Genetic Dissection of Stem Water-Soluble Carbohydrates and Agronomic Traits in Wheat under Different Water Regimes
Bibliographic record
Abstract
Drought is a major environmental stress threatening wheat (Triticum aestivum L.) productivity worldwide. Although drought impedes wheat performance at all growth stages, it is more critical during the flowering and grain-filling phases and results in substantial yield losses. In this context, stem water-soluble carbohydrates (SWSC) were dissected at flowering and grain filling stages under drought stress (DS) and well-watered (WW) conditions using a population consisted of 116 wheat accessions in this research. The main goal was to dissect the genetic basis of water-soluble carbohydrates and the agronomic traits using association mapping approach and identify linked molecular markers. The results showed significant and positive correlations for stem water-soluble carbohydrates at grain filling (SWSCG) with accumulating efficiency of stem water-soluble carbohydrates (AESWSC) and grain filling efficiency at the late stage (GFEL). The accumulating and grain filling efficiency at grain filling stage could play an important role for SWSC especially under DS condition. Four favorable alleles for plant height (PH) and grain yield (GY) were identified in two water environments. Xbarc78-4A163and Xbarc78-4A155 were variant alleles for PH which were identified in both water regimes. Whereas Xwmc25-2D151 and Xgwm165-4B191 positively linked with GY in WW. Although Xwmc420-4A121and Xwmc112-2D215 were alleles for stem water-soluble carbohydrates at flowering (SWSCF) and SWSCG in DS but the frequency were < 5% so they were considered as rare alleles. These SSR markers which explained significant level of phenotypic variability for chosen traits could be used for selection of genotypes in wheat breeding programs through marker-assisted selection.
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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".