Method R estimates of heritability for milk, fat, and protein yields of United States dairy cattle

Identification of candidate SNPs associated with embryo mortality and fertility traits in lactating Holstein cows

Introduction: Targeted single nucleotide polymorphisms (SNPs) have been used in genomic prediction methodologies to enhance the accuracy of associated genetic transmitting abilities in Holstein cows. The objective of this study was to identify and validate SNPs associated with fertility traits impacting early embryo mortality. Methods: The mRNA sequencing data from day 16 normal (n = 9) and embryo mortality (n = 6) conceptuses from lactating multiparous Holstein cows were used to detect SNPs. The selection of specific genes with SNPs as preliminary candidates was based on associations with reproductive and fertility traits. Validation of candidate SNPs and genotype-to-phenotype analyses were conducted in a separate cohort of lactating primiparous Holstein cows (n = 500). After genotyping, candidate SNPs were filtered using a quality control pipeline via PLINK software. Continuous numeric and binary models from reproductive traits were evaluated using the mixed procedure for a generalized linear model-one way ANOVA or logistic regression, respectively. Results: Sixty-nine candidate SNPs were initially identified, but only 23 passed quality control procedures. Ultimately, the study incorporated 466 observations for statistical analysis after excluding animals with missing genotypes or phenotypes. Significant (p <0.05) associations with fertility traits were identified in seven of the 23 SNPs: DSC2 (cows with the A allele were older at first calving); SREBF1 and UBD (cows with the T or G alleles took longer to conceive); DECR1 and FASN (cows with the C allele were less likely to become pregnant at first artificial insemination); SREBF1 and BOLA-DMB (cows with the T allele were less likely to be pregnant at 150 days in milk). It was also determined that two candidate SNPs within the DSC2 gene were tag SNPs. Only DSC2 SNPs had an important allele substitution effect in cows with the G allele, which had a decreased age at first calving by 10 days. Discussion: Candidate SNPs found in this study could be used to develop genetic selection tools to improve fertility traits in dairy production systems.

Developmental programming of production and reproduction in dairy cows: IV. Association of maternal milk fat and protein percentage and milk fat to protein ratio with offspring's birth weight, survival, productive and reproductive performance and AMH concentration from birth to the first lactation period

Although the association of maternal milk production with developmental programming of offspring has been investigated, there is limited information available on the relationship of maternal milk components with productive and reproductive performance of the offspring. Therefore, the present study was conducted to analyze the association of maternal milk fat and protein percentage and milk fat to protein ratio with birth weight, survival, productive and reproductive performance and AMH concentration in the offspring. In study I, data of birth weight, milk yield and reproductive variables of offspring born to lactating dams (n = 14,582) and data associated with average maternal milk fat percentage (MFP), protein percentage (MPP) and fat to protein ratio (MFPR) during 305-day lactation were retrieved. Afterwards, offspring were classified in various categories of MFP, MPP and MFPR. In study II, blood samples (n = 339) were collected from offspring in various categories of MFP, MPP and MFPR for measurement of serum AMH. Maternal milk fat percentage was positively associated with birth weight and average percentage of milk fat (APMF) and protein (APMP) and milk fat to protein ratio (FPR) during the first lactation, but negatively associated with culling rate during nulliparity in the offspring (P < 0.05). Maternal milk protein percentage was positively associated with birth weight, APMF, APMP, FPR and culling rate, but negatively associated with milk yield and fertility in the offspring (P < 0.05). Maternal FPR was positively associated with APMF and FPR, but negatively associated with culling rate, APMP and fertility in the offspring (P < 0.05). However, concentration of AMH in the offspring was not associated with MFP, MPP and MFPR (P > 0.05). In conclusion, the present study revealed that maternal milk fat and protein percentage and their ratio were associated with birth weight, survival, production and reproduction of the offspring. Yet it was a preliminary research and further studies are required to elucidate the mechanisms underlying these associations.

