Optimizing female allocation to reproductive technologies considering merit,inbreeding and cost in nucleus breeding programmes with genomic selection

Female reproductive technologies such as multiple ovulation and embryo transfer (MOET) and juvenile in vitro fertilization and embryo transfer (JIVET) have been shown to accelerate genetic gain by increasing selection intensity and decreasing generation interval. Genomic selection (GS) increases the accuracy of selection of young candidates which can further accelerate genetic gain. Optimal contribution selection (OCS) is an effective method of keeping the rate of inbreeding at a sustainable level while increasing genetic merit. OCS could also be used to selectively and optimally allocate reproductive technologies in mate selection while accounting for their cost. This study uses stochastic simulation to simulate breeding programmes that use a combination of artificial insemination (AI) or natural mating (N), MOET and JIVET with GS. OCS was used to restrict inbreeding to 1.0% increase per generation and also to optimize use of reproductive technologies, considering their effect on genetic gain as well as their cost. Two Australian sheep breeding objectives were used as an example to illustrate the methodology—a terminal sire breeding objective (A) and a dual‐purpose self‐replacing breeding objective (B). The objective function used for optimization considered genetic merit, constrained inbreeding and cost of technologies where costs were offset by a premium paid to the seedstock breeder investing in female reproductive technologies. The premium was based on the cumulative discounted expression of genetic merit in the progeny of a commercial tier in the breeding programme multiplied by the proportion of that benefit received by the breeder. With breeding objective B, the highest premium of 64% paid to the breeder resulted in the highest allocation of reproductive technologies (4%–10% for MOET and 19%–54% for JIVET) and hence the highest annual genetic gain. Conversely, breeding objective A, which had a lower dollar value of the breeding objective and a maximum of 5% mating types for JIVET and zero for MOET were optimal, even when highest premiums were paid. This study highlights that the level of investment in breeding technologies to accelerate genetic gain depends on the investment of genetic improvement returned to the breeder per index point gain achieved. It also demonstrates that breeding programmes can be optimized including allocation of reproductive technologies at the individual animal level. Accounting for revenue to the breeder and cost of the technologies can facilitate more practical decision support for beef and sheep breeders.     

Genomic selection for maternal traits in pigs

The aim of this study was to compare alternative designs for implementation of genomic selection to improve maternal traits in pigs, with a conventional breeding scheme and a progeny testing scheme. The comparison was done through stochastic simulation of a pig population. It was assumed that selection was performed based on a trait that could be measured on females after the first litter, with a heritability of 0.1. Genomic selection increased genetic gain and reduced the rate of inbreeding, compared with conventional selection without progeny testing. Progeny testing could also increase genetic gain and decrease the rate of inbreeding, but because of the increased generation interval, the increase in annual genetic gain was only 7%. When genomic selection was applied, genetic gain was increased by 23 to 91%, depending on which and how many animals were genotyped. Genotyping dams in addition to the male selection candidates gave increased accuracy of the genomic breeding values, increased genetic gain, and decreased rate of inbreeding. To genotype 2 or 3 males from each litter, in order to perform within-litter selection, increased genetic gain 8 to 12%, compared with schemes with the same number of genotyped females but only 1 male candidate per litter. Comparing schemes with the same total number of genotyped animals revealed that genotyping more females caused a greater increase in genetic gain than genotyping more males because greater accuracy of selection was more advantageous than increasing the number of male selection candidates. When more than 1 male per litter was genotyped, and thereby included as selection candidates, rate of inbreeding increased because of coselection of full sibs. The conclusion is that genomic selection can increase genetic gain for traits that are measured on females, which includes several traits with economic importance in maternal pig breeds. Genotyping females is essential to obtain a high accuracy of selection.     

