Development of a genetic linkage map for Sharon goatgrass (<i>Aegilops sharonensis</i>) and mapping of a leaf rust resistance gene
Bibliographic record
Abstract
Aegilops sharonensis (Sharon goatgrass), a diploid wheat relative, is known to be a rich source of disease resistance genes for wheat improvement. To facilitate the transfer of these genes into wheat, information on their chromosomal location is important. A genetic linkage map of Ae. sharonensis was constructed based on 179 F2 plants derived from a cross between accessions resistant (1644) and susceptible (1193) to wheat leaf rust. The linkage map was based on 389 markers (377 Diversity Arrays Technology (DArT) and 12 simple sequence repeat (SSR) loci) and was comprised of 10 linkage groups, ranging from 2.3 to 124.6 cM. The total genetic length of the map was 818.0 cM, with an average interval distance between markers of 3.63 cM. Based on the chromosomal location of 115 markers previously mapped in wheat, the four linkage groups of A, B, C, and E were assigned to Ae. sharonensis (S(sh)) and homoeologous wheat chromosomes 6, 1, 3, and 2. The single dominant gene (designated LrAeSh1644) conferring resistance to leaf rust race THBJ in accession 1644 was positioned on linkage group A (chromosome 6S(sh)) and was flanked by DArT markers wpt-9881 (at 1.9 cM distal from the gene) and wpt-6925 (4.5 cM proximal). This study clearly demonstrates the utility of DArT for genotyping uncharacterized species and tagging resistance genes where pertinent genomic information is lacking.
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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 | 0.001 |
| 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) | 0.002 | 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".