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Biplot

About: Biplot is a research topic. Over the lifetime, 1178 publications have been published within this topic receiving 24080 citations.


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Journal ArticleDOI
TL;DR: This paper presents a GGE (i.e., G + GE) biplot, which is constructed by the first two symmetrically scaled principal components (PC1 and PC2) derived from singular value decomposition of environment-centered MET data.
Abstract: Cultivar evaluation and mega-environment identification are among the most important objectives of multi-environment trials (MET). Although the measured yield is a combined result of effects of genotype (G), environment (E), and genotype × environment interaction (GE), only G and GE are relevant to cultivar evaluation and mega-environment identification. This paper presents a GGE (i.e., G + GE) biplot, which is constructed by the first two symmetrically scaled principal components (PC1 and PC2) derived from singular value decomposition of environment-centered MET data. The GGE biplot graphically displays G plus GE of a MET in a way that facilitates visual cultivar evaluation and mega-environment identification. When applied to yield data of the 1989 through 1998 Ontario winter wheat (Triticum aestivum L.) performance trials, the GGE biplots clearly identified yearly winning genotypes and their winning niches. Collective analysis of the yearly biplots suggests two winter wheat mega-environments in Ontario: a minor mega-environment (eastern Ontario) and a major one (southern and western Ontario), the latter being traditionally divided into three subareas. There were frequent crossover GE interactions within the major mega-environment but the location groupings were variable across years. It therefore could not be further divided into meaningful subareas. It was revealed that in most years PC1 represents a proportional cultivar response across locations, which leads to noncrossover GE interactions, while PC2 represents a disproportional cultivar response across locations, which is responsible for any crossover GE interactions. Consequently, genotypes with large PC1 scores tend to give higher average yield, and locations with large PC1 scores and near-zero PC2 scores facilitates identification of such genotypes

1,509 citations

Book
09 Dec 2019
TL;DR: GGE Biplot Continues to Evolve Cultivar Evaluation Based on Multiple Traits Why multiple Traits?
Abstract: GENOTYPE-BY-ENVIRONMENT INTERACTION AND STABILITY ANALYSIS Genotype-by-Environment Interaction Heredity and Environment Genotype-by-Environment Interaction Implications of GEI in Crop Breeding Causes of Genotype-by-Environment Interaction Stability Analyses in Plant Breeding and Performance Trials Stability Analysis in Plant Breeding and Performance Trials Stability Concepts and Statistics Dealing with Genotype-by-Environment Interaction GGE Biplot: Genotype + GE Interaction GGE BIPLOT AND MULTI-ENVIRONMENTAL TRIAL ANALYSIS Theory of Biplot The Concept of Biplot The Inner-Product Property of a Biplot Visualizing the Biplot Relationships among Columns and among Rows Biplot Analysis of Two-Way Data Introduction to GGE Biplot The Concept of GGE and GGE Biplot The Basic Model for a GGE Biplot Methods of Singular Value Partitioning An Alternative Model for GGE Biplot Three Types of Data Transformation Generating a GGE Biplot Using Conventional Methods Biplot Analysis of Multi-Environment Trial Data Objectives of Multi-Environment Trial Data Analysis Simple Comparisons Using GGE Biplot Mega-Environment Investigation Cultivar Evaluation for a Given Mega-Environment Evaluation of Test Environments Comparison with the AMMI Biplot Interpreting Genotype-by-Environment Interaction GGE BIPLOT SOFTWARE AND APPLICATIONS TO OTHER TYPES OF TWO-WAY DATA GGE Biplot Software-The Solution for GGE Biplot Analyses The Need for GGE Biplot Software The Terminology of Entries and Testers Preparing Data File for GGE Biplot Organization of GGE Biplot Software Functions for a Genotype-by-Environment Dataset Function for a Genotype-by-Strain Dataset Application of GGE Biplot to Other Types of Two-way Data GGE Biplot Continues to Evolve Cultivar Evaluation Based on Multiple Traits Why Multiple Traits? Cultivar Evaluation Based on Multiple Traits Identifying Traits for Indirect Selection for Loaf Volume Identification of Redundant Traits Comparing Cultivars as Packages of Traits Investigation of Different Selection Strategies Systems Understanding of Crop Improvement Three-Mode Principal Component Analysis and Visualization QTL Identification Using GGE Biplot Why Biplot? Data Source and Model Grouping of Linked Markers Gene Mapping Using Biplot QTL Identification via GGE Biplot Interconnectedness among Traits and Pleiotropic Effects of a Given Locus Understanding DH Lines through the Biplot Pattern QTL and GE Interaction Biplot Analysis of Diallel Data Model for Biplot Analysis of Diallel Data General Combining Ability of Parents Specific Combining Ability of Parents Heterotic Groups The Best Testers for Assessing General Combining Ability of Parents The Best Crosses Hypothesis on the Genetic Constitution of Parents Targeting a Large Dataset Advantages and Disadvantages of the Biplot Approach Biplot Analysis of Host Genotype-by-Pathogen Strain Interactions Vertical vs. Horizontal Resistance Genotype-By-Strain Interaction for a Barley Net Blotch Genotype-by-Strain Interaction for Wheat Fusarium Head Blight Biplot Analysis to Detect Synergism between Genotypes of Different Species Genotype-by-Strain Interaction for Nitrogen-Fixation Wheat-Maize Interaction for Wheat Haploid Embryo Formation References Index

