Genotype by environment interaction in Nelore cattle from five Brazilian states

Records from 75,941 Nelore cattle were used to determine the importance of genotype by environment interaction (GEI) in five Brazilian states. (Co)variance components were estimated by single-trait analysis (with yearling weight, W450, considered to be the same trait in all states) and multiple-trait analysis (with the record from each state considered to be a different trait). The direct heritability estimates for yearling weight were 0.51, 0.39, 0.44, 0.37 and 0.41 in the states of Goiás, Mato Grosso, São Paulo, Mato Grosso do Sul and Minas Gerais, respectively. The across-state genetic correlation estimates between Goiás and Mato Grosso, Goiás and Minas Gerais, São Paulo and Minas Gerais, and Mato Grosso do Sul and Minas Gerais ranged from 0.67 to 0.75. These estimates indicate that GEIs are biologically important. No interactions were observed between Goiás and São Paulo, Goiás and Mato Grosso do Sul, Mato Grosso and São Paulo, Mato Grosso and Mato Grosso do Sul, Mato Grosso and Minas Gerais, or São Paulo and Mato Grosso do Sul (0.82 to 0.97). Comparison of single and multiple-trait analyses showed that selection based on the former was less efficient in the presence of GEI, with substantial losses (up to 10%) during selection

Relative contribution of effects included in contemporary groups for adjusted and actual 120-day and 210-day weights in Nelore cattle in Brazil

The objective of this research was to estimate the relative magnitude of effects included in contemporary groups (CG) and their interactions with adjusted and actual 120 d and 210 d weights in 72,731 male and female Nelore calves born from 1985 to 2005 in 40 herds from PMGRN (Genetic Improvement Program of Nelore). Ten models with different CG structures were compared. The analyses were done using the general linear models (GLM) procedure run in SAS software. All of the effects included in the CG for each model were significant (p < 0.001) for the four traits analyzed. Inclusion of semester or trimester of birth as part of a CG was more appropriate than its use as an independent effect in the model because it accounted for interactions with the other effects in the CG. Calf sex (CS) and dam age at calving (DAC) had similar effects across the models, which suggested independence from other effects in these models. The corresponding age deviation effect had a larger impact on actual weight at 120 d than any other effect in all of the models tested. The use of actual weights in models with no CS effect in CG provides an alternative that would allow better genetic connectedness among CGs and greater accuracy in genetic evaluations

Estimativas de parâmetros genéticos para características de crescimento e produtividade em vacas da raça Canchim, utilizando-se inferência bayesiana

The objective of this study was to estimate genetic parameters for body weights at weaning (PD), 12 months old (P12) and adult age (PAD), culling age (TPR, days in herd), number (ND10) and kilograms (QD10) of calves weaned up to ten years of age, total number (NDT) and total kilograms (QDT) of calves weaned during herd life, and kilograms of calves weaned per year in herd (QTPR) of Canchim (5/8 Charolais + 3/8 Zebu) females from one herd. Data consisted of 3,249, 3.111, 1,138, 1,340, 1,362, 1,362, 1,340, 1,340 and 1,340 records of PD, P12, PAD, TPR, ND10, QD10, NDT, QDT and QTPR. respectively. Variance and covariance components were estimated by bivariate analyses between PD, P12 and PAD and other production traits using Bayesian inference. The models included the additive direct, permanent environmental and residual random effects and the fixed effects year and month of birth or calving, calving age and age of the animal, depending on the trait. QD10, QDT and QTPR of each female were obtained by adjusting the weaning weights of calves for year and month of birth, sex and age of cow. Average of heritability estimates were 0.38 (PD), 0.40 (P12), 0.54 (PAD), 0.22 (TPR), 0.22 (ND10), 0.24 (QD10), 0.23 (NDT), 0.23 (QDT) and 0.32 (QTPR), indicating genetic variability to obtain response by selection. Genetic correlations between TPR (-0.02, 0.26 and -0.12), ND10 (0.04, 0.10 and -0.29), QD10 (0.37, 0.39 and -0.13), NDT (-0.03, 0.14 and -0.25), QDT (0.20, 0.33 and -0.16), QTPR (0.21, 0.28 and -0.19) and body weights (PD, P12 and PAD) suggest that selection of females based on weaning and 12-month body weights will not affect productivity. However, it may be decreased by increasing female adult body weight