Genetic evaluation strategies for multiple traits and countries

Genetic evaluation strategies were studied using simulated data for three lactation traits in each of two importing and two exporting countries, each running a typical progeny test program. Conversion (CNV), multiple-trait across-country evaluation (MACE) and global animal model (GAM) strategies were considered. Base populations were either unselected, or all males were above average, and exporting countries had higher genetic means than importing countries. With unselected base populations, errors of prediction (ERP) for top (highest predicted merit) bulls were equivalent (P>0.10) for MACE and GAM strategies and higher for CNV (P<0.01), while GAM had lower ERP (P<0.01) for all bulls. With selected base populations, MACE strategies had lower ERP than GAM for top bulls (P<0.01) and lower but similar ERP (P>0.05) for all bulls. Strategies to evaluate multiple traits per country consistently had lower ERP than strategies evaluating one trait per country. Evaluations were biased, favouring bulls from importing countries on foreign scales of evaluation, for all strategies and with either selected or unselected base populations. True merits of top bulls selected with MACE or GAM were similar (P>0.05) and greater than CNV (P<0.01), and 90-95% of the top 1% selected bulls were the same for multiple-trait MACE and GAM strategies.     

全基因组选择及其在奶牛育种中应用进展

全基因组选择是指基于基因组育种值（GEBV）的选择方法,指通过检测覆盖全基因组的分子标记,利用基因组水平的遗传信息对个体进行遗传评估,以期获得更高的育种值估计准确度。由于可显著缩短世代间隔,全基因组选择作为一项育种新技术在奶牛育种中具有广阔的应用前景,目前已经成为各国的研究热点。不同国家的试验结果表明,奶牛育种中基于GEBV的遗传评估可靠性在20%~67%之间,如果代替常规后裔测定体系,可节省92%的育种成本。本文综述了全基因组选择的基本原理及其在各国奶牛育种中的应用现状和所面临的问题。     

Genetic evaluation of Australian dairy cattle for somatic cell scores using multi-trait random regression test-day model

Summary A multi-trait (MT) random regression (RR) test day (TD) model has been developed for genetic evaluation of somatic cell scores for Australian dairy cattle, where first, second and third lactations were considered as three different but correlated traits. The model includes herd-test-day, year-season, age at calving, heterosis and lactation curves modelled with Legendre polynomials as fixed effects, and random genetic and permanent environmental effects modelled with Legendre polynomials. Residual variance varied across the lactation trajectory. The genetic parameters were estimated using asreml . The heritability estimates ranged from 0.05 to 0.16. The genetic correlations between lactations and between test days within lactations were consistent with most of the published results. Preconditioned conjugate gradient algorithm with iteration on data was implemented for solving the system of equations. For reliability approximation, the method of Tier and Meyer was used. The genetic evaluation system was validated with Interbull validation method III by comparing proofs from a complete evaluation with those from an evaluation based on a data set excluding the most recent 4 years. The genetic trend estimate was in the allowed range and correlations between the two sets of proofs were very high. Additionally, the RR model was compared to the previous test day model. The correlations of proofs between both models were high (0.97) for bulls with high reliabilities. The correlations of bulls decreased with increasing incompleteness of daughter performance information. The correlations between the breeding values from two consecutive runs were high ranging from 0.97 to 0.99. The MT RR TD model was able to make effective use of available information on young bulls and cows, and could offer an opportunity to breeders to utilize estimated breeding values for first and later lactations.     

Reducing bias in the dairy cattle single‐step genomic evaluation by ignoring bulls without progeny

The number of genotyped animals has increased rapidly creating computational challenges for genomic evaluation. In animal model BLUP, candidate animals without progeny and phenotype do not contribute information to the evaluation and can be discarded. In theory, genotyped candidate animal without progeny can bring information into single‐step BLUP (ssGBLUP) and affect the estimation of other breeding values. We studied the effect of including or excluding genomic information of culled bull calves on genomic breeding values (GEBV) from ssGBLUP. In particular, GEBVs of genotyped bulls with daughters and GEBVs of young bulls selected into AI to be progeny tested (test bulls) were studied. The ssGBLUP evaluation was computed using Nordic test day (TD) model and TD data for the Nordic Red Dairy Cattle. The results indicate that genomic information of culled bull calves does not affect the GEBVs of progeny tested reference animals, but if genotypes of the culled bulls are used in the TD ssGBLUP, the genetic trend in the test bulls is considerably higher compared to the situation when genomic information of the culled bull calves is excluded. It seems that by discarding genomic information of culled bull calves without progeny, upward bias of GEBVs of test bulls is reduced.     

