Association mapping for phenological, morphological, and quality traits in canola quality winter rapeseed (<i>Brassica napus</i>L.)This 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
QTL mapping by association analysis has recently gained interest in plant breeding research as an alternative to QTL mapping in segregating populations from biparental crosses. In a first experiment on whole-genome association analysis in rapeseed, 684 mapped AFLP markers were tested for association with 14 traits in a set of 84 canola quality winter rapeseed cultivars. For association analysis a general linear model was used. By testing significance of marker-trait associations against a false discovery rate of 0.2, between 1 and 34 associated markers were found for 10 of the 14 traits. Taking into account linkage disequilibrium between the significant markers, these markers represent between 1 and 22 putative QTL for the respective traits. The minimum phenotypic variance explained by the QTL for the different traits ranged from 15% to 53%. A subset of 27 markers were significantly associated with two or more traits. These markers were predominantly shared between traits that were significantly correlated at the phenotypic level. The results show clearly that in rapeseed, QTL mapping by association analysis is a viable alternative to QTL mapping in segregating populations.
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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 | 0.001 | 0.001 |
| 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".