Within-herd heritability estimated with daughter-parent regression for yield and somatic cell score

Estimates of heritability within herd () that were generated with daughter-dam regression, daughter-sire regression, and REML were compared, and effects of adjusting lactation records for within-herd heritability on genetic evaluations were evaluated. Holstein records for milk, fat, and protein yields and somatic cell score (SCS) from the USDA national database represented herds in the US Northeast, Southeast, Midwest, and West. Four data subsets (457 to 499 herds) were randomly selected, and a large-herd subset included the 15 largest herds from the West and 10 largest herds from other regions. Subset heritabilities for yield and SCS were estimated assuming a regression model that included fixed covariates for effects of dam yield or SCS, sire predicted transmitting ability (PTA) for yield or SCS, herd-year-season of calving, and age within parity. Dam records and sire PTA were nested within herd as random covariates to generate within-herd heritability estimates that were regressed toward mean for the random subset. Heritabilities were estimated with REML using sire models (REML_SIRE), sire-maternal grandsire models (REML_MGS), and animal models (REML_ANIM) for each herd individually in the large-herd subset. Phenotypic variance for each herd was estimated from herd residual variance after adjusting for effects of year-season and age within parity. Deviations from herd-year-season mean were standardized to constant genetic variance across herds, and records were weighted according to estimated error variance to accommodate when estimating breeding values. Mean tended to be higher with daughter-dam regression (0.35 for milk yield) than with daughter-sire regression (0.24 for milk yield). Heritability estimates varied widely across herds (0.04 to 0.67 for milk yield estimated with daughter-dam regression), and deviated from subset means more for large herds than for small herds. Correlation with REML_ANIM was 0.68 for daughter-dam and was 0.45 for daughter-sire for milk yield. The correlation between daughter-sire and REML_MGS was greater than the correlation between daughter-dam and REML_MGS. Data adjustments had a minimal impact on breeding value bias. Within-herd heritability can be estimated rapidly using regression techniques with moderate accuracy, but adjusting lactation records for resulted in only a small improvement in the accuracy of genetic evaluations.     

Genotype x environment interaction for grazing vs. confinement. II. Health and reproduction traits

Continual selection for increased milk yield for more than 40 yr, combined with the antagonistic relationship between increasing yield, somatic cell count, and fertility, have resulted in sires that may not be optimal for producing daughters for grazing systems where seasonal calving is very important. The objective of this study was to investigate the possible existence of a genotype x environment interaction (G x E) in grazing vs. confinement herds within the United States for lactation average somatic cell score (LSCS), days open (DO), days to first service (DFS), and number of services per conception (SPC). Grazing herds were defined as those that utilized grazing for at least 6 mo each year and were enrolled in Dairy Herd Improvement (DHI). Control herds were confinement DHI herds of similar size in the same states. For LSCS, the performance of daughters in grazing and control herds was examined using linear regression of LSCS on the November 2000 USDA-DHIA sire predicted transmitting abilities (PTA) for SCS. Genetic parameters for all traits were estimated using REML in a bivariate animal model that treated the same trait in different environments as different traits. Rank correlations were calculated between sires' estimated breeding values for LSCS, calculated separately for sires in both environments. The coefficient of regression of daughter LSCS on sire PTA was less in grazing herds than in control herds. The coefficient of regression for control herds was closer to expectation. Estimates of heritability were approximately 0.12 for LSCS, and less than 0.05 for the reproduction traits. Heritabilities for DO, DFS, and SPC were slightly higher for control herds. Estimates of genetic correlation for each reproductive trait between the 2 environments were high and not significantly different from unity. Generally, these traits appear to be under similar genetic control, but a lower coefficient of regression of LSCS on sire PTA for SCS in grazing herds suggests differences in daughter performance in grazing herds may be overstated based on current PTA for SCS.     

