Association mapping of seed oil content in Brassica napus and comparison with quantitative trait loci identified from linkage mappingThis article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”.
Bibliographic record
Abstract
Association mapping has been used increasingly in natural populations with rich genetic diversity to detect DNA-based markers that are associated with important agronomic traits. Brassica napus is an important oil crop with limited genetic diversity. "New-type" B. napus that is introgressed with subgenomic components from related species has been developed to broaden the genetic basis of "traditional" B. napus. In this study, new-type B. napus lines and a collection of traditional B. napus varieties from different countries were used as two different populations to evaluate seed oil content and to determine the efficacy of association mapping by comparison with previous study of linkage mapping. Relatively rich genetic diversity, but a higher level of linkage disequilibrium was observed in the new-type B. napus as compared with the traditional B. napus. Similarly, a larger variation in oil content and a greater number of associated markers were detected in the population of new-type B. napus. Meanwhile, more than half of the genetic loci, to which the associated markers corresponded, were located within the quantitative trait loci intervals identified previously in linkage mapping experiments, which demonstrated the power of association mapping in B. napus.
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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 | 0.000 |
| Bibliometrics | 0.002 | 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".