Genetic parameters for visual scores, growth and carcass traits in Nellore Cattle.

Growth and carcass traits are essential selection criteria for beef cattle breeding programs. However, it is necessary to combine these measurements with body composition traits to meet the demand of the consumer market. This study aimed to estimate the genetic parameters for visual scores, growth (pre and post-weaning weights), and carcass (rib eye area (REA), back and rump fat thickness) traits in Nellore cattle using Bayesian inference. Data from 12,060 animals belonging to the HoRa Hofig Ramos herd were used. Morphological traits were evaluated by the MERCOS methodology. The heritability estimates obtained ranged from low to high magnitude, from 0.15 to 0.28 for visual scores, 0.13 to 0.44 for growth, and from 0.42 to 0.46 for carcass traits. Genetic correlations between visual scores and growth traits were generally of moderate to high magnitudes, however, visual scores showed low correlations with carcass traits, except between sacral bone and structure and REA. Selection for visual score traits can lead to favorable responses in body weight and vice versa, but the same is not true for carcass traits. Morphological categorical traits can be used as complementary tools that add value to selection.

Selection Indices and Multivariate Analysis Show Similar Results in the Evaluation of Growth and Carcass Traits in Beef Cattle.

This research evaluated a multivariate approach as an alternative tool for the purpose of selection regarding expected progeny differences (EPDs). Data were fitted using a multi-trait model and consisted of growth traits (birth weight and weights at 120, 210, 365 and 450 days of age) and carcass traits (longissimus muscle area (LMA), back-fat thickness (BF), and rump fat thickness (RF)), registered over 21 years in extensive breeding systems of Polled Nellore cattle in Brazil. Multivariate analyses were performed using standardized (zero mean and unit variance) EPDs. The k mean method revealed that the best fit of data occurred using three clusters (k = 3) (P < 0.001). Estimates of genetic correlation among growth and carcass traits and the estimates of heritability were moderate to high, suggesting that a correlated response approach is suitable for practical decision making. Estimates of correlation between selection indices and the multivariate index (LD1) were moderate to high, ranging from 0.48 to 0.97. This reveals that both types of indices give similar results and that the multivariate approach is reliable for the purpose of selection. The alternative tool seems very handy when economic weights are not available or in cases where more rapid identification of the best animals is desired. Interestingly, multivariate analysis allowed forecasting information based on the relationships among breeding values (EPDs). Also, it enabled fine discrimination, rapid data summarization after genetic evaluation, and permitted accounting for maternal ability and the genetic direct potential of the animals. In addition, we recommend the use of longissimus muscle area and subcutaneous fat thickness as selection criteria, to allow estimation of breeding values before the first mating season in order to accelerate the response to individual selection.

Bayesian inference on field data for genetic parameters for some reproductive and related traits of Nellore cattle (Bos indicus)

We used Gibbs sampling in single and two-trait animal models to estimate genetic parameters for some reproductive and related traits of Nellore cattle (Bos indicus). Female traits were age at first calving (AFC), cumulative productivity (CP) and adult weight (AW). For males, scrotal circumferences at 365 and 450 days of age were analyzed. Gibbs sampling using the Multiple Trait using Gibbs Sampling under Animal Model (MTGSAM) program of Van Tassel and Van Vleck was used to estimate the (co) variance components of the traits and conduct genetic analyses. Heritabilities were AFC = 0.26, AW = 0.36 and CP = 0.25 under the single-trait animal model. The mean, mode and median estimates for genetic parameters obtained from the marginal posterior distributions were similar for all traits except AW, which presented lower values for the mode than the mean and median. However, the marginal posterior distributions for the traits studied presented a tendency toward normality. Favorable, but low, negative genetic correlations were found between male scrotal circumference and female age at first calving. The reproductive traits showed medium-magnitude heritabilities, which indicates that these traits should respond to selection and therefore should be included in genetic improvement programs.

Bayesian inference in a quantitative genetic study of growth traits in Nelore cattle (Bos indicus)

The objective of this study was to estimate (co)variance components and genetic parameters for weights (W) and scrotal circumferences (SC) at 365 and 450 days of age, of Nelore (Bos indicus) cattle, using Bayesian inference in single and multiple-trait animal models. The fitted linear models included, besides the animal and residual random effects, the contemporary group (herd-year-season-sex-management group) and age-of-dam as "fixed effects". The analyses were carried out using a Gibbs sampler implemented through the MTGSAM program. The heritabilities (in parentheses) obtained fitting single-trait models were W365 (0.49), W450 (0.52), SC365 (0.68) and SC450 (0.66). Estimates of means, modes and medians for genetic parameters obtained from marginal posterior distributions were similar for all traits. The W365 and SC365 can be considered as suitable traits to be included as selection criteria in genetic improvement programs, not only because of their relatively high heritabilities but also due to the fact that they can be measured when the animals are relatively young compared to the corresponding traits W450 and SC450. The Bayesian approach appears to be an appropriate alternative for estimating genetic parameters, and has the advantage over point estimation methods of allowing inferences on marginal posterior distributions.