Effects of including a quantitative trait locus in selection under different waiting plans of young bulls

The advantage of using the genotype of a quantitative trait locus (QTL) in selection schemes of dairy cattle was quantified using stochastic simulation. Three selection plans were studied. In the first plan, young bulls waited for 3 yr until their sisters completed a lactation and then were evaluated and selected based on an animal model. In a second plan, young bulls waited for 5 yr until their daughters completed a lactation. An intermediate 4-yr waiting plan was also studied. Simulation was for 16 yr with overlapping generations. Population and model parameters were proportional to the U.S. Holstein population. The advantage of using a QTL was quantified as the percentage of superiority of QTL-assisted over QTL-free selection using cumulative genetic response. Percentage of superiority was reported for four selection pathways: active sires, young bulls, bull dams, and first lactation cows. A general trend was observed: low superiority in early years of selection that increased to a plateau in later years and then decreased. The superiority of the QTL information was greatest in the 3-yr waiting plan and least in the 4-yr waiting plan. Superiority at plateau for selection pathways ranged from 16 to 26% for the 3-yr waiting plan, from 3 to 12% for the 4-yr waiting plan, and from 5 to 13% for the 5-yr waiting plan. The contribution to selection response attributed to the QTL and the polygenes was quantified. The rate at which the favorable allele approached fixation and the accuracy of predicting breeding values on the percentage of superiority were studied.     

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.     

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.     

Adding cows to the reference population makes a small dairy population competitive

Small dairy breeds are challenged by low reliabilities of genomic prediction. Therefore, we evaluated the effect of including cows in the reference population for small dairy cattle populations with a limited number of sires in the reference population. Using detailed simulations, 2 types of scenarios for maintaining and updating the reference population over a period of 15 yr were investigated: a turbo scheme exclusively using genotyped young bulls and a hybrid scheme with mixed use of genotyped young bulls and progeny-tested bulls. Two types of modifications were investigated: (1) number of progeny-tested bulls per year was tested at 6 levels: 15, 40, 60, 100, 250, and 500; and (2) each year, 2,000 first-lactation cows were randomly selected from the cow population for genotyping or, alternatively, an additional 2,000 first-lactation cows were randomly selected and typed in the first 2 yr. The effects were evaluated in the 2 main breeding schemes. The breeding schemes were chosen to mimic options for the Danish Jersey cattle population. Evaluation criteria were annual monetary genetic gain, rate of inbreeding, reliability of genomic predictions, and variance of response. Inclusion of cows in the reference population increased monetary genetic gain and decreased the rate of inbreeding. The increase in genetic gain was larger for the turbo schemes with shorter generation intervals. The variance of response was generally higher in turbo schemes than in schemes using progeny-tested bulls. However, the risk was reduced by adding cows to the reference population. The annual genetic gain and the reliability of genomic predictions were slightly higher with more cows in the reference population. Inclusion of cows in the reference population is a rapid way to increase reliabilities of genomic predictions and hence increase genetic gain in a small population. An economic evaluation shows that genotyping of cows is a profitable investment.     

Selection with inbreeding control in simulated young bull schemes for local dairy cattle breeds

Local breeds are rarely subject to modern selection techniques; however, selection programs will be required if local breeds are to remain a viable livelihood option for farmers. Selection in small populations needs to take into account accurate inbreeding control. Optimum contribution selection (OCS) is efficient in controlling inbreeding and maximizes genetic gain. The current paper investigates genetic progress in simulated dairy cattle populations from 500 to 6,000 cows undergoing young bull selection schemes with OCS compared with truncation selection (TS) at an annual inbreeding rate of 0.003. Selection is carried out for a dairy trait with a base heritability of 0.3. A young bull selection scheme was used because of its simplicity in implementation. With TS, annual genetic gain from 0.111 standard deviation units with 500 cows increases rapidly to 0.145 standard deviation units with 4,000 cows. Then, genetic gain increases more slowly up to 6,000 cows. At the same inbreeding rate, OCS produces higher genetic progress than TS. Differences in genetic gain between OCS and TS vary from to 2 to 6.3%. Genetic gain is also improved by increasing the number of years that males can be used as sires of sires. When comparing OCS versus TS at different heritabilities, we observe an advantage of OCS only at high heritability, up to 8% with heritability of 0.9. By increasing the constraint on inbreeding, the difference of genetic gain between the 2 selection methods increases in favor of OCS, and the advantage at the inbreeding rate of 0.001 per generation is 6 times more than at the inbreeding rate of 0.003. Opportunities exist for selection even in dairy cattle populations of a few hundred females. In any case, selection in local breeds will most often require specific investments in infrastructure and manpower, including systems for accurate data recording and selection skills and the presence of artificial insemination and breeders organizations. A cost-benefit analysis is therefore advisable before considering the implementation of selection schemes in local dairy cattle breeds.     

