Estimativas de componentes de (co)variância para peso e escores visuais de conformação frigorífica em bovinos Nelore / Variance component estimates for weight and visual scores of slaughter conformation in Nellore cattle

Estimaram-se os componentes de (co)variância e herdabilidade da conformação frigorífica à desmama (CFD), conformação frigorífica ao sobreano (CFS), peso à desmama (PD) e peso ao sobreano (PS) de animais Nelore, e as correlações genéticas entre essas características. Um modelo animal multicaracterística foi proposto para analisar 6.397 informações de peso e escores visuais de conformação frigorífica, obtidas à desmama e ao sobreano. Esse modelo incluiu os efeitos aleatórios genético aditivo direto, genético aditivo materno, ambiente permanente materno e residual, além dos efeitos fixos de grupo contemporâneo e das covariáveis idade da mãe ao parto - para peso e conformação frigorífica à desmama e ao sobreano - e idade do animal à data da avaliação - para conformação frigorífica, à desmama e ao sobreano. As herdabilidades estimadas para CFD, CFS, PD e PS foram, respectivamente, 0,13, 0,25, 0,22 e 0,29. Correlações genéticas positivas e de alta magnitude entre as características de peso e as características de avaliação visual sugerem que a seleção para uma delas pode resultar em resposta indireta na outra. A característica de conformação frigorífica pode ser selecionada em idade mais precoce em razão da correlação genética alta e positiva entre mensurações feitas nas duas idades estudadas.

The aim of this study was to estimate variance components, heritability and genetic correlation for slaughter conformation at weaning (SCW), slaughter conformation at yearling age (SCY), weaning weight (WW) and yearling age weight (YW) of Nellore cattle. A total of 6,397 records of all traits measured at weaning and at yearling age were used in the analysis. A multiple trait animal model which included the direct genetic additive, maternal genetic additive, maternal permanent environmental and residual random effects, as well as the fixed effect of contemporary group and the covariates age at calving (for weight and slaughter conformation at weaning and yearling age) and age at the evaluation time (slaughter conformation at weaning and yearling age) was proposed. The heritability estimates for SCW, SCY, WW and YW were, respectively, 0.13, 0.25, 0.22 and 0.29. Positive and high genetic correlations between body weight traits and visual evaluation traits suggested that direct selection for one trait results in positive indirect response in the remaining trait. Slaughter conformation trait can be selected at earlier age due to the high and positive genetic correlation between conformation scores at different age.

Genetic evaluation for large data sets by random regression models in Nellore cattle / Avaliação genética para grandes massas de dados por meio de modelos de regressão aleatória em gado Nelore

Expected progeny differences (EPD) of Nellore cattle estimated by random regression model (RRM) and multiple trait model (MTM) were compared. Genetic evaluation data included 3,819,895 records of up nine sequential weights of 963,227 animals measured at ages ranging from one day (birth weight) to 733 days. Traits considered were weights at birth, ten to 110-day old, 102 to 202-day old, 193 to 293-day old, 283 to 383-day old, 376 to 476-day old, 551 to 651-day old, and 633 to 733-day old. Seven data samples were created. Because the parameters estimates biologically were better, two of them were chosen: one with 84,426 records and another with 72,040. Records preadjusted to a fixed age were analyzed by a MTM, which included the effects of contemporary group, age of dam class, additive direct, additive maternal, and maternal permanent environment. Analyses were carried out by REML, with five traits at a time. The RRM included the effects of age of animal, contemporary group, age of dam class, additive direct, permanent environment, additive maternal, and maternal permanent environment. Different degree of Legendre polynomials were used to describe random effects. MTM estimated covariance components and genetic parameters for weight at birth and sequential weights and RRM for all ages. Due to the fact that correlation among the estimates EPD from MTM and all the tested RM were not equal to 1.0, it is not possible to recommend RRM to genetic evaluation to large data sets.

Compararam-se as diferenças esperadas nas progênies (DEPs) de gado Nelore, estimadas por meio de um modelo de características múltiplas (MTM), com um modelo de regressão aleatória (RRM). Foram utilizados 3.819.895 dados de peso corporal sequenciais para a avaliação genética de 963.227 animais, coletados do nascer aos 733 dias de idade. As características consideradas foram: peso ao nascer e pesos dos 10 aos 110, dos 102 aos 202, dos 193 aos 293, dos 283 aos 383, dos 376 aos 476, dos 467 aos 567, dos 551 aos 651, e dos 633 aos 733 dias. Sete amostras foram geradas. Duas amostras resultaram em estimativas de parâmetros mais consistentes do ponto de vista biológico, sendo, portanto consideradas representativas da população em estudo. A primeira amostra constituiu-se de 84.426 medidas, e a segunda, de 72.040. Os pesos pré-ajustados para as idades fixas foram analisados por meio de um MTM, com cinco características por processamento, no qual se incluíram efeito de grupo contemporâneo, classe de idade da vaca, aditivo direto, aditivo materno e ambiente materno permanente, utilizando-se a metodologia de máxima verossimilhança restrita (REML). Diferentes graus dos polinômios de Legendre foram utilizados em um RRM, para os efeitos aleatórios. As correlações entre as DEPs estimadas por meio do modelo para características múltiplas e de regressão aleatória não foram iguais a 1,0, portanto, não se recomenda a utilização dos modelos de regressão aleatória para avaliação genética para grande massa de dados.

Analyses of growth curves of Nellore cattle by Bayesian method via Gibbs sampling

Growth curves of Nellore cattle were analyzed using body weights measured at ages ranging from 1 day (birth weight) to 733 days. Traits considered were birth weight, 10 to 110 days weight, 102 to 202 days weight, 193 to 293 days weight, 283 to 383 days weight, 376 to 476 days weight, 551 to 651 days weight, and 633 to 733 days weight. Two data samples were created: one with 79,849 records from herds that had missing traits and another with 74,601 from herds with no missing traits. Records preadjusted to a fixed age were analyzed by a multiple trait model (MTM), which included the effects of contemporary group, age of dam class, additive direct, additive maternal, and maternal permanent environment. Analyses were carried out by a Bayesian method for all nine traits. The random regression model (RRM) included the effects of age of animal, contemporary group, age of dam class, additive direct, permanent environment, additive maternal, and maternal permanent environment. Legendre cubic polynomials were used to describe random effects. MTM estimated covariance components and genetic parameters for birth weight and sequential weights and RRM for all ages. Due to the fact that covariance components based on RRM were inflated for herds with missing traits, MTM should be used and converted to covariance functions.