Correlation analysis and QTL mapping of osmotic potential in <i>japonica</i> rice under upland and lowland conditions
Bibliographic record
Abstract
Mu, P. and Li, Z. 2013. Correlation analysis and QTL mapping of osmotic potential in japonica rice under upland and lowland conditions. Can. J. Plant Sci. 93: 785–792. In order to reveal the genetic basis of differences in osmotic potential (OP) of japonica rice under various water regimes, a recombinant inbreed line (RIL) population including 120 plants derived from a cross between an upland japonica rice IRAT109 and a paddy japonica rice Yuefu was used in this study. A complete genetic linkage map with 201 simple sequence repeat (SSR) molecular markers was constructed. Osmotic potentials of the RIL population and their parents were measured pre-dawn and at mid-day under lowland (no soil water stress) and upland (soil water stress) conditions. Significant correlations between osmotic difference (ODl) and (ODu) and index of drought resistance (IDR, based on relative yield in upland and lowland treatments) was found under lowland and upland conditions. Quantitative trait loci (QTL) associated with osmotic potentials and osmotic differences under lowland and upland conditions were detected by using the software QTL MAPPER version 2.0. Twelve additive QTL and 10 pairs of epistatic QTL for OP and OD were detected. Three additive QTL and a pair of epitatic QTL for IDR were also obtained. There were more Q×E QTL and epistatic QTLs under upland conditions than under lowland conditions, indicating that the genetic pattern was more complex under upland conditions than under lowland conditions. Two QTL near RM493–RM1287 on chromosome 1 and RM332–RM1124 on chromosome 11 were found to have a high general contribution of over 10% and may be the key QTL controlling osmotic potential. These results reveal that osmotic potential is a quantitative trait controlled by both additive and epistatic QTL, which are mainly distributed on chromosomes 1, 2, 8 and 9.
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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 | 0.001 |
| 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) | 0.000 | 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".