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The objective of this work was to determine the effect of two different feeding plans on quantity and quality of oocytes and in vitro embryo production, as well as on the body development of prepubertal Nellore heifers. After weaning, thirty-three Nellore heifers were divided into two experimental groups and supplemented with two nutritional plans according to dry and rainy seasons. For nutritional plan 2 the animals received diets containing 26% and 19% more metabolizable energy (ME) related to NP1 in the dry and rainy seasons, respectively. After 3.5 months of supplementation (i.e., at 10 months old), animals from both groups underwent follicular aspiration (OPU) every 21 days and in vitro produced embryos (IEP). Withers height, chest depth and body weight of all animals were measured monthly. Subcutaneous fat deposition was measured at the end of the study by ultrasonography. The number of retrieved COCs per OPU and viable oocytes was respectively 49% and 42% greater in NP2 heifers (p=0.018 and p=0.049). Regarding the blastocysts rate, heifers administered NP2 produced 29.7% of blastocysts, a percentage higher than NP1 treatment animals that produced 24.40% of embryos (p<0.05). Consequently, the females from NP2 treatment presented a greater final weight, chest depth, carcass finish, in addition to a greater accumulation of subcutaneous fat. These results indicating a positive effect of diet slightly higher in energy in the assisted reproduction of Nellore prepubertal heifers and also on weight gain and fat deposition in the carcass, allowing these females to reach the suggested early mating conditions.

Genetic parameters were estimated for scrotal circumference adjusted to 365 (SC365) and 450 (SC450) days of age, age at first calving (AFC), accumulated productivity (AP), stayability (STAY), loin eye area (LMA), thickness of subcutaneous fat over the 12th-13th ribs (BF), thickness of subcutaneous fat over the rump (RF), and shear force measured by Warner-Bratzler shear force (WBSF) of polled Nellore cattle. Bayesian analysis bi characteristics were performed by adopting linear animal model, whereas STAY analyzes used the linear threshold model. Heritability estimates were equal to 0.31 (SC365), 0.37 (SC450), 0.16 (AFC), 0.25 (AP), 0.16 (STAY), 0.30 (LMA), 0.13 (BF), 0.24 (RF), and 0.15 (WBSF), indicating moderate response to selection. Genetic and residual correlations between SC365 and SC450 were high and positive (0.91 and 0.74, respectively), as well as the genetic correlations of AP with SC365, SC450, AFC, and STAY (0.61, 0.62, -0.69, and 0.83, respectively). Genetic and residual correlations of WBSF with reproductive and carcass characteristics exhibited high standard deviations, however favorable.

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner-Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.