Significance and Stability Analysis of Gene-Environment Interaction using RGxEStat

2026-04-03

AI Summary

Gene-by-environment interaction (G×E) substantially reduces the accuracy of phenotypic prediction and hinders the efficiency of precision breeding. To address this challenge, this study develops a mixed-effects model that, for the first time, integrates significance testing and stability analysis of G×E interactions within a unified framework. Furthermore, we introduce RGxEStat, a lightweight, programming-free, interactive R tool that seamlessly combines modeling, computation, and visualization functionalities. This tool greatly streamlines the analytical workflow for breeding data, enabling efficient evaluation of genotype performance across multiple environments and accelerating breeding decision cycles. Both the source code and example datasets are openly available, significantly lowering the technical barrier for agricultural researchers engaging in advanced genomic analyses.

Abstract

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.

Problem

• Gene-Environment Interaction
• GxE
• Phenotypic Predictability
• Genotype Performance
• Breeding

Innovation

• Gene-Environment Interaction
• Mixed Effect Model
• Stability Analysis
• RGxEStat
• Breeding Data Analysis