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.     

Short communication: Genetic analysis of nonreturn rate and mastitis in first-lactation Norwegian Red cows

Associations between clinical mastitis (CM) and nonreturn rate within 56 d after first insemination (NR56) were examined in Norwegian Red (NRF) cows. Records on absence or presence of CM within each of the intervals, −30 to 30, 31 to 150, and 151 to 300 d after first calving, and records on NR56 for 620,492 first-lactation daughters of 3,064 NRF sires were analyzed with a Bayesian multivariate threshold liability model. Point estimates of genetic correlations between NR56 and the 3 CM traits were between −0.05 and −0.02. Residual correlations were close to zero, and correlations between herd-5-yr effects on NR56 and CM in the 3 lactation intervals ranged from −0.15 to −0.17. It appears that CM and NR56 in first lactation are independent traits.     

Genetic analysis of fertility-related diseases and disorders in Norwegian Red cows

Heritability of and genetic correlations among silent heat (SH), cystic ovaries (CO), metritis (MET), and retained placenta (RP) were inferred. These traits were chosen because they are the 4 most frequent fertility-related diseases and disorders among first-lactation cows in Norway. Records of 503,683 first-lactation daughters of 1,058 Norwegian Red sires with first calving from 2000 through 2006 were analyzed with a 4-variate threshold sire model. Presence or absence of each of the 4 diseases was scored as 1 or 0 based on whether or not the cow had at least 1 veterinary treatment for the disease. The mean frequency was 3.1% for SH, 0.9% for MET, 0.5% for CO, and 1.5% for RP. The model for liability had effects of age at calving and of month-year of calving, herd, sire of the cow, and a residual. Posterior mean (SD) of heritability of liability was 0.06 (0.01) for SH, 0.03 (0.01) for MET, 0.07 (0.01) for CO, and 0.06 (0.01) for RP. The genetic correlation between MET and RP was strong, with posterior mean (SD) 0.64 (0.10). A negative genetic correlation (−0.26) was found between RP and CO. The posterior distributions of the other genetic correlations included zero with high density, and could not be considered different from zero. The frequency of fertility-related diseases and disorders is very low in the Norwegian Red population at present, so there is limited scope for genetic improvement. However, this study indicates that reasonably precise genetic evaluation of sires is feasible for these traits given information from large daughter groups.     

Genetic analysis of number of mastitis cases and number of services to conception using a censored threshold model

The objective of this study was to infer genetic parameters and genetic change for number of clinical mastitis cases (NCM) and number of services to conception (STC) in first-lactation Norwegian Red (NRF) cows. Records on 620,492 daughters of 3,064 NRF sires, with first calving from 1980 through 2004, were analyzed with a bivariate threshold liability model that takes censoring into account. Posterior mean (SD) of heritability of liability was 0.08 (0.004) for NCM and 0.03 (0.002) for STC. The mean (SD) of the posterior distribution of the genetic correlation between the 2 traits was 0.21 (0.04). Posterior means of the correlation between herd-5-yr effects, and between residuals for NCM and STC were 0.17 and 0.05, respectively. To evaluate effects of taking censoring into account, the data were also analyzed with a bivariate ordered threshold model ignoring censoring. The genetic correlation between NCM and STC was lower than in the censored threshold model (0.09 vs. 0.21). Heritability of liability to NCM and STC from this model was also slightly lower, whereas the point estimates of herd-5-yr and residual correlations were 0.15, and −0.01, respectively. These results suggest that genetic (co)variance may be understated in models ignoring censoring. For comparison purposes, the data were analyzed with a bivariate linear sire model and standard REML-BLUP procedures. The correlation (rank correlation) between sire evaluations from the censored threshold model and sire predicted transmitting abilities from the linear model was 0.90 (0.90) for NCM and 0.87 (0.86) for STC. The evolution of average sire posterior means by birth year of daughters was used to assess genetic change, and results indicated genetic reduction (i.e., genetic improvement) of NCM and little or no genetic change for STC in the NRF population.     

