Advancing anthracnose resistance in dry beans through the transition from traditional to computational breeding efforts

), causing significant yield losses worldwide. Despite considerable progress in breeding resistant varieties using traditional methods such as phenotypic selection and crossbreeding, the ongoing challenges posed by the pathogen's genetic diversity and environmental variability call for more sustainable solutions. Traditional breeding methods have made notable advancements, but with the increasing pressure of climate change and evolving disease dynamics, there is a growing need to complement these approaches with modern computational tools. The integration of genomics, phenomics, and bioinformatics has introduced new possibilities in disease resistance breeding. Techniques such as high-throughput sequencing, genome-wide association studies (GWAS), and marker-assisted selection have accelerated the identification of resistance genes, while machine learning and multi-omics approaches provide a deeper understanding of host-pathogen-environment interactions. Therefore, this review aims to provide a comprehensive synthesis of the historical development of anthracnose resistance breeding, highlighting the role of traditional methods and the transition toward computational strategies. It emphasizes how combining both approaches can enhance the development of durable, high-yielding, anthracnose-resistant dry beans, offering more effective solutions to global production challenges.