Significance and Stability Analysis of Gene-Environment Interaction using RGxEStat

TL;DR

This paper introduces models for analyzing gene-environment interactions and presents RGxEStat, a user-friendly tool that simplifies complex statistical analysis for breeders and agronomists.

Contribution

It develops significance and stability models for GxE interactions and provides RGxEStat, a lightweight, interactive software tool for streamlined analysis and visualization.

Findings

RGxEStat effectively identifies significant GxE interactions.

The models reveal genotype stability across diverse environments.

The tool accelerates breeding research cycles.

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.