-component model, and the others were 2-component Gaussian mixtures consisting of similar but distinct linear models. All mixture models (i.e., 2 to 5) included separate means for the 2 components. Model 2 assumed entirely homogeneous variances for both components. Models 3 and 4 assumed heterogeneous variances for either residual (model 3) or genetic and permanent environmental variances (model 4). Model 5 was the most complex, in which variances of all random effects were allowed to vary across components. A Bayesian approach was applied and Gibbs sampling was used to obtain posterior estimates. Five chains of 205,000 cycles were generated for each model. Estimates of variance components were based on posterior means. Models were compared by use of the deviance information criterion. Based on the deviance information criterion, all mixture models were superior to the linear model for analysis of SCS. The best model was one in which genetic and PE variances were heterogeneous, but residual variances were homogeneous. The genetic analysis suggested that SCS in healthy and infected cattle are different traits, because the genetic correlation between SCS in the 2 components of 0.13 was significantly different from unity

Breed and season influence on milk quality parameters and in mastitis occurrence

The aims of the present study were to evaluate the performance of Jersey and Holstein cows under different rainfall conditions (dry and rainy seasons) by monitoring aspects related to subclinical mastitis (somatic cell count, microbiological isolation, type of isolated pathogen), milk quality (lactose, protein, fat, total solids) and production (mean milk production) of both breeds. The study was carried out in a dairy farm located in the state of São Paulo, Brazil. Eight visitations were done to the farm, four in a period of high rainfall and four in a period of low rainfall. Milk samples were collected from 79 Holstein cows and 37 Jersey cows for electronic somatic cell count and determination of the main milk components (protein, fat, total solids, lactose). Milk fat, protein, total solids and production were influenced by breed and the season, with similar tendencies for both breeds in both seasons. Somatic cell count (SCC) showed similar results for both breeds. Holstein cows with intramammary infections (IMI) presented a higher increase in SCC when compared to Jersey cows (P<0.001). In the dry season, 53 animals had IMI in at least one month during the study, which 32 were Holstein and 21 were Jersey cows. In the rainy season, 65 animals had intramammary infection, being 43 Holstein and 22 Jersey cows. The frequency of IMI cases was larger in the rainy season than in the dry season. Jersey cows had a lower chance of showing IMI signs and symptoms than Holstein cows in the rainy season (odds ratio=0.52). The larger number of IMI cases in the rainy season may have led to a lower milk lactose rate for both breeds, thus milk lactose rate can be considered an indicator of IMI status. There was prevalence of contagious pathogens overall in the study. The applied model showed that environmental pathogens were more frequently isolated from the breed Jersey, regardless of the study season. There seems to be differences in the immune response of Jersey and Holstein breeds