Reaction norms for protein yield and days open in Swedish red and white dairy cattle in relation to various environmental variables

Various environmental variables were used in analysis of genotype by environment interaction (GxE) for first lactation protein yield and days open in Swedish Red and White dairy cattle. The environmental variables described the herd level of production and fertility, herd size, geographic position, and weather conditions of the herds. Fixed effects of the environmental variables were analysed using a fixed regression sire model. All studied environmental variables, except the average rainfall during summer, had significant effect on both protein yield and days open. Possible effects of GxE were evaluated using random regression of protein yield and days open on the environmental variables. GxE was indicated between protein yield and herd level of production, protein yield and herd size, and days open and herd level of fertility. Correlations between the traits expressed at average and deviating levels of the environment were high indicating that GxE did not result in reranking of sires within the range of environments found in Sweden.     

Adjusting for heterogeneity of variance for carcass traits affects single and multiple trait selections in genetic evaluation of Japanese Black cattle

Heterogeneity of variance among subclasses of an effect is a potential source of bias in genetic evaluation. Degrees of the heterogeneity of variance among farm‐market‐year‐sex (FMYS) subclasses for carcass weight, beef marbling standard number, rib‐eye area, rib thickness and subcutaneous fat thickness were investigated in Japanese Black cattle. Consequences of adjusting for the heterogeneity on the predicted breeding values (PBVs) or on the genetic indexes derived from the PBVs of the five carcass traits were assessed. A total of 57 461 records were collected between 1997 and 2002 from steers and heifers fattened at farms across Japan. These records were grouped into 1591 FMYS subclasses. Bartlett's test showed that the degree of the heterogeneity of variance among the FMYS subclasses was sizeable in all traits (P < 0.0001). By applying a two‐step adjustment procedure it was possible to reduce the standard deviation, the coefficient of variation and the Gini coefficient of the phenotypic variances by 67.5% to 75.0% in the different traits. The applied adjustment caused a substantial re‐ranking of elite dams in the PBV for each trait as well as in the genetic index. This study provided evidence that the applied adjustment reduces the bias in the PBVs due to heterogeneous variances and increases the accuracy of bull‐dam selection.     

Comparison of models for genetic evaluation of survival traits in dairy cattle: a simulation study.

Three models for the analysis of functional survival data in dairy cattle were compared using stochastic simulation. The simulated phenotype for survival was defined as a month after the first calving (from 1 to 100) in which a cow was involuntarily removed from the herd. Parameters for simulation were based on survival data of the Canadian Jersey population. Three different levels of heritability of survival (0.100, 0.050 and 0.025) and two levels of numbers of females per generation (2000 or 4000) were considered in the simulation. Twenty generations of random mating and selection (on a second trait, uncorrelated with survival) with 20 replicates were simulated for each scenario. Sires were evaluated for survival of their daughters by three models: proportional hazard (PH), linear multiple-trait (MT), and random regression (RR) animal models. Different models gave different ranking of sires with respect to survival of their daughters. Correlations between true and estimated breeding values for survival to five different points in a cow's lifetime after the first calving (120 and 240 days in milk after first, second, third and fourth calving) favoured the PH model, followed by the RR model evaluations. Rankings of models were independent of the heritability level, female population size and sire progeny group size (20 or 100). The RR model, however, showed a slight superiority over MT and PH models in predicting the proportion of sire's daughters that survived to the five different end-points after the first calving.     

