Assessing the accuracy of prediction for milk fatty acids by using a small reference population of tropical Holstein cows

Fatty acids (FA) have been related to effects on human health, sensory quality and shelf life of dairy products, cow's health and methane emission. However, despite their importance, they are not regularly measured in all dairy herds yet, which can affect the accuracy of estimated breeding values (EBV) for these traits. In this case, an alternative is to use genomic selection. Thus, the aim was to assess the use of genomic information in the genetic evaluation for milk traits in a tropical Holstein population. Monthly records (n = 36,457) of milk FA percentage, daily milk yield and quality traits from 4,203 cows as well as the genotypes of 755 of these cows for 57,368 single nucleotide polymorphisms (SNP) were used. Polygenic and genomic–polygenic models were applied for EBV prediction, and both models were compared through the EBV accuracy calculated from the prediction error and Spearman's correlation among EBV rankings. Prediction accuracy was assessed by using cross‐validation. In this case, the accuracy was the correlation between the genomic breeding values (GEBV) obtained as the sum of SNP effects and the EBV obtained in the polygenic model in each validation group. For all traits, the use of the genomic–polygenic model did not alter the animals' ranking, with correlations higher than 0.87. Nevertheless, through this model, the accuracy increased from 1.5% to 6.8% compared to the polygenic model. The correlations between GEBV and EBV varied from 0.52 to 0.68. Therefore, the use of a small group of genotyped cows in the genetic evaluation can increase the accuracy of EBV for milk FA and other traditional milk traits.     

Estimates of genetic variance and variance of predicted genetic merits using pedigree or genomic relationship matrices in six Brown Swiss cattle populations for different traits

The amount of variance captured in genetic estimations may depend on whether a pedigree‐based or genomic relationship matrix is used. The purpose of this study was to investigate the genetic variance as well as the variance of predicted genetic merits (PGM) using pedigree‐based or genomic relationship matrices in Brown Swiss cattle. We examined a range of traits in six populations amounting to 173 population‐trait combinations. A main aim was to determine how using different relationship matrices affect variance estimation. We calculated ratios between different types of estimates and analysed the impact of trait heritability and population size. The genetic variances estimated by REML using a genomic relationship matrix were always smaller than the variances that were similarly estimated using a pedigree‐based relationship matrix. The variances from the genomic relationship matrix became closer to estimates from a pedigree relationship matrix as heritability and population size increased. In contrast, variances of predicted genetic merits obtained using a genomic relationship matrix were mostly larger than variances of genetic merit predicted using pedigree‐based relationship matrix. The ratio of the genomic to pedigree‐based PGM variances decreased as heritability and population size rose. The increased variance among predicted genetic merits is important for animal breeding because this is one of the factors influencing genetic progress.     

Genetic parameters of milk production traits and fatty acid contents in milk for Holstein cows in parity 1 – 3

The objective of this study was to estimate genetic parameters of milk, fat, and protein yields, fat and protein contents, somatic cell count, and 17 groups and individual milk fatty acid (FA) contents predicted by mid‐infrared spectrometry for first‐, second‐ and third‐parity Holstein cows. Edited data included records collected in the Walloon region of Belgium from 37 768 cows in parity 1, 22 566 cows in parity 2 and 8221 in parity 3. A total of 69 (23 traits for three parities) single‐trait random regression animal test‐day models were run. Approximate genetic correlations among traits were inferred from pairwise regressions among estimated breeding values of cow having observations. Heritability and genetic correlation estimates from this study reflected the origins of FA: de novo synthetized or originating from the diet and the body fat mobilization. Averaged daily heritabilities of FA contents in milk ranged between 0.18 and 0.47. Average daily genetic correlations (averaged across days in milk and parities) among groups and individual FA contents in milk ranged between 0.31 and 0.99. The genetic variability of FAs in combination with the moderate to high heritabilities indicated that FA contents in milk could be changed by genetic selection; however, desirable direction of change in these traits remains unclear and should be defined with respect to all issues of importance related to milk FA.     

