Estimating variance components and breeding values for number of oocytes and number of embryos in dairy cattle using a single-step genomic evaluation

Reproductive technologies such as multiple ovulation and embryo transfer (MOET) and ovum pick-up (OPU) accelerate genetic improvement in dairy breeding schemes. To enhance the efficiency of embryo production, breeding values for traits such as number of oocytes (NoO) and number of MOET embryos (NoM) can help in selection of donors with high MOET or OPU efficiency. The aim of this study was therefore to estimate variance components and (genomic) breeding values for NoO and NoM based on Dutch Holstein data. Furthermore, a 10-fold cross-validation was carried out to assess the accuracy of pedigree and genomic breeding values for NoO and NoM. For NoO, 40,734 OPU sessions between 1993 and 2015 were analyzed. These OPU sessions originated from 2,543 donors, from which 1,144 were genotyped. For NoM, 35,695 sessions between 1994 and 2015 were analyzed. These MOET sessions originated from 13,868 donors, from which 3,716 were genotyped. Analyses were done using only pedigree information and using a single-step genomic BLUP (ssGBLUP) approach combining genomic information and pedigree information. Heritabilities were very similar based on pedigree information or based on ssGBLUP [i.e., 0.32 (standard error = 0.03) for NoO and 0.21 (standard error = 0.01) for NoM with pedigree, 0.31 (standard error = 0.03) for NoO, and 0.22 (standard error = 0.01) for NoM with ssGBLUP]. For animals without their own information as mimicked in the cross-validation, the accuracy of pedigree-based breeding values was 0.46 for NoO and NoM. The accuracies of genomic breeding values from ssGBLUP were 0.54 for NoO and 0.52 for NoM. These results show that including genomic information increases the accuracies. These moderate accuracies in combination with a large genetic variance show good opportunities for selection of potential bull dams.     

Genomic selection strategies in a small dairy cattle population evaluated for genetic gain and profit

The objective of this study was to evaluate a genomic breeding scheme in a small dairy cattle population that was intermediate in terms of using both young bulls (YB) and progeny-tested bulls (PB). This scheme was compared with a conventional progeny testing program without use of genomic information and, as the extreme case, a juvenile scheme with genomic information, where all bulls were used before progeny information was available. The population structure, cost, and breeding plan parameters were chosen to reflect the Danish Jersey cattle population, being representative for a small dairy cattle population. The population consisted of 68,000 registered cows. Annually, 1,500 bull dams were screened to produce the 500 genotyped bull calves from which 60 YB were selected to be progeny tested. Two unfavorably correlated traits were included in the breeding goal, a production trait (h² = 0.30) and a functional trait (h² = 0.04). An increase in reliability of 5 percentage points for each trait was used in the default genomic scenario. A deterministic approach was used to model the different breeding programs, where the primary evaluation criterion was annual monetary genetic gain (AMGG). Discounted profit was used as an indicator of the economic outcome. We investigated the effect of varying the following parameters: (1) increase in reliability due to genomic information, (2) number of genotyped bull calves, (3) proportion of bull dam sires that are young bulls, and (4) proportion of cow sires that are young bulls. The genomic breeding scheme was both genetically and economically superior to the conventional breeding scheme, even in a small dairy cattle population where genomic information causes a relatively low increase in reliability of breeding values. Assuming low reliabilities of genomic predictions, the optimal breeding scheme according to AMGG was characterized by mixed use of YB and PB as bull sires. Exclusive use of YB for production cows increased AMGG up to 3 percentage points. The results from this study supported our hypothesis that strong interaction effects exist. The strongest interaction effects were obtained between increased reliabilities of genomic estimated breeding values and more intensive use of YB. The juvenile scheme was genetically inferior when the increase in reliability was low (5 percentage points), but became genetically superior at higher reliabilities of genomic estimated breeding values. The juvenile scheme was always superior according to discounted profit because of the shorter generation interval and minimizing costs for housing and feeding waiting bulls.     

