Abstract

Genotype-environment (G×E) interactions play a critical role in shaping the phenotype of crops and fruit trees. In horticulture and arboriculture, G×E interaction analysis enables the identification of genotypes best suited to specific environmental conditions, which is essential for plant breeding, optimizing production, and adapting to climate change. This paper discusses classical and modern methods of G×E interaction analysis, including statistical models, advanced bioinformatics tools, and the potential for integrating environmental and genetic data. Practical applications in horticulture and arboriculture are highlighted, and directions for future research are proposed.

Keywords:Genotype-Environment; Phenotype; Transcriptomics; Resistance Breeding; Epigenetics

Abbreviations:AUDPC: Area Under the Disease-Progress Curve, AMMI: Additive Main effects and Multiplicative Interaction, ANOVA: Analysis of Variance, GE: Genotype-Environment, MAS: Marker-Assisted Selection, GS: Genomic Selection, PCA: Principal Component Analysis

Introduction

Genotype-environment (G×E) interactions refer to the phenomenon where different genotypes respond differently to environmental changes and represent one of the most important topics in agricultural sciences, especially in horticulture and arboriculture [1,2]. These fields rely on traits such as yield, fruit quality, and resistance to biotic and abiotic stresses, making G×E interaction analysis crucial for understanding genotype stability and adaptive potential [3]. Plant phenotype results from the interplay between genotype (G) and environment (E), yet in practice, genotypes often exhibit varying reactions to changing environmental conditions [4,5]. This variability presents both challenges and opportunities for breeders and producers. With climate change and the need for sustainable management of natural resources, precise tools for G×E interaction analysis are increasingly necessary [6]. Knowledge of G×E interactions enable breeders to select genotypes best suited to local conditions or exhibiting broad phenotypic stability [7]. Genotype defines the genetic potential of a plant, while environmental factors, both abiotic (e.g., temperature, humidity, nutrient availability) and biotic (e.g., pathogens, interspecies competition), influence plant development. At the molecular level, G×E interactions encompass gene expression differences, epigenetics, and regulation of metabolic pathways. This interplay leads to unpredictable phenotypic variability resulting from the interaction between genotype and specific environmental conditions [8].

Classical Methods of G×E Interaction Analysis

Analysis of Variance (ANOVA): ANOVA separates phenotypic variability into components due to genotype, environment, and their interaction. It is widely used to assess the statistical significance of G×E interactions.

Stability Analysis: Regression models are applied to evaluate genotype performance relative to environmental factors, identifying genotypes with high stability and minimal deviations from the regression line. Early methods for estimating G×E interaction effects were proposed by Yates and Cochran [9], Wricke [10], and subsequently refined by Finlay and Wilkinson [11], Eberhart and Russell [12], Perkins and Jinks [13], and Hanson [14].

Stability Indices: Stability parameters introduced by Shukla [15] and Kang [16] enable comparisons of genotype stability across diverse environments.

Modern Methods of G×E Interaction Analysis

AMMI Model (Additive Main Effects and Multiplicative Interaction): Combines ANOVA with principal component analysis (PCA) to visualize G×E interaction patterns. It is extensively used to identify genotypes well-suited to specific environments [17- 35].

GGE Biplot (Genotype + G×E): Focuses on genotype effects and G×E interaction on performance, enabling the identification of high-performing genotypes across different environments [36- 38].

Mixed Models: Incorporate random and fixed effects for genotypes, environments, and their interactions. These models are particularly useful for analyzing large and unbalanced datasets [39-41].

Multidimensional Analyses: Techniques such as canonical correlation analysis (CCA) and clustering group genotypes with similar performance across environments [42].

Integration of Multi-Source Data: Combines genomics, transcriptomics, and environmental data (e.g., GIS) to identify genotypes best adapted to diverse conditions. Tools such as marker-assisted selection (MAS) and genomic selection (GS) facilitate the identification of genotypes with beneficial G×E traits. High-throughput phenomics allows for detailed data collection on plant responses to environmental conditions [43].

G×E Interactions in Horticulture and Arboriculture

Plant Breeding: Development of varieties with high stability and productivity across diverse conditions.

Selection of genotypes tailored to specific environmental conditions, e.g., drought-tolerant varieties.

Resistance Breeding: Identification of genotypes resistant to biotic stresses (e.g., diseases) and abiotic stresses (e.g., drought).

Yield Optimization: Selection of high-performing genotypes in specific cultivation conditions.

Stability in fruit tree yields is particularly crucial, as variability from G×E interactions can lead to production unpredictability.

Improving Product Quality: Analysis of genotypeenvironment influences on sensory and nutritional traits of fruits and vegetables.

Challenges and Future Directions in G×E Analysis

• The increasing number of genotypes and environments requires advanced multidimensional analysis tools.
• There is a growing emphasis on studying genotypes resistant to extreme weather conditions.
• Analyzing large datasets of genetic and environmental data necessitates advanced statistical and computational methods.
• The integration of artificial intelligence tools may automate G×E analysis and improve predictive capabilities.

Summary

G×E interaction analysis forms the foundation of modern breeding for horticultural crops and fruit trees. Combining classical statistical methods with advanced bioinformatics tools enables more precise evaluations of genotype stability and adaptability. The significance of G×E interactions in horticulture and arboriculture cannot be overstated. Understanding and analyzing G×E interactions allow for the development of more productive, resilient, and stable plant varieties. In light of climate change, intensifying G×E interactions research and integrating knowledge from genomics, climatology, and statistics will drive the development of sustainable horticulture and arboriculture capable of addressing future challenges.

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