Genome-Wide Mapping of Loci Associated With Resistance to Clubroot in Brassica napus ssp. napobrassica (Rutabaga) Accessions From Nordic Countries
Bibliographic record
Abstract
Rutabaga (Brassica napus ssp. napobrassica (L.) Hanelt) is reported to be an excellent source of clubroot (Plasmodiophora brassicae) resistance genes. In this study, 124 rutabaga accessions from the Nordic countries (Norway, Sweden, Finland, Denmark, and Iceland) were evaluated for their reaction to five single-spore isolates representing P. brassicae pathotypes 2F, 3H, 5I, 6M, and 8N and 12 field isolates representing pathotypes 2B, 3A, 3O, 5C, 5G, 5K, 5L, 5X (two isolates, L-G2 and L-G3), 8E, 8J and 8P. The accessions were also genotyped using a 15K Brassica SNP array and 60 PCR-based primers linked to previously identified clubroot resistance genes. Six thousand eight hundred sixty-one SNP markers were retained after filtering with TASSEL 5.0, and used to evaluate four general linear models (GLM) and four mixed linear models (MLM). The PCA + K and Q + K MLM models gave the minimal deviance of the observed from the expected distribution in quantile-quantile plots, and hence were used for SNP-clubroot association analyses; in addition, 108 alleles derived from the PCR-based markers and the phenotypic data were analyzed with the PCA + K model. Forty-five SNPs and four PCR-based markers were identified to be associated strongly with resistance to isolates representing 13 pathotypes (2F, 3H, 5I, 6M, 8N, 2B, 3A, 3O, 5C, 5G, 5K, 5L and 8P). These markers revealed the top and bottom segments of rutabaga chromosome A03 and the middle segment of chromosome A08 as genomic hotspots associated with resistance to the different P. brassicae pathotypes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".