An interactive biplot implementation in R for modeling genotype-by-environment interaction

Frutos, Elisa; Vicente‐Galindo, Purificación; Leiva, Víctor

doi:10.1007/s00477-013-0821-z

Cited by 185 publications

(132 citation statements)

References 38 publications

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“…GGE biplots were generated using the GGEBiplotGUI-package (Frutos et al, 2014) implemented in R Software. GGE biplot analysis was based on model 13 (Yan and Tinker, 2006), since it simultaneously considers genotypic main effects and genotype x environment interactions.…”

Section: Methodsmentioning

confidence: 99%

Adaptation of introduced mungbean genotypes in Uganda

Mbeyagala¹,

Amayo²,

Obuo³

2016

Afr. Crop Sci. J.

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Section: Methodsmentioning

confidence: 99%

Adaptation of introduced mungbean genotypes in Uganda

Mbeyagala¹,

Amayo²,

Obuo³

2016

Afr. Crop Sci. J.

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“…Additionally, we compared the MEs formed with the best models to those of the simplest models: GGE2 and AMMI2. Finally, since the MEs created by the different strategies were similar, a GGE2 augmented biplot representation was used to visually represent the GEI structure using the R statistical software (R Development Core with the GGEBiplotGUI package (Frutos et al, 2014).…”

Section: Phenotypic Data Analysismentioning

confidence: 99%

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

et al. 2016

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Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype × environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location–year combinations genotyped with genotyping‐by‐sequencing (GBS). Mixed models were used to obtain either overall or by‐environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega‐environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context.

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“…Center and scale transformation were applied to data and biplots were created separately for parents and crosses. Secondly, the GGE biplot method was applied with the GGEBiplotGUI package in R statistical software (Frutos et al, 2014).…”

Section: (Fs) -2cov (Hs) + Trcov (Hs)mentioning

confidence: 99%

Comparison of graphical analyses for maize genetic experiments: Application of biplots and polar plot to line × tester design

Kahrıman

Egesel

Orhun

et al. 2016

Chilean J. Agric. Res.

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Graphical techniques have become important tools to show results of maize (Zea mays L.) breeding experiments in current literature. The present study compared different graphical techniques to determine the best parental lines and cross combinations for yield and kernel quality traits in maize breeding experiments. We measured single plant yield, protein content, oil content, carotenoid content, oleic acid, and linoleic acid in a 5 × 2 line × tester design. Genotype + genotype × environment (GE) biplot, principal component analysis (PCA) biplot, and polar plot were used to analyze data and compare them with conventional line × tester analysis. In the conventional analysis, parents with high means and positive general combining ability (GCA) values were A680 and HYA for single plant yield, IHP for protein content, IHO and HYA for oil content, A680 and Q2 for carotenoid content, IHP for oleic acid content, and A680 for linoleic acid content. The B73 tester exhibited positive GCA values for most investigated traits. The HYA × B73 combination was the best cross in terms of single plant yield, protein, and oil contents. Results showed that biplot methods had both advantages and disadvantages. The PCA biplots can be used alone while the GGE biplot and polar plots are both useful for combining ability, heterosis, and gene action analysis in a line × tester design. Overall, graphical analysis results were very similar to conventional analysis. Consequently, it was assumed that the graphical methods used could be useful to analyze/present data from maize breeding experiments carried out with a line × tester design.

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An interactive biplot implementation in R for modeling genotype-by-environment interaction

Cited by 185 publications

References 38 publications

Adaptation of introduced mungbean genotypes in Uganda

Adaptation of introduced mungbean genotypes in Uganda

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

Comparison of graphical analyses for maize genetic experiments: Application of biplots and polar plot to line × tester design

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