Genetic analysis of clinical mastitis and somatic cell count traits in Austrian Fleckvieh cows

The objectives of this study were to investigate genetic associations between clinical mastitis (CM) and different somatic cell count traits, and to examine their relationships, in terms of estimated breeding values, with other traits that are routinely evaluated in Austrian Fleckvieh dual-purpose cows. Records on veterinary treatments of CM were available from the Austrian health-monitoring project. For CM, 3 intervals in early lactation were considered: -10 to 50 d, 51 to 150 d, and -10 to 150 d after calving. Within each interval, absence or presence of CM was scored as 1 or 0 based on whether or not the cow had recorded at least one veterinary treatment of CM. The average somatic cell score of the first 2 test-days after calving was defined as early lactation average somatic cell score, and lactation mean somatic cell score was the average of all test-day somatic cell scores from 8 to 305 d after calving. Subclinical mastitis was expressed as a binary trait based on prolonged elevated somatic cell counts. If somatic cell counts on 3 consecutive test-days in the interval from 8 to 305 d after calving were above 200,000 cells/mL, the binary variable subclinical mastitis was defined as 1 and otherwise 0. Records of Austrian Fleckvieh cows, with calving from January 1, 2007, to February 28, 2009, were analyzed using univariate and bivariate sire models. Threshold liability models were applied for binary traits, and Gaussian models were used for early lactation average somatic cell score and lactation mean somatic cell score. A Bayesian approach using Gibbs sampling was applied for genetic analyses. Posterior means of heritability of liability to CM were 0.06 and 0.02 in the first and second interval, respectively, and 0.05 in the full period (-10 to 150 d). Heritability estimates of somatic cell count traits were higher (0.09 to 0.13). The posterior mean of the genetic correlation between CM in lactation period 1 (-10 to 50 d after calving) and 2 (51 to 150 d after calving) was close to unity. Posterior means of genetic correlations between CM and somatic cell count traits ranged from 0.64 to 0.77. Because CM and somatic cell count describe different aspects of udder health, information on both traits should be considered for selection of bulls. Correlations of sire breeding values revealed that especially the udder conformation trait udder depth may be useful as additional information to reduce both CM and somatic cell count.     

Bayesian analysis of liability of clinical mastitis in Norwegian cattle with a threshold model: effects of data sampling method and model specification.

First-lactation records of Norwegian Cattle were used to infer heritability of liability to clinical mastitis with a threshold sire model. Mastitis was defined as a binary response (presence or absence) in a defined period of first lactation (opportunity period). Length of opportunity period (from 30 d before calving up to 120 or 300 d of lactation) had less effect on heritability estimates than data sampling methods (include or exclude records of cows culled before the end of the opportunity period) whereas sire ranking was more affected by the former. Including all cows, whether culled before the end of the opportunity period or not, gave a sharper and more symmetric posterior distribution of heritability of liability to clinical mastitis. When we analyzed data for all cows, model specification had a small effect on heritability estimates, while sire ranking was affected markedly. Posterior means of heritability range from 0.058 to 0.074. A model regressing on the length of the opportunity period for culled cows without mastitis, was shown favorable for the two opportunity periods using Bayes factors and the deviance information criterion for model comparison. This model, in which liability of mastitis depends on time to culling, may allow utilizing information from all first lactations in genetic evaluation, irrespectively of duration and culling outcome.     

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.     

Genetic selection for health traits using producer-recorded data. II. Genetic correlations, disease probabilities, and relationships with existing traits

