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Genotype by environment interaction for 450-day weight of Nelore cattle analyzed by reaction norm models
Pégolo, Newton T; Oliveira, Henrique N; Albuquerque, Lúcia G; Bezerra, Luiz Antonio F; Lôbo, Raysildo B.
Afiliação
  • Pégolo, Newton T; Universidade de São Paulo. Faculdade de Medicina de Ribeirão Preto. Departamento de Genética. Ribeirão Preto. BR
  • Oliveira, Henrique N; Universidade Estadual Paulista Júlio de Mesquita Filho. Faculdade de Medicina Veterinária e Zootecnia. Departamento de Melhoramento e Nutrição Animal. Botucatu. BR
  • Albuquerque, Lúcia G; Universidade Estadual Paulista Júlio de Mesquita Filho. Faculdade de Ciências Agrárias e Veterinárias. Departamento de Zootecnia. Jaboticabal. BR
  • Bezerra, Luiz Antonio F; Universidade de São Paulo. Faculdade de Medicina de Ribeirão Preto. Departamento de Genética. Ribeirão Preto. BR
  • Lôbo, Raysildo B; Universidade de São Paulo. Faculdade de Medicina de Ribeirão Preto. Departamento de Genética. Ribeirão Preto. BR
Genet. mol. biol ; 32(2): 281-287, 2009. graf, tab
Article em En | LILACS | ID: lil-513946
Biblioteca responsável: BR1.1
ABSTRACT
Genotype by environment interactions (GEI) have attracted increasing attention in tropical breeding programs because of the variety of production systems involved. In this work, we assessed GEI in 450-day adjusted weight (W450) Nelore cattle from 366 Brazilian herds by comparing traditional univariate single-environment model analysis (UM) and random regression first order reaction norm models for six environmental variables standard deviations of herd-year (RRMw) and herd-year-season-management (RRMw-m) groups for mean W450, standard deviations of herd-year (RRMg) and herd-year-season-management (RRMg-m) groups adjusted for 365-450 days weight gain (G450) averages, and two iterative algorithms using herd-year-season-management group solution estimates from a first RRMw-m and RRMg-m analysis (RRMITw-m and RRMITg-m, respectively). The RRM results showed similar tendencies in the variance components and heritability estimates along environmental gradient. Some of the variation among RRM estimates may have been related to the precision of the predictor and to correlations between environmental variables and the likely components of the weight trait. GEI, which was assessed by estimating the genetic correlation surfaces, had values < 0.5 between extreme environments in all models. Regression analyses showed that the correlation between the expected progeny differences for UM and the corresponding differences estimated by RRM was higher in intermediate and favorable environments than in unfavorable environments (p < 0.0001).
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Texto completo: 1 Coleções: 01-internacional Base de dados: LILACS Tipo de estudo: Prognostic_studies Idioma: En Revista: Genet. mol. biol Assunto da revista: GENETICA Ano de publicação: 2009 Tipo de documento: Article País de afiliação: Brasil
Texto completo: 1 Coleções: 01-internacional Base de dados: LILACS Tipo de estudo: Prognostic_studies Idioma: En Revista: Genet. mol. biol Assunto da revista: GENETICA Ano de publicação: 2009 Tipo de documento: Article País de afiliação: Brasil
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