Fine mapping of a major QTL <i>qPA7-1</i> for low hydrocyanic acid content in sorghum–sudangrass hybrid
Bibliographic record
Abstract
The purpose of this study was to study the genetic mechanism of low hydrocyanic acid (HCN) content. The segregation of HCN content trait in fresh stems and leaves was determined in the sorghum ( Sorghum bicolor (L.) Moench)–sudangrass ( Sorghum sudanense (Piper) Stapf) hybrid F2 population ( N = 1200), also used to detect a quantitative trait locus (QTL) for HCN content. Our hypothesis was that the additive effect of QTL was negative, showing that QTL was associated with low HCN. In the present research, a total of 11 simple sequence repeats (SSR) polymorphic primers were screened, and four SSR markers associated with low HCN content were developed based on the bulked segregant analysis method. A high-resolution genetic linkage group of the previously known qPA7-1 locus of the low HCN trait was constructed by analyzing different populations, families, and recombinants. Then, the QTL qPA7-1 of sorghum–sudangrass hybrid was fine-mapped to a 203.6 kb region between markers SORBI4G4-120 and SORBI4G4-680, and seven candidate genes for low HCN were predicted in this region based on sequence comparison with the sorghum reference genome. According to gene annotation, the candidate genes related to low HCN content may be different from those involved in the known regulation mode of sorghum dhurrin biosynthesis and metabolism.
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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 | 0.000 |
| 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 | 0.000 |
| 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".