Can Nitrogen Excretion of Dairy Cows Be Reduced by Genetic Selection for Low Milk Urea Nitrogen Concentration?

The objectives of this study were two-fold. Firstly, to estimate the likely correlated responses in milk urea nitrogen (MUN) concentration, lactation yields of milk (MY), fat (FY) and crude protein (CPY) and mature cow liveweight (LWT) under three selection scenarios which varied in relative emphasis for MUN; 0% relative emphasis (MUN_0%: equivalent to current New Zealand breeding worth index), and sign of the economic value; 20% relative emphasis positive selection (MUN_+20%), and 20% relative emphasis negative selection (MUN_-20%). Secondly, to estimate for these three scenarios the likely change in urinary nitrogen (UN) excretion under pasture based grazing conditions. The predicted genetic responses per cow per year for the current index were 16.4 kg MY, 2.0 kg FY, 1.4 kg CPY, -0.4 kg LWT and -0.05 mg/dL MUN. Positive selection on MUN in the index resulted in annual responses of 23.7 kg MY, 2.0 kg FY, 1.4 kg CPY, 0.6 kg LWT and 0.10 mg/dL MUN, while negative selection on MUN in the index resulted in annual responses of 5.4 kg MY, 1.6 kg FY, 1.0 kg CPY, -1.1 kg LWT and -0.17 mg/dL MUN. The MUN_-20% reduced both MUN and cow productivity, whereas the MUN_+20% increased MUN, milk production and LWT per cow. Per cow dry matter intake (DMI) was increased in all three scenarios as milk production increased compared to base year, therefore stocking rate (SR) was adjusted to control pasture cover. Paradoxically, ten years of selection with SR adjusted to maintain annual feed demand under the MUN_+20% actually reduced per ha UN excretion by 3.54 kg, along with increases of 63 kg MY, 26 kg FY and 16 kg CPY compared to the base year. Ten years of selection on the MUN_0% index generated a greater reductions of 10.45 kg UN and 30 kg MY, and increases of 32 kg FY and 21 kg CPY per ha, whereas the MUN_-20% index reduced 14.06 kg UN and 136 kg MY with increases of 32 kg FY and 18 kg CPY compared to base year. All three scenarios increased partitioning of nitrogen excreted as feces. The selection index that excluded MUN was economically beneficial in the current economic circumstances over selection indices including MUN regardless of whether selection was either for or against MUN. There was no substantial benefit from an environmental point of view from including MUN in the Breeding Worth index, because N leaching is more a function of SR rather than of individual cow UN excretion. This study demonstrates that attention needs to be paid to the whole system consequences of selection for environmental outcomes in pastoral grazing circumstances.

Design of selection schemes to include tick resistance in the breeding goal for Hereford and Braford cattle

Ticks are one of the main causes of losses in cattle, causing economic impact by reducing productivity and fertility and by transmission of diseases. The objective of this study was to analyze the genetic gains obtained through different strategies to include traditional (EBV) or genomic EBV (GEBV) for tick count (TC) in selection indexes for Hereford and Braford cattle. Besides TC, we also considered traits currently included in the Delta G Breeding Program Index (DGI): preweaning gain, weaning conformation, weaning precocity, weaning muscling, postweaning gain, yearling conformation, yearling precocity, yearling muscling, and scrotal circumference. Genetic gain per generation (ΔG) was evaluated using the current DGI and including TC in 8 alternative scenarios with TC relative weightings of 10, 50, or 100% and using phenotype or GEBV. Genomic EBV accuracy () ranged between 0.1 and 0.9. As expected, increasing increases the accuracy of the index () for all scenarios in which GEBV were considered. As the relative weight of TC was increased to 50%, greater ΔG differences in relation to the baseline DGI ($53.03) scenario were observed when the GEBV information was included with equal to or greater than 0.7 only for TC (ΔG between $61.06 and $74.26) or equal to or greater than 0.5 for all traits (ΔG between $56.03 and $83.36). To achieve these accuracies for traits with low heritability, a large calibration data set would be required. Focusing only on TC, the availability of genomic information would be desirable to avoid the need to count ticks and the exposure of animals to parasitism risks. However, for = 0.7, the respective numbers for Hereford and Braford would be 4,703 and 6,522 animals. As expected, when comparing the relative index weights of 10, 50, and 100% for TC, the highest response to selection per generation (RS) for TC was in the scenario was with 100% relative weight and GEBV for this trait (SR = -0.09 SD with = 0.9). This would be the recommended scenario to form tick-resistant lines in Hereford and Braford cattle. However, with 50% relative weight for TC, including GEBV information for TC only or for all traits in index ( = 0.9), it should yield 93 or 84% of RS, respectively, compared to that obtained with full emphasis on TC (100% relative weight) and GEBV information. This indicates that in the presence of highly accurate GEBV, despite slightly slower gain for TC, indexes with 50% relative weight for TC are interesting alternatives to jointly improve tick resistance and other relevant traits.

