Relationships between milk yield and somatic cell score in Canadian Holsteins from simultaneous and recursive random regression models

Multiple-trait random regression animal models with simultaneous and recursive links between phenotypes for milk yield and somatic cell score (SCS) on the same test day were fitted to 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. Regressions were Legendre polynomials of order 4 on a scale from 5 to 305 d in milk (DIM). Bayesian methods via Gibbs sampling were used for the estimation of model parameters. Heterogeneity of structural coefficients was modeled across (the first 3 lactations) and within (4 DIM intervals) lactation. Model comparisons in terms of Bayes factors indicated the superiority of simultaneous models over the standard multiple-trait model and recursive parameterizations. A moderate heterogeneous (both across- and within-lactation) negative effect of SCS on milk yield (from −0.36 for 116 to 265 DIM in lactation 1 to −0.81 for 5 to 45 DIM in lactation 3) and a smaller positive reciprocal effect of SCS on milk yield (from 0.007 for 5 to 45 DIM in lactation 2 to 0.023 for 46 to 115 DIM in lactation 3) were estimated in the most plausible specification. No noticeable differences among models were detected for genetic and environmental variances and genetic parameters for the first 2 regression coefficients. The curves of genetic and permanent environmental variances, heritabilities, and genetic and phenotypic correlations between milk yield and SCS on a daily basis were different for different models. Rankings of bulls and cows for 305-d milk yield, average daily SCS, and milk lactation persistency remained the same among models. No apparent benefits are expected from fitting causal phenotypic relationships between milk yield and SCS on the same test day in the random regression test-day model for genetic evaluation purposes.     

Genetic relationships of clinical mastitis,cystic ovaries,and lameness with milk yield and somatic cell score in first-lactation Canadian Holsteins

The objective of this study was to investigate the genetic relationships of the 3 most frequently reported dairy cattle diseases (clinical mastitis, cystic ovaries, and lameness) with test-day milk yield and somatic cell score (SCS) in first-lactation Canadian Holstein cows using random regression models. Health data recorded by producers were available from the National Dairy Cattle Health System in Canada. Disease traits were defined as binary traits (0 = healthy, 1 = affected) based on whether or not the cow had at least one disease case recorded within 305 d after calving. Mean frequencies of clinical mastitis, cystic ovaries, and lameness were 12.7, 8.2, and 9.1%, respectively. For genetic analyses, a Bayesian approach using Gibbs sampling was applied. Bivariate linear sire random regression model analyses were carried out between each of the 3 disease traits and test-day milk yield or SCS. Random regressions on second-degree Legendre polynomials were used to model the daily sire additive genetic and cow effects on test-day milk yield and SCS, whereas only the intercept term was fitted for disease traits. Estimated heritabilities were 0.03, 0.03, and 0.02 for clinical mastitis, cystic ovaries, and lameness, respectively. Average heritabilities for milk yield were between 0.41 and 0.49. Average heritabilities for SCS ranged from 0.10 to 0.12. The average genetic correlations between daily milk yield and clinical mastitis, cystic ovaries, and lameness were 0.40, 0.26, and 0.23, respectively; however, the last estimate was not statistically different from zero. Cows with a high genetic merit for milk yield during the lactation were more susceptible to clinical mastitis and cystic ovaries. Estimates of genetic correlations between daily milk yield and clinical mastitis were moderate throughout the lactation. The genetic correlations between daily milk yield and cystic ovaries were near zero at the beginning of lactation and were highest at mid and end lactation. The average genetic correlation between daily SCS and clinical mastitis was 0.59 and was consistent throughout the lactation. The average genetic correlation between daily SCS and cystic ovaries was near zero (−0.01), whereas a moderate, but nonsignificant, correlation of 0.27 was observed between SCS and lameness. Unfavorable genetic associations between milk yield and diseases imply that production and health traits should be considered simultaneously in genetic selection.     