Genomic additive and dominance variance of milk performance traits

Milk performance traits are likely influenced by both additive and non‐additive (e.g. dominance) genetic effects. Genetic variation can be partitioned using genomic information. The objective of this study was to estimate genetic variance components of production and milk component traits (e.g. acetone, fatty acids), which are particularly important for milk processing or which can provide information on the health status of cows. A genomic relationship approach was applied to phenotypic and genetic information of 1295 Holstein cows for estimating additive genetic and dominance variance components. Most of the 17 investigated traits were mainly affected by additive genetic effects, but protein content and casein content also showed a significant contribution of dominance. The ratio of dominance to additive variance was estimated as 0.64 for protein content and 0.56 for casein content. This ratio was highest for SCS (1.36) although dominance was not significant. Dominance effects were negligible in other moderately heritable milk traits.     

Comparison of heritabilities of dairy traits in Australian Holstein‐Friesian cattle from genomic and pedigree data and implications for genomic evaluations

The reliability of genomic evaluations depends on the proportion of genetic variation explained by the DNA markers. In this study, we have estimated the proportion of variance in daughter trait deviations (DTDs) of dairy bulls explained by 45 993 genome wide single‐nucleotide poly‐ morphism (SNP) markers for 29 traits in Australian Holstein‐Friesian dairy cattle. We compare these proportions to the proportion of variance in DTDs explained by the additive relationship matrix derived from the pedigree, as well as the sum of variance explained by both pedigree and marker information when these were fitted simultaneously. The propor‐ tion of genetic variance in DTDs relative to the total genetic variance (the total genetic variance explained by the genomic relationships and pedigree relationships when both were fitted simultaneously) varied from 32% for fertility to approximately 80% for milk yield traits. When fitting genomic and pedigree relationships simultaneously, the variance unexplained (i.e. the residual variance) in DTDs of the total variance for most traits was reduced compared to fitting either individually, suggesting that there is not complete overlap between the effects. The proportion of genetic variance accounted by the genomic relationships can be used to modify the blending equations used to calculate genomic estimated breeding value (GEBV) from direct genomic breeding value (DGV) and parent average. Our results, from a validation population of young dairy bulls with DTD, suggest that this modification can improve the reliability of GEBV by up to 5%.     

Genetic analysis of fat‐to‐protein ratio,milk yield and somatic cell score of Holstein cows in Japan in the first three lactations by using a random regression model

We estimated the genetic parameters of fat‐to‐protein ratio (FPR) and the genetic correlations between FPR and milk yield or somatic cell score in the first three lactations in dairy cows. Data included 3 079 517 test‐day records of 201 138 Holstein cows in Japan from 2006 to 2011. Genetic parameters were estimated with a multiple‐trait random regression model in which the records within and between parities were treated as separate traits. The phenotypic values of FPR increased soon after parturition and peaked at 10 to 20 days in milk, then decreased slowly in mid‐ and late lactation. Heritability estimates for FPR yielded moderate values. Genetic correlations of FPR among parities were low in early lactation. Genetic correlations between FPR and milk yield were positive and low in early lactation, but only in the first lactation. Genetic correlations between FPR and somatic cell score were positive in early lactation and decreased to become negative in mid‐ to late lactation. By using these results for genetic evaluation it should be possible to improve energy balance in dairy cows.     

Genetic relationships of fertility traits with test‐day milk yield and fat‐to‐protein ratio in tropical smallholder dairy farms

The test‐day milk fat‐to‐protein ratio (TD‐FPR) could serve as a measure of energy balance status and might be used as a criterion to improve metabolic stability and fertility through genetic selection. Therefore, genetic parameters for fertility traits, test‐day milk yield (TD‐MY) and TD‐FPR, as well as, their relationships during different stages of lactation, were estimated on data collected from 25 968 primiparous Thai dairy crossbred cows. Gibbs sampling algorithms were implemented to obtain (co)variance components using both univariate linear and threshold animal models and bivariate linear‐linear and linear‐threshold animal models with random regression. Average TD‐MY and TD‐FPR were 12.60 and 1.15. Heritability estimates for TD‐MY, TD‐FPR and selected fertility traits ranged from 0.31 to 0.58, 0.17 to 0.19 and 0.02 to 0.05, respectively. Genetic correlations among TD‐FPR and TD‐MY, TD‐FPR and fertility traits, and TD‐MY and fertility traits ranged from 0.05 to ‐0.44, from ‐0.98 to 0.98 and ‐0.22 to 0.79, respectively. Selection for lower TD‐FPR would decrease numbers of inseminations per conception and increase conception at first service and pregnancy within 90 days. In addition, cow selection based only on high milk production has strong effects to prolong days to first service, days open and calving interval.     

