Genomic structure analysis of a set of Oryza nivara introgression lines and identification of yield-associated QTLs using whole-genome resequencing
Bibliographic record
Abstract
Oryza nivara, an annual wild AA-genome species of rice, is an important gene pool for broadening the genetic diversity of cultivated rice (O. sativa L.). Towards identifying and utilizing favourable alleles from O. nivara, we developed a set of introgression lines (ILs) by introducing O. nivara segments into the elite indica rice variety 93-11 background through advanced backcrossing and repeated selfing. Using whole-genome resequencing, a high-density genetic map containing 1,070 bin-markers was constructed for the 131 ILs, with an average length of 349 kb per bin. The 131 ILs cover 95% of O. nivara genome, providing a relatively complete genomic library for introgressing O. nivara alleles for trait improvement. Using this high-density bin-map, QTL mapping for 13 yield-related traits was performed and a total of 65 QTLs were detected across two environments. At ~36.9% of detected QTLs, the alleles from O. nivara conferred improving effects on yield-associated traits. Six cloned genes, Sh4/SHA1, Bh4, Sd1, TE/TAD1, GS3 and FZP, colocalised in the peak intervals of 9 QTLs. In conclusion, we developed new genetic materials for exploration and use of beneficial alleles from wild rice and provided a basis for future fine mapping and cloning of the favourable O. nivara-derived QTLs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".