Short communication: The role of genotypes from animals without phenotypes in single-step genomic evaluations

In a 2-step genomic system, genotypes of animals without phenotypes do not influence genomic prediction of other animals, but that might not be the case in single-step systems. We investigated the effects of including genotypes from culled bulls on the reliability of genomic predictions from single-step evaluations. Four scenarios with a constant amount of phenotypic information and increasing numbers of genotypes from culled bulls were simulated and compared with respect to prediction reliability. With increasing numbers of genotyped culled bulls, there was a corresponding increase in prediction reliability. For instance, in our simulation scenario the reliability for selection candidates was twice as large when all culled bulls from the last 4 generations were included in the analysis. Single-step evaluations imply the imputation of all nongenotyped animals in the pedigree. We showed that this imputation was increasingly more accurate as increasingly more genotypic information from the culled bulls was taken into account. This resulted in higher prediction reliabilities. The extent of the benefit from including genotypes from culled bulls might be more relevant for small populations with low levels of reliabilities.     

Genomic predictions for New Zealand dairy bulls and integration with national genetic evaluation

A method is described for the prediction of breeding values incorporating genomic information. The first stage involves the prediction of genomic breeding values for genotyped individuals. A novel component of this is the estimation of the genomic relationship matrix in the context of a multi-breed population. Because not all ancestors of genotyped animals are genotyped, a selection index procedure is used to blend genomic predictions with traditional ancestral information that is lost between the process of deregression of the national breeding values and subsequent re-estimation using the genomic relationship matrix. Finally, the genomically enhanced predictions are filtered through to nongenotyped descendants using a regression procedure.     

Genetic evaluation of dairy cattle using test-day models

Recently there has been considerable interest in modeling individual test-day records (TDR) for genetic evaluation of dairy cattle as a replacement for the traditional use of estimated accumulated 305-d yields. Some advantages of test-day models (TDM) include the ability to account for environmental effects of each test day, the ability to model the trajectory of the lactation for individual genotypes or groups of animals, and the possibility of genetic evaluations for persistency of production. Also, the use of test-day models avoids the necessity of extending short lactations on culled animals and animals with records in progress. The disadvantages of TDM include computational difficulties associated with analyzing much larger datasets and the need to estimate many more parameters than in a traditional 305-d lactation model. Several different models have been proposed to model the trajectory of the lactation, including so-called "biological functions," various polynomials and character process models. At present, there is not universal agreement on which models to use in routine prediction of breeding values and better methods to compare models are desirable. Obtaining accurate estimates of the dispersion parameters to use in TDM remains a challenge. Methods used include a two-step procedure in which the dispersion parameters are estimated in a series of multivariate models followed by a reduction in order of fit using covariance functions, and a one-step procedure in which the parameters of TDM are estimated using restricted maximum likelihood or Bayesian methods in a random regression model. Further research should focus on including multiple lactation data and accounting for heterogeneity variance.     

Strategies for estimating genetic parameters in marker-assisted best linear unbiased predictor models in dairy cattle

An appropriate strategy to estimate variance components and breeding values in genetic models with quantitative trait loci (QTL) was developed for a dairy cattle breeding scheme by utilizing simulated data. Reliable estimates for variance components in QTL models are a prerequisite in fine-mapping experiments and for marker-assisted genetic evaluations. In cattle populations, only a small fraction of the population is genotyped at genetic markers, and only these animals are included in marker-assisted genetic evaluation models. Phenotypic information in these models are precorrected phenotypes [daughter yield deviations (DYD) for bulls, yield deviations (YD) for cows] estimated by standard animal models from the entire population. Because DYD and YD may represent different amounts of information, the problem of weighting these 2 types of information appropriately arises. To detect the best combination of phenotypes and weighting factors, a stochastic simulation for a trait representing milk yield was used. The results show that DYD models are generally optimal for estimating QTL variance components, but properties of estimates depend strongly on weighting factors. An example for the benefit in selection of using YD is shown for the selection among paternal half-sibs inheriting alternative QTL alleles. Even if QTL effects are small, marker-assisted best unbiased linear prediction can improve the selection among half-sibs, because the Mendelian sampling variance within family can be exploited, especially in DYD-YD models. Marker-assisted genetic evaluation models should also include YD for cows to ensure that marker-assisted selection improves selection even for moderate QTL effects (≥10%). A useful strategy for practical implementation is to estimate variance components in DYD models and breeding values in DYD-YD models.     

