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This Article Full Text Full Text (PDF) All Versions of this Article: jas.2007-0653v1 86/5/1047    most recent Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Hickey, J. M. Articles by Veerkamp, R. F. PubMed PubMed Citation Articles by Hickey, J. M. Articles by Veerkamp, R. F.

Estimation of accuracy and bias in genetic evaluations with genetic groups using sampling¹

J. M. Hickey^*,,,2, M. G. Keane, D. A. Kenny, A. R. Cromie, H. A. Mulder^* and R. F. Veerkamp^*

^* Animal Breeding and Genomics Centre, Animal Sciences Group, PO Box 65, 8200 AB, Lelystad, the Netherlands; and Grange Beef Research Centre, Teagasc, Dunsany, Co. Meath, Ireland; and School of Agriculture, Food and Veterinary Medicine, College of Life Sciences, University College Dublin, Belfield, Dublin 4, Ireland; and and Irish Cattle Breeding Federation, Shinagh House, Bandon, Co. Cork, Ireland

² Corresponding author: John.Hickey{at}wur.nl

Accuracy and bias of EBV are important measures of the quality of genetic evaluations. A sampling method that accounts for the uncertainty in the estimation of genetic group effects was used to calculate accuracy and bias of estimated effects. The method works by repeatedly simulating phenotypes for multiple traits for a defined data and pedigree structure. These simulated values are analyzed using BLUP with genetic groups in the relationship matrix. Accuracies and biases are then calculated as correlations among and differences between true and estimated values across all replicates, respectively. The method was applied to the Irish beef production data set for 15 traits and with 15 genetic groups to account for differences in breed means. Accuracy and bias of estimated genetic group effects, estimated comparisons between genetic group effects, EBV within genetic group, and EBV across genetic group were calculated. Small biases were detected for most estimated genetic group effects and most estimated comparisons between genetic group effects. Most of these were not important relative to the phenotypic SD of the traits involved. For example, a bias of 0.78% of the phenotypic SD was detected for carcass conformation in Aberdeen Angus. However, one trait, calf quality, which had few performance records in the data set, displayed larger bias, ranging from –10.31 to 5.85% of the phenotypic SD across the different estimated genetic group effects. Large differences were observed in the accuracies of genetic group effects, ranging from 0.02 for feed intake in Holstein, which had no data recorded, to >0.97 for carcass conformation, a trait with large amounts of data recorded in the different genetic groups. Large differences were also observed in the accuracies of the comparisons among genetic group effects. The accuracies of the EBV within genetic group and EBV across genetic group were sometimes different; for example, carcass conformation in Belgian Blue had an average accuracy within genetic group of 0.69 compared with an average accuracy across genetic group of 0.89. This suggests that the accuracy of genetic groups should be taken into account when publishing EBV across genetic groups.

Key Words: accuracy • bias • data quality • genetic groups • multiple breed genetic evaluations