Genetic analysis of somatic cell score in danish holsteins using a liability-normal mixture model

Mixture models are appealing for identifying hidden structures affecting somatic cell score (SCS) data, such as unrecorded cases of subclinical mastitis. Thus, liability-normal mixture (LNM) models were used for genetic analysis of SCS data, with the aim of predicting breeding values for such cases of mastitis. Here, putative mastitis statuses and breeding values for liability to putative mastitis were inferred solely from SCS observations. In total, there were 395,906 test-day records for SCS from 50,607 Danish Holstein cows. Four different statistical models were fitted: A) a classical (nonmixture) random regression model for test-day SCS; B1) an LNM test-day model assuming homogeneous (co)variance components for SCS from healthy (IMI-) and infected (IMI+) udders; B2) an LNM model identical to B1, but assuming heterogeneous residual variances for SCS from IMI- and IMI+ udders; and C) an LNM model assuming fully heterogeneous (co)variance components of SCS from IMI- and IMI+ udders. For the LNM models, parameters were estimated with Gibbs sampling. For model C, variance components for SCS were lower, and the corresponding heritabilities and repeatabilities were substantially greater for SCS from IMI- udders relative to SCS from IMI+ udders. Further, the genetic correlation between SCS of IMI- and SCS of IMI+ was 0.61, and heritability for liability to putative mastitis was 0.07. Models B2 and C allocated approximately 30% of SCS records to IMI+, but for model B1 this fraction was only 10%. The correlation between estimated breeding values for liability to putative mastitis based on the model (SCS for model A) and estimated breeding values for liability to clinical mastitis from the national evaluation was greatest for model B1, followed by models A, C, and B2. This may be explained by model B1 categorizing only the most extreme SCS observations as mastitic, and such cases of subclinical infections may be the most closely related to clinical (treated) mastitis.     

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.     

Detection of mastitis in dairy cattle by use of mixture models for repeated somatic cell scores: a Bayesian approach via Gibbs sampling

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 (Pm). 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 Pm 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.     

Application of a finite mixture model to somatic cell scores of Italian goats

The objectives of this study were to apply a finite mixture model (FMM) to data for somatic cell count in goats and to compare the fit of the FMM with that of a standard linear mixed effects model. Bacteriological information was used to assess the ability of the model to classify records from healthy or infected goats. Data were 4518 observations of somatic cell score (SCS) and bacterial infection from both udder halves of 310 goats from 5 herds in Northern Italy. The records were from a complete production season, and were taken monthly from February to November 2000. Explanatory factors in both models included a 3-parameter regression on days in milk (DIM); fixed class effects of herd-test-day, parity group, and udder side (left or right); and random effects of goat and udder half within goat. In addition, the 2-component FMM included a fixed mean for the second component of the model (theoretically corresponding to infected udder halves), as well as an unknown probability of membership to a given putative infection status. A Bayesian statistical approach was used for the analysis with Gibbs sampling used to obtain draws from posterior distributions of parameters of interest. Two sampling chains of 200,000 cycles each were generated for each model. The FMM yielded a much lower estimate of residual variance than the standard model (1.28 vs. 3.02 SCS²), and a slightly higher estimate for the between-goat variance (1.79 vs. 1.48). The deviance information criterion (DIC) was used to compare the fit of the 2 models. The DIC was much lower for the FMM, indicating a better fit to the data. The FMM was able to classify correctly 60 and 48% of the healthy and infected observations, respectively. This was slightly higher than what would be expected from random classification, but not high enough for useful mastitis diagnosis. Nevertheless, increased precision of genetic evaluation is the goal of applying the FMM, rather than timely and accurate mastitis diagnosis. The results suggest that more research on FMM for SCS is merited and necessary for proper application.     

Joint analysis of quantitative trait loci for clinical mastitis and somatic cell score on five chromosomes in three Nordic dairy cattle breeds

Five chromosomes were selected for joint quantitative trait loci (QTL) analyses for clinical mastitis (CM) and somatic cell score (SCS) in 3 breeds: Finnish Ayrshire (FA), Swedish Red and White (SRB), and Danish Red (DR). In total, 19 grandsires and 672 sons in FA, 19 grandsires and 499 sons in SRB, and 8 grandsires and 258 sons in DR were used in the study. These individuals were genotyped with the 61 microsatellite markers used in any of the previous QTL scans on the selected chromosomes. Within-family QTL analyses based on linear regression models were carried out for CM and SCS to identify the segregating sires for each region. On the segregating families, joint single-trait and 2-trait analyses were performed using variance components models. The analyses confirmed that QTL affecting CM or SCS, or both, segregate on Bos taurus autosomes (BTA) 9, 11, 14, and 18, whereas a QTL on BTA29 could not be confirmed. Our results indicate that there may be at least 2 linked QTL on BTA9, one that primarily affects CM and a second that primarily affects SCS. On chromosomes BTA11, 14, and 18, the joint analyses were only significant for SCS.     

Recursive relationships between milk yield and somatic cell score of Canadian Holsteins from finite mixture random regression models

Finite mixture, multiple-trait, random regression animal models with recursive links between phenotypes for milk yield and somatic cell score (SCS) on the same test-day were applied to first lactation Canadian Holstein data. All models included fixed herd-test-day effects and fixed regressions within region-age at calving-season of calving classes, and animal additive genetic and permanent environmental regressions with random coefficients. Causal links between phenotypes for milk yield and SCS were fitted separately for records from healthy cows and cows with a putative, subclinical form of mastitis. Bayesian methods via Gibbs sampling were used for the estimation of model parameters. Bayes factors indicated superiority of the model with recursive link from milk to SCS over the reciprocal recursive model and the standard multiple-trait model. Differences between models measured by other, single-trait model comparison criteria (i.e., weighted mean squared error, squared bias, and correlation between observed and expected data) were negligible. Approximately 20% of test-day records were classified as originating from cows with mastitis in recursive mixture models. The proportion of records from cows infected with mastitis was largest at the beginning of lactation. Recursive mixture models exhibited different distributions of data from healthy and infected cows in different parts of lactation. A negative effect of milk to SCS (up to −0.15 score points for every kilogram of milk for healthy cows from 5 to 45 d in milk) was estimated for both mixture components (healthy and infected) in all stages of lactation for the most plausible model. The magnitude of this effect was stronger for healthy cows than for cows infected with mastitis. Different patterns of genetic and environmental correlations between milk and SCS for healthy and infected records were revealed, due to heterogeneity of structural coefficients between mixture components. Estimated breeding values for SCS from the best fitting model for sires of infected daughters were more related to estimated breeding values for the same trait from the regular multiple-trait model than evaluations for sires of mastitis-free cows.     

Genetic change for clinical mastitis in Norwegian cattle: a threshold model analysis

Records of clinical mastitis on 1.6 million first-lactation daughters of 2,411 Norwegian Cattle sires that were progeny tested from 1978 through 1998 were analyzed with a threshold model. The main objective was to infer genetic change for the disease in the population. A Bayesian approach via Gibbs sampling was used. The model for the underlying liability had age at first calving, month x year of calving, herd x 3-year-period, and sire of the cow as explanatory variables. Posterior mean (SD) of heritability of liability to clinical mastitis was 0.066 (0.003). Genetic evaluations (posterior means) of sires both in the liability and observable scales were computed. Annual genetic change of liability to clinical mastitis for progeny tested bulls born from 1973 to 1993 was assessed. The linear regression of mean sire effect on year of birth had a posterior mean (SD) of -0.00018 (0.0004), suggesting a nearly constant genetic level for clinical mastitis. However, an analysis of sire posterior means by birth-year of daughters indicated an approximately constant genetic level in the cow population from 1976 to 1990 (-0.02%/yr), and a genetic improvement thereafter (-0.27%/yr). This reflects more emphasis on mastitis in selection of bulls in recent years. Corresponding results obtained with a standard linear model analysis were -0.01% and -0.23% per year, respectively (regression of sire predicted transmitting ability on birth-year of daughters). Genetic change seems to be slightly understated with the linear model, assuming the threshold model holds true.     