Partition of sire effects for international sire evaluation in the presence of genotype×environment interaction

Both simulation and field data were analyzed to compare the current MACE (multiple-trait across-country evaluation) and a proposed partition model (MACE_P) for international sire evaluation in the presence of genotype–environment (GE) interaction. The partition model decomposes the sire effect into two components: constant effect that is stable genetically across countries and interaction effect that varies from country to country. Simulation results indicate that interaction effect was estimated more accurately by MACE_P when GE interaction is strong than when it is weak. The accuracy of estimating sire effects increased as GE interaction and/or heterogeneity of sire variances between countries lessened, indicating the importance of accounting for GE interaction and heterogeneous sire variances in the international genetic evaluation. A joint analysis of Japanese and the US Holstein populations reveals that the estimated interaction correlation (r_I) as a new measure of GE interaction was 0.924, suggesting little evidence of sire by country interaction for milk yield. A general formula was developed to convert the estimates of sire effects from MACE to MACE_P. Although MACE_P demands more computing resources than MACE, the use of this simple conversion formula would greatly reduce the computing requirements by MACE_P. Both constant and interaction effects estimated by MACE_P sums to the sire effect estimated by MACE. Sire (co)variances obtained as a sum of constant and interaction (co)variances estimated by MACE_P were very similar to those estimated by MACE. The partition of sire effect into the constant and interaction effects by MACE_P allows the indexing of these two component traits with different weights to maximize overall response in the presence of GE interaction.     

Model for international evaluation of dairy siresModèle pour une évaluation internationale des taureaux laitiersModell für die internationale Bewertung von Milchviehbullen

A linear statistical model is put forward for use in comparing the genetic level of dairy sires based on their progeny test evaluations from one or more countries. Additive genetic relationships among bulls are included to provide more connections or comparisons among countries. Several definitions of genetic differences among countries are presented. A small example illustrates the use of this model.     

Genomic predictions across Nordic Holstein and Nordic Red using the genomic best linear unbiased prediction model with different genomic relationship matrices

This study investigated genomic predictions across Nordic Holstein and Nordic Red using various genomic relationship matrices. Different sources of information, such as consistencies of linkage disequilibrium (LD) phase and marker effects, were used to construct the genomic relationship matrices (G‐matrices) across these two breeds. Single‐trait genomic best linear unbiased prediction (GBLUP) model and two‐trait GBLUP model were used for single‐breed and two‐breed genomic predictions. The data included 5215 Nordic Holstein bulls and 4361 Nordic Red bulls, which was composed of three populations: Danish Red, Swedish Red and Finnish Ayrshire. The bulls were genotyped with 50 000 SNP chip. Using the two‐breed predictions with a joint Nordic Holstein and Nordic Red reference population, accuracies increased slightly for all traits in Nordic Red, but only for some traits in Nordic Holstein. Among the three subpopulations of Nordic Red, accuracies increased more for Danish Red than for Swedish Red and Finnish Ayrshire. This is because closer genetic relationships exist between Danish Red and Nordic Holstein. Among Danish Red, individuals with higher genomic relationship coefficients with Nordic Holstein showed more increased accuracies in the two‐breed predictions. Weighting the two‐breed G‐matrices by LD phase consistencies, marker effects or both did not further improve accuracies of the two‐breed predictions.     

A method of comparing sires evaluated in different countries

Cattle breeders often wish to compare the genetic merit of bulls from other countries to that of bulls from their own country. This paper proposes a general method of predicting breeding values in one country from progeny test results in another that does not make the assumption of no genotype × environment interaction and which requires few external parameter estimates. The method is used to predict breeding values in Australia from the sire evaluations in New Zealand of Friesian bulls.     

我国荷斯坦青年公牛基因组选择效果分析

本研究基于我国荷斯坦奶牛基因组遗传评估和生产性能测定（DHI）结果,旨在分析我国荷斯坦公牛基因组选择的效果。选择1 686头既有基因组遗传评估成绩又有后裔测定成绩的荷斯坦公牛,利用2019年12月基因组遗传评估结果及其女儿的产奶和体型性状数据,通过R软件与Excel计算公牛基因组评估结果与公牛女儿表型数据间的相关性,对我国荷斯坦青年公牛基因组选择效果进行分析。相关性分析结果表明,荷斯坦公牛的基因组性能指数（GCPI）与后裔测定性能指数（CPI）呈正相关（r_s>0.3）,其中产奶量和体细胞评分的基因组育种值（GEBV）与估计育种值（EBV）呈较强的正相关（0.4 < r_s < 0.8）。对公牛女儿表型数据分析结果表明,女儿产奶量、乳蛋白率、乳脂率与肢蹄评分的表型值与公牛GEBV分组趋势一致,且公牛不同产奶性状GEBV组间的女儿性状表型值大部分达到极显著差异（P<0.01）;北京及上海地区公牛产奶性状、体细胞评分和肢蹄评分的GEBV分组与女儿表型值趋势较其他省市（地区）更一致,且GEBV高组与低组之间差值均高于其他省市（地区）。基于1 686头荷斯坦公牛基因组选择及其女儿表型数据的分析结果表明,我国荷斯坦公牛的基因组遗传评估准确性较好,其中产奶量、乳蛋白率、体细胞评分和肢蹄评分的表型数据更好地反映了基因组选择的效果;北京及上海地区较其他省市（地区）更能反映我国荷斯坦公牛基因组选择的效果。     

