Development of Two Functional Markers of <i>Badh2</i> Gene in Guangxi Fragrant Rice
Bibliographic record
Abstract
Fragrance in rice is one of the most important quality traits, which resulted from the loss of function of betaine aldehyde dehydrogenase ( Badh2 ) gene on chromosome 8. The mutation of Badh2 leads to accumulation of 2-acetyl-1-pyrroline (2-AP), which is known as the main volatile in fragrant rice. At least 18 allelic variations have been identified in Badh2 genes in rice. Marker assisted selection has proved to be an effective way of fragrant rice breeding. Traditional marker detection methods, such as Sanger sequencing or SSR molecular marker, are found to be low efficient. To develop a more dynamic method, we adopt Real-time PCR method to detect the common mutation site in Guangxi. In this study, Badh2 gene of 40 fragrant rice accessions collected from Guangxi province was sequenced. Most of the fragrant rice accessions belonged to 806 bp deletion between exon 4-5 ( badh2-E4-5.1 ) and 8 bp deletion in exon 7 ( badh2-E7 ). Two Real-time PCR SNP molecular markers were developed, and were used to verify the 40 sequenced fragrant rice accessions. 93.75% of the detection results using Real-time PCR were consistent with the results of Sanger sequencing. Further, 50 local varieties were examined by Real-time PCR. A total of 24 accessions carry badh-E4-5 allele and 22 accessions detected with badh-E7 . The two functional SNP molecular markers common in Guangxi fragrant rice were developed and proved to be useful in rice breeding. These functional markers will improve the efficiency of fragrant rice breeding.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".