Development of single nucleotide polymorphism‐based functional molecular markers from the <i>Lr22a</i> gene sequence in wheat (<scp><i>Triticum aestivum</i></scp>)
Bibliographic record
Abstract
Abstract The adult‐plant leaf rust resistant gene Lr22a confers broadly effective resistance against the fungal pathogen Puccinia triticina Eriks. (Pt) in wheat that has not been extensively utilized in wheat cultivars. The objective of this study was to develop robust functional molecular markers using the Lr22a gene sequence to facilitate integration of Lr22a in breeding programmes. The Lr22a coding sequence was used to identify isolated SNPs and four kompetitive allele specific polymerase chain reaction (PCR) (KASP) markers were developed. For marker testing, a F2:3 population was developed by crossing a near‐isogenic line RL4495 (Lr22a carrier) with Thatcher and phenotyped with Pt race TDBJ and genotyped with a 90K iSelect SNP array, four KASP markers and two SSR markers. A linkage map of chromosome arm 2DS that included Lr22a was constructed. The KASP markers co‐segregated with Lr22a and were validated for cross‐applicability on a panel of wheat lines. KASP markers Kwh636, Kwh637 and Kwh638 reliably detected the presence or absence of Lr22a. The markers developed in this study will facilitate Lr22a selection in breeding programmes.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".