Prediction of genetic gain in finite populations with heterogeneous predicted breeding values accuracies

The algebraic expression of the genetic selection differential (expected genetic superiority of breeders after a selection on their Predicted Breeding Values) was derived when a limited number of individuals were selected from a limited sample of candidates on the basis of their predicted genetic value, with heterogeneous reliabilities. A formula is proposed for situations in which these reliabilities can be clustered in a few classes. We show that the expected genetic selection differential increases with the number of classes, the mean reliability being constant. In the panel of cases simulated, this increase reached up to 18% of the values obtained in the homogeneous situation. We used the proposed formulae to estimate selection differentials and compared it numerically with performing simulations. In terms of speed of computation, our algebraic formulae performed better than simulations in populations of limited size.

Review: Opportunities and challenges for small populations of dairy cattle in the era of genomics

In modern dairy cattle breeding, genomic breeding programs have the potential to increase efficiency and genetic gain. At the same time, the requirements and the availability of genotypes and phenotypes present a challenge. The set-up of a large enough reference population for genomic prediction is problematic for numerically small breeds but also for hard to measure traits. The first part of this study is a review of the current literature on strategies to overcome the lack of reference data. One solution is the use of combined reference populations from different breeds, different countries, or different research populations. Results reveal that the level of relationship between the merged populations is the most important factor. Compiling closely related populations facilitates the accurate estimation of marker effects and thus results in high accuracies of genomic prediction. Consequently, mixed reference populations of the same breed, but from different countries are more promising than combining different breeds, especially if those are more distantly related. The use of female reference information has the potential to enlarge the reference population size. Including females is advisable for small populations and difficult traits, and maybe combined with genotyping females and imputing those that are un-genotyped. The efficient use of imputation for un-genotyped individuals requires a set of genotyped related animals and well-considered selection strategies which animals to choose for genotyping and phenotyping. Small populations have to find ways to derive additional advantages from the cost-intensive establishment of genomic breeding schemes. Possible solutions may be the use of genomic information for inbreeding control, parentage verification, within-herd selection, adjusted mating plans or conservation strategies. The second part of the paper deals with the issue of high-quality phenotypes against the background of new, difficult and hard to measure traits. The use of contracted herds for phenotyping is recommended, as additional traits, when compared to standard traits used in dairy cattle breeding can be measured at set moments in time. This can be undertaken even for the recording of health traits, thus resulting in complete contemporary groups for health traits. Future traits to be recorded and used in genomic breeding programs, at least partly will be traits for which traditional selection based on widespread phenotyping is not possible. Enabling phenotyping of sufficient numbers to enable genomic selection will rely on cooperation between scientists from different disciplines and may require multidisciplinary approaches.

Genomic selection in a pig population including information from slaughtered full sibs of boars within a sib-testing program