Pathogen-specific effects of quantitative trait loci affecting clinical mastitis and somatic cell count in Danish Holstein cattle

The aim of this study was to investigate whether quantitative trait loci (QTL) affecting the risk of clinical mastitis (CM) and QTL affecting somatic cell score (SCS) exhibit pathogen-specific effects on the incidence of mastitis. Bacteriological data on mastitis pathogens were used to investigate pathogen specificity of QTL affecting treatments of mastitis in first parity (CM1), second parity (CM2), and third parity (CM3), and QTL affecting SCS. The 5 most common mastitis pathogens in the Danish dairy population were analyzed: Streptococcus dysgalactiae, Escherichia coli, coagulase-negative staphylococci, Staphylococcus aureus, and Streptococcus uberis. Data were analyzed using 2 approaches: an independence test and a generalized linear mixed model. Three different data sets were used to investigate the effect of data sampling: all samples, only samples that were followed by antibiotic treatment, and samples from first-crop daughters only. The results showed with high certainty that 2 QTL affecting SCS exhibited pathogen specificity against Staph. aureus and E. coli, respectively. The latter result might be explained by a pleiotropic QTL that also affects CM2 and CM3. Less certain results were found for QTL affecting CM. A QTL affecting CM1 was found to be specific against Strep. dysgalactiae and Staph. aureus, a QTL affecting CM2 was found to be specific against E. coli, and finally a QTL affecting CM3 was found to be specific against Staph. aureus. None of the QTL analyzed was found to be specific against coagulase-negative staphylococci and Strep. uberis. Our results show that particular mastitis QTL are highly likely to exhibit pathogen-specificity. However, the results should be interpreted carefully because the results are sensitive to the sampling method and method of analysis. Field data were used in this study. These kind of data may be heavily biased because there is no standard procedure for collecting milk samples for bacteriological analysis in Denmark. Furthermore, using only the mean SCS from d 10 to 180 after parturition may lead to truncated effects of SCS-QTL when samples collected after d 180 are used. Additionally, repeated samples were used, which could boost the difference in incidence of pathogens between daughters of sires inheriting the positive and negative QTL allele, respectively. However, the magnitude of these effects in this study is unclear.     

Genetic improvement in mastitis resistance: comparison of selection criteria from cross-sectional and random regression sire models for somatic cell score

Several selection criteria for reducing incidence of mastitis were developed from a random regression sire model for test-day somatic cell score (SCS). For comparison, sire transmitting abilities were also predicted based on a cross-sectional model for lactation mean SCS. Only first-crop daughters were used in genetic evaluation of SCS, and the different selection criteria were compared based on their correlation with incidence of clinical mastitis in second-crop daughters (measured as mean daughter deviations). Selection criteria were predicted based on both complete and reduced first-crop daughter groups (261 or 65 daughters per sire, respectively). For complete daughter groups, predicted transmitting abilities at around 30 d in milk showed the best predictive ability for incidence of clinical mastitis, closely followed by average predicted transmitting abilities over the entire lactation. Both of these criteria were derived from the random regression model. These selection criteria improved accuracy of selection by approximately 2% relative to a cross-sectional model. However, for reduced daughter groups, the cross-sectional model yielded increased predictive ability compared with the selection criteria based on the random regression model. This result may be explained by the cross-sectional model being more robust, i.e., less sensitive to precision of (co)variance components estimates and effects of data structure.     

Cow, farm, and herd management factors in the dry period associated with raised somatic cell counts in early lactation

This study investigated cow characteristics, farm facilities, and herd management strategies during the dry period to examine their joint influence on somatic cell counts (SCC) in early lactation. Data from 52 commercial dairy farms throughout England and Wales were collected over a 2-yr period. For the purpose of analysis, cows were separated into those housed for the dry period (6,419 cow-dry periods) and those at pasture (7,425 cow-dry periods). Bayesian multilevel models were specified with 2 response variables: ln SCC (continuous) and SCC >199,000 cells/mL (binary), both within 30 d of calving. Cow factors associated with an increased SCC after calving were parity, an SCC >199,000 cells/mL in the 60 d before drying off, increasing milk yield 0 to 30 d before drying off, and reduced DIM after calving at the time of SCC estimation. Herd management factors associated with an increased SCC after calving included procedures at drying off, aspects of bedding management, stocking density, and method of pasture grazing. Posterior predictions were used for model assessment, and these indicated that model fit was generally good. The research demonstrated that specific dry-period management strategies have an important influence on SCC in early lactation.     

