With the global human population growing rapidly, agricultural production must increase to meet crop demand. Improving crops through breeding is a sustainable approach to increase yield and yield stability without intensifying the use of fertilisers and pesticides. Current advances in genomics and bioinformatics provide opportunities for accelerating crop improvement. The rise of third generation sequencing technologies is helping overcome challenges in plant genome assembly caused by polyploidy and frequent repetitive elements. As a result, high-quality crop reference genomes are increasingly available, benefitting downstream analyses such as variant calling and association mapping that identify breeding targets in the genome. Machine learning also helps identify genomic regions of agronomic value by facilitating functional annotation of genomes and enabling real-time high-throughput phenotyping of agronomic traits in the glasshouse and in the field. Furthermore, crop databases that integrate the growing volume of genotype and phenotype data provide a valuable resource for breeders and an opportunity for data mining approaches to uncover novel trait-associated candidate genes. As knowledge of crop genetics expands, genomic selection and genome editing hold promise for breeding diseases-resistant and stress-tolerant crops with high yields.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited