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Multi-trait multi-environment models in the genetic selection of segregating soybean progeny.
Volpato, Leonardo; Alves, Rodrigo Silva; Teodoro, Paulo Eduardo; Vilela de Resende, Marcos Deon; Nascimento, Moysés; Nascimento, Ana Carolina Campana; Ludke, Willian Hytalo; Lopes da Silva, Felipe; Borém, Aluízio.
Afiliação
  • Volpato L; Federal University of Viçosa-Department of Plant Science, University Campus, Viçosa, Minas Gerais, Brazil.
  • Alves RS; Federal University of Viçosa-Department of General Biology, University Campus, Viçosa, Minas Gerais, Brazil.
  • Teodoro PE; Federal University of Mato Grosso do Sul-Department of Plant Science, University Campus, Chapadão do Sul, Mato Grosso do Sul, Brazil.
  • Vilela de Resende MD; Federal University of Viçosa-Department of Statistics, University Campus, Viçosa, Minas Gerais, Brazil.
  • Nascimento M; Federal University of Viçosa-Department of Statistics, University Campus, Viçosa, Minas Gerais, Brazil.
  • Nascimento ACC; Federal University of Viçosa-Department of Statistics, University Campus, Viçosa, Minas Gerais, Brazil.
  • Ludke WH; Federal University of Viçosa-Department of Plant Science, University Campus, Viçosa, Minas Gerais, Brazil.
  • Lopes da Silva F; Federal University of Viçosa-Department of Plant Science, University Campus, Viçosa, Minas Gerais, Brazil.
  • Borém A; Federal University of Viçosa-Department of Plant Science, University Campus, Viçosa, Minas Gerais, Brazil.
PLoS One ; 14(4): e0215315, 2019.
Article em En | MEDLINE | ID: mdl-30998705
At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; [Formula: see text]) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of [Formula: see text]. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Seleção Genética / Glycine max / Herança Multifatorial / Interação Gene-Ambiente / Genótipo / Modelos Genéticos Tipo de estudo: Prognostic_studies Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Seleção Genética / Glycine max / Herança Multifatorial / Interação Gene-Ambiente / Genótipo / Modelos Genéticos Tipo de estudo: Prognostic_studies Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos