Construction of a high-density genetic linkage map and QTL mapping of oleic acid content and three agronomic traits in sunflower (<i>Helianthus annuus</i> L.) using specific-locus amplified fragment sequencing (SLAF-seq)
Bibliographic record
Abstract
High-density genetic linkage maps are particularly important for quantitative trait loci (QTL) mapping, genome assembly, and marker-assisted selection (MAS) in plants.In this study, a high-density genetic linkage map of sunflower (Helianthus annuus L.) was constructed using an F 2 population generated from a cross between Helianthus annuus L. '86-1' and 'L-1-OL-1' via specific-locus amplified fragment sequencing (SLAFseq).After sequence preprocessing, 530.50 M reads (105.60 Gb) were obtained that contained a total of 343,197 SLAFs, of which 39,589 were polymorphic.Of the polymorphic SLAFs, 6,136 were organized into a linkage map consisting of 17 linkage groups (LGs) spanning 2,221.86cM, with an average genetic distance of 0.36 cM between SLAFs.Based on this high-density genetic map, QTL analysis was performed that focused on four sunflower phenotypic traits: oleic acid content (OAC), plant height (PH), head diameter (HD), and stem diameter (SD).Subsequently, for these four traits eight QTLs were detected that will likely be useful for increasing our understanding of genetic factors underlying these traits and for use in marker-assisted selection (MAS) for future sunflower breeding.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".