Sire Evaluation Research Articles

Short Communication: Genetic Analysis of Respiratory Disease in Norwegian Red Calves

A genetic analysis of respiratory disease (RD) in Norwegian Red calves was conducted. This is the first genetic study of calf diseases based on data from the Norwegian health recording system, and RD was chosen because it is the most frequent disease in calves. Records on absence or presence of RD before 180 d of age for 250,212 calves (progeny of 728 Norwegian Red artifical insemination sires) were analyzed with a threshold model. A total of 0.7% of the calves had veterinary treatment of RD before 180 d of age. Heritability of RD in the underlying scale was 0.05, with a 95% credibility interval from 0.02 to 0.09. The “worst” sire had a predicted probability of RD that was more than twice as high as that of the “best” one (1.15 vs. 0.44%). Their 95% credibility intervals for predicted probability of RD, however, overlapped. With improved and more extensive recording of calf diseases, the precision in genetic evaluation of sires could increase considerably. The frequency of RD is very low in the Norwegian Red population at present, so there is not much scope for genetic improvement. However, this study indicates that reasonably precise genetic evaluation of sires for resistance to RD could be feasible.

Genetic analysis of liability to clinical mastitis, with somatic cell score and production traits using bivariate threshold–linear and linear–linear models

Inferences about genetic and residual correlation estimates and sire evaluations involving a categorical trait with linear model are ambiguous and mostly based on data simulations. In this study, estimates of variance components and prediction of breeding values in a model with a categorical and a continuous trait were compared between threshold–linear (TLM) and linear–linear models (LLM) in analysis of large clinical mastitis (CM) field data. Data on CM, somatic cell score (SCS), 305-day milk (MY), protein (PY) and fat yield (FY) from first-lactation Finnish Ayrshire cows were used. Four bivariate analyses were made using a TLM in Bayesian analysis. Each analysis fitted CM and one continuous trait at a time. Corresponding bivariate analyses were made using a Gaussian linear model. Estimates of heritabilities for CM were 0.06 and 0.02 from TLM and LLM, respectively whilst heritability estimates of the continuous traits were similar from both models. Genetic correlations between CM–SCS, CM–MY, CM–PY, and CM–FY from TLM and LLM were 0.63 and 0.63; 0.36 and 0.36; 0.32 and 0.32; 0.30 and 0.29, respectively. Estimates of residual correlations were 0.11 and 0.06; − 0.04 and − 0.02; − 0.03 and − 0.02; − 0.05 and − 0.03 between CM–SCS, CM–MY, CM–PY, and CM–FY, respectively. Comparison between the models indicates similar estimates of genetic correlations with no underestimation with the linear model analysis. In CM evaluation, the comparison of model's predictive ability showed greater improvements in accuracy with the bivariate than with the univariate models. There was, however no clear advantage of univariate threshold model over univariate linear model, except for less accuracy sires.

Multi-trait and random regression approaches for addressing the wide range of weaning ages in Asturiana de los Valles beef cattle for genetic parameter estimation1

Weaning weight (WW) records of 24,066 Asturiana de los Valles beef cattle, including the progeny of 557 sires and 10,653 dams, were analyzed using a multitrait animal model (MAM) and a random regression model (RRM) in order to estimate the variance components and the breeding value of the animals. Three definitions of WW were used: early weaning (EW) for animals weaned before the age of 180 d; standard weaning (SW) for animals weaned between 180 and 240 d old; and late weaning (LW) for animals weaned between the ages of 240 and 365 d. The heritabilities (h(2)) were high (from 0.49 to 0.63), which fully agrees with previous estimates for this breed. The genetic correlations between EW and SW were 0.86, and lower between EW and LW at 0.543 to 0.622, using MAM and RRM models, respectively. Ranking of sires displayed changes depending on age at weaning of their offspring, which could explain the modest genetic progress reached using MAM evaluation. The first 2 eigenvalues of the random regression coefficient matrix explained 66 and 30% of the genetic variance, which implies important genetic variation underlying the form of the growth curve of the animals during the weaning period. The evaluation of sires according to the official method, as currently carried out by the breeders' association (WW adjusted previously to the age of 180 d), does not exploit the genetic differences in response to their production system where the calf is weaned at variable ages.

