Allelic variation of vernalization and photoperiod response genes in a diverse set of North American high latitude winter wheat genotypes
Bibliographic record
Abstract
The major physiological determinants of wheat (Triticum aestivum L.) phenology in a given area are a response to vernalization temperature and day length, which are at least in part, regulated by the allelic variation at the vernalization (VRN) and photoperiod (PPD) loci, respectively. Characterization of the existing genetic variation for plant phenology in winter wheat can assist breeding programs improve adaptation to local environments and to optimize wheat phenology for the changing climate. The objectives of this research were to characterize the allelic variation at the major VRN and PPD loci in a diverse panel of high latitude winter wheat genotypes (n = 203) and to associate the allelic variation with phenologic, agronomic and adaptation traits. The panel was genotyped using allele-specific markers at vernalization (VRN-A1, VRN-B1, VRN-D1 and VRN-B3) and photoperiod (PPD-A1, PPD-B1, and PPD-D1) loci and phenotyped for agronomically-important traits. Though photoperiod sensitivity was more prevalent, most of the variation in the phenology of the winter wheat panel was explained by allelic variation at PPD-D1, PPD-A1, and the interaction between these loci. While a typical high latitude winter wheat genotype is one that carries winter alleles at all major VRN loci and photoperiod sensitive alleles at the major PPD loci, in lower latitudes where winters are milder, the presence of one or two photoperiod insensitive alleles seems to contribute to higher yield and wider adaptation.
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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) | 0.001 | 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".