Relationship of test-day somatic cell score with test-day and lactation milk yields

To determine the relationship of test-day (TD) somatic cell score (SCS) to TD and lactation milk yields, 1,320,590 records from Holstein first and second calvings from 1995 through 2002 were examined. All lactations had recorded yield and SCS for at least the first 4 TD. Least square analyses were conducted for yields on TD 2 through 10 within herd and cow. The model included regressions on current TD SCS and mean SCS of all previous TD with separate estimates by parity; effects for parity and calving year were included as well as regression on days in milk on TD 1. Corresponding analyses were conducted without regression on current SCS. An analysis of lactation yield was performed with a similar model and regression on all TD SCS. The SCS was highest most often on TD 1 for parity 1 (22.5%) and on TD 10 for parity 2 (18.5%). Regression of TD milk yield on mean of previous TD SCS was highest during the latter half of lactation (maximum of -0.346 kg/SCS unit on TD 9) for parity 1 and during TD through 7 (maximum of -0.366 kg/SCS unit on TD 4) for parity 2. Regression of TD yield on current TD SCS tended to be larger for later lactation. Regression of lactation yield on TD SCS was negative and important for TD 1 through 6 for parity 1 and for all TD for parity 2. To minimize milk loss, mastitis control is most important immediately pre- and postcalving for parity 1 and throughout lactation for parity 2.     

Inferring relationships between somatic cell score and milk yield using simultaneous and recursive models

A Bayesian analysis via Markov chain Monte Carlo methods extending the simultaneous and recursive model of Gianola and Sorensen (2004) was proposed to account for possible population heterogeneity. The method was used to infer relationships between milk yield and somatic cell scores of Norwegian Red cows. Data consisted of test-day records of milk yield and somatic cell score of first-lactation cows during the first 120 d of lactation. Results suggested large negative direct effects from somatic cell score to milk yield and small reciprocal effects from milk yield to somatic cell score. The direct effects were larger in the first 60 d of lactation than in the subsequent period. Bayesian model selection strongly favored the simultaneous and recursive models for milk yield and somatic cell score over the corresponding mixed model without considering simultaneity or recursiveness. Estimated effects between milk yield and somatic cell score seemed to be yield-dependent, larger in higher producing cows than in lower producing cows. Heritability estimates from the simultaneous and recursive models were similar to those from the mixed model, but some genetic correlations differed considerably among models.     

Short communication: Genetic relationships between claw disorders,protein yield,and somatic cell score by days in milk

The aim of the present study was to infer daily genetic relationships between the selected claw disorders digital dermatitis, sole ulcer (SU), and interdigital hyperplasia (IH) and protein yield and the udder health indicator somatic cell score (SCS). Data were from 26,651 Holstein cows kept in 15 selected large-scale herds located in the region of Thuringia in the eastern part of Germany. Herds are characterized by organized data recording for novel health traits, and for the present study, claw disorders from the years 2008 to 2012 were used. A longitudinal and binary health data structure was created by assigning claw disorders to adjacent official test days. No entry of a claw disorder within a given interval of approximately 30 d implied a score of 0 (healthy), and otherwise, a score of 1 (diseased). Threshold random regression models (RRM) were applied to binary health data, and linear RRM to Gaussian-distributed protein yield and SCS. Genetic correlations between protein yield and SCS for identical days in milk (DIM) only revealed a tendency for genetic antagonisms between DIM 40 and DIM 180, with a maximal genetic correlation (r_g) of 0.14 at DIM 100. With regard to protein yield and claw disorders, the largest and moderate values of r_g (~0.30), indicating a genetic antagonism between productivity and claw health, were found when correlating protein yield from DIM 300 with SU from DIM 160. Especially for SU and protein yield, time-lagged relationships were more pronounced than genetic relationships from the same test days. Genetic correlations between IH and protein yield were favorable and negative from calving to DIM 300. Generally, on the genetic scale, we found heterogeneous associations between protein yield and claw disorders (i.e., different r_g at identical test days for different claw disorders, and also an alteration of r_g for identical traits at different DIM). The SCS measured at d 20, 160, and 300 was genetically positively correlated with SU over the whole trajectory of 365 d, indicating a common genetic background for claw and udder health. A maximal value of 0.36 was found for the r_g between SCS from d 300 and SU early in lactation. Additionally, a recursive effect was observed (i.e., r_g = 0.26 between SCS from d 20 and SU from d 340). Genetic correlations between SCS and IH, and between SCS and digital dermatitis, were close to zero and partly negative during lactation. Results showed the feasibility of threshold RRM applications to binary claw health data, and a changing genetic background in the course of lactation. From a practical perspective, and with regard to the herds used in this study, continuation of breeding on productivity will have different effects on incidences of different claw disorders, with the highest susceptibility to SU.     

