Integrating multi-omics and machine learning for disease resistance prediction in legumes

Multi-omics assisted prediction of disease resistance mechanisms using machine learning has the potential to accelerate the breeding of resistant legume varieties. Grain legumes, such as soybean (Glycine max (L.) Merr.), chickpea (Cicer arietinum L.), and lentil (Lens culinaris Medik.) play an important role in combating micronutrient malnutrition in the growing human population. However, plant diseases significantly reduce grain yield, causing 10-40% losses in major food crops. The genetic mechanisms associated with disease resistance in legumes have been widely studied using genomic approaches. Multi-omics data encompassing various biological layers such as the transcriptome, epigenome, proteome, and metabolome, in addition to the genome, enables researchers to gain a deeper understanding of these complementary layers and their roles in complex legume-pathogen interactions. Genomic prediction, used to select the best genotypes with desirable traits for breeding, has largely relied on genome-wide markers and statistical approaches to estimate the breeding values of individuals. Integrating multi-omics data into genomic prediction can be achieved using machine learning models, which can capture nonlinear relationships prevalent in high-dimensional data better than traditional statistical methods. This integration may enable more accurate predictions and identification of resistance mechanisms for breeding resistant legumes. Despite its potential, multi-omics integration for disease resistance prediction in legumes has been largely unexplored. In this review, we explore omics studies focusing on disease resistance in legumes and discuss how machine learning models can integrate multi-omics data for disease resistance prediction. Such multi-omics assisted prediction has the potential to reduce the breeding cycle for developing disease-resistant legume varieties.