Molecular and cytogenetic tools for selecting and fixing disease resistance genes in wheat (Triticum aestivum L.) populations
Bibliographic record
Abstract
This thesis integrates classical, cytogenetic and molecular approaches to selecting disease resistance gene stacks in common wheat.Wheat leaf rust resistance (Lr) gene Lr22a was previously introgressed into wheat from Aegilops tauschü Coss and is located on chromosome 2DS.Lr22a was mapped with microsatellite (SSR) markers to allow stacking with other Lr genes; the closest marker was GWM296 (2.9 cM distal).Genetic size of the Ae.tauschü introgression was determined with SSRs and was tracked through the ancestry of various Canadian wheat varieties.Disease resistance genes are often more effective and durable when they are stacked.To investigate the use of telocentric chromosomes to increase the frequency of desirable alleles in breeding populations four populations were produced each with a different combination of disease resistance genes to either leaf rust or fusarium head blight (FHB).Each population had Fr plants that were either double monotelodisomic (dmtd), with both resistance genes in the hemizygous state, ot were dihybrid.F3 families were produced and tested for disease resistance.The families derived from dmtd F1 plants showed an increased frequency in disease resistance compared to the families derived from dihybrids.Testing the female and male transmissions of the four double telo combinations revealed no gametic selection against telosomes in ovules while there was reduced transmission of telosomes through pollen.Pollen competition increased the frequency of gene stacks.Nineteen of 21 monosomics were isolated in elite germplasm by screening the progeny of haploid by diploid crosses.All monosomics were crossed with a normal parent to generate telocentric chromosomes through the misdivision of the univalent.Eleven telocentrics were recovered out of a possible 38.Eight of these represented telosome pairs i.e. long and short arm telosomes
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".