Mauricio Oliveira

Genomic prediction for tick resistance in Braford and Hereford cattle.

One of the main animal health problems in tropical and subtropical cattle production is the bovine tick, which causes decreased performance, hide devaluation, increased production costs with acaricide treatments, and transmission of infectious diseases. This study investigated the utility of genomic prediction as a tool to select Braford (BO) and Hereford (HH) cattle resistant to ticks. The accuracy and bias of different methods for direct and blended genomic prediction was assessed using 10,673 tick counts obtained from 3,435 BO and 928 HH cattle belonging to the Delta G Connection breeding program. A subset of 2,803 BO and 652 HH samples were genotyped and 41,045 markers remained after quality control. Log transformed records were adjusted by a pedigree repeatability model to estimate variance components, genetic parameters, and breeding values (EBV) and subsequently used to obtain deregressed EBV. Estimated heritability and repeatability for tick counts were 0.19 ± 0.03 and 0.29 ± 0.01, respectively. Data were split into 5 subsets using k-means and random clustering for cross-validation of genomic predictions. Depending on the method, direct genomic value (DGV) prediction accuracies ranged from 0.35 with Bayes least absolute shrinkage and selection operator (LASSO) to 0.39 with BayesB for k-means clustering and between 0.42 with BayesLASSO and 0.45 with BayesC for random clustering. All genomic methods were superior to pedigree BLUP (PBLUP) accuracies of 0.26 for k-means and 0.29 for random groups, with highest accuracy gains obtained with BayesB (39%) for k-means and BayesC (55%) for random groups. Blending of historical phenotypic and pedigree information by different methods further increased DGV accuracies by values between 0.03 and 0.05 for direct prediction methods. However, highest accuracy was observed with single-step genomic BLUP with values of 0.48 for -means and 0.56, which represent, respectively, 84 and 93% improvement over PBLUP. Observed random clustering cross-validation breed-specific accuracies ranged between 0.29 and 0.36 for HH and between 0.55 and 0.61 for BO, depending on the blending method. These moderately high values for BO demonstrate that genomic predictions could be used as a practical tool to improve genetic resistance to ticks and in the development of resistant lines of this breed. For HH, accuracies are still in the low to moderate side and this breed training population needs to be increased before genomic selection could be reliably applied to improve tick resistance.

Genome-wide association study for backfat thickness in Canchim beef cattle using Random Forest approach

BackgroundMeat quality involves many traits, such as marbling, tenderness, juiciness, and backfat thickness, all of which require attention from livestock producers. Backfat thickness improvement by means of traditional selection techniques in Canchim beef cattle has been challenging due to its low heritability, and it is measured late in an animal’s life. Therefore, the implementation of new methodologies for identification of single nucleotide polymorphisms (SNPs) linked to backfat thickness are an important strategy for genetic improvement of carcass and meat quality.ResultsThe set of SNPs identified by the random forest approach explained as much as 50% of the deregressed estimated breeding value (dEBV) variance associated with backfat thickness, and a small set of 5 SNPs were able to explain 34% of the dEBV for backfat thickness. Several quantitative trait loci (QTL) for fat-related traits were found in the surrounding areas of the SNPs, as well as many genes with roles in lipid metabolism.ConclusionsThese results provided a better understanding of the backfat deposition and regulation pathways, and can be considered a starting point for future implementation of a genomic selection program for backfat thickness in Canchim beef cattle.

Influência da castração e da idade de abate sobre as características subjetivas e instrumentais da carne de cordeiros Corriedale

The objective of this trial was to evaluate the effects of castration and slaughtering age on subjective and instrumental characteristics of meat from Corriedale lambs raised on native pasture. Sixty male lambs (30 castrated and 30 intact) slaughtered at 120, 210 and 360 days of age were used in this experiment. The Longissimus dorsi muscle was used for all meat evaluations and analysis. No significant castration ´ slaughtering age interaction was observed for the studied variables. A significant slaughtering age effect was found for meat fat thickness and marbling, which had lower scores in animals slaughtered at more advanced age as well as for all instrumental characteristics of the meat, mainly tenderness that was reduced in older lambs. However, a significant castration effect was observed only for meat color (brightness according to the CIELAB system). It can be concluded that meat quality from Corriedale lambs grazing native pasture was not affected by castration. Slaughtering age affected meat quality with lambs slaughtered at 120 days of age showing the best results.

Multiple-breed reaction norm animal model accounting for robustness and heteroskedastic in a Nelore-Angus crossed population.

Our objective was to genetically characterize post-weaning weight gain (PWG), over a 345-day period after weaning, of Brangus-Ibagé (Nelore×Angus) cattle. Records (n=4016) were from the foundation herd of the Embrapa South Livestock Center. A Bayesian approach was used to assess genotype by environment (G×E) interaction and to identify a suitable model for the estimation of genetic parameters and use in genetic evaluation. A robust and heteroscedastic reaction norm multiple-breed animal model was proposed. The model accounted for heterogeneity of residual variance associated with effects of breed, heterozygosity, sex and contemporary group; and was robust with respect to outliers. Additive genetic effects were modeled for the intercept and slope of a reaction norm to changes in the environmental gradient. Inference was based on Monte Carlo Markov Chain of 110 000 cycles, after 10 000 cycles of burn-in. Bayesian model choice criteria indicated the proposed model was superior to simpler sub-models that did not account for G×E interaction, multiple-breed structure, robustness and heteroscedasticity. We conclude that, for the Brangus-Ibagé population, these factors should be jointly accounted for in genetic evaluation of PWG. Heritability estimates increased proportionally with improvement in the environmental conditions gradient. Therefore, an increased proportion of differences in performance among animals were explained by genetic factors rather than environmental factors as rearing conditions improved. As a consequence response to selection may be increased in favorable environments.