Comparing traditional and Bayesian analyses of selection experiments in animal breeding.

摘要

The mixed linear model is commonly used in animal breeding to predict or estimate the breeding values of individual animals. Breeding values are used to select animals for use in breeding subsequent generations. A traditional analysis of data underthis model involves estimating the variance components with restricted maximum likelihood. These estimates are then used to find best linear unbiased predictors for the animal breeding values. A Bayesian analysis of data under this model involves treating all of the parameters, including the variance components, as random and finding the joint posterior distribution of all of the parameters given the data. Because the selection decision depends on the values of the variance components, a Bayesian analysis can yield different selection outcomes than the traditional analysis. We demonstrate both types of data analysis on data from an animal breeding experiment and compare the resulting selections.