Genetic connection between cell-wall composition and grain yield via parallel QTL analysis in indica and japonica subspecies
Bibliographic record
Abstract
Grain yield is a complicated trait, which is highly associated with biomass productivity. The cell wall is a central element of biomass, and its biogenesis contributes to plant architecture and development. However, the genetic link between cell-wall property and grain yield is largely unclear. Here, we report on identification of quantitative trait loci (QTLs) for grain yield-related traits and cell-wall composition with a set of chromosomal segment substitution lines (CSSLs) that were generated by using 9311, an indica cultivar as donor, and Nipponbare, a japonica cultivar as recipient. Nipponbare and 9311 showed significant differences in grain yield-related traits and cell-wall composition. Genotyping with molecular markers, 125 lines covering 95.6% of the whole genome of 9311 were employed for phenotypic and chemical examinations. Thirty-seven QTLs for grain yield-related traits and nineteen QTLs for cell-wall composition have been identified. In addition to correlation analysis, we found overlapped and closely linked QTLs for two sets of traits. Fine-mapping further narrowed a QTL for cellulose content together with HD17, a known QTL for heading date and grain yield, suggesting that plants may regulate cell wall biogenesis and grain yield via related means. Our study provided genetic clues for cloning QTLs for both complicated traits.
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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".