Economic weights of production and functional traits for Holstein‐Friesian cattle in Hungary

A bio‐economic model was used to estimate economic values of 15 milk production, functional, growth and carcass traits for Hungarian Holstein‐Friesian cattle. The calculations were carried out for the situation in Hungary from 2000 to 2007, assuming no production quotas. The marginal economic values were defined as partial derivatives of the profit function with respect to each trait in a production system with dairy cow herds and with sales of surplus male calves. The economic weights for maternal and direct components of traits were calculated multiplying the marginal economic values by the number of discounted expression summed over a 25‐year investment period for 2‐year‐old bulls (candidates for selection). The standardized economic weight (economic weight × genetic standard deviation) of the trait or trait component expressed as percentage of the sum of the standardized economic weights for all traits and trait components represented the relative economic importance of this trait or trait component. The highest relative economic importance was obtained for milk yield (25%), followed by productive lifetime of cows (23%), protein yield and the direct component of a cow’s total conception rate (9% each), the maternal effect of the total conception rate of cows and the somatic cell score (approximately 7% each), fat yield (5%) and mature weight of cows and daily gain in rearing of calves (approximately 4% each). Other functional traits (clinical mastitis incidence, calving difficulty score, total conception rate of heifers and calf mortality) reached a relative economic importance between 0.5% and 2%. Birth weight and dressing percentage were least important (<0.5%). Based on these results, the inclusion of productive lifetime and cow fertility in the breeding programme for Holstein‐Friesian cattle in Hungary is advisable.     

Comparing deregression methods for genomic prediction of test‐day traits in dairy cattle

We aimed to investigate the performance of three deregression methods (VanRaden, VR; Wiggans, WG; and Garrick, GR) of cows’ and bulls’ breeding values to be used as pseudophenotypes in the genomic evaluation of test‐day dairy production traits. Three scenarios were considered within each deregression method: (i) including only animals with reliability of estimated breeding value (REL_EBV ) higher than the average of parent reliability (REL_PA ) in the training and validation populations; (ii) including only animals with REL_EBV higher than 0.50 in the training and REL_EBV higher than REL_PA in the validation population; and (iii) including only animals with REL_EBV higher than 0.50 in both training and validation populations. Individual random regression coefficients of lactation curves were predicted using the genomic best linear unbiased prediction (GBLUP), considering either unweighted or weighted residual variances based on effective records contributions. In summary, VR and WG deregression methods seemed more appropriate for genomic prediction of test‐day traits without need for weighting in the genomic analysis, unless large differences in REL_EBV between training population animals exist.     

Reducing bias in the dairy cattle single‐step genomic evaluation by ignoring bulls without progeny

The number of genotyped animals has increased rapidly creating computational challenges for genomic evaluation. In animal model BLUP, candidate animals without progeny and phenotype do not contribute information to the evaluation and can be discarded. In theory, genotyped candidate animal without progeny can bring information into single‐step BLUP (ssGBLUP) and affect the estimation of other breeding values. We studied the effect of including or excluding genomic information of culled bull calves on genomic breeding values (GEBV) from ssGBLUP. In particular, GEBVs of genotyped bulls with daughters and GEBVs of young bulls selected into AI to be progeny tested (test bulls) were studied. The ssGBLUP evaluation was computed using Nordic test day (TD) model and TD data for the Nordic Red Dairy Cattle. The results indicate that genomic information of culled bull calves does not affect the GEBVs of progeny tested reference animals, but if genotypes of the culled bulls are used in the TD ssGBLUP, the genetic trend in the test bulls is considerably higher compared to the situation when genomic information of the culled bull calves is excluded. It seems that by discarding genomic information of culled bull calves without progeny, upward bias of GEBVs of test bulls is reduced.     

Across breed multi‐trait random regression genomic predictions in the Nordic Red dairy cattle

The current study evaluates reliability of genomic predictions in selection candidates using multi‐trait random regression model, which accounts for interactions between marker effects and breed of origin in the Nordic Red dairy cattle (RDC). The population structure of the RDC is admixed. Data consisted of individual animal breed proportions calculated from the full pedigree, deregressed proofs (DRP) of published estimated breeding values (EBV) for yield traits and genotypic data for 37 595 single nucleotide polymorphic markers. The analysed data included 3330 bulls in the reference population and 812 bulls that were used for validation. Direct genomic breeding values (DGV) were estimated using the model under study, which accounts for breed effects and also with GBLUP, which assume uniform population. Validation reliability was calculated as a coefficient of determination from weighted regression of DRP on DGV (), scaled by the mean reliability of DRP. Using the breed‐specific model increased the reliability of DGV by 2 and 3% for milk and protein, respectively, when compared to homogeneous population GBLUP. The exception was for fat, where there was no gain in reliability. Estimated validation reliabilities were low for milk (0.32) and protein (0.32) and slightly higher (0.42) for fat.     

