The objectives of this study were to estimate heritabilities of, and genetic correlations among, clinical mastitis (CM), subclinical mastitis (SCM), and alternative somatic cell count (SCC) traits in the first 3 lactations of Swedish Holstein cows, and to estimate genetic correlations for the alternative traits across lactations. Data from cows having their first calving between 2002 and 2009 were used. The alternative SCC traits were based on information on CM and monthly test-day (TD) records of SCC traits of 178,613, 116,079, and 64,474 lactations in first, second, or third parity, respectively. Sires had an average of 230, 165, or 124 daughters in the data (parities 1, 2, or 3, respectively). Subclinical mastitis was defined as the number of periods with an SCC >150,000 cell/mL and without a treatment for CM. Average TD SCC between 5 and 150 d was used as a reference trait. The alternative SCC traits analyzed were 1) presence of at least 1 TD SCC between 41,000 and 80,000 cell/mL (TD41-80), 2) at least 1 TD SCC >500,000 cells/mL, 3) standard deviation of log SCC over the lactation, 4) number of infection peaks, and 5) average days diseased per peak. The same variables in different parities were treated as distinct traits. The statistical model considered the effects of herd-year, year, month, age at calving, animal, and residual. Heritability estimates were 0.07 to 0.08 for CM, 0.12 to 0.17 for SCM, and 0.14 for SCC150. For the alternative traits, heritability estimates were 0.12 to 0.17 for standard deviation of log SCC, TD SCC >500,000 cells/mL, and average days diseased per peak, and 0.06 to 0.10 for TD41-80 and number of infection peaks. Genetic correlations between CM with SCM were 0.62 to 0.74, and correlations for these traits with the alternative SCC traits were positive and very high (0.67 to 0.82 for CM, and 0.94 to 0.99 for SCM). Trait TD41-80 was the only alternative trait that showed negative, favorable, genetic correlations with CM (-0.22 to -0.50) and SCM (-0.48 to -0.85) because it is associated with healthy cows. Genetic correlations among the alternative traits in all 3 parities were high (0.93 to 0.99, 0.92 to 0.98, and 0.78 to 0.99, respectively). The only exception was TD41-80, which showed moderate to strong negative correlations with the rest of the traits. Genetic correlations of the same trait across parities were in general positive and very high (0.83 to 0.99). In conclusion, these alternative SCC traits could be used in practical breeding programs aiming to improve udder health in dairy cattle.

Calving records (n = 6,763) obtained from first, second, and third parities of 3,442 spring-calving, Uruguayan Aberdeen Angus cows were used to estimate heritabilities and genetic correlations for the linear trait calving day (CD) and the binary trait calving success (CS), using models that considered CD and CS at 3 calving opportunities as separate traits. Three approaches were defined to handle the CD observations on animals that failed to calve: 1) the cows were assigned a penalty value of 21 d beyond the last observed CD record within contemporary group (PEN); 2) the censored CD values were randomly obtained from a truncated normal distribution (CEN); and 3) the CD records were treated as missing, and the parameters were estimated in a joint threshold-linear analysis including CS traits (TLMISS). The models included the effects of contemporary group (herd x year of calving x mating management), age at calving (3 levels), physiological status at mating (nonlactating or lactating), animal additive genetic effects, and residual. Estimates of heritability for CD traits in the PEN and CEN data sets ranged from 0.20 to 0.31, with greater values in the first calving opportunity. Genetic correlations were positive and medium to high in magnitude, 0.57 to 0.59 in the PEN data set and 0.38 to 0.91 in the CEN data set. In the TLMISS data set, heritabilities ranged from 0.19 to 0.23 for CD and 0.37 to 0.42 for CS. Genetic correlations between CD traits varied between 0.82 and 0.88; between CS traits, genetic correlations varied between 0.56 and 0.80. Negative (genetically favorable), medium to high genetic correlations (-0.54 to -0.91) were estimated between CD and CS traits, suggesting that CD could be used as an indicator trait for CS. Data recording must improve in quality for practical applications in genetic evaluation for fertility traits.

The objectives of this study were (1) to explore traits that better capture weekly or monthly changes in somatic cell counts (SCC) than does the commonly used lactation-average SCC, (2) to estimate their heritabilities and relationships to clinical mastitis (CM), and (3) to determine if these traits are feasible for use in monthly testing schemes. Clinical mastitis and weekly test-day (TD) records of SCC and milk production traits from 1,006 lactations of Swedish Red and Holstein cows collected from 1989 to 2004 were used (data set W). A data subset was also created to mimic monthly recording (data set M, 980 lactations). Twenty SCC traits were defined, taking into account SCC general levels and variation along the lactation curve, time and level of infection, and time of recovery. To reduce dimensionality, cluster and stepwise logistic regression procedures were applied. In data set W, 3 traits, "standard deviation of SCC over the lactation," a discrete (0/1) indicator of "at least one TD with SCC >500,000 cells/mL", and "number of days sick in the widest SCC peak" (DWidest) were the variables kept both with cluster procedures and a stepwise logistic regression with the logit of CM as dependent variable. In data set M, DWidest was replaced by "number of SCC peaks" and "average number of days sick per peak" (ADSick). Lactation-average SCC (in the first 150 d or between 150 and 305 d) did not enter into the logistic regression. Heritability estimates obtained for these new traits under a Bayesian setting and a Gibbs sampling approach were 10 to 16% (except for ADSick: 5%). Heritabilities were at least as high in the monthly data set as in the weekly data set. Thus, these SCC traits seem promising for use in breeding programs based on monthly milk recording.

Background Many methods for the genetic analysis of mastitis use a cross-sectional approach, which omits information on, e.g., repeated mastitis cases during lactation, somatic cell count fluctuations, and recovery process. Acknowledging the dynamic behavior of mastitis during lactation and taking into account that there is more than one binary response variable to consider, can enhance the genetic evaluation of mastitis. Methods Genetic evaluation of mastitis was carried out by modeling the dynamic nature of somatic cell count (SCC) within the lactation. The SCC patterns were captured by modeling transition probabilities between assumed states of mastitis and non-mastitis. A widely dispersed SCC pattern generates high transition probabilities between states and vice versa. This method can model transitions to and from states of infection simultaneously, i.e. both the mastitis liability and the recovery process are considered. A multilevel discrete time survival model was applied to estimate breeding values on simulated data with different dataset sizes, mastitis frequencies, and genetic correlations. Results Correlations between estimated and simulated breeding values showed that the estimated accuracies for mastitis liability were similar to those from previously tested methods that used data of confirmed mastitis cases, while our results were based on SCC as an indicator of mastitis. In addition, unlike the other methods, our method also generates breeding values for the recovery process. Conclusions The developed method provides an effective tool for the genetic evaluation of mastitis when considering the whole disease course and will contribute to improving the genetic evaluation of udder health.