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

Elisa Frutos, M. Purificación Galindo, VICTOR ELISEO LEIVA SANCHEZ

Research output: Contribution to journalReview articlepeer-review

86 Scopus citations

Abstract

Classical and GGE biplot methods are graphical procedures that allow multivariate data to be analyzed. In particular, the GGE biplot displays the genotype main effect (G) and the genotype by environment interaction (GE) in two-way data. The GGE biplot originates from data graphical analysis of multi-environment trials (MET). Thus, agronomists, crop scientists and geneticists are potential users of this method. However, it can also be used to visualize and analyze other types of data. In this paper, we propose a new interactive computational implementation in R language to perform the main functions of the classical and GGE biplot methods, so it is also useful for MET data visual analysis. This implementation is organized in an R package named GGEBiplotGUI. This package is the only interactive, noncommercial and open source software that currently exists, offering a free alternative to available commercial software. In addition, it can be used without to practically have knowledge of the R programming language. Here, we present and discuss the capabilities and features of the GGEBiplotGUI package and illustrate them by using real data. The GGEBiplotGUI package graphically addresses the questions that a researcher likely asks. This R package is not only a tool for visual data analysis of multi-environment trials, useful for plant breeders and geneticists, in order to study yields from genotypes and interactions between genotype and environment, but also data from other areas can be analyzed by the GGEBiplotGUI package.

Original languageEnglish
Pages (from-to)1629-1641
Number of pages13
JournalStochastic Environmental Research and Risk Assessment
Volume28
Issue number7
DOIs
StatePublished - 1 Jan 2014

Keywords

  • Graphical display
  • Least squares method
  • Multivariate data analysis
  • Singular value decomposition
  • Statistical models
  • Statistical software

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