Effect of enlarging the reference population with (un)genotyped animals on the accuracy of genomic selection in dairy cattle

Genomic selection (GS) permits accurate breeding values to be obtained for young animals, shortening the generation interval and accelerating the genetic gain, thereby leading to reduced costs for proven bulls. Genotyping a large number of animals using high-density single nucleotide polymorphism marker arrays is nevertheless expensive, and therefore, a method to reduce the costs of GS is desired. The aim of this study was to investigate an influence of enlarging the reference population, with either genotyped animals or individuals with predicted genotypes, on the accuracy of genomic estimated breeding values. A dairy cattle population was simulated in which proven bulls with 100 daughters were used as a reference population for GS. Phenotypic records were simulated for bulls with heritability equal to the reliability of daughter yield deviations based on 100 daughters. The simulated traits represented heritabilities at the level of individual daughter performance of 0.3, 0.05, and 0.01. Three scenarios were considered in which (1) the reference population consisted of 1,000 genotyped animals, (2) 1,000 ungenotyped animals were added to the reference population, and (3) the 1,000 animals added in scenario 2 were genotyped in addition to the 1,000 animals from scenario 1. Genotypes for ungenotyped animals were predicted with an average accuracy of 0.58. Additionally, an adjustment of the diagonal elements of the G matrix was proposed for animals with predicted genotypes. The accuracy of genomic estimated breeding values for juvenile animals was the highest for the scenario with 2,000 genotyped animals, being 0.90, 0.79, and 0.60 for the heritabilities of 0.3, 0.05, and 0.01, respectively. Accuracies did not differ significantly between the scenario with 1,000 genotyped animals only and the scenario in which 1,000 ungenotyped animals were added and the adjustment of the G matrix was applied. The absence of significant increase in the accuracy of genomic estimated breeding values was attributed to the low accuracy of predicted genotypes. Although the differences were not significant, the difference between scenario 1 and 2 increased with decreasing heritability. Without the adjustment of the diagonal elements of the G matrix, accuracy decreased. Results suggest that inclusion of ungenotyped animals is only expected to enhance the accuracy of GS when the unknown genotypes can be predicted with high accuracy.     

Genetic evaluation of test-day milk yields from smallholder dairy production systems in Kenya using genomic relationships

Efforts to improve dairy production in smallholder farming systems of East Africa over the past decade have had limited impact because of the lack of records on performance to guide targeted breeding programs. Estimates of genetic parameters in these systems are lacking. Using data generated through a project (“Germplasm for Dairy Development in East Africa”) in Kenya and a genomic relationship matrix from genotypic records, we examined the potential impact of different models handling contemporary groups or herd effects on estimates of genetic parameters using a fixed regression model (FRM) for test-day (TD) milk yields, and the covariance structure for TD milk yield at various stages of lactation for animals using a random regression model (RRM). Models in which herd groups were defined using production levels derived from the data fitted the data better than those in which herds were grouped depending on management practices or were random. Lactation curves obtained for animals under different production categories did not display the typical peak yield characteristic of improved dairy systems in developed countries. Heritability estimates for TD milk yields using the FRM varied greatly with the definition of contemporary herd groups, ranging from 0.05 ± 0.03 to 0.27 ± 0.05 (mean ± standard error). The analysis using the RRM fitted the data better than the FRM. The heritability estimates for specific TD yields obtained by the RRM were higher than those obtained by the FRM. Genetic correlations between TD yields were high and positive for measures within short consecutive intervals but decreased as the intervals between TD increased beyond 60 d and became negative with intervals of more than 5 mo. The magnitude of the genetic correlation estimates among TD records indicates that using TD milk records beyond a 60-d interval as repeated measures of the same trait for genetic evaluation of animals on smallholder farms would not be optimal. Although each individual smallholder farmer retains only a few animals, using the genomic relationship between animals to link the large number of farmers operating under specified environments provides a sufficiently large herd-group for which a breeding program could be developed.     

