Pangenomics and Machine Learning in Improvement of Crop Plants

The growing global human population requires improvement in crop production to meet food demand. Crop improvementCrop improvement via breeding can sustainably increase yield production and stability and decrease dependence on fertilisers and pesticides. Recent progresses in pangenomicsPangenomics and machine learningMachine learning provide opportunities for crop improvementCrop improvement. The development of long-read sequencing technologies is helping overcome challenges in crop genome assembly caused by highly repeated regions or heterozygous sequences. As a result, high-quality crop reference genomes and pangenomes are becoming increasingly accessible, enhancing downstream analyses such as variant discovery and association mappingAssociation mapping, which are crucial for identifying breeding targets for crop improvementCrop improvement. Machine learningMachine learning approaches help to characterise the growing volume of plant genomicPlant genomics data and facilitating real-time high-throughput phenotyping of agronomic traitsAgronomic traits. Moreover, crop databases that integrate the increasing amount of genotypes identified using pangenomes and machine learningMachine learning approaches are valuable for uncovering novel trait-associated candidate genes. With an increasing understanding of crop geneticsCrop genetics, genomic selectionGenomic selection (GS) and genome editingGenome editing emerge as powerful tools for cultivating crops that are resistant to both biotic and abiotic stressesAbiotic stress, while also achieving high productivity.