Genotype x environment interaction for grazing versus confinement. I. Production traits

The objective of this study was to investigate the possible existence of a genotype x environment interaction (GxE) for production traits of US Holsteins in grazing versus confinement herds. Grazing herds were defined as those that utilized grazing for at least 6 mo and were enrolled in dairy herd improvement (DHI). Control herds were confinement DHI herds of comparable size in similar regions. The performance of daughters in grazing herds and control herds was examined using linear regression of mature equivalent milk, fat, and protein yield on the November 2000 USDA-DHI predicted transmitting abilities (PTA) of their sires for those traits. Heritabilities and genetic correlations were estimated using restricted maximum likelihood in a bivariate animal model that considered the same trait in different environments as different traits. Product-moment and rank correlations were calculated between sires' estimated breeding values, estimated separately in both environments. For grazing herds, the coefficient of regression of milk, fat and protein on PTA were 0.78, 0.76, and 0.78, respectively. Corresponding coefficients in the control herds were 0.99, 0.96, and 0.98. Estimates of heritability for the traits ranged from 0.2 to 0.25, and differences between grazing and control environments were small. Estimates of the genetic correlations for the traits in both environments were 0.89, 0.88, and 0.91 for milk, fat, and protein, respectively. Within-quartile analyses revealed a lower correlation for milk and protein between the upper and lower grazing quartiles, while the same quartiles for the control herds did not differ from unity. Rank correlation coefficients between sire estimated breeding values from the 2 environments were 0.59, 0.63, and 0.66 for milk, fat, and protein, respectively. The mean rank change for the top 100 sires between the two environments was 27. The regression coefficients indicate that expected daughter differences may be overstated by current sire PTA in grazing herds. Genetic correlations less than unity suggests that there is, at least, some reranking among sires in both environments, while the rank correlations indicate the possibility of sire reranking when evaluations were performed within management system. However, differences are not so large as to justify separate genetic evaluations for each system.     

Short communication: variance estimates among herds stratified by individual herd heritability

The objectives of this study were to compare (co)variance parameter estimates among subsets of data that were pooled from herds with high, medium, or low individual herd heritability estimates and to compare individual herd heritability estimates to REML heritability estimates for pooled data sets. A regression model was applied to milk yield, fat yield, protein yield, and somatic cell score (SCS) records from 20,902 herds to generate individual-herd heritability estimates. Herds representing the 5th percentile or less (P5), 47th through the 53rd percentile (P50), and the 95th percentile or higher (P95) for herd heritability were randomly selected. Yield or SCS from the selected herds were pooled for each percentile group and treated as separate traits. Records from P5, P50, and P95 were then analyzed with a 3-trait animal model. Heritability estimates were 23, 31, 26, and 8% higher in P95 than in P5 for milk yield, fat yield, protein yield, and SCS, respectively. The regression techniques successfully stratified individual herds by heritability, and additive genetic variance increased progressively, whereas permanent environmental variance decreased progressively as herd heritability increased.     

Relationships of sire predicted transmitting ability for somatic cell score with measures of daughter performance.

The relationship between sire predicted transmitting ability (PTA) for somatic cell score (SCS) and occurrence of mastitis in daughters was characterized with 304 Holsteins first freshening since 1991 in the Virginia Tech dairy herd. No direct sire selection for PTA SCS was practiced in the herd. Linear regressions of first lactation average SCS (1.3), number of cases of mastitis (0.80), days clinical (7.0), and number of treatments for mastitis (2.0) on PTA SCS were significant. Linear regression coefficients for second and third lactations were generally positive, but like quadratic and cubic coefficients in this study, were not significant. Relationship of sire PTA SCS and measures of lifetime (84 mo opportunity) performance were determined for 2,494,195 Holsteins born between 1979 and 1987. Linear coefficients from regression on PTA SCS for number of lactations (-0.31), days of productive life (-87.0), total days in milk (-72.2), lifetime milk (-1609.1), fat (-80.7), and protein (-48.2) production, relative net income adjusted for opportunity cost for fluid markets (+13.47), first lactation average SCS (+1.00), and first lactation mature equivalent milk (+429.9) were different from zero. Partial regressions holding PTA milk or PTA protein constant were substantially larger than linear coefficients. When PTA productive life was held constant, the relationship between PTA SCS and measures of herd life and lifetime yield became positive, and relative net income adjusted for opportunity cost variables became moderately positive. One of the major expenses associated with higher susceptibility (higher PTA SCS) may be shorter herd life.     

Prediction of most recent evaluations of Holstein bulls from first available pedigree information

The objectives of this study were to predict most recent evaluations of young bulls entering artificial insemination (AI) sampling programs from pedigree information available at time of sampling and investigate whether prediction equations differ among AI organizations. Data were pedigree information and most recent USDA evaluations on bulls entering AI sampling programs from 1989 through 1994. Pedigree information included earliest available parent average, predicted transmitting abilities (PTA) of sire, dam, and maternal grand sire. Most recent evaluations were from May 2000 evaluations and included PTA and daughter yield deviations for milk, fat, and protein. Regression coefficients on PTA of sire and PTA of dam were less than the expected coefficient of 0.50. Accuracy of prediction as determined by R-square values was less than 12%. Inclusion of PTA of maternal grand sire after PTA of sire and dam increased the accuracy of prediction by less than 1%, but regression coefficients on PTA of maternal grand sire differed from 0. Regressions on parent average were not different among AI organizations for prediction of PTA and daughter yield deviations. Partial regression coefficients on PTA of sire differed among AI organizations for prediction of fat and protein but did not differ for milk. Coefficients on PTA of dam did not differ among organizations. These results indicate that AI organizations put different emphasis on PTA of sire in selection of sons for fat and protein.     

Sensitivity of Methods of Variance Component Estimation to Culling Type of Selection

Two pseudoexpectation methods of variance component estimation were examined for selection bias from culling and were compared with Henderson's simple method and restricted maximum likelihood. Milk yield data were simulated for 100 herds, 50 sires, and approximately 2000 cows per replicate. Heritability was .25, and repeatability was .50. Each cow had two records. Twenty replicates were made. Two data sets were created; one was unselected and included both records of the cow, and the second excluded second records of cows with first records below the herd mean. Sire, cow, and error variances were estimated according to a mixed model that included fixed herd, parity, and random sire, cow, and error effects. Estimates of sire, cow, and error variances and heritability and repeatability were unbiased and were similar for all methods for unselected data. For selected data, sire and cow variances were overestimated and error variances were underestimated with Henderson's simple method and the pseudoexpectation methods. Accordingly, heritabilities and repeatabilities were overestimated. Biases were greatest for Henderson's simple method and differences between the two pseudoexpectation methods were small. With restricted maximum likelihood, sire variances were unbiased but error variances were slightly overestimated and cow variances underestimated. Use of restricted maximum likelihood is preferred with selected data.     

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.     

Impact of genetic merit for milk somatic cell score of sires and maternal grandsires on herd life of their daughters

A retrospective study of the impact of the estimated breeding values of sires and maternal grandsires for somatic cell score (SCS) on productive life (PL) of Holsteins and Jerseys was conducted. Data included records from 2,626,425 Holstein and 142,725 Jersey cows. The sires and maternal grandsires of cows were required to have been available through artificial insemination and to have predicted transmitting ability (PTA) SCS evaluations based on 35 or more daughters. A weighted function (WPTA) of sire and maternal grandsire PTA for SCS was used: (sire PTA + 0.5 maternal grandsire PTA)/1.5. The 3 dependent variables were PL, frequency of cows culled for mastitis, and first-lactation SCS. The model included effects of herd, birth year, and WPTA (WPTA was categorized into groups: <2.70, 2.70 to 2.79, …, 3.20 to 3.29, ≥3.30). For analysis of first-lactation SCS, calving year and calving month were substituted for birth year. Differences among WPTA groups were highly significant: as WPTA increased, PL decreased, whereas percentage culled for mastitis and first-lactation SCS increased. The range in PL from lowest to highest WPTA was 5.07 mo for Holsteins and 4.73 mo for Jerseys. Corresponding differences for percentage culled for mastitis were 7.0 and 5.6% and for SCS were 0.95 and 1.04 (for Holsteins and Jerseys, respectively). Although phenotypic studies suggest that cows with extremely low SCS were less resistant to mastitis, our results showed consistent improvements in PL, percentage culled for mastitis, and SCS of daughters when bulls were chosen for low PTA SCS.     

