A machine learning approach to genome-wide association mapping of disease resistance and geographic origin in sorghum

Authors: Ahn, Ezekiel; Baek, Insuck; Park, Sunchung; Prom, Louis; LIM, SEUNGHYUN - Orise Fellow; Jang, Jae Hee; HONG, SEOK MIN - Ulsan National Institute Of Science And Technology (UNIST); Kim, Moon; Meinhardt, Lyndel; MAGILL, CLINT - Texas A&M University

Submitted to: BMC Plant Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/24/2026
Publication Date: 2/28/2026
Citation: Ahn, E.J., Baek, I., Park, S., Prom, L.K., Lim, S., Jang, J., Hong, S., Kim, M.S., Meinhardt, L.W., Magill, C. 2026. A machine learning approach to genome-wide association mapping of disease resistance and geographic origin in sorghum. The Plant Genome. https://doi.org/10.1186/s12870-026-08468-z.
DOI: https://doi.org/10.1186/s12870-026-08468-z

Interpretive Summary: Sorghum, the fifth most important cereal crop globally and widely grown across tropical and temperate regions of Africa, Asia, and the Americas, faces persistent threats from fungal diseases that limit productivity and resilience. To investigate the genetic basis of disease resistance and geographic adaptation, machine learning–enabled genome wide analysis was applied to a diverse sorghum panel, prominently featuring a unique Senegalese collection, integrating nearly 300,000 DNA markers and phenotypic data to identify genomic regions associated with geographic origin and resistance to anthracnose, head smut, and downy mildew. We discovered that the sorghum from Senegal possesses a distinct genetic makeup, hinting at a unique evolutionary history and potentially harboring valuable genes for disease resistance. Furthermore, we pinpointed specific regions in the sorghum genome linked to both geographic origin and disease resistance. This knowledge empowers scientists and breeders to develop improved sorghum varieties tailored to specific regions and resistant to prevalent diseases, contributing to increased food security and more sustainable agricultural practices.

Technical Abstract: Sorghum, the fifth most important cereal crop globally, faces persistent threats from fungal diseases that limit productivity and resilience. To investigate the genetic basis of disease resistance and geographic adaptation, we applied a machine learning-enabled genome-wide association study (GWAS) to a panel of 377 genetically diverse sorghum accessions, incorporating nearly 300,000 SNP markers and phenotypic evaluations for resistance to anthracnose, head smut, and downy mildew. While disease resistance phenotypes did not cluster strictly by geographic origin, SNP-based analyses revealed significant genetic differentiation among accessions from different regions, particularly involving a genetically distinct group from Senegal. Bootstrap Forest models highlighted candidate SNPs predictive of geographic origin, most notably on Chromosome 10, near genes encoding transcription factors (e.g., bHLH, EREBP-like) and DUF6598-domain proteins with potential roles in plant defense. For disease resistance, top-ranked SNPs were located near genes implicated in canonical immune pathways, including zinc-binding proteins (anthracnose), NB-ARC and LRR-containing proteins (head smut), and F-box proteins (downy mildew). Although exploratory in nature, these findings suggest that local adaptation to pathogen pressure may have shaped sorghum’s genomic landscape. The identified candidate genes and associated SNPs help prioritize targets for marker-assisted selection and follow-up functional validation, contributing to the development of sorghum varieties with enhanced resistance and adaptability.