Dairy management practices associated with incidence rate of clinical mastitis in low somatic cell score herds in France

An epidemiological prospective study was carried out in French dairy herds with Holstein, Montbéliarde, or Normande cows and with low herd somatic cell scores. The objective was to identify dairy management practices associated with herd incidence rate of clinical mastitis. The studied herds were selected on a national basis, clinical cases were recorded through a standardized system, and a stable dairy management system existed. In the surveyed herds, mean milk yield was 7420 kg/cow per yr and mean milk somatic cell score was 2.04 (132,000 cells/mL). Overdispersion Poisson models were performed to investigate risk factors for mastitis incidence rate. From the final model, the herds with the following characteristics had lower incidence rates of clinical mastitis: 1) culling of cows with more than 3 cases of clinical mastitis within a lactation; 2) more than 2 person-years assigned to dairy herd management; 3) balanced concentrate in the cow basal diet. Moreover, herds with the following characteristics had higher incidence rates of clinical mastitis: 1) milking cows loose-housed in a straw yard; 2) no mastitis therapy performed when a single clot was observed in the milk; 3) clusters rinsed using water or soapy water after milking a cow with high somatic cell count; 4) 305-d milk yield >7435 kg; 5) herd located in the South region; 6) herd located in the North region; 7) cows with at least 1 nonfunctional quarter; and 8) premilking holding area with a slippery surface. The underlying mechanisms of some highlighted risk factors, such as milk production level and dietary management practices, should be investigated more thoroughly through international collaboration.     

Refined positioning of a quantitative trait locus affecting somatic cell score on chromosome 18 in the German Holstein using linkage disequilibrium

Combined linkage and linkage disequilibrium analysis (LALD) was conducted to more accurately map a previously reported quantitative trait locus (QTL) affecting somatic cell score on bovine chromosome 18. A granddaughter design consisting of 6 German Holstein grandsire families with 1,054 progeny-tested genotyped sons was used in this study. Twenty microsatellite markers, 5 single nucleotide polymorphisms, and an erythrocyte antigen marker with an average marker spacing of 1.95 cM were analyzed along a chromosomal segment of 50.80 cM. Variance components were estimated and restricted maximum likelihood test statistics were calculated at the midpoint of each marker interval. The test statistics calculated in single-QTL linkage analysis exceeded the genome-wide significance threshold at several putative QTL positions. Using LALD, we were successful in assigning a genome-wide significant QTL to a confidence interval of 10.8 cM between the markers ILSTS002 and BMS833. The QTL in this marker interval was estimated to be responsible for between 5.89 and 13.86% of the genetic variation in somatic cell score. In contrast to the single-QTL linkage analysis model, LALD analyses with a 2-QTL model confirmed the position of one QTL, but gave no conclusive evidence for the existence or position of a second QTL. Ultimately, the QTL position was narrowed down considerably compared with previous results with a refined confidence interval of less than 11 cM.     