Interaction of DGAT1 polymorphism, parity, and acetate supplementation on feeding behavior, milk synthesis, and plasma metabolites

Acetate supplementation increases milk fat production, but interactions with animal-related factors have not been investigated. The objective of this study was to characterize the interaction of acetate supplementation with parity and genetic potential for milk fat synthesis including the DGAT1 K232A polymorphism (AA and KA genotypes). In total, 47 primiparous and 49 multiparous lactating cows were used in 2 blocks in a crossover design. The basal diet was formulated to have a low risk of biohydrogenation-induced milk fat depression and had 32.8% and 32.0% neutral detergent fiber and 21.7% and 23.6% starch [all on a dry matter (DM) basis] in block 1 and 2, respectively. The control treatment received the basal diet, and the acetate supplementation treatment included anhydrous sodium acetate supplemented to the basal diet at 3.2% and 3.1% of DM of the diet for block 1 and 2, respectively (targeting 10 mol/d of acetate). The DGAT1 genotype frequency of the experimental cows was 45% AA and 51% KA, with 4% cows with either a KK or unimputable genotype. Acetate supplementation increased DM intake (DMI) in KA multiparous cows, but acetate did not change DMI in AA multiparous or primiparous cows of either genotype. Acetate supplementation increased the frequency of meals by 8% and decreased the length of each meal by ∼5 min compared with control. There was no effect of acetate on milk yield. Acetate supplementation increased milk fat yield and concentration by 117 g/d and 0.31 percentage units, respectively, regardless of DGAT1 polymorphism or parity. The increase in milk fat yield was mostly due to an increase in yield of 16C mixed-sourced fatty acids, suggesting that acetate supplementation drives mammary de novo synthesis toward completion. Response to acetate supplementation was not related to genomic predicted transmitting ability of milk fat concentration and yield or to pretrial milk fat percent and yield, suggesting that acetate increases milk fat production regardless of genetic potential for milk fat yield and level of milk fat synthesis. Interestingly, analyzing the temporal effect on the interaction between treatment and DGAT1 polymorphism on milk fat yield suggested that DGAT1 polymorphism may affect the short-term response to acetate supplementation during the first ≤7 d on treatment. Acetate supplementation also increased plasma β-hydroxybutyrate concentration and decreased plasma glucose concentration. In conclusion, acetate supplementation consistently increased milk fat synthesis regardless of parity or genetic potential for milk fat synthesis.

Effects of maternal age and offspring sex on milk yield, composition and calf growth of red deer (Cervus elaphus)

Differential maternal allocation theory states that mothers will invest more heavily in the offspring sex that will secure higher reproductive output. Senescence theory is concerned with the gradual deterioration of physiological function with age. We analysed the offspring sex-dependent response of calf growth and milk traits to mother age in an Iberian population of captive red deer (Cervus elaphus) using a 22 year time series longitudinal data set. Previous studies revealed that there was little evidence for the differential allocation theory on milk traits and that most studies lacked proper control for confounding factors. Our results indicated that (i) calf growth was offspring male-biased, negatively affected by mother age and positively influenced by mother weight and parity, and (ii) there was no support for differential allocation offspring sex-dependence in milk traits (yield, energy density, fat, protein and lactose content). Our findings suggest that maternal allocation responds to offspring energy requirements, which are mainly driven by offspring body weight, and contingent on mother age and weight and previous maternal reproductive effort.

Tropical milk production systems and milk quality: a review

We reviewed information on dairy cattle production systems in the tropics, the factors involved, and their influence on milk composition. Genetic factors had greater influence on milk production; specialized breeds produced more milk, and there was an inverse relation between the content of fat, protein, total solids, and the amount of milk produced. Season was related to the availability of forage, and the type of grazing system. Greater pasture area increased individual production, while a greater supply of feed concentrate did not increase milk production. The number of calvings positively affected milk production through the fifth calving, with subsequent declines in production. Milk production increased to a maximum and then declined as lactation progressed. Specialized systems had higher production and better hygienic milk quality; milking and container equipment are critical for maintaining milk sanitary quality. Factor interaction is highly complex, preventing the generation of specific recommendations and general principles applicable to the specific conditions for each system.

