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Multi‐breed genetic evaluation in a Gelbvieh population
Author(s) -
Legarra A.,
Bertrand J.K.,
Strabel T.,
Sapp R.L.,
Sánchez J.P.,
Misztal I.
Publication year - 2007
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/j.1439-0388.2007.00671.x
Subject(s) - breed , heterosis , statistics , random effects model , bayesian probability , population , selection (genetic algorithm) , biology , mathematics , zoology , meta analysis , demography , computer science , machine learning , medicine , botany , hybrid , sociology
Summary A multi‐breed model was presented for the genetic evaluation of growth traits in beef cattle. In addition to the fixed effects, random direct and maternal genetic effects, and random maternal permanent environmental effects are considered; the model also fits direct and maternal heterosis and direct and maternal breed‐of‐founder (BOF) × generation group effects using a Bayesian approach that weights prior literature estimates relative to information supplied by the dataset to which the model will be applied. The multi‐breed evaluation procedures also allow the inclusion of external evaluations for animals of other breeds. The multi‐breed model was applied to a dataset provided by the American Gelbvieh Association. Different analyses were conducted by varying the weights given to the prior literature relative to the information provided by the dataset. Large differences were observed for the heterosis estimates, the BOF × generation group effect estimates, and the predicted breeding values across breeds due to the weights posed on prior literature estimates versus estimates derived directly from data. However, the rankings within breed were observed to be relatively robust to the different weights on prior information.