Genetic evaluation of growth in a multibreed beef cattle population using random regression linear spline models

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 examine the feasibility of using random regression, spline (RR-spline) models for fitting growth traits in a multibreed beef cattle population. To meet the objective, the evaluation results from the RR-spline model were compared to the widely used multi-trait (MT) model when both were fit to a data set (1.8 million records and 1.1 million animals) provided by the American Gelbvieh Association. The effect of prior information on the EBV of sires was also investigated. In both RR-spline and MT models, the following effects were considered: individual direct and maternal additive genetic, contemporary group, age of the animal at measurement, direct and maternal heterosis, and direct and maternal additive genetic mean effect of the breed. Additionally, the RR-spline model included an individual direct permanent environmental effect. When both MT and RR-spine models were applied to a data set containing records for weaning weight (WWT) and yearling weight (YWT) within specified age ranges, the rankings of bulls direct EBV (as measured via Pearson correlations) provided by both models were comparable, with slightly greater differences in the re-ranking of bulls observed for YWT evaluations (>= .99 for BWT and WWT and >= .98 for YWT), also some bulls dropped from the top 100 list when these lists were compared across methods. For maternal effects, the estimated correlations were slightly smaller, particularly for YWT, again some drops from the top 100 animals were observed. As in regular MT multibreed genetic evaluations, the heterosis effects and the additive genetic effects of the breed could not be estimated from field data because there were not enough contemporary groups with the proper composition of purebred and crossbred animals, thus prior information based on literature values had to be included. The inclusion of prior information had negligible effect in the overall ranking for bulls with greater than 20 BWT progeny records; however, the effect of prior information for breeds or groups poorly represented in the data was very important. The Pearson correlations for direct and maternal WWT and YWT ranged from .95 to .98 when comparing evaluations of data sets where out of range age records were removed or retained. Random regression allows for avoiding the discarding of records that are outside the usual age ranges of measurement, thus higher accuracies are achieved and higher genetic progress could be expected.

Key Words: Beef Cattle, Growth, Multibreed, Random Regression Model, Splines

This article has been cited by other articles:

				J. P. Sanchez, I. Misztal, and J. K. Bertrand Evaluation of methods for computing approximate accuracies of predicted breeding values in maternal random regression models for growth traits in beef cattle J Anim Sci, May 1, 2008; 86(5): 1057 - 1066. [Abstract] [Full Text] [PDF]