Genetic Evaluation Of Beef Cattle Research Articles

95 Evaluation of variation attributable to lab and technician for measurements of beef carcass traits made using ultrasound

Abstract Carcass data that is collected using ultrasound contributes to national genetic evaluation of beef cattle. Cattle are scanned by many technicians and the resulting images are processed by three laboratories providing data to breed associations. The Ultrasound Guidelines Council certifies technicians and laboratories as being proficient. However, there has been no comprehensive evaluation of non-genetic sources of variation that affect the data. The objective of this study was to partition the variance of ultrasound carcass measurements to animal genetic effects, ultrasound scanning technician and image interpretation laboratory. Ultrasound carcass data for longissimus muscle area, percent intramuscular fat, and subcutaneous fat depth were provided by the American Angus Association (n=65,971), American Hereford Association (n=43,380), and American Simmental Association (n=48,298). For each breed variance components were estimated for additive genetic effects of each animal, technician, contemporary group nested within technician and residual. Genetic correlations were estimated treating measurements from the different interpretation laboratories as separate traits. Across all three breeds, additive genetic effects explained between 15 and 30%, 11 and 25%, and 6 and 13% of the phenotypic variance for IMF, SQF, and LMA, respectively. Similarly, technician explained 12-27%, 4-28%, and 4-26% of the phenotypic variance; contemporary groups within technician accounted for 25-45%, 20-54%, and 43-73% of phenotypic variance. Among the image interpretation laboratories, the ratio of greatest to least residual variance was 3.96, 1.62, and 1.50 for IMF, SQF, and LMA, respectively. Genetic correlations between labs ranged from 0.79 to 0.95 for IMF, 0.64 to 0.94 for SQF and 0.78 to 0.98 for LMA. These results suggest the potential for the residual variances to be heterogeneous, particularly for IMF, depending on the image interpretation laboratory.

Genetic and phenotypic trends for weights of major beef and dual-purpose cattle breeds in Slovak Republic

Genetic evaluation of beef cattle in Slovakia started in first years of this century. After the first decade of running the routine evaluations it is important to review the progress made and to discuss the further development. The aim of this paper was to compile and deliver genetic and phenotypic trends in order to review importance of evaluated traits with respect to four major beef (Charolais, Limousine) and dual-purpose breeds (Slovak Pinzgau cattle, Slovak Spotted cattle). The study showed different progress made across the breeds. Higher genetic progress was observed in Charolais purebreds compared to Limousine counterparts. Moreover, almost similar progress to that in Charolais was observed in dual-purpose Slovak Pinzgau cattle. On the other hand no progress was observed in Slovak Spotted purebreds. Results also showed higher progress in bulls compared to cows. Phenotypic trends showed reserves for improvement in Charolais and closing to breed standards in Limousine. In dualpurpose breeds improvement was shown only in Slovak Pinzgau, while no improvement or decrease in actual weights was shown in Slovak Spotted purebreds.

Unraveling the effect of body structure score on phenotypic plasticity for body weight at different ages in Guzerat cattle

The Body structure (BS) is a trait to be considered in the genetic evaluation of beef cattle because it is associated with economically important traits, maintenance requirement, and physiological maturity. Therefore, the objective of this study was to determine the effect of genotype-environment (GxE) interaction considering contemporary group solutions for BW and BS as an environmental gradient (EG) to assess the changes in the body weight (BW). For this BW was measured at 120, 205, 365, 450 and 550 days of age in Guzerat cattle. Phenotypic data from 21,973 Guzerat animals were used to explore the GxE effect among BW, obtained from 150 farms distributed in five Brazilian States provided by the Brazilian Association of Zebu Breeders. A reaction norm model in two-steps was used to estimate the animals’ response to environmental changes. As evidence of GxE, heterogeneity of genetic variance across environments was observed for BW at different ages. Changes in heritability estimates ranged from 0.29 to 0.44 for BW and provided differences in the selection response across different EG levels. The genetic correlations between extreme EG levels indicated a strong effect of GxE interactions. Breeding value for BW at 120 and 205 days of age showed less plasticity than BW at 365, 450, and 550 days of age due to changes in EG. Nevertheless, these results indicate that the BS is an important factor to be considered in genetic evaluations for BW according to the growth phase.

