M. J. Yokoo

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.

Evaluation of productivity of sexually precocious Nelore heifers

The objectives of the present study were to investigate the influence of yearling weight on the occurrence of early pregnancy and to determine differences between precocious and non-precocious heifers in terms of pre- and postnatal calf mortality, calf weight and height, mature cow weight, and stayability of the cow in the herd. Data from 26 977 females of a Nelore herd that participated in the breeding season between 1986 and 2004 were analyzed. The influence of yearling weight on sexual precocity and differences between precocious and non-precocious heifers in pre- and postnatal calf mortality and stayability were analyzed using the GENMOD procedure of the SAS program. Differences in the growth traits between precocious and non-precocious animals were estimated by contrast analysis. Three groups were analyzed for postnatal mortality: first calving of the heifers, calves born from the third calving and all offspring of the cow. In order to have a standardized calving to conception period for all females (precocious and non-precocious), calves born from the second calving were not included in the analysis. This was necessary because the first calving to conception period (days open) of precocious heifers was longer than for non-precocious due to farm reproductive management. No differences in postnatal mortality rates, from the third calving, were observed between the two groups of heifers studied. Analysis of all offspring of the dams showed a 9% higher probability of death of calves born to precocious heifers compared with calves born to non-precocious heifers. With respect to stayability, precocious heifers presented 33% greater odds to remain in the herd until 5 and 6 years of age and 28% greater odds to remain in the herd until 7 years when compared with non-precocious heifers. Precocious heifers weaned calves (205 days) significantly heavier than non-precocious ones, 1.410 and 0.797 kg considering the weaning weights of all offspring and of the third calving, respectively. With respect to sexual precocity, the results suggest that the probability of heifers to become pregnant at 16 months of age increases with increasing weight. However, heifers weighing more than 240 kg present practically the same pregnancy probability. Nevertheless, exposure of heifers during the early breeding season is recommended for beef cattle herds raised in tropical regions in view of the numerous benefits demonstrated here.

Design of selection schemes to include tick resistance in the breeding goal for Hereford and Braford cattle1

Ticks are one of the main causes of losses in cattle, causing economic impact by reducing productivity and fertility and by transmission of diseases. The objective of this study was to analyze the genetic gains obtained through different strategies to include traditional (EBV) or genomic EBV (GEBV) for tick count (TC) in selection indexes for Hereford and Braford cattle. Besides TC, we also considered traits currently included in the Delta G Breeding Program Index (DGI): preweaning gain, weaning conformation, weaning precocity, weaning muscling, postweaning gain, yearling conformation, yearling precocity, yearling muscling, and scrotal circumference. Genetic gain per generation (ΔG) was evaluated using the current DGI and including TC in 8 alternative scenarios with TC relative weightings of 10, 50, or 100% and using phenotype or GEBV. Genomic EBV accuracy () ranged between 0.1 and 0.9. As expected, increasing increases the accuracy of the index () for all scenarios in which GEBV were considered. As the relative weight of TC was increased to 50%, greater ΔG differences in relation to the baseline DGI (

Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle

53.03) scenario were observed when the GEBV information was included with equal to or greater than 0.7 only for TC (ΔG between

Threshold and linear models for genetic evaluation of visual scores in Hereford and Braford cattle

61.06 and

Economic selection indexes for Hereford and Braford cattle raised in southern Brazil1

74.26) or equal to or greater than 0.5 for all traits (ΔG between

Goodness of Fit Comparisons among Five Bayesian Models in Genome-Wide Association of Tick Resistance in Brazilian Hereford and Braford Beef Cattle

56.03 and

Genetic parameters and trends for traits of the Hereford and Braford breeds in Brazil

83.36). To achieve these accuracies for traits with low heritability, a large calibration data set would be required. Focusing only on TC, the availability of genomic information would be desirable to avoid the need to count ticks and the exposure of animals to parasitism risks. However, for = 0.7, the respective numbers for Hereford and Braford would be 4,703 and 6,522 animals. As expected, when comparing the relative index weights of 10, 50, and 100% for TC, the highest response to selection per generation (RS) for TC was in the scenario was with 100% relative weight and GEBV for this trait (SR = -0.09 SD with = 0.9). This would be the recommended scenario to form tick-resistant lines in Hereford and Braford cattle. However, with 50% relative weight for TC, including GEBV information for TC only or for all traits in index ( = 0.9), it should yield 93 or 84% of RS, respectively, compared to that obtained with full emphasis on TC (100% relative weight) and GEBV information. This indicates that in the presence of highly accurate GEBV, despite slightly slower gain for TC, indexes with 50% relative weight for TC are interesting alternatives to jointly improve tick resistance and other relevant traits.

Impact of embryo transfer phenotypic records on large-scale beef cattle genetic evaluations

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.

Tag-SNP selection using Bayesian genome-wide association study for growth and adaptation traits in Hereford and Braford cattle

Data from 127 539 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 >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.