Genetic analysis of somatic cell score in Norwegian cattle using random regression test-day models

The dataset used in this analysis contained a total of 341,736 test-day observations of somatic cell scores from 77,110 primiparous daughters of 1965 Norwegian Cattle sires. Initial analyses, using simple random regression models without genetic effects, indicated that use of homogeneous residual variance was appropriate. Further analyses were carried out by use of a repeatability model and 12 random regression sire models. Legendre polynomials of varying order were used to model both permanent environmental and sire effects, as did the Wilmink function, the Lidauer-M?ntysaari function, and the Ali-Schaeffer function. For all these models, heritability estimates were lowest at the beginning (0.05 to 0.07) and higher at the end (0.09 to 0.12) of lactation. Genetic correlations between somatic cell scores early and late in lactation were moderate to high (0.38 to 0.71), whereas genetic correlations for adjacent DIM were near unity. Models were compared based on likelihood ratio tests, Bayesian information criterion, Akaike information criterion, residual variance, and predictive ability. Based on prediction of randomly excluded observations, models with 4 coefficients for permanent environmental effect were preferred over simpler models. More highly parameterized models did not substantially increase predictive ability. Evaluation of the different model selection criteria indicated that a reduced order of fit for sire effects was desireable. Models with zeroth- or first-order of fit for sire effects and higher order of fit for permanent environmental effects probably underestimated sire variance. The chosen model had Legendre polynomials with 3 coefficients for sire, and 4 coefficients for permanent environmental effects. For this model, trajectories of sire variance and heritability were similar assuming either homogeneous or heterogeneous residual variance structure.     

Effect of gradual or abrupt cessation of milking at dry off on milk yield and somatic cell score in the subsequent lactation

The objective of this study was to assess the effect of milk cessation method (abrupt or gradual) at dry off on milk yield and somatic cell score (SCS) up to 120 d in milk during the subsequent lactation. Data from 428 cows from 8 dairy herds in Ohio were analyzed. Abrupt cessation cows kept the farm's regular milking schedule (2 or 3 times) through dry off and gradual cessation cows were milked once daily for the final week of lactation. Milk yield and SCS were collected using Dairy Herd Improvement Association test-day records. Aseptic quarter milk samples were collected approximately 1 wk before dry off, at dry off, and within 1 wk after calving for bacterial culture to determine the presence of intramammary infections. Overall, milk cessation method was not significantly associated with either milk yield or SCS in early lactation; however, interaction between the milk cessation method and herd was highly significant. Cows producing greater amounts of milk around dry off had significantly higher SCS in the following lactation. Shorter dry periods were significantly associated with decreased milk yield in the following lactation, especially among abruptly dried off cows. Additionally, as expected, several other factors, such as parity of cows and stage of lactation, were significantly associated with both outcomes. No interactions between the milk cessation method and the other explanatory variables in the final models were significant. The results of the current study suggest that higher milk yield at dry off was associated with higher SCS in the following lactation, even though milk cessation method at the end of lactation had a varying effect on test-day milk yield and SCS in different herds during the first 120 d in milk in the following lactation. The specific herd characteristics influencing this could not be identified within this study, warranting further research.     

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 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.     

Genetic and phenotypic relationships among milk yield and somatic cell count before and after clinical mastitis

This paper studies whether cows with originally lower somatic cell count (SCC) are more susceptible to clinical mastitis (CM) than cows with higher somatic cell count, and evaluates the correlations between CM, SCC, and milk yield. Data were extracted from the Finnish national milk-recording database and from the health recording system. First and second lactation records of 87,861 Ayrshire cows calving between January 1998 and December 2000 were included. Traits studied were incidence of CM, test-day SCC, and test-day milk yield before and following CM. Genetic parameters were estimated using multitrait REML with a sire model. Results did not indicate that cows with genetically low SCC would be more susceptible to CM. The genetic correlation between CM in the first and second lactation was reasonably high (0.73), suggesting that susceptibility to mastitis remains similar across lactations. The genetic correlation between CM and milk yield traits was positive (from 0.38 to 0.56), confirming the genetic antagonism between production and udder health traits. The genetic correlation between SCC and milk was positive in the first lactation, but negative, or near zero in the second lactation. This indicates that breeding for lower SCC might not affect milk production in later lactations. The results of this study support the use of SCC as an indicator of mastitis and a tool for selection for mastitis resistance.     

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.     

