Identification of Novel Quantitative Trait Loci for Culm Thickness of Rice Derived from Strong-Culm Landrace in Japan, Omachi
Bibliographic record
Abstract
Abstract Increasing the lodging resistance of rice through genetic improvement has been an important target in breeding. To further enhance the lodging resistance of high-yielding rice varieties amidst climate change, it is necessary to not only shorten culms but strengthen them as well. A landrace rice variety, Omachi, which was established more than 100 years ago, has the largest culm diameter and bending moment at breaking in the basal internodes among 135 temperate japonica accessions. Using unused alleles in such a landrace is an effective way to strengthen the culm. In this study, we performed quantitative trait locus (QTL) analysis to identify the genetic factors of culm strength of Omachi using recombinant inbred lines (RILs) derived from a cross between Omachi and Koshihikari, a standard variety in Japan. We identified three QTLs for the culm diameter of the 5th internode on chromosomes 3 (qCD3) and 7 (qCD7-1, qCD7-2). Among them, qCD7-2 was verified by QTL analysis using the F2 population derived from a cross between one of the RILs and Koshihikari. RNA-seq analysis of shoot apex raised 10 candidate genes underlying the region of qCD7-2. The increase in culm strength by accumulating Omachi alleles of qCD3, qCD7-1 and qCD7-2 was 25.0% in 2020. These QTLs for culm diameter pleiotropically increased spikelet number per panicle but did not affect days to heading or culm length. These results suggest that the Omachi alleles of qCD3, qCD7-1 and qCD7-2 are useful for breeding to increase lodging resistance and yield.
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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 | 0.001 | 0.001 |
| 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".