Quantitative Trait Loci Affecting Oil Content, Oil Composition, and Other Agronomically Important Traits in Oat
Bibliographic record
Abstract
Groat oil content and composition are important determinants of oat ( Avena sativa L.) quality. We investigated these traits in a population of 146 recombinant inbred lines from a cross between ‘Dal’ (high oil) and ‘Exeter’ (low oil). A linkage map consisting of 475 Diversity Arrays Technology (DArT) markers spanning 1271.8 cM across 40 linkage groups was constructed. Quantitative trait locus (QTL) analysis for groat oil content and composition was conducted using grain samples grown at Aberdeen, ID, in 1997. Quantitative trait locus analysis for multiple agronomic traits was also conducted using data collected from hill plots and field plots in Ottawa, ON, in 2010. Using simple and composite interval mapping methods, QTLs for oil content, palmitic acid (16:0), stearic acid (18:0), oleic acid (18:1), linoleic acid (18:2), and linolenic acid (18:3) were identified. Two of the loci associated with oil content were associated with all of the fatty acids examined in this study, and most oil‐related QTL showed similar patterns of effect on the fatty acid profile. These results suggest the presence of pleiotropic effects on oil‐related traits through influences at specific nodes of the oil synthesis pathway. In addition, 12 QTL‐associated markers (likely representing nine unique regions) were associated with plant height, heading date, lodging, and protein content.
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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".