5 resultados para Multibreed


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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Data from a multibreed commercial flock located at Mid-West of Brazil, supported by Programa de Melhoramento Genético de Caprinos e Ovinos de Corte (GENECOC), were used to estimate genetic parameters of traits related to ewe productivity by Average Information Restricted Maximum Likelihood method applied to an animal model. The analyzed traits were litter weight at birth (LWB) and at weaning (LWW), ewe weight at weaning (EW) and ewe production efficiency, estimated by WEE=LWW/EW 0.75. The heritabilities were 0.26±0.05, 0.32±0.06, 0.37±0.03 and 0.10±0.02 for LWB, LWW, EW and WEE, respectively. Significant effects for direct heterosis were observed for LWW and EW. Recombination losses were important for EW and WEE. Genetic correlations of LWB with LWW, EW and WEE were 0.68, 0.37 and 0.15, respectively; of LWW with EW and WEE were 0.30 and 0.34, respectively; and between EW and WEE was -0.25. Even though it is a low heritability trait, WEE can be indicated as a selection criteria for improving the ewe productivity without increasing the mature weight of animals due to its genetic correlations with LWW and other traits. © 2011 Elsevier B.V.

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The objective of this study was to investigate, in a population of crossbred cattle, the obtainment of the non-additive genetic effects for the characteristics weight at 205 and 390 days and scrotal circumference, and to evaluate the consideration of these effects in the prediction of breeding values of sires using different estimation methodologies. In method 1, the data were pre-adjusted for the non-additive effects obtained by least squares means method in a model that considered the direct additive, maternal and non-additive fixed genetic effects, the direct and total maternal heterozygosities, and epistasis. In method 2, the non-additive effects were considered covariates in genetic model. Genetic values for adjusted and non-adjusted data were predicted considering additive direct and maternal effects, and for weight at 205 days, also the permanent environmental effect, as random effects in the model. The breeding values of the categories of sires considered for the weight characteristic at 205 days were organized in files, in order to verify alterations in the magnitude of the predictions and ranking of animals in the two methods of correction data for the non-additives effects. The non-additive effects were not similar in magnitude and direction in the two estimation methods used, nor for the characteristics evaluated. Pearson and Spearman correlations between breeding values were higher than 0.94, and the use of different methods does not imply changes in the selection of animals.

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The objectives of the present study were to characterize and define homogenous production environments of composite beef cattle in Brazil in terms of climatic and geographic variables using multivariate exploratory techniques and to use them to assess the presence of G x E for birth weight (BW) and weaning weight (WW). Data from animals born between 1995 and 2008 on 36 farms located in 27 municipalities of the Brazilian states were used. Fifteen years of climate observations (mean minimum and maximum annual temperature and mean annual rainfall) and geographic (latitude, longitude and altitude) data were obtained for each municipality where the farms were located for characterization of the production environments. Hierarchical and nonhierarchical cluster analysis was used to group farms located in regions with similar environmental variables into clusters. Six clusters of farms were formed. The effect of sire-cluster interaction was tested by single-trait analysis using deviance information criterion (DIC). Genetic parameters were estimated by multi-trait analysis considering the same trait to be different in each cluster. According to the values of DIC, the inclusion of sire-cluster effect did not improve the fit of the genetic evaluation model for BW and WW. Estimates of genetic correlations among clusters ranged from -0.02 to 0.92. The low genetic correlation among the most studied regions permits us to suggest that a separate genetic evaluation for some regions should be undertaken. (C) 2012 Elsevier B.V. All rights reserved.