Genotype × environment interaction in individual performance and progeny tests in beef cattle

The study reported here evaluated genotype × environment interaction in individual performance and progeny tests in beef cattle. Genetic parameters for final weight (FW), ADG, and scrotal circumference (SC) of 33,013 Nellore young bulls tested on pasture or in feedlots were analyzed. The posterior means (and highest posterior density interval with 90% of samples [HPD90]) of heritability for traits measured on pasture-raised and feedlot-raised animals were 0.44 (HPD90 = 0.40 to 0.48) and 0.50 (HPD90 = 0.43 to 0.56) for FW, 0.26 (HPD90 = 0.23 to 0.29) and 0.26 (HPD90 = 0.20 to 0.32) for ADG, and 0.53 (HPD90 = 0.48 to 0.59) and 0.65 (HPD90 = 0.55 to 0.74) for SC, respectively. The posterior means (and HPD90) of genetic correlations for FW, ADG, and SC on pasture and in feedlots were 0.75 (HPD90 = 0.66 to 0.87), 0.49 (HPD90 = 0.31 to 0.66), and 0.89 (HPD90 = 0.83 to 0.97), respectively. When the selection intensity was kept the same for both the environments, the greatest direct responses for FW and ADG were exhibited by the animals reared and selected in feedlots. The correlated responses relative to production on pasture and based on selection in feedlots were similar to the direct responses, whereas the correlated responses for production in feedlots and based on selection on pasture were lower than the direct responses. When the selection intensity on pasture was higher than the selection intensity in feedlots, the responses to direct selection were similar for both the environments and correlated responses obtained in feedlots by selection on pasture were similar to the direct responses in feedlots. Analyses of few or poor indicators of genotype × environment interaction result in incorrect interpretations of its existence and implications. The present work demonstrated that traits with lower heritability are more susceptible to genotype × environment interaction and that selection intensity plays an important role in the study of genotype × environment interaction in beef cattle.

Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle

Several methods have been used for genome-enabled prediction (or genomic selection) of complex traits, for example, multiple regression models describing a target trait with a linear function of a set of genetic markers. Genomic selection studies have been focused mostly on single-trait analyses. However, most profitability traits are genetically correlated, and an increase in prediction accuracy of genomic breeding values for genetically correlated traits is expected when using multiple-trait models. Thus, this study was carried out to assess the accuracy of genomic prediction for carcass and meat quality traits in Nelore cattle, using single- and multiple-trait approaches. The study considered 15 780, 15 784, 15 742 and 526 records of rib eye area (REA, cm²), back fat thickness (BF, mm), rump fat (RF, mm) and Warner-Bratzler shear force (WBSF, kg), respectively, in Nelore cattle, from the Nelore Brazil Breeding Program. Animals were genotyped with a low-density single nucleotide polymorphism (SNP) panel and subsequently imputed to arrays with 54 and 777 k SNPs. Four Bayesian specifications of genomic regression models, namely, Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression; blending methods, BLUP; and single-step genomic best linear unbiased prediction (ssGBLUP) methods were compared in terms of prediction accuracy using a fivefold cross-validation. Estimates of heritability ranged from 0.20 to 0.35 and from 0.21 to 0.46 for RF and WBSF on single- and multiple-trait analyses, respectively. Prediction accuracies for REA, BF, RF and WBSF were all similar using the different specifications of regression models. In addition, this study has shown the impact of genomic information upon genetic evaluations in beef cattle using the multiple-trait model, which was also advantageous compared to the single-trait model because it accounted for the selection process using multiple traits at the same time. The advantage of multi-trait analyses is attributed to the consideration of correlations and genetic influences between the traits, in addition to the non-random association of alleles.

Effects of a mastitis J5 bacterin vaccination on the productive performance of dairy cows: An observational study using propensity score matching techniques

Inferring causal effects between variables when utilizing observational data is challenging due to confounding factors not controlled through a randomized experiment. Propensity score matching can decrease confounding in observational studies and offers insights about potential causal effects of prophylactic management interventions such as vaccinations. The objective of this study was to determine potential causality and impact of vaccination with an Escherichia coli J5 bacterin on the productive performance of dairy cows applying propensity score matching techniques to farm-recorded (e.g., observational) data. Traits of interest included 305-d milk yield (MY305), 305-d fat yield (FY305), 305-d protein yield (PY305), and somatic cell score (SCS). Records from 6,418 lactations generated by 5,121 animals were available for the analysis. Vaccination status of each animal was obtained from producer-recorded information. Confounding variables considered were herd-year-season groups (56 levels), parity (5 levels: 1, 2, 3, 4, and ≥5), and genetic quartile groups (4 levels: top 25% through bottom 25%) derived from genetic predictions for MY305, FY305, PY305, and SCS, as well as for the genetic susceptibility to mastitis. A logistic regression model was applied to estimate the propensity score (PS) for each cow. Subsequently, PS values were used to form pairs of animals (1 vaccinated with 1 unvaccinated control), depending on their PS similarities (difference in PS values of cows within a match required to be <20% of 1 standard deviation of the logit of PS). After the matching process, 2,091 pairs of animals (4,182 records) remained available to infer the causal effects of vaccinating dairy cows with the E. coli J5 bacterin. Causal effects estimation was performed using 2 approaches: simple matching and a bias-corrected matching. According to the PS methodology, causal effects of vaccinating dairy cows with a J5 bacterin on their productive performance were identified for MY305. The simple matched estimator suggested that vaccinated cows produced 163.89 kg more milk over an entire lactation when compared with nonvaccinated counterparts, whereas the bias-corrected estimator suggested that such increment in milk production was of 150.48 kg. Conversely, no causal effects of immunizing dairy cows with a J5 bacterin were identified for FY305, PY305, or SCS. In conclusion, the utilization of PS matching techniques applied to farm-recorded data was feasible and allowed us to identify that vaccination with an E. coli J5 bacterin relates to an overall milk production increment without compromising milk quality.