Genome-Wide Association Study (GWAS) for Phenotypic Traits Associated with Drought Tolerance in an Ecologically-Diverse Wheat Core Collection
Bibliographic record
Abstract
Drought stress occurred at early growth stages in wheat affecting the following growth stages. Therefore, selecting promising drought-tolerant genotypes with highly adapted traits at the seedling stage is an important task for wheat breeders and geneticists. Few research efforts were conducted on the genetic control for drought-adaptive traits at the seedling stage in wheat. This study evaluated a set of 146 highly diverse spring wheat core collection representing 28 countries under drought stress at the seedling stage. Two main experiments were conducted. In Experiment I, the whole set of genotypes were assessed for drought tolerance by water withholding for 13 days when all plants reached to one-leaf stage. Shoot and leaf traits including SHL (shoot length), LA (leaf area), SLW (sum of leaf wilting), DTW (days to wilting), LR (leaf rolling) were scored, Additionally, a total of five traits were scored in roots such as RL (root length), RW (root width), REA (root emergence angle), RTA (root tip angle), NOR (number of roots). Drought-tolerant genotypes as well as drought-susceptible genotypes were selected from this Experiment. In the Experiment II, the effect of SA on improving the performance of the selected eight genotypes originated from different countries; IPK_040 (TRI 4113, Afghanistan), IPK_046 (TRI 3633, Canada), IPK_050 (TRI 3564, Portugal), IPK_071 (TRI 4940, USA), IPK_105 (TRI 4126, Italy), WAS_007 (MISR1, Egypt), WAS_024 (QDRIY 005, Egypt), WAS_031 (Sohag-5, Egypt). The eight spring wheat genotypes were used under four treatments: normal irrigation (NI), drought stress (D) (30% (FC) field capacity), normal irrigation with 0.5 mM SA (NSA), and drought treated with SA (DSA). Vegetative and physiological traits were scored on each genotype under each treatment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".