Natural variations of wheat EARLY FLOWERING 3 highlight their contributions to local adaptation through fine-tuning of heading time
Bibliographic record
Abstract
Abstract Heading/flowering at the appropriate time in each cultivation region can greatly contribute to stabilizing and maximizing yield. Genotype combinations of Vrn-1 and Ppd-1, the major genes determining heading time, can explain the variation in heading time. However, the genes that can explain the remaining variations in heading time are still unknown. In this study, we aimed to identify the genes conferring early heading using doubled haploid lines derived from Japanese wheat varieties. Quantitative trait loci (QTL) analysis revealed a significant QTL on the long arm of chromosome 1B in multiple growing seasons. Genome sequencing using Illumina short reads and Pacbio HiFi reads revealed a large deletion of a ~500 kb region containing TaELF-B3, an orthologue of Arabidopsis clock gene EARLY FLOWERING 3 (ELF3). Plants with the deleted allele of TaELF-B3 (ΔTaELF-B3allele) headed earlier only under short-day vernalization conditions. Higher expression levels of clock- and clock-output genes, such as Ppd-1 and TaGI, were observed in plants with the ΔTaELF-B3 allele. These results suggest that the deletion of TaELF-B3 causes early heading. Of the TaELF-3 homoeoalleles conferring early heading, the ΔTaELF-B3 allele showed the greatest effect on the early heading phenotype in Japan. The higher allele frequency of the ΔTaELF-B3 allele in western Japan suggests that the ΔTaELF-B3 allele was preferred during recent breeding to adapt to the environment. TaELF-3 homoeolog will help to expand the cultivated area by fine-tuning the optimal timing of heading in each environment.
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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".