New DNA chip helps track fungal genetics and diversity

Jenn Hoskins
21st January, 2026

New DNA chip helps track fungal genetics and diversity

The symptoms of wheat stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (pictured), are the result of a highly interconnected pathogen population whose extensive gene flow and migration patterns were revealed by a novel genotyping chip.

Public Domain Photograph

Key Findings

  • In northwest China, a new chip rapidly analyzes wheat stripe rust fungus genetics, overcoming slow traditional methods
  • Analysis of fungus samples revealed three distinct genetic groups within the region’s pathogen population
  • Gene flow occurs between these groups, particularly between Qinghai and Gansu provinces, likely aided by wind patterns
Wheat stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst), is a major threat to wheat production globally, leading to significant yield losses. Effectively managing this disease requires understanding how the pathogen evolves and spreads. Traditional methods for studying Pst genetics have been hampered by the difficulty of growing the fungus in the lab, a time-consuming and costly process. Researchers at Northwest A&F University and Qinghai University have developed a new tool to overcome these limitations: a genotyping-by-target sequencing (GBTS) chip[1]. This new technology, GBTS, allows for rapid and efficient genetic analysis of Pst directly from infected wheat leaves, eliminating the need for culturing. It’s a more streamlined and economic approach compared to older methods that rely on isolating and growing the fungus before analysis. This is particularly important because detailed genetic studies are crucial for tracking pathogen movements and identifying potential outbreaks. Previously, techniques like SSR multiplexing combined with direct DNA extraction from leaves have been used to improve genotyping efficiency[2], but GBTS represents a further advancement in speed and scalability. The study focused on Pst populations in the northwest oversummering region of China, where the pathogen survives during warmer months. Researchers analyzed 225 infected leaf samples, identifying three distinct genetic groups within the Pst population. Importantly, the analysis revealed significant gene flow – the transfer of genetic material – between these groups. Specifically, there was a stronger connection observed between Pst populations in Qinghai and Gansu provinces than between either of those and Ningxia province. This pattern of connectivity aligns with wind trajectory models, suggesting that wind plays a key role in spreading Pst spores between Qinghai and Gansu. This highlights a highly interconnected regional epidemic system, meaning that the pathogen is readily moving between these areas. Understanding these connections is vital for predicting disease outbreaks and implementing effective control measures. The ability to rapidly genotype large numbers of isolates is critical for understanding pathogen evolution and migration patterns[3]. Earlier studies using multilocus microsatellite genotyping demonstrated the power of population-level analysis to identify the origin and spread of Pst[3][4]. The GBTS chip presented in this study builds upon these foundations, offering a more efficient way to achieve the same goals. Furthermore, the challenges of population stratification in genetic association studies, where genetic diversity can confound results, are well-documented[5]. While not directly addressing stratification, the increased throughput of GBTS allows for more comprehensive genetic sampling, potentially mitigating some of these issues by providing a more accurate representation of the pathogen's genetic diversity. The technology’s ability to analyze a larger number of markers than previous methods, as highlighted in comparison to techniques like structure and EIGENSTRAT[5], further supports this. The methodology developed by the research team provides a scalable framework for studying other fungal pathogens, promising to enhance disease monitoring and management in agriculture. By enabling rapid and detailed genetic analysis, this tool will be invaluable for tracking pathogen evolution, identifying new threats, and developing more effective disease control strategies.

GeneticsPlant ScienceMycology

References

Main Study

1) The development and validation of a genotyping-by-target sequencing chip for fungal population genetic analysis

Published 19th January, 2026

https://doi.org/10.1007/s44154-025-00281-2

Related Studies

2) A rapid genotyping method for an obligate fungal pathogen, Puccinia striiformis f.sp. tritici, based on DNA extraction from infected leaf and Multiplex PCR genotyping.

https://doi.org/10.1186/1756-0500-4-240

3) Origin, migration routes and worldwide population genetic structure of the wheat yellow rust pathogen Puccinia striiformis f.sp. tritici.

https://doi.org/10.1371/journal.ppat.1003903

4) Molecular Characterization of Wheat Stripe Rust Pathogen (Puccinia striiformis f. sp. tritici) Collections from Nine Countries.

https://doi.org/10.3390/ijms22179457

5) Fast model-based estimation of ancestry in unrelated individuals.

https://doi.org/10.1101/gr.094052.109


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