Genotype by environment interaction for yearling weight in Nellore cattle applying reaction norms models

In the present study, a possible existence of genotype × environment interaction was verified for yearling weight in Nellore cattle, utilising a reaction norms model. Therefore, possible changes in the breeding value were evaluated for 46 032 animals, from three distinct herds, according to the environmental gradient variation of the different contemporary groups. Under a Bayesian approach, analyses were carried out utilising INTERGEN software resulting in solutions of contemporary groups dispersed in the environmental gradient from –90 to +100 kg. The estimates of heritability coefficients ranged from 0.19 to 0.63 through the environmental gradient and the genetic correlation between intercept and slope of the reaction norms was 0.76. The genetic correlation considering all animals of the herds in the environmental gradient ranged from 0.83 to 1.0, and the correlation between breeding values of bulls in different environments ranged from 0.79 to 1.0. The results showed no effect of genotype × environment interaction on yearling weight in the herds of this study. However, it is important to verify a possible influence of the genotype × environment in the genetic evaluation of beef cattle, as different environments might cause interference in gene expression and consequently difference in phenotypic response.

Genotype by environment interaction effects in genetic evaluation of preweaning gain for Line 1 Hereford cattle from Miles City, Montana

It has long been recognized that genotype × environment interaction potentially influences genetic evaluation of beef cattle. However, this recognition has largely been ignored in systems for national cattle evaluation. The objective of this investigation was to determine if direct and maternal genetic effects on preweaning gain would be reranked depending on an environmental gradient as determined by year effects. Data used were from the 76-yr selection experiment with the Line 1 Hereford cattle raised at Miles City, MT. The data comprised recorded phenotypes from 7,566 animals and an additional 1,862 ancestral records included in the pedigree. The presence of genotype × environment interaction was examined using reaction norms wherein year effects on preweaning gain were hypothesized to linearly influence the EBV. Estimates of heritability for direct and maternal effects, given the average environment, were 10 ± 2 and 26 ± 3%, respectively. In an environment that is characterized by the 5th (95th) percentile of the distribution of year effects, the corresponding estimates of heritability were 18 ± 3 (22 ± 3%) and 30 ± 3% (30 ± 3%), respectively. Rank correlations of direct and maternal EBV appropriate to the 5th and 95th percentiles of the year effects were 0.67 and 0.92, respectively. In the average environment, the genetic trends were 255 ± 1 g/yr for direct effects and 557 ± 3 g/yr for maternal effects. In the fifth percentile environment, the corresponding estimates of genetic trend were 271 ± 1 and 540 ± 3 g/yr, respectively, and in the 95th percentile environment, they were 236 ± 1 and 578 ± 3 g/yr, respectively. Linear genetic trends in environmental sensitivity were observed for both the direct (-8.06 × 10 ± 0.49 × 10) and maternal (8.72 × 10 ± 0.43 × 10) effects. Therefore, changing systems of national cattle evaluation to more fully account for potential genotype × environment interaction would improve the assessment of breeding stock, particularly for direct effects. Estimates of environmental sensitivity parameters could also facilitate identification of genetic limitations to production.

Accounting for unknown foster dams in the genetic evaluation of embryo transfer progeny.

Animals born by embryo transfer (ET) are usually not included in the genetic evaluation of beef cattle for preweaning growth if the recipient dam is unknown. This is primarily to avoid potential bias in the estimation of the unknown age of dam. We present a method that allows including records of calves with unknown age of dam. Assumptions are as follows: (i) foster cows belong to the same breed being evaluated, (ii) there is no correlation between the breeding value (BV) of the calf and the maternal BV of the recipient cow, and (iii) cows of all ages are used as recipients. We examine the issue of bias for the fixed level of unknown age of dam (AOD) and propose an estimator of the effect based on classical measurement error theory (MEM) and a Bayesian approach. Using stochastic simulation under random mating or selection, the MEM estimating equations were compared with BLUP in two situations as follows: (i) full information (FI); (ii) missing AOD information on some dams. Predictions of breeding value (PBV) from the FI situation had the smallest empirical average bias followed by PBV obtained without taking measurement error into account. In turn, MEM displayed the highest bias, although the differences were small. On the other hand, MEM showed the smallest MSEP, for either random mating or selection, followed by FI, whereas ignoring measurement error produced the largest MSEP. As a consequence from the smallest MSEP with a relatively small bias, empirical accuracies of PBV were larger for MEM than those for full information, which in turn showed larger accuracies than the situation ignoring measurement error. It is concluded that MEM equations are a useful alternative for analysing weaning weight data when recipient cows are unknown, as it mitigates the effects of bias in AOD by decreasing MSEP.

Comparison of bivariate and multivariate joint analyses on the selection loss of beef cattle.

For genetic evaluation of beef cattle, univariate or bivariate analyses are often performed as an alternative to decrease the complexity of matrices and mathematical models compared to multivariate analysis, which considers a larger number of joint traits. The use of bivariate methods to calculate genetic predictors may cause bias in the estimation of breeding values and, as a consequence, reclassification of the rank of top-selected sires, resulting in a loss of genetic gain in future generations. The objective of this study was to compare the bivariate and multivariate joint methods of genetic evaluation, verifying the selection loss, and reclassification of the ranking of the best animals with different selection intensities. Records of 431,224 Nellore breed animals were evaluated for birth weight, weaning weight, post-weaning gain, muscle score, scrotal circumference, and selection index. The pedigree file consisted of 505,848 animals, including 218,727 males and 287,121 females. The predicted breeding values were obtained using the program PEST 2, and the complete pedigree analysis was performed by the PopReport software. The results showed that, for the four different selection intensities considered (TOP 10 and 1, 10, and 30%), selection loss and reclassification of animals in ranking, were detected for all traits evaluated when the two methods of analysis were compared.

Bayesian analysis of autoregressive panel data model: application in genetic evaluation of beef cattle

The animal breeding values forecasting at futures times is a relevant technological innovation in the field of Animal Science, since its enables a previous indication of animals that will be either kept by the producer for breeding purposes or discarded. This study discusses an MCMC Bayesian methodology applied to panel data in a time series context. We consider Bayesian analysis of an autoregressive, AR(p), panel data model of order p, using an exact likelihood function, comparative analysis of prior distributions and predictive distributions of future observations. The methodology was tested by a simulation study using three priors: hierarchical Multivariate Normal-Inverse Gamma (model 1), independent Multivariate Student's t Inverse Gamma (model 2) and Jeffrey's (model 3). Comparisons by Pseudo-Bayes Factor favored model 2. The proposed methodology was applied to longitudinal data relative to Expected Progeny Difference (EPD) of beef cattle sires. The forecast efficiency was around 80%. Regarding the mean width of the EPD interval estimation (95%) in a future time, a great advantage was observed for the proposed Bayesian methodology over usual asymptotic frequentist method.

Measuring connectedness among herds in mixed linear models: From theory to practice in large-sized genetic evaluations

A procedure to measure connectedness among groups in large-sized genetic evaluations is presented. It consists of two steps: (a) computing coefficients of determination (CD) of comparisons among groups of animals; and (b) building sets of connected groups. The CD of comparisons were estimated using a sampling-based method that estimates empirical variances of true and predicted breeding values from a simulated n-sample. A clustering method that may handle a large number of comparisons and build compact clusters of connected groups was developed. An aggregation criterion (Caco) that reflects the level of connectedness of each herd was computed. This procedure was validated using a small beef data set. It was applied to the French genetic evaluation of the beef breed with most records and to the genetic evaluation of goats. Caco was more related to the type of service of sires used in the herds than to herd size. It was very sensitive to the percentage of missing sires. Disconnected herds were reliably identified by low values of Caco. In France, this procedure is the reference method for evaluating connectedness among the herds involved in on-farm genetic evaluation of beef cattle (IBOVAL) since 2002 and for genetic evaluation of goats from 2007 onwards.