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.     

Effects of genomic selection on genetic improvement, inbreeding, and merit of young versus proven bulls

Genomic selection has the potential to revolutionize dairy cattle breeding because young animals can be accurately selected as parents, leading to a much shorter generation interval and higher rates of genetic gain. The aims of this study were to assess the effects of genomic selection and reduction of the generation interval on the rate of genetic gain and rate of inbreeding. Furthermore, the merit of proven bulls relative to young bulls was studied. This is important for breeding organizations as it determines the relative importance of progeny testing. A closed nucleus breeding scheme was simulated in which 1,000 males and 1,000 females were born annually, 200 bulls were progeny tested, and 20 sires and 200 dams were selected to produce the next generation. In the “proven” (PROV) scenario, only cows with own performance records and progeny-tested bulls were selected as parents. The proportion of the genetic variance that was explained by simulated marker information (M) was varied from 0 to 100%. When M increased from 0 to 100%, the rate of genetic gain increased from 0.238 to 0.309 genetic standard deviations (σ) per year (+30%), whereas the rate of inbreeding reduced from 1.00 to 0.42% per generation. Alternatively, when young cows and bulls were selected as parents (YNG scenario), the rate of genetic gain for M = 0% was 0.292 σ/yr but the corresponding rate of inbreeding increased substantially to 3.15% per generation. A realistic genomic selection scheme (YNG with M = 40%) gave 108% higher rate of genetic gain (0.495 σ/yr) and approximately the same rate of inbreeding per generation as the conventional system without genomic selection (PROV with M = 0%). The rate of inbreeding per year, however, increased from 0.18 to 0.52% because the generation interval in the YNG scheme was much shorter. Progeny-testing fewer bulls reduced the rate of genetic gain and increased the rate of inbreeding for PROV, but had negligible effects for YNG because almost all sires were young bulls. In scenario YNG with M = 40%, the best young bulls were superior to the best proven bulls by 1.27 σ difference in genomic estimated breeding value. This superiority increased even further when fewer bulls were progeny tested. This stochastic simulation study shows that genomic selection in combination with a severe reduction in the generation interval can double the rate of genetic gain at the same rate of inbreeding per generation, but with a higher rate of inbreeding per year. The number of progeny-tested bulls can be greatly reduced, although this will slightly affect the quality of the proven bull team. Therefore, it is important for breeding organizations to predict the future demand for proven bull semen in light of the increasing superiority of young bulls.     

Optimization of a genomic breeding program for a moderately sized dairy cattle population

Although it now standard practice to genotype thousands of female calves, genotyping of bull calves is generally limited to progeny of elite cows. In addition to genotyping costs, increasing the pool of candidate sires requires purchase, isolation, and identification of calves until selection decisions are made. We economically optimized via simulation a genomic breeding program for a population of approximately 120,000 milk-recorded cows, corresponding to the Israeli Holstein population. All 30,000 heifers and 60,000 older cows of parities 1 to 3 were potential bull dams. Animals were assumed to have genetic evaluations for a trait with heritability of 0.25 derived by an animal model evaluation of the population. Only bull calves were assumed to be genotyped. A pseudo-phenotype corresponding to each animal's genetic evaluation was generated, consisting of the animal's genetic value plus a residual with variance set to obtain the assumed reliability for each group of animals. Between 4 and 15 bulls and between 200 and 27,000 cows with the highest pseudo-phenotypes were selected as candidate bull parents. For all progeny of the founder animals, genetic values were simulated as the mean of the parental values plus a Mendelian sampling effect with variance of 0.5. A probability of 0.3 for a healthy bull calf per mating, and a genomic reliability of 0.43 were assumed. The 40 bull calves with the highest genomic evaluations were selected for general service for 1 yr. Costs included genotyping of candidate bulls and their dams, purchase of the calves from the farmers, and identification. Costs of raising culled calves were partially recovered by resale for beef. Annual costs were estimated as $10,922 + $305 × candidate bulls. Nominal profit per cow per genetic standard deviation was $106. Economic optimum with a discount rate of 5%, first returns after 4 yr, and a profit horizon of 15 yr were obtained with genotyping 1,620 to 1,750 calves for all numbers of bull sires. However, 95% of the optimal profit can be achieved with only 240 to 300 calves. The higher reliabilities achieved through addition of genomic information to the selection process contribute not only in obtaining higher genetic gain, but also in obtaining higher absolute profits. In addition, the optimal profits are obtained for a lower number of calves born in each generation. Inbreeding, as allowed within genomic selection for the Israeli herd, had virtually no effect on genetic gain or on profits, when compared with the case of exclusion of all matings that generate inbreeding. Annual response to selection ranged from 0.35 to 0.4 genetic standard deviation for 4 to 15 bull sires, as compared with 0.25 to 0.3 for a comparable half-sib design without genomic selection.     

