The objective of this study was to identify genomic regions that are associated with meat quality traits in the Nellore breed. Nellore steers were finished in feedlots and slaughtered at a commercial slaughterhouse. This analysis included 1,822 phenotypic records of tenderness and 1,873 marbling records. After quality control, 1,630 animals genotyped for tenderness, 1,633 animals genotyped for marbling, and 369,722 SNPs remained. The results are reported as the proportion of variance explained by windows of 150 adjacent SNPs. Only windows with largest effects were considered. The genomic regions were located on chromosomes 5, 15, 16 and 25 for marbling and on chromosomes 5, 7, 10, 14 and 21 for tenderness. These windows explained 3,89% and 3,80% of the additive genetic variance for marbling and tenderness, respectively. The genes associated with the traits are related to growth, muscle development and lipid metabolism. The study of these genes in Nellore cattle is the first step in the identification of causal mutations that will contribute to the genetic evaluation of the breed.

The objective of this study was to evaluate the accuracy of genomic predictions for rib eye area (REA), backfat thickness (BFT), and hot carcass weight (HCW) in Nellore beef cattle from Brazilian commercial herds using different prediction models. Phenotypic data from 1756 Nellore steers from ten commercial herds in Brazil were used. Animals were offspring of 294 sires and 1546 dams, reared on pasture, feedlot finished, and slaughtered at approximately 2 years of age. All animals were genotyped using a 777k Illumina Bovine HD SNP chip. Accuracy of genomic predictions of breeding values was evaluated by using a 5-fold cross-validation scheme and considering three models: Bayesian ridge regression (BRR), Bayes C (BC) and Bayesian Lasso (BL), and two types of response variables: traditional estimated breeding value (EBV), and phenotype adjusted for fixed effects (Y*). The prediction accuracies achieved with the BRR model were equal to 0.25 (BFT), 0.33 (HCW) and 0.36 (REA) when EBV was used as response variable, and 0.21 (BFT), 0.37 (HCW) and 0.46 (REA) when using Y*. Results obtained with the BC and BL models were similar. Accuracies increased for traits with a higher heritability, and using Y* instead of EBV as response variable resulted in higher accuracy when heritability was higher. Our results indicate that the accuracy of genomic prediction of carcass traits in Nellore cattle is moderate to high. Prediction of genomic breeding values from adjusted phenotypes Y* was more accurate than from EBV, especially for highly heritable traits. The three models considered (BRR, BC and BL) led to similar predictive abilities and, thus, either one could be used to implement genomic prediction for carcass traits in Nellore cattle.

Carcass traits measured after slaughter are economically relevant traits in beef cattle. In general, the slaughter house payment system is based on HCW. Ribeye area (REA) is associated with the amount of the meat in the carcass, and a minimum of backfat thickness (BFT) is necessary to protect the carcass during cooling. The aim of this study was to identify potential genomic regions harboring candidate genes affecting those traits in Nellore cattle. The data set used in the present study consisted of 1,756 Nellore males with phenotype records. A subset of 1,604 animals had both genotypic and phenotypic information. Genotypes were generated based on a panel with 777,962 SNPs from the Illumina Bovine HD chip. The SNP effects were calculated based on the genomic breeding values obtained by using the single-step GBLUP approach and a genomic matrix re-weighting procedure. The proportion of the variance explained by moving windows of 100 consecutive SNPs was used to assess potential genomic regions harboring genes with major effects on each trait. The top 10 non-overlapping SNP-windows explained 8.72%, 11.38%, and 9.31% of the genetic variance for REA, BFT, and HCW, respectively. These windows are located on chromosomes 5, 7, 8, 10, 12, 20, and 29 for REA; chromosomes 6, 8, 10, 13, 16, 17, 18, and 24 for BFT; and chromosomes 4, 6, 7, 8, 14, 16, 17, and 21 for HCW. For REA, there were identified genes (CDKN2A and CDKN2B) involved in the cell cycle biological process which affects many aspects of animal growth and development. The SLC38A1 and SLC38A2 genes, both from SLC38 AA transporter family, was also associated with REA. The AA transporters are essential for cell growth and proliferation, acting as carriers of tissue nutrient supplies. Various genes identified for BFT (SORCS2, AQP3, AQP7, CDC42BPA, ASIP, and ACSS2) have been associated with lipid metabolism in different mammal species. One of the most promising genes identified for HCW was the PLAG1. There is evidence, in the literature, that this gene is located in putative QTL affecting carcass weight in beef cattle. Our results showed several genomic regions containing plausible candidate genes that may be associated with carcass traits in Nellore cattle. Besides contributing to a better understanding of the genetic control of carcass traits, the identified genes can also be helpful for further functional genomic studies.

Subcutaneous fat deposition measured as backfat thickness (BFT) increases protection for the bovine carcass during cooling, conferring to BFT an important characteristic for the meat industry. To study the influence of BFT on meat quality traits of Nellore bulls (Bos indicus), data from 1652 animals aged 20–24 months in feedlot finishing were used. The principal component analysis (PCA) was performed to characterise meat quality variables in longissimus thoracis muscle. Measurements comprised the rib eye area, BFT, marbling, shear force, myofibril fragmentation index, cooking losses, intramuscular lipid content and colour (lightness, yellowness, redness, chromaticity and hue). Considering BFT as a separation criterion, the K-means cluster analysis was applied to classify beef samples. The first four PC explained roughly 66% of total variability and meat colour (yellowness and chromaticity) was more effective to define the first PC. Tenderness or toughness (shear force and cooking losses) and fatness (BFT and intramuscular lipid content) were more effective to define the second and third PC, respectively. Three BFT groups were formed and projected in the gradient defined by PC 2 and PC 3. BFT means in the clusters were 10.82 ± 3.19 (I), 5.03 ± 1.01 (II) and 2.54 ± 0.63 (III) mm with 185, 947 and 520 animals in each group, respectively. The projection of I, II and III in the gradient allowed to distinguish fatness between beef samples and tenderness between I and III. Additionally, 57.32% of animals (Group II) were placed between the two previous groups. Beef samples with higher values of shear force and cooking losses (tough meat) showed lower BFT and myofibril fragmentation index values, possibly due to fibre shortening. PCA and K-means cluster analysis presented as interesting multivariate techniques to identify Nellore bulls regarding meat quality as some of the traits used in the study are difficult to measure. The three-cluster solution represented the main biological type of Nellore bulls finished on feedlot in Brazil showing that only 11.2% of beef samples (Cluster I) can be considered tender. This information can be useful for breeding programs of Nellore bulls. In this study, Cluster I shows optimal beef quality (SF = 4.52 ± 1.17 kg) with better marbling level and less cooking losses. Nellore cattle producers should target BFT at least 5.00 mm to prevent fibre shortening. However, the only condition which provides optimal beef tenderness (i.e. shear force values lower than 4.9 kg) was found in Cluster I. The BFT does not seem to be a suitable characteristic for the selection of animals to improve tenderness due the weak relationship between BFT and shear force.