Abstract
The implementation of genetic groups in BLUP evaluations accounts for different expectations of breeding values in base animals. Notwithstanding, many feasible structures of genetic groups exist and there are no analytical tools described to compare them easily. In this sense, the recent development of a simple and stable procedure to calculate the Bayes factor between nested competing models allowed us to develop a new approach of that method focused on compared models with different structures of random genetic groups. The procedure is based on a reparameterization of the model in terms of intraclass correlation of genetic groups. The Bayes factor can be easily calculated from the output of a Markov chain Monte Carlo sampling by averaging conditional densities at the null intraclass correlation. It compares two nested models, a model with a given structure of genetic groups against a model without genetic groups. The calculation of the Bayes factor between different structures of genetic groups can be quickly and easily obtained from the Bayes factor between the nested models. We applied this approach to a weaning weight data set of the Bruna dels Pirineus beef cattle, comparing several structures of genetic groups, and the final results showed that the preferable structure was an only group for unknown dams and different groups for unknown sires for each year of calving.
Highlights
The best linear unbiased prediction (BLUP) assumes that the base population is unselected animals sampled from a normal distribution with a zero mean and a variance equal to the genetic variance [11]
Notwithstanding, a simple and stable Bayes factor procedure has been described to test between nested models that only differ in a bounded variable [4, 28]. This methodology shows an important advantage in terms of dependence to the prior distributions for all parameters, with the only exception of the boundary variable, because they are the same in both competing models and they are cancelled in the final calculation [28]. Taking this as a starting point, we developed a new approach to test between different structures of random genetic groups
The number of required genetic groups differed greatly depending upon the structure used
Summary
The best linear unbiased prediction (BLUP) assumes that the base population is unselected animals sampled from a normal distribution with a zero mean and a variance equal to the genetic variance [11]. It implies an extensive knowledge of the pedigree of our livestock, which is often impossible. Including genetic groups in the evaluation leads to an unbiased estimation of differences between those groups, and leads to less accurate estimated breeding values due to an increased parameterization of the model [20] In this sense, appropriate analytical tools to determine the preferable structure of the genetic groups become essential in the selection programs of our livestock
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