GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists
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Summary
Research data is expensive and precious, yet it is seldom fully utilized due to our ability of comprehension. Graphical display is desirable, if not absolutely necessary, for fully understanding large data sets with complex interconnectedness and interactions. The newly developed GGE biplot methodology is a superior approach to the graphical analysis of research data and may revolutionize the way researchers analyze data. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists introduces the theory of the GGE biplot methodology and describes its applications in visual analysis of multi-environment trial (MET) data and other types of research data.
The text includes three parts: I) Genotype by environment interaction and stability analysis, II) GGE biplot and multi-environment trial (MET) data analysis, and III) GGE biplot software and applications in analyzing other types of two-way data. Part I presents a comprehensive but succinct treatment of genotype-by-environment (G x E) interaction in order to provide an overall picture of the entire G x E issue and to show how GGE biplot methodology fits in. Part II describes and demonstrates the numerous utilities of a GGE biplot in visualizing MET data. Part III describes the "GGE biplot" software and extends its application to the analysis of genotype by trait data, QTL mapping data, diallel cross data, and host by pathogen data. Altogether, this book demonstrates that the GGE biplot methodology is a superior data-visualization tool and allows the researcher to graphically extract and utilize the information from MET data and other types of two-way data to the fullest extent.
GGE Biplot Analysis makes this useful technology accessible on a wider scale to plant and animal breeders, geneticists, agronomists, ecologists, and students in these and other related research areas. The information presented here will greatly enhance researchers' ability to understand their data and will make a significant contribution toward helping to meet the challenges of food production and food security that currently face the world. Readers will be amazed to see how much more they can extract from their data by implementing the new and easily understood GGE biplot methods presented here and will soon agree that any delay in using this technique is a loss to their research achievement.
Table of Contents
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
Editorial Reviews
"The objective (of this book) is to demonstrate the power of this (biplot) graphical analysis and enable other scientists to effectively use the methodology and software to gain more insight into their own data. I believe that Yan and Kang have achieved this objective and that someone who has not used this ordination approach before could do so with this book as their guide.
"A major advantage of biplot methodology is that it can be applied to any two-way data with multiple entries and multiple attributes in order to obtain a better understanding of the relationships among entries, relationships among attributes, and the interactions between entries and attributes. Hence the material in this book will be of benefit to any scientists working with such data. I believe no other book covers this methodology and software in equivalent depth and breadth.
"I have no hesitation in recommending this book to breeders, geneticists, agronomists, ecologists, and to biometricians working with them. The biplot methodology greatly enhances the ability to understand and interpret two-way data and this graphical analysis can be readily implemented with the GGEbiplot software."
- K. E. Basford, in Biometrics, Vol 59, 2003
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