Genomic selection is becoming a common practise in dairy cattle, but only few works have studied its introduction in pig selection programs. Results described for this species are highly dependent on the considered traits and the specific population structure. This paper aims to simulate the impact of genomic selection in a pig population with a training cohort of performance-tested and slaughtered full sibs. This population is selected for performance, carcass and meat quality traits by full-sib testing of boars. Data were simulated using a forward-in-time simulation process that modeled around 60K single nucleotide polymorphisms and several quantitative trait loci distributed across the 18 porcine autosomes. Data were edited to obtain, for each cycle, 200 sires mated with 800 dams to produce 800 litters of 4 piglets each, two males and two females (needed for the sib test), for a total of 3200 newborns. At each cycle, a subset of 200 litters were sib tested, and 60 boars and 160 sows were selected to replace the same number of culled male and female parents. Simulated selection of boars based on performance test data of their full sibs (one castrated brother and two sisters per boar in 200 litters) lasted for 15 cycles. Genotyping and phenotyping of the three tested sibs (training population) and genotyping of the candidate boars (prediction population) were assumed. Breeding values were calculated for traits with two heritability levels (h 2=0.40, carcass traits, and h 2=0.10, meat quality parameters) on simulated pedigrees, phenotypes and genotypes. Genomic breeding values, estimated by various models (GBLUP from raw phenotype or using breeding values and single-step models), were compared with the classical BLUP Animal Model predictions in terms of predictive ability. Results obtained for traits with moderate heritability (h 2=0.40), similar to the heritability of traits commonly measured within a sib-testing program, did not show any benefit from the introduction of genomic selection. None of the considered genomic models provided improvements in prediction ability of pigs with no recorded phenotype. However, a few advantages were found for traits with low heritability (h 2=0.10). These heritability levels are characteristic for meat quality traits recorded after slaughtering or for reproduction or health traits, typically recorded on field and not in performance stations. Other scenarios of data recording and genotyping should be evaluated before considering the implementation of genomic selection in a pig-selection scheme based on sib testing of boars.

Effects of a breeding scheme combined by genomic pre-selection and progeny testing on annual genetic gain in a dairy cattle population

The effectiveness of the incorporation of genomic pre-selection into dairy cattle progeny testing (GS-PT) was compared with that of progeny testing (PT) where the fraction of dam to breed bull (DB) selected was 0.01. When the fraction of sires to breed bulls (SB) selected without being progeny tested to produce young bulls (YB) in the next generation was 0.2, the annual genetic gain from GS-PT was 13% to 43% greater when h(2)  = 0.3 and 16% to 53% greater when h(2)  = 0.1 compared with that from PT. Given h(2)  = 0.3, a selection accuracy of 0.8 for both YB and DB, and selected fractions of 0.117 for YB and 0.04 for DB, GS-PT produced 40% to 43% greater annual genetic gain than PT. Given h(2)  = 0.1, a selection accuracy of 0.6 for both YB and DB, and selected fractions of 0.117 for YB and 0.04 for DB, annual genetic gain from GS-PT was 48% to 53% greater than that from PT. When h(2)  = 0.3, progeny testing capacity had little effect on annual genetic gain from GS-PT. However, when h(2)  = 0.1, annual genetic gain from GS-PT increased with increasing progeny testing capacity.

Genomic selection using beef commercial carcass phenotypes

In this study, an industry terminal breeding goal was used in a deterministic simulation, using selection index methodology, to predict genetic gain in a beef population modelled on the UK pedigree Limousin, when using genomic selection (GS) and incorporating phenotype information from novel commercial carcass traits. The effect of genotype-environment interaction was investigated by including the model variations of the genetic correlation between purebred and commercial cross-bred performance (ρX). Three genomic scenarios were considered: (1) genomic breeding values (GBV)+estimated breeding values (EBV) for existing selection traits; (2) GBV for three novel commercial carcass traits+EBV in existing traits; and (3) GBV for novel and existing traits plus EBV for existing traits. Each of the three scenarios was simulated for a range of training population (TP) sizes and with three values of ρX. Scenarios 2 and 3 predicted substantially higher percentage increases over current selection than Scenario 1. A TP of 2000 sires, each with 20 commercial progeny with carcass phenotypes, and assuming a ρX of 0.7, is predicted to increase gain by 40% over current selection in Scenario 3. The percentage increase in gain over current selection increased with decreasing ρX; however, the effect of varying ρX was reduced at high TP sizes for Scenarios 2 and 3. A further non-genomic scenario (4) was considered simulating a conventional population-wide progeny test using EBV only. With 20 commercial cross-bred progenies per sire, similar gain was predicted to Scenario 3 with TP=5000 and ρX=1.0. The range of increases in genetic gain predicted for terminal traits when using GS are of similar magnitude to those observed after the implementation of BLUP technology in the United Kingdom. It is concluded that implementation of GS in a terminal sire breeding goal, using purebred phenotypes alone, will be sub-optimal compared with the inclusion of novel commercial carcass phenotypes in genomic evaluations.

Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies

Genomic selection holds the promise to be particularly beneficial for traits that are difficult or expensive to measure, such that access to phenotypes on large daughter groups of bulls is limited. Instead, cow reference populations can be generated, potentially supplemented with existing information from the same or (highly) correlated traits available on bull reference populations. The objective of this study, therefore, was to develop a model to perform genomic predictions and genome-wide association studies based on a combined cow and bull reference data set, with the accuracy of the phenotypes differing between the cow and bull genomic selection reference populations. The developed bivariate Bayesian stochastic search variable selection model allowed for an unbalanced design by imputing residuals in the residual updating scheme for all missing records. The performance of this model is demonstrated on a real data example, where the analyzed trait, being milk fat or protein yield, was either measured only on a cow or a bull reference population, or recorded on both. Our results were that the developed bivariate Bayesian stochastic search variable selection model was able to analyze 2 traits, even though animals had measurements on only 1 of 2 traits. The Bayesian stochastic search variable selection model yielded consistently higher accuracy for fat yield compared with a model without variable selection, both for the univariate and bivariate analyses, whereas the accuracy of both models was very similar for protein yield. The bivariate model identified several additional quantitative trait loci peaks compared with the single-trait models on either trait. In addition, the bivariate models showed a marginal increase in accuracy of genomic predictions for the cow traits (0.01-0.05), although a greater increase in accuracy is expected as the size of the bull population increases. Our results emphasize that the chosen value of priors in Bayesian genomic prediction models are especially important in small data sets.

Estimation of genetic parameters for novel functional traits in Brown Swiss cattle

The aim of this study was to estimate genetic parameters and accuracies of breeding values for a set of functional, behavior, and conformation traits in Brown Swiss cattle. These traits were milking speed, udder depth, position of labia, rank order in herd, general temperament, aggressiveness, milking temperament, and days to first heat. Data of 1,799 phenotyped Brown Swiss cows from 40 Swiss dairy herds were analyzed taking the complete pedigree into account. Estimated heritabilities were within the ranges reported in literature, with results at the high end of the reported values for some traits (e.g., milking speed: 0.42±0.06, udder depth: 0.42±0.06), whereas other traits were of low heritability and heritability estimates were of low accuracy (e.g., milking temperament: 0.04±0.04, days to first heat: 0.02±0.04). For most behavior traits, we found relatively high heritabilities (general temperament: 0.38±0.07, aggressiveness: 0.12±0.08, and rank order in herd: 0.16±0.06). Position of labia, arguably an indicator trait for pathological urovagina, was genetically analyzed in this study for the first time, and a moderate heritability (0.28±0.06) was estimated.

A proposed selection index for jersey cattle in zimbabwe

A multitrait selection index (I_T) for Zimbabwean Jersey cattle was constructed. The breeding objective was defined in terms of production and functionality traits. The production component of the index included milk yield (M), butterfat yield (F), protein yield (P), butterfat percent (F%), and protein percent (P%), while the functional component included the somatic cell count (SCC). The index was termed as I_T = 0.0004M + 0.0109F + 0.0313P + 1.0004F% + 2.4491P% − 0.1905SCC. The accuracy of the index was 91.1%, and the correlation between this index and the aggregate breeding objective was 0.954. A selection index is more important in the selection of sires and cows. This leads to the greatest genetic progress and hence productivity in the dairy sector. Therefore, the application of the selection index developed is necessary if the dairy cattle industry is to maximise the exploitation of genetics and to improve its relative competitive position.

Potential benefits of genomic selection on genetic gain of small ruminant breeding programs

