Abstract

Animal model evaluation systems are replacing sire model evaluation systems as the basis of ranking animals. Mixed model techniques underlying these evaluations provide a powerful tool for further improvement of predictions. Improvements in the data to be analyzed, the models and algorithms used in the analysis, and the delivery of results to the industry all are possible. Inclusion of more data for more traits allows more animals to be compared and each animal’s total genetic merit to be predicted more accurately. Evaluation models and variance assumptions should match actual distribution of records as closely as possible, but records themselves often can be adjusted or transformed to meet assumptions of a simpler model. Effects usually excluded from animal models that may deserve inclusion are inbreeding, heterosis, other nonadditive genetic effects, effects of individual genes, and genotype-by-environment interactions. Variances and covariances required in evaluations usually are not known with certainty and are difficult to estimate without selection bias from the large, unbalanced data sets available. Nonlinear mixed model equations offer improved predictions if data are not distributed normally. Enhanced models often demand enhanced algorithms or more powerful computers. Results delivered to the industry must be timely, well documented, and easily explained to those actually making selection decisions if they are to have maximum impact.
 

P. M van Raden

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume XIII. Plenary lectures, molecular genetics and mapping, selection, prediction and estimation., , 357–363, 1990
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