Identification and validation of functional markers in a global rice collection by association mapping
Bibliographic record
Abstract
Recent results indicate that marker-assisted selection is an effective approach to reduce the cost and to improve the efficacy and accuracy of selection in plant breeding. This study was conducted to identify and validate molecular markers linked to important breeding traits by association mapping. The association was evaluated between 81 molecular markers (STS, SSR, Indel, CAPS, and PCR-based SNP) and 15 morphological traits in a global panel of 100 rice (Oryza sativa) accessions. The population structure analysis identified three main subpopulations. Obvious kinship relationships were also detected between the rice accessions. Association analysis was performed based on the mixed linear model by considering population structure and family relatedness. In addition, the false discovery rate method was used to correct the multiple testing. A total of 47 marker-trait associations were identified, including 22 markers for 14 traits. Among all, the polymorphism at the loci DDR-GL was highly associated with grain characters (grain length, grain width, and length/width ratio). In addition, marker RM3148 was responsible for five important traits simultaneously. Results demonstrated that such informative markers can be very useful for rice breeding programs using marker-assisted selection. Moreover, the diverse populations of rice accessions are a valuable resource for association mapping of morphological traits.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".