The objective of the study was to determine the factors related to beef cattle carcass bruises. Handling, transportation and unloading data from cattle purchased by a slaughterhouse in southern Brazil were obtained to assess their associations with carcass bruising (occurrence or not) and the number of bruises per load. In total, 4338 carcasses from 142 loads of cattle purchased by the company were assessed. The females were cull cows and the males were steers. Independent variables studied included sex (females or males), loading facilities (good, regular or poor), cattle handling (good, regular or poor), transport time from the farm to the slaughterhouse (hours), hauling-truck types, truck animal-load density (kg/m2) and slaughterhouse unloading time (min). For both traits studied (carcasses bruised and number of bruises per load), all of the independent variables showed a significant (P < 0.05) effect. Animal sex is the most important factor on carcass bruising and the number of bruises per load, with females showing 91% more bruises than males. Moderate load densities caused greater bruising than did low (<370 kg/m2) and high (>431 kg/m2) densities. In general, greater bruising was caused by longer transport and unloading times. Bruises were reduced by improving loading facilities from regular to good. Transportation in vehicles with higher load capacities (animal numbers) resulted in greater bruising. Therefore, special handling practices and personnel training measures should be undertaken to mitigate losses pre-slaughter, especially when handling female cattle, which are more vulnerable to developing carcass bruises, possibly due to higher cow reactivity.

The objective of the present study was to evaluate the accuracy and bias of direct and blended genomic predictions using different methods and cross-validation techniques for growth traits (weight and weight gains) and visual scores (conformation, precocity, muscling, and size) obtained at weaning and at yearling in Hereford and Braford breeds. Phenotypic data contained 126,290 animals belonging to the Delta G Connection genetic improvement program, and a set of 3,545 animals genotyped with the 50K chip and 131 sires with the 777K. After quality control, 41,045 markers remained for all animals. An animal model was used to estimate (co)variance components and to predict breeding values, which were later used to calculate the deregressed estimated breeding values (DEBV). Animals with genotype and phenotype for the traits studied were divided into 4 or 5 groups by random and k-means clustering cross-validation strategies. The values of accuracy of the direct genomic values (DGV) were moderate to high magnitude for at weaning and at yearling traits, ranging from 0.19 to 0.45 for the k-means and 0.23 to 0.78 for random clustering among all traits. The greatest gain in relation to the pedigree BLUP (PBLUP) was 9.5% with the BayesB method with both the k-means and the random clustering. Blended genomic value accuracies ranged from 0.19 to 0.56 for k-means and from 0.21 to 0.82 for random clustering. The analyses using the historical pedigree and phenotypes contributed additional information to calculate the GEBV, and in general, the largest gains were for the single-step (ssGBLUP) method in bivariate analyses with a mean increase of 43.00% among all traits measured at weaning and of 46.27% for those evaluated at yearling. The accuracy values for the marker effects estimation methods were lower for k-means clustering, indicating that the training set relationship to the selection candidates is a major factor affecting accuracy of genomic predictions. The gains in accuracy obtained with genomic blending methods, mainly ssGBLUP in bivariate analyses, indicate that genomic predictions should be used as a tool to improve genetic gains in relation to the traditional PBLUP selection.

The aim of this study was to perform a Bayesian genomewide association study (GWAS) to identify genomic regions associated with growth traits in Hereford and Braford cattle, and to select Tag‐SNPs to represent these regions in low‐density panels useful for genomic predictions. In addition, we propose candidate genes through functional enrichment analysis associated with growth traits using Medical Subject Headings (MeSH). Phenotypic data from 126,290 animals and genotypes for 131 sires and 3,545 animals were used. The Tag‐SNPs were selected with BayesB (π = 0.995) method to compose low‐density panels. The number of Tag‐single nucleotide polymorphism (SNP) ranged between 79 and 103 SNP for the growth traits at weaning and between 78 and 100 SNP for the yearling growth traits. The average proportion of variance explained by Tag‐SNP with BayesA was 0.29, 0.23, 0.32 and 0.19 for birthweight (BW), weaning weight (WW205), yearling weight (YW550) and postweaning gain (PWG345), respectively. For Tag‐SNP with BayesA method accuracy values ranged from 0.13 to 0.30 for k‐means and from 0.30 to 0.65 for random clustering of animals to compose reference and validation groups. Although genomic prediction accuracies were higher with the full marker panel, predictions with low‐density panels retained on average 76% of the accuracy obtained with BayesB with full markers for growth traits. The MeSH analysis was able to translate genomic information providing biological meanings of more specific gene products related to the growth traits. The proposed Tag‐SNP panels may be useful for future fine mapping studies and for lower‐cost commercial genomic prediction applications.

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Data from 127539 Hereford and Braford cattle were used to compare estimates of genetic parameters for navel, conformation, precocity, muscling and size visual scores at yearling, using linear and threshold animal models. In a second step, these models were cross-validated using a multinomial logistic regression in order to quantify the association between phenotype and genetic merit for each trait. For navel score, higher heritability was obtained with the threshold model (0.42 ± 0.02) in relation to the linear model (0.22 ± 0.02). However, similar heritability was estimated in both models for conformation, precocity, muscling and size, with values of 0.18 ± 0.01, 0.19 ± 0.01, 0.19 ± 0.01 and 0.26 ± 0.01, respectively, using linear model, and of 0.19 ± 0.01, 0.19 ± 0.01, 0.20 ± 0.01, and 0.29 ± 0.01, respectively, using threshold model. For navel score, Spearman correlations between sires’ breeding values predicted using linear and threshold models ranged from 0.60 (1% of the best sires are selected) to 0.96 (all sires are selected). For conformation, precocity, muscling and size scores, low changes in sires’ rank are expected using these models (Spearman correlations &gt;0.86), regardless of the proportion of sires selected. Except for navel with the linear model, the direction of the associations between phenotype and genetic merit were in accordance with its expectation, as there were increases in the phenotype per unit of change in the breeding value. Thus, the threshold model would be recommended to perform genetic evaluation of navel score in this population. However, linear and threshold models showed similar predictive ability for conformation, precocity, muscling and size scores.