Evidence of Heterogeneity of Variance in Milk Yield among Holstein-Friesian Herds in Kenya

Three thousand, nine hundred and seventy five lactation records of Holstein- Friesian cows between 1968 and 1984 were used to investigate the existence of heterogeneity of variance in milk yield in Kenya. Coefficient of variation and standard deviations across herds were used to test heterogeneity of variance. Average herd standard deviation and coefficient of variation after correcting for the fixed affects were 672.2kg and 26.7% respectively, while scaled standard deviations between herds were 180.1kg and 3.4%.The scaling of observation s within individual herds by sample standard deviation minimized heterogeneity of variance of milk yield thereby increasing accuracy of selection. It was concluded that significant heterogeneity of variance in milk yield existed among herds and should be accounted for in sire and cow evaluation. The Kenya Veterinarian Vol. 25 2002: pp. 12-17

Short Communication: Modification of Genetic Evaluation of Herd Life from a Three-Trait to a Five-Trait Model in Canadian Dairy Cattle

The national genetic evaluation of herd life for Canadian dairy breeds was modified from a 3-trait to a 5-trait animal model. The genetic evaluation incorporates information from daughter survival (direct herd life) and information from conformation, fertility, and udder health traits that are related to longevity (indirect herd life). Genetic evaluations for direct herd life were based on cows’ survival from first calving to 120 days in milk (DIM), from 120 to 240 DIM, from 240 DIM to second calving, survival to third calving, and survival to fourth calving, which were analyzed using a multiple-trait animal model. Sire evaluations obtained for each of the 5 survival traits were combined into an overall sire evaluation for direct herd life. Sire evaluations for indirect herd life were based on an index of sire evaluations for dairy strength, feet and legs, overall mammary, rump angle, somatic cell score, milking speed, nonreturn rate in cows, and interval from calving to first service. A multiple-trait sire model based on multiple-trait across-country evaluation methodology was used to combine direct and indirect genetic evaluations for herd life into an overall genetic evaluation for herd life. Sire evaluations for herd life were expressed as an estimated transmitting ability for the number of lactations. The transmitting ability represents expected differences among daughters for herd life; and the average herd life was set to 3 lactations.

DOD incidence in thoroughbred foals between 23 and 45 weeks of age – growth, nutrition and genetic factors

The aim of this work was to understand the extent of developmental orthopaedic disease (DOD) multifactorial aetiology factors. Growth pattern, nutritional and genetic factors were studied on a population of 89 thoroughbred foals aged between 23 and 45 weeks: growth curve characteri- sation, feeding plan analysis and genetic study approach to stallion progeny were accomplished in order to evaluate their influence on DOD frequency. A statistically significant difference was found between DOD positive and DOD negative foals' weight; fillies were found to be the sub-population responsible for this difference. A shortage of energy and protein was found during the period con- sidered and there was an excess in calcium and phosphorus by the end of the period. Genetic factor analysis indicated an intensive utilisation of stallions with the higher results of DOD positive foals although the breeding system's objectives regarding this aspect remained unclear. Developmental Orthopaedic Diseases (DOD) / osteochondrosis / growth pattern / nutrition / sire evaluation

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.

Development of a Survival Model with Piecewise Weibull Baselines for the Analysis of Length of Productive Life of Holstein Cows in Japan

A total of 100,000 records of Holstein dairy cows in Hokkaido, Japan, were used to study the current characteristics of their length of productive life, to build up a suitable model for genetic survival analysis, and to apply the piecewise Weibull baseline model. The data for this analysis only included records of cows belonging to herds in which more than 60% of cows had type score. Compared with other studies, the proportions of cows with only 1 or 2 calvings were low (24 and 46%, respectively), indicating a very low culling rate in first or second parity in Hokkaido. The median length of productive life was about 1,250 d (3 yr and 5mo). Four different definitions of stages of lactation were studied. The best fit of the data was obtained when stages of lactation were chosen with cutpoints at 0, 60, 250, and 350 d after calving. Whatever the definition of stages of lactation, the piecewise Weibull baseline model was better than a unique baseline model. The estimated shape Weibull parameters ρ for the different parity×stage of lactation combinations varied greatly (1.0 to 5.0). The estimated hazard functions based on the piecewise Weibull baseline model were smooth and reflected the important changes in observed hazard within lactation. The results of this study are expected to contribute to the development of a genetic model for sire evaluation and to genetic improvement of length of productive life of Holstein cattle in Hokkaido, Japan.

