Fine mapping and marker development for the wheat leaf rust resistance gene <i>Lr32</i>
Bibliographic record
Abstract
Wheat leaf rust is caused by the fungal pathogen Puccinia triticina and is one of the wheat diseases of concern globally. Among the known leaf rust resistance genes (Lr) genes, Lr32 is a broadly effective gene derived from the diploid species Aegilops tauschii coss. accession RL5497-1 and has been genetically mapped to chromosome arm 3DS. However, Lr32 resistance has not been utilized in current cultivars in part due to the lack of modern, predictive DNA markers. The goals of this study were to fine map the Lr32 region and develop SNP-based kompetitive allele-specific polymerase chain reaction markers. The genomic analysis was conducted by using doubled haploid and F2-derived mapping populations. For marker development, a 90K wheat chip array, 35K and 820K Axiom R SNPs, A. tauschii pseudomolecules v4.0 and International Wheat Genome Sequencing Consortium ReqSeq v2.1 reference genomes were used. Total 28 kompetitive allele-specific polymerase chain reaction and 2 simple sequence repeat markers were developed. The Lr32 region was fine mapped between kompetitive allele-specific polymerase chain reaction markers Kwh142 and Kwh355 that flanked 34-35 Mb of the diploid and hexaploid reference genomes. Leaf rust resistance mapped as a Mendelian trait that cosegregated with 20 markers, recombination restriction limited the further resolution of the Lr32 region. A total of 10-11 candidate genes associated with disease resistance were identified between the flanking regions on both reference genomes, with the majority belonging to the nucleotide-binding domain and leucine-rich repeat gene family. The validation analysis selected 2 kompetitive allele-specific polymerase chain reaction markers, Kwh147 and Kwh722, for marker-assisted selection. The presence of Lr32 along with other Lr genes such as Lr67 and Lr34 would increase the resistance in future wheat breeding lines and have a high impact on controlling wheat leaf rust.
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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 | 0.000 |
| 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".