High-resolution genetic dissection of the major QTL for tipburn resistance in lettuce,<i>Lactuca sativa</i>
Bibliographic record
Abstract
Tipburn is an important physiological disorder of lettuce, Lactuca sativa L., related to calcium deficiency that can result in leaf necrosis and unmarketable crops. The major quantitative trait locus (QTL), qTPB5.2, can account for up to 70% of the phenotypic variance for tipburn incidence in the field. This QTL was genetically dissected to identify candidate genes for tipburn by creating lines with recombination events within the QTL and assessing their resistance to tipburn. By comparing lines with contrasting haplotypes, the genetic region was narrowed down to ∼877 Kb that was associated with a reduction of tipburn by ∼60%. Analysis of the lettuce reference genome sequence revealed 12 genes in this region, one of which is a calcium transporter with a single nucleotide polymorphism in an exon between haplotypes with contrasting phenotypes. RNA-seq analysis of recombinants revealed two genes that were differentially expressed between contrasting haplotypes consistent with the tipburn phenotype. One encodes a Teosinte branched1/Cycloidea/Proliferating Cell factor transcription factor; however, differential expression of the calcium transporter was detected. The phenotypic data indicated that there is a second region outside of the ∼877 Kb region but within the QTL, at which a haplotype from the susceptible parent decreased tipburn by 10-20%. A recombinant line was identified with beneficial haplotypes in each region from both parents that showed greater tipburn resistance than the resistant parent; this line could be used as the foundation for breeding cultivars with more resistance than is currently available.
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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 | 0.001 |
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".