Animal Model Versus Conventional Methods of Sire Evaluation in Sahiwal Cattle

A total of 1,367 first lactation records of daughters of 81 sires, having 5 or more progeny were used to evaluate sires by 3 different methods viz., least squares (LS), best linear unbiased prediction (BLUP) and derivative free restricted maximum likelihood (DFREML) method. The highest and lowest overall average breeding value of sires for first lactation 305 days or less milk yield was obtained by BLUP (1,520.72 kg) and LS method (1,502.22 kg), respectively. The accuracy, efficiency and stability of different sire evaluation methods were compared to judge their effectiveness. The error variance of DFREML method was lowest (191,112 kg 2 ) and its coefficient of determination of fitting the model was highest (33.39%) revealing that this method of sire evaluation was most efficient and accurate as compared to other methods. However, the BLUP method was most stable amongst all the methods having coefficient of variation (%) very near to unadjusted data (18.72% versus 19.89%). The higher rank correlations (0.7979 to 0.9568) between different sire evaluation methods indicated that there was higher degree of similarity of ranking sires by different methods ranging from about 80 to 96 percent. However, the DFREML method seemed to be the most effective sire evaluation method as compared to other methods for the present set of data.

Genetic parameters and single versus multi-trait evaluation of udder health traits

Genetic parameters were estimated for lactation average somatic cell score (SCS) and clinical mastitis (CM) for the first three lactations of multiparous Finnish Ayrshire cows. A multi-trait linear sire model was used for estimation of covariance components, and the efficiencies of single- versus multi-trait multi-lactation (MT) sire evaluations were compared. Heritability of SCS and CM in the first three lactations ranged from 0.11 to 0.13 and 0.02 to 0.03, respectively. Within lactation, genetic correlations between SCS and CM ranged from 0.68 to 0.72. Within both traits, across-lactation genetic correlations were lowest between 1 and 3, and highest between 2 and 3, with estimates ranging from 0.75 to 0.86 and from 0.81 to 0.98 for CM and SCS, respectively. Residual and phenotypic correlations were low and ranged from 0.09 to 0.13 and from 0.10 to 0.13, respectively. The absolute difference between genetic and residual correlations was from 0.5 to 0.6. Within-lactation genetic correlations between traits that are much less than unity suggest a multi-trait model for genetic evaluation of mastitis resistance. Comparison of model prediction performance between single-trait (ST) and MT models using a data splitting method showed that the MT model was more stable in predicting breeding values in future records of animals. Especially, for young sires and CM, the SD of EBVs from the MT model was 14 to 23% higher than the ST model, indicating more effective use of information in terms of revealing more genetic variation.

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.

Estimation of Genetic Correlations Among Countries in International Dairy Sire Evaluations with Structural Models

The increase in the number of participating countries and the lack of genetic ties between some countries has lead to statistical and computational difficulties in estimating the genetic (co)variance matrix needed for international sire evaluation of milk yield and other traits. Structural models have been proposed to reduce the number of parameters to estimate by exploiting patterns in the genetic correlation matrix. Genetic correlations between countries are described as a simple function of unspecified country characteristics that can be mapped in a space of limited dimensions. Two link functions equal to the exponential of minus the Euclidian distance between the coordinates of two countries and the exponential of minus the square of this Euclidian distance were used for the study on international simulated and field data. On simulated data, it was shown that structural models might allow an easier estimation of genetic correlations close to the border of the parameter space. This is not always possible with an unstructured model. On milk yield data, genetic correlations obtained from 22 countries for structural models based on 2 and 7 dimensions, respectively, were analyzed. Only a structural model with a large number of axes gave reasonable estimates of genetic correlations compared with correlations obtained for an unstructured model: 76.7% of correlations deviated by less than 0.030. Such a model reduces the number of parameters from 231 genetic correlations to 126 coordinates. On foot angle data, large deviations were observed between genetic correlations estimated with an unstructured model and correlations estimated with a structural model, regardless of the number of axes taken into account.

Comparison of models using different censoring scenarios for days open in Spanish Holstein cows

AbstractDays open data from 113 569 lactation records in 774 Spanish Holstein herds were analysed using standard linear models under two different editing procedures, and with two alternative methodologies that account for censoring: a censored linear model (CLM) and a Weibull survival analysis (SA) model. The first editing procedure excluded from the linear model all censored records for days open (LMnc), and the second defined days open as days from calving to the last known insemination or culling date, treating censored records as complete (LM). Sire variance estimates for days open were 61, 70 and 139 for LMnc, LM and CLM, respectively, and 0·026 for SA on a logarithmic scale. Heritability estimates were 0·05, 0·06 and 0·08 with LMnc, LM and CLM, respectively. Rankings of sires varied between methodologies: sire evaluations from LMnc and LM had rank correlations with evaluations from SA equal to −0·65 and −0·82, respectively, and of 0·71 and 0·87 with evaluations from CLM. The rank correlation between evaluations from SA and CLM was −0·98, suggesting stronger agreement of sire rankings between models that take censoring into account.The SA model had a better predictive ability of daughter fertility at early stages of lactation than the other methods, as measured by chi-squared statistics for predicted pregnancy status at 75, 103, 140, or 200 days post partum in a split data set. The CLM also predicted daughter fertility more accurately than any of the two standard linear models.

Genetic analysis of clinical mastitis in different risk periods by linear and threshold models

Understanding the genetic basis of clinical mastitis (CM) during lactation is essential in deciding on the best measure of CM for breeding value evaluation. CM is one of the leading reasons for premature disposal of dairy cows. Culled cows have incomplete lactation records, and in statistical analysis this may cause sampling biases, because culled cows are seldom a random sample. It is, therefore, essential to study the effects of culling information on the genetic analysis of CM. The objectives of this study were first: to estimate heritability and genetic association between CM during the different stages of first lactation using linear and threshold models, second: to assess the effects culling information on the estimates of variance (co)variance components and associated genetic parameters. First lactation was divided into six lactation stages (risk periods) by days (d) after calving: CM1, (-7 to 150 d); CM2, (-30 to 30 d); CM3, (-30 to 150 d); CM4, (31 to 150 d); CM5, (150 to 300 d); CM6, (-30 to 300 d). To assess the effect of including or excluding records of cows that had been culled from the herd before the end of the risk period, two data sets were prepared. In the first data set (Data I), records of cows culled before the end of the risk period were not included. In the second data set (Data II), records of cows culled during the risk period were included; if the cow had been culled for mastitis reasons or had completed two-thirds of the opportunity period if culled for other reasons. Variance (co)variance components were estimated by linear and threshold-liability models employing Bayesian approach. Heritability estimates using the linear model ranged from 0.005 to 0.024 for Data I, and from 0.005 to 0.029 for Data II, depending on the stages of lactation or risk period defined. The corresponding estimates from the threshold-liability model ranged from 0.034 to 0.076, and from 0.043 to 0.083 for Data I and Data II, respectively. In general, increased sire variance and higher heritability were observed with the inclusion of culling information. Estimated genetic correlations between the CM traits were medium to high, and ranged from 0.42 to 0.99 in Data I, and from 0.61 to 0.99 in Data II. Thus CM cannot be regarded the same trait in the course of lactation. Results from the threshold analyses showed that the heritability of liability to CM was highest for CM2 and followed by CM6, CM3 and CM1with estimates of 0.083, 0.073, 0.073 and 0.072, respectively. Estimates for CM4 and CM 5 were the lowest with heritability of 0.043 and 0.047, respectively. The high heritability estimate for CM2 indicates that most genetic information is in early lactation. Thus, the best measure of CM would be to consider only cases in early lactation. However, as the genetic correlation between CM traits defined over the different risk periods were less than one, mastitis cannot be regarded as the same trait in the different parts of lactation. Thus, a multivariate model, treating mastitis in different stages of lactation as different traits, would be the best model for sire evaluation.

