Molecular mapping of QTLs conferring stay-green in grain sorghum (<i>Sorghum bicolor</i>L. Moench)
Bibliographic record
Abstract
Drought resistance is of enormous importance in crop production. The identification of genetic factors involved in plant response to drought stress provides a strong foundation for improving drought tolerance. Stay-green is a drought resistance trait in sorghum (Sorghum bicolor L. Moench) that gives plants resistance to premature senescence under severe soil moisture stress during the post-flowering stage. The objective of this study was to map quantitative trait loci (QTLs) that control the stay-green and chlorophyll content in sorghum. By using a restriction fragment length polymorphism (RFLP) map, developed from a recombinant inbred line (RIL) population, we identified four stay-green QTLs, located on three linkage groups. The QTLs (Stg1 and Stg2) are on linkage group A, with the other two, Stg3 and Stg4, on linkage groups D and J, respectively. Two stay-green QTLs, Stg1 and Stg2, explaining 13-20% and 20-30% of the phenotypic variability, respectively, were consistently identified in all trials at different locations in two years. Three QTLs for chlorophyll content (Chl1, Chl2, and Chl3), explaining 25-30% of the phenotypic variability were also identified under post-flowering drought stress. All coincided with the three stay-green QTL regions (Stg1, Stg2, and Stg3) accounting for 46% of the phenotypic variation. The Stg1 and Stg2 regions also contain the genes for key photosynthetic enzymes, heat shock proteins, and an abscisic acid (ABA) responsive gene. Such spatial arrangement shows that linkage group A is important for drought- and heat-stress tolerance and yield production in sorghum. High-resolution mapping and cloning of the consistent stay-green QTLs may help to develop drought-resistant hybrids and to understand the mechanism of drought-induced senescence in plants.
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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) | 0.000 | 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".