Inheritance and development of EST-SSR marker associated with turnip mosaic virus resistance in Chinese cabbage
Bibliographic record
Abstract
Li, Q., Tong, H., Zhang, Z., Zhao, Z. and Song, X. 2011. Inheritance and development of EST-SSR marker associated with turnip mosaic virus resistance in Chinese cabbage. Can. J. Plant Sci. 91: 707–715. Turnip mosaic virus (TuMV) is one of the major pathogens infecting Brassica crops (including Chinese cabbage), and often causes serious reductions in yield and quality. Breeding for resistant cultivars is complicated by the existence of numerous TuMV strains and isolates. The objective of this research was to determine the mode of inheritance and to develop molecular markers associated with TuMV resistance in Chinese cabbage. F1and F2populations were developed from the cross between 71-36-2 (susceptible) and 73 (resistant) lines and mechanically inoculated with TuMV-C4. Inheritance analysis by visual scoring and enzyme-linked immunosorbent assay (ELISA) indicated that resistance to TuMV-C4 in this cross was controlled by one recessive gene. A total of 132 EST-SSR primers were designed from EST sequences available in public databases. Seven primers detected polymorphism between parental genotypes. Marker HCC259 was associated with the TuMV resistance Ph-retr02. The distance between the marker and the TuMV resistance gene retr02 was 3.8 cM. This is the first co-dominant marker linked to the TuMV resistance gene with a distance less than 5.0 cM in Chinese cabbage. This marker was suitable for TuMV-C4 resistance screening in progenies from the cross between lines 73 (resistant) and susceptible line, 71-36-2. The usefulness of this marker was validated in 21 additional resistant and susceptible lines. This marker has the potential to simplify and accelerate breeding Chinese cabbage cultivars resistant to TuMV-C4.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".