Genetic relationships between calving interval and body condition score conditional on milk yield

Body condition score (BCS) is a useful tool in assessing the energy status of dairy cattle. Previous research has shown that it is heritable and genetically correlated to reproductive performance. Currently, interest exists in developing selection indexes for fertility that include BCS information. Before such indexes are developed, it is important to assess the genetic covariance between BCS and fertility after fully accounting for the covariance of both traits with milk yield, as indices to predict selection responses require knowledge of these (co)variances. In the present study, calving interval (CI) was used as a measure of reproductive performance. The genetic correlations between BCS and CI before and after genetically adjusting for milk yield were -0.48 and -0.22, respectively. Thus, cows with low BCS have longer CI, which is exacerbated by high levels of milk production. Using selection index theory, we showed that selecting for milk yield alone will result in an increase of 768 kg of milk, an increase of 4.46 d in CI and a reduction of 0.41 BCS units for every standard deviation change in the index. Restricting BCS to no genetic change, whereas still selecting for milk yield will result in an increase of 653.1 kg of milk per standard deviation of the selection index. However, CI will still continue to increase at a rate of 3.20 d per standard deviation of the selection index. The selection indices used here are not optimum, in that they are not economically driven and do not consider all traits that contribute to profitability. However, they demonstrate that, even though restricting BCS may be seen as an attractive way of limiting reliance of body tissue mobilization to fuel milk production, this is unlikely to result in improvements in CI, although the rate of increase in CI will be reduced.     

Genetic correlation patterns between somatic cell score and protein yield in the Italian Holstein-Friesian population

Genetic parameters for somatic cell score (SCS) in the Italian Holstein-Friesian population were estimated addressing the pattern of genetic correlation with protein yield in different parities (first, second, and third) and on different days in milk within each parity. Three approaches for parameter estimation were applied using random samples of herds from the national database of the Italian Holstein Association. Genetic correlations for lactation measures (305-d protein yield and lactation SCS) were positive in the first parity (0.31) and close to zero in the second (0.01) and third (0.09) parities. These results indicated that larger values of SCS were genetically associated with increased production. The second and third sets of estimates were based on random regression test-day models, modeling the shape of lactation curve with the Wilmink function and fourth-order Legendre polynomials, respectively. Genetic correlations from both random regression models showed a specific pattern associated with days in milk within and across parities. Estimates varied from positive to negative in the first and second parity, and from null to negative in the third parity. Patterns were similar for both random regression models. The average overall correlation between SCS and protein yield was zero or slightly positive in the first lactation and ranged from zero to negative in later lactations. Correlation estimates differed by parity and stage of lactation. They also demonstrated the dubiousness of applying a single genetic correlation measure between SCS and protein in setting selection strategies. Differences in magnitude and the sign of genetic correlations between SCS and yields across and within parities should be accounted for in selection schemes.     

Association between somatic cell count during the first lactation and the cumulative milk yield of cows in Irish dairy herds

Reduced potential milk yield is an important component of mastitis costs in dairy cows. The first aim of this study was to assess associations between somatic cell count (SCC) during the first lactation, and cumulative milk yield over the first lactation and subsequent lifetime of cows in Irish dairy herds. The second aim was to assess the association between SCC at 5 to 30 d in milk during parity 1 (SCC1), and SCC over the entire first lactation for cows in Irish dairy herds. The data set studied included records from 51,483 cows in 5,900 herds. Somatic cell count throughout the first lactation was summarized using the geometric mean and variance of SCC. Data were analyzed using linear models that included random effects to account for the lack of independence between observations, and herd-level variation in coefficients. Models were developed in a Bayesian framework and parameters were estimated from 10,000 Markov chain Monte Carlo simulations. The final models were a good fit to the data. A 1-unit increase in mean natural logarithm SCC over the first lactation was associated with a median decrease in first lactation and lifetime milk yield of 135 and 1,663 kg, respectively. A 1-unit increase in the variance of natural logarithm SCC over the first lactation was associated with a median decrease in lifetime milk yield of 719 kg. To demonstrate the context of lifetime milk yield results, microsimulation was used to model the trajectory of individual cows and evaluate the expected outcomes for particular changes in herd-level geometric mean SCC over the first lactation. A 75% certainty of savings of at least €199/heifer in the herd was detected if herd-level geometric mean SCC over the first lactation was reduced from ≥120,000 to ≤72,000 cells/mL. The association between SCC1 and SCC over the remainder of the first lactation was highly herd dependent, indicating that control measures for heifer mastitis should be preferentially targeted on an individual-herd basis toward either the pre- and peripartum period, or the lactating period, to optimize the lifetime milk yield of dairy cows.     