In conventional small ruminant breeding programs, only pedigree and phenotype records are used to make selection decisions but prospects of including genomic information are now under consideration. The objective of this study was to assess the potential benefits of genomic selection on the genetic gain in French sheep and goat breeding designs of today. Traditional and genomic scenarios were modeled with deterministic methods for 3 breeding programs. The models included decisional variables related to male selection candidates, progeny testing capacity, and economic weights that were optimized to maximize annual genetic gain (AGG) of i) a meat sheep breeding program that improved a meat trait of heritability (h(2)) = 0.30 and a maternal trait of h(2) = 0.09 and ii) dairy sheep and goat breeding programs that improved a milk trait of h(2) = 0.30. Values of ±0.20 of genetic correlation between meat and maternal traits were considered to study their effects on AGG. The Bulmer effect was accounted for and the results presented here are the averages of AGG after 10 generations of selection. Results showed that current traditional breeding programs provide an AGG of 0.095 genetic standard deviation (σa) for meat and 0.061 σa for maternal trait in meat breed and 0.147 σa and 0.120 σa in sheep and goat dairy breeds, respectively. By optimizing decisional variables, the AGG with traditional selection methods increased to 0.139 σa for meat and 0.096 σa for maternal traits in meat breeding programs and to 0.174 σa and 0.183 σa in dairy sheep and goat breeding programs, respectively. With a medium-sized reference population (nref) of 2,000 individuals, the best genomic scenarios gave an AGG that was 17.9% greater than with traditional selection methods with optimized values of decisional variables for combined meat and maternal traits in meat sheep, 51.7% in dairy sheep, and 26.2% in dairy goats. The superiority of genomic schemes increased with the size of the reference population and genomic selection gave the best results when nref > 1,000 individuals for dairy breeds and nref > 2,000 individuals for meat breed. Genetic correlation between meat and maternal traits had a large impact on the genetic gain of both traits. Changes in AGG due to correlation were greatest for low heritable maternal traits. As a general rule, AGG was increased both by optimizing selection designs and including genomic information.

Genomic selection for two traits in a maternal pig breeding scheme

The objective of this study was to compare different implementations of genomic selection to a conventional maternal pig breeding scheme, when selection was based partly on production traits and partly on maternal traits. A nucleus pig breeding population with size and structure similar to Norwegian Landrace was simulated where equal weight was used for maternal and production traits. To genotype the boars at the boar station and base the final selection of boars on genomic breeding values increased total genetic gain by 13% and reduced the rate of inbreeding by 40%, without significantly affecting the relative contribution of each trait to total genetic gain. To increase the size of the reference population and thereby accuracy of selection, female sibs in the selected litters can also be genotyped to increase genetic gain for maternal traits more than for production traits, thereby resulting in an increased relative contribution of maternal traits to total genetic gain. Genotyping 2,400 females each year increased the relative contribution of maternal traits to total genetic gain from 16 to 32%. Performing preselection of males by allowing genotyping of 2 males per litter and allowing for selection across and within litters before the boar test increased genetic gain by 5 to 11%, compared with genotyping the boars at the boar station, without significant effects on the relative contribution of each trait to total genetic gain. Genotyping more animals consequently increased genetic gain. Genotyping females to build a larger reference base for maternal traits gave similar genetic gain as genotyping the same amount of additional males but with a lower rate of inbreeding and a greater contribution of maternal traits to total genetic gain. In conclusion, genotyping females should be prioritized before genotyping more males than the tested boars if the breeding goal is to increase maternal traits specifically over production traits or genomic selection is used as a tool to reduce the rate of inbreeding.

Hot topic: Effect of breeding strategies using genomic information on fitness and health

A complex deterministic approach was used to model the breeding goal and breeding structure for the Austrian Fleckvieh (dual-purpose Simmental) breed. The reference breeding goal corresponded to the current total merit index (TMI-R), where dairy traits have a relative weight of 37.9% and fitness traits of 43.7% (beef traits 16.5%; milkability 2%). The breeding program was characterized by 280,000 cows under performance recording, 3,200 bull dams, 100 test bulls with a test capacity of 25%, and 15 proven bulls and 8 bull sires per year. The annual monetary genetic gain (AMGG) was generated mainly by increases in milk fat and milk protein yield (80.6%) and only to a small extent by fitness traits (6.6%). The inclusion of direct health traits (early reproductive disorders, cystic ovaries, and mastitis) with their economic weights increased the relative AMGG for fitness traits from 6.6 to 11.2%. The presently slightly negative AMGG for fertility index and udder health changed in a positive direction. Increasing the weight on the direct health traits by 50% resulted in a further shift toward fitness and health. The effect of strategies using genomic information in a total merit index (TMI) with varying weights on fitness and health traits was also analyzed. The conventional progeny-testing scheme was defined as the reference breeding program. A breeding program was considered to be genomically enhanced (GS50) when 50% of inseminations of herdbook cows and of bull dams were from young bulls with a genomic TMI, and a second program (GS100) did not rely on progeny-tested bulls at all. For GS50, a clear shift of the relative gain in AMGG toward fitness and health traits was observed for all 3 TMI scenarios, as a result of larger progeny groups and a shorter generation interval. For GS100, where no gene flow from progeny-tested bulls was assumed, the genetic gain per generation was lower for the fertility and udder health index but higher per year. The results based on natural genetic gain per year showed that no positive genetic response for fertility and udder health index were achieved for TMI-R (without the inclusion of direct health traits) in GS50 and GS100. The direction of the genetic trend was determined by the weights given to fertility and udder health indices within the TMI. When appropriate weights generated a clear positive trend, GS50 and GS100 reinforced this trend.