Comparisons of milk quality on North Carolina organic and conventional dairies

The organic dairy industry is growing rapidly across the United States and has recently expanded into the southeastern states. To date, no published comparisons of milk quality exist between organic and conventional dairies in the Southeastern United States. Maintaining high milk quality is challenging in this region due to the longer periods of high heat and humidity. The objective of this observational study was to compare milk quality on organic and conventional dairies in North Carolina during the warm summer months of the year. Data were compared from 7 organically and 7 conventionally managed herds in North Carolina. To assess milk quality, milk samples were aseptically collected from each functional quarter of each cow in the milking herds at the time of sampling and linear somatic cell scores (SCS) were obtained for individual cows. A total of 4,793 quarter milk samples (2,526 conventional and 2,267 organic) were collected from 1,247 cows (652 conventional and 595 organic). Milk samples were cultured and bacterial growth was identified using protocols consistent with those of the National Mastitis Council (Verona, WI). Subclinical mastitis was defined as the presence of SCS ≥4 and also a microbiological infection in at least 1 quarter. The proportion of cows with subclinical mastitis did not differ between conventional (20.8%) and organic (23.3%) herds. No significant difference was observed between herd management types in the proportion of cows without microbiological growth in milk samples. Also, no significant differences were observed between organic and conventional herds for cow-level prevalence of Staphylococcus aureus, coagulase-negative Staphylococcus spp., Streptococcus spp., or Corynebacterium spp. Two of the organic herds had a notably higher prevalence of Corynebacterium spp. and higher SCS. Coliforms were found in 5 of 7 conventional herds and in only 1 of 7 organic herds. Mean SCS did not differ between conventional (3.3 ± 0.2) and organic (3.5 ± 0.2) herds. Despite differences in herd management, milk quality was remarkably similar between the organic and conventional dairies compared for this study.     

Enhanced clinical mastitis resistance in Holsteins with a FEZL p.Gly105(12_13) polymorphism

Mastitis is a common infectious disease of the mammary gland and a major problem in the dairy industry. We previously reported that forebrain embryonic zinc finger-like (FEZL) encoding a stretch of 12 glycines (p.Gly105[12]) instead of 13 glycines (p.Gly105[13]) is associated with a lower somatic cell score (SCS) in a family derived from Walkway Chief Mark. Here we report that the p.Gly105[12] allele is associated with a significantly decreased incidence of clinical mastitis in a large Holstein population. We genotyped the FEZL polymorphism in 918 randomly collected Holstein sires, and investigated the effect of the polymorphism on the estimated breeding value (EBV) for SCS and milk, fat, solids-not-fat, and protein yield, and on the number of cattle with clinical mastitis among daughters derived from these sires. The average EBV for SCS among sires carrying the heterozygous p.Gly105[12] was significantly lower than that among sires carrying the homozygous p.Gly105[13], whereas we found no unfavorable effects of this polymorphism on EBV for milk, fat, solids-not-fat, and protein yield. The proportion of cows with clinical mastitis derived from sires carrying heterozygous p.Gly105[12] was significantly lower than that of daughters derived from sires carrying the homozygous p.Gly105[13]. Thus, selection of sires carrying p.Gly105[12] could be beneficial in the dairy industry by reducing the incidence of mastitis.     

A zero-inflated poisson model for genetic analysis of the number of mastitis cases in Norwegian Red cows

The objective was to extend a zero-inflated Poisson (ZIP) model to account for correlated genetic effects, and to use this model to analyze the number of clinical mastitis cases in Norwegian Red cows. The ZIP model is suitable for analysis of count data containing an excess of zeros relative to what is expected from Poisson sampling. A ZIP model was developed and compared with a corresponding Poisson model. The Poisson parameter followed a hierarchical structure, and a residual term accounting for overdispersion was included. In both models, the Poisson parameter was regressed 1) on the year, month, and age at first calving; 2) on the logarithm of the number of days elapsed from calving to the end of first lactation; and c) on herd and sire effects. Herd and sire effects were assigned normal prior distributions in a Bayesian analysis, corresponding to a random effects treatment in a frequentist analysis. An analysis of residuals favored the Poisson model when there were 2 or more cases of mastitis during first lactation, with very small differences between the ZIP and Poisson models at 0 and 1 cases. However, the residual assessment was not satisfactory for either of the models. The ZIP model, on the other hand, had a better predictive ability than the corresponding Poisson model. Posterior means of the sire, herd, and residual variances in the ZIP model (log scale) were 0.09, 0.37, and 0.36, respectively, highlighting the importance of herds as a source of variation in clinical mastitis. The correlation between sire rankings from the ZIP and Poisson models was 0.98. A weaker correlation would be expected in a population with more severe inflation at zero than the present one. The estimate of the perfect state probability p was 0.32, indicating that 32% of the animals would be in the perfect state, either because they are resistant or because they were not exposed to mastitis.     