牛乳体细胞数与乳蛋白含量相关性的研究

对呼和浩特郊区一牧场30头荷斯坦乳牛进行6个月单个采样，共得427个有效样本，检测乳样中体细胞数、酪蛋白（包括α-酪蛋白、β-酪蛋白、κ-酪蛋白）、乳清蛋白、总蛋白、游离氨基氮、酪蛋白／总蛋白和乳清蛋白／总蛋白。结果表明，乳中总蛋白、游离氨基氮含量及乳清蛋白／总蛋白与SCC呈显著正相关；酪蛋白／总蛋白与SCC呈显著负相关；乳清蛋白含量与SCC呈极显著正相关。酪蛋白、α-酪蛋白／酪蛋白、β-酪蛋白／酪蛋白、κ-酪蛋白／酪蛋白与SCC的相关性不显著。酪蛋白含量与总蛋白含量呈极显著的正相关，与游离氨基氮含量、乳清蛋白／总蛋白呈极显著的负相关。乳清蛋白含量与游离氨基氮、总蛋白含量呈极显著正相关。     

Parameters for milk somatic cell score and relationships with production traits in primiparous dairy sheep

A total of 13,066 first-lactation test-day records of 2,277 Valle del Belice ewes from 17 flocks were used to estimate genetic parameters for somatic cell scores (SCS) and milk production traits, using a repeatability test-day animal model. Heritability estimates were low and ranged from 0.09 to 0.14 for milk, fat, and protein yields, and contents. For SCS, the heritability of 0.14 was relatively high. The repeatabilities were moderate and ranged from 0.29 to 0.47 for milk production traits. The repeatability for SCS was 0.36. Flock-test-day explained a large proportion of the variation for milk production traits, but it did not have a big effect on SCS. The genetic correlations of fat and protein yields with fat and protein percentages were positive and high, indicating a strong association between these traits. The genetic correlations of milk production traits with SCS were positive and ranged from 0.16 to 0.31. The results showed that SCS is a heritable trait in Valle del Belice sheep and that single-trait selection for increased milk production will also increase SCS.     

Analysis of the relationship between somatic cell score and functional longevity in Canadian dairy cattle

The aim of this study was to assess the level of somatic cell count (SCC) and to explore the impact of somatic cell score (SCS) on the functional longevity of Canadian dairy cattle by using a Weibull proportional hazards model. Data consisted of 1,911,428 cows from 15,970 herds sired by 7,826 sires for Holsteins, 80,977 cows in 2,036 herds from 1,153 sires for Ayrshires, and 53,114 cows in 1,372 herds from 1,758 sires for Jerseys. Functional longevity was defined as the number of days from the first calving to culling, death, or censoring. The test-day SCC was transformed to a linear score, and the resulting SCS were averaged within each lactation. The average SCS were grouped into 10 classes. The statistical model included the effects of stage of lactation; season of production; annual change in herd size; type of milk recording supervision; age at first calving; effects of milk, fat, and protein yields, calculated as within-herd-year-parity deviations; herd-year-season of calving; SCS class; and sire. The relative culling rate was calculated for animals in each SCS class after accounting for the aforementioned effects. The overall average SCC for Holsteins was 167,000 cells/mL, for Ayrshires was 155,000 cells/mL, and for the Jerseys was 212,000 cells/mL. In all breeds there were no appreciable differences in the relative risk of culling among classes of SCS breed averages (i.e., up to a SCS of 5). However, as the SCS increased beyond the breed average, the relative risk of cows being culled increased considerably. For instance, Holstein, Ayrshire, and Jersey cows with the highest classes of SCS had, respectively, a 4.95, 6.73, and 6.62 times greater risk of being culled than cows with average SCS.     

Genetic parameters for milk production and persistency for Danish Holsteins estimated in random regression models using REML

(Co)variance components for milk, fat, and protein yield of 8075 first-parity Danish Holsteins (DH) were estimated in random regression models by REML. For all analyses, the fixed part of the model was held constant, whereas four different functions were applied to model the additive genetic effect and the permanent environment effect. Homogeneous residual variance was assumed throughout lactation. Univariate models were compared using a minimum of -2 ln(restricted likelihood) as the criterion for best fit. Heritabilities as a function of time were calculated from the estimated curve parameters from univariate analyses. Independent of the function applied and the trait in question, heritabilities were lowest in the beginning of the lactation. Heritabilities for persistency of fat yield were slightly higher than heritabilities for persistency of milk and protein yield. Genetic correlations between persistency and 305-d production were higher for protein and milk yield than for fat yield. Bivariate analyses between the production traits were carried out in sire models using the models with the best 3-parameter curve fit in the univariate analyses. Correlations between traits were calculated from covariance components for curve parameters estimated in bivariate analyses. Genetic correlations between milk and protein yield were higher than between milk and fat yield.     