Linear model vs. survival analysis for genetic evaluation of sires for longevity in Chianina beef cattle

The objective of this study was to compare sire EBVs for longevity in Chianina beef cattle estimated with linear models and survival analysis. Two datasets were created, one considered all data (SURVall), the other only uncensored records (SURVun). The linear models were used to analyze longevity measured as three correlated dichotomous (yes/no) measures of survival in the first three parities (LIN-S3) and as an overall measure of lifespan in months (LIN-LPL). Correlation between sire EBVs from the two survival analyses were 0.85. For LIN-S3 the correlations of EBVs across parities were between 0.69 to 0.93. Medium correlations (from 0.50 to 0.62) were found when only uncensored data (SURVun) were compared to the linear model (LIN-S3). Higher correlations (from 0.71 to 0.93) were found when EBV based on both censored and uncensored data (SURVall) were compared to LIN-S3. Heritability was estimated at 0.11, 0.09 and 0.08 for SURVall, SURVun and LIN-LPL, respectively; and 0.05, 0.02 and 0.02, respectively, for survival in the first three parities according to LIN-S3. Linear and non-linear models differed in many aspects; the most precise EBV were obtained when all data was used in the evaluation.

Identification of genetic markers for fat deposition and meat tenderness on bovine chromosome 5: Development of a low-density single nucleotide polymorphism map1,2

As genetic markers, SNP are well suited for the development of genetic tests for production traits in livestock. They are stable through many generations and can provide direct assessment of individual animal's genetic merit if they are in linkage disequilibrium and phase with functional genetic variation. Bovine chromosome 5 has been shown to harbor genetic variation affecting production traits in multiple cattle populations; thus, this chromosome was targeted for SNP-based marker development and subsequent association analysis with carcass and growth phenotypes. Discovery of SNP was performed in a panel of 16 sires representing two sires from each of seven beef breeds and two Holstein sires by PCR amplification and sequencing using primers designed from genomic sequence obtained by low-coverage sequencing of bacterial artificial chromosome (BAC) clones. From 550 SNP, 296 (54%) were tentatively identified as having a minor allele frequency >10%. Forty-five SNP derived from 15 BAC were chosen based on minor allele frequency and were genotyped in 564 steers and their sires. Production and carcass data were collected on the steers as a part of the Germplasm Evaluation (GPE), Cycle VII Project at the U.S. Meat Animal Research Center (Clay Center, NE), which involves of the evaluation of sires from seven of the most popular U.S. breeds. Haplotypes based on seven SNP derived from a BAC containing the bovine genes HEM1 and PDE1B were associated with traits related to carcass fat. Steers homozygous for the major haplotype had 0.15 +/- 0.04 cm less subcutaneous fat, 0.57 +/- 0.18 kg less rib fat, 0.18 +/- 0.07 lower yield grade, 1.11 +/- 0.35% less predicted fat yield, and 0.79 +/- 0.3% greater predicted retail product yield than heterozygotes. The frequency of the major haplotype was 0.70 in the steers, and it ranged from 0.44 (Limousin) to 0.98 (Simmental and Gelbvieh) in a panel consisting of an average of 20 purebred sires from each of the seven breeds. A second set of haplotypes based on four SNP derived from a BAC containing the genes NOL1 and CHD4 was associated with Warner-Bratzler shear force. Steers homozygous for the major haplotype had 0.27 +/- 0.11 kg greater shear force than those heterozygous for the major haplotype and one of two minor haplotypes. The frequency of the major haplotype was 0.59 in the steers and ranged from 0.27 (Hereford) to approximately 0.95 (Angus and Red Angus) in the panel of purebred sires. These results demonstrate the feasibility of targeting QTL regions for SNP-based marker development and that a low level of coverage can identify markers associated with phenotypic traits.