Genomic selection strategies in dairy cattle breeding programmes: Sexed semen cannot replace multiple ovulation and embryo transfer as superior reproductive technology

The aim of this study was to test whether the use of X-semen in a dairy cattle population using genomic selection (GS) and multiple ovulation and embryo transfer (MOET) increases the selection intensity on cow dams and thereby the genetic gain in the entire population. Also, the dynamics of using different types of sexed semen (X, Y or conventional) in the nucleus were investigated. The stochastic simulation study partly supported the hypothesis as the genetic gain in the entire population was elevated when X-semen was used in the production population as GS exploited the higher selection intensity among heifers with great accuracy. However, when MOET was applied, the effect was considerably diminished as was the exchange of females between the nucleus and the production population, thus causing modest genetic profit from using X-sorted semen in the production population. In addition, the effect of using sexed semen on the genetic gain was very small compared with the effect of MOET and highly dependent on whether cow dams or bull dams were inseminated with sexed semen and on what type of semen that was used for the bull dams. The rate of inbreeding was seldom affected by the use of sexed semen. However, when all young bull candidates were born following MOET, the results showed that the use of Y-semen in the breeding nucleus tended to decrease the rate of inbreeding as it enabled GS to increase within-family selection. This implies that the benefit from using sexed semen in a modern dairy cattle breeding scheme applying both GS and MOET may be found in its beneficial effect on the rate of inbreeding.     

Improving production efficiency in the presence of genotype by environment interactions in pig genomic selection breeding programmes

We simulated a genomic selection pig breeding schemes containing nucleus and production herds to improve feed efficiency of production pigs that were cross‐breed. Elite nucleus herds had access to high‐quality feed, and production herds were fed low‐quality feed. Feed efficiency in the nucleus herds had a heritability of 0.3 and 0.25 in the production herds. It was assumed the genetic relationships between feed efficiency in the nucleus and production were low (r_g = 0.2), medium (r_g = 0.5) and high (r_g = 0.8). In our alternative breeding schemes, different proportion of production animals were recorded for feed efficiency and genotyped with high‐density panel of genetic markers. Genomic breeding value of the selection candidates for feed efficiency was estimated based on three different approaches. In one approach, genomic breeding value was estimated including nucleus animals in the reference population. In the second approach, the reference population was containing a mixture of nucleus and production animals. In the third approach, the reference population was only consisting of production herds. Using a mixture reference population, we generated 40–115% more genetic gain in the production environment as compared to only using nucleus reference population that were fed high‐quality feed sources when the production animals were offspring of the nucleus animals. When the production animals were grand offspring of the nucleus animals, 43–104% more genetic gain was generated. Similarly, a higher genetic gain generated in the production environment when mixed reference population was used as compared to only using production animals. This was up to 19 and 14% when the production animals were offspring and grand offspring of nucleus animals, respectively. Therefore, in genomic selection pig breeding programmes, feed efficiency traits could be improved by properly designing the reference population.     

The potential of embryo transfer in a German horse‐breeding programme

A reference horse‐breeding programme with 13 500 foals each year was modelled with ZPLAN+. This new software for the optimization of the structures in breeding programmes is based on ZPLAN. In two scenarios, the implementation of a rigorous selection of mares was implemented. In scenario I, the mare performance test was the point of selection, while in scenario II, further information on 20 competitions in two more years is available. These selected mares were used for embryo transfer (ET), partly in combination with multiple ovulation (MOET). The selection intensity and the number of foals out of (MO)ET were varied in both scenarios. It was expected that 250, 500 and 1000 mares are available for selecting 20, 50, 100 or 200 donor mares each year. The number of foals out of (MO)ET was varied between one and six foals per donor mare and year. Donor mares were used for ET for 4 years. It became clear that with high selection intensities of donor mares and high reproduction rates of them, the yearly genetic gain in a horse‐breeding programme could increase over a large range. In scenario II, the additional information on 20 competitions increased the accuracy of the selection index to 0.85. With 200 selected donor mares of 1000 available mares and six foals per year, the genetic gain could almost be doubled compared to the reference scenario. The implementation of ET and a related higher usage of few selected mares entails rising costs and a reduction in the genetic variance. In the most extreme MOET scenario, the effective population size was reduced by 19% relative to the reference scenario. Only if the increase in genetic gain can be converted into higher return for the breeders, the implementation of (MO)ET schemes is a realistic and sensible option for horse‐breeding programmes.     

