Fine mapping of a major QTL controlling early flowering in tomato using QTL-seq
Bibliographic record
Abstract
Early flowering is one of the major earliness traits in tomato and is also an important agronomical trait in crop plants; thus, this trait is important for plant breeding and crop improvement. With the innovation of rapid and cost-effective technologies, quantitative trait locus (QTL)-seq has become the preferred method of performing QTL identification. In the present study, we identified a candidate QTL of an early flowering trait in tomato (Solanum lycopersicum) using QTL-seq. Two DNA pools of the extreme phenotype of the F2 progeny from crosses between the ‘Bone MM’ cultivar (early flowering, P1) and ‘071-440’ cultivar (late flowering, P2) were bulked for sequencing and an alignment analysis. We observed 220 single nucleotide polymorphism markers, seven candidate QTLs, and genes that may be associated with early flowering located between 1.6 and 71.8 Mb on chromosome 1. Using traditional QTL analysis, the location of one QTL was confirmed in the physical region between 23.5 and 25.3 Mb, which corresponded to the region identified using QTL-seq, and was referred to as EF1 (Solyc01g017060). A real-time quantitative reverse transcription polymerase chain reaction analysis showed that EF1 was the most highly expressed among the candidate genes and significantly expressed in early flowering parents and furthermore, we found that EF1, which had a similar sequence to the Ycf2 gene, may relate to the early flowering phenotype.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".