Studies on multiple trait and random regression models for genetic evaluation of beef cattle for growth

A simulation study examined issues important for genetic evaluation of growth in beef cattle by random regression models with cubic Legendre polynomials (RRML) and linear splines with three knots (RRMS) compared with multiple-trait models (MTM). Parameters for RRML were obtained by conversion from covariance functions. Parameters for MTM and RRMS were extracted from RRML at 1, 205, and 365 d; parameters for RRMS were the same as MTM for all effects except the permanent environment and the residual. Four data sets were generated assuming RRML included records at 1, 205, and 365 d; at 1, 160 to 250, and 320 to 410 d; at 1, 100, 205, 300, and 365 d; and at 1, 55 to 145, 160 to 250, 275 to 325, and 320 to 410 d. Accuracies were computed as correlations between the true (simulated) and predicted breeding values. With the first data set, excellent agreement in accuracy was obtained for all models. With the second data set, the accuracy of MTM dropped by up to 1.5% compared with the first data set, but accuracy was unchanged for both RRML and RRMS. With the third (fourth) data set, accuracies of RRML were up to 2.4% (2.5%) higher than with the first (second) data set. Small differences in accuracy between RRML and RRMS were found with the third and fourth data sets, which were traced to inflated correlations especially between 1 and 205 d in RRMS; inflation could be decreased by adding one extra knot at 100 d to RRMS. Diagonalization of random coefficients was crucial for RRML but not for RRMS, resulting in approximately six (two) times faster convergence with RRML (RRMS). Reduction of dimensionality in RRML associated with small eigenvalues caused a less accurate evaluation for birth weight. Genetic evaluation of growth by RRM requires careful implementation. The RRMS is simpler to implement than the RRML.

Genetic evaluation of beef cattle accounting for uncertain paternity

A hierarchical Bayes (HIER) model for the quantitative genetic analysis of performance data was compared to a model based on the use of Henderson's average numerator relationship matrix (ANRM) when some pedigrees are characterized by uncertain paternity. A simulation study consisted of ten datasets characterized by 30% of animals having uncertain paternity for each of two traits: one having moderate heritabilities for direct and maternal genetic effects on weaning weight (WWT) and another having a high heritability for direct genetic effects on postweaning gain (PWG). Posterior inference on the variance components was very similar between the two models. In an application to WWT and PWG data from Brazilian Herefords, posterior inference on variance components was also very similar between ANRM and HIER. Furthermore, rank correlations on posterior means for genetic effects between the two models exceeded 0.90. Nevertheless, large differences in posterior means between these two models were observed for some animals. Furthermore, animals with uncertain paternity had generally larger posterior standard deviations of genetic effects using the HIER model compared to the ANRM model, likely because the HIER model infers upon sire assignment probabilities. Bayesian model choice criteria consistently favored the HIER model over the ANRM model in both simulation and Hereford data analysis studies.

Scope for a random regression model in genetic evaluation of beef cattle for growth

Potential improvement in accuracy of genetic evaluation of beef cattle for growth from replacing the current multi-trait (MT) model comprising birth, weaning, yearling and final weights as separate traits, with a random regression (RR) model analysis is examined by simulation. Maintaining the original data and pedigree structure for three beef cattle data sets, data were simulated assuming a cubic regression on polynomials of age for direct and maternal, genetic and permanent environmental effects and heterogeneous measurement error variances. Ages at weighing from birth to 730 days were considered. Data set I represented records from an experimental herd with monthly weighing of animals. Data sets II and III were field data, selecting a subset of herds in which at least 50% of animals had four or more weights recorded and records for all herds for a small breed, respectively. Simulated records were analysed fitting a MT model and RR models. Field data sets were expanded by adding a fictitious weight approximately 3 months after the original records. Accuracy of genetic evaluation was calculated as correlation between true and estimated values for each analysis. For the same subset of data, accuracies from a RR analysis were consistently higher than for a MT analysis, due to more appropriate modelling of variances and genetic parameters. Using all records available, RR accuracies for breeding value estimates for 200, 400 and 600 days in data set I were 0.023–0.034 or 4.3–5.9% higher than for MT. Corresponding gains were 3.1–3.6% for data set II and 1.5–1.7% for data set III. Expanding the field data sets by 100%, increased RR accuracies by 0.027–0.038 over those from MT analyses. While small in absolute terms, this was equivalent to a proportional increase of 5.7–8.3%. Results showed that substantial benefits could be obtained from the implementation of a RR model, if additional weight records were collected.

