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

## TL;DR

A new sequencing tool called GBTS is developed to study fungal populations, offering a more efficient way to analyze genetic diversity and track disease spread in crops.

## Contribution

The novel GBTS chip enables efficient and scalable population genetic analysis of fungal pathogens without the need for culturing.

## Key findings

- The GBTS chip was successfully applied to profile Puccinia striiformis f. sp. tritici, revealing three genetic groups.
- Gene flow analysis showed closer connectivity between Qinghai and Gansu provinces, consistent with wind trajectory models.
- The methodology provides a scalable framework for genomic epidemiology of other fungal pathogens.

## Abstract

Advanced genotyping technologies for understanding the genetic intricacies of fungal pathogens have broad applications in crop protection. Here, we introduce a novel genotyping-by-target sequencing (GBTS) chip, a versatile tool designed for comprehensive genetic analysis of fungal populations. This technology overcomes key limitations of traditional molecular marker-based approaches by providing a more efficient, economic, and streamlined solution while bypassing the need for labor-intensive pathogen culturing. We demonstrate its utility by applying it to profile Puccinia
striiformis f. sp. tritici (Pst), the causal agent of wheat stripe rust. Our analysis involved 225 infected leaves collected from wheat fields in the northwest oversummering region for Pst in China. We delineated three genetic groups and revealed frequent gene flow, with closer connectivity between Qinghai and Gansu than either province with Ningxia, a pattern consistent with wind trajectory models. These findings illustrate a highly connected regional epidemic system and highlight the value of the GBTS chip for genomic epidemiology. The methodology established here provides a scalable framework for population genetic studies in other fungal pathogens, promising to enhance disease monitoring and management across agricultural systems.

The online version contains supplementary material available at 10.1007/s44154-025-00281-2.