QTL analysis for flowering time using backcross population between Oryza sativa Nipponbare and O. rufipogon
Bibliographic record
Abstract
In the near future, global average temperature is expected to increase due to the accumulation of greenhouse gases, and increased temperatures will cause severe sterility in many crop species. In rice, since wild species show high genetic variation, they may have the potential to improve the flowering characters of cultivars. In this study, we investigated flowering characters under natural conditions by comparing an Asian wild rice accession of Oryza rufipogon W630 (originated from Myanmar) with a Japanese rice cultivar, O. sativa Japonica cv. Nipponbare. Further, QTL analysis for days to heading (DH) and spikelet opening time (SOT: the time of day when the spikelet opens) was carried out using BC(2)F(8) backcross population derived from the cross between them. Regarding DH, four QTLs were detected, and two of them were found to have wild alleles with strong effects leading to longer days to heading during the Japanese summer. These wild alleles may be used to produce late-heading cultivars that do not flower during the high summer temperatures anticipated in the future. As for SOT, two parameters of SOTb (beginning time when the first spikelet opens) and SOTm (median time when 50% of the spikelets open) were recorded and the time differences from Nipponbare were investigated. Two QTLs on chromosomes 5 and 10 and two QTLs on chromosomes 4 and 5 were detected for SOTb and SOTm, respectively. The wild alleles were responsible for early spikelet opening time at all loci. If the wild alleles detected in this study have the same effects in the genetic background of other cultivars, they will be very useful in producing early-flowering rice cultivars that complete fertilization in the morning before the temperature rises.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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".