Genetic analyses of field-recorded growth and carcass traits for Swedish beef cattle

To investigate the possibility of using carcass data from commercial slaughterhouses for the genetic evaluation of beef cattle, genetic parameters for carcass traits and their correlations with field-recorded growth traits were estimated. The data comprised information on carcass weight (CWT), carcass fleshiness (FLESH) and carcass fatness grade (FAT) according to the (S)EUROP scale for 5870, 1509 and 1119 young bulls of the Charolais, Hereford and Simmental breeds, respectively. In addition, information on field-recorded pre-weaning gain (WG) and post-weaning gain (PWG) was available for more than 23 200 and 15 700 bulls, respectively. The breeds were analysed separately using BLUP animal models. The results showed that it is feasible to include field-recorded carcass traits in the genetic evaluation. The estimated direct heritabilities of FLESH, FAT, CWT, WG and PWG were in the ranges 0.21–0.39, 0.23–0.45, 0.42–0.70, 0.31–0.41 and 0.29–0.42, respectively. The genetic correlations differed between breeds, though for all breeds studied the genetic correlations with pre-weaning gain were weaker for FAT and FLESH than for CWT. The effect of selection on pre-weaning gain appeared to be stronger for CWT than for the other traits.

Genetic evaluation of growth in nellore cattle by multiple-trait and random regression models.

The objective of this study was to identify issues in genetic evaluation of beef cattle for growth by a random regression model (RRM). Genetic evaluation data included 2,946,847 records of up to nine sequential weights of 812,393 Nellore cattle measured at ages ranging from birth to 733 d. Models considered were a five-trait multiple-trait model (MTM) and a cubic RRM. The MTM included the effects of contemporary group, age of dam class, additive direct, additive maternal, and maternal permanent environment. Both additive effects were assumed correlated. The RRM included the same effects as MTM, with the addition of permanent and random error effects. The purpose of the random error effect, which was in addition to a residual effect with constant variance, was to model heterogeneous residual variances. All effects in RRM were modeled as cubic Legendre polynomials. Expected progeny differences (EPD) were obtained iteratively using a preconditioned conjugate gradient algorithm. Numerically accurate solutions with RRM were not obtained until the random regressions were orthogonalized. Computing requirements of RRM were reduced by more than 50%, without affecting the accuracy by removing regressions corresponding to very low eigen-values and by replacing the random error effects with weights. Afterward, the correlations between EPD from RRM and from MTM for EPD on selected weights were between 0.84 and 0.89. For sires with at least 50 progeny, these correlations increased to 0.92 to 0.97. Low correlations were caused by differences in parameters. The RRM applied to growth i s prone to numerical problems. Estimates of EPD with RRM may be more accurate than those with MTM only if accurate parameters are applied.

Efeitos da Heterogeneidade de Variância Residual entre Grupos de Contemporâneos na Avaliação Genética de Bovinos de Corte

