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1 Department of Genetics and Biotechnology, Danish Institute of Agricultural Sciences, P.O. Box 50, 8830 Tjele, Denmark; Department of Natural Sciences, Royal Veterinary and Agricultural University, Thorvaldsensvej 40, 1870 Frederiksberg C, Denmark
* To whom correspondence should be addressed. E-mail: lars.damgaard{at}agrsci.dk.
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This paper deals with Bayesian inferences of animal models using Gibbs sampling. First, we suggest a general and efficient method for updating additive genetic effects, where the computational cost is independent of the pedigree depth and only increases linearly with the size of the pedigree. Second, we show how this approach can be used to draw inferences from a wide range of animal models using the computer package Winbugs. Finally we illustrate the approach in a simulation study, where data are generated and analysed (using Winbugs) according to a linear model with i.i.d errors having Student-t distributions. In conclusion, Winbugs can be used to make inferences in small size quantitative genetic data sets applying a wide range of animal models that are not yet standard in the animal breeding literature.
Key Words: Bayesian inference, Winbugs, genetics
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