Application of genomics tools to animal breeding

The main goal in animal breeding is to select individuals that have high breeding values for traits of interest as parents to produce the next generation and to do so as quickly as possible. To date, most programs rely on statistical analysis of large data bases with phenotypes on breeding populations by linear mixed model methodology to estimate breeding values on selection candidates. However, there is a long history of research on the use of genetic markers to identify quantitative trait loci and their use in marker-assisted selection but with limited implementation in practical breeding programs. The advent of high-density SNP genotyping, combined with novel statistical methods for the use of this data to estimate breeding values, has resulted in the recent extensive application of genomic or whole-genome selection in dairy cattle and research to implement genomic selection in other livestock species is underway. The high-density SNP data also provides opportunities to detect QTL and to encover the genetic architecture of quantitative traits, in terms of the distribution of the size of genetic effects that contribute to trait differences in a population. Results show that this genetic architecture differs between traits but that for most traits, over 50% of the genetic variation resides in genomic regions with small effects that are of the order of magnitude that is expected under a highly polygenic model of inheritance.

Controlling inbreeding and maximizing genetic gain using semi-definite programming with pedigree-based and genomic relationships

Because of the relatively high levels of genetic relationships among potential bull sires and bull dams, innovative selection tools should consider both genetic gain and genetic relationships in a long-term perspective. Optimum genetic contribution theory using official estimated breeding values for a moderately heritable trait (production index, Index-PROD), and a lowly heritable functional trait (index for somatic cell score, Index-SCS) was applied to find optimal allocations of bull dams and bull sires. In contrast to previous practical applications using optimizations based on Lagrange multipliers, we focused on semi-definite programming (SDP). The SDP methodology was combined with either pedigree (a(ij)) or genomic relationships (f(ij)) among selection candidates. Selection candidates were 484 genotyped bulls, and 499 preselected genotyped bull dams completing a central test on station. In different scenarios separately for PROD and SCS, constraints on the average pedigree relationships among future progeny were varied from a(ij)=0.08 to a(ij)=0.20 in increments of 0.01. Corresponding constraints for single nucleotide polymorphism-based kinship coefficients were derived from regression analysis. Applying the coefficient of 0.52 with an intercept of 0.14 estimated for the regression pedigree relationship on genomic relationship, the corresponding range to alter genomic relationships varied from f(ij) = 0.18 to f(ij) = 0.24. Despite differences for some bulls in genomic and pedigree relationships, the same trends were observed for constraints on pedigree and corresponding genomic relationships regarding results in genetic gain and achieved coefficients of relationships. Generally, allowing higher values for relationships resulted in an increase of genetic gain for Index-PROD and Index-SCS and in a reduction in the number of selected sires. Interestingly, more sires were selected for all scenarios when restricting genomic relationships compared with restricting pedigree relationships. For example, at constraint of f(ij)=0.185 and selection on Index-PROD, the number of selected sires was 35. In contrast, only 21 sires were selected at the comparable constraint on additive genetic relationship of a(ij)=0.09. A further reduction in relationships is possible when using SDP output (i.e., suggested genetic contributions of selected parents) and applying a simulated annealing algorithm to define specific mating plans. However, the advantage of this strategy is limited to a short-term perspective and probably not successful in the period of genomic selection allowing a substantial reduction of generation intervals.

Optimising multistage dairy cattle breeding schemes including genomic selection using decorrelated or optimum selection indices

Background

The prediction of the outcomes from multistage breeding schemes is especially important for the introduction of genomic selection in dairy cattle. Decorrelated selection indices can be used for the optimisation of such breeding schemes. However, they decrease the accuracy of estimated breeding values and, therefore, the genetic gain to an unforeseeable extent and have not been applied to breeding schemes with different generation intervals and selection intensities in each selection path.

