Abstract
Assuming that selection in closed herds can promote reduction in additive genetic variance, multiple regression models were used to estimate this change in additive genetic (co)variance component, over the years when the selection was done. Weights at 550 days (W550) were studied using simulated data of herds submitted to 20 years of selection. (Co)variance components were estimated assuming that the weight at 550 days was a new trait every five years, by multiple-trait analyses involving four traits in the animal model. Three multiple regression equations were fitted—RMI, RMM, RMF—estimating thus the additive genetic (co)variance components for the 20 years of selection and eight years prior to the selection process. The initial years of each generation of selection were used as a covariate in the RMI. In the RMM, intermediate years were used, and the final years were considered in the RMF. The equations showed high coefficients of determination. However, there was no difference in the adjustment between the models. It was observed that the multiple regression models can be used in the estimation of genetic (co)variance components, when heteroscedasticity is assumed over time due to the selection process.
Highlights
In genetic evaluations, it is frequently assumed that the variances remain constant over generations of selection, in closed herds, it is expected that selection changes the mean of traits and their additive genetic variance
From the generation 1 to 4, the mean genetic variances were 159.04, 127.13, 81.25, and 67.49, respectively. It was observed a reduction in genetic variances for Weights at 550 days (W550) with the advancing generations of selection, and reduction of genetic covariance between the generations, proportional to the distance between them
For Gomez-Raya and Burnside (1990), the higher the accuracy of selection, the greater is the reduction of variances, considering the accuracy of selection defined as the genetic correlation between the true breeding value and the predicted one
Summary
In genetic evaluations, it is frequently assumed that the variances remain constant over generations of selection, in closed herds, it is expected that selection changes the mean of traits and their additive genetic variance.Considering that the mean and variance describe statistically the basic characteristics of a population, it is expected changes in these parameters when theActa Scientiarum. In genetic evaluations, it is frequently assumed that the variances remain constant over generations of selection, in closed herds, it is expected that selection changes the mean of traits and their additive genetic variance. Considering that the mean and variance describe statistically the basic characteristics of a population, it is expected changes in these parameters when the. The difficulty to assume heteroscedasticity over the generations lies in obtaining precise estimates of the (co)variance components, since the number of observations at each heteroscedasticity class is reduced with increasing the number of classes. With a very large number, the computational effort to estimate the (co)variance components becomes very high, and if the classes are less numerous the genetic connections are weaker, leading to inaccurate estimates of the components.
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