Application of resistance gene analog markers to analyses of genetic structure and diversity in rice
Bibliographic record
Abstract
Plant disease resistance gene analog (RGA) markers were designed according to the conserved sequence of known RGAs and used to map resistance genes. We used genome-wide RGA markers for genetic analyses of structure and diversity in a global rice germplasm collection. Of the 472 RGA markers, 138 were polymorphic and these were applied to 178 entries selected from the USDA rice core collection. Results from the RGA markers were similar between two methods, UPGMA and STRUCTURE. Additionally, the results from RGA markers in our study were agreeable with those previously reported from SSR markers, including cluster of ancestral classification, genetic diversity estimates, genetic relatedness, and cluster of geographic origins. These results suggest that RGA markers are applicable for analyses of genetic structure and diversity in rice. However, unlike SSR markers, the RGA markers failed to differentiate temperate japonica, tropical japonica, and aromatic subgroups. The restricted way for developing RGA markers from the cDNA sequence might limit the polymorphism of RGA markers in the genome, thus limiting the discriminatory power in comparison with SSR markers. Genetic differentiation obtained using RGA markers may be useful for defining genetic diversity of a suite of random R genes in plants, as many studies show a differentiation of resistance to a wide array of pathogens. They could also help to characterize the genetic structure and geographic distribution in crops, including rice, wheat, barley, and banana.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".