Identification of cold tolerance QTLs at the bud burst stage in 211 rice landraces by GWAS
Bibliographic record
Abstract
BACKGROUND: Rice is a crop that is very sensitive to low temperature, and its morphological development and production are greatly affected by low temperature. Therefore, understanding the genetic basis of cold tolerance in rice is of great significance for mining favorable genes and cultivating excellent rice varieties. However, there have been limited studies focusing on cold tolerance at the bud burst stage; therefore, considerable attention should be given to the genetic basis of cold tolerance at this stage. RESULTS: In this study, a natural population consisting of 211 rice landraces collected from 15 provinces in China and other countries was used for the first time to evaluate cold tolerance at the bud burst stage. Population structure analysis showed that this population was divided into two groups and was rich in genetic diversity. Our evaluation results confirmed that japonica rice was more tolerant to cold at the bud burst stage than indica rice. A genome-wide association study (GWAS) was performed with the phenotypic data of 211 rice landraces and a 36,727 SNP dataset under a mixed linear model. Twelve QTLs (P < 0.0001) were identified for the seedling survival rate (SR) after treatment at 4 °C, in which there were five QTLs (qSR2-2, qSR3-1, qSR3-2, qSR3-3 and qSR9) that were colocalized with those from previous studies and seven QTLs (qSR2-1, qSR3-4, qSR3-5, qSR3-6, qSR3-7, qSR4 and qSR7) that were reported for the first time. Among these QTLs, qSR9, harboring the most significant SNP, explained the most phenotypic variation. Through bioinformatics analysis, five genes (LOC_Os09g12440, LOC_Os09g12470, LOC_Os09g12520, LOC_Os09g12580 and LOC_Os09g12720) were identified as candidates for qSR9. CONCLUSION: This natural population consisting of 211 rice landraces combined with high-density SNPs will serve as a better choice for identifying rice QTLs/genes in the future, and the detected QTLs associated with cold tolerance at the bud burst stage in rice will be conducive to further mining favorable genes and breeding rice varieties under cold stress.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".