Application of single‐step genomic best linear unbiased prediction with a multiple‐lactation random regression test‐day model for Japanese Holsteins

This study aimed to evaluate a validation reliability of single‐step genomic best linear unbiased prediction (ssGBLUP) with a multiple‐lactation random regression test‐day model and investigate an effect of adding genotyped cows on the reliability. Two data sets for test‐day records from the first three lactations were used: full data from February 1975 to December 2015 (60 850 534 records from 2 853 810 cows) and reduced data cut off in 2011 (53 091 066 records from 2 502 307 cows). We used marker genotypes of 4480 bulls and 608 cows. Genomic enhanced breeding values (GEBV) of 305‐day milk yield in all the lactations were estimated for at least 535 young bulls using two marker data sets: bull genotypes only and both bulls and cows genotypes. The realized reliability (R²) from linear regression analysis was used as an indicator of validation reliability. Using only genotyped bulls, R² was ranged from 0.41 to 0.46 and it was always higher than parent averages. The very similar R² were observed when genotyped cows were added. An application of ssGBLUP to a multiple‐lactation random regression model is feasible and adding a limited number of genotyped cows has no significant effect on reliability of GEBV for genotyped bulls.     

Genetic correlations between horse show jumping competition traits in five European countries

Genetic correlations were computed for show jumping competition traits from national estimated breeding values (EBV) of stallions of five countries: Belgium, Denmark, France, Ireland and Sweden. Data involved 24,390 horses, i.e. 8993 stallions with EBV and their ancestors. There were 617 stallions with several EBVs in more than one country. Method involved MACE (Multi-Trait Across Country Evaluation) methodology based on deregressed proofs adapted to the existence of own performance for stallions and to missing EBVs for some ancestors. ASREML was used to estimate covariance with an equivalent multiple trait model described as a random regression model. Within country sire variances were considered as known. The estimated genetic correlations were high (0.86 to 0.88) especially for reliable estimates between Belgium, France, Sweden and between Sweden and Denmark, and relatively high (0.70 to 0.91) for other pairs. These results open perspectives for the international evaluation of sporthorses.     

国内外肉牛遗传评估体系概况

种公牛的选育是肉牛育种工作的核心。传统选育肉用种公牛需要经过后裔测定进行选择,其优点是准确性高,但存在周期长、屠宰和肉质性状难以收集、成本高等问题,致其选择效率低。自2001年全基因组选择概念提出后,该技术迅速成为动植物育种领域研究的热点。利用全基因组选择进行肉用种公牛的选育,进行早期选择从而大幅度缩短世代间隔,可以提高繁殖性状等低遗传力性状的选择准确性,加快遗传进展,并大大降低育种成本。2014年,美国安格斯协会开始应用全基因组选择技术,其他欧美发达国家也陆续使用,肉牛育种进入基因组时代。中国自2017年开始使用全基因组选择技术选择青年肉用种公牛,并于2020年在全国范围内使用该技术进行基因组遗传评估。本文综述了国内外肉牛遗传评估现状,以期为我国肉牛育种工作提供参考和借鉴。     

Use of genomic recursions and algorithm for proven and young animals for single‐step genomic BLUP analyses – a simulation study

