Pre-weaning performance evaluation of a multibreed Aberdeen Angus × Nellore population using different genetic models

Jader Silva Lopes,Tomás Weber,Mariana De Almeida Dornelles,Paulo Roberto Nogara Rorato,Juliana Grigolleto Comin,Ronyere Olegário De Araújo

doi:10.1590/s1516-35982010001100014

Jader Silva Lopes, Tomás Weber + Show 4 more

Open Access

https://doi.org/10.1590/s1516-35982010001100014

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Abstract

This work aimed at estimating the genetic effects that affect the pre-weaning performance of animals from multibreed crosses. In order to do so, it was used information of the weight at weaning of 79,521 animals, sired by 1,020 bulls and 61,898 cows from Aberdeen Angus and Nellore breeds and from many genetic groups resulted from their crosses. Five genetic models were tested: model 1, containing the fixed breed genetic effects (additive direct and maternal effects, heterozygote direct and maternal effects, epystatic direct and maternal effects, joint additive direct and maternal effects); model 2, equal to model 1, excluding direct and maternal joint additive effects; model 3, equal to model 1, excluding direct and maternal epystatic effects; model 4, equal to model 1, excluding direct and maternal epystactic effect and direct and maternal joint additive effects; and model 5, equal to model 1, excluding direct and maternal heterozygotic effects, direct and maternal epystatic effects and direct and maternal joint additive effects. The models were analyzed by the following methods: least square means method, ridge regression method, and the restricted maximum likelihood method. The dominant additive models usually used for genetic evaluations do not give a good description of the pre-weaning performance variations, making it necessary to add the heterozygote and epystatic effects; the joint additive effects do not significantly improve the adjustment of the analysis model and the heterozygote effects are efficient in representing a quadratic breed additive effect, in addition to insert an unnecessary bias assigned to multicollinearity related to the joint additive effects.

Highlights

In Brazil, as in other countries, the use of multibreed populations for commercial purposes has been growing, given the possibility to increase the efficiency of production determined by heterosis and complementarity between breeds used in crossing, enhanced by the selection of superior animals.The response to selection is proportional to the accuracy of prediction of breeding values (Falconer & Mackay, 1996)
The dominant additive models usually used for genetic evaluations do not give a good description of the pre-weaning performance variations, making it necessary to add the heterozygote and epystatic effects; the joint additive effects do not significantly improve the adjustment of the analysis model and the heterozygote effects are efficient in representing a quadratic breed additive effect, in addition to insert an unnecessary bias assigned to multicollinearity related to the joint additive effects
The models explained about 60% of the variation in weaning weight, whereby model 5, which contained only additive fixed genetic effects, showed the lowest setting, with a loss of about seven percentage points in the coefficient of determination (R2) confirmed by the high estimate for the C(p) statistic

Summary

Introduction

In Brazil, as in other countries, the use of multibreed populations for commercial purposes has been growing, given the possibility to increase the efficiency of production determined by heterosis and complementarity between breeds used in crossing, enhanced by the selection of superior animals. The response to selection is proportional to the accuracy of prediction of breeding values (Falconer & Mackay, 1996). The accurate estimation of genetic value of individuals subjected to a breeding program depends, in large part, on the effects considered in the statistical model used for evaluation of the animals. When there are strong linear relationships between independent variables (multicollinearity), the simple estimation of individual regression coefficients by least squares tends to be unstable, often with high standard error, which can lead to erroneous inferences (Bergmann & Hohenboken, 1995)

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