iGS: A Zero-Code Dual-Engine Graphical Software for Polygenic Trait Prediction

Abstract Genomic selection (GS) has become the core driving force in modern plant and animal breeding. However, state-of-the-art comprehensive GS tools often rely on complex underlying environment configurations and command-line operations, posing significant technical barriers for breeders lacking programming expertise. To address this critical pain point, this study developed a fully “zero-code” graphical user interface (GUI) decision support system for genomic selection. The platform innovatively employs a “portable dual-engine architecture” (R-Portable and Python-Portable) to achieve completely dependency-free, “out-of-the-box” deployment, and integrates a standardized six-step end-to-end workflow from data quality control to result export. Furthermore, the platform comprehensively integrates 33 cutting-edge prediction models across four major paradigms, linear, Bayesian, machine learning, and deep learning, and features an original intelligent parameter configuration system that dynamically renders algorithm parameters to provide a minimalist UI interaction experience. Benchmark testing on the Wheat2000 dataset across six complex agronomic and quality traits, including thousand-kernel weight (TKW) and grain protein content (PROT), demonstrated that classic linear models remain highly robust for polygenic additive traits, while tree-based machine learning and hybrid deep learning architectures exhibit superior predictive potential and noise resilience when resolving complex epistatic effects and low-heritability traits. The successful deployment of this platform fundamentally liberates biologists from the constraints of computational science, providing robust digital infrastructure to accelerate the popularization and practical application of GS technologies in agricultural production.