Identification of factors that cause genotype by environment interaction between herds of Holstein cattle in seventeen countries

Currently, the International Bull Evaluation Service calculates international dairy sire evaluations using the multiple-trait across country evaluation procedure. This method depends implicitly on political boundaries between countries, because the input data are national evaluations from each participating country. Therefore, different countries are treated as different production environments. The goal of this study was to identify factors that describe the production system on each farm. Such factors could be used to group herds across countries for borderless genetic evaluations. First lactation milk records of Holstein cows calving between January 1, 1990 and December 31, 1997 in Australia, Austria, Belgium, Canada, Czech Republic, Estonia, Finland, Germany, Hungary, Ireland, Israel, Italy, The Netherlands, New Zealand, South Africa, Switzerland, and the USA were used in this study. Thirteen genetic, management, and climatic variables were considered as potential indicators of production environments: peak milk yield, persistency, herd size, age at first calving, seasonality of calving, standard deviation of milk yield, culling rate, days to peak yield, fat to protein ratio, sire PTA milk, percentage of North American Holstein genes, maximum monthly temperature, and annual rainfall. Herds were grouped into quintiles based on herd averages for each of these variables. Genetic correlations for lactation milk yield between quintiles were significantly less than one for maximum monthly temperature, sire PTA milk, percent North American Holstein genes, herd size, and peak milk yield. The variables can be used to group herds into similar production environments, regardless of country borders, for the purpose of accounting for genotype by environment interaction in international dairy sire evaluation.     

Genetic parameters and evaluation of rear legs (rear view) for Brown Swiss and Guernseys

Genetic parameters were estimated for rear legs (rear view; RLRV) and 15 current linear type traits of Brown Swiss and Guernsey dairy cattle. The Brown Swiss Cattle Breeders’ Association of the USA and the American Guernsey Association began scoring RLRV in 2004. For Brown Swiss, 8,502 records were available for 7,676 cows in 417 herds; Guernsey data included 5,437 records for 4,749 cows in 229 herds. Nine unknown-parent groups were defined for each breed, each with 2 birth years. The model included fixed effects for the interaction of herd, appraisal date, and parity; appraisal age within parity; and lactation stage within parity and random effects for animal, permanent environment, and residual error. The multitrait analysis for RLRV and the 15 linear type traits used canonical transformation, multiple diagonalization, and a decelerated expectation-maximization REML algorithm. For Brown Swiss, heritability was 0.102 for RLRV and ranged from 0.099 for rear legs (side view) to 0.453 for stature. For Guernseys, heritability ranged from 0.078 for RLRV to 0.428 for stature. For Brown Swiss, the highest genetic correlation with RLRV was 0.71 for rear udder width; the most negative correlation was −0.19 with rump angle. For Guernseys, the highest genetic correlations with RLRV were 0.43 for rear udder width and 0.42 for body depth; the most negative correlation was −0.46 with rear legs (side view). With heritability near 0.10, RLRV should be useful in selection for improved locomotion. Release of genetic evaluations for RLRV began in May 2006 for Brown Swiss and Guernseys.     

