Identification of resistance loci in Chinese and Canadian canola/rapeseed varieties against Leptosphaeria maculans based on genome-wide association studies
Bibliographic record
Abstract
Abstract Background The fungal pathogen Leptosphaeria maculans (Lm). causes blackleg disease on canola/rapeseed in many parts of the world. It is important to use resistant cultivars to manage the disease and minimize yield losses. In this study, twenty-two Lm isolates were used to identify resistance genes in a collection of 243 canola/rapeseed (Brassica napus L.) accessions from Canada and China. These Lm isolates carry different compliments of avirulence genes, and the investigation was based on a genome-wide association study (GWAS) and genotype-by-sequencing (GBS). Results Using the CROP-SNP pipeline, a total of 81,471 variants, including 78,632 SNPs and 2839 InDels, were identified. The GWAS was performed using TASSEL 5.0 with GLM + Q model. Thirty-two and 13 SNPs were identified from the Canadian and Chinese accessions, respectively, tightly associated with blackleg resistance with P values < 1 × 10− 4. These SNP loci were distributed on chromosomes A03, A05, A08, A09, C01, C04, C05, and C07, with the majority of them on A08 followed by A09 and A03. The significant SNPs identified on A08 were all located in a 2010-kb region and associated with resistance to 12 of the 22 Lm isolates. Furthermore, 25 resistance gene analogues (RGAs) were identified in these regions, including two nucleotide binding site (NBS) domain proteins, fourteen RLKs, three RLPs and six TM-CCs. These RGAs can be the potential candidate genes for blackleg resistance. Conclusion This study provides insights into potentially new genomic regions for discovery of additional blackleg resistance genes. The identified regions associated with blackleg resistance in the germplasm collection may also contribute directly to the development of canola varieties with novel resistance genes against blackleg of canola.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".