Prediction of Maize Single Cross Hybrids Using the Total Effects of Associated Markers Approach Assessed by Cross-Validation and Regional Trials

Melo, Wagner Mateus Costa; Pinho, Renzo Garcia Von; Balestre, Márcio

doi:10.1155/2014/924348

Cited by 2 publications

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References 23 publications

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“…The experimental data were described by Melo et al [31]. These data are related to grain yield traits measured in 50 maize single-cross hybrids (G1-G50).…”

Section: Methodsmentioning

confidence: 99%

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Bayesian factor analytic model: An approach in multiple environment trials

et al. 2019

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One of the main challenges in plant breeding programs is the efficient quantification of the genotype-by-environment interaction (GEI). The presence of significant GEI may create difficulties for breeders in the selection and recommendation of superior genotypes for a wide environmental network. Among the diverse statistical procedures developed for this purpose, we highlight those based on mixed models and factor analysis that are called factor analytic (FA) models. However, some inferential issues are related to the factor analytic model, such as Heywood cases that make the model non-identifiable. Moreover, the representation of the loads and factors in the conventional biplot does not involve any measurement of uncertainty. In this work, we propose dealing with the FA model using the Bayesian framework with direct sampling of factor loadings via spectral decomposition; this guarantees identifiability in the estimation process and eliminates the need for the rotationality of factor loadings or imposition of any ad hoc constraints. We used simulated and real data to illustrate the method’s application in multi-environment trials (MET) and to compare it with traditional FA mixed models on controlled unbalancing. In general, the Bayesian FA model was robust under different simulated unbalanced levels, presenting the superior predictive ability of missing data when compared to competing models, such as those based on FA mixed models. In addition, for some scenarios, the classical FA mixed model failed in estimating the full FA model, illustrating the parametric problems of convergence in these models. Our results suggest that Bayesian factorial models might be successfully used in plant breeding for MET analysis.

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“…The experimental data were described by Melo et al [31]. These data are related to grain yield traits measured in 50 maize single-cross hybrids (G1-G50).…”

Section: Methodsmentioning

confidence: 99%

“…These hybrids are originated from crossing among lineages of different backgrounds (tropical–Flint, Lancaster and Stiff Stalk Synthetic sources–see Melo et al [31]). The crossing design was in a partial diallel system.…”

Section: Methodsmentioning

confidence: 99%

Bayesian factor analytic model: An approach in multiple environment trials

et al. 2019

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Heterogeneity of Variances in the Bayesian AMMI Model for Multienvironment Trial Studies

et al. 2019

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In analyses of multienvironment trials, it is common to assume homogeneity of variances in additive main effect and multiplicative interaction (AMMI) models for further inferences about the genotypes × environment interaction (GEI). However, it is not always reasonable to adopt such an assumption because it could mislead the evaluation and selection of the best genotypes. In this context, modeling the heterogeneity of variance jointly with GEI models has been of particular interest in plant breeding, since the experimental accuracy may float across the trial network. In this study, we used the Bayesian AMMI model in real and simulated frameworks to study GEI effects in the presence of heterogeneous variances (BAMMI‐H) across environments, highlighting the differences that can arise when this scenario is neglected. The findings indicate that neglecting the differences among the experimental variances across environments can influence the biplot precision and the conclusions regarding adaptability and stability. The main differences observed between the naive AMMI (assuming homogeneity) and BAMMI‐H biplots were related to the biplot precision for the genotypic and environment scores and the ability to recover information about experimental differences among the trials in the biplot. The results observed in this study suggest the importance of taking the heterogeneity of variance into account in the AMMI analysis to select genotypes for stability and adaptability.

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Prediction of Maize Single Cross Hybrids Using the Total Effects of Associated Markers Approach Assessed by Cross-Validation and Regional Trials

Cited by 2 publications

References 23 publications

Bayesian factor analytic model: An approach in multiple environment trials

Bayesian factor analytic model: An approach in multiple environment trials

Heterogeneity of Variances in the Bayesian AMMI Model for Multienvironment Trial Studies

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