Highly predictive SNP markers for efficient selection of the wheat leaf rust resistance gene Lr16
Bibliographic record
Abstract
BACKGROUND: Lr16 is a widely deployed leaf rust resistance gene in wheat (Triticum aestivum L.) that is highly effective against the North American Puccinia triticina population when pyramided with the gene Lr34. Lr16 is a seedling leaf rust resistance gene conditioning an incompatible interaction with a distinct necrotic ring surrounding the uredinium. Lr16 was previously mapped to the telomeric region of the short arm of wheat chromosome 2B. The goals of this study were to develop numerous single nucleotide polymorphism (SNP) markers for the Lr16 region and identify diagnostic gene-specific SNP marker assays for marker-assisted selection (MAS). RESULTS: Forty-three SNP markers were developed and mapped on chromosome 2BS tightly linked with the resistance gene Lr16 across four mapping populations representing a total of 1528 gametes. Kompetitive Allele Specific PCR (KASP) assays were designed for all identified SNPs. Resistance gene analogs (RGAs) linked with the Lr16 locus were identified and RGA-based SNP markers were developed. The diagnostic potential of the SNPs co-segregating with Lr16 was evaluated in a diverse set of 133 cultivars and breeding lines. Six SNP markers were consistent with the Lr16 phenotype and are accurately predictive of Lr16 for all wheat lines/cultivars in the panel. CONCLUSIONS: Lr16 was mapped relative to SNP markers in four populations. Six SNP markers exhibited high quality clustering in the KASP assay and are suitable for MAS of Lr16 in wheat breeding programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".