Creation and judicious application of a wheat resistance gene atlas
Bibliographic record
Abstract
Disease-resistance (R) gene cloning in wheat (Triticum aestivum) has been accelerated by the recent surge of genomic resources, facilitated by advances in sequencing technologies and bioinformatics. However, with the challenges of population growth and climate change, it is vital not only to clone and functionally characterize a few handfuls of R genes, but also to do so at a scale that would facilitate the breeding and deployment of crops that can recognize the wide range of pathogen effectors that threaten agroecosystems. Pathogen populations are continually changing, and breeders must have tools and resources available to rapidly respond to those changes if we are to safeguard our daily bread. To meet this challenge, we propose the creation of a wheat R-gene atlas by an international community of researchers and breeders. The atlas would consist of an online directory from which sources of resistance could be identified and deployed to achieve more durable resistance to the major wheat pathogens, such as wheat rusts, blotch diseases, powdery mildew, and wheat blast. We present a costed proposal detailing how the interacting molecular components governing disease resistance could be captured from both the host and the pathogen through biparental mapping, mutational genomics, and whole-genome association genetics. We explore options for the configuration and genotyping of diversity panels of hexaploid and tetraploid wheat, as well as their wild relatives and major pathogens, and discuss how the atlas could inform a dynamic, durable approach to R-gene deployment. Set against the current magnitude of wheat yield losses worldwide, recently estimated at 21%, this endeavor presents one route for bringing R genes from the lab to the field at a considerable speed and quantity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".