Association analysis of molecular markers with traits under drought stress in safflower
Bibliographic record
Abstract
This study was performed to identify marker loci associated with important agronomic traits and oil content under two moisture conditions and find stable associations in test environments in a worldwide collection of safflower (Carthamus tinctorius L.). Association analysis was conducted between eight important traits and 341 polymorphic AFLP markers produced by 10 primer combinations (EcoRI/MseI) in 100 safflower genotypes. The results of population structure analysis identified three main subpopulations possessing significant genetic differences revealed by analysis of molecular variance. Association analysis explained the highest percentage of trait variation for seed yield (38%) under drought-stress conditions and number of seeds per capitulum (27.75%) under normal conditions. Four markers (M51/E41-6, M51/E41-4, M61/E40-6 and M62/E40-17) in drought-stress conditions and two markers (M62/E40-35 and M47/E37-13) in normal conditions were simultaneously associated with seed and oil yield. The markers stably associated with traits in all test environments included M62/E40-35 with seed yield in normal conditions, M62/E40-17 with seed yield in drought stress conditions, and M62/E41-11 with oil yield in drought-stress conditions. Significant relationships were identified between oil content and three markers (M61/E40-6, M47/E37-8 and M51/E32-9) under drought-stress conditions, and three markers (M61/E2-2, M61/E40-6 and M51/E41-12) under normal conditions. Among them, M51/E32-9 and M61/E2-2 markers showed stable association with oil content under drought-stress and normal conditions, respectively. Detected markers would be useful in marker-assisted breeding programs for safflower improvement in arid and semi-arid area.
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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 | 0.001 | 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 | 0.000 |
| 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".