981 resultados para random regression


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A total of 5575 monthly test-day yield records from 796 lactations buffaloes first in the north coast of Colombia. The model included random direct additive genetic and permanent environment effects. As fixed effects were included, contemporary groups, and age of cow at calving as covaraible, linear and quadratic effects. Test-day (PLDC) yield was 3.89 ± 1.14 kg. The PLDC ranged from 2.86 kg to 4.26 kg while the highest values towards the middle of lactation. The heritability estimates obtained for PLDC ranged from 0.23 to 0.47. Genetic correlations between PLDC, declining steadily increased the distance between PLDC. Phenotypic variances were higher in the initial PLDC and decreasing towards the end of lactation. The results found in this study indicate that there is a high genetic variability for the PLDC in the population studied using a random regression model.

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Data set of 17.767 weight records of 4.210 Santa Inês lambs were used aiming to evaluate the importance of the inclusion of the maternal effect in the model to estimate components of (Co) variance and resulting genetic parameters for the growth curve through random regression models. The fixed and random regressions were fitted using Legendre Polynomials of order three, being fit four models that differed in relation to the inclusion of the additive genetic and permanent environmental maternal effects. Considerable increase was observed in Log L and decrease in the criteria AIC and BIC when the maternal effect was included (genetic or permanent environmental), evidencing its importance. The maternal genetic effect explained larger proportion of the phenotypic variance than the maternal permanent environmental along the growth curve. The direct additive genetic variance was inflated by maternal effect, when this last one was not considered in the analysis model, reflecting the same behavior in the heritabilities. The maternal permanent environmental effect contributed to maternal variance, as well as, it inflated maternal genetic variance, when it was not considered in the model. Similar behavior was verified with maternal heritability. The correlation estimated for the four models hardly differed in function of maternal effect. The maternal effect should be considered in the genetic studies of the growth curve of Santa Inês sheep.

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The objectives of the present study were to estimate genetic parameters of monthly test-day milk yield (TDMY) of the first lactation of Brazilian Holstein cows using random regression (RR), and to compare the genetic gains for milk production and persistency, derived from RR models, using eigenvector indices and selection indices that did not consider eigenvectors. The data set contained monthly TDMY of 3,543 first lactations of Brazilian Holstein cows calving between 1994 and 2011. The RR model included the fixed effect of the contemporary group (herd-month-year of test days), the covariate calving age (linear and quadratic effects), and a fourth-order regression on Legendre orthogonal polynomials of days in milk (DIM) to model the population-based mean curve. Additive genetic and nongenetic animal effects were fit as RR with 4 classes of residual variance random effect. Eigenvector indices based on the additive genetic RR covariance matrix were used to evaluate the genetic gains of milk yield and persistency compared with the traditional selection index (selection index based on breeding values of milk yield until 305 DIM). The heritability estimates for monthly TDMY ranged from 0.12 ± 0.04 to 0.31 ± 0.04. The estimates of additive genetic and nongenetic animal effects correlation were close to 1 at adjacent monthly TDMY, with a tendency to diminish as the time between DIM classes increased. The first eigenvector was related to the increase of the genetic response of the milk yield and the second eigenvector was related to the increase of the genetic gains of the persistency but it contributed to decrease the genetic gains for total milk yield. Therefore, using this eigenvector to improve persistency will not contribute to change the shape of genetic curve pattern. If the breeding goal is to improve milk production and persistency, complete sequential eigenvector indices (selection indices composite with all eigenvectors) could be used with higher economic values for persistency. However, if the breeding goal is to improve only milk yield, the traditional selection index is indicated. © 2013 American Dairy Science Association.

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

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Genética e Melhoramento Animal - FCAV

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Pós-graduação em Genética e Melhoramento Animal - FCAV