Comparison between linear models and survival analysis for genetic evaluation of clinical mastitis in dairy cattle

Clinical mastitis was analyzed with mixed linear models (LM) and survival analysis (SA) using data from the first 3 lactations of >200,000 Swedish Holstein cows having their first calving between 1995 and 2000. The model for both methods included fixed effects of year-month and age at calving, fixed regressions of proportions of heterosis and North American Holstein genes, and random effects of herd-year at calving and sire. For the LM, clinical mastitis was defined as a binary trait measured from 10 d before to 150 d after calving. For the SA, clinical mastitis was defined either as the time period from 10 d before calving to the day of first treatment or culling because of mastitis (uncensored record) or from 10 d before to the day of next calving, culling for reasons other than mastitis, movement to a new herd, or to lactation d 240 (censored record). The heritability estimates from SA (0.03 to 0.04) were higher than those obtained with the LM (0.01 to 0.03). Consequently, the accuracies of estimated transmitting abilities were also higher for the trait analyzed with SA. The difference between estimates from the 2 methods was greater for later lactations. This study reveals the potential of analyzing clinical mastitis data with SA.     

A comparison of dairy cattle breeding designs that use genomic selection

Different dairy cattle breeding schemes were compared using stochastic simulations, in which the accuracy of the genomic breeding values was dependent on the structure of the breeding scheme, through the availability of new genotyped animals with phenotypic information. Most studies that predict the gain by implementing genomic selection apply a deterministic approach that requires assumptions about the accuracy of the genomic breeding values. The achieved genetic gain, when genomic selection was the only selection method to directly identify elite sires for widespread use and progeny testing was omitted, was compared with using genomic selection for preselection of young bulls for progeny testing and to a conventional progeny test scheme. The rate of inbreeding could be reduced by selecting more sires every year. Selecting 20 sires directly on their genomic breeding values gave a higher genetic gain than any progeny testing scheme, with the same rate of inbreeding as the schemes that used genomic selection for preselection of bulls before progeny testing. The genomic selection breeding schemes could reduce the rate of inbreeding and still increase genetic gain, compared with the conventional breeding scheme. Since progeny testing is expensive, the breeding scheme omitting the progeny test will be the cheapest one. Keeping the progeny test and use of genomic selection for preselection still has some advantages. It gives higher accuracy of breeding values and does not require a complete restructuring of the breeding program. Comparing at the same rate of inbreeding, using genomic selection for elite sire selection only gives a 13% increase in genetic gain, compared with using genomic selection for preselection. One way to reduce the costs of the scheme where genomic selection was used for preselection is to reduce the number of progeny tested bulls. This was here achieved without getting lower genetic gain or a higher rate of inbreeding.     

Optimal strategies for the use of genomic selection in dairy cattle breeding programs

The objective of the present study was to conduct a stochastic simulation study on the possible benefits of an application of genomic selection in dairy cattle breeding programs according to a variety of selection schemes. In addition, the heritability of the trait in question, the accuracy of genomic breeding values, and the number of animals to be genotyped were varied. Specifically, the question of genotyping males, females, or both, was addressed. Selection schemes were compared with a young bull breeding program. The main criterion for comparison was the average of true breeding values of selected young males to be used as replacements for artificial insemination bulls. Stochastic simulations were run with 50 repetitions each to generate individuals with phenotypes, breeding values estimated by BLUP, and true breeding values. Genomic breeding values were generated from true breeding values with defined accuracy. Examined scenarios included a group of selection schemes that featured genotyping of parents of future bulls only. Such schemes can be viewed as improvements of young bull programs, and they were found to be competitive with or superior to a classical young bull program. However, a genomic breeding program usually involves at least genotyping young male candidates. A second group of selection schemes reflected this requirement. Scenarios in this group were found to be superior over the young bull program by 1.0 to 1.2 standard deviations of the average true breeding value of young male candidates. Within this group of scenarios, one scheme referred to an ideal situation under which genotypes for male calves were available without limitation. Using the average of true breeding values as the criterion for comparison, this idealistic scenario was competitive with other scenarios only if the reliability of genomic breeding values was larger than 0.50. Conventionally, not all males available will have genotypes, and the 2 most promising scenarios included a preselection step for dams of future bulls. This preselection step can be based on conventional BLUP estimated breeding values for bull dams, because differences with a scheme under which both parents and the resulting male offspring are genotyped were marginal. Genotyping of young male candidates should be the focus of activities of today's breeding organizations.     