Mammary pathogens and their relationship to somatic cell count and milk yield losses in dairy ewes

A total of 9592 samples of half udder milk were collected monthly throughout lactation for bacteriological and somatic cell count (SCC) study from 1322 Churra ewe lactations from seven separate flocks enrolled in the recording scheme of the National Association of Spanish Churra Breeders in the Castile-Le6n region of Spain. Statistical analyses were carried out from a mixed model with random factor half udder or ewe for repeated measures. Test of significance of fixed effects of this mixed model showed significant effects of organisms, flock, parity, lactation stage, and birth type on SCC. Special reference must be made to novobiocin-sensitive coagulase-negative staphylococci, which represented more than 50% of the isolates and which elicited SCC geometric means of around 106/ml. In addition, the analysis of 4352 monthly test-day records for milk yield, SCC, and bacteriology showed that the ewes that were uninfected and infected by minor pathogens had the lowest SCC and the highest milk yields, whereas those infected by major pathogens had high SCC and milk yield losses between 8.8 and 10.1% according to the uni- or bilateral character of the infection. Finally, ewes infected by novobiocin-sensitive coagulase-negative staphylococci elicited SCC values similar to those of infections by major pathogens and milk yield losses ranging between those caused by minor and major pathogens. As a result, emphasis should be put on prevention of subclinical mastitis, particularly mastitis caused by novobiocin-sensitive coagulase-negative staphylococci in dairy sheep herds to improve microbiological and hygienic milk quality and to minimize losses in milk yield.     

Association of candidate gene polymorphisms with milk technological traits,yield, composition,and somatic cell score in Italian Holstein-Friesian sires

Advances in DNA-based marker technology have enabled the identification of genomic regions underlying complex phenotypic traits in livestock species. The incorporation of detected quantitative trait loci into genetic evaluation provides great potential to enhance selection accuracies, hence expediting the genetic improvement of economically important traits. The objective of the present study was to investigate 96 single nucleotide polymorphisms (SNP) located in 53 candidate genes previously reported to have effects on milk production and quality traits in a population of highly selected Holstein-Friesian bulls. A total of 423 semen samples were used to genotype the bulls through a custom oligo pool assay. Forty-five SNP in 32 genes were found to be associated with at least 1 of the tested traits. Most significant and favorable SNP trait associations were observed for polymorphisms located in CCL3 and AGPAT6 genes for fat yield (0.037 and 0.033 kg/d, respectively), DGKG gene for milk yield (0.698 kg/d), PPARGC1A, CSN1S1, and AGPAT6 genes for fat percentage (0.127, 0.113, and 0.093%, respectively), GHR gene for protein (0.064%) and casein percentage (0.053%), and TLR4 gene for fat (0.090%), protein (0.066%), and casein percentage (0.050%). Somatic cell score was favorably affected by GHR (?0.095) and POU1F1 (?0.137), and interesting SNP-trait associations were observed for polymorphisms located in CSN2, POU1F1, and AGPAT6 genes for rennet coagulation time (?0.592, ?0.558, and ?0.462 min, respectively), and GHR and CSN2 genes for curd firmness 30 min after rennet addition (1.264 and 1.183 mm, respectively). In addition to the influence of individual SNP, the effects of composite genotypes constructed by grouping SNP according to their individual effects on traits considered in the analysis were also examined. Favorable and significant effects on milk traits were observed for 2 composite genotypes, one including 10 SNP and the other 4 SNP. The former was associated with an increase of milk (0.075 kg/d), fat (0.097 kg/d), protein (0.083 kg/d), and casein yields (0.065 kg/d), and the latter was associated with an increase of fat (0.244%), protein (0.071%), and casein percentage (0.047%). Although further research is required to validate the identified SNP loci in other populations and breeds, our results can be considered as a preliminary foundation for further replication studies on gene-assisted selection programs.