Adjusting for heterogeneity of experimental data in genetic evaluation of dry matter intake in dairy cattle

The objectives of the present study were (i) to find the best fitted model for repeatedly measured daily dry matter intake (DMI) data obtained from different herds and experiments across lactations and (ii) to get better estimates of the genetic parameters and better genetic evaluations. After editing, there were 572,512 daily DMI records of 3,495 animals (Holstein cows) from 11 different herds across 13 lactations and the animals were under 110 different nutritional experiments. The fitted model for this data set was a univariate repeated‐measure animal model (called model 1) in which additive genetic and permanent environmental (within and across lactations) effects were fitted as random. Model 1 was fitted as two distinct models (called models 2 and 3) based on alternative fixed effect corrections. For unscaled data, each model (models 2 and 3) was fitted as a homoscedastic (HOM) model first and then as a heteroscedastic (HET) model. Then, data were scaled by multiplying with particular herd‐scaling factors, which were calculated by accounting for heterogeneity of phenotypic within‐herd variances. Models were selected based on cross‐validation and prediction accuracy results. Scaling factors were re‐estimated to determine the effectiveness of accounting for herd heterogeneity. Variance components and respective heritability and repeatability were estimated based on a pedigree‐based relationship matrix. Results indicated that the model fitted for scaled data showed better fit than the models (HOM or HET) fitted for unscaled data. The heritability estimates of the models 2 and 3 fitted for scaled data were 0.30 and 0.08, respectively. The repeatability estimates of the model fitted for scaled data ranged from 0.51 to 0.63. The re‐estimated scaling factor after accounting for heterogeneity of residual variances was close to 1.0, indicating the stabilization of residual variances and herd accounted for most of the heterogeneity. The rank correlation of EBVs between scaled and unscaled data ranged from 0.96 to 0.97.     

Single step genomic evaluation for female fertility in Nordic Red dairy cattle

Joint Nordic (Denmark, Finland, Sweden) genetic evaluation of female fertility is currently based on the multiple trait multilactation animal model (BLUP). Here, single step genomic model (ssGBLUP) was applied for the Nordic Red dairy cattle fertility evaluation. The 11 traits comprised of nonreturn rate and days from first to last insemination in heifers and first three parities, and days from calving to first insemination in the first three parities. Traits had low heritabilities (0.015–0.04), but moderately high genetic correlations between the parities (0.60–0.88). Phenotypic data included 4,226,715 animals with records and pedigree 5,445,392 animals. Unknown parents were assigned into 332 phantom parent groups (PPG). In mixed model equations animals were associated with PPG effects through the pedigree or both the pedigree and genomic information. Genotype information of 46,914 SNPs was available for 33,969 animals in the pedigree. When PPG used pedigree information only, BLUP converged after 2,420 iterations whereas the ssGBLUP evaluation needed over ten thousand iterations. When the PPG effects were solved accounting both the pedigree and the genomic information, the ssGBLUP model converged after 2,406 iterations. Also, with the latter model breeding values by ssGBLUP and BLUP became more consistent and genetic trends followed each other well. Models were validated using forward prediction of the young bulls. Reliabilities and variance inflation of predicted genomic breeding values (values for parent averages in brackets) for the 11 traits ranged 0.22–0.31 (0.10–0.27) and 0.81–0.95 (0.83–1.06), respectively. The ssGBLUP model gave always higher validation reliabilities than BLUP, but largest increases were for the cow fertility traits.     

Genotype by environment interaction for length of productive life in Swedish Red and White dairy cattle

In this study the amount of genotype by environment interaction (G×E) for length of productive life of Swedish Red and White dairy cattle was studied. The environmental variables used were the herd-year averages of number of first parity cows, peak yield, protein yield, and productive life. Data were analysed using multi-trait models (using low and high quartile herds) and reaction norm models (using the whole continuous scale). Considerable G×E was found for the trait productive life between environments (herds) with short or long average productive life, and a genetic correlation of 0.74 was found. For productive life in relation to herd size, G×E was negligible, and the genetic correlation was 0.95. For productive life in relation to herd-year average of peak yield or protein yield some G×E was indicated by the reaction norm model, especially between extreme environments. The G×E between herds with short or long average productive life should be studied further and might need to be considered in breeding programmes for dairy cattle.     

