Genotype by environment interaction for birth and weaning weights of composite beef cattle in different regions of Brazil

Júnior, Mário Luiz Santana; Eler, Joanir Pereira; Cardoso, F. F.; Albuquerque, Lúcia Galvão de; Bignardi, Annaíza Braga; Ferraz, José Bento Sterman

doi:10.1016/j.livsci.2012.07.017

Cited by 16 publications

(24 citation statements)

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“…The largest differences were observed between models that omitted or included S × CG (Tables 3 and 4). These estimates are similar to those reported in other studies involving the present population of composite beef cattle raised in a (sub)tropical environment (Mourão et al, 2007;Santana et al 2012). A higher direct heritability was observed for BW, whereas maternal heritability and fraction of phenotypic variance due to maternal permanent environmental effects were higher for WW.…”

Section: Resultssupporting

confidence: 92%

“…A higher direct heritability was observed for BW, whereas maternal heritability and fraction of phenotypic variance due to maternal permanent environmental effects were higher for WW. The same pattern of genetic parameters has been reported by Albuquerque and Meyer (2001) and Santana et al (2012) for the weight of Nelore and composite cattle, respectively. These findings indicate that the traits studied should respond satisfactorily to selection in both favorable and unfavorable environments.…”

Section: Resultssupporting

confidence: 86%

“…Therefore, the genotype × production environment interaction may be important for genetic evaluations of favorable and other environments. The genetic correlations between direct effects were low as also reported by Bradfield et al (1997) for WW of Australian Santa Gertrudis cattle (0.64), by Lopes et al (2008) for the BW and WW of Nelore cattle raised in southern Brazil (−0.04 to 0.79), and by Santana et al (2012) for the BW and WW of composite beef cattle raised in different regions of Brazil (−0.02 to 0.92).…”

Section: Resultsmentioning

confidence: 62%

“…The out crossing percentage was defined as h ij ¼ P i ≠ j r i r j for ith sire breed and jth dam breed, where r is the proportion of the biological type based on NABC definition. Further information about the present composite beef cattle and distribution of farms throughout Brazil can be found in Santana et al (2012).…”

Section: Datamentioning

confidence: 99%

“…In this respect, Bradfield et al (1997) defined favorable and unfavorable production environments based on contemporary groups and demonstrated the importance of including sire × contemporary group interaction in the genetic evaluation of Santa Gertrudis cattle in Australia. Diaz et al (2011) and Santana et al (2012) observed G × E in beef cattle raised in different parts of Brazil. The impact of this G × E on genetic evaluations in Brazil was, however, not yet quantified.…”

Section: Introductionmentioning

confidence: 97%

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Genotype by production environment interaction for birth and weaning weights in a population of composite beef cattle

Júnior

Eler

Bignardi

et al. 2014

Animal

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The objectives of the present study were: (1) to evaluate the importance of genotype × production environment interaction for the genetic evaluation of birth weight (BW) and weaning weight (WW) in a population of composite beef cattle in Brazil, and (2) to investigate the importance of sire × contemporary group interaction (S × CG) to model G × E and improve the accuracy of prediction in routine genetic evaluations of this population. Analyses were performed with one, two (favorable and unfavorable) or three (favorable, intermediate, unfavorable) different definitions of production environments. Thus, BW and WW records of animals in a favorable environment were assigned to either trait 1, in an intermediate environment to trait 2 or in an unfavorable environment to trait 3. The (co)variance components were estimated using Gibbs sampling in single-, bi-or three-trait animal models according to the definition of number of production environments. In general, the estimates of genetic parameters for BW and WW were similar between environments. The additive genetic correlations between production environments were close to unity for BW; however, when examining the highest posterior density intervals, the correlation between favorable and unfavorable environments reached a value of only 0.70, a fact that may lead to changes in the ranking of sires across environments. The posterior mean genetic correlation between direct effects was 0.63 in favorable and unfavorable environments for WW. When S × CG was included in two-or three-trait analyses, all direct genetic correlations were close to unity, suggesting that there was no evidence of a genotype × production environment interaction. Furthermore, the model including S × CG contributed to prevent overestimation of the accuracy of breeding values of sires, provided a lower error of prediction for both direct and maternal breeding values, lower squared bias, residual variance and deviance information criterion than the model omitting S × CG. Thus, the model that included S × CG can therefore be considered the best model on the basis of these criteria. The genotype × production environment interaction should not be neglected in the genetic evaluation of BW and WW in the present population of beef cattle. The inclusion of S × CG in the model is a feasible and plausible alternative to model the effects of G × E in the genetic evaluations.

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Section: Resultssupporting

confidence: 92%

Section: Resultssupporting

confidence: 86%