“Concept of G × E Interaction and Its Application in Plant Breeding”

UNIVERSITY OF AGRICULTURAL SCIENCES, BANGALORE

DEPARTMENT OF GENETICS AND PLANT BREEDING

M.Sc. Seminar - GPB 582 (0+1)

“Concept of G × E Interaction and Its Application in Plant Breeding”

Economically important traits in plants are typically polygenic and strongly influenced by environmental factors, resulting in genotype × environment interaction (GEI). GEI can mask the true relationship between genotype and phenotype, making it difficult to accurately estimate gene effects and identify superior genotypes. This complexity often limits the genetic gain achievable in breeding programs. To overcome these challenges, multi-environment trials (METs) are crucial, as they allow for the assessment of genotype performance across a range of environmental conditions, thereby improving selection accuracy and breeding efficiency.²

Differences between genotypes over environments may vary in magnitude or involve changes in ranking. GEI can be detected and investigated by pooled ANOVA, which integrates data from multiple environments. Various statistical methods are employed to evaluate genotype stability, including basic univariate statistics, regression-based models, and multivariate models like Additive Main Effects and Multiplicative Interaction (AMMI) and genotype main effect and genotype by environment interaction (GGE) biplot. Univariate and regression-based models focus on selecting stable genotypes but may overlook specific adaptation to certain environments. In contrast, multivariate models, such as AMMI and GGE, use PCA to reduce dimensionality and analyze the interaction effects more comprehensively. GGE, primarily a graphical approach, uses singular value decomposition (SVD) of environment-centered data to display the effects of genotype and GEI⁴, while AMMI decomposes the data into genotype, environment, and interaction components, subjecting the latter to decomposition.³

Development of novel genetic variants in Kalmegh through gamma radiation and stability assessment of mutant lines for andrographolide and neo-andrographolide content across environments using AMMI and GGE further exemplifies the practical application of these models.¹

GEI influences both the type of variety to be developed and the range of environments under which selection should be conducted. A breeder must decide whether a single variety can be grown across a wide range of environments or if separate varieties should be developed for specific environments. Therefore, the presence of GEI impacts both the breeding objectives and strategies, which may be aimed either at avoiding or exploiting these interactions.

REFERENCES

1. Hiremath, C., Ashwini, K.V., Gupta, N. and Shanker, K., 2024. Genotype–environment interaction of andrographolide and neo-andrographolide in high therapeutic potential medicinal plants Kalmegh (Andrographis paniculata (Burm. f.) Wall. ex Nees) by AMMI and GGE biplot analysis. Genet. Resour. Crop Evol., 71(8): 4157–4169.

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 Sci., 53(5): 1860–1869.

4. Yan, W., Kang, M.S., Ma, B., Woods, S. and Cornelius, P.L., 2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci., 47(2): 643–653.