The objectives of this study were to calculate genetic correlations between health traits that were recorded in on-farm herd management software programs and to assess relationships between these traits and other traits that are routinely evaluated in US dairy sires. Data consisted of 272,576 lactation incidence records for displaced abomasum (DA), ketosis (KET), mastitis (MAST), lameness (LAME), cystic ovaries (CYST), and metritis (MET) from 161,622 cows in 646 herds. These data were collected between January 1, 2001 and December 31, 2003 in herds using the Dairy Comp 305, DHI-Plus, or PCDART herd management software programs. Binary incidence data for all disorders were analyzed simultaneously using a multiple-trait threshold sire model that included random sire and herd-year-season of calving effects. Although data from multiple lactations were available for some animals, our genetic analysis included only first parity records due to concerns about selection bias and improper modeling of the covariance structure. Heritability estimates for the presence or absence of each disorder during first lactation were 0.14 for DA, 0.06 for KET, 0.09 for MAST, 0.03 for LAME, 0.04 for CYST, and 0.06 for MET. Estimated genetic correlations were 0.45 between DA and KET, 0.42 between KET and CYST, 0.20 between MAST and LAME, 0.19 between KET and LAME, 0.17 between DA and CYST, 0.17 between KET and LAME, 0.17 between KET and MET, and 0.16 between LAME and CYST. All other correlations were negligible. Correlations between predicted transmitting abilities for the aforementioned health traits and existing production, type, and fitness traits were low, though it must be noted that these estimates may have been biased by low reliability of the health trait evaluations. Based on results of this study, it appears that genetic selection for health disorders recorded in on-farm software programs can be effective. These traits can be incorporated into selection indices directly, or they can be combined into composite traits, such as "reproductive disorders", "metabolic disorders", or "early lactation disorders".     

Genetic relationship between culling, milk production, fertility, and health traits in Norwegian red cows

First-lactation records on 836,452 daughters of 3,064 Norwegian Red sires were used to examine associations between culling in first lactation and 305-d protein yield, susceptibility to clinical mastitis, lactation mean somatic cell score (SCS), nonreturn rate within 56 d in heifers and primiparous cows, and interval from calving to first insemination. A Bayesian multivariate threshold-linear model was used for analysis. Posterior mean of heritability of liability to culling of primiparous cows was 0.04. The posterior means of the genetic correlations between culling and the other traits were −0.41 to 305-d protein yield, 0.20 to lactation mean SCS, 0.36 to clinical mastitis, 0.15 to interval from calving to first insemination, −0.11 to 56-d nonreturn as heifer, and −0.04 to 56-d nonreturn as primiparous cow. As much as 66% of the genetic variation in culling was explained by genetic variation in protein yield, clinical mastitis, interval of calving to first insemination, and 56-d nonreturn in heifers, whereas contribution from the SCS and 56-d nonreturn as primiparous cow was negligible, after taking the other traits into account. This implies that for breeds selected for a broad breeding goal, including functional traits such as health and fertility, most of the genetic variation in culling will probably be covered by other traits in the breeding goal. However, in populations where data on health and fertility is scarce or not available at all, selection against early culling may be useful in indirect selection for improved health and fertility. Regression of average sire posterior mean on birth-year of the sire indicate a genetic change equivalent to an annual decrease of the probability of culling in first-lactation Norwegian Red cattle by 0.2 percentage units. This genetic improvement is most likely a result of simultaneous selection for improved milk yield, health, and fertility over the last decades.     

Genetic evaluation of mastitis in dairy cattle using linear models, threshold models, and survival analysis: a simulation study

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.     

Genetic associations between clinical mastitis and somatic cell score in early first-lactation cows

The objectives of this study were to examine genetic associations between clinical mastitis and somatic cell score (SCS) in early first-lactation cows, to estimate genetic correlations between SCS of cows with and without clinical mastitis, and to compare genetic evaluations of sires based on SCS or clinical mastitis. Clinical mastitis records from 15 d before to 30 d after calving and first test-day SCS records (from 6 to 30 d after calving) from 499,878 first-lactation daughters of 2,043 sires were analyzed. Results from a bivariate linear sire model analysis of SCS in cows with and without clinical mastitis suggest that SCS is a heterogeneous trait. Heritability of SCS was 0.03 for mastitic cows and 0.08 for healthy cows, and the genetic correlation between the 2 traits was 0.78. The difference in rank between sire evaluations based on SCS of cows with and without clinical mastitis varied from −994 to 1,125, with mean 0. A bivariate analysis with a threshold-liability model for clinical mastitis and a linear Gaussian model for SCS indicated that heritability of liability to clinical mastitis is at least as large as that of SCS in early lactation. The mean (standard deviation) of the posterior distribution of heritability was 0.085 (0.006) for liability to clinical mastitis and 0.070 (0.003) for SCS. The posterior mean (standard deviation) of the genetic correlation between liability to clinical mastitis and SCS was 0.62 (0.03). A comparison of sire evaluations showed that genetic evaluation based on SCS was not able to identify the best sires for liability to clinical mastitis. The association between sire posterior means for liability to clinical mastitis and sire predicted transmitting ability for SCS was far from perfect.     