大白猪基因组选择在不同应用策略下的比较研究

目前,基因组选择（genomic selection,GS）技术已经在种猪育种中开展,但为获得较高的收益,还需研究一些应用策略,如确定仔猪基因分型个体比例和早期仔猪留种比例。本试验选择温氏集团出生于2011—2016年的大白种猪作为研究对象,共有超过4.5万条的生长测定记录,超过7万条繁殖记录,和2 090个个体的简化基因组测序（GBS）数据,其中,出生于2016年7~12月的440个体作为候选群体。研究性状包括两个生长性状（校正100 kg日龄和校正100 kg背膘厚）和一个繁殖性状（总产仔数）。为对比预测效果,在候选群体进行育种值预测时,按照是否利用其基因型或表型信息分为4种预测方案,比较不同方案的预测可靠性和个体选择指数的排名情况。结果显示,在预测候选群育种值时,利用其表型或基因型信息均比不利用时的预测结果更加可靠。对生长性状终测前、后进行基因组选择指数计算,发现,终测后指数排名前30%的个体都位于终测前指数排名前60%内。若仔猪出生后仅选择常规BLUP预测指数排名前60%的个体,会导致有接近15%的具有优秀潜力的个体被遗漏。本研究建议,对所有新生健康仔猪都进行基因分型并计算基因组选择指数,然后对指数排名靠前60%的个体进行性能测定。     

Comparative Analysis of Different Implementation Strategies on Genomic Selection in Large White Pigs

At present, genomic selection (GS) has been applied in pigs breeding, but some implementation strategies, such as the determination of genotyping ratios or early selection rates for piglets, are required to obtain a higher benefit using this technology. The Large White pigs born from 2011 to 2016 at WENS Foodstuff Group Co.,Ltd were chose as the research objects, including more than 45 000 growth measurement records, more than 70 000 reproduction records and 2 090 individuals with genotyping-by-sequencing (GBS) data. The 440 individuals born from July to December in 2016 were used as the candidate individuals. The traits included two growth traits, age at 100 kg and backfat thickness at 100 kg, and one reproduction trait, number of total born. To compare the prediction effects, four prediction scenarios were designed according to including or ignoring the phenotypic or genotypic information of candidate individuals when predicting their breeding values. The predictive reliability of different scenarios and rankings of selection indices of individuals would be compared. The results showed that the results using the phenotypic and genotypic information was more reliable than ignoring them to predict the breeding values of candidate individuals. When genomic selection indices were calculated before and after performances testing for the growth traits, the individuals ranking in the top 30% of indices after testing were all found in the individuals ranking in the top 60% of indices before testing. If the piglets with the top 60% of traditional BLUP indices were only selected, around 15% of individuals with good genetic potentials would be omitted. This study suggests that all healthy piglets after birth are genotyped and their genomic selection indices are calculated, and then the individuals ranking in the top 60% of indices are chose to perform growth measurement.     

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 h² = 0.3 and 16% to 53% greater when h² = 0.1 compared with that from PT. Given h² = 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 h² = 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 h² = 0.3, progeny testing capacity had little effect on annual genetic gain from GS‐PT. However, when h² = 0.1, annual genetic gain from GS‐PT increased with increasing progeny testing capacity.     

Effect of selection and selective genotyping for creation of reference on bias and accuracy of genomic prediction

Reference populations for genomic selection usually involve selected individuals, which may result in biased prediction of estimated genomic breeding values (GEBV). In a simulation study, bias and accuracy of GEBV were explored for various genetic models with individuals selectively genotyped in a typical nucleus breeding program. We compared the performance of three existing methods, that is, Best Linear Unbiased Prediction of breeding values using pedigree‐based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single‐Step approach (SSGBLUP) using both. For a scenario with no‐selection and random mating (RR), prediction was unbiased. However, lower accuracy and bias were observed for scenarios with selection and random mating (SR) or selection and positive assortative mating (SA). As expected, bias disappeared when all individuals were genotyped and used in GBLUP. SSGBLUP showed higher accuracy compared to GBLUP, and bias of prediction was negligible with SR. However, PBLUP and SSGBLUP still showed bias in SA due to high inbreeding. SSGBLUP and PBLUP were unbiased provided that inbreeding was accounted for in the relationship matrices. Selective genotyping based on extreme phenotypic contrasts increased the prediction accuracy, but prediction was biased when using GBLUP. SSGBLUP could correct the biasedness while gaining higher accuracy than GBLUP. In a typical animal breeding program, where it is too expensive to genotype all animals, it would be appropriate to genotype phenotypically contrasting selection candidates and use a Single‐Step approach to obtain accurate and unbiased prediction of GEBV.     