Estimation of Variances and Covariances of Milk Traits by REML with an Individual Animal Model

Abstract

Records form Finnish Ayrshire cattle were used to estimate variances and covariances of milk traits by the restricted maximum likelihood (REML) method using the individual animal model (IAM). Two data sets were analyzed. The first data set consisted of 1423 sires and 16363 cows, of which 11911 had records on first lactation. The heritabilities estimated from this data set for milk yield, protein yield, protein content and fat content were 0.40, 0.31, 0.63 and 0.68, respectively. The second data set was a subset of first data set with herds with less than ten observations excluded and consisted of 1335 sires and 11262 cows with 8140 first, 5688 second and 3717 third lactation records. The heritability estimates from the second data set under a repeatability model for milk yield, protein yield, protein content and fat content were 0.30, 0.26, 0.59 and 0.66, respectively. The repeatability estimates for the same traits were 0.53, 0.51, 0.67 and 0.76, respectively. The second data set was also used to estimate genetic and phenotypic correlations among milk traits in first lactation. Both genetic and phenotypic correlations among protein yield and protein and fat content traits were small. The genetic correlation between milk yield and protein content was -0.61, between milk yield and fat content -0.50 and between protein content and fat content 0.67. Absolute values of phenotypic correlations for the same pairs of traits were somewhat smaller than respective genetic correlations.     

Genetic analysis of the prevalence of abomasal displacement and its relationship to milk output characteristics in German Holstein cows

Genetic parameters for the prevalence of abomasal displacement and for milk yield traits were estimated using a data set of 3578 cows. The animals originated from 50 farms near Hanover being under the official milk recording scheme. At these farms all cases of abomasal displacement in German Holsteins were registered from July 2001 to January 2003. Using REML heritability estimates in linear animal models were h2 = 0.034 +/- 0.014, h2 = 0.017 +/- 0.013 and h2 = 0.029 +/- 0.011 for all cases of abomasal displacement, leftsided abomasal displacement and rightsided abomasal displacement, respectively. Additive genetic correlations between all cases of abomasal displacement and milk yield traits were small, ranging from rg = -0.20 (fat content) to rg = 0.08 (milk kg). However, there was a highly positive additive genetic correlation between leftsided abomasal displacement and milk yield of rg = 0.683 +/- 0.227. Leftsided abomasal displacement was correlated additive genetically to fat and protein yield, fat and protein content with rg = 0.595 +/- 0.297, r9 = 0.653 +/- 0.250, rg = -0.768 +/- 0.3280 und rg = -0.643 +/- 0.354, respectively. The additive genetic correlation to the ratio between fat and protein content was rg = -0.585 +/- 0.470. For rightsided abomasal displacement, additive genetic correlations were of similar size but with reversed signs. The estimates obtained for the residual correlations were negligibly small throughout.     

Genetic and phenotypic parameters and annual trends for milk production and fertility traits of the Sahiwal cattle in semi arid Kenya

Data comprising 7211 lactation records of 2894 cows were used to estimate genetic and phenotypic parameters for milk production (lactation milk yield, LMY and lactation length, LL) and fertility (calving interval, CI; number of services per conception, NSC and age at first calving, AFC) traits. Genetic, environmental and phenotypic trends were also estimated. Variance components were estimated using univariate, bivariate and trivariate animal models on based restricted maximum likelihood procedures. Univariate models were used for each trait, while bivariate models were used to estimate genetic and phenotypic correlations between milk production and fertility traits and between LMY, LL, CI and NSC within each lactation. Trivariate models were used in the analysis of LMY, LL, CI and NSC in the first three lactations. Heritability estimates from the univariate model were 0.16, 0.07, 0.03, 0.04 and 0.01 for LMY, LL, CI, AFC and NSC, respectively. The heritability estimates from trivariate analysis were higher for milk production traits than those from univariate analyses. Genetic correlations were high and undesirable between milk production and fertility traits, while phenotypic correlations were correspondingly low. Genetic trends were close to zero for all traits, while environmental and phenotypic trends fluctuated over the study period.     

Autoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle

Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and −0.019 (−0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and −0.022 (−0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.     