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.     

Predictive abilities of different statistical models for analysis of survival data in dairy cattle

The objective of this study was to compare alternative trait definitions and statistical models for genetic evaluation of survival in dairy cattle. Data from the first 5 lactations of 808,750 first-crop daughters of 3,064 Norwegian Red sires were analyzed. Seven sire models were used for genetic analyses: linear and threshold cross-sectional models for binary survival scores from first lactation; a linear multi-trait model for survival scores from the first 3 lactations; linear and threshold repeatability models for survival scores from the first 5 lactations; a Weibull frailty model for herd life in first lactation; and a Weibull frailty model for herd life in the first 5 lactations. The models were compared to assess predictive ability of sire estimated breeding values with respect to average survival 365 d after first calving for second-crop daughters (not included in calculation of predicted transmitting abilities) of 375 elite sires. Generally, the linear multi-trait model analyzing survival in the first 3 lactations as correlated traits gave more-accurate predicted sire breeding values compared with both linear and Weibull frailty models using data from first lactation only, even when the latter models were extended to include data up to the sixth lactation. The Weibull frailty models did not improve predictive ability of sire estimated breeding values over what was obtained using a simple cross-sectional linear model for binary survival in first lactation.     

Comparison between bivariate models for 56-day nonreturn and interval from calving to first insemination in Norwegian red

A bivariate threshold-linear (TL) and a bivariate linear-linear (LL) model were assessed for the genetic analysis of 56-d nonreturn (NR56) and interval from calving to first insemination (CFI) in first-lactation Norwegian Red (former Norwegian Dairy Cattle) (NRF). Three different datasets were used to infer genetic parameters and to predict transmitting abilities for NRF sires. Mean progeny group sizes were 147.8, 102.7, and 56.5 daughters, and the corresponding number of sires were 746, 743, and 742 in the 3 datasets. Otherwise, the structures of the 3 datasets were similar. When the TL model was used, heritability of liability to NR56 was 2.8% in the 2 larger datasets and 3.8% in the smallest dataset. In the LL model, the heritability of NR56 in the largest dataset and in the 2 smaller datasets was 1.2 and 0.9%, respectively. For CFI, the heritability was similar in TL and LL models, ranging from 2.4 to 2.7%. The small heritability of the 2 reproductive traits implies that most of the variation is environmental and that large progeny groups are required to get accurate sire PTA. The point estimates of the genetic correlation between NR56 and CFI were near zero in both models. The 2 bivariate models were compared in terms of predictive ability using logistic regression and a χ² statistic based on differences between observed and predicted outcomes for NR56 in a separate dataset. Comparison was also with respect to ranking of sires and correlations between sire posterior means (TL model) and PTA (LL model). We found very small differences in ability to predict NR56 between the 2 bivariate models, regardless of the dataset used. Correlations between sire posterior means (TL) and sire PTA (LL) and rank correlations between sire evaluations were all >0.98 in the 3 datasets. At present, the LL model is preferred for sire evaluations of NR56 and CFI in NRF. This is because the LL model is less computationally demanding and more robust with respect to the structure of the data than TL.     

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.     

