A review of stability analysis methods in plant breeding with an emphasis on cereals II: An Exploring of Multivariate Techniques and Future Prospects

Articles in Press

Document Type : Review Paper

Authors

1 Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran.

2 Urmia University

3 Department of Plant Production and Genetics, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

4 Institut des Sciences du Cerveau de Toulouse, France.

5 Department of Plant Production and Genetics, Faculty of Agriculture, University of Maragheh, Maragheh, Iran.

6 Department of Plant Production and Genetics, Faculty of Agriculture, Higher Education Complex of Saravan, Saravan, Iran.

Abstract

Abstract

Introduction: In the field of cereal breeding, understanding the genotype-environment interaction (GEI) and the stability of various traits is recognized as the key to successfully producing high-quality agricultural products. The GEI complicates the optimal selection of genotypes for target environments, making it essential to use appropriate statistical methods for analyzing and identifying stable and high-performing genotypes. Multivariate methods are powerful tools for analyzing complex GEI data.

Materials and Methods: In this article, multivariate statistical methods such as cluster analysis (CA), principal component analysis (PCA), principal coordinates analysis (PCOA), factor analysis (FA), as well as AMMI, GGE, and SHMM models used in the analysis of multi-environment trial (MET) data are reviewed. In the following, the application of genome-wide association studies (GWAS) and genomic prediction (GP) in the genetic analysis of stability, as well as the softwares used for calculating various multivariate stability methods, are introduced.

Findings: Based on the studies, the AMMI model, which combines analysis of variance and principal component analysis, has a high capability in evaluating main effects and interactions. The GGE plot effectively displays the main effects of genotype and its interaction with the environment. QTL analysis and the study of QTL-environment interaction (QEI) in MET data lead to the identification of stable linked markers.

Conclusion: This article comprehensively and practically introduces and examines multivariate methods in GEI analysis with the aim of better understanding GE interactions and identifying genotypes with broad adaptability and stable performance. Based on comprehensive studies, it has been determined that breeders should consider all stability statistics, both univariate and multivariate, in the analysis of MET data in order to make better decisions in selecting genotypes. Recent advancements in genomic technologies, including whole genome sequencing and GWAS, can significantly aid in understanding the complexities of GE and genetic of stability.

Keywords

Main Subjects

Cereal Research

Articles in Press, Accepted Manuscript
Available Online from 02 May 2025

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