Random regression test day models to estimate genetic parameters for milk yield and milk components in Philippine dairy buffaloes

Heritabilities and genetic correlations for milk production traits were estimated from first‐parity test day records on 1022 Philippine dairy buffalo cows. Traits analysed included milk (MY), fat (FY) and protein (PY) yields, and fat (Fat%) and protein (Prot%) concentrations. Varying orders of Legendre polynomials (Leg_m) as well as the Wilmink function (Wil) were used in random regression models. These various models were compared based on log likelihood, Akaike's information criterion, Bayesian information criterion and genetic variance estimates. Six residual variance classes were sufficient for MY, FY, PY and Fat%, while seven residual classes for Prot%. Multivariate analysis gave higher estimates of genetic variance and heritability compared with univariate analysis for all traits. Heritability estimates ranged from 0.25 to 0.44, 0.13 to 0.31 and 0.21 to 0.36 for MY, FY and PY, respectively. Wilmink's function was the better fitting function for additive genetic effects for all traits. It was also the preferred function for permanent environment effects for Fat% and Prot%, but for MY, FY and PY, the Leg_m was the appropriate function. Genetic correlations of MY with FY and PY were high and they were moderately negative with Fat% and Prot%. To prevent deterioration in Fat% and Prot% and improve milk quality, more weight should be applied to milk component traits.     

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。     

Autoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle

Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and −0.019 (−0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and −0.022 (−0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.     

Genetic correlations between body condition scores and fertility in dairy cattle using bivariate random regression models

Genetic correlations between body condition score (BCS) and fertility traits in dairy cattle were estimated using bivariate random regression models. BCS was recorded by the Swiss Holstein Association on 22,075 lactating heifers (primiparous cows) from 856 sires. Fertility data during first lactation were extracted for 40,736 cows. The fertility traits were days to first service (DFS), days between first and last insemination (DFLI), calving interval (CI), number of services per conception (NSPC) and conception rate to first insemination (CRFI). A bivariate model was used to estimate genetic correlations between BCS as a longitudinal trait by random regression components, and daughter's fertility at the sire level as a single lactation measurement. Heritability of BCS was 0.17, and heritabilities for fertility traits were low (0.01-0.08). Genetic correlations between BCS and fertility over the lactation varied from: -0.45 to -0.14 for DFS; -0.75 to 0.03 for DFLI; from -0.59 to -0.02 for CI; from -0.47 to 0.33 for NSPC and from 0.08 to 0.82 for CRFI. These results show (genetic) interactions between fat reserves and reproduction along the lactation trajectory of modern dairy cows, which can be useful in genetic selection as well as in management. Maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in mid lactation when the genetic variance for BCS is largest, and the genetic correlations between BCS and fertility is strongest.     

Estimation of genetic parameters for heat stress,including dominance gene effects,on milk yield in Thai Holstein dairy cattle

Heat stress in tropical regions is a major cause that strongly negatively affects to milk production in dairy cattle. Genetic selection for dairy heat tolerance is powerful technique to improve genetic performance. Therefore, the current study aimed to estimate genetic parameters and investigate the threshold point of heat stress for milk yield. Data included 52 701 test‐day milk yield records for the first parity from 6247 Thai Holstein dairy cattle, covering the period 1990 to 2007. The random regression test day model with EM‐REML was used to estimate variance components, genetic parameters and milk production loss. A decline in milk production was found when temperature and humidity index (THI) exceeded a threshold of 74, also it was associated with the high percentage of Holstein genetics. All variance component estimates increased with THI. The estimate of heritability of test‐day milk yield was 0.231. Dominance variance as a proportion to additive variance (0.035) indicated that non‐additive effects might not be of concern for milk genetics studies in Thai Holstein cattle. Correlations between genetic and permanent environmental effects, for regular conditions and due to heat stress, were ? 0.223 and ? 0.521, respectively. The heritability and genetic correlations from this study show that simultaneous selection for milk production and heat tolerance is possible.     