Adjusting for heterogeneity of experimental data in genetic evaluation of dry matter intake in dairy cattle

The objectives of the present study were (i) to find the best fitted model for repeatedly measured daily dry matter intake (DMI) data obtained from different herds and experiments across lactations and (ii) to get better estimates of the genetic parameters and better genetic evaluations. After editing, there were 572,512 daily DMI records of 3,495 animals (Holstein cows) from 11 different herds across 13 lactations and the animals were under 110 different nutritional experiments. The fitted model for this data set was a univariate repeated‐measure animal model (called model 1) in which additive genetic and permanent environmental (within and across lactations) effects were fitted as random. Model 1 was fitted as two distinct models (called models 2 and 3) based on alternative fixed effect corrections. For unscaled data, each model (models 2 and 3) was fitted as a homoscedastic (HOM) model first and then as a heteroscedastic (HET) model. Then, data were scaled by multiplying with particular herd‐scaling factors, which were calculated by accounting for heterogeneity of phenotypic within‐herd variances. Models were selected based on cross‐validation and prediction accuracy results. Scaling factors were re‐estimated to determine the effectiveness of accounting for herd heterogeneity. Variance components and respective heritability and repeatability were estimated based on a pedigree‐based relationship matrix. Results indicated that the model fitted for scaled data showed better fit than the models (HOM or HET) fitted for unscaled data. The heritability estimates of the models 2 and 3 fitted for scaled data were 0.30 and 0.08, respectively. The repeatability estimates of the model fitted for scaled data ranged from 0.51 to 0.63. The re‐estimated scaling factor after accounting for heterogeneity of residual variances was close to 1.0, indicating the stabilization of residual variances and herd accounted for most of the heterogeneity. The rank correlation of EBVs between scaled and unscaled data ranged from 0.96 to 0.97.     

Comparison of models for genetic evaluation of survival traits in dairy cattle: a simulation study.

Three models for the analysis of functional survival data in dairy cattle were compared using stochastic simulation. The simulated phenotype for survival was defined as a month after the first calving (from 1 to 100) in which a cow was involuntarily removed from the herd. Parameters for simulation were based on survival data of the Canadian Jersey population. Three different levels of heritability of survival (0.100, 0.050 and 0.025) and two levels of numbers of females per generation (2000 or 4000) were considered in the simulation. Twenty generations of random mating and selection (on a second trait, uncorrelated with survival) with 20 replicates were simulated for each scenario. Sires were evaluated for survival of their daughters by three models: proportional hazard (PH), linear multiple-trait (MT), and random regression (RR) animal models. Different models gave different ranking of sires with respect to survival of their daughters. Correlations between true and estimated breeding values for survival to five different points in a cow's lifetime after the first calving (120 and 240 days in milk after first, second, third and fourth calving) favoured the PH model, followed by the RR model evaluations. Rankings of models were independent of the heritability level, female population size and sire progeny group size (20 or 100). The RR model, however, showed a slight superiority over MT and PH models in predicting the proportion of sire's daughters that survived to the five different end-points after the first calving.     

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.     

Effects of a novel missense polymorphism within the SIGLEC5 gene on fertility traits in Holstein‐Friesian cattle

The aim of the study was to find functional polymorphism within two exons of the SIGLEC5 (sialic acid‐binding Ig‐like lectin‐5) gene and to examine its effects on the production and fertility traits of cows and bulls. Two hundred seventytwo Holstein‐Friesian cows and 574 bulls were included in the study. Novel missense polymorphism (A > G) within exon 3 causing substitution of amino acid arginine by glutamate in position 260 of SIGLEC5 protein (R260Q) was identified by sequencing and digestion by restriction enzyme Msp I. Basic production and fertility traits of cows and estimated breeding values (EBV) of bulls were analysed. The study demonstrated a significant association of SIGLEC5 R260Q polymorphism with days open and calving interval in cows as well as with breeding value for calving interval in bulls. An opposite effect of SIGLEC5 alleles for production and fertility traits was observed: the allele G increased the breeding value for the protein yield, while the allele A increased the breeding value for the calving interval. The current study suggests the involvement of SIGLEC5 R260Q polymorphism in biological processes related to fertility traits. This finding can be applied as a biomarker for a genetic improvement programme in Holstein‐Friesian cattle.     