Methods

A grid search was applied in order to identify optimum breeding plans to maximise the genetic gain per year in a multistage, multipath dairy cattle breeding program. In this program, different values of the accuracy of estimated genomic breeding values and of their costs per individual were applied, whereby the total breeding costs were restricted. Both decorrelated indices and optimum selection indices were used together with fast multidimensional integration algorithms to produce results.

Results

In comparison to optimum indices, the genetic gain with decorrelated indices was up to 40% less and the proportion of individuals undergoing genomic selection was different. Additionally, the interaction between selection paths was counter-intuitive and difficult to interpret. Independent of using decorrelated or optimum selection indices, genomic selection replaced traditional progeny testing when maximising the genetic gain per year, as long as the accuracy of estimated genomic breeding values was ≥ 0.45. Overall breeding costs were mainly generated in the path "dam-sire". Selecting males was still the main source of genetic gain per year.

Conclusion

Decorrelated selection indices should not be used because of misleading results and the availability of accurate and fast algorithms for exact multidimensional integration. Genomic selection is the method of choice when maximising the genetic gain per year but genotyping females may not allow for a reduction in overall breeding costs. Furthermore, the economic justification of genotyping females remains questionable.

Designing dairy cattle breeding schemes under genomic selection: a review of international research

High rates of genetic gain can be achieved through (1) accurate predictions of breeding values (2) high intensities of selection and (3) shorter generation intervals. Reliabilities of ~60% are currently achievable using genomic selection in dairy cattle. This breakthrough means that selection of animals can happen at a very early age (i.e. as soon as a DNA sample is available) and has opened opportunities to radically redesign breeding schemes. Most research over the past decade has focussed on the feasibility of genomic selection, especially how to increase the accuracy of genomic breeding values. More recently, how to apply genomic technology to breeding schemes has generated a lot of interest. Some of this research remains the intellectual property of breeding companies, but there are examples in the public domain. Here we review published research into breeding scheme design using genomic selection and evaluate which designs appear to be promising (in terms of rates of genetic gain) and those that may have unfavourable side-effects (i.e. increasing the rate of inbreeding). The schemes range from fairly conservative designs where bulls are screened genomically to reduce numbers entering progeny testing, to schemes where very large numbers of bull calves are screened and used as sires as soon as they reach sexual maturity. More radical schemes that incorporate the use of reproductive technologies (in juveniles) and genomic selection in nucleus herds are also described. The models used are either deterministic and more recently tend to be stochastic, simulating populations of cattle. A key driver of the rate of genetic gain is the generation interval, which could range from being similar to that in conventional testing (~5 years), down to as little as 1.5 years. Generally, the rate of genetic gain is between 12% and 100% more than in conventional progeny testing, while the rate of inbreeding tends to be lower per generation than in progeny testing because Mendelian sampling terms can be estimated more accurately. However, short generation intervals can lead to higher rates of inbreeding per year in genomic breeding programs.

Accuracy of genotype imputation in sheep breeds

Although genomic selection offers the prospect of improving the rate of genetic gain in meat, wool and dairy sheep breeding programs, the key constraint is likely to be the cost of genotyping. Potentially, this constraint can be overcome by genotyping selection candidates for a low density (low cost) panel of SNPs with sparse genotype coverage, imputing a much higher density of SNP genotypes using a densely genotyped reference population. These imputed genotypes would then be used with a prediction equation to produce genomic estimated breeding values. In the future, it may also be desirable to impute very dense marker genotypes or even whole genome re-sequence data from moderate density SNP panels. Such a strategy could lead to an accurate prediction of genomic estimated breeding values across breeds, for example. We used genotypes from 48 640 (50K) SNPs genotyped in four sheep breeds to investigate both the accuracy of imputation of the 50K SNPs from low density SNP panels, as well as prospects for imputing very dense or whole genome re-sequence data from the 50K SNPs (by leaving out a small number of the 50K SNPs at random). Accuracy of imputation was low if the sparse panel had less than 5000 (5K) markers. Across breeds, it was clear that the accuracy of imputing from sparse marker panels to 50K was higher if the genetic diversity within a breed was lower, such that relationships among animals in that breed were higher. The accuracy of imputation from sparse genotypes to 50K genotypes was higher when the imputation was performed within breed rather than when pooling all the data, despite the fact that the pooled reference set was much larger. For Border Leicesters, Poll Dorsets and White Suffolks, 5K sparse genotypes were sufficient to impute 50K with 80% accuracy. For Merinos, the accuracy of imputing 50K from 5K was lower at 71%, despite a large number of animals with full genotypes (2215) being used as a reference. For all breeds, the relationship of individuals to the reference explained up to 64% of the variation in accuracy of imputation, demonstrating that accuracy of imputation can be increased if sires and other ancestors of the individuals to be imputed are included in the reference population. The accuracy of imputation could also be increased if pedigree information was available and was used in tracking inheritance of large chromosome segments within families. In our study, we only considered methods of imputation based on population-wide linkage disequilibrium (largely because the pedigree for some of the populations was incomplete). Finally, in the scenarios designed to mimic imputation of high density or whole genome re-sequence data from the 50K panel, the accuracy of imputation was much higher (86-96%). This is promising, suggesting that in silico genome re-sequencing is possible in sheep if a suitable pool of key ancestors is sequenced for each breed.

