Variations in <scp><i>CYP</i></scp><i>78</i><scp><i>A</i></scp><i>13</i> coding region influence grain size and yield in rice
Bibliographic record
Abstract
Grain size is one of the most important determinants of crop yield in cereals. Here, we identified a dominant mutant, big grain2 (bg2-D) from our enhancer-trapping population. Genetic analysis and SiteFinding PCR (polymerase chain reaction) revealed that BG2 encodes a cytochrome P450, OsCYP78A13. Sequence search revealed that CYP78A13 has a paralogue Grain Length 3.2 (GL3.2, LOC_Os03g30420) in rice with distinct expression patterns, analysis of transgenic plants harbouring either CYP78A13 or GL3.2 showed that both can promote grain growth. Sequence polymorphism analysis with 1529 rice varieties showed that the nucleotide diversity at CYP78A13 gene body and the 20 kb flanking region in the indica varieties were markedly higher than those in japonica varieties. Further, comparison of the genomic sequence of CYP78A13 in the japonica cultivar Nipponbare and the indica cultivar 9311 showed that there were three InDels in the promoter region and eight SNPs (single nucleotide polymorphism) in its coding sequence. Detailed examination of the transgenic plants with chimaeric constructs suggested that variation in CYP78A13 coding region is responsible for the variation of grain yield. Taken together, our results suggest that the variations in CYP78A13 in the indica varieties hold potential in rice breeding for application of grain yield improvement.
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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".