Quantitative Trait Locus Locating Analysis of Bud Set time in an F1 Hybrid Population of <i>Populus deltoides</i> and <i>Populus simonii</i>
Bibliographic record
Abstract
Growth and dormancy are the two most important biological processes in the life cycle of perennial plants. Studying dormancy-related traits is of great significance for understanding the adaptability of forest growth and improving the efficiency of plant molecular breeding. In order to reveal the genetic mechanism of poplar bud set, we performed quantitative trait locus (QTL) locating analysis of the trait with the KW method in the software MapQTL, based on the F 1 hybrid population derived from a cross of Populus deltoides and Populus simonii and the two parental linkage maps. As a result, 15 QTLs controlling bud set were identified distributing on 7 linkage groups, among which 12 QTLs were located in linkage groups LG-1, LG-7, LG-9, LG-11 and LG-15 on the maternal P. deltoides map, whereas the other 3 QTLs were distributed in linkage groups LG-9 and LG-12 on the paternal P. simonii map. According to the location of the QTLs on the P. trichocarpa genome, a total of 45 candidate genes for bud set were identified. Furthermore, GO and KEGG enrichment analyses revealed that 71% of the candidate genes possessed potential functions in light and hormone signal transduction pathways. The research results provide a valuable resource for exploring the genes involved with bud set in poplar and for the application in molecular marker-assisted 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".