Interactive effects of multiple vernalization (Vrn-1)- and photoperiod (Ppd-1)-related genes on the growth habit of bread wheat and their association with heading and flowering time
Bibliographic record
Abstract
BACKGROUND: The precise identification of Winterness/Springness (growth habit) for bread wheat, which is determined by genes involved in vernalization and photoperiod, will contribute to the effective utilization of bread wheat varieties. Here, 198 varieties from the Yellow and Huai wheat production region (YHW) in China were collected to identify their vernalization (Vrn-1) and photoperiod (Ppd-1) gene composition via a series of functional markers and their association with vernalization and photoperiod requirements at three locations during two years of experiments. The growth habits were measured during the spring sowing season. RESULTS: The results showed that the semi-winter varieties (grades1-4) were most prevalent in the population. The relative effects of single Vrn alleles on the growth period, such as heading date (HD) and/or flowering date (FD), were as follows: Vrn-B1b > Vrn-B1a > Vrn-D1b > Vrn-D1a > vrn-D1 = vrn-B1. The interactive effects of Vrn-B1 and Vrn-D1 on HD and FD were identical to those of Vrn-B1b. Approximately 35.3% of the cultivars carried Ppd-B1a (photoperiod-insensitive) and exhibited the earliest HD and FD. The Ppd-D1a-insensitive allele (Hapl II) was carried by just 0.5% of the varieties; however, the other two sensitive alleles were present at a higher frequency, and their effects were slightly weaker than those of Ppd-B1a. In addition, strong interactive effects between Ppd-B1 and Ppd-D1 were detected. In terms of mean values among various genotypes, the effects followed the order of Vrn-1 > Ppd-1. CONCLUSIONS: According to the results of ANOVA and least significant range (LSR) tests, we can conclude that Vrn-1 rather than Ppd-1 played a major role in controlling vernalization and photoperiod responses in this region. This research will be helpful for precisely characterizing and evaluating the HD, FD and even growth habit of varieties in the YHW at molecular levels.
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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 | 0.000 | 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 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".