Heifer fertility and its relationship with cow fertility and production traits in Holstein dairy cattle

Breeding receipts from three AI units were merged with Ontario Dairy Herd Improvement Corporation and Record of Performance production records. Data comprised 53,705 heifer, 41,253 lactation 1, 14,688 lactation 2, and 3054 lactation 3 records by daughters of 2150 sires represented in 15,877 herd-year-seasons of birth. Three measures of heifer fertility, three measures of cow fertility, and three measures of production were investigated. Measures of heifer fertility were ages at first and last breeding and number of inseminations per conception. Cow fertility traits were days from calving to first breeding, days open, and number of inseminations per conception. Production traits were breed class average milk, breed class average fat, and fat percentage. Relationships among these nine traits for the first three lactations were estimated using a maximum likelihood multiple-trait procedure. The linear mixed model for each trait included fixed effects of herd-year-season of birth and genetic groups of sire and the random effect of sire. Transformations of the data for nonnormality had no influence on the estimates of genetic and phenotypic parameters. The heritability of .12 for age at first insemination, which was higher than other heifer fertility traits, indicated that selection would result in genetic response. Genetic and phenotypic correlations between heifer fertility and cow fertility and production traits in all three lactations were not different from zero. There was no genetic antagonism between fertility and subsequent production traits.     

Genetic parameters for health traits and their relationship to different persistency traits in German Holstein dairy cattle

Data from 3,200 Holstein cows from 3 commercial dairy farms in Germany were used to estimate heritabilities and breeding values for liability to udder diseases (UD), fertility diseases (FD), metabolic diseases (MD), and claw and leg diseases (CLD) using single-trait threshold sire models. A total of 92,722 medical treatments recorded from 1998 to 2003 were included in the analysis. Approximate genetic correlations between persistency of milk yield, fat yield, protein yield, and persistency of milk energy yield and liability to the health traits were calculated based on correlations between EBV. Posterior means of heritability of liability ranged from 0.05 to 0.08 for UD, from 0.04 to 0.07 for FD, from 0.08 to 0.12 for MD, and from 0.04 to 0.07 for CLD. Approximate genetic correlations of the disease traits with the persistency traits were favorable, except for MD in all lactations, which were unfavorable, and UD, which were around zero. Highest correlations in the range of 0.13 to 0.46 were found between the different persistency traits and CLD.     

Genetic selection for health traits using producer-recorded data. I. Incidence rates, heritability estimates, and sire breeding values

The objective of this study was to determine the feasibility of genetic selection for health traits in dairy cattle using data recorded in on-farm herd management software programs. Data regarding displaced abomasum (DA), ketosis (KET), mastitis (MAST), lameness (LAME), cystic ovaries (CYST), and metritis (MET) were collected between January 1, 2001 and December 31, 2003 in herds using Dairy Comp 305, DHI-Plus, or PCDART herd management software programs. All herds in this study were either participants in the Alta Genetics (Watertown, WI) Advantage progeny testing program or customers of the Dairy Records Management Systems (Raleigh, NC) processing center. Minimum lactation incidence rates were applied to ensure adequate reporting of these disorders within individual herds. After editing, DA, KET, MAST, LAME, CYST, and MET data from 75,252 (313), 52,898 (250), 105,029 (429), 50,611 (212), 65,080 (340), and 97,318 (418) cows (herds) remained for analysis. Average lactation incidence rates were 0.03, 0.10, 0.20, 0.10, 0.08, and 0.21 for DA, KET, MAST, LAME, CYST, and MET (including retained placenta), respectively. Data for each disorder were analyzed separately using a threshold sire model that included a fixed parity effect and random sire and herd-year-season of calving effects; both first lactation and all lactation analyses were carried out. Heritability estimates from first lactation (all lactation) analyses were 0.18 (0.15) for DA, 0.11 (0.06) for KET, 0.10 (0.09) for MAST, 0.07 (0.06) for LAME, 0.08 (0.05) for CYST, and 0.08 (0.07) for MET. Corresponding heritability estimates for the pooled incidence rate of all diseases between calving and 50 d postpartum were 0.12 and 0.10 for the first and all lactation analyses, respectively. Mean differences in PTA for probability of disease between the 10 best and 10 worst sires were 0.034 for DA, 0.069 for KET, 0.130 for MAST, 0.054 for LAME, 0.039 for CYST, and 0.120 for MET. Based on the results of this study, it appears that genetic selection against common health disorders using data from on-farm recording systems is possible.     