The purpose of this study was to examine accuracy of genomic selection via single‐step genomic BLUP (ssGBLUP) when the direct inverse of the genomic relationship matrix ( G ) is replaced by an approximation of G ^?1 based on recursions for young genotyped animals conditioned on a subset of proven animals, termed algorithm for proven and young animals (APY). With the efficient implementation, this algorithm has a cubic cost with proven animals and linear with young animals. Ten duplicate data sets mimicking a dairy cattle population were simulated. In a first scenario, genomic information for 20k genotyped bulls, divided in 7k proven and 13k young bulls, was generated for each replicate. In a second scenario, 5k genotyped cows with phenotypes were included in the analysis as young animals. Accuracies (average for the 10 replicates) in regular EBV were 0.72 and 0.34 for proven and young animals, respectively. When genomic information was included, they increased to 0.75 and 0.50. No differences between genomic EBV (GEBV) obtained with the regular G ^?1 and the approximated G ^?1 via the recursive method were observed. In the second scenario, accuracies in GEBV (0.76, 0.51 and 0.59 for proven bulls, young males and young females, respectively) were also higher than those in EBV (0.72, 0.35 and 0.49). Again, no differences between GEBV with regular G ^?1 and with recursions were observed. With the recursive algorithm, the number of iterations to achieve convergence was reduced from 227 to 206 in the first scenario and from 232 to 209 in the second scenario. Cows can be treated as young animals in APY without reducing the accuracy. The proposed algorithm can be implemented to reduce computing costs and to overcome current limitations on the number of genotyped animals in the ssGBLUP method.     

Economic evaluation of breeding strategies for improvement of dairy cattle in Kenya

Cost-benefit analysis using net present value (NPV) as the economic evaluation criterion was used to investigate the economic merits of four breeding strategies used for genetic improvement of dairy cattle in Kenya. The breeding strategies were evaluated over a 25-year period. The costs involved in setting up and running each strategy were obtained from large-scale dairy cattle farms, and government and private institutions involved in genetic improvement of dairy cattle. Only benefits from genetic improvement were considered. The impact on NPV due to changes in genetic and economic parameters was investigated. The ranking of the breeding strategies greatly differed with genetic ranking. Among the local selection programs, a strategy utilizing young bulls, sons of local bulls, was more profitable than one utilizing old progeny tested bulls. Continuous semen importation was not an economically viable alternative. The strategy utilizing young bulls progeny of imported bulls (PIB) was only viable if imports were from countries which are >2.00 SD in genetic merit above the local dairy cattle population. The ranking of strategies was not sensitive to changes in genetic parameters but to economic parameters. The use of local semen from young bulls progeny of local proven bulls is recommended. Alternatively, PIB can be utilized but the semen will have to be imported from countries which are >2.00 SD above the local dairy cattle population or the cost of imported semen should be?≤?US$40 per straw.     

The impact of genomic relatedness between populations on the genomic estimated breeding values

In genomic selection, prediction accuracy is highly driven by the size of animals in the reference population(RP).Combining related populations from different countries and regions or using a related population with large size of RP has been considered to be viable strategies in cattle breeding. The genetic relationship between related populations is important for improving the genomic predictive ability. In this study, we used 122 French bulls as test individuals. The genomic estimated breeding values(GEBVs) evaluated using French RP, America RP and Chinese RP were compared.The results showed that the GEBVs were in higher concordance using French RP and American RP compared with using Chinese population. The persistence analysis, kinship analysis and the principal component analysis(PCA) were performed for 270 French bulls, 270 American bulls and 270 Chinese bulls to interpret the results. All the analyses illustrated that the genetic relationship between French bulls and American bulls was closer compared with Chinese bulls. Another reason could be the size of RP in China was smaller than the other two RPs. In conclusion, using RP of a related population to predict GEBVs of the animals in a target population is feasible when these two populations have a close genetic relationship and the related population is large.     

Genetic evaluation of breeding strategies for improvement of dairy cattle in Kenya

Cost-benefit analysis using net present value (NPV) as the economic evaluation criterion was used to investigate the economic merits of four breeding strategies used for genetic improvement of dairy cattle in Kenya. The breeding strategies were evaluated over a 25-year period. The costs involved in setting up and running each strategy were obtained from large-scale dairy cattle farms, and government and private institutions involved in genetic improvement of dairy cattle. Only benefits from genetic improvement were considered. The impact on NPV due to changes in genetic and economic parameters was investigated. The ranking of the breeding strategies greatly differed with genetic ranking. Among the local selection programs, a strategy utilizing young bulls, sons of local bulls, was more profitable than one utilizing old progeny tested bulls. Continuous semen importation was not an economically viable alternative. The strategy utilizing young bulls progeny of imported bulls (PIB) was only viable if imports were from countries which are >2.00 SD in genetic merit above the local dairy cattle population. The ranking of strategies was not sensitive to changes in genetic parameters but to economic parameters. The use of local semen from young bulls progeny of local proven bulls is recommended. Alternatively, PIB can be utilized but the semen will have to be imported from countries which are >2.00 SD above the local dairy cattle population or the cost of imported semen should be ≤ US$40 per straw.     

