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Interbull Centre, Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, S-750 07, Uppsala, Sweden
Corresponding author: W. F. Fikse; e-mail: Freddy.Fikse{at}hgen.slu.se.
The purpose of this investigation was to compare accuracy and precision of variance components and breeding values for international genetic evaluations based on national breeding values or animal performance records. A conventional progeny test scheme was simulated for 3 countries. True breeding values and observations were generated specific to production environments. Two production environments were considered, and both balanced and unbalanced distribution of production environments over countries were considered. True breeding values for both production environments were generated as bivariate normal deviates, and low (0.70) and high (0.90) genetic correlations between performance in production environments were considered. Each cow had an observation in one country only. Performance records were generated as the sum of the true breeding value, a contemporary group effect, and a random residual. Eight generations of data were simulated, and the entire simulated data set was used to compare 3 methods for international genetic evaluation: 1) multiple-trait across-country evaluation based on national predicted breeding values of bulls (MACE), 2) international genetic evaluation across country using performance records, and 3) international genetic evaluation across production environment using performance records. Estimated genetic parameters were biased for all models in this study. Genetic correlations between countries were generally more biased for MACE than for the across-country analyses using performance records. Bias in within-country genetic variances was smaller for MACE. Even genetic parameters obtained with the international evaluation across production environment using performance records were biased, despite the fact that this model was closest to the true, simulated model. The root mean square error of predicted breeding values was similar between models for most of the situations considered. The difference between models was largest when the distribution of production environments over countries was unbalanced and the genetic correlation between performance in production environments was low (0.70). Using breeding values obtained with the across-production environment international genetic evaluation based on performance records will increase the response to selection.
Key Words: simulation • borderless evaluation • multiple-trait across-country evaluation
Abbreviation key: GAMcou = international evaluation on country basis using performance records, GAMenv = international evaluation on production environment basis using performance records, MACE = multiple-trait across-country evaluation, NAMcou = national evaluation using performance records, PBV = predicted breeding value, RMSE = root mean square error, TBV = true breeding value
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