Document Type : Review Paper
Authors
1
Post-Doctoral Researcher, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
2
Ph. D. Student, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
3
Professor, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
4
Professor, Department of Plant Production and Genetics, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
5
Associate Professor, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
6
M. Sc. Graduate, Institut des Sciences du Cerveau de Toulouse, Toulouse, France
7
Associate Professor, Department of Plant Production and Genetics, Faculty of Agriculture, University of Maragheh, Maragheh, Iran
8
Assistant Professor, Department of Plant Production and Genetics, Faculty of Agriculture, University of Saravan, Saravan, Iran
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. 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 statistical methods are powerful tools for analyzing complex multi-environment trial (MET) data. Statistical methods such as cluster analysis (CA), principal component analysis (PCA), principal coordinate analysis (PCoA), factor analysis (FA), as well as additive main effects and multiplicative interaction (AMMI), genotype main effects and genotype × environment interaction biplot (GGE-Biplot), shifted multiplicative model (SHMM), and best linear unbiased prediction (BLUP) have been well used with high accuracy in analyzing MET data. In this study, multivariate statistical methods used in the analysis of GEI and genotypes stability from MET data and their advantages and disadvantages are reviewed. Moreover, the application of genome-wide association studies (GWAS), quantitative trait locus (QTL) analysis and QTL-environment interaction (QEI), and genomic prediction (GP) in the genetic analysis of stability, as well as the softwares used for calculating various multivariate stability methods, are introduced.
Research findings
The results of this study showed that the AMMI model, which combines analysis of variance and principal component analysis, has a high capability to evaluate main effects and interactions. Also, the GGE-Biplot method and various diagrams presented in this method effectively displays the main effects of genotype and its interaction with the environment. QTL analysis and the study of QEI in MET data also lead to the identification of linked markers to stability that can be used in molecular breeding of crop plants.
Conclusion
In the current study, multivariate methods used in GEI analysis were comprehensively and practically reviewed and introduced with the aim of better understanding GE interactions and identifying genotypes with broad adaptability and stable performance. The results of this study based on comprehensive studies showed that to make better decisions in selecting genotypes, it is necessary to consider all stability statistics, both univariate and multivariate, in the analysis of MET data. Recent advances in genomic technologies, including whole genome sequencing and GWAS, can significantly aid in understanding the complexities of GE and genetic of stability.
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