Detection of main-effect and epistatic QTL for yield-related traits in rice under drought stress and normal conditions
Bibliographic record
Abstract
Xing, W., Zhao, H. and Zou, D. 2014. Detection of main-effect and epistatic QTL for yield-related traits in rice under drought stress and normal conditions. Can. J. Plant Sci. 94: 633–641. Drought-resistant cultivars play an important role in maintaining high and stable crop yields under drought-stress conditions. However, the genetic mechanism of drought resistance must first be elucidated. Therefore, 220 recombinant inbred lines from a cross between Xiaobaijingzi (upland rice) and Kongyu 131 (Oryza sativa L.) were used to identify quantitative trait loci (QTLs) for yield and yield-component traits under drought stress and control conditions in Heilongjiang and Tieli. As a result, 23 main-effect QTLs and 11 digenic interactions were detected for four traits under the above two conditions. Of the main-effect QTLs, 10 and 8 were detected under control and drought-stress conditions, respectively; and five common QTLs were observed. In addition, five QTLs were found to be responsible for the difference across the two conditions. Among all epistatic QTLs, three types of epistatic QTLs were observed: one was between two main-effect QTLs, such as qPH-3-1 and qPH-7-2; one was between one locus with and another without main-effect, e.g., qPN-4 and qPN-3-2; and one was between two loci without main-effect, e.g., qYP-6-1 and qYP-12-2. In the above epistatic examples, their recombinant genotypes tended to reduce plant height and the number of grains per panicle and increase yield, respectively. Our results provide a good foundation for designed molecular breeding of drought-resistant rice.
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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 | 0.001 |
| 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".