Genetic improvement of mastitis resistance: validation of somatic cell score and clinical mastitis as selection criteria

Mean daughter deviations for clinical mastitis among second-crop daughters were regressed on predicted transmitting abilities for clinical mastitis and lactation mean somatic cell score in first-crop daughters to validate the predictive ability of these traits as selection criteria for reduced incidence of clinical mastitis. A total of 321 sires had 684,897 second-crop daughters, while predicted transmitting abilities were calculated for 2159 sires, based on 495,681 records of first-crop daughters. Predictive ability, as a measure of efficiency of selection, was 23 to 43% higher for clinical mastitis than for lactation mean somatic cell score. Compared to single-trait selection, predictive ability improved 8 to 13% from utilizing information on both traits. The relative weight that should be assigned to standardized predicted transmitting abilities from univariate genetic analyses were 60 to 67% for clinical mastitis and 33 to 40% for lactation mean somatic cell score. No significant nonlinear genetic relationship between the two traits was found.     

Genetic analysis of longitudinal trajectory of clinical mastitis in first-lactation Norwegian cattle

Clinical mastitis records for 36,178 first-lactation daughters of 245 Norwegian Cattle (NRF) sires were analyzed with a Bayesian longitudinal threshold model. For each cow, the period going from 30 d before calving to 300 d after calving was divided into 11 intervals of 30 d length each. Absence or presence of clinical mastitis within each interval was scored as "0" or "1", respectively. A Bayesian threshold model consisting of a set of explanatory variables plus Legendre polynomials on time of order four was used to describe the trajectory of liability to clinical mastitis. Heritability ranged between 0.07 and 0.13 before calving, from 0.04 to 0.15 during the first 270 d after calving, and increased sharply thereafter, as a consequence of the form of the polynomial. Genetic correlations between adjacent days were close to 1, and decreased when days were further apart. Most genetic correlations were moderate to high. A measure of probability of future daughters contracting clinical mastitis during lactation was computed for each sire. A typical curve had a peak near calving followed by a decrease thereafter. The best sires had a low peak around calving and a low expected probability of mastitis among daughters throughout lactation. Expected fraction of days without mastitis was derived from the probability curves and used for ranking of sires. Rank correlations with genetic evaluations of sires obtained from cross-sectional models were high. However, sire selection was affected markedly, especially at high selection intensity. An advantage of the longitudinal model for clinical mastitis is its ability to take multiple treatments and time aspects into account.     

Associations among body condition score, body weight, somatic cell count, and clinical mastitis in seasonally calving dairy cattle

The objective of this study was to determine if an association existed among body condition score (BCS), body weight (BW), and udder health, as indicated by somatic cell score (SCS) and cases of clinical mastitis (CM). The data consisted of 2,635 lactations from Holstein-Friesian (n = 523) and Jersey (n = 374) cows in a seasonal calving pasture-based research herd between the years 1986 and 2000, inclusive. Increased BCS at calving was associated with reduced SCS in first- and second-parity cows, and greater SCS in cows of third parity or greater. This relationship persisted for most BCS traits throughout lactation. Body weight was positively associated with SCS, although the effect was greater in Jersey cows than in Holstein-Friesians. Increased BCS and BW loss in early lactation were associated with lower SCS and a reduced probability of a high test-day SCC. Body condition score was not significantly related to CM with the exception of a curvilinear relationship between the daily rate of BCS change to nadir and CM in early lactation. Several BW variables were positively associated with a greater likelihood of CM. Nevertheless, most associations with udder health lacked biological significance within the ranges of BCS and BW generally observed on-farm. Results are important in assuring the public that modern dairy systems, where cows are subjected to substantial amounts of BCS mobilization in early lactation, do not unduly compromise cow udder health.     

