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

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 cow's 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.