Genetic gain in dairy cattle populations is increased using sexed semen in commercial herds

Using stochastic simulation, the effect of using sexed semen to cow dams (CD) in a dairy cattle breeding scheme, with or without use of multiple ovulation and embryo transfer (MOET) to bull dams (BD), on annual genetic gain at the population level was examined. Three levels of sexed semen were combined with three levels of MOET: no sexed semen, sexed semen to the best CD and sexed semen to all heifers, combined with no MOET, MOET on all BD and MOET randomly on 20% of the BD. In total, nine scenarios were compared. The simulated population was monitored for 30 years and included 450 herds with 100 cows each. Each year 50 young bulls (YB), 10 active sires and 215 BD were selected on best linear unbiased prediction estimated breeding values by truncation selection across the simulated population, and the YB were tested within the population. Use of sexed semen alone gave a positive increase in the annual genetic gain of 2.1% when used on the best CD and 2.7% when used on all heifers, but only the latter was statistically significant. The increased annual genetic gain was caused by a larger contribution from the CD to the BD. Use of sexed semen together with MOET on BD increased the annual genetic gain by 1.8-2.5% compared with schemes without sexed semen and MOET on all BD. Performing MOET on all BD enables selection of offspring with high Mendelian deviations, which increase the annual genetic gain. Use of sexed semen decreased the genetic lag between the sires and the CD by 12-14% when used on the best CD and by 6% when used to all heifers. The decrease in the genetic lag is caused by the increased selection intensity of the cow dams.     

畜禽基因组选择方法研究进展

畜禽的选种选育在生产中至关重要,育种值估计是选种选育的核心。基因组选择（genomic selection,GS）是利用全基因组范围内的高密度标记估计个体基因组育种值的一种新型分子育种方法,目前已在牛、猪、鸡等畜禽育种中得到应用并取得了良好的效果。该方法可实现畜禽育种早期选择,降低测定费用,缩短世代间隔,提高育种值估计准确性,加快遗传进展。基因组选择主要是通过参考群体中每个个体的表型性状信息和单核苷酸多态性（single nucleotide polymorphism,SNP）基因型估计出每个SNP的效应值,然后测定候选群体中每个个体的SNP基因型,计算候选个体的基因组育种值,根据基因组育种值的高低对候选群体进行合理的选择。随着基因分型技术快速发展和检测成本不断降低,以及基因组选择方法不断优化,基因组选择已成为畜禽选种选育的重要手段。作者对一些常用的基因组选择方法进行了综述,比较了不同方法之间的差异,分析了基因组选择存在的问题与挑战,并展望了其在畜禽育种中的应用前景。     

Genotyping of groups of cows to improve genomic breeding values of new traits

Genotyping females and including them into the reference set for genomic predictions in dairy cattle is considered to provide gains in reliabilities of estimated breeding values for selection candidates. This should especially be true for low heritability traits. By the use of simulation, we extended a genomic reference set for an existing trait by including a fixed number of genotyped first‐crop daughters for one or two generations of reference sires. Moreover, we calculated results for the effects of a similar strategy in a situation where for a new trait the recording of phenotypes has recently started. For this case, we compared the effect of two different genotyping strategies: first, to phenotype cows but to genotype their sires only, and second, to collect phenotypes and genotypes on the same cows. We studied the effects on validation reliabilities and unbiasedness of predicted values for selection candidates. We found that by extending the reference set with genotyped daughters it is possible to increase the validation reliability of genomic breeding values. In the case of a new trait, it is always better to collect and use genotypes and phenotypes on the same animals instead of using only sire genotypes. We found that the benefits that can be achieved are sensitive to the sampling strategy used when selecting females for genotyping.     