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.     

Pedigree verification and parentage assignment using genomic information in the Mexican Holstein population

Genealogical information is an essential tool for carrying out any genetic improvement program. The objective of this study was to determine the accuracy of pedigree information in the Mexican registered Holstein population using genomic data available in Mexico and for the US Holstein population. The study included 7,508 animals (158 sires and 7,350 cows) that were born from 2002 through 2014, registered with Holstein de México, and genotyped with single nucleotide polymorphism arrays of different densities. Parentage could not be validated for 17% of sires of cows and 12% of sires of bulls. Most (79%) of the dams of cows and the dams of bulls had no genotype available and could not be validated. A parentage test was possible for only 6,104 sires of cows, 139 sires of bulls, 1,519 dams of cows, and 33 dams of bulls. Of the animals with a parentage test, parent assignment was confirmed for 89% of sires of cows, 92% of dams of cows, 95% of sires of bulls, and 97% of dams of bulls. Parent discovery was possible for some animals without confirmed parents: 17% for sires of cows, 2.5% for dams of cows, 43% for sires of bulls, and 0% for dams of bulls. Of the 7,795 progeny tests, 777 had parent conflicts, which is an error rate of 9.97% for parental recording in the population, a rate that is similar to those recently reported for other populations. True parents for some progeny conflicts (15%) were discovered for the Mexican population, and the remaining parents were assigned as unknown. Expected effects of misidentification on rate of genetic gain could be decreased by half if genealogical errors were decreased to 5%. This study indicates that genotyping and genealogy recovery may help in increasing rates of genetic improvement in the Mexican registered Holstein population.     

Optimizing the design of small-sized nucleus breeding programs for dairy cattle with minimal performance recording

Dairy cattle breeding programs in developing countries are constrained by minimal and erratic pedigree and performance recording on cows on commercial farms. Small-sized nucleus breeding programs offer a viable alternative. Deterministic simulations using selection index theory were performed to determine the optimum design for small-sized nucleus schemes for dairy cattle. The nucleus was made up of 197 bulls and 243 cows distributed in 8 non-overlapping age classes. Each year 10 sires and 100 dams were selected to produce the next generation of male and female selection candidates. Conception rates and sex ratio were fixed at 0.90 and 0.50, respectively, translating to 45 male and 45 female candidates joining the nucleus per year. Commercial recorded dams provided information for genetic evaluation of selection candidates (bulls) in the nucleus. Five strategies were defined: nucleus records only [within-nucleus dam performance (DP)], progeny records in addition to nucleus records [progeny testing (PT)], genomic information only [genomic selection (GS)], dam performance records in addition to genomic information (GS+DP), and progeny records in addition to genomic information (GS+PT). Alternative PT, GS, GS+DP, and GS+PT schemes differed in the number of progeny per sire and size of reference population. The maximum number of progeny records per sire was 30, and the maximum size of the reference population was 5,000. Results show that GS schemes had higher responses and lower accuracies compared with other strategies, with the higher response being due to shorter generation intervals. Compared with similar sized progeny-testing schemes, genomic-selection schemes would have lower accuracies but these are offset by higher responses per year, which might provide additional incentive for farmers to participate in recording.     