The value of using DNA markers for beef bull selection in the seedstock sector

The objective of this study was to estimate the value derived from using DNA information to increase the accuracy of beef sire selection in a closed seedstock herd. Breeding objectives for commercial production systems targeting 2 diverse markets were examined using multiple-trait selection indexes developed for the Australian cattle industry. Indexes included those for both maternal (self-replacing) and terminal herds targeting either a domestic market, where steers are finished on pasture, or the export market, where steers are finished on concentrate rations in feedlots and marbling has a large value. Selection index theory was used to predict the response to conventional selection based on phenotypic performance records, and this was compared with including information from 2 hypothetical marker panels. In 1 case the marker panel explained a percentage of additive genetic variance equal to the heritability for all traits in the breeding objective and selection criteria, and in the other case to one-half of this amount. Discounted gene flow methodology was used to calculate the value derived from the use of superior bulls selected using DNA test information and performance recording over that derived from conventional selection using performance recording alone. Results were ultimately calculated as discounted returns per DNA test purchased by the seedstock operator. The DNA testing using these hypothetical marker panels increased the selection response between 29 to 158%. The value of this improvement above that obtained using traditional performance recording ranged from $89 to 565 per commercial bull, and $5,332 to 27,910 per stud bull. Assuming that the entire bull calf crop was tested to achieve these gains, the value of the genetic gain derived from DNA testing ranged from $204 to 1,119 per test. All values assumed that the benefits derived from using superior bulls were efficiently transferred along the production chain to the seedstock producer incurring the costs of genotyping. These results suggest that the development of greater-accuracy DNA tests for beef cattle selection could be beneficial from an industry-wide perspective, but the commercial viability will strongly depend on price signaling throughout the production chain.

Deterministic models of breeding scheme designs that incorporate genomic selection

A deterministic model to calculate rates of genetic gain and inbreeding was used to compare a range of breeding scheme designs under genomic selection (GS) for a population of 140,000 cows. For most schemes it was assumed that the reliability of genomic breeding values (GEBV) was 0.6 across 4 pathways of selection. In addition, the effect of varying reliability on the ranking of schemes was also investigated. The schemes considered included intense selection in male pathways and genotyping of 1,000 young bulls (GS-Y). This scheme was extended to include selection in females and to include a "worldwide" scheme similar to GS-Y, but 6 times as large and assuming genotypes were freely exchanged between 6 countries. An additional worldwide scheme was modeled where GEBV were available through international genetic evaluations without exchange of genotypes. Finally, a closed nucleus herd that used juvenile in vitro embryo transfer in heifers was modeled so that the generation interval in female pathways was reduced to 1 or 2 yr. When the breeding schemes were compared using a GEBV reliability of 0.6, the rates of genetic gain were between 59 and 130% greater than the rate of genetic gain achieved in progeny testing. This was mainly through reducing the generation interval and increasing selection intensity. Genomic selection of females resulted in a 50% higher rate of genetic gain compared with restricting GS to young bulls only. The annual rates of inbreeding were, in general, 60% lower than with progeny testing, because more sires of bulls and sires of cows were selected, thus increasing the effective population size. The exception was in nucleus breeding schemes that had very short generation intervals, resulting in higher rates of both gain and inbreeding. It is likely that breeding companies will move rapidly to alter their breeding schemes to make use of genomic selection because benefits to the breeding companies and to the industry are considerable.