O objetivo deste estudo foi investigar, por meio de dados simulados, o efeito da heterogeneidade de variância residual entre grupos de contemporâneos (GC) sobre as avaliações genéticas de bovinos de corte, e comparar o uso de uma avaliação genética ponderada (R<FONT FACE=Symbol>¹</FONT>Isigmae²) em relação à avaliação que pressupõe homogeneidade de variância (R=Isigmae²). A característica estudada foi ganho de peso pós-desmame corrigido para 345 dias, sendo esta simulada com variância fenotípica de 300 kg² e herdabilidade igual a 0,4. A estrutura de um conjunto real de dados foi utilizada para fornecer os GC e os pais referentes às observações de cada animal. Cinco níveis de heterogeneidade de variância residual foram considerados de forma que os componentes de variância fossem, na média, iguais aos da situação de homogeneidade de variância. Na medida em que níveis mais acentuados de heterogeneidade de variância residual foram considerados, os animais foram selecionados dos GC com maior variabilidade, especialmente com pressão de seleção intensa. Em relação à consistência de predição, os produtos e as vacas tiveram seus valores genéticos preditos mais afetados pela heterogeneidade de variância residual do que os touros. O fator de ponderação utilizado reduziu, mas não eliminou o efeito da heterogeneidade de variância. As avaliações genéticas ponderadas apresentaram resultados iguais ou superiores àqueles obtidos pelas avaliações que assumiram homogeneidade de variância. Mesmo quando não necessário, o uso de avaliações ponderadas produziu resultados não inferiores às avaliações que assumiram homogeneidade de variância.

Multiple trait prediction for a type of model with heterogeneous genetic and residual covariance structures.

A restricted set of models is defined that allows for heterogeneous genetic and residual covariance structures. Multiple trait models and models with multiple random factors are included. The restriction on the model is that the correlations among genetic effects in different classes are the same. Equivalently, the genetic covariance matrices are assumed to differ between classes due to scaling. This assumption greatly reduces the number of parameters that must be specified and does not adversely affect the computational burden of a mixed model analysis. An application of the model for genetic evaluation of beef cattle is described and illustrated numerically.

New approaches to genetic evaluation of beef cattle

The beef cattle industry in the United States has undergone dramatic changes over the past decade with the adoption of genetic evaluation programs. The method of choice has been Henderson's mixed model methodology for best linear unbiased prediction (BLUP). The most prevalently used model is the animal model (Henderson and Quaas, 1976) computed by the equivalent reduced animal model (Quaas and Pollak, 1980).Neither the methodology or the models being used are particularly new. What is new in this industry is the widespread application of these techniques to the analysis of the data banks maintained by the breed organizations. Today many breed associations publish a national sire evaluation, and most of these have published their first in the last three years. This rapid proliferation of published evaluations has coincided with an attitude in the industry of promoting specification beef and predictable performance. Genetic evaluations provide information not only to achieve goals in selection but as well for merchandizing cattle based on quantifiable potential. The enthusiasm for genetic evaluations right now in the U.S. beef industry is high.

Comparison of within-herd genetic evaluation procedures for beef cattle weaning weight.

The practicability of a specific procedure for within-herd genetic evaluation of beef cattle for weaning weight was tested with a data set of 503 Angus weaning weight records obtained from 1958 through 1976. Three different sets of genetic evaluations were obtained with the same model. The model included herd-year means, direct additive genetic values, maternal additive genetic values and random environmental errors. The evaluations of set 1 were best linear unbiased predictions (BLUP) with a full complement of equations, i.e., one for both the direct and maternal trait of all animals in the pedigree. To reduce computational load by eliminating predictions of little interest, either the direct or maternal equation for certain animals was deleted from the full complement. The resulting set 2 evaluations were also BLUP. A third method permitted further reduction in the number of equations and, therefore, computations, but the resulting set 3 evaluations were not BLUP. Thirty-seven and 43% less computer time was necessary to obtain evaluations of sets 2 and 3, respectively, than to obtain the set 1 evaluations. The correlations among the three sets of evaluations were very high, the average differences in rank small and the maximum absolute differences of predictions of the same genetic value small. The genetic, environmental and phenotypic trends obtained from each set of evaluations were similar. The results indicated that the genetic progress made from selection decisions based on evaluation sets 1, 2 and 3 would be similar.

Genetic evaluation of beef cattle for weaning weight.

Genetic evaluation of beef cattle for weaning weight.

Effect of Inbreeding on within-Herd Genetic Evaluation of Beef Cattle

Effect of Inbreeding on within-Herd Genetic Evaluation of Beef Cattle