Genomic and single-step evaluations of carcass traits of young bulls in dual-purpose cattle

Genetic evaluations for carcass traits of young bulls in Normande and Montbeliarde breeds are currently being developed in France. In order to determine a suitable genomic evaluation for three carcass traits of young bulls, genomic breeding values were estimated for young candidates to selection using different approaches. Records of 111,789 Normande and 118,183 Montbeliarde were used. Average progeny pre-adjusted performances (DYD) were calculated for sires. Evaluation approaches were compared based on an assessment of their accuracy (correlation between DYD and estimated breeding values [EBVs]) and bias (regression coefficient of DYD on EBVs) on the 20% youngest AI sires. All genomic approaches were generally more accurate than BLUP (+.045 to +.116 correlation points), except for age at slaughter where single-step GBLUP (SSGBLUP) was the only genomic method leading to a greater accuracy (+.038 to +.126 points). The best setting of the SSGBLUP relationship matrix was characterized by a weight of 30% for pedigree information in the genomic relationship matrix. SSGBLUP was the most valuable evaluation approach for the evaluation of carcass traits of Normande and Montbeliarde young bulls.     

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.     

Genetic parameters for milk fatty acids,milk yield and quality traits of a Holstein cattle population reared under tropical conditions

Information about genetic parameters is essential for selection decisions and genetic evaluation. These estimates are population specific; however, there are few studies with dairy cattle populations reared under tropical and sub‐tropical conditions. Thus, the aim was to obtain estimates of heritability and genetic correlations for milk yield and quality traits using pedigree and genomic information from a Holstein population maintained in a tropical environment. Phenotypic records (n = 36 457) of 4203 cows as well as the genotypes for 57 368 single nucleotide polymorphisms from 755 of these cows were used. Covariance components were estimated using the restricted maximum likelihood method under a mixed animal model, considering a pedigree‐based relationship matrix or a combined pedigree‐genomic matrix. High heritabilities (around 0.30) were estimated for lactose and protein content in milk whereas moderate values (between 0.19 and 0.26) were obtained for percentages of fat, saturated fatty acids and palmitic acid in milk. Genetic correlations ranging from −0.38 to −0.13 were determined between milk yield and composition traits. The smaller estimates compared to other similar studies can be due to poor environmental conditions, which may reduce genetic variability. These results highlight the importance in using genetic parameters estimated in the population under evaluation for selection decisions.     

Estimation of variance and genomic prediction using genotypes imputed from low‐density marker subsets for carcass traits in Japanese black cattle

The influence of genotype imputation using low‐density single nucleotide polymorphism (SNP) marker subsets on the genomic relationship matrix (G matrix), genetic variance explained, and genomic prediction (GP) was investigated for carcass weight and marbling score in Japanese Black fattened steers, using genotype data of approximately 40,000 SNPs. Genotypes were imputed using equally spaced SNP subsets of different densities. Two different linear models were used. The first (model 1) incorporated one G matrix, while the second (model 2) used two different G matrices constructed using the selected and remaining SNPs. When using model 1, the estimated additive genetic variance was always larger when using all SNPs obtained via genotype imputation than when using only equally spaced SNP subsets. The correlations between the genomic estimated breeding values obtained using genotype imputation with at least 3,000 SNPs and those using all available SNPs without imputation were higher than 0.99 for both traits. While additive genetic variance was likely to be partitioned with model 2, it did not enhance the accuracy of GP compared with model 1. These results indicate that genotype imputation using an equally spaced low‐density panel of an appropriate size can be used to produce a cost‐effective, valid GP.     

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.     