Comparisons of cows and herds in two progeny testing programs and two corresponding states

Data were USDA genetic evaluations of cows and DHI herd profiles from 4154 Holstein progeny-test herds from two artificial insemination organizations. 21st Century Genetics (Shawano, WI) and Genex (Ithaca, NY), and from 6361 additional herds from Minnesota and New York. We grouped herds into four categories: 21st Century Genetics herds, other Minnesota herds, Genex herds, and other New York herds. Herds were eliminated if they contributed fewer than 10 cows with genetic evaluations and birth dates from January 1989 to March 1995. Data included 83 and 74%, respectively, of first-crop daughters of 21st Century Genetics and Genex progeny-test bulls with genetic evaluations from January 1995 to February 1997. Herds were characterized by DHI profile and cow evaluation data. Daughters of progeny-test bulls with extreme production records (outside of 3 SD) relative to herd mean and variance did not appear in disproportionate numbers among the progeny of bulls likely culled or considered for further use. The two organizations appear to have selected larger, genetically superior, and better managed herds from within their respective regions for progeny-testing purposes. We were not able to predict whether a bull in the progeny-testing programs of these two organizations was going to exceed or fail to meet the pedigree prediction from characteristics of herds in which his daughters performed. Differences between parent average and daughter yield deviations for typical young sires appear to result from Mendelian segregation of genes.     

Genotype by environment interaction for production traits while accounting for heteroscedasticity

Grazing (G) provides an alternative management system for dairy production. Heteroscedasticity (HV) of the data may bias estimates of genetic correlations of yield traits between environments, an indicator of genotype-by-environment interaction (G×E). The objective of this study was to investigate the effect of HV on estimates of heritabilities and genetic correlations for mature-equivalent milk, protein, and fat yield, and lactation-average somatic cell scores of daughters, and to determine if HV affects the ability of sire's predicted transmitting ability (PTA) to predict daughter production in G and confinement (C) herds. Data consisted of 72,489 records from 35,674 cows in 366 G herds from 11 states, and 117,629 records from 50,963 cows in 373 C herds from the same 11 states plus 1 geographically contiguous state. Herds were divided into variance quartiles (Q_V1-Q_V4) based on milk yield. A transformation was used to reduce HV by standardizing the within-herd standard deviation to the average across-herd standard deviation of a base year for each parity, and was similar to the method used in current USDA-DHIA genetic evaluations. Regression of daughter yield on sire PTA showed that PTA overestimated production of all traits in Q_V1-Q_V3 and of milk in Q_V4 of G herds. For C herds, yields of milk in Q_V1 and Q_V2, and of protein and fat in Q_V1 were overestimated, and protein was underestimated in Q_V4. Reducing HV had little effect on G herds, but for C herds, regression did not differ from unity for milk and protein in Q_V1 and Q_V2. For milk, protein, and fat in G, heritabilities were approximately 0.17, 0.17, and 0.19, respectively. The heritabilities for milk, protein, and fat in C herds were approximately 0.16, 0.17, and 0.21, respectively. Genetic correlations between C and G did not suggest a G×E in 3 upper quartiles, but a possible G×E (correlation = 0.21, estimated standard error = 0.22) for the lowest quartile. Reducing HV did not affect estimates of heritabilities or genetic correlations. Results indicated that modest evidence for existence of G×E did not arise solely from HV.     

Stillbirth (co)variance components for a sire-maternal grandsire threshold model and development of a calving ability index for sire selection

