Identification of Early Tomato Fruit Ripening Loci by QTL-seq
Bibliographic record
Abstract
QTL-seq has been successfully studied in identifying major QTLs, markers, and candidate genes associated with traits that are important for crop improvement. Tomato earliness is an economically important trait and is a major current research focus recently. This paper reports the identification of tomato early ripening fruit locus facilitated by QTL-seq using a novel next-generation sequencing technology. Two DNA pools of phenotypes of F2 offspring from crosses between the Bone ММ (early ripening fruit, P1) and 071-440 (late ripening fruit, P2) cultivars of (Solanum lycopersicum) were bulked for sequencing and alignment analysis. Sequencing results revealed 434 SNP markers on chromosome 11, a candidate QTL at position 52,048,208 bp (named er-fruit) and a candidate gene, Solyc11g071510.1.1. The “er-fruit” as confirmed by the traditional QTL method was related to the early fruit ripening trait in tomato. Additionally, BLAST analysis to known homologies for Solyc11g071510.1.1 gene encodes glycoside hydrolases (GHs). GHs are functionally associated with cell wall degradation, fruit softening and ripening. Thus, GHs may be important in fruit softening, stimulating early fruit ripening in tomato. Our results confirmed that QTL-seq is effective method to identify candidate QTL loci, candidate genes and candidate markers.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".