Genotype-by-Environment Interactions in Transgenic Crops: A Critical Review of Gene Function Validation under Variable Agronomic Conditions

Genotype-by-environment (G×E) interactions frequently derail transgenic crops that excel under controlled conditions. Abiotic stress, soil chemistry, and photoperiod can suppress promoters, destabilize transcripts, or misfold proteins, reshaping phenotypes across locations. Literature from the past two decades reveals recurring patterns: Bt Cry toxins lose potency during drought and heat; OsTZF5 rice yields rise only within moderate moisture deficits; safe-harbor insertions and stress-inducible promoters reduce but do not eliminate variability. Contemporary toolkits, AMMI and GGE biplots, UAV phenomics, multi-omics tracking, and crop simulators detect interaction signals earlier, while machine-learning models direct trials toward environments with high crossover risk. A staged validation pipeline is proposed: factorial stress screens, multi-environment trials, molecular bookkeeping, data integration, and iterative redesign through predictive modeling. Environment-responsive constructs tested in this framework produce evidence that satisfies regulatory comparators, improves deployment targeting, and strengthens confidence that laboratory performance will translate to climate-volatile farms.