Random regression test-day model for clinical mastitis: Genetic parameters,model comparison,and correlations with indicator traits

The objective was to study genetic (co)variance components for binary clinical mastitis (CM), test-day protein yield, and udder health indicator traits [test-day somatic cell score (SCS) and type traits of the udder composite] in the course of lactation with random regression models (RRM). The study used a data set from selected 15 large-scale contract herds including 26,651 Holstein cows. Test-day production and CM data were recorded from 2007 to 2012 and comprised parities 1 to 3. A longitudinal CM data structure was generated by assigning CM records to adjacent official test dates. Bivariate threshold-linear RRM were applied to estimate genetic (co)variance components between longitudinal binary CM (0 = healthy; 1 = diseased) and longitudinal Gaussian distributed protein yield and SCS test-day data. Heritabilities for liability to CM (heritability ~0.15 from 0 to 305 d after calving) were slightly higher than for SCS for corresponding days in milk (DIM) in the course of lactation. Daily genetic correlations between CM and SCS were moderate to high (genetic correlation ~0.70), but substantially decreased at the very end of lactation. Genetic correlations between CM at different test days were close to 1 for adjacent test days, but were close to zero for test days far apart. Daily genetic correlations between CM and protein yield were low to moderate. For identical DIM (e.g., DIM 20, 160, and 300), genetic correlations were −0.03, 0.11, and 0.18, respectively, and disproved pronounced genetic antagonisms between udder health and productivity. Correlations between estimated breeding values (EBV) for CM from the RRM and official EBV for linear type traits of the udder composite, including EBV from 74 influential sires (sires with >60 daughters), were −0.31 for front teat placement, −0.01 for rear teat placement, −0.31 for fore udder attachment, −0.32 for udder depth, and −0.08 for teat length. Estimated breeding values for CM from the RRM were compared with EBV from a multiple-trait model and with EBV from a repeatability model. For test days covering an identical time span and on a lactation level, correlations between EBV from RRM, multiple-trait model, and repeatability model were close to 1. Most relevant results suggest the routine application of threshold RRM to binary CM to (1) allow selection of genetically superior sires for distinct stages of lactation and (2) achieve higher selection response in CM compared with selection strategies based on indicator type traits or based on the indicator-trait SCS.     

Heritabilities and correlations among body condition scores, production traits, and reproductive performance

The objectives of this study were to estimate the heritability of body condition scores (BCS) from producer and consultant-recorded data and to describe the genetic and phenotypic relationships among BCS, production traits, and reproductive performance. Body condition scores were available at calving, postpartum, first service, pregnancy check, before dry off, and at dry off from the Dairy Records Management Systems in Raleigh, NC, through the PCDART program. Heritabilities, genetic correlations, and phenotypic correlations were estimated assuming an animal model using average information REML. Herd-year-season effects and age at calving were included in all models. Prior calving interval was included in models for second and third lactations. Analyses that included reproductive traits were conducted with and without mature equivalent milk as a covariable. Heritability estimates for BCS ranged from 0.09 at dry-off to 0.15 at postpartum in first lactation. Heritability estimates ranged from 0.07 before dry-off to 0.20 at pregnancy check in second lactation and from 0.08 before dry-off to 0.19 at first service in third lactation. Genetic correlations between adjacent BCS within first lactation were greater than 0.96 with the exception of calving and postpartum (0.74). In second lactation, adjacent genetic correlations were 1.0 with the exception of calving and postpartum (0.84). Genetic correlations across lactations were greater than 0.77. Phenotypic correlations between scoring periods were highest for adjacent scoring periods and when BCS was lowest. Phenotypic correlations were lower than genetic correlations, i.e., less than 0.70. Higher BCS during the lactation were negatively related to production, both genetically and phenotypically, but the relationship was moderate. Higher BCS were favorably related genetically to reproductive performance during the lactation.