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ANIMAL GENETICS |
Department of Animal Science, Michigan State University, East Lansing 48824
3 Correspondence: 1205 Anthony Hall, East Lansing, MI 48824-1225 (phone: 517-355-8445; fax: 517-353-1699; e-mail: tempelma{at}msu.edu).
Abstract
The primary objective of this study was to demonstrate the utility of a hierarchical Bayes implementation of a multiple-breed animal model (MBAM) to estimate breed composition means and additive genetic variances as well as on variances due to the segregation between breeds. The MBAM and a conventional animal model (AM) were both applied to five simulated data sets derived from each of two different populations. Population I consisted of crosses between two breeds having a twofold difference in genetic variance and a nonzero segregation variance. Population II had the same population structure as Population I, except that the two breeds had the same genetic variance with no segregation variance; that is, Population II was essentially single breed in its genetic architecture. For Population I, posterior means of all variance components obtained by MBAM were unbiased, with 95% posterior probability intervals (PPI) having the expected coverage based on five replicates. The MBAM showed a slightly superior performance over the AM for genetic predictions in Population I, although there was no evidence that the use of the MBAM translated into greater genetic gains relative to the use of the AM. Nevertheless, the MBAM was clearly demonstrated to have superior fit to the data using pseudo-Bayes factors (PBF) as the basis for model choice. As expected, the MBAM and AM performed equally well in Population II. A data set consisting of 22,717 postweaning gain (PWG) records of a Nelore-Hereford population (40,082 animals in the pedigree) also was analyzed. The MBAM inference on Nelore and Hereford additive heritabilities 
substantially differed. Herefords had a posterior mean 
of 0.20 with a 95% PPI of 0.15 to 0.25, whereas the corresponding values for the Nelores were 0.07 and 0.04 to 0.11, respectively. The posterior mean genetic variance due to the segregation between these breeds was 8.4 kg2, with a 95% PPI of 2.3 to 24.8 kg2, and represented 35.4% of the Nelore but only 9.9% of the Hereford posterior mean genetic variance. The posterior mean using the AM was 0.15, presumed common across the two breeds, with a 95% PPI of 0.11 to 0.19. The PBF heavily favored the MBAM over the AM for the PWG data. Accordingly, the MBAM represents a viable alternative to AM for multiple-breed genetic evaluations, providing the necessary flexibility in modeling heteroskedastic genetic variances of breed composition groups.
Key Words: Bayesian Inference • Beef Cattle • Crossbreeding • Genetic Evaluation • Multiple-Breed • Postweaning Gain
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