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metadata.artigo.dc.title: Credible intervals for scores in the AMMI with random effects for genotype
metadata.artigo.dc.creator: Oliveira, Luciano Antonio de
Silva, Carlos Pereira da
Nuvunga, Joel Jorge
Silva, Alessandra Querino Da
Balestre, Marcio
metadata.artigo.dc.subject: Plant breeding
Genotype × environment interaction
Maize hybrids
Bayesian AMMI
Melhoramento de plantas
Interação genótipo × ambiente
Híbridos de milho
metadata.artigo.dc.publisher: Crop Science Society of America 2015
metadata.artigo.dc.identifier.citation: OLIVEIRA, L. A. de et al. Credible intervals for scores in the AMMI with random effects for genotype. Crop Science, [S.I.], v. 55, n. 2, p. 465-476, Mar./Apr. 2015. DOI:
metadata.artigo.dc.description.abstract: The additive main effects and multiplicative interaction (AMMI) model is frequently applied in plant breeding for studying the genotype × environment (G × E) interaction. One of the main difficulties related to this method of analysis is the incorporation of inference to the bilinear terms that compose the biplot representation. This study aimed to incorporate credible intervals for the genotypic and environmental scores in the AMMI model by using an informative prior for the genotype effect. This approach differs from the Bayesian methods that have been presented so far, which assume the same restrictions as the fixed effects model. The method was exemplified by using data from a study with 55 maize hybrids in nine different environments for which variable being studied was the yield of unhusked ears. Our results demonstrated that the credible intervals allowed for the identification of genotypes and environments that did not contribute to the G × E interaction. In addition, it facilitated recognition of homogeneous subgroups of genotypes and environments (with respect to the effect of the interaction) and the adaptability of genotypes to specific environments of great interest to breeders. The posterior distributions of singular vectors were bimodal but with the same density peaks in absolute value. This reflects the arbitrary choice of signs of the main component that was used in different mathematical algorithms. Although our data set was based on unrelated single cross hybrids, the choice of genotypes as random effects enabled the Bayesian AMMI to accommodate the additive and nonadditive relationship matrices. Additionally, the flexibility of the analysis facilitated working with unbalanced data and the incorporation of heterogeneity of variances.
metadata.artigo.dc.language: en
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