Effective management of clubroot disease in canola, caused by Plasmodiophora brassicae, depends
largely on the use of resistant varieties. However, because host resistance can vary in effectiveness
against different clubroot pathotypes, it is essential to quickly and accurately identify which pathotypes
are present in a field. Traditional pathotype identification involves inoculating a set of host plants with
unknown pathogen strains, a process that is time-consuming and labor-intensive.
To streamline this, we are developing a DNA-based method to distinguish clubroot pathotypes using
unique genetic fingerprints. We assembled the genomes of seven clubroot isolates from different
pathotypes using advanced High Fidelity (HiFi) sequencing and identified regions of DNA variation. We
also analyzed genetic data from 45 additional isolates representing 13 pathotypes using Illumina
sequencing to detect both small DNA changes (single-character variants) and larger structural
differences.

From this work, we identified variable DNA regions that can serve as pathotype-specific markers. These
markers could form the basis of a high-throughput sequencing assay capable of detecting multiple
pathotypes in a single sample, such as soil, greatly reducing the time and effort required for diagnostics.
We have developed computational tools to compare these DNA fingerprints and will conduct laboratory
testing to validate the approach. In particular, we are evaluating a technique called metabarcoding, which
uses short, unique DNA sequences as ‘barcodes’ for rapid detection. This method can be applied to both
plant and soil samples for early detection of clubroot pathotypes.
By improving pathotype identification and tracking, this approach will support breeding programs, guide
variety selection, and ultimately help growers manage clubroot more effectively.