Economic evaluation of progeny-testing and genomic selection schemes for small-sized nucleus dairy cattle breeding programs in developing countries

In developing countries minimal and erratic performance and pedigree recording impede implementation of large-sized breeding programs. Small-sized nucleus programs offer an alternative but rely on their economic performance for their viability. We investigated the economic performance of 2 alternative small-sized dairy nucleus programs [i.e., progeny testing (PT) and genomic selection (GS)] over a 20-yr investment period. The nucleus was made up of 453 male and 360 female animals distributed in 8 non-overlapping age classes. Each year 10 active sires and 100 elite dams were selected. Populations of commercial recorded cows (CRC) of sizes 12,592 and 25,184 were used to produce test daughters in PT or to create a reference population in GS, respectively. Economic performance was defined as gross margins, calculated as discounted revenues minus discounted costs following a single generation of selection. Revenues were calculated as cumulative discounted expressions (CDE, kg) × 0.32 (€/kg of milk) × 100,000 (size commercial population). Genetic superiorities, deterministically simulated using pseudo-BLUP index and CDE, were determined using gene flow. Costs were for one generation of selection. Results show that GS schemes had higher cumulated genetic gain in the commercial cow population and higher gross margins compared with PT schemes. Gross margins were between 3.2- and 5.2-fold higher for GS, depending on size of the CRC population. The increase in gross margin was mostly due to a decreased generation interval and lower running costs in GS schemes. In PT schemes many bulls are culled before selection. We therefore also compared 2 schemes in which semen was stored instead of keeping live bulls. As expected, semen storage resulted in an increase in gross margins in PT schemes, but gross margins remained lower than those of GS schemes. We conclude that implementation of small-sized GS breeding schemes can be economically viable for developing countries.     

Quantitative trait loci mapping for dairy cattle production traits using a maximum likelihood method

A maximum likelihood method was developed for QTL mapping in half-sib designs and compared to the regression method in analyses of both field and simulated data. The field data consisted of milk production evaluations of 433 progeny tested sons of 6 sires and 64 microsatellite markers distributed over 12 chromosomes. Based on permutation tests, 5 significant QTL were detected in the field data by the regression method compared with 10 by the maximum likelihood method (P < 0.05). In field data analysis, the maximum likelihood method detected more significant QTL and had a smaller residual variance than the regression method. The simulation included 9 scenarios differing in number of families, family size, QTL variance, and marker density, each replicated 100 times. The simulation results suggested that, as for the regression method, the precision of estimating QTL from the maximum likelihood method improves with increasing number of sons per sire, increasing the ratio of QTL to phenotypic variance, and decreasing marker interval. The maximum likelihood method had a smaller dispersion of estimated QTL positions than the regression method in 6 of 9 scenarios simulated. Overall, the maximum likelihood method shows potential advantage in QTL detection over the regression method, especially in the situations with less favorable conditions for QTL detection.     