Bayesian model selection of contemporary groups for BLUP genetic evaluation in Latxa dairy sheep

Milk yield data from the Latxa dairy sheep breed were used for model comparison. Three possible models for genetic evaluation, differing in the definition of the contemporary groups, were compared using a Bayesian approach. Contemporary groups were: flock-year+flock-month of lambing-number of parity and age, flock-year-month, and flock-year in models 1, 2, and 3, respectively. All available data from the two strains of the breed (Black-Faced and Blond-Faced) were used. The comparison criteria were: Bayes factor, posterior Bayes factor, pseudo-Bayes factor, two checking functions based on univariate predictive distributions, and the deviance information criteria. The two checking functions consisted in the measure of accuracy (d₁) and systematic bias (d₂) in the prediction of observations. All criteria except d₂ indicated the superiority of model 2 in both strains. Based on these results, it was concluded that model 2 reaches the best equilibrium between fit and predictive ability.     

Genetic correlations between production with disease resistance and fertility in dairy cattle and consequences for total merit selection

Twelve years of data from progeny‐test results of 1486 Swedish Red and White (SRB) and 756 Swedish Black and White (SLB) AI bulls were analysed to provide estimates of genetic correlations between yield of protein and three health‐ and fertility traits. For both breeds, the correlations were unfavourable (r_G= —0.13 to —0.37). The effects of negative genetic correlations (compared to a situation with genetic correlations with zero values) on the b‐values in the total merit index of bulls were rather small but the effects on the estimated genetic gain were large. The effect of not including health and fertility traits in the index, although included in the breeding goal, resulted in a 9–10% reduced accuracy of estimated breeding values for total merit and thus a corresponding loss in total gain.     

Autoregressive model for genetic evaluation of longitudinal reproductive traits in Brazilian Holstein cattle

Reproductive efficiency is major determinant of the dairy herd profitability. Thus, reproductive traits have been widely used as selection objectives in the current dairy cattle breeding programs. We aimed to evaluate strategies to model days open (DO), calving interval (CI) and daughter pregnancy rate (DPR) in Brazilian Holstein cattle. These reproductive traits were analysed by the autoregressive (AR) model and compared with classical repeatability (REP) model using 127,280, 173,092 and 127,280 phenotypic records, respectively. The first three calving orders of cows from 1,469 Holstein herds were used here. The AR model reported lower values for Akaike Information Criteria and Mean Square Errors, as well as larger model probabilities, for all evaluated traits. Similarly, larger additive genetic and lower residual variances were estimated from AR model. Heritability and repeatability estimates were similar for both models. Heritabilities for DO, CI and DPR were 0.04, 0.07 and 0.04; and 0.05, 0.06 and 0.04 for AR and REP models, respectively. Individual EBV reliabilities estimated from AR for DO, CI and DPR were, in average, 0.29, 0.30 and 0.29 units higher than those obtained from REP model. Rank correlation between EBVs obtained from AR and REP models considering the top 10 bulls ranged from 0.72 to 0.76; and increased from 0.98 to 0.99 for the top 100 bulls. The percentage of coincidence between selected bulls from both methods increased over the number of bulls included in the top groups. Overall, the results of model-fitting criteria, genetic parameters estimates and EBV predictions were favourable to the AR model, indicating that it may be applied for genetic evaluation of longitudinal reproductive traits in Brazilian Holstein cattle.     

Analyses of genetic diversity in five Canadian dairy breeds using pedigree data

The issue of loss of animal genetic diversity, worldwide in general and in Canada in particular, has become noteworthy. The objective of this study was to analyze the trend in within‐breed genetic diversity and identify the major causes of loss of genetic diversity in five Canadian dairy breeds. Pedigrees were analyzed using the software EVA (evolutionary algorithm) and CFC (contribution, inbreeding, coancestry), and a FORTRAN package for pedigree analysis suited for large populations (PEDIG). The average rate of inbreeding in the last generation analyzed (2003 to 2007) was 0.93, 1.07, 1.26, 1.09 and 0.80% for Ayrshire, Brown Swiss, Canadienne, Guernsey and Milking Shorthorn, respectively, and the corresponding estimated effective population sizes were 54, 47, 40, 46 and 66, respectively. Based on coancestry coefficients, the estimated effective population sizes in the last generation were 62, 76, 43, 61 and 76, respectively. The estimated percentage of genetic diversity lost within each breed over the last four decades was 6, 7, 11, 8 and 5%, respectively. The relative proportion of genetic diversity lost due to random genetic drift in the five breeds ranged between 59.3% and 89.7%. The results indicate that each breed has lost genetic diversity over time and that the loss is gaining momentum due to increasing rates of inbreeding and reduced effective population sizes. Therefore, strategies to decrease rate of inbreeding and increase the effective population size are advised.     