1,213 citations

Journal ArticleDOI
Weikai Yan1
TL;DR: GGEbiplot as mentioned in this paper is a Windows application that performs biplot analysis of two-way MET data analysis, and it also produces an WHAT IS A GGE BIPLOT? interactive show of the data.
Abstract: facilitate the application of the GGE biplot methodology in MET data analysis and in analyses of other types Plant breeding trials produce quantities of data and finding the of two-way data, a Windows application, the GGEbiplot useful information within that data has historically been a major challenge of plant breeding. A recently developed graphical data software, was developed. This paper describes the funcsummary, called GGEbiplot, can aid in data exploration. GGEbiplot tions built in this software and exemplifies their use in is a Windows application that performs biplot analysis of two-way MET data analysis. data that assume an entry tester structure. GGEbiplot analyzes the data and outputs the results as an image, and it also produces an WHAT IS A GGE BIPLOT? interactive show of the data. It allows interactive visualization of the biplot from various perspectives. A multienvironment trial data set, The Concept of Biplot in which cultivars are entries and environments are testers, was used The concept of biplot was first proposed by Gabriel to demonstrate the functions of GGEbiplot. These include but are not limited to: (i) ranking the cultivars based on their performance (1971). The main ideas follow. Any two-way table or in any given environment, (ii) ranking the environments based on matrix X that contains n rows and m columns can be the relative performance of any given cultivar, (iii) comparing the regarded as the product of two matrices: A with n rows performance of any pair of cultivars in different environments, (iv) and r columns and B with r rows and m columns. Thereidentifying the best cultivar in each environment, (v) grouping the fore, Matrix X can always be decomposed to its two environments based on the best cultivars, (vi) evaluating the cultivars component matrices, A and B. If r happens to be 2, based on both average yield and stability, (vii) evaluating the environ- Matrix X is referred to as a rank-two matrix. Each row ments based on both discriminating ability and representativeness, in Matrix A has two values, which define a point in a and (viii) visualizing all of these aspects for a subset of the data by two-dimensional plot. Similarly, each column in Matrix removing some of the cultivars or environments. GGEbiplot has been

969 citations

Journal ArticleDOI
TL;DR: The basic principles of biplot analysis are reviewed and recent developments in its application in analyzing multi-environment trail (MET) data are reviewed, with the aim of providing a working guide for breeders, agronomists, and other agricultural scientists on bi plot analysis and interpretation.
Abstract: Biplot analysis has evolved into an important statistical tool in plant breeding and agricultural research. Here we review the basic principles of biplot analysis and recent developments in its application in analyzing multi-environment trail (MET) data, with the aim of providing a working guide for breeders, agronomists, and other agricultural scientists on biplot analysis and interpretation. The review is divided into four sections. The first section is a complete but succinct description of the principles of biplot analysis. The second section is a detailed treatment of biplot analysis of genotype by environment data. It addresses environment and genotype evaluation from all perspectives. The third section deals with biplot analysis of various two-way tables that can be generated from a three-way MET dataset, which is an integral and essential part to a fuller understanding and exploration of MET data. The final section discusses questions that are frequently asked about biplot analysis. Methods descri...

952 citations

Journal ArticleDOI
TL;DR: The main conclusions are: both GGE biplot analysis and AMMI analysis combine rather than separate G and GE in mega-environment analysis and genotype evaluation, and the G GE biplot is superior to the AMMI1 graph in Mega-Environment analysis and Genotype evaluation.
Abstract: The use of genotype main effect (G) plus genotype-by-environment (GE) interaction (G+GE) biplot analysis by plant breeders and other agricultural researchers has increased dramatically during the past 5 yr for analyzing multi-environment trial (MET) data. Recently, however, its legitimacy was questioned by a proponent of Additive Main Effect and Multiplicative Interaction (AMMI) analysis. The objectives of this review are: (i) to compare GGE biplot analysis and AMMI analysis on three aspects of genotype-by-environment data (GED) analysis, namely mega-environment analysis, genotype evaluation, and test-environment evaluation; (ii) to discuss whether G and GE should be combined or separated in these three aspects of GED analysis; and (iii) to discuss the role and importance of model diagnosis in biplot analysis of GED. Our main conclusions are: (i) both GGE biplot analysis and AMMI analysis combine rather than separate G and GE in mega-environment analysis and genotype evaluation, (ii) the GGE biplot is superior to the AMMI1 graph in mega-environment analysis and genotype evaluation because it explains more G+GE and has the inner-product property of the biplot, (iii) the discriminating power vs. representativeness view of the GGE biplot is effective in evaluating test environments, which is not possible in AMMI analysis, and (iv) model diagnosis for each dataset is useful, but accuracy gain from model diagnosis should not be overstated.

939 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023161
2022285
202173
202082
201978
201888