Identification and molecular marker analysis of PHS resistance of high generation wheat materials
Bibliographic record
Abstract
Pre-harvest sprouting (PHS) of wheat will significantly reduce the yield and quality of wheat and threaten the safety of wheat production in China. Screening and utilization of resistant germplasm and functional molecular markers is the fundamental way to reduce the harm of PHS. In this study, 238 high generation lines were used to identify and evaluate PHS resistance by grain germination method, and the distribution of PHS function markers Vp1B3, myb10-D, PM19-A1 and MFT-A2 in resistant germplasm was determined and their breeding effects were evaluated. Phenotypic identification showed that there were significant differences in the relative seed germination index (RSGI) of 238 wheat germplasm resources. The RSGI ranged from 0.03 to 1, and the average RSGI was 0.31. The difference significance analysis showed that the RSGI of the alleles of functional markers Vp1B3, PM19-A1 and MFT-A2 were significantly different, suggesting that Vp1B3, PM19-A1 and MFT-A2 could be used for detection of PHS resistance genotypes and marker-assisted breeding. Based on the phenotype and genotype results, three red wheat materials with high PHS resistance (23JD392, 23JD393 and 23JD481) and four white wheat materials with high PHS resistance (23JD025, 23JD085, 23JD541 and 23JD655) were selected. At the same time, the high resistance materials 23JD392 and 23JD393 which amplified TaVp-1Bc/TaPM19-A1a/TaMFT-A2a had the lowest RSGI. These results can be used for genetic breeding and layout of wheat varieties resistant to PHS, indicating that resistance can be significantly improved by using functional markers. This study combined molecular markers and phenotypic identification to screen anti-PHS materials, which is expected to improve the level of wheat PHS resistance.
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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 | 0.001 |
| 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".