Genome-wide association for milk production and lactation curve parameters in Holstein dairy cows

The aim of this study was to identify genomic regions associated with 305-day milk yield and lactation curve parameters on primiparous (n = 9,910) and multiparous (n = 11,158) Holstein cows. The SNP solutions were estimated using a weighted single-step genomic BLUP approach and imputed high-density panel (777k) genotypes. The proportion of genetic variance explained by windows of 50 consecutive SNP (with an average of 165 Kb) was calculated, and regions that accounted for more than 0.50% of the variance were used to search for candidate genes. Estimated heritabilities were 0.37, 0.34, 0.17, 0.12, 0.30 and 0.19, respectively, for 305-day milk yield, peak yield, peak time, ramp, scale and decay for primiparous cows. Genetic correlations of 305-day milk yield with peak yield, peak time, ramp, scale and decay in primiparous cows were 0.99, 0.63, 0.20, 0.97 and −0.52, respectively. The results identified three windows on BTA14 associated with 305-day milk yield and the parameters of lactation curve in primi- and multiparous cows. Previously proposed candidate genes for milk yield supported by this work include GRINA, CYHR1, FOXH1, TONSL, PPP1R16A, ARHGAP39, MAF1, OPLAH and MROH1, whereas newly identified candidate genes are MIR2308, ZNF7, ZNF34, SLURP1, MAFA and KIFC2 (BTA14). The protein lipidation biological process term, which plays a key role in controlling protein localization and function, was identified as the most important term enriched by the identified genes.     

Random regression test day models to estimate genetic parameters for milk yield and milk components in Philippine dairy buffaloes

Heritabilities and genetic correlations for milk production traits were estimated from first‐parity test day records on 1022 Philippine dairy buffalo cows. Traits analysed included milk (MY), fat (FY) and protein (PY) yields, and fat (Fat%) and protein (Prot%) concentrations. Varying orders of Legendre polynomials (Leg_m) as well as the Wilmink function (Wil) were used in random regression models. These various models were compared based on log likelihood, Akaike's information criterion, Bayesian information criterion and genetic variance estimates. Six residual variance classes were sufficient for MY, FY, PY and Fat%, while seven residual classes for Prot%. Multivariate analysis gave higher estimates of genetic variance and heritability compared with univariate analysis for all traits. Heritability estimates ranged from 0.25 to 0.44, 0.13 to 0.31 and 0.21 to 0.36 for MY, FY and PY, respectively. Wilmink's function was the better fitting function for additive genetic effects for all traits. It was also the preferred function for permanent environment effects for Fat% and Prot%, but for MY, FY and PY, the Leg_m was the appropriate function. Genetic correlations of MY with FY and PY were high and they were moderately negative with Fat% and Prot%. To prevent deterioration in Fat% and Prot% and improve milk quality, more weight should be applied to milk component traits.     

Comparison of selection criteria for milk yield traits of Valle del Belice dairy sheep

The objectives of this work were to investigate alternative selection criteria for milk yield traits in the Valle del Belice sheep breed, which are either corrected for lactation length or not affected by lactation length, to estimate genetic parameters for these alternative selection criteria and to compare the estimated breeding values. The genetic correlations show that corrected milk yield (CMY), maximum test-day yield (MTY) and milk yield at hundred days (MYH), are moderately or weakly correlated with lactation length (LL) (r_g = 0.58, 0.16 and 0.39, respectively). Higher genetic correlation was found between total milk yield (TMY) and LL (r_g = 0.73). Rank correlations between selection criteria for estimated breeding values for the entire data set were above 0.90 for CMY vs. TMY and CMY vs. MYH and were similar for rams and ewes. Very low were the rank correlations for LL vs. MTY and LL vs. MYH in comparison with 0.75 for LL vs. TMY. Under high selection intensity, rank correlations between breeding values from CMY vs. TMY, CMY vs. MYH and MTY vs. MYH were lower, ranging from 0.53 to 0.75, but higher than all other contrasts between selection criteria. The general results obtained in this study show that MYH is a selection criterion that could improve the genetic evaluation in the Valle del Belice dairy sheep.     

