by Weikai Yan
and Manjit S. Kang

• Includes over 160 figures to enhance visualization and interpretation of a biplot based on various types of two-way data
• Provides a holistic picture of MET data analysis
• Details biplot analysis of genotype by trait data and shows how a biplot can help physiologists to achieve a systems understanding of the research subject and help breeders in selecting superior varieties/parents
• Presents a new and easily understood method of QTL identification
• Contains a new methodology for diallel data analysis
• Gives a detailed description of biplot analysis of host-by-pathogen interaction
• Supplies a comprehensive but succinct treatment of genotype-by-environment interaction and shows how GGE biplot methodology fits in

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.

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.

Contents

1. 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

2. 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

3. 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

Index

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