Effects of Embryo Transfer on Genetic Change in Dairy Cattle

Impact of embryo transfer on rate of genetic gain was examined for a) development of bulls for progeny test, b) development of replacement females, and c) progeny testing of dams of bulls and replacement females. Increased selection intensity by embryo transfer potentially could improve genetic merit of bull dams by 17% when applied to production of sires for progeny test. Additional benefits would arise from increased availability of sisters to such bulls. Genetic merit of dams of replacement females increases more than genetic merit of dams of bulls with embryo transfer. However, current costs of embryo transfer limit its application to production of replacement females when increased yield is the sole source of added income. Increases in generation interval offset improvement in rate of genetic gain per generation from progeny testing females. Therefore, mass selection on own performance and pedigree produce a higher rate of genetic gain per year than progeny testing females. Application of embryo transfer to selection schemes for multiple traits may prove beneficial.     

Genomic prediction of milk-production traits and somatic cell score using single-step genomic best linear unbiased predictor with random regression test-day model in Thai dairy cattle

Cow genotypes are expected to improve the accuracy of genomic estimated breeding values (GEBV) for young bulls in relatively small populations such as Thai Holstein-Friesian crossbred dairy cattle in Thailand. The objective of this study was to investigate the effect of cow genotypes on the predictive ability and individual accuracies of GEBV for young dairy bulls in Thailand. Test-day data included milk yield (n = 170,666), milk component traits (fat yield, protein yield, total solids yield, fat percentage, protein percentage, and total solids percentage; n = 160,526), and somatic cell score (n = 82,378) from 23,201, 82,378, and 13,737 (for milk yield, milk component traits, and SCS, respectively) cows calving between 1993 and 2017, respectively. Pedigree information included 51,128; 48,834; and 32,743 animals for milk yield, milk component traits, and somatic cell score, respectively. Additionally, 876, 868, and 632 pedigreed animals (for milk yield, milk component traits, and SCS, respectively) were genotyped (152 bulls and 724 cows), respectively, using Illumina Bovine SNP50 BeadChip. We cut off the data in the last 6 yr, and the validation animals were defined as genotyped bulls with no daughters in the truncated set. We calculated GEBV using a single-step random regression test-day model (SS-RR-TDM), in comparison with estimated breed value (EBV) based on the pedigree-based model used as the official method in Thailand (RR-TDM). Individual accuracies of GEBV were obtained by inverting the coefficient matrix of the mixed model equations, whereas validation accuracies were measured by the Pearson correlation between deregressed EBV from the full data set and (G)EBV predicted with the reduced data set. When only bull genotypes were used, on average, SS-RR-TDM increased individual accuracies by 0.22 and validation accuracies by 0.07, compared with RR-TDM. With cow genotypes, the additional increase was 0.02 for individual accuracies and 0.06 for validation accuracies. The inflation of GEBV tended to be reduced using cow genotypes. Genomic evaluation by SS-RR-TDM is feasible to select young bulls for the longitudinal traits in Thai dairy cattle, and the accuracy of selection is expected to be increased with more genotypes. Genomic selection using the SS-RR-TDM should be implemented in the routine genetic evaluation of the Thai dairy cattle population. The genetic evaluation should consider including genotypes of both sires and cows.     

Benefits of cooperation between breeding programs in the presence of genotype by environment interaction

Dairy cattle breeding programs and dairy farmers are selecting sires and dams across environments. Genotype × environment interaction (G × E) limits the possibilities for cooperation between breeding programs operating in different environments. The objectives of this study were 2-fold: 1) to investigate the effects of heritability, selection intensity, number of progeny per bull, and size of breeding programs on possibilities for cooperation between dairy cattle breeding programs in the short and long term in the presence of G × E, and 2) to quantify the effect of such cooperation on genetic gain. A dairy cattle situation with 2 breeding programs operating in 2 environments was simulated using a deterministic pseudo-BLUP selection index model. Long-term cooperation between the 2 breeding programs was possible in the presence of G × E, when the genetic correlation was higher than 0.80 to 0.90, resulting in up to 15% extra genetic gain. In addition, in the initial generations of selection, the breeding programs could benefit from mutually selecting sires and dams from each other when the genetic correlation was as low as 0.40 to 0.60. With more intense selection, breeding programs were less likely to benefit from cooperation with breeding programs in other environments. Heritability and number of progeny per bull had little effect on possibilities for cooperation, unless the heritabilities and the number of progeny per bull were extremely different in the 2 environments. Small breeding programs benefited more from cooperation than did large breeding programs, and benefits were possible even at lower values (i.e., <0.80) of the genetic correlation. Possibilities for cooperation across environments would affect the optimal design of dairy cattle breeding programs considering genetic gain, inbreeding, and costs.     

