Identification and exploitation of genome structural variants for trait improvement in Prairie crops
Term: 4 years, ending July 2025
Status: Completed
Researcher: Andrew Sharpe, Sampath Perumal, Kevin Koh, Raju Chaudhary (GIFS/University of Saskatchewan); Isobel Parkin (AAFC); Curtis Gutwin (U of S)
SaskCanola Investment: $172,500
Total Project Cost: $690,000
Funding Partners: Alberta Grains, Sask Wheat, WGRF
Objectives
Develop a catalogue of structural variation (SV) in canola and wheat, which will be associated with key complex agronomic traits.
Develop a cost-effective platform for capturing such variation that can be applied in canola and wheat.
Application of SVs for trait association and candidate gene/locus identification in canola and wheat.
Project Description
Structural variants (SVs) are large-scale changes in DNA such as deletions, duplications, or insertions and are an untapped resource for improving crop traits like yield, disease resistance, and climate resilience. This four-year project focused on discovering, cataloging, and applying SV markers in two of the Prairies’ most important crops: canola and wheat. By combining advanced genome sequencing technologies and novel data analysis pipelines, the team created new reference genome assemblies, developed cost-effective SV genotyping tools, and demonstrated the power of SVs to reveal trait associations often missed by standard single nucleotide polymorphism (SNP) based approaches.
In canola, over 50 globally diverse parental lines were sequenced and assembled to create a pan-SV atlas, along with matching datasets for SNPs, gene expression (transcriptome), DNA methylation, and mobile elements (mobilome). A nested association mapping (NAM) population of ~2,600 recombinant inbred lines (RILs) was also analyzed using an SNP-array-based method to infer SVs (called SNaPs). These SVs were used to study traits such as blackleg resistance, shattering, and oil quality. In wheat, a similar strategy was used with 14 genome assemblies, including synthetic wheat lines, to develop a pan-SV resource and identify SVs using SNP array data in structured breeding populations.
The project successfully developed an adaptive sequencing-based SV genotyping pipeline, although it remains limited for large-scale applications due to sequencing cost and throughput. Instead, a scalable alternative using SNaP data was implemented across populations.
Key Results
• Developed new high-quality genome assemblies and a pan-SV atlas for 50 canola founder lines and 14 wheat lines.
• Discovered over 173,000 SVs in canola and over 2 million SVs in wheat.
• Demonstrated that SVs provide improved trait discovery resolution over SNPs alone.
• Identified a copy number variation at the Rlm gene locus that determines blackleg resistance in canola that is undetectable via SNP markers.
• Used SNaP markers to perform GWAS and genomic selection for key agronomic traits, improving prediction accuracy by 5–15%.
• Linked SVs in wheat to heading date and other traits by integrating with known QTL regions.
Grower Benefits
This project provides breeders with new genetic tools to select for better-performing canola and wheat germplasm for variety development. The SV resources and genotyping methods developed here are applicable to traits that are complex or have been historically hard to map.
These innovations will ultimately help develop varieties that are more productive, resilient to disease, and better adapted to Prairie growing conditions.