Comparative sequencing and SNP marker validation for oat stem rust resistance gene Pg6 in a diverse collection of Avena accessions
Bibliographic record
Abstract
Abstract Key message Comparative sequence analysis was used to design a SNP marker that aided in the identification of new sources of oat stem rust resistance. Abstract New races of Puccinia graminis f. sp. avenae ( Pga ) threaten global oat production. An A. strigosa accession known to carry the broadly effective oat stem rust resistance gene, Pg6 , was crossed with two susceptible A. strigosa accessions to generate 198 F 2:3 families and 190 F 5:6 RILs. The RIL population was used to determine that Pg6 was a single dominant gene located between 475 and 491 Mbp on diploid chromosome AA2 of the A. atlantica genome. This region was further refined by identifying SNPs associated with Pg6 resistance in a panel of previously sequenced A-genome accessions. Twenty-four markers were developed from SNPs that showed perfect association between the Pg6 phenotype and 11 sequenced Avena diploid accessions. These markers were validated in the RILs and F 2:3 families, and the markers most closely linked with resistance were tested in a diverse panel of 253 accessions consisting of oat stem rust differentials, all available diploid Avena spp. accessions, and 41 A. vaviloviana accessions from the National Small Grains Collection. One SNP marker located at 483, 439, 497 bp on AA2, designated as AA2_483439497, was perfectly associated with the Pg6 phenotype in Avena strigosa diploids and was within several Kb of a resistance gene analog, RPP13. The marker results and seedling testing against Pga races DBD, KBD, TJS, and TQL enabled the postulation of Pg6 and potential new sources of resistance in the Avena panel. These results will be used to infer Pg6 presence in other germplasm collections and breeding programs and can assist with introgression, gene pyramiding, and cloning of Pg6 .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".