Calculating economic values for growth and functional traits in non‐dairy sheep

A bio‐economic model including a computer program was developed where a large number of parameters can be modified to describe a great variety of production systems. Economic values for up to 27 traits can be calculated. Seasonal feeding is considered and protein requirement in addition to net energy may be taken into account when calculating feed requirement. Furthermore, the model allows for the evaluation of individual parts of the growth curve. The model was applied to the Czech Suffolk breed used in pure‐breeding. An extensive pasture system with spring lambing and sale of surplus progeny for slaughter after weaning or after a finishing period was assumed. The economic values (in Euro cent per ewe, per year and per genetic standard deviation of the trait) were as follows: 9.9 for birth weight, ?72.6 for mature weight, 142, 32.1 and 16.9 for growth rates till weaning, in rearing of breeding animals and in fattening, respectively, 19.9 and 101 for conception rate of female lambs and ewes, respectively, 690 for litter size per ewe lambing, 172 for productive lifetime of ewes, 402 and 503 for survival rate of lambs at lambing and till weaning, respectively, and 16.5 for fleece weight.     

Genomic evaluation of regional dairy cattle breeds in single‐breed and multibreed contexts

An important prerequisite for high prediction accuracy in genomic prediction is the availability of a large training population, which allows accurate marker effect estimation. This requirement is not fulfilled in case of regional breeds with a limited number of breeding animals. We assessed the efficiency of the current French routine genomic evaluation procedure in four regional breeds (Abondance, Tarentaise, French Simmental and Vosgienne) as well as the potential benefits when the training populations consisting of males and females of these breeds are merged to form a multibreed training population. Genomic evaluation was 5–11% more accurate than a pedigree‐based BLUP in three of the four breeds, while the numerically smallest breed showed a < 1% increase in accuracy. Multibreed genomic evaluation was beneficial for two breeds (Abondance and French Simmental) with maximum gains of 5 and 8% in correlation coefficients between yield deviations and genomic estimated breeding values, when compared to the single‐breed genomic evaluation results. Inflation of genomic evaluation of young candidates was also reduced. Our results indicate that genomic selection can be effective in regional breeds as well. Here, we provide empirical evidence proving that genetic distance between breeds is only one of the factors affecting the efficiency of multibreed genomic evaluation.     

Estimation of dominance genetic variances for reproductive traits and growth traits of calves in Japanese Black cattle

The dominance genetic effects for reproductive and calf growth abilities in the practical Japanese Black populations were examined using average information (AI) algorithm restricted maximum likelihood (REML) under animal models. The reproductive traits were observed in Japanese Black cattle maintained at Tottori and Okinawa prefectures, and growth traits of calves were observed in cattle at Okinawa. The average of dominance relationships in Tottori ranged from 0.2 to 0.4%, while the level in Okinawa was lower and sparse compared with Tottori. The proportions of the dominance variances to sum of additive and dominance variances () were all 0.02 for reproductive traits in Tottori. In contrast, the proportion was 0.02–0.64 in Okinawa regardless of the level of dominance relationships. These proportions suggested that the dominance might affect the expression of calving interval, days open and gestation length in Okinawa, where breeding units were spread over many islands. Although the dominance variances could not estimate birthweight, w as 0.34 for calf market weight and 0.27 for average daily gain from birth to calf market in Okinawa. These values also suggested that the dominance might affect the early growth of calves. In the near future, genetic relationships will become more complicated with continuation of the current selection and mating systems. Therefore, genetic evaluation accounting for dominance effects would be necessary for particular traits and populations.     

Detecting effective starting point of genomic selection by divergent trends from best linear unbiased prediction and single-step genomic best linear unbiased prediction in pigs,beef cattle,and broilers

Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree best linear unbiased prediction (BLUP) became biased because this method does not consider selection using genomic information. Hence, the effective starting point of genomic selection can be detected in two possible ways including the divergence of genetic trends and Realized Mendelian sampling (RMS) trends obtained with BLUP and single-step genomic BLUP (ssGBLUP). This study aimed to find the start date of genomic selection for a set of economically important traits in three livestock species by comparing trends obtained using BLUP and ssGBLUP. Three datasets were used for this purpose: 1) a pig dataset with 117k genotypes and 1.3M animals in pedigree, 2) an Angus cattle dataset consisted of ~842k genotypes and 11.5M animals in pedigree, and 3) a purebred broiler chicken dataset included ~154k genotypes and 1.3M birds in pedigree were used. The genetic trends for pigs diverged for the genotyped animals born in 2014 for average daily gain (ADG) and backfat (BF). In beef cattle, the trends started diverging in 2009 for weaning weight (WW) and in 2016 for postweaning gain (PWG), with little divergence for birth weight (BTW). In broiler chickens, the genetic trends estimated by ssGBLUP and BLUP diverged at breeding cycle 6 for two out of the three production traits. The RMS trends for the genotyped pigs diverged for animals born in 2014, more for ADG than for BF. In beef cattle, the RMS trends started diverging in 2009 for WW and in 2016 for PWG, with a trivial trend for BTW. In broiler chickens, the RMS trends from ssGBLUP and BLUP diverged strongly for two production traits at breeding cycle 6, with a slight divergence for another trait. Divergence of the genetic trends from ssGBLUP and BLUP indicates the onset of the genomic selection. The presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agrees with industry practices across the three species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP.     

