Quantitative trait loci underlying domestication- and yield-related traits in an<i>Oryza sativa</i> ×<i>Oryza rufipogon</i>advanced backcross population
Bibliographic record
Abstract
To understand the genetic characteristics of the traits related to differentiation between cultivated rice and its wild progenitor, genetic factors controlling domestication- and yield-related traits were identified using a BC3F2 population derived from an accession of common wild rice (donor, Oryza rufipogon Griff.) collected from Yuanjiang, Yunnan province, China, and an indica cultivar, Teqing (recipient, Oryza sativa L.). A genetic linkage map consisting of 125 simple sequence repeat (SSR) markers was constructed. Based on the phenotypes of the 383 BC3F2 families evaluated in two environments, two domestication-related morphological traits, panicle shape and growth habit, were found to be controlled by single Mendelian factors. This implies that the recessive mutations of single genes controlling some morphological traits could have been easily selected during early domestication. By single-point analysis and interval mapping, 59 putative quantitative trait loci (QTLs) that influence 11 quantitative traits were detected at two sites, and 37.5% of the QTL alleles originating from O. rufipogon had a beneficial effect for yield-related traits in the Teqing background. Regions with significant QTLs for domestication- and yield-related traits were detected on chromosomes 1, 4, 5, 7, 8, and 12. Fine mapping and cloning of these domestication-related genes and QTLs will be useful in elucidating the origin and differentiation of Asian cultivated rice in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".