Abstract

Hierarchical extensions to linear and generalized linear mixed models are recommended for routine use in animal breeding. These extensions provide a richer class of genetic models, leading to potentially better model fit and different inferences compared to traditional animal breeding models. Inferences under multi-stage hierarchical models are facilitated by Bayesian methods, particularly MCMC. Also, MCMC allows a clearer assessment of model fit and choice than empirical Bayes or BLUP approaches. An example is used to illustrate the impact of model misspecification on genetic parameters and inferences on breeding values.

Robert J Tempelman, M. Z Firat

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume 25: Lactation; growth and efficiency; meat quality; role of exotic breeds in the tropics; design of village breeding programmes;, , 605–612, 1998
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