(Co)variance components for stillbirth in US Holsteins were estimated under a sire-maternal grandsire threshold model using subsets of data from the national calving ease database, which includes over 6 million calving records with associated stillbirth scores. Stillbirth was coded as a binomial trait indicating whether the calf was alive 48 h postpartum. Records were selected for calves whose sire and maternal grandsire (MGS) were among the 2,600 most frequently appearing bulls (2,578 sires and 2,586 MGS). Herd-years were required to contain at least 20 records and only single births were used. After editing, the data set included 2,083,979 calving records from 5,765 herds and 33,304 herd-years. Six sample datasets of approximately 250,000 records each were created by randomly selecting herd codes. Quasi-REML and Bayesian approaches were used to estimate (co)variance components from each sample. The model included fixed year-season, parity-sex, birth year group of sire, and birth year group of MGS effects and random herd-year, sire, MGS, and residual effects. Quasi-REML and Bayesian analyses produced similar results, although the Bayesian estimates were slightly larger. Marginal posterior means (and standard deviations) from the Bayesian analysis averaged 0.0085 (0.0015), 0.0181 (0.0020), 0.0872 (0.0538), and 0.00410 (0.0001) for sire, MGS, and herd-year variances and the sire-MGS covariance, respectively. Mean direct and maternal heritabilities were 0.030 (0.003) and 0.058 (0.005), respectively, and the mean genetic correlation between the 2 effects was −0.02 (0.16). A calving ability index combining stillbirth (SB) and calving ease (CE) was developed for inclusion in the Lifetime Net Merit index. The index was calculated as −4(sire CE)−3(daughter CE)−4(sire SB) −8(daughter SB).     

Characterization of dairy production systems in countries that participate in the International Bull Evaluation Service.

The International Bull Evaluation Service Centre has routinely calculated international dairy sire evaluations since 1994. Production systems vary between countries and between herds within a country, and these differences can cause significant genotype x environment interactions. First-lactation records of Holstein cows calving from January 1, 1990 through December 31, 1997, were used in this study. Countries that provided data for this study included Australia, Austria, Belgium, Canada, Czech Republic, Estonia, Finland, Germany, Hungary, Ireland, Israel, Italy, The Netherlands, New Zealand, South Africa, Switzerland, and the United States. Country means were calculated for 13 variables related to climate, herd management, and genetic background. These variables were considered as possible causes of genotype by environment interaction. Highest peak yields were found in Israel and the United States at 31.4 and 30.5 kg, respectively. New Zealand and Estonia had the lowest daily peak yields at 17.1 and 18.9 kg, respectively. This was consistent with genetic differences between these countries, because Israel had the highest average predicted transmitting abilities (PTA) milk among sires, while Estonia had the lowest PTA milk. Persistency of lactation, defined as milk yield at 240 d postpartum divided by milk yield at 60 d postpartum, was highest in the Czech Republic and Estonia at 1.34, and lowest in Israel at 1.05. Herd size also varied substantially between countries, ranging from 2.3 first-lactation cows per herd-year in Finland to 62 per herd-year in Hungary. In summary, tremendous variation exists between the leading dairy countries in management, genetic, and climatic factors.     

Prediction of longevity breeding values for US Holstein sires using survival analysis methodology

Breeding values of Holstein sires for daughter longevity in each of 9 geographical regions of the United States were predicted using a Weibull proportional hazards model. Longevity (also commonly referred to as herd life or length of productive life) was defined as the number of days from first calving until culling or censoring. Records from 2,322,389 Holstein cows with first calving from 1990 to 2000 were used. In addition to the sire's additive genetic merit, our failure time model included time-dependent effects of herd-year-season of calving, parity-stage of lactation, and within-herd-year quintiles for mature equivalent fat plus protein yield, as well as the time-independent effect of age at first calving. Sire variances and parameters of the Weibull distribution were estimated separately for each region. The relative risk of culling for daughters of each individual sire was expressed relative to that of daughters of an average sire (within a specific region). Predicted breeding values for functional longevity, expressed as relative risk ratios, ranged from 0.7 to 1.3. Sizable differences were observed between geographical regions in sire rankings, as well as estimated sire variances and gamma parameters (of the distribution of herd-year-season effects), suggesting that a single national ranking may not be appropriate for every region. Two random samples of herds were selected from the full national data set; these contained 375,086 records and 256,751 records, respectively. Predicted transmitting abilities (PTA) of sires for daughter longevity were calculated using the Weibull proportional hazards (sire) model described previously but without the correction for milk production. These were compared with predictions from a linear (animal) model, as currently used for routine genetic evaluation of length of productive life in the United States. Logistic regression of daughters' stayability to 36, 48, 60, 72, or 84 mo of life (among animals that had opportunity to stay that long) on sires' PTA indicated that the proportional hazards model yielded more accurate predictions of daughter longevity than the linear animal model, even though the latter relied on denser pedigree information.     

