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This paper introduces two statistical models for analyzing Genotype-by-Environment (GxE) interactions: significance analysis using a mixed-effects model to identify significant genetic and GxE effects on phenotypes, and stability analysis to characterize gene-environment relationships and genotype performance across environments. To facilitate the application of these models, the authors developed RGxEStat, a user-friendly software tool that streamlines the construction, solution, and visualization of these models, eliminating the need for specialized programming skills. The tool and models aim to accelerate breeding research by enabling more efficient analysis of breeding data.
Unlock faster breeding cycles: RGxEStat offers a user-friendly tool for dissecting gene-environment interactions without complex programming.
Genotype-by-Environment (GxE) interactions influence the performance of genotypes across diverse environments, reducing the predictability of phenotypes in target environments. In-depth analysis of GxE interactions facilitates the identification of how genetic advantages or defects are expressed or suppressed under specific environmental conditions, thereby enabling genetic selection and enhancing breeding practices. This paper introduces two key models for GxE interaction research. Specifically, it includes significance analysis based on the mixed effect model to determine whether genes or GxE interactions significantly affect phenotypic traits; stability analysis, which further investigates the interactive relationships between genes and environments, as well as the relative superiority or inferiority of genotypes across environments. Additionally, this paper presents RGxEStat, a lightweight interactive tool, which is developed by the authors and integrates the construction, solution, and visualization of the aforementioned models. Designed to eliminate the need for breeders and agronomists to learn complex SAS or R programming, RGxEStat provides a user-friendly interface for streamlined breeding data analysis, significantly accelerating research cycles. Codes and datasets are available at https://github.com/mason-ching/RGxEStat.