Application of multiple‐trait finite mixture model to test‐day records of milk yield and somatic cell score of Canadian Holsteins

Multiple‐trait (MT) finite mixture random regression (MIX) model was applied using Bayesian methods to first lactation test‐day (TD) milk yield and somatic cell score (SCS) of Canadian Holsteins, allowing for heterogeneity of distributions with respect to days in milk (DIM) in lactation. The assumption was that the associations between patterns of variation in these traits and mastitis would allow revealing the hidden structure in the data distribution because of unknown health status of cows. The MIX model assumed separate means and residual co‐variance structures for two components in four intervals of lactation, in addition to fitting the fixed effect of herd‐test‐day, and fixed and random regressions with Legendre polynomials. Results indicated that the mixture model was superior to standard MT model, as supported by the Bayes factor. Approximately 20% of TD records were classified as originated from cows with a putative, sub‐clinical form of mastitis. The proportion of records from mastitic cows was the largest at the beginning of lactation. The MIX model exhibited different distributions of data from healthy and infected cows in different parts of lactation. Records from sick cows were characterized by larger (smaller) means for SCS (milk) and larger variances. Residual, and daily genetic and environmental correlations between milk and SCS were smaller from the MIX model when compared with MT estimates. Heritabilities of both traits differed significantly among records from healthy, sick and MT model estimates. Both models fitted milk records from healthy cows relatively well. The ability of the MT model in handling SCS records, measured by model residuals, was low, but improved substantially, however, where the data were allowed to be separated into two components in the MIX parameterization. Correlations between estimated breeding values (EBV) for sires from both models were very high for cumulative milk yield (>0.99) and slightly lower (0.95 in the interval from 5 to 45 DIM) for daily SCS. EBV for SCS from MT and MIX models were weakly correlated with posterior probability of sub‐clinical mastitis on the phenotypic scale.     

Estimation of genetic and crossbreeding parameters for daily milk yield of Ayrshire × Sahiwal × Ankole crossbred cows in Burundi

The pedigree of 317 cows of which 184 were controlled for milk production has been used to estimate crossbreeding parameters for daily milk yield of Ayrshire, Sahiwal and Ankole crosses in the Mahwa station. Lactating cows belonged to one of 6 different genetic groups defined on the basis of the mating system used to produce them. REML estimates of the genetic parameters were obtained with a repeated animal model using daily milk records. Estimated heritability (h²) and repeatability (r²) were 0.27 and 0.36, respectively. The genetic group effects were used to estimate crossbreeding parameters following Dickerson's genetic model. Estimates for the additive effects for daily milk yield of Ankole, Sahiwal and Ayrshire breeds were − 1.66l, − 0.48l and 5.22l, respectively. Estimates of direct heterosis for daily milk yield for Sahiwal × Ankole, Ayrshire × Ankole, and Ayrshire × Sahiwal crosses were 1.97l, 2.30l and − 2.33l, respectively.     

Dual-purpose Guzerá cattle exhibit high dairy performance under heat stress

The present study evaluated the heat stress response pattern of dual-purpose Guzerá cattle for test-day (TD) milk yield records of first lactation and estimated genetic parameters and trends related to heat stress. A total of 31,435 TD records from 4,486 first lactations of Guzerá cows, collected between 1986 and 2012, were analysed. Two random regression models considered days in milk (DIM) and/or temperature × humidity-dependent (THI) covariate. Impacts of −0.037, −0.019 and −0.006 kg/day/THI for initial and intermediate stages of lactation were observed when considering the mean maximum daily temperature and humidity to calculate THI. Heritability estimates ranged from 0.16 to 0.35 throughout lactation and THI values, suggesting the possibility to expect gains from selection for such trait. The variable trajectory of breeding values for dual-purpose Guzerá sires in response to changes in THI values confirms that the genotype × environment interaction due to heat stress can have some effect on TD milk yield. Despite the high dairy performance of Guzerá cattle under heat stress, estimated genetic trends showed a progressive reduction in heat tolerance. Therefore, new strategies should be adopted to prevent negative impacts of heat stress over milk production in Guzerá animals in future.     