Performance Evaluation of Highly Admixed Tanzanian Smallholder Dairy Cattle Using SNP Derived Kinship Matrix

The main purpose of this study was to understand the type of dairy cattle that can be optimally used by smallholder farmers in various production environments such that they will maximize their yields without increasing the level of inputs. Anecdotal evidence and previous research suggests that the optimal level of taurine inheritance in crossbred animals lies between 50 and 75% when considering total productivity in tropical management clusters. We set out to assess the relationship between breed composition and productivity for various smallholder production systems in Tanzania. We surveyed 654 smallholder dairy households over a 1-year period and grouped them into production clusters. Based on supplementary feeding, milk productivity and sale as well as household wealth status four clusters were described: low-feed-low-output subsistence, medium-feed-low-output subsistence, maize germ intensive semi-commercial and feed intensive commercial management clusters. About 839 crossbred cows were genotyped at approximately 150,000 single nucleotide polymorphism (SNP) loci and their breed composition determined. Percentage dairyness (proportion of genes from international dairy breeds) was estimated through admixture analysis with Holstein, Friesian, Norwegian Red, Jersey, Guernsey, N'Dama, Gir, and Zebu as references. Four breed types were defined as RED-GUE (Norwegian Red/Friesian-Guernsey; Norwegian Red/Friesian-Jersey), RED-HOL (Norwegian Red/Friesian-Holstein), RED-Zebu (Norwegian Red/Friesian-Zebu), Zebu-RED (Zebu-Norwegian Red/Friesian) based on the combination of breeds that make up the top 76% breed composition. A fixed regression model using a genomic kinship matrix was used to analyze milk yield records. The fitted model accounted for year-month-test-date, parity, age, breed type and the production clusters as fixed effects in the model in addition to random effects of animal and permanent environment effect. Results suggested that RED-Zebu breed type with dairyness between 75 and 85% is the most appropriate for a majority of smallholder management clusters. Additionally, for farmers in the feed intensive management group, animals with a Holstein genetic background with at least 75% dairy composition were the best performing. These results indicate that matching breed type to production management group is central to maximizing productivity in smallholder systems. The findings from this study can serve as a basis to inform the development of the dairy sector in Tanzania and beyond.

Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection

Seven years after the introduction of genomic selection in the United States, it is now possible to evaluate the impact of this technology on the population. Selection differential(s) (SD) and generation interval(s) (GI) were characterized in a four-path selection model that included sire(s) of bulls (SB), sire(s) of cows (SC), dam(s) of bulls (DB), and dam(s) of cows (DC). Changes in SD over time were estimated for milk, fat, and protein yield; somatic cell score (SCS); productive life (PL); and daughter pregnancy rate (DPR) for the Holstein breed. In the period following implementation of genomic selection, dramatic reductions were seen in GI, especially the SB and SC paths. The SB GI reduced from ∼7 y to less than 2.5 y, and the DB GI fell from about 4 y to nearly 2.5 y. SD were relatively stable for yield traits, although modest gains were noted in recent years. The most dramatic response to genomic selection was observed for the lowly heritable traits DPR, PL, and SCS. Genetic trends changed from close to zero to large and favorable, resulting in rapid genetic improvement in fertility, lifespan, and health in a breed where these traits eroded over time. These results clearly demonstrate the positive impact of genomic selection in US dairy cattle, even though this technology has only been in use for a short time. Based on the four-path selection model, rates of genetic gain per year increased from ∼50-100% for yield traits and from threefold to fourfold for lowly heritable traits.

Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle

Background

Estimates of dominance variance in dairy cattle based on pedigree data vary considerably across traits and amount to up to 50% of the total genetic variance for conformation traits and up to 43% for milk production traits. Using bovine SNP (single nucleotide polymorphism) genotypes, dominance variance can be estimated both at the marker level and at the animal level using genomic dominance effect relationship matrices. Yield deviations of high-density genotyped Fleckvieh cows were used to assess cross-validation accuracy of genomic predictions with additive and dominance models. The potential use of dominance variance in planned matings was also investigated.

