Genome-Wide Association Mapping Reveals Multiple QTLs Governing Tolerance Response for Seedling Stage Chilling Stress in Indica Rice
Bibliographic record
Abstract
rice. Six phenotypic classes were exposed to six low temperature stress regimes under control phenotyping facility to investigate response pattern. A panel of 66 genotypes representing all phenotypic classes was used for ensuring genetic diversity, population structure and association mapping for the trait using 58 simple sequence repeat (SSR) and 2 direct trait linked markers. A moderate level of genetic diversity was detected in the panel population for the trait. Deviation of Hardy-Weinberg's expectation was detected in the studied population using Wright's F statistic. The panel showed 30% variation among population and 70% among individuals. The entire population was categorized into three sub-populations through STRUCTURE analysis. This revealed tolerance for the trait had a common primary ancestor for each sub-population with few admix individuals. The panel population showed the presence of many QTLs for cold stress tolerance in the individuals representing like genome-wide expression of the trait. Nineteen SSR markers were significantly associated at chilling stress of 8°C to 4°C for 7-21 days duration. Thus, the primers linked to the seedling stage cold tolerance QTLs namely qCTS9, qCTS-2, qCTS6.1, qSCT2, qSCT11, qSCT1a, qCTS-3.1, qCTS11.1, qCTS12.1, qCTS-1b, and CTB2 need to be pyramided for development of strongly chilling tolerant variety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".