Genetic progress in multistage dairy cattle breeding schemes using genetic markers

The aim of this paper was to explore general characteristics of multistage breeding schemes and to evaluate multistage dairy cattle breeding schemes that use information on quantitative trait loci (QTL). Evaluation was either for additional genetic response or for reduction in number of progeny-tested bulls while maintaining the same response. The reduction in response in multistage breeding schemes relative to comparable single-stage breeding schemes (i.e., with the same overall selection intensity and the same amount of information in the final stage of selection) depended on the overall selection intensity, the selection intensity in the various stages of the breeding scheme, and the ratio of the accuracies of selection in the various stages of the breeding scheme. When overall selection intensity was constant, reduction in response increased with increasing selection intensity in the first stage. The decrease in response was highest in schemes with lower overall selection intensity. Reduction in response was limited in schemes with low to average emphasis on first-stage selection, especially if the accuracy of selection in the first stage was relatively high compared with the accuracy in the final stage.Closed nucleus breeding schemes in dairy cattle that use information on QTL were evaluated by deterministic simulation. In the base scheme, the selection index consisted of pedigree information and own performance (dams), or pedigree information and performance of 100 daughters (sires). In alternative breeding schemes, information on a QTL was accounted for by simulating an additional index trait. The fraction of the variance explained by the QTL determined the correlation between the additional index trait and the breeding goal trait. Response in progeny test schemes relative to a base breeding scheme without QTL information ranged from +4.5% (QTL explaining 5% of the additive genetic variance) to +21.2% (QTL explaining 50% of the additive genetic variance). A QTL explaining 5% of the additive genetic variance allowed a 35% reduction in the number of progeny tested bulls, while maintaining genetic response at the level of the base scheme. Genetic progress was up to 31.3% higher for schemes with increased embryo production and selection of embryos based on QTL information. The challenge for breeding organizations is to find the optimum breeding program with regard to additional genetic progress and additional (or reduced) cost.     

Genetic evaluation of fertility traits of dairy cattle using a multiple-trait animal model

A genetic evaluation system was developed for 5 fertility traits of dairy cattle: interval from first to successful insemination and nonreturn rate to 56 d of heifers, and interval from calving to first insemination, nonreturn rate to 56 d, and interval first to successful insemination of cows. Using the 2 interval traits of cows as components, breeding values for days open were derived. A multiple-trait animal model was applied to evaluate these fertility traits. Fertility traits of later lactations of cows were treated as repeated measurements. Genetic parameters were estimated by REML. Mixed model equations of the genetic evaluation model were solved with preconditioned conjugate gradients or the Gauss-Seidel algorithm and iteration on data techniques. Reliabilities of estimated breeding values were approximated with a multi-trait effective daughter contribution method. Daughter yield deviations and associated effective daughter contributions were calculated with a multiple trait approach. The genetic evaluation software was applied to the insemination data of dairy cattle breeds in Germany, Austria, and Luxembourg, and it was validated with various statistical methods. Genetic trends were validated. Small heritability estimates were obtained for all the fertility traits, ranging from 1% for nonreturn rate of heifers to 4% for interval calving to first insemination. Genetic and environmental correlations were low to moderate among the traits. Notably, unfavorable genetic trends were obtained in all the fertility traits. Moderate to high correlations were found between daughter yield-deviations and estimated breeding values (EBV) for Holstein bulls. Because of much lower heritabilities of the fertility traits, the correlations of daughter yield deviations with EBV were significantly lower than those from production traits and lower than the correlations from type traits and longevity. Fertility EBV were correlated unfavorably with EBV of milk production traits but favorably with udder health and longevity. Integrating fertility traits into a total merit selection index can halt or reverse the decline of fertility and improve the longevity of dairy cattle.     