Short communication: Genetic parameters of production traits in Chinese Holsteins using a random regression test-day model

The objective of this study was to estimate genetic parameters of production traits in the first 3 parities in Chinese Holsteins. Data were a random sample of complete herds (109,005 test-day records of 9,706 cows from 54 herds) extracted from the original data set, which included 362,304 test-day records of 30,942 Holstein cows from 105 herds. A test-day animal model with multiple-trait random regression and the Gibbs sampling method were used for parameter estimation. Regression curves were modeled using Legendre polynomials of order 4. The multiple-trait analysis included milk, fat, and protein yield, and somatic cell score (SCS). Average daily heritabilities ranged between 0.222 and 0.346 for the yield traits and between 0.092 and 0.187 for SCS. Heritabilities were higher in the third lactation for all traits. Within-parity genetic correlations were very high among the yield traits (>0.806) and were close to zero between SCS and yield traits, especially for first-parity cows. Results were similar to previous literature estimates from studies that used the same model as applied to this study. The estimates found in this study will be used to perform breeding value estimation for national genetic evaluations in Chinese Holsteins.     

Comparison of heritability estimates from daughter on dam regression with three models to account for production level of dam

Three models were used to estimate heritabilities for milk yields at different production levels and for different years as twice the regression of daughter residual effects on dam residual effects. The denominator is the residual mean square for dams. The numerator is the difference between the residual term for sum of dam's and daughter's records and sum of residual terms for records of dams and daughters. Model 1 included sire of daughter and herd-year-season of daughters only. Model 2 included sire of daughter, herd-year-season of dam, and herd-year-season of daughter. Model 3 included sire of daughter and herd-year-season of dam and herd-year-season of daughter combination. The weighted mean estimates for each method were, respectively, .35, .38, .38 for milk production and .61, .67, .67 for fat test. Yearly time trends in heritability were slightly positive for both milk production and fat test. Standard errors of heritability estimates from model 1 were 40 to 50% smaller than those from models 2 and 3 due to the smaller number of effects in the model. Estimates for model 2 from low to high production levels averaged .30, .38, .38, and .42 for milk yield and .64, .68, .67, and .71 for fat test.     

Impact of area and sire by herd interaction on heritability estimates for somatic cell count in Italian Holstein Friesian cows.

The aim of the paper was to estimate variance components for somatic cell scores for Italian Holsteins using data from three different areas of the country. A total of 2,202,804 first-parity test-day records, collected from 1990 to 1997 in three areas of Italy (Mantova, Milano, and Parmigiano cheese area), were available for study. The areas differ in herd size, feeding systems and especially in milk use. A minimum standard of quality is also required by some specific methods of cheese production, as for example from the Parmigiano Reggiano cheese chain. These reasons, taken together, affect the attention given to the quality of milk production in herds, and, therefore, to the sanitation levels. A pedigree file was extracted from the national database of Holstein Friesian breed. For computational reasons, eight samples of the data were extracted per area. Variance components were estimated by sample using two different test-day repeatability models. The first model included fixed effects of herd-test date, days in milk (30-d intervals) and calving month, and random effects of permanent environment, additive genetic and residual error. Estimated heritabilities in the first model ranged from 0.06 to 0.09 and repeatabilities from 0.36 to 0.45. Only small differences were detected among areas. In the second model, a random sire x herd interaction effect was added. Including the sire x herd effect resulted in heritability estimates ranging between 0.05 and 0.08 and repeatabilities from 0.35 to 0.45. The analysis revealed that only a small fraction of the total variance (0.35 to 1.5%) could be explained by sire x herd interaction effect. Based on this research, it appears that parameter estimates for somatic cell count do not differ by region, and inclusion of a sire x herd interaction effect is unnecessary.