Discovery and genotyping of high‐throughput SNP markers for crown rust resistance gene <i>Pc94</i> in cultivated oat
Bibliographic record
Abstract
Abstract Crown rust caused by Puccinia coronata f. sp. avenae Eriks is a serious problem for oat production worldwide and pyramiding multiple resistance genes into new cultivars is a key objective of breeders. Many race specific resistance genes have been mapped and markers that are closely linked to them have been identified. However, the use of these markers in oat breeding practice has been limited due to the economics of marker assisted selection (MAS) deployment. Single nucleotide polymorphism (SNP) markers have been demonstrated to have a high‐throughput capability with relatively low cost and numerous semi‐automated SNP scoring platforms exist. Gene Pc94 has remained highly effective since it was first tested on the Canadian crown rust populations in 1993 and is one of the few effective genes available in Western Canada. In the present study, PCR products were amplified using primers derived from sequences of amplified fragment length polymorphism bands which have been shown to be linked to Pc94. Genomic DNA from genotypes, with and without the Pc94 gene, were used as the PCR templates. By comparative sequence alignment amongst the PCR fragments, many putative SNP sites were identified. From these sites, four SNP sites were selected and validated by the single base extension method. One SNP site, Pc94‐SNP1a, was tested on two F2:3 populations segregating for the resistance gene. The map distances between the SNP marker and Pc94 were 2.1 and 5.4 cM in the two different populations. Various oat cultivars and germplasm lines were also tested for a wider application of the SNP marker. Fluorescent technology and capillary electrophoresis allowed for the semi‐automated, fairly high‐throughput scoring of the SNP markers.
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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 | 0.001 |
| 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".