Crossbreeding effects on milk yield traits and calving interval in spring‐calving dairy cows

The aim of the study was to assess crossbreeding effects for 305‐day milk, fat, and protein yield and calving interval (CI) in Irish dairy cows (parities 1 to 5) calving in the spring from 2002 to 2006. Data included 188 927 records for production traits and 157 117 records for CI. The proportion of genes from North American Holstein Friesian (HO), Friesian (FR), Jersey (JE) and Montbéliarde (MO) breeds, and coefficients of expected heterosis for HO×FR, HO×JE and HO×MO crosses were calculated from the breed composition of cows’ parents. The model used to assess crossbreeding effects accounted for contemporary group, age at calving within parity, linear regression on gene proportions for FR, JE and MO, and linear regression on coefficients of expected heterosis for HO×FR, HO×JE and HO×MO, as fixed effects, and additive genetic, permanent environmental and residual as random. Breed effects for production traits were in favour of HO, while for CI were in favour of breeds other than HO. The highest heterosis estimates for production were for HO×JE, with first‐generation crosses yielding 477 kg more milk, 25.3 kg more fat, and 17.4 kg more protein than the average of the parental breeds. The highest estimate for CI was for HO×MO, with first‐generation crosses showing 10.2 days less CI than the average of the parental breeds. Results from this study indicate breed differences and specific heterosis effects for milk yield traits and fertility exist in Irish dairy population.     

Estimation of additive and non-additive genetic effects for fertility and reproduction traits in North American Holstein cattle using genomic information

Non-additive genetic effects are usually ignored in animal breeding programs due to data structure (e.g., incomplete pedigree), computational limitations and over-parameterization of the models. However, non-additive genetic effects may play an important role in the expression of complex traits in livestock species, such as fertility and reproduction traits. In this study, components of genetic variance for additive and non-additive genetic effects were estimated for a variety of fertility and reproduction traits in Holstein cattle using pedigree and genomic relationship matrices. Four linear models were used: (a) an additive genetic model; (b) a model including both additive and epistatic (additive by additive) genetic effects; (c) a model including both additive and dominance effects; and (d) a full model including additive, epistatic and dominance genetic effects. Nine fertility and reproduction traits were analysed, and models were run separately for heifers (N = 5,825) and cows (N = 6,090). For some traits, a larger proportion of phenotypic variance was explained by non-additive genetic effects compared with additive effects, indicating that epistasis, dominance or a combination thereof is of great importance. Epistatic genetic effects contributed more to the total phenotypic variance than dominance genetic effects. Although these models varied considerably in the partitioning of the components of genetic variance, the models including a non-additive genetic effect did not show a clear advantage over the additive model based on the Akaike information criterion. The partitioning of variance components resulted in a re-ranking of cows based solely on the cows’ additive genetic effects between models, indicating that adjusting for non-additive genetic effects could affect selection decisions made in dairy cattle breeding programs. These results suggest that non-additive genetic effects play an important role in some fertility and reproduction traits in Holstein cattle.     

Genetics of milk yield and fertility traits in Holstein-Friesian cattle on large-scale Kenyan farms

Purebred Holstein-Friesian cows are the main exotic breed used for milk production on large, medium, and small farms in Kenya. A study was undertaken on seven large-scale farms to investigate the genetic trends for milk production and fertility traits between 1986 and 1997 and the genetic relationships between the traits. This involved 3,185 records from 1,614 cows, the daughters of 253 sires. There was a positive trend in breeding value for 305-d milk yield of 12.9 kg/ yr and a drop in calving interval of 0.9 d/yr over the 11-yr period. Bulls from the United States (U.S.) had an average total milk yield breeding value 230 kg higher than the mean of all bulls used; Canada (+121 kg), Holland (+15 kg), the United Kingdom (U.K., 0 kg), and Kenya (-71 kg) were the other major suppliers of bulls. Average breeding values of bulls for calving interval by country of origin were -1.31 (Canada), -1.27 (Holland), -0.83 (U.S.), -0.63 (Kenya), and 0.68 d (U.K.). The genetic parameters for 305-d milk yield were 0.29 (heritability), 0.05 (permanent environment effect as proportion of phenotypic variance) resulting in an estimated repeatability of 0.34. Using complete lactation data rather than 305-d milk yield resulted in similar estimates of the genetic parameters. However, when lactation length was used as a covariate heritability was reduced to 0.25 and the permanent environment effect proportion increased to 0.09. There was little genetic control of either lactation length (heritability, 0.09) or calving interval (heritability, 0.05); however, there were strong genetic correlations between first lactation milk yield, calving interval, and age at first calving.