Multiple trait genetic evaluation of ewe traits in Icelandic sheep

The prolificacy of the ewes was measured as the number of lambs born per ewe mated (NLB) when the ewes were 1–4 years of age. The ewe productivity related to the same age interval was measured by special ewe production indices (EPI). The genetic parameters for these traits were estimated by a series of bivariate REML analyses using animal models. The material used for the genetic analysis contained records on 193 213 ewes. The heritability estimates for NLB were h² = 0.17, 0.13, 0.11, 0.10 for the four respective age classes. Corresponding estimates for EPI were h² = 0.16, 0.17, 0.17, 0.15. The genetic correlations among NLB at different ages ranged from 0.63 to 0.98 and among EPI from 0.82 to 0.99. The genetic correlations between NLB and EPI were generally low. The material used for estimating the breeding values by the MT‐BLUP Animal Model consisted of 1.5 million individuals in the pedigree file. In total 815 782 ewes had records for the NLB and 763 491 ewes had production index (at least 1 year). The records were registered in the years 1990–2006. All possible missing patterns were present in the data. In the iteration process expected values for missing traits were generated and solutions were obtained on canonical transformed scale. The genetic evaluations were run independently for NLB and EPI for computational convenience given the correlations between these traits were negligible.     

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.     

Comparing deregression methods for genomic prediction of test‐day traits in dairy cattle

We aimed to investigate the performance of three deregression methods (VanRaden, VR; Wiggans, WG; and Garrick, GR) of cows’ and bulls’ breeding values to be used as pseudophenotypes in the genomic evaluation of test‐day dairy production traits. Three scenarios were considered within each deregression method: (i) including only animals with reliability of estimated breeding value (REL_EBV ) higher than the average of parent reliability (REL_PA ) in the training and validation populations; (ii) including only animals with REL_EBV higher than 0.50 in the training and REL_EBV higher than REL_PA in the validation population; and (iii) including only animals with REL_EBV higher than 0.50 in both training and validation populations. Individual random regression coefficients of lactation curves were predicted using the genomic best linear unbiased prediction (GBLUP), considering either unweighted or weighted residual variances based on effective records contributions. In summary, VR and WG deregression methods seemed more appropriate for genomic prediction of test‐day traits without need for weighting in the genomic analysis, unless large differences in REL_EBV between training population animals exist.     

Genetic evaluation using random regression models with different covariance functions for test‐day milk yield in an admixture population of Thailand goats

The objectives of this study were to compare covariance functions (CF) and estimate the heritability of milk yield from test‐day records among exotic (Saanen, Anglo‐Nubian, Toggenburg and Alpine) and crossbred goats (Thai native and exotic breed), using a random regression model. A total of 1472 records of test‐day milk yield were used, collected from 112 does between 2003 and 2006. CF of the study were Wilmink function, second‐ and third‐order Legendre polynomials, and linear splines 4 knots located at 5, 25, 90 and 155 days in milk (SP25–90) and 5, 35, 95 and 155 of days in milk (SP35–95). Variance components were estimated by restricted maximum likelihood method (REML). Goodness of fit, Akaike information criterion (AIC), percentage of squared bias (PSB), mean square error (MSE), and empirical correlation (RHO) between the observed and predicted values were used to compare models. The results showed that CF had an impact on (co)variance estimation in random regression models (RRM). The RRM with splines 4 knots located at 5, 25, 90 and 155 of days in milk had the lowest AIC, PSB and MSE, and the highest RHO. The heritability estimated throughout lactation obtained with this model ranged from 0.13 to 0.23.