Multibreed genomic evaluation for production traits of dairy cattle in the United States using single-step genomic best linear unbiased predictor

Official multibreed genomic evaluations for dairy cattle in the United States are based on multibreed BLUP evaluation followed by single-breed estimation of SNP effects. Single-step genomic BLUP (ssGBLUP) allows the straight computation of genomic (G)EBV in a multibreed context. This work aimed to develop ssGBLUP multibreed genomic predictions for US dairy cattle using the algorithm for proven and young (APY) to compute the inverse of the genomic relationship matrix. Only purebred Ayrshire (AY), Brown Swiss (BS), Guernsey (GU), Holstein (HO), and Jersey (JE) animals were considered. A 3-trait model with milk (MY), fat (FY), and protein (PY) yields was applied using about 45 million phenotypes recorded from January 2000 to June 2020. The whole data set included about 29.5 million animals, of which almost 4 million were genotyped. All the effects in the model were breed specific, and breed was also considered as fixed unknown parent groups. Evaluations were done for (1) each single breed separately (single); (2) HO and JE together (HO_JE); (3) AY, BS, and GU together (AY_BS_GU); (4) all the 5 breeds together (5_BREEDS). Initially, 15k core animals were used in APY for AY_BS_GU and 5_BREEDS, but larger core sets with more animals from the least represented breeds were also tested. The HO_JE evaluation had a fixed set of 30k core animals, with an equal representation of the 2 breeds, whereas HO and JE single-breed analysis involved 15k core animals. Validation for cows was based on correlations between adjusted phenotypes and (G)EBV, whereas for bulls on the regression of daughter yield deviations on (G)EBV. Because breed was correctly considered in the model, BLUP results for single and multibreed analyses were the same. Under ssGBLUP, predictability and reliability for AY, BS, and GU were on average 7% and 2% lower in 5_BREEDS compared with single-breed evaluations, respectively. However, validation parameters for these 3 breeds became better than in the single-breed evaluations when 45k animals were included in the core set for 5_BREEDS. Evaluations for Holsteins were more stable across scenarios because of the greatest number of genotyped animals and amount of data. Combining AY, BS, and GU into one evaluation resulted in predictions similar to the ones from single breed, especially when using about 30k core animals in APY. The results showed that single-step large-scale multibreed evaluations are computationally feasible, but fine tuning is needed to avoid a reduction in reliability when numerically dominant breeds are combined. Having evaluations for AY, BS, and GU separated from HO and JE may reduce inflation of GEBV for the first 3 breeds.     

Genetic parameters for health traits using data collected from genomic information nucleus herds

As with many other countries, data availability has been a limitation in Australia for developing breeding values for health traits. A genomic information nucleus of approximately 100 herds across the country, selected on the basis of their record keeping, has recently been established in Australia and is known as Ginfo. The objective of this study was to evaluate the feasibility of developing genomic breeding values for health traits using farmer-collected data from Ginfo herds. Having this genotyped population opens up opportunities to develop new genomic breeding values, such as dairy health traits. In these herds, the 4 most recorded groups of health diseases identified were mastitis, reproductive problems, lameness, and metabolic disorders with incidence levels of 16, 9, 2, and 1.5%, respectively. Heritability estimates from pedigree and genomic analysis ranged from 0.01 to 0.03 for mastitis, 0.005 to 0.02 for reproductive disorders, 0 to 0.02 for lameness, and 0.00 to 0.06 for metabolic disorders. However, although heritability is low, there is sufficient genetic variation to make genetic progress feasible (coefficient of variation ranging from 3 to 26.3%). Genetic correlations between health and milk production traits (0.08 to 0.48) and overall type (?0.00 to 0.57) are unfavorable, but favorable with other economical traits, such as fertility (0.10 to 0.51), survival (?0.16 to ?0.37), and somatic cell count (0.07 to 0.55). For a genomic reference population of 11,458 cows, the reliability of genomic predictions is comparatively low (<30%), but is promising for health traits, such as mastitis (～30%) and a broader-based all disease trait (～28%), when bulls have daughters in the reference population, but not when they only have genomic information (0 to 18%). Further improvement of the reliabilities of health breeding values continues to be an important objective. The study has provided a good foundation for future research to develop breeding values for health traits.     