Controlling inbreeding by constraining the average relationship between parents of young bulls entering AI progeny test programs

In breeding is known to impair the health, fertility, and productivity of dairy cattle and other livestock species. Mating programs can address inbreeding concerns on the farm, at least in the short term, but long-term control of inbreeding in a dairy population requires consideration of relationships between young bulls entering AI progeny test programs. The present study discusses an application of optimal contribution methodology to selection of young AI bulls in the five major US dairy breeds. Elite cows and active AI sires from the Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey breeds were considered as potential bull parents. Genetic merit of selected sires and dams was maximized subject to various constraints on the mean additive genetic relationship within the selected group. Relationships between selected parents can be reduced substantially relative to current levels, but the corresponding reduction in genetic merit may be large. This loss in genetic merit occurs due to lower selection intensity, although it is mainly a reflection of a larger number of bull parents (with progeny more evenly distributed among these parents), rather than selection of genetically inferior "outcross" parents that wouldn't otherwise have been considered. Selected parents were generally older and slightly less inbred than those that would have been chosen had inbreeding been ignored. Although severe restrictions on relationships can be costly, in terms of lost genetic progress, it appears that moderate constraints can keep relationships at a manageable level without a significant loss in genetic merit. Cooperation between breed associations and several competing AI companies may be required to facilitate implementation of this methodology in dispersed populations, but if this can be accomplished, prospects for achieving a balance between inbreeding and selection seem positive.     

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.     

Use of insemination data for joint evaluation of male and female fertility in predominantly seasonal-calving dairy herds

Conception in dairy cattle is influenced by the fertility of the cow and the bull and their interaction. Despite genetic selection for female fertility in many countries, selection for male fertility is largely not practiced. The primary objective of this study was to quantify variation in male and female fertility using insemination data from predominantly seasonal-calving herds. Nonreturn rate (NRR) was derived by coding each insemination as successful (1) or failed (0) based on a minimum of at least 25 d. The NRR was treated as a trait of the bull with semen (male fertility) and the cow that is mated (female fertility). The data (805,463 cows that mated to 5,776 bulls) were used to estimate parameters using either models that only included bulls with mating data or models that fitted the genetic and permanent environmental (PE) effects of bulls and cows simultaneously. We also evaluated whether fitting genetic and PE effects of bulls as one term is better for ranking bulls based on NRR compared with a model that ignored genetic effect. The age of cows that were mated, age of the bulls with semen data, season of mating, breed of cow that mated, inbreeding of cows and bulls, and days from calving to mating date were found to have a significant effect on NRR. Only about 3% of the total variance was explained by the random effects in the model, despite fitting the genetic and PE effects of the bull and cow. The 2 components of fertility (male and fertility) were not correlated. The heritability of male fertility was low (0.001 to 0.008), and that of female fertility was also low (~0.016). The highest heritability estimate for male fertility was obtained from the model that fitted the additive genetic relationship matrix and PE component of the bull as one term. When this model was used to calculate bull solutions, the difference between bulls with at least 100 inseminations was up to 19.2% units (−9.6 to 9.6%). Bull solutions from this model were compared with bull solutions that were predicted fitting bull effects ignoring pedigree. Bull solutions that were obtained considering pedigree had (1) the highest accuracy of prediction when early insemination was used to predict yet-to-be observed insemination data of bulls, and (2) improved model stability (i.e., a higher correlation between bull solutions from 2 randomly split herds) compared with the model which fitted bull with no pedigree. For practical purposes, the model that fitted genetic and PE effect as one term can provide more accurate semen fertility values for bulls than the model without genetic effect. To conclude, insemination data from predominantly seasonal-calving herds can be used to quantify variability between bulls for male fertility, which makes their ranking on NRR feasible. Potentially this information can be used for monitoring bulls and can supplement efforts to improve herd fertility by avoiding or minimizing the use of semen from subfertile bulls.     

