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Related Experiment Video

Updated: May 14, 2026

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot
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Identification of Genomic Regions for Partial Resistance to Soybean Rust Under Field Conditions Using FarmCPU and

António Daniel Pedro Maquil1,2,3, Tonny Obua1,2, David L Nsibo4

  • 1Department of Crop Science and Horticulture, School of Agricultural Sciences, College of Agricultural and Environmental Sciences, Makerere University, Kampala P.O. Box 7062, Uganda.

Plants (Basel, Switzerland)
|May 13, 2026
PubMed

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Correction: Genome‑wide association study of soybean germplasm derived from modern Canadian and Chinese soybean cultivars to identify novel genes conferring soybean cyst nematode resistance.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026
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Genome-wide association study of soybean germplasm derived from modern Canadian and Chinese soybean cultivars to identify novel genes conferring soybean cyst nematode resistance.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026
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Soybean Rust Resistance and Yield Performance of Elite Soybean Genotypes Across Diverse Environments in Uganda.

Plant-environment interactions (Hoboken, N.J.)·2026
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Unraveling the genetic architecture of anti-nutritional factors in soybean (Glycine max.) for nutritional enhancement.

Scientific reports·2025
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Merging traditional practices and modern technology through computational plant breeding.

Plant physiology·2025
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Multi-locus GWAS analysis identifies genomic regions associated with resistance to ergot (Claviceps africana) in sorghum.

PloS one·2025
Summary

Identifying genes for durable soybean rust resistance is crucial for crop protection. This study used GWAS and machine learning to find key genetic loci and candidate genes, paving the way for improved soybean breeding strategies.

Area of Science:

  • Plant Pathology and Genetics
  • Agricultural Science

Background:

  • Soybean rust, caused by *Phakopsora pachyrhizi*, poses a significant threat to global soybean production, leading to substantial yield losses.
  • While race-specific resistance genes (*Rpp*) offer limited protection due to pathogen variability, partial resistance (PR) provides more durable, broad-spectrum defense, but its genetic underpinnings are not well understood.

Purpose of the Study:

  • To identify genomic regions and candidate genes associated with partial resistance (PR) to soybean rust.
  • To leverage genome-wide association study (GWAS) and machine learning (ML) approaches for a comprehensive understanding of PR's genetic architecture.

Main Methods:

  • A panel of 312 soybean accessions was phenotyped for rust index (RI) across six Ugandan environments under natural infection.
  • Genome-wide association study (GWAS) using Fixed and Random Model Circulating Probability Unification (FarmCPU) and machine learning (ML) methods (Random Forest and Support Vector Regression) were employed.
Keywords:
GWAScandidate genemachine learningpartial resistancesoybean rust

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  • Analysis involved 8272 SNPs within a ±60 kb linkage disequilibrium (LD) window, with heritability (H²) estimated and genotypes ranked using Best Linear Unbiased Predictions (BLUPs).

    • Main Results:

    • Multi-environmental analysis revealed significant genetic effects on RI (p < 0.01) with high heritability (H² = 0.57-0.68).
    • A total of 61 loci associated with PR were detected, with ML methods enhancing the discovery rate compared to FarmCPU alone.
    • Candidate genes identified include *Glyma.01G128100* (WRKY transcription factor), *Glyma.13G228000* (receptor-like kinase), and *Glyma.20G173100* (WD40-domain regulator).

    Conclusions:

    • Integrating ML with GWAS effectively identified loci underlying partial resistance (PR) to soybean rust, confirming its polygenic nature.
    • The findings support the potential for genomic selection and locus pyramiding strategies to develop durable soybean rust resistance.
    • This research provides valuable genetic resources for breeding programs aiming to enhance soybean resilience against *Phakopsora pachyrhizi*.