Deep Learning Genome-wide Linkage Association Study for Wheat Fusarium Head Blight Resistance Genes Discovery
Bibliographic record
Abstract
Abstract Background Fusarium head blight (FHB) is one of the most devastating diseases of wheat worldwide and artificial intelligence can assist with understanding resistance to the disease. Considering different sample populations, marker types, reference maps, and statistical methods, we developed a Deep Learning Genome-wide Linkage Association Study (dpGLAS) of FHB resistance in wheat. Results The dpGLAS was first applied to two bi-parental population datasets in which the cultivar AC Barrie was a common parent for FHB resistance. Eight candidate gene markers were discovered in the one AC Barrie population and 10 in the other associated with FHB resistance. Eight of these markers were also supported by the conventional QTL mapping. Most of these candidate marker genes were found associated with the Reactive Oxygen Species (ROS) and Abscisic acid (ABA) axes. These ROS and ABA pathways were further supported by RNA-seq transcriptome data of FHB resistant cv. AAC Tenacious, a parent of the third bi-parental population. In this dataset, the ROS-centered Panther protein families were significantly enriched in those genes that had most different response to FHB when compared the resistance Tenacious and the susceptible Roblin. Conclusions This study developed the framework of dpGLAS and identified candidate genes for FHB resistance in the Canadian spring wheat cultivars AC Barrie and AAC Tenacious.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".