Genetic architecture of seed glycerolipids in Asian cultivated rice
Bibliographic record
Abstract
Glycerolipids are essential for rice development and grain quality but its genetic regulation remains unknown. Here we report its genetic base using metabolite-based genome-wide association study and metabolite-based quantitative traits locus (QTL) analyses based on lipidomic profiles of seeds from 587 Asian cultivated rice accessions and 103 chromosomal segment substitution lines, respectively. We found that two genes encoding phosphatidylcholine (PC):diacylglycerol cholinephosphotransferase (OsLP1) and granule-bound starch synthase I (Waxy) contribute to variations in saturated triacylglycerol (TAG) and lyso-PC contents, respectively. We demonstrated that allelic variation in OsLP1 sequence between indica and japonica results in different enzymatic preference for substrate PC-16:0/16:0 and different saturated TAG levels. Further evidence demonstrated that OsLP1 also affects heading date, and that co-selection of OsLP1 and a flooding-tolerant QTL in Aus results in the abundance of saturated TAGs associated with flooding tolerance. Moreover, we revealed that the sequence polymorphisms in Waxy has pleiotropic effects on lyso-PC and amylose content. We proposed that rice seed glycerolipids have been unintentionally shaped during natural and artificial selection for adaptive or import seed quality traits. Collectively, our findings provide valuable genetic resources for rice improvement and evolutionary insights into seed glycerolipid variations in rice.
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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".