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

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

Multiple ovulation and embryo manipulation in the improvement of beef cattle: relative theoretical rates of genetic change

Theoretical rates of annual genetic responses to selection in beef cattle were compared for conventional and multiple ovulation and embryo transfer (MOET) breeding schemes. Several combinations of replacement policy, mating ratio and type of selection were considered for both schemes with low, medium and high heritabilities. For MOET, four rates of embryo transfers per donor were used to represent low to moderate MOET levels. The results indicated that annual genetic responses to selection could be up to 1.3, 1.6 and 1.8 times as great for MOET compared with conventional breeding for traits of low, medium and high heritability, respectively; however, the annual inbreeding rates also were high for the MOET schemes considered. Embryo splitting, or cloning, was shown to increase accuracy of selection by 8 to 35% through the production of identical genotypes. The use of MOET in conjunction with embryo splitting in elite nucleus units could substantially increase genetic improvement for traits with low, medium and high heritabilities in beef cattle populations.     

基因组选择在我国种猪育种中应用的探讨

种猪育种对我国养猪业起着极其重要的作用。基因组选择在我国猪育种生产中的应用水平尚不及欧美发达国家的种猪企业。完整的性能记录、高效的数据系统和资金投入的缺乏是制约基因组选择在我国猪育种生产中应用的重要因素。基因组选择能够增加不同性状遗传评估的育种值准确性,尤其是增加低遗传力性状的准确性。基因组选择在杂交优势、选配和多品种评估方面均具有应用优势。我国种猪企业需要进一步完善表型和性能数据的收集,制定长期的育种规划。通过区域性的联合评估和基因组选择技术的应用,加速群体的遗传进展,加速提升我国商品猪的生产性能。     

Persistency of accuracy of genomic breeding values for different simulated pig breeding programs in developing countries

Genetic improvement of pigs in tropical developing countries has focused on imported exotic populations which have been subjected to intensive selection with attendant high population‐wide linkage disequilibrium (LD). Presently, indigenous pig population with limited selection and low LD are being considered for improvement. Given that the infrastructure for genetic improvement using the conventional BLUP selection methods are lacking, a genome‐wide selection (GS) program was proposed for developing countries. A simulation study was conducted to evaluate the option of using 60 K SNP panel and observed amount of LD in the exotic and indigenous pig populations. Several scenarios were evaluated including different size and structure of training and validation populations, different selection methods and long‐term accuracy of GS in different population/breeding structures and traits. The training set included previously selected exotic population, unselected indigenous population and their crossbreds. Traits studied included number born alive (NBA), average daily gain (ADG) and back fat thickness (BFT). The ridge regression method was used to train the prediction model. The results showed that accuracies of genomic breeding values (GBVs) in the range of 0.30 (NBA) to 0.86 (BFT) in the validation population are expected if high density marker panels are utilized. The GS method improved accuracy of breeding values better than pedigree‐based approach for traits with low heritability and in young animals with no performance data. Crossbred training population performed better than purebreds when validation was in populations with similar or a different structure as in the training set. Genome‐wide selection holds promise for genetic improvement of pigs in the tropics.     

Assessment of the genetic and economic impact of performance recording and genotyping in Australian commercial sheep operations

With the new opportunities from DNA technology, multitier breeding schemes have the potential to become more effective and more integrated. Integrated breeding schemes can also be better adapted to account for potential genotype by environment interactions (G × E) between tiers. In this case, phenotypic and genotype information from lower tiers becomes more valuable as it involves measurement of traits that directly represent the breeding objective. The objective of this study was to compare scenarios that represented different selection strategies and their economic effectiveness in fine‐wool commercial sheep operations that exploit multitier breeding structures. Genomic selection (GS) applied in the multiplier and the commercial tier presented the largest additional revenue among all scenarios, as it resulted in the largest amount of genetic progress. The largest benefits from GS were outweighed by the genotyping costs, which made DNA parentage the most feasible strategy for the multiplier tier, resulting in the highest cumulative net present value (CNPV). The benefits of phenotypes and genotype information from the commercial environment were larger in the presence of G × E between the nucleus and the commercial tier. The CNPV was larger with a 50% reduction in genotyping costs, which increased the returns of GS scenarios by 2.7‐fold on average. Higher selection intensity when selecting multiplier rams also resulted in larger benefits. In this case, returns for the breeding scheme were 3.5‐fold higher when 33% of multiplier males were selected based on commercial information, compared to scenarios selecting 50% of the available multiplier rams. The benefits of collecting commercial phenotypes and genotypes were long term, which means that return on investment often took more than 10 years to be achieved, and were largely dependent on two‐stage selection to reduce cost while maintaining selection efficiency and on the cost of a genotype test.     