Results

Variance components of nine milk production and conformation traits were estimated with additive and dominance models using yield deviations of 1996 Fleckvieh cows and ranged from 3.3% to 50.5% of the total genetic variance. REML and Gibbs sampling estimates showed good concordance. Although standard errors of estimates of dominance variance were rather large, estimates of dominance variance for milk, fat and protein yields, somatic cell score and milkability were significantly different from 0. Cross-validation accuracy of predicted breeding values was higher with genomic models than with the pedigree model. Inclusion of dominance effects did not increase the accuracy of the predicted breeding and total genetic values. Additive and dominance SNP effects for milk yield and protein yield were estimated with a BLUP (best linear unbiased prediction) model and used to calculate expectations of breeding values and total genetic values for putative offspring. Selection on total genetic value instead of breeding value would result in a larger expected total genetic superiority in progeny, i.e. 14.8% for milk yield and 27.8% for protein yield and reduce the expected additive genetic gain only by 4.5% for milk yield and 2.6% for protein yield.

Conclusions

Estimated dominance variance was substantial for most of the analyzed traits. Due to small dominance effect relationships between cows, predictions of individual dominance deviations were very inaccurate and including dominance in the model did not improve prediction accuracy in the cross-validation study. Exploitation of dominance variance in assortative matings was promising and did not appear to severely compromise additive genetic gain.

Richer milk for sons but more milk for daughters: Sex-biased investment during lactation varies with maternal life history in rhesus macaques

Lactation represents the greatest postnatal energetic expenditure for human and non-human primate females, and the ability to sustain the costs of lactation is influenced by a mother's physical condition. This is especially true for young mothers that initiate reproduction shortly after adolescence. These mothers have fewer bodily reserves available for lactation and face tradeoffs between reproduction and their own growth. Milk synthesis among captive rhesus macaques (Macaca mulatta) was investigated at the California National Primate Research Center from 2005 to 2007 (N = 114). Rhesus macaques produced low energy density milk typical of the primate order, but there was substantial individual variation among mothers in both milk energy density and yield. As a consequence, the available milk energy (AME), the product of milk energy density and milk yield, to support infant growth, development, and activity, varied tenfold among mothers. Primiparous mothers (N = 40) had fewer bodily resources, as measured by mass and body mass index, available for lactation than did multiparous mothers (N = 74) and showed poorer lactational performance. Mothers of sons produced milk of higher energy density, especially primiparous mothers, but lower milk yield, such that AME was the same for sons and daughters. Although AME from the mother was the same for sons and daughters, there was significant sexual dimorphism in infant mass. These data indicate that selection has likely favored sex-specific regulation of growth and development that is not necessarily contingent on greater maternal investment.

A structural equation model for describing relationships between somatic cell score and milk yield in first-lactation dairy cows

Relationships between production and diseases may involve recursive or simultaneous effects between traits. Four structural equation models (SEqM) for somatic cell score and milk yield, with varying specifications for the effects relating the 2 traits, were compared. Data consisted of repeated records of milk yield and somatic cell score of 33,453 first-lactation daughters of 245 Norwegian Red sires that had their first progeny test in 1991 and 1992. All models included random effects of the sire and of the cow and were fitted using the LISREL software. The Bayesian information criterion clearly favored a model with a recursive effect from somatic cell score on milk yield over the 3 other models fitted (absence of recursive effects; an effect from milk yield on somatic cell score; simultaneity of effects between the 2 traits). This provides evidence that the negative association between milk yield and somatic cell score is more likely due to an effect of infection (measured indirectly by the somatic cell score) on production than to a dilution effect. Estimates indicated that a mastitis event would reduce milk yield in the following 15 d by about 900 g/d. The estimated genetic (co)variances did not change sizably when the specification of recursive or simultaneous effects was varied. However, estimates of the phenotypic covariance were altered when a recursive effect from somatic cell score on milk yield was included in the model.

