Evaluation of methods for computing approximate accuracies of predicted breeding values in maternal random regression models for growth traits in beef cattle

¹ Animal and Dairy Science Department, University of Georgia, 425 River Road, Athens, GA, 30602, US; Departamento de Producción Animal, Facultad de Veterinaria, Universidad de León, Campus de Vegazana, León, 24071, Spain
² Animal and Dairy Science Department, University of Georgia, 425 River Road, Athens, GA, 30602, US

^* To whom correspondence should be addressed. E-mail: juansan{at}uga.edu.

	Abstract

The objective of this study was to determine the suitability of two methods for computing approximate accuracies of predicted breeding values, where accuracy was defined as the squared correlation between the predicted and true breeding value, when modeling growth traits in beef cattle using random regression (RR) models. The first method (S-M-B) was designed for use with multitrait models, thus its use with RR models requires the clustering of measurements into different traits. The second method (T-M) was more general as it accounted for random coefficients other than zeros and ones, thus it could be used directly when fitting RR models. To investigate the performance of both methods, their results were compared to the true accuracies using a balanced simulated data set. The largest difference between approximate and true average accuracies for direct effects was observed at 205 d when S-M-B was used (4.6% males and 8.8% females). With regard to maternal effects, the largest differences in average accuracies were observed at 205 d in males when S-M-B was used (31.8%) and at the same age in females but when using T-M (33.3%). In general, bias increased for direct effect accuracies in males at the tails of the accuracy range, but for females and for maternal effect accuracies in both sexes, bias increased as accuracy increased. When a population was simulated to create large numbers of progeny for base females that did not have individual records, much greater errors were observed in the regression of approximate values on the true ones. When both approximate methods were compared using a real beef cattle data set, a fairly good agreement was observed, particularly for direct effect accuracies in sires; i.e., at 205 d the regressions were 0.98 (direct) and 0.95 (maternal) with r² over 0.99. The largest discrepancies for sires between the methods were observed at 205 d for direct (2.7%) and maternal (16.3%) effect accuracies. For dams, the largest differences between methods were also observed at 205 d, 9.3% (direct) and 15.2% (maternal). The differences between methods for non parent animals were greater than for dams for maternal effect accuracies but intermediate between sires and dams for direct effect accuracies. In spite of the less biased results provided by T-M, its use could be problematic when employed in evaluations of large populations due to its higher memory and computation requirements (170% and 478% more than S-M-B for a population of 11 million).

Key Words: Accuracies, Beef Cattle, Growth, Linear Spline, Random Regression

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