Integrating multi-omics and genome editing for biotic stress resistance and quality improvement in groundnut
Groundnut ( Arachis hypogaea L.) is a major legume and oilseed crop, yet its productivity is constrained by biotic stresses, including fungal, bacterial, and viral pathogens and insect pests, which reduce yield and seed quality. These constraints are difficult to address through conventional breeding, because stress tolerance is polygenic and is further complicated by the allotetraploid genome and strong genotype × environment interactions. To overcome these limitations, multi-omics approaches are now used to dissect stress responses at the mechanistic level; in particular, the integration of transcriptomic and metabolomic data has revealed coordinated regulatory networks involving resistance genes, transcription factors, hormone-mediated signalling pathways, and secondary metabolite biosynthesis. Building on these insights, genome editing, particularly CRISPR/Cas together with base and prime editing, provides precise tools to study and modify these pathways, although in groundnut it has so far been applied mainly to seed oil quality and herbicide resistance. In parallel, machine-learning approaches are beginning to support candidate-gene prioritisation and trait prediction, though their use in groundnut remains limited and largely unvalidated. Realising the potential of these approaches will therefore depend less on the tools themselves than on resolving gene redundancy in the polyploid genome, achieving efficient transformation, and validating candidate genes under field conditions. By integrating multi-omics analysis, precision genome editing, and predictive modelling, this review outlines a framework to improve biotic stress resistance and seed quality in groundnut.