Using a mixed linear animal model, genetic parameters were estimated for clinical mastitis (MAST), lactation average somatic cell score (LSCS), and milk production traits in the first 3 lactations of more than 200,000 Swedish Holstein cows with first calving from 1995 to 2000. Heritability estimates for MAST (0.01 to 0.03) were distinctly lower than those for LSCS (0.10 to 0.14) and production traits (0.23 to 0.36). The genetic correlation between MAST and LSCS was high for all lactations (mean 0.70), implying that selection for low LSCS will reduce the incidence of mastitis. Undesirable genetic relationships with production were found for both MAST and LSCS with genetic correlations ranging from 0.01 to 0.45. This emphasizes the need for including udder health traits in the breeding goal. Genetic correlations across lactations for the same trait were positive and high for both MAST (>0.7), LSCS (>0.8), and production traits (>0.9), with the strongest correlations between second and third parity for all traits (>0.9 for udder health traits and close to unity for production traits).

The objective was to study, by simulation, whether survival analysis results in a more precise genetic evaluation for mastitis in dairy cattle than cross-sectional linear models and threshold models by using observation periods for mastitis of 2 lengths (the first 150 d of lactation, and the full lactation, respectively). True breeding values for mastitis liability on the underlying scale were simulated for daughters of 400 sires (average daughter group size, 60 or 150), and the possible event of a mastitis case within lactation for each cow was created. For the linear models and the threshold models, mastitis was defined as a binary trait within either the first 150 d of lactation or the full lactation. For the survival analysis, mastitis was defined as the number of days from calving to either the first case of mastitis (uncensored record) or to the day of censoring (i.e., day of culling, lactation d 150 or day of next calving; censored record). Cows could be culled early in lactation (within 10 d after calving) for calving-related reasons or later on because of infertility. The correlation between sire true breeding values for mastitis liability and sire predicted breeding values was greater when using the full lactation data (0.76) than when using data from the first 150 d (0.70) with an average of 150 daughters per sire. The corresponding results were 0.60 and 0.53, respectively, with an average of 60 daughters per sire. Under these simulated conditions, the method used had no effect on accuracy. The higher accuracy of sire breeding values can be translated into a greater genetic gain, unless counteracted by a longer generation interval.

Clinical mastitis was analyzed with mixed linear models (LM) and survival analysis (SA) using data from the first 3 lactations of >200,000 Swedish Holstein cows having their first calving between 1995 and 2000. The model for both methods included fixed effects of year-month and age at calving, fixed regressions of proportions of heterosis and North American Holstein genes, and random effects of herd-year at calving and sire. For the LM, clinical mastitis was defined as a binary trait measured from 10 d before to 150 d after calving. For the SA, clinical mastitis was defined either as the time period from 10 d before calving to the day of first treatment or culling because of mastitis (uncensored record) or from 10 d before to the day of next calving, culling for reasons other than mastitis, movement to a new herd, or to lactation d 240 (censored record). The heritability estimates from SA (0.03 to 0.04) were higher than those obtained with the LM (0.01 to 0.03). Consequently, the accuracies of estimated transmitting abilities were also higher for the trait analyzed with SA. The difference between estimates from the 2 methods was greater for later lactations. This study reveals the potential of analyzing clinical mastitis data with SA.

A genetic analysis of longitudinal binary clinical mastitis (CM) data recorded on about 90 000 first-lactation Swedish Holstein cows was carried out using linear random regression models (RRM). This method for genetic evaluation of CM has theoretical advantages compared to the method of linear cross-sectional models (CSM), which is currently being used. The aim of this study was to investigate the feasibility and suitability of estimating genetic parameters and predicting breeding values for CM with a linear sire RRM. For validation purposes, the estimates and predictions from the RRM were compared to those from linear sire longitudinal multivariate models (LMVM) and CSM. For each cow, the period from 10 days before to 241 days after calving was divided into four 1-week intervals followed by eight 4-week intervals. Within each interval, presence or absence of CM was scored as '1' or '0'. The linear RRM used to explain the trajectory of CM over time included a set of explanatory variables plus a third-order Legendre polynomial function of time for the sire effect. The time-dependent heritabilities and genetic correlations from the chosen RRM corresponded fairly well with estimates obtained from the linear LMVM for the separate intervals. Some discrepancy between the two methods was observed, with the more unstable results being obtained from the linear LMVM. Both methods indicated clearly that CM was not genetically the same trait throughout lactation. The correlations between predicted sire breeding values from the RRM, summarized over different time periods, and from linear CSM were rather high. They were, however, less than unity (0.74 to 0.96), which indicated some re-ranking of sires. Sire curves based on the time-specific breeding values from the RRM illustrated differences in intercept and slope among the best and the worst sires. To conclude, a linear sire RRM seemed to work well for genetic evaluation purposes, but was sensitive for estimation of genetic parameters.Keywords: dairy cattle, genetic evaluation, clinical mastitis, random regression model ImplicationsLinear random regression models (RRM) were used as a new longitudinal approach for genetic evaluation of clinical mastitis data in dairy cattle. This approach has theoretical advantages compared to the method of linear cross-sectional models (CSM) currently used. The estimated genetic parameters and predicted breeding values from the RRM were in good agreement with the results from linear longitudinal multivariate and linear CSM. However, the RRM approach was sensitive to parameter estimation in the current setting.

Claw lesions are one of the most important health issues in dairy cattle. Although the frequency of claw lesions depends greatly on herd management, the frequency can be lowered through genetic selection. A genetic evaluation could be developed based on trimming records collected by claw trimmers; however, not all cows present in a herd are usually selected by the breeder to be trimmed. The objectives of this study were to investigate the importance of the preselection of cows for trimming, to account for this preselection, and to estimate genetic parameters of claw health traits. The final data set contained 25,511 trimming records of French Holstein cows. Analyzed claw lesion traits were digital dermatitis, heel horn erosion, interdigital hyperplasia, sole hemorrhage circumscribed, sole hemorrhage diffused, sole ulcer, and white line fissure. All traits were analyzed as binary traits in a multitrait linear animal model. Three scenarios were considered: including only trimmed cows in a 7-trait model (scenario 1); or trimmed cows and contemporary cows not trimmed but present at the time of a visit (considering that nontrimmed cows were healthy) in a 7-trait model (scenario 2); or trimmed cows and contemporary cows not trimmed but present at the time of a visit (considering lesion records for trimmed cows only), in an 8-trait model, including a 0/1 trimming status trait (scenario 3). For scenario 3, heritability estimates ranged from 0.02 to 0.09 on the observed scale. Genetic correlations clearly revealed 2 groups of traits (digital dermatitis, heel horn erosion, and interdigital hyperplasia on the one hand, and sole hemorrhage circumscribed, sole hemorrhage diffused, sole ulcer, and white line fissure on the other hand). Heritabilities on the underlying scale did not vary much depending on the scenario: the effect of the preselection of cows for trimming on the estimation of heritabilities appeared to be negligible. However, including untrimmed cows as healthy caused bias in the estimation of genetic correlations. The use of a trimming status trait to account for preselection appears promising, as it allows consideration of the exhaustive population of cows present at the time a trimmer visited a farm without causing bias in genetic parameters.