Principal Components and Factorial Approaches for Estimating Genetic Correlations in International Sire Evaluation

The increasing number of participating countries and the lack of genetic links among some of them lead to statistical and computational difficulties in estimating the genetic (co)variance matrix needed for international sire evaluation of milk yield. Reparameterization using principal components or factorial approaches is proposed to exploit patterns in the genetic correlation matrix in order to reduce the number of parameters to be estimated without much loss of information. A 2-step approach was used. First, the genetic matrix between 8 or 9 “base” countries was used to determine a reduced number of principal components or factors. Then, the contributions of the remaining countries to these principal components or factors were computed. The resulting genetic correlations for the 18 countries were compared with the “reference” genetic correlations obtained with a classical model. The impact of using reparameterized genetic correlation matrices on breeding value prediction was investigated for both approaches. A better agreement between predicted breeding values and stability of their rankings was found when an approximate factor analysis was used, whatever the number of factors considered. The estimation of genetic correlations among 18 countries using an approximate factorial approach with 5 factors taken into account led to a reduction of the number of parameters to estimate from 171 to 80. The average absolute deviation of the correlations estimated with an approximate factorial approach from the “reference” genetic correlations was 0.014, which is considered very satisfactory in light of the computational ease.

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 Evaluation of Dairy Sires for Milking Duration Using Electronically Recorded Milking Times of Their Daughters

The potential of using electronically recorded data from on-farm milking parlor and herd management software programs for genetic evaluation of dairy sires for milking duration of their daughters was assessed in the present study. Single measurements of milking duration were collected weekly from 29 herds between June 1, 2003 and April 1, 2004. These included 73,547 observations corresponding to 10,152 Holstein cows from 1551 sires. Average milking duration for a single milking in our data set was 4.5min. Estimated heritability of milking duration was 0.17, and predicted transmitting abilities (PTA) of individual sires ranged from −0.48min for sires with the fastest milking daughters to 0.59min for sires with the slowest milking daughters. The correlation between PTA for milking duration and PTA for somatic cell score (SCS) was −0.15, indicating that sires whose daughters milk most quickly also tend to transmit higher SCS to their progeny. Correlations between PTA milking duration and PTA for teat placement and teat length were −0.14 and 0.20, respectively, indicating that sires that transmit wide teat placement and long teats tend to have daughters that milk slowly. Based on the results presented herein, it appears that genetic selection based on objective, electronically recorded milking times is possible. This approach would greatly improve the quality and efficiency of data collection relative to conventional evaluations of milking speed, which are based on farmer surveys. The number of herds currently equipped to routinely capture milking times is limited, but this number is increasing very rapidly. Future research should focus on refinement of data reporting and validation systems, as well as estimation of the economic value of milking duration. This trait may have an intermediate optimum, because cows that milk too slowly will disrupt parlor flow and reduce milking efficiency, but cows that milk too quickly may be at greater risk for mastitis.

Genetic Evaluation of Mastitis Resistance Using a First-Passage Time Model for Wiener Processes for Analysis of Time to First Treatment

A Wiener process is a Brownian-motion process initiated in a certain state in a state space, and the first passage time is defined as the time of the process to reach a predefined absorbing state where the process stops. Time from 31 d prepartum to first treatment of clinical mastitis (CM) was modeled as first passage times of such Wiener processes. Two processes were used to allow for several risk factors, and for each process, initiation was at some arbitrary time point, in a certain health state with drift toward or away from absorption (disease). The drift parameter of each process was expressed as linear functions of covariates (year of calving and sire). First passage time was de-fined as the time from process initiation until the first health status process reached zero (absorption). The model was fitted to records for 36,178 first-lactation daughters of 245 Norwegian cattle sires using a Bayesian approach and Markov chain Monte Carlo methods. Genetic evaluation of sires was carried out by calculating the posterior probability of no CM (the value of the survival function) by d 331, i.e., 300 d after first calving. Alternatively, sire evaluation was based on the integrated area under the survival curve. These measures were highly correlated (0.999), which indicates a small degree of crossings of the sire-dependent survival curves. Hence, sire-specific hazards were close to proportional, resulting in a higher rank-correlation to sire evaluations from a survival model with proportional hazards than to the results from a multivariate threshold model.