Identification of Quantitative Trait Loci Controlling Floral Morphology of Rice Using a Backcross Population between Common Cultivated Rice, Oryza sativa and Asian Wild Rice, O. rufipogon
Bibliographic record
Abstract
Differences in floral morphologies affect pollination behaviour in many flowering plants. In the genus Oryza, several differences in the size of floral organs are known. In this study, we focused on the differences in the size of floral organs between common cultivated rice, Oryza sativa L. and its wild ancestor, O. rufipogon. We compared floral morphologies between cultivated rice O. sativa cv. Nipponbare and O. rufipogon W630. We first evaluated temporal changes in filament and anther lengths. W630 had longer filaments with rapid elongation within 15 min after spikelet opening. W630 also had longer anthers than Nipponbare, and size of anther was consistent throughout all time examined. We also analysed other six floral traits, and found that W630 had higher stigma and style length, as well as lemma and palea length, but lower lemma and palea width. Quantitative trait locus (QTL) analysis was performed to identify the loci controlling these floral traits, using backcross recombinant inbred lines derived from a cross between Nipponbare and W630. A total of 11 significant QTLs were identified. Of these, two pairs of QTLs for lemma and palea length and one pair for lemma and palea width overlapped, suggesting that common genetic factors may be the reason for the differences in these traits. In addition, we performed QTL analysis for grain size, and found that QTLs for grain size coincided with those for lemma and palea size, indicating that grain size is partly controlled by glume capacity. The QTLs identified in this study will be informative for understanding genetic changes associated with rice domestication.
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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 | 0.001 |
| 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".