Test characteristics of milk amyloid A ELISA,somatic cell count,and bacteriological culture for detection of intramammary pathogens that cause subclinical mastitis

Bovine mastitis is an important disease in the dairy industry, causing economic losses as a result of withheld milk and treatment costs. Several studies have suggested milk amyloid A (MAA) as a promising biomarker in the diagnosis of mastitis. In the absence of a gold standard for diagnosis of subclinical mastitis, we estimated the diagnostic test accuracy of a commercial MAA-ELISA, somatic cell count (SCC), and bacteriological culture using Bayesian latent class modeling. We divided intramammary infections into 2 classes: those caused by major pathogens (e.g., Escherichia coli, Staphylococcus aureus, streptococci, and lacto-/enterococci) and those caused by all pathogens (major pathogens plus Corynebacterium bovis, coagulase-negative staphylococci, Bacillus spp., Streptomyces spp.). We applied the 3 diagnostic tests to all samples. Of 433 composite milk samples included in this study, 275 (63.5%) contained at least 1 colony of any bacterial species; of those, 56 contained major pathogens and 219 contained minor pathogens. The remaining 158 samples (36.5%) were sterile. We determined 2 different thresholds for the MAA-ELISA using Bayesian latent class modeling: 3.9 µg/mL to detect mastitis caused by major pathogens and 1.6 µg/mL to detect mastitis caused by all pathogens. The optimal SCC threshold for identification of subclinical mastitis was 150,000 cells/mL; this threshold led to higher specificity (Sp) than 100,000 cells/mL. Test accuracy for major-pathogen intramammary infections was as follows: SCC, sensitivity (Se) 92.6% and Sp 72.9%; MAA-ELISA, Se 81.4% and Sp 93.4%; bacteriological culture, Se 23.8% and Sp 95.2%. Test accuracy for all-pathogen intramammary infections was as follows: SCC, sensitivity 90.3% and Sp 71.8%; MAA-ELISA, Se 88.0% and Sp 65.2%; bacteriological culture, Se 83.8% and Sp 54.8%. We suggest the use of SCC and MAA-ELISA as a combined screening procedure for situations such as a Staphylococcus aureus control program. With Bayesian latent class analysis, we were able to identify a more differentiated use of the 3 diagnostic tools. The MAA-ELISA is a valuable addition to existing tools for the diagnosis of subclinical mastitis.     

Multivariate threshold model analysis of clinical mastitis in multiparous norwegian dairy cattle

A Bayesian multivariate threshold model was fitted to clinical mastitis (CM) records from 372,227 daughters of 2411 Norwegian Dairy Cattle (NRF) sires. All cases of veterinary-treated CM occurring from 30 d before first calving to culling or 300 d after third calving were included. Lactations were divided into 4 intervals: -30 to 0 d, 1 to 30 d, 31 to 120 d, and 121 to 300 d after calving. Within each interval, absence or presence of CM was scored as "0" or "1" based on the CM episodes. A 12-variate (3 lactations x 4 intervals) threshold model was used, assuming that CM was a different trait in each interval. Residuals were assumed correlated within lactation but independent between lactations. The model for liability to CM had interval-specific effects of month-year of calving, age at calving (first lactation), or calving interval (second and third lactations), herd-5-yr-period, sire of the cow, plus a residual. Posterior mean of heritability of liability to CM was 0.09 and 0.05 in the first and last intervals, respectively, and between 0.06 and 0.07 for other intervals. Posterior means of genetic correlations of liability to CM between intervals ranged from 0.24 (between intervals 1 and 12) to 0.73 (between intervals 1 and 2), suggesting interval-specific genetic control of resistance to mastitis. Residual correlations ranged from 0.08 to 0.17 for adjacent intervals, and between -0.01 and 0.03 for nonadjacent intervals. Trends of mean sire posterior means by birth year of daughters were used to assess genetic change. The 12 traits showed similar trends, with little or no genetic change from 1976 to 1986, and genetic improvement in resistance to mastitis thereafter. Annual genetic change was larger for intervals in first lactation when compared with second or third lactation. Within lactation, genetic change was larger for intervals early in lactation, and more so in the first lactation. This reflects that selection against mastitis in NRF has emphasized mainly CM in early first lactation, with favorable correlated selection responses in second and third lactations suggested.