Identification and characterization of stripe rust, leaf rust, leaf spot, and common bunt resistance in spring wheat
Bibliographic record
Abstract
Abstract Hundreds of quantitative trait loci (QTLs) have been reported in diverse types of hexaploid wheat (Triticum aestivum L.) populations, but direct comparisons of QTLs in different studies and populations are still challenging due to the lack of physical positions for most QTLs. Here, we used the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map of all markers to map QTLs associated with leaf spot (Ls), leaf rust (Lr), stripe rust (Yr), and common bunt (Cbt) resistance in two recombinant inbred line populations. QTL mapping was conducted using the IWGSC physical map of 3158 and 5732 markers and disease severity data of Peace/Carberry and Attila/CDC Go populations evaluated in three to eight environments. We uncovered a total of 82 QTLs associated with Yr (36), Ls (18), Lr (15), and Cbt (13) resistance in the individual and overall means of all combined environments. Among them, 29 were associated with all combined environments, which accounted for 0.5%–20.9% individually and 12.4%–41.2% of the total disease severity per trait. Three (QLr.dms‐2D.2, QLs.dms‐5B, and QYr.dms‐5B.2) of the 29 QTLs were common in both populations. Fourteen out of the 29 QTLs were stable as they were identified both in the overall means and most of the individual environments. Ten chromosome arms harbored a cluster of QTLs associated with resistance to two to four diseases. This methodology would serve as one of the resources to compare QTLs identified in different populations and studies based on the improved physical information of all markers instead of population‐specific and consensus linkage maps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| 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 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".