A potential resolution to the lek paradox through indirect genetic effects

Females often prefer males with elaborate traits, even when they receive no direct benefits from their choice. In such situations, mate discrimination presumably has genetic advantages; selective females will produce offspring of higher genetic quality. Over time, persistent female preferences for elaborate secondary-sexual traits in males should erode genetic variance in these traits, eventually eliminating any benefit to the preferences. Yet, strong female preferences persist in many taxa. This puzzle is called the lek paradox and raises two primary questions: do females obtain genetic benefits for offspring by selecting males with elaborate secondary-sexual characteristics and, if so, how is the genetic variation in these male traits maintained? We suggest that indirect genetic effects may help to resolve the lek paradox. Maternal phenotypes, such as habitat selection behaviours and offspring provisioning, often influence the condition and the expression of secondary-sexual traits in sons. These maternal influences are commonly genetic based (i.e. they are indirect genetic effects). Females choosing mates with elaborate traits may receive 'good genes' for daughters in the form of effective maternal characteristics. Recognizing the significance of indirect genetic effects may be important to our understanding of the process and consequences of sexual selection.

HERITABILITY OF PARENTAL EFFORT IN A PASSERINE BIRD

The study of the evolution of parental care is central to our understanding of social systems, sexual selection, and interindividual conflict, yet we know virtually nothing about the genetic architecture of parental care traits in natural populations. In this paper, we use data from a long term field study of a passerine bird, the long-tailed tit (Aegithalos caudatus), to examine the heritability of the rate at which parents feed offspring. This measure of effort is positively related to offspring survival, is repeatable within individuals, and does not appear to be confounded by environmental effects. Using both parent-offspring regression, and an animal model approach, with a pedigree derived from ringing data, we show that our measure of effort has a significant heritable component.

Within-Herd Heritability Estimated with Daughter–Parent Regression for Yield and Somatic Cell Score

Estimates of heritability within herd (h(WH)(2) ) 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 h(WH)(2) 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 h(WH)(2) when estimating breeding values. Mean h(WH)(2) 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 h(WH)(2) deviated from subset means more for large herds than for small herds. Correlation with REML(ANIM) h(WH)(2) was 0.68 for daughter-dam and was 0.45 for daughter-sire h(WH)(2) for milk yield. The correlation between daughter-sire h(WH)(2) and REML(MGS) was greater than the correlation between daughter-dam h(WH)(2) 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 h(WH)(2) resulted in only a small improvement in the accuracy of genetic evaluations.

Major advances in genetic evaluation techniques

The past quarter-century in genetic evaluation of dairy cattle has been marked by evolution in methodology and computer capacity, expansion in the array of evaluated traits, and globalization. Animal models replaced sire and sire-maternal grandsire models and, more recently, application of Bayesian theory has become standard. Individual test-day observations have been used more effectively in estimation of lactation yield or directly as input to evaluation models. Computer speed and storage are less limiting in choosing procedures. The increased capabilities have supported evaluation of additional traits that affect the net profitability of dairy cows. The importance of traits other than yield has increased, in a few cases due to an antagonistic relationship with yield. National evaluations combined internationally provide evaluations for bulls from all participating countries on each of the national scales, facilitating choices from among many more bulls. Selection within countries has increased inbreeding and the use of similar genetics across countries reduces the previously available outcross population. Concern about inbreeding has prompted changes in evaluation methodology and mating practices, and has promoted interest in crossbreeding. In just the past decade, distribution of genetic evaluations has gone from mailed paper or computer tapes for a limited audience to publicly accessible, request-driven distribution via the Internet. Among the distributed information is a choice of economic indices that combine an increasing array of traits into numbers reflecting breeding goals under different milk-pricing conditions. Considerable progress in genomics and the mapping of the bovine genome have identified markers for some deleterious recessive genes, but broader benefits of marker-assisted selection are still in the future. A possible exception is the proprietary use of DNA testing by semen producers to select among potential progeny test bulls. The collection and analysis of industry-wide data to evaluate genetic merit will continue to be the most important tool for genetic progress into the foreseeable future.

Estimation of quantitative genetic parameters

This paper gives a short review of the development of genetic parameter estimation over the last 40 years. This shows the development of more statistically and computationally efficient methods that allow the fitting of more biologically appropriate models. Methods have evolved from direct methods based on covariances between relatives to methods based on individual animal models. Maximum-likelihood methods have a natural interpretation in terms of best linear unbiased predictors. Improvements in iterative schemes to give estimates are discussed. As an example, a recent estimation of genetic parameters for a British population of dairy cattle is discussed. The development makes a connection to relevant work by Bill Hill.

