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.

Estimates of genetic and phenotypic parameters for feeding behaviour and feed efficiency-related traits in Nelore cattle.

The objective of this study was to obtain (co)variance components, heritability, and genetic and phenotypic correlation estimates for feed efficiency and feed behaviour-related indicator traits. Further, it aimed to predict the direct and correlated responses for feed efficiency traits when selection was applied for feeding behaviour-related traits in Nelore cattle. Phenotypic records (n = 4840) from 125 feed efficiency tests (RFI: Residual feed intake and DMI: Dry matter intake) carried out between 2011 and 2018 were considered in this study. Animals belonged to five farms located in two Brazilian geographical regions (Midwest and Southeast). Animals under similar management and environmental conditions in the feedlot were evaluated when they attained an average of 13.5 ± 4.15 months of age. Feed behaviour-related traits were also obtained, including meal criteria (MC), meal frequency (MF), average meal duration (AMD), meal duration (MD), average consumption per meal (ACM), and consumption rate (CR) through the GrowSafe System® electronic bunk system. The contemporary groups for all traits were composed of farm, management group, feed efficiency test, sex, and birth year. The (co)variance components were estimated using the restricted maximum likelihood method considering a multi-trait (n = 8) animal model. The heritability estimates for RFI (0.23 ± 0.02), DMI (0.31 ± 0.02), MF (0.65 ± 0.02), AMD (0.29 ± 0.02), ACM (0.24 ± 0.02), MD (0.41 ± 0.02), MC (0.48 ± 0.02), and CR (0.42 ± 0.02) were moderate to high. The highest genetic correlation was obtained between CR and MD (-0.91 ± 0.04), MD and AMD (0.73 ± 0.03), CR and AMD (-0.68 ± 0.04), and RFI and DMI (0.81 ± 0.02). The highest phenotypic correlation was between ACM and AMD (0.76 ± 0.02), DMI and MD (0.77 ± 0.02), and DMI and RFI (0.77 ± 0.02). Genetic improvement for feed efficiency and feeding behaviour-related traits is feasible and the results obtained herein provided valuable information regarding the genetic background of Nelore feeding behaviour-related traits. The genetic association between feeding behaviour and feed efficiency-related traits suggested that animals spending less time feeding at a low feeding rate also had lower DMI and higher feed efficiency (RFI), and likely had lower energy maintenance requirements. The relative efficiency of selection showed that feeding behaviour-related traits were not adequate indicator traits to improve RFI and DMI. The DMI might be an effective selection criterion to improve RFI and reduce the herd's maintenance requirements.

Early growth, backfat thickness and body condition has major effect on early heifer pregnancy in Nellore cattle.

The aim was to evaluate the association between growth, carcass and visual scores traits with precocious calving in Nellore cattle. Birth weight (BW), weight at 120, 210, 365 and 450 days of age, pre and post-weaning average daily gain, rib eye area, backfat thickness (BF), rump fat thickness and visual scores obtained at 18 months were used for the analysis. Records from 700 females born between 2009 and 2015, exposed to mating starting at 11 months of age were analyzed. Discriminant analyzes were performed with the software Statistica. BW and BF showed the highest (P>0.01) discrimination value for early heifer pregnancy (EP). Extreme intrauterine growth retardation can result in slower growth, which reflects in the worst reproductive performance, confirmed by the variation in BW between precocious and conventional heifers. The results also demonstrate that the level of body fat affects begin of puberty. Bone structure, musculature, depth, tail insertion and rump scores presented the highest discrimination value for EP. These traits can be used as selection tools to improve sexual precocity in female Nellore cattle. The results obtained in this study would support farmers to guide the heifer management and decisions in order to enhance the EP.

Weighted single-step genome-wide association study and pathway analyses for feed efficiency traits in Nellore cattle.

The aim was to conduct a weighted single-step genome-wide association study to detect genomic regions and putative candidate genes related to residual feed intake, dry matter intake, feed efficiency (FE), feed conversion ratio, residual body weight gain, residual intake and weight gain in Nellore cattle. Several protein-coding genes were identified within the genomic regions that explain more than 0.5% of the additive genetic variance for these traits. These genes were associated with insulin, leptin, glucose, protein and lipid metabolisms; energy balance; heat and oxidative stress; bile secretion; satiety; feed behaviour; salivation; digestion; and nutrient absorption. Enrichment analysis revealed functional pathways (p-value < .05) such as neuropeptide signalling (GO:0007218), negative regulation of canonical Wingless/Int-1 (Wnt) signalling (GO:0090090), bitter taste receptor activity (GO:0033038), neuropeptide hormone activity (GO:0005184), bile secretion (bta04976), taste transduction (bta0742) and glucagon signalling pathway (bta04922). The identification of these genes, pathways and their respective functions should contribute to a better understanding of the genetic and physiological mechanisms regulating Nellore FE-related traits.

Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.

The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non-autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10-6 ), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree-based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle.