Economically important traits in plants are often polygenic, meaning they are controlled by multiple genes, and are significantly influenced by environmental factors. This leads to a phenomenon known as Genotype by Environment Interaction (GEI), where different genotypes respond differently to varying environments. GEI can obscure the true relationship between genotype and phenotype, complicating the estimation of gene effects and making it challenging to select the best genotypes. Consequently, this can reduce the genetic improvement achieved through breeding programs. To address these challenges, multi-environment trials are crucial for evaluating genotype performance across diverse conditions.

The differences between genotypes across environments may vary in magnitude or involve changes in ranking. GEI can be detected and analyzed using Pooled ANOVA, which combines data from multiple environments. Various statistical methods are used to evaluate genotype stability, including basic univariate statistics, regression-based models, and multivariate models such as Additive Main Effects and Multiplicative Interaction (AMMI) and genotype main effect and genotype by environment interaction (GGE) biplot.

Univariate and regression-based models primarily focus on selecting stable genotypes but may not capture specific adaptations to particular environments. On the other hand, multivariate models like AMMI and GGE utilize Principal Component Analysis (PCA) to reduce dimensionality and provide a more comprehensive analysis of interaction effects. The GGE biplot is mainly a graphical approach that employs Singular Value Decomposition (SVD) of environmental centered data to illustrate the effects of genotype and GEI. In contrast, AMMI decomposes the data into genotype, environment, and interaction components, further breaking down the interaction component.

A comparison of AMMI and GGE biplots for analyzing GEI using multi-environmental data from 50 wheat genotypes revealed that while their approaches differ, both models are complementary and yield equivalent results. This enhances the reliability of selecting superior cultivars and optimal test environments.

GEI influences the decision regarding whether to develop a single variety suitable for a broad range of environments or separate varieties for specific environments. Thus, the presence of GEI impacts both breeding objectives and strategies, which may either aim to avoid or exploit these interactions.

References:

  1. Neisse, A. C., Kirch, J. L., & Hongyu, K. (2018). AMMI and GGE Biplot for genotype × environment interaction: A medoid-based hierarchical cluster analysis approach for high-dimensional data. Biometrics Letters, 55(2), 97-121.
  2. Bernardo, R. (2002). Breeding for quantitative traits in plants. Woodbury: Stemma Press.
  3. Gauch, H. G. (2013). A simple protocol for AMMI analysis of yield trials. Crop Science, 53(5), 1860-1869.
  4. Yan, W., Kang, M. S., Ma, B., Woods, S., & Cornelius, P. L. (2007). GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Science, 47(2), 643-653.