Economic Returns to Holstein and Jersey Herds Under Multiple Component Pricing

This study analyzed component data from herds participating in the Mideast Federal Milk Marketing Order from 2000 through 2002, and its implications for herd profitability. A monthly simulation model was developed to evaluate the economic returns for a representative Holstein and Jersey herd in Pennsylvania under multiple component pricing. Component levels were highly seasonal and variable from farm to farm. A third of the herds during the course of a year realized a 1- to 3-mo temporary reduction in milk fat or protein greater than one standard deviation. Consistently producing milk fat and protein one standard deviation below the mean reduced the Class III value by $0.82/cwt (100 pounds), or 7.09%. The simulation model indicated that a herd of 100 Holstein cows generated $31,221 more income over feed costs (IOFC) a year than a herd of 100 Jersey cows. Although Jersey milk had greater gross value than Holstein milk due to higher component levels, total volume of milk and components produced by Holsteins offset this difference. Simulation results confirm that increasing milk fat and protein percentages by one standard deviation increased IOFC 7.7% for Holsteins and 9.2% for Jerseys relative to the baseline IOFC, with similar losses for component reductions. Increasing milk yield by one standard deviation increased IOFC by 19.6% for Holsteins and 23.9% for Jerseys relative to the baseline IOFC, again with similar losses for reductions in milk production. In all of the scenarios analyzed, the most important factor affecting IOFC was total amount of milk fat and protein produced, not the component percentage levels.

Stability of genetic evaluations for active artificial insemination bulls

Genetic evaluations for milk, fat, and protein from 1995 through August 2003 for 17,987 Holstein bulls in active artificial insemination (AI) service were examined for changes to the November 2003 evaluation. Evaluations for active AI bulls at each of 31 evaluation dates showed mean declines to November 2003. No evidence was seen of a worsening situation over time. Bulls' early evaluations with active AI status showed much larger declines, but this overevaluation diminished and essentially disappeared after 3 yr. The bulls with first active AI evaluations since 1995 were the primary focus of the study. The influx of second-crop daughters did not appear to cause a decline in evaluations for these bulls, attesting to the successful modification to the genetic evaluation system by expanding the genetic variance of short records. Mean declines and the variation of those differences were generally similar by bull sampling organization. A change from active to inactive AI status was generally concurrent with a decline in predicted transmitting ability (PTA). Bulls coded as having standard AI sampling declined less than bulls coded as having other sampling, but the differences were much less than in previous reports. Larger increases in reliability were generally associated with greater declines in PTA, and the magnitude of these changes decreased over time (increasing evaluation number). Change in reliability underpredicted the variance of change in PTA, indicating that other important factors contribute or that the assumptions for the calculation of the expected change in PTA are not met. Declines in estimated merit over time are not sufficient to alter present genetic selection programs, but reasons for the declines continue to elude explanation.

Method R estimates of additive genetic, dominance genetic, and permanent environmental fraction of variance for yield and health traits of Holsteins

Fractions of variance accounted for by additive genetic, dominance genetic, and permanent environmental effects for milk, fat, and protein yields; somatic cell score; and productive life were estimated from Holstein data used for national genetic evaluations. Contemporary group assignments were determined using the national procedure. Data included 1,973,317 milk and fat records for 812,659 cows, 1,019,421 protein records for 462,067 cows, 468,374 lactation average somatic cell score (SCS) records for 232,909 cows, and 735,256 cows with productive-life records. Variance components were estimated with the JAADOM program, which uses iteration on data and second-order Jacobi iteration for obtaining solutions to the mixed-model equations and Method R for estimation of variance components. Ten different random data subsets were used to estimate parameters for each trait. Estimated additive genetic, dominance genetic, and permanent environmental fractions of variance were 0.34, 0.05, and 0.10 for milk yield; 0.34, 0.05, and 0.11 for fat yield; 0.31, 0.05, and 0.10 for protein yield; and 0.17, 0.01, and 0.16 for lactation average SCS. Estimated additive genetic and dominance genetic fractions of variance were 0.12 and 0.06 for productive life. Mean empirical standard errors of additive genetic, dominance genetic, and permanent environmental variance fractions were 0.003, 0.006, and 0.006.