Harnessing multi-omics approaches to combat Karnal bunt of wheat: a review of advances and future prospects

Karnal bunt of wheat, caused by the fungus Tilletia indica , is a major quarantine disease that not only affects global wheat trade but also leads to yield loss and reduced grain quality. With global climate change, the disease has spread to new areas across continents, increasing vulnerabilities and creating a worrisome scenario, as once established, it is extremely difficult to eradicate. Host resistance remains the most effective strategy to combat Karnal bunt. However, only a few resistant sources have been identified so far and are being deployed in breeding programs. Various omics approaches including genomics, transcriptomics, proteomics and metabolomics have gained considerable attention for their role in enhancing disease resistance and improving agronomic yield in wheat. Notably, the integration of multiple omics and epiomics strategies has led to substantial advancements in identifying candidate genes, analyzing pathways, and understanding key elements of stress responses, thereby improving yields. Renowned for its data-mining capabilities, Machine Learning offers an opportunity to enhance the precision of current trait association methods. Nonetheless, its application in predicting disease resistance is still not widespread. In this review, we explore various omics technologies and platforms employed in wheat research to deepen the understanding of the molecular mechanisms involved in host-pathogen interactions, thereby advancing resistance to Karnal bunt of wheat. Furthermore, we emphasize the potential of Machine Learning as a significant tool for pinpointing genetic loci that contribute to host resistance.