The implementation of genetic groups in BLUP evaluations accounts for different expectations of breeding values in animals of the base generation. Notwithstanding, many feasible structures of genetic groups exist and there are not analytical tools described to compare among them easily. In this sense, we have developed a Bayes factor approach focused on comparing models with different structures of random genetic groups. It compares two nested models, a model with a given structure of genetic groups against the model without genetic groups. The Bayes factor between different structures of genetic groups can be 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. The results showed that the preferable structure was an only level for unknown dams and a different group for unknown sires for each year of calving.
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume , , 24.24, 2006
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