Genetic and genomic analyses for predicted methane‐related traits in Japanese Black steers

The objectives of this study were to estimate genetic parameters and to perform a genome‐wide association study (GWAS) for predicted methane‐related traits in Japanese Black steers. The methane production and yield traits were predicted using on‐farm measurable traits, such as dry matter intake and average daily gain. A total of 4,578 Japanese Black steers, which were progenies of 362 sires genotyped with imputed 551,995 single nucleotide polymorphisms (SNPs), had phenotypes of predicted methane‐related traits during the total fattening period (52 weeks). For the estimation of genetic parameters, the estimated heritabilities were moderate (ranged from 0.57 to 0.60). In addition, the estimated genetic correlations of methane production traits with most of carcass traits and feed‐efficiency traits were unfavorable, but those of methane yield traits were favorable or low. For the GWAS, no genome‐wide significant SNP was detected, but a total of four quantitative trait locus (QTL) regions that explained more than 5.0% of genetic variance were localized on the genome, and some candidate genes associated with growth and feed‐efficiency traits were located on the regions. Our results suggest that the predicted methane‐related traits are heritable and some QTL regions for the traits are localized on the genome in Japanese Black steers.     

Impact of including information from bulls and their daughters in the training population of multiple‐step genomic evaluations in dairy cattle: A simulation study

The objective of this study was to investigate the impact of accounting for parent average (PA) and genotyped daughters’ average (GDA) on the estimation of deregressed estimated breeding values (dEBVs) used as pseudo‐phenotypes in multiple‐step genomic evaluations. Genomic estimated breeding values (GEBVs) were predicted, in eight different simulated scenarios, using dEBVs calculated based on four methods. These methods included PA and GDA in the dEBV (VR) or only GDA (VRpa) and excluded both PA and GDA from the dEBV with either all information or only information from PA and GDA (JA and NEW, respectively). In general, VR and NEW showed the lowest and highest GEBV reliabilities across scenarios, respectively. Among all deregression methods, VRpa and NEW provided the most consistent bias estimates across the majority of scenarios, and they significantly yielded the least biased GEBVs. Our results indicate that removing PA and GDA information from dEBVs used in multiple‐step genomic evaluations can increase the reliability of GEBVs, when both bulls and their daughters are included in the training population.     

Genetic parameters for total lactation yields of milk,fat, protein,and somatic cell score in New Zealand dairy goats

The aim of this study was to estimate genetic parameters for lactation yields of milk (MY), fat (FY), protein (PY), and somatic cell score (SCS) of New Zealand dairy goats. The analysis used 64,604 lactation records from 23,583 does, kidding between 2004 and 2017, distributed in 21 flocks and representing 915 bucks. Estimates of genetic and residual (co) variances, heritabilities, and repeatabilities were obtained using a multiple‐trait repeatability animal model. The model included the fixed effects of contemporary group (does kidding in the same flock and year), age of the doe (in years), and as covariates, kidding day, proportion of Alpine, Nubian, Toggenburg, and “unknown” breeds (Saanen was used as the base breed), and heterosis. Random effects included additive animal genetic and doe permanent environmental effects. Estimates of heritabilities were 0.25 for MY, 0.24 for FY, 0.24 for PY, and 0.21 for SCS. The phenotypic correlations between MY, FY, and PY ranged from 0.90 to 0.96, and the genetic correlations ranged from 0.81 to 0.93. These results indicate lactation yield traits exhibit useful heritable variation and that multiple trait selection for these traits could improve milk revenue produced from successive generations of New Zealand dairy goats.