Research Progress on Genomic Selection Methods in Livestock and Poultry

Selection and breeding are very important in production of livestock and poultry,and breeding value estimation is the core of selection and breeding.Genomic selection (GS) is a novel molecular breeding method to estimate genomic breeding value using high-density markers across the whole genome.At present,GS has been successfully applied in cattle,pig,chicken and so on,and made significant progress.This method can achieve early selection,decrease the testing costs,shorten generation interval,improve the accuracy of breeding value estimation and accelerate genomic progress.GS estimates the effect of SNP by phenotype information and SNP genotype of each individual in the reference population,and measures the SNP genotype to calculate the genomic estimated breeding value in the candidate population,then selects the best individuals according to the genomic estimated breeding value.With the rapid development of genotyping technology and the decrease of detection cost,and the continuous optimization and high efficiency of genomic selection methods,genomic selection has become an important research method in the selection and breeding of livestock and poultry.The authors reviewed some of the widely used genomic selection methods,compared the differences between different methods,analyzed the problems and challenges of genomic selection,and looked forward to its application prospects in breeding.     

Strategy for applying genome-wide selection in dairy cattle

Animals can be genotyped for thousands of single nucleotide polymorphisms (SNPs) at one time, where the SNPs are located at roughly 1‐cM intervals throughout the genome. For each contiguous pair of SNPs there are four possible haplotypes that could be inherited from the sire. The effects of each interval on a trait can be estimated for all intervals simultaneously in a model where interval effects are random factors. Given the estimated effects of each haplotype for every interval in the genome, and given an animal's genotype, a ‘genomic’ estimated breeding value is obtained by summing the estimated effects for that genotype. The accuracy of that estimator of breeding values is around 80%. Because the genomic estimated breeding values can be calculated at birth, and because it has a high accuracy, a strategy that utilizes these advantages was compared with a traditional progeny testing strategy under a typical Canadian‐like dairy cattle situation. Costs of proving bulls were reduced by 92% and genetic change was increased by a factor of 2. Genome‐wide selection may become a popular tool for genetic improvement in livestock.     

Genetic and economic evaluation of Japanese Black (Wagyu) cattle breeding schemes

Deterministic simulation was used to evaluate 10 breeding schemes for genetic gain and profitability and in the context of maximizing returns from investment in Japanese Black cattle breeding. A breeding objective that integrated the cow-calf and feedlot segments was considered. Ten breeding schemes that differed in the records available for use as selection criteria were defined. The schemes ranged from one that used carcass traits currently available to Japanese Black cattle breeders (Scheme 1) to one that also included linear measurements and male and female reproduction traits (Scheme 10). The latter scheme represented the highest level of performance recording. In all breeding schemes, sires were chosen from the proportion selected during the first selection stage (performance testing), modeling a two-stage selection process. The effect on genetic gain and profitability of varying test capacity and number of progeny per sire and of ultrasound scanning of live animals was examined for all breeding schemes. Breeding schemes that selected young bulls during performance testing based on additional individual traits and information on carcass traits from their relatives generated additional genetic gain and profitability. Increasing test capacity resulted in an increase in genetic gain in all schemes. Profitability was optimal in Scheme 2 (a scheme similar to Scheme 1, but selection of young bulls also was based on information on carcass traits from their relatives) to 10 when 900 to 1,000 places were available for performance testing. Similarly, as the number of progeny used in the selection of sires increased, genetic gain first increased sharply and then gradually in all schemes. Profit was optimal across all breeding schemes when sires were selected based on information from 150 to 200 progeny. Additional genetic gain and profitability were generated in each breeding scheme with ultrasound scanning of live animals for carcass traits. Ultrasound scanning of live animals was more important than the addition of any other traits in the selection criteria. These results may be used to provide guidance to Japanese Black cattle breeders.