Selection responses for disease resistance in two selection experiments with Norwegian red cows

Genetic trends for clinical mastitis (CM), ketosis (KET), retained placenta (RP), and 305-d protein yield (PY305) were calculated for 2 Norwegian dairy cattle selection experiments. The first experiment, accomplished from 1978 to 1989, included groups selected for high (HMP) and low milk production (LMP). The second experiment started in 1989 and included selection for high protein yield (HPY) and low mastitis frequency (LCM). In both experiments proven sires from the active breeding program of Norwegian Red were used as sires. To take into account that selection of sires was external to the experiment, all available data from the Norwegian Red population, including disease records for 2.7 million first-lactation cows, were analyzed with a multivariate animal model. Estimated breeding values for cows in the experiments were extracted from this analysis to calculate genetic trends in the selection groups. Genetic trends for PY305 were, as expected, positive for the HMP and HPY groups, and negative for LMP and LCM. The HMP group showed increasing genetic trends for all 3 diseases, arguably a correlated response after selection for increased milk production, whereas the LCM group showed decreasing genetic trends for CM, KET, and RP. The genetic trends for KET and RP in the LCM group are most likely correlated responses after selection against CM. After 5 cow-generations the genetic difference between HPY and LCM was 10 percentage units CM, 1.5 percentage units KET, and 0.5 percentage units RP.     

Optimum multistage genomic selection in dairy cattle

The availability of different single nucleotide polymorphism (SNP) chips and the development of imputation algorithms allow for multistage dairy cattle breeding schemes applying various genomic selection strategies. These SNP genotypes yield genomically estimated breeding values (GEBV) with different accuracies at different costs. Thus, the optimum allocation of investments to different selection paths and strategies to maximize the genetic gain per year (ΔG(a)) and its sensitivity to changes in cost and accuracies of GEBV is of great interest. This is even more relevant under the constraints of limited financial resources. With deterministic methods, optimum multistage breeding plans maximizing ΔG(a) were identified in which selection could take place on GEBV derived from high-density (GEBV(HD)) and low-density (GEBV(LD)) SNP genotypes. To account for the uncertainty of cost and accuracies of GEBV, these parameters were varied in a semi-continuous manner. Overall breeding costs were limited to the crucial expenses of a traditional breeding program with 50 progeny-tested young bulls per year. Results clearly show that, in an optimal selection strategy, selection on GEBV(LD) is predominantly used for the identification of future bull dams but the main part of ΔG(a) is still generated from selection of sires. The low selection intensity in the path dam to sire induced a higher sensitivity of ΔG(a) to changes in cost and accuracies of GEBV(LD) compared with the same changes of GEBV(HD). On the contrary, the genetic gain generated from selection of males was only affected by changes in accuracies of GEBV(HD) but almost unaffected by any changes in cost. Thus, changes in cost and accuracies of GEBV(LD) put the most pressure on the breeding scheme structure to maintain a high ΔG(a). Furthermore, genomic selection of bull dams produced by far the majority of breeding cost but the lowest genetic gain.     

Infrequent milk progesterone measurements in daughters enable bull selection for cow fertility

The interval from calving to first luteal activity (CLA) has been suggested as an unbiased and, therefore, preferable measure for selection on female fertility in dairy cattle. However, measurement of this interval for individual cows is not feasible for reasons of cost and labor associated with the necessary frequent (milk) progesterone measurements. The objective of this study was to test the hypothesis that mean sire progesterone profiles based on individual progesterone measurements of daughters at 3- to 6-wk intervals have prospects as a measure for female fertility when selecting sires in a progeny testing scheme. In this study, progesterone concentrations were measured in milk samples collected at routinely performed milk recordings during the first 100 d of lactation of daughters of 20 test bulls. It is demonstrated that a) mean progesterone profiles can be used to calculate the earliest stage of lactation at which at least 50% of the daughters of a test bull has a milk progesterone level >3 ng/mL (indicating luteal activity) and that b) this stage, at which 50% of the daughters of a bull have an active corpus luteum (CLA50%), varies largely between test bulls. We conclude that selecting sires based on daughter CLA50% may improve female fertility.