A New Dominant Gene <i>E9</i> Conditions Early Flowering and Maturity in Soybean
Bibliographic record
Abstract
ABSTRACT Adaptability of soybean [Glycine max (L.) Merr.] to a wide range of latitudes is attributed to the natural variation in the major genes and quantitative trait loci (QTL) that control flowering time and maturity. Identification of novel genes and understanding their molecular basis is critical to improving soybean productivity. We identified a new locus conditioning days to flowering and maturity that was detected in hybrid progeny between cultivated and wild soybeans. A backcross was made between the recurrent parent Tokei 780 and two early‐flowering recombinant inbred lines (RILs; from the cross Tokei 780 × Hidaka 4, a wild soybean accession, all of which possessed an identical genotype at the major four maturity loci, E1 to E4). The segregation patterns observed in the F2 and F3 progeny derived from the two crosses revealed that early‐flowering was controlled by a single dominant gene. The gene was fine‐mapped to a 245‐kb interval between markers M5 and M7 on Gm16. A tagging marker ID1 was significantly associated with the variation in days to flowering (0.82, p < 0.01) and maturity (0.76, p < 0.01) in the F2 population. The new early‐flowering gene and its tagging marker are very useful for molecular breeding towards early maturity and stable productivity of soybean under high‐latitude environments. The gene symbol E9e9 has been assigned. E9E9 results in early maturity and e9e9 results in late maturity.
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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".