Genetic and phenotypic relationships among locomotion type traits, profit, production, longevity, and fertility in Spanish dairy cows

The phenotypic and genetic relationships of 3 locomotion traits with profit, production, longevity, and fertility traits were studied to determine the importance of locomotion traits for dairy producers. Two data sets including official milk records and type classification scores of 62,293 cows, and reproductive records of 24,561 cows from the Basque and Navarra Autonomous Regions were analyzed. Higher scores for feet and legs (FL), foot angle (FA), and rear legs set (RLS) were positively related to production and functional traits, whereas fertility was not significantly affected. The cows that scored the highest for FL were $213/yr more profitable, produced 575 kg more milk per year, and remained in the herd for 307 more functional days than the cows scoring the lowest. Feet and legs was the trait most genetically correlated to profit, although a low value (0.10) was obtained, whereas RLS was the trait most correlated to milk production (0.12). Genetic correlations among FL, FA, RLS, and longevity traits (from −0.10 to 0.05) were low. Quadratic curves were the best fit for both profit and functional herd life for EBV of each of the 3 locomotion traits. Further studies dealing with profitability and lameness, instead of using conformation traits, could be performed directly if a larger data pool of lameness was routinely recorded.     

Using the unified relationship matrix adjusted by breed-wise allele frequencies in genomic evaluation of a multibreed population

The observed low accuracy of genomic selection in multibreed and admixed populations results from insufficient linkage disequilibrium between markers and trait loci. Failure to remove variation due to the population structure may also hamper the prediction accuracy. We verified if accounting for breed origin of alleles in the calculation of genomic relationships would improve the prediction accuracy in an admixed population. Individual breed proportions derived from the pedigree were used to estimate breed-wise allele frequencies (AF). Breed-wise and across-breed AF were estimated from the currently genotyped population and also in the base population. Genomic relationship matrices (G) were subsequently calculated using across-breed (G_AB) and breed-wise (G_BW) AF estimated in the currently genotyped and also in the base population. Unified relationship matrices were derived by combining different G with pedigree relationships in the evaluation of genomic estimated breeding values (GEBV) for genotyped and ungenotyped animals. The validation reliabilities and inflation of GEBV were assessed by a linear regression of deregressed breeding value (deregressed proofs) on GEBV, weighted by the reliability of deregressed proofs. The regression coefficients (b₁) from G_AB ranged from 0.76 for milk to 0.90 for protein. Corresponding b₁ terms from G_BW ranged from 0.72 to 0.88. The validation reliabilities across 4 evaluations with different G were generally 36, 40, and 46% for milk, protein, and fat, respectively. Unexpectedly, validation reliabilities were generally similar across different evaluations, irrespective of AF used to compute G. Thus, although accounting for the population structure in G_BW tends to simplify the blending of genomic- and pedigree-based relationships, it appeared to have little effect on the validation reliabilities.     

Use of a single-step approach for integrating foreign information into national genomic evaluation in Holstein cattle

The use of multi-trait across-country evaluation (MACE) and the exchange of genomic information among countries allows national breeding programs to combine foreign and national data to increase the size of the training populations and potentially increase accuracy of genomic prediction of breeding values. By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (GBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. A single-step genomic BLUP approach, which enables integration of data from MACE evaluations, can be used to obtain genomic predictions while avoiding double-counting of information. The objectives of this study were to apply a single-step approach that simultaneously includes domestic and MACE information for genomic evaluation of workability traits in Canadian Holstein cattle, and compare the results obtained with this methodology with those obtained using a multi-step approach (msGBLUP). By including MACE bulls in the training population, msGBLUP led to an increase in reliability of genomic predictions of 4.8 and 15.4% for milking temperament and milking speed, respectively, compared with a traditional evaluation using only pedigree and phenotypic information. Integration of MACE data through a single-step approach (ssGBLUP_IM) yielded the highest reliabilities compared with other considered methods. Integration of MACE data also helped reduce bias of genomic predictions. When using ssGBLUP_IM, the bias of genomic predictions decreased by half compared with msGBLUP using domestic and MACE information. Therefore, the reliability and bias of genomic predictions for both traits improved substantially when a single-step approach was used for evaluation compared with a multi-step approach. The use of a single-step approach with integration of MACE information provides an alternative to the current method used in Canadian genomic evaluations.