Natural Variation in the Flag Leaf Morphology of Rice Due to a Mutation of the NARROW LEAF 1 Gene in Oryza sativa L.
Bibliographic record
Abstract
We investigated the natural variations in the flag leaf morphology of rice. We conducted a principal component analysis based on nine flag leaf morphology traits using 103 accessions from the National Institute of Agrobiological Sciences Core Collection. The first component explained 39% of total variance, and the variable with highest loading was the width of the flag leaf (WFL). A genome-wide association analysis of 102 diverse Japanese accessions revealed that marker RM6992 on chromosome 4 was highly associated with WFL. In analyses of progenies derived from a cross between Takanari and Akenohoshi, the most significant quantitative trait locus (QTL) for WFL was in a 10.3-kb region containing the NARROW LEAF 1 (NAL1) gene, located 0.4 Mb downstream of RM6992. Analyses of chromosomal segment substitution lines indicated that a mutation (G1509A single-nucleotide mutation, causing an R233H amino acid substitution in NAL1) was present at the QTL. This explained 13 and 20% of total variability in WFL and the distance between small vascular bundles, respectively. The mutation apparently occurred during rice domestication and spread into japonica, tropical japonica, and indica subgroups. Notably, one accession, Phulba, had a NAL1 allele encoding only the N-terminal, or one-fourth, of the wild-type peptide. Given that the Phulba allele and the histidine-type allele showed essentially the same phenotype, the histidine-type allele was regarded as malfunctional. The phenotypes of transgenic plants varied depending on the ratio of histidine-type alleles to arginine-type alleles, raising the possibility that H(233)-type products function differently from and compete with R(233)-type products.
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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 | 0.000 |
| 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".