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Machine learning reveals complex genetics of fungal resistance in sorghum grain mold

Heredity volume 134, pages 485–499 (2025)Cite this article

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Abstract

Plant disease resistance is often a complex, polygenic trait, making its genetic dissection with traditional genome-wide association studies (GWAS) challenging. Grain mold in sorghum, a devastating disease caused by a fungal complex, exemplifies this complexity. We hypothesized that a machine learning (ML)-driven GWAS, employing diverse phenotypic representations from a panel of 306 sorghum accessions, could more effectively unravel the genetic basis of resistance. Phenotypic data, including raw disease scores, a ‘difference phenotype’ (inoculated vs. control), and principal components, were analyzed using Boosted Tree and Bootstrap Forest models, demonstrating strong explanatory power for phenotypic variance when trained on the entire dataset. This ML-GWAS approach confirmed a highly polygenic architecture for grain mold resistance, identifying numerous SNPs across the sorghum genome. Notably, several SNPs were consistently associated with resistance across multiple analytical models and phenotypic representations. These robustly identified SNPs were frequently located near genes with predicted functions integral to plant defense. Gene ontology (GO) analyses of the candidate gene set confirmed enrichment in categories supporting roles in pathogen recognition, DNA repair, and stress response modulation, indicating a multifaceted defense mechanism. This study provides valuable candidate genes for breeding sorghum with enhanced grain mold resistance and offers a refined methodological framework for dissecting complex traits in this crop. The successful application of this ML-based strategy in sorghum suggests its potential utility for studying similar complex traits in other plant species.

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Fig. 1: Population structure and genetic-phenotypic correlations in sorghum grain mold resistance.
Fig. 2: Manhattan plots showing genome-wide association results for grain mold resistance in sorghum under different treatments.
Fig. 3: Manhattan plots showing genome-wide association results for the difference in grain mold response between treatment and control conditions in sorghum SAP lines.
Fig. 4: Genome-wide association results for combined phenotypic score and PC 1 in sorghum grain mold resistance.
Fig. 5: Venn diagrams illustrating the overlap among the top 100 candidate SNPs identified by three different methodologies for various grain mold resistance phenotypes in 306 sorghum accessions.
Fig. 6: GO enrichment analysis of candidate genes associated with grain mold resistance.

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Data availability

The complete list of top SNPs, their associated LD blocks, and the complete results of the Gene Ontology (GO) enrichment analysis are available in Supplementary Data 1. All other relevant data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work is supported by the U.S. Department of Agriculture, Agricultural Research Service, In-House Projects No. 8042-21220-258-000-D and 8042-21000-303-000-D. Mention of any trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer, and all agency services are available without discrimination.

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Authors and Affiliations

  1. Sustainable Perennial Crops Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, USA

    Ezekiel Ahn, Sunchung Park, Jishnu Bhatt, Seunghyun Lim & Jae Hee Jang

  2. Insect Control and Cotton Disease Research, Agricultural Research Service, Southern Plains Agricultural Research Center, United States Department of Agriculture, College Station, TX, USA

    Louis K. Prom

  3. Soybean Genomics & Improvement Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, USA

    Dongho Lee

  4. Molecular Plant Sciences, Washington State University, Pullman, WA, USA

    Vishnutej Ellur

  5. Molecular Plant Pathology Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, USA

    Dilip Lakshman

  6. Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA

    Clint Magill

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  1. Ezekiel Ahn
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  2. Louis K. Prom
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  3. Sunchung Park
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Contributions

Conceptualization: EA. Methodology: LKP, SP, DL, JB, VE, SL, JJ, DL. Formal analysis: EA. Validation: EA, SP, DL, JJ, CM. Investigation: LKP, SP. Resources: LKP, JHJ, VE, DL, CM. Writing—Original Draft: EA. Writing—Review & Editing: EA, LKP, SP, DL, JB, VE, SL, JJ, DL, CM. Visualization: EA, LKP, SP, CM. Supervision: EA, CM. Project Administration: EA, CM. Funding Acquisition: EA, LKP, SP, CM.

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Correspondence to Ezekiel Ahn.

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Ahn, E., Prom, L.K., Park, S. et al. Machine learning reveals complex genetics of fungal resistance in sorghum grain mold. Heredity 134, 485–499 (2025). https://doi.org/10.1038/s41437-025-00783-9

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