Genomic and single-step evaluations of carcass traits of young bulls in dual-purpose cattle

Genetic evaluations for carcass traits of young bulls in Normande and Montbeliarde breeds are currently being developed in France. In order to determine a suitable genomic evaluation for three carcass traits of young bulls, genomic breeding values were estimated for young candidates to selection using different approaches. Records of 111,789 Normande and 118,183 Montbeliarde were used. Average progeny pre-adjusted performances (DYD) were calculated for sires. Evaluation approaches were compared based on an assessment of their accuracy (correlation between DYD and estimated breeding values [EBVs]) and bias (regression coefficient of DYD on EBVs) on the 20% youngest AI sires. All genomic approaches were generally more accurate than BLUP (+.045 to +.116 correlation points), except for age at slaughter where single-step GBLUP (SSGBLUP) was the only genomic method leading to a greater accuracy (+.038 to +.126 points). The best setting of the SSGBLUP relationship matrix was characterized by a weight of 30% for pedigree information in the genomic relationship matrix. SSGBLUP was the most valuable evaluation approach for the evaluation of carcass traits of Normande and Montbeliarde young bulls.     

Genetic evaluation of somatic cell score in dairy cattle considering first and later lactations as two different but correlated traits

A test‐day (TD) random regression model (RRM) was described for the genetic evaluation of somatic cell score (SCS) where first and later lactations were considered as two different but correlated traits. A two‐step covariance function procedure was used to estimate variance–covariances and associated genetic parameters. Analysis of estimated breeding values (EBV), ranking of top bulls and cows and some computational aspects were used to compare RRM with TD repeatability model (RPM) and lactation average model (LAM). Residuals were analysed to assess the relative fit of TD models. Comparison between RRM and RPM showed that RRM has lower mean squared error and gave better fit to the data. For young bulls and cows, the standard deviation (SD) of EBVs was highest for RRM and lowest for LAM implying efficient utilization of information on SCS, in terms of revealing more genetic variation. A much lower correlation of EBVs ranging from 0.80 to 0.92 and significant re‐ranking of top bulls and cows were observed between RRM and LAM. The lower across‐lactation correlation between RRM and LAM indicated that LAM is directed to give more weight to first lactation breeding values. The RRM, where SCS in the first and later lactations was considered as two different but correlated traits was able to make effective use of available information on young bulls and cows, and could offer an opportunity to breeders to utilize EBVs for first and later lactations.     

Top down preselection using marker assisted estimates of breeding values in dairy cattle

Top down preselection of young bulls before entering progeny testing has been proposed as a practicable form of marker‐assisted selection (MAS), especially in dairy cattle populations with large male paternal half‐sib families. Linkage phase between the superior (Q) and the inferior (q) QTL alleles of heterozygous sires (Qq at the QTL) with informative markers is established within each paternal half‐sib family and may be used for selection among grand‐progeny. If, additionally to sires, bulldams are also genotyped and data from consecutive generations are used, then a marker‐assisted best linear unbiased prediction (MA‐BLUP) model can be employed to connect the information of all generations and families of a top down design, and to select across all families. A customized ‘augmented’ sire model (with sires and dams of sires as random effects) is introduced for this purpose. Adapted formulae for the mixed model equations are given and their equivalence to a corresponding animal model and to a certain variant of previously proposed reduced animal models is shown. The application of the augmented sire model in MA‐BLUP estimation from daughter‐yield deviations and effective daughter contributions is presented.     

Genetic correlations between similar traits in the Danish and Swedish Warmblood sport horse populations

Genetic correlations between phenotypically similar or related traits tested at young horse performance tests for Danish Warmblood (DWB) and Swedish Warmblood (SWB) horses were calculated using Multi-trait Across Country Evaluation (MACE). Data comprised stallions with an estimated breeding value (EBV) from the national genetic evaluations (NGE) based on at least 10 progeny tested in performance tests, and the ancestors of those stallions in two generations. The DWB data included 349 stallions and the SWB data 426 stallions. Of these, 28 had EBVs in both DWB and SWB. Additionally 151 pedigree animals were common between DWB and SWB. The dependent variables used were NGE results of stallions born 1980 and later, which reduced the number of common stallions with EBVs to 23. The genetic correlations were very high for jumping traits (0.99) and dressage related traits (0.89–0.97). For conformation traits correlations varied between 0.10 and 0.98. Because of the high genetic correlations and frequent use of same or closely related foreign stallions, breeders of both DWB and SWB would benefit from using the NGEs for performance traits across countries, although the genetic correlations do not consider differences in genetic merit levels between the populations. It would be feasible to perform a joint genetic evaluation using MACE, which would improve the reliability of estimated breeding values, and enable ranking of all stallions according to the national scale of each country.