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Resumo -O objetivo deste trabalho foi determinar os parâmetros genéticos para o peso de bovinos Nelore, do nascimento até 1.000 dias de idade, por meio de modelos de regressão aleatória. Utilizaram-se 115.096 registros de peso de 19.417 animais. Os parâmetros genéticos foram obtidos por modelos de regressão aleatória via inferência bayesiana. A trajetória média de crescimento foi ajustada com um polinômio de Legendre quártico. O efeito genético aditivo direto foi ajustado com um polinômio quadrático de Legendre. Os efeitos de ambiente permanente direto e materno foram ajustados com polinômios de Legendre quíntico e quadrático, respectivamente. A variância residual foi modelada com três classes de idades. Os valores genéticos dos pesos, do nascimento até 1.000 dias de idade, foram utilizados para a análise da tendência genética, por meio de superfícies de resposta. As herdabilidades variaram entre 0,16 e 0,47. Os efeitos de ambiente permanente direto e materno foram responsáveis por 5 a 77% e 0,2 a 11% da variância fenotípica, respectivamente. As correlações genéticas dos pesos em diferentes idades foram altas e superiores a 0,40. Os valores genéticos foram crescentes ao longo dos anos, e a tendência genética foi máxima para peso aos 500 dias.Termos para indexação: Bos indicus, inferência bayesiana, parâmetro genético, seleção, superfície de resposta. Genetic analysis of body weight in a Nellore cattle herdAbstract -The objective of this work was to determine the genetic parameters for body weight of Nellore cattle, from birth to 1,000 days of age, by random regression models. Data from 115,096 body weight records of 19,417 animals were used. The genetic parameters were obtained by random regression models and Bayesian inference. The average growth trajectory was fitted by a fourth-order Legendre polynomial. The additive direct effect was fitted by a quadratic Legendre polynomial. Direct and maternal permanent environmental effects were fitted by the fifth-and second-order Legendre polynomials, respectively. Residual variance was modeled by three classes of age. The breeding values of weights obtained from birth to 1,000 days of age were used for the analysis of genetic trend by response surfaces. Heritability ranged from 0.16 to 0.47. The direct and maternal permanent environmental effects explained 5 to 77% and 0.2 to 11% of the phenotypic variance, respectively. The genetic correlations of body weights at different ages were high and above 0.40. Breeding values increased throughout the years, and genetic trend was maximum for body weight at 500 days.

BackgroundCentral testing is used to select young bulls which are likely to contribute to increased net income of the commercial beef cattle herd. We present genetic parameters for growth and reproductive traits on performance-tested young bulls and commercial animals that are raised on pasture and in feedlots.MethodsRecords on young bulls and heifers in performance tests or commercial herds were used. Genetic parameters for growth and reproductive traits were estimated. Correlated responses for commercial animals when selection was applied on performance-tested young bulls were computed.ResultsThe 90% highest posterior density (HPD90) intervals for heritabilities of final weight (FW), average daily gain (ADG) and scrotal circumference (SC) ranged from 0.41 to 0.49, 0.23 to 0.30 and 0.47 to 0.57, respectively, for performance-tested young bulls on pasture, from 0.45 to 0.60, 0.20 to 0.32 and 0.56 to 0.70, respectively, for performance-tested young bulls in feedlots, from 0.29 to 0.33, 0.14 to 0.18 and 0.35 to 0.45, respectively, for commercial animals on pasture, and from 0.24 to 0.44, 0.13 to 0.24 and 0.35 to 0.57 respectively, for commercial animals in feedlots. The HPD90 intervals for genetic correlations of FW, ADG and SC in performance-tested young bulls on pasture (feedlots) with FW, ADG and SC in commercial animals on pasture (feedlots) ranged from 0.86 to 0.96 (0.83 to 0.94), 0.78 to 0.90 (0.40 to 0.79) and from 0.92 to 0.97 (0.50 to 0.83), respectively. Age at first calving was genetically related to ADG (HPD90 interval = −0.48 to −0.06) and SC (HPD90 interval = −0.41 to −0.05) for performance-tested young bulls on pasture, however it was not related to ADG (HPD90 interval = −0.29 to 0.10) and SC (HPD90 interval = −0.35 to 0.13) for performance-tested young bulls in feedlots.ConclusionsHeritabilities for growth and SC are higher for performance-tested young bulls than for commercial animals. Evaluating and selecting for increased growth and SC on performance-tested young bulls is efficient to improve growth, SC and age at first calving in commercial animals. Evaluating and selecting performance-tested young bulls is more efficient for young bulls on pasture than in feedlots.

-This study aimed to evaluate associations among final weight (FW), average daily gain (ADG), scrotal circumference (SC), and visual score (VS) of beef cattle in performance tests on pasture or in feedlots. Genetic parameters for FW, ADG, SC, and VS of young Nellore bulls performance-tested on pasture or in feedlots were evaluated by mixed model. The performance test and final age were considered as fixed effects and additive genetic and residual effects were considered as random effects. Additive genetic and residual variances for final weight and average daily gain were smaller on pasture than in feedlots. There was no difference between genetic and residual variances and heritability for scrotal circumference on pasture or in feedlots. Genetic variance and heritability for visual score on pasture were smaller than those in feedlots. The posterior means (and highest posterior density intervals with 90% of samples (HPD90)

Background: Host resilience (HR) to parasites can affect growth in pastured raised cattle. This study is a detailed investigation of the genetic mechanisms of HR to ticks (TICK), gastrointestinal nematodes (GIN), and Eimeria spp. (EIM) under natural infestation. HR was defined as the slope coefficient of random regression models of body weight (BW) when TICK, GIN, and EIM burdens were used as environmental gradients. The BW was evaluated in five measurement events (ME): when animals were 331, 385, 443, 498, and 555 days old on average. 7307 BW records were available from 1712 animals weighted at least in one ME. Out of those, 1075 animals had valid genotypic information after quality control analysis that were used in genome-wide association studies (GWAS) and GWAS meta-analyses to identify genomic regions associated with HR. Results: Both the genetic correlations between intercept and HR to each parasite, and the genetic correlations between BW measured in animals submitted to different parasite burden indicated that there was genotype x parasite burden interaction for BW, and selection for BW under environment with controlled parasite burden might be an efficient strategy to improve both, BW and HR. Furthermore, there was no impact of age of measurement on genetic variance estimates for HR to different parasites. However, genetic correlation between HR to the same parasite measured in different ages ranged from low to moderate in magnitude, with a posteriori means (high posterior density interval with 90% of samples) varying from 0.13 (-0.05; 0.35) to 0.40 (0.15; 0.63) for TICK, from 0.11 (-0.06; 0.29) to 0.52 (0.37; 0.67) for GIN and from 0.25 (0.07; 0.43) to 0.56 (0.34; 0.77) for EIM. These results indicate the importance of age of measurement in studies on HR. Conclusions: HR to GIN and EIM can be used as a complementary tool to parasitic control management, and a multiple trait selection method that combine BW and HR to parasites should be used in parasitic endemic areas to avoid economic losses due parasitic diseases.