Large-Scale Data Integration Reveals Colocalization of Gene Functional Groups with Meta-QTL for Multiple Disease Resistance in Barley
Bibliographic record
Abstract
Race-nonspecific and durable resistance of plant genotypes to major pathogens is highly relevant for yield stability and sustainable crop production but difficult to handle in practice due to its polygenic inheritance by quantitative trait loci (QTL). As far as the underlying genes are concerned, very little is currently known in the most important crop plants such as the cereals. Here, we integrated publicly available data for barley (Hordeum vulgare subsp. vulgare) in order to detect the most important genomic regions for QTL-mediated resistance to a number of fungal pathogens and localize specific functional groups of genes within these regions. This identified 20 meta-QTL, including eight hot spots for resistance to multiple diseases that were distributed over all chromosomes. At least one meta-QTL region for resistance to the powdery mildew fungus Blumeria graminis was found to be co-linear between barley and wheat, suggesting partial evolutionary conservation. Large-scale genetic mapping revealed that functional groups of barley genes involved in secretory processes and cell-wall reinforcement were significantly over-represented within QTL for resistance to powdery mildew. Overall, the results demonstrate added value resulting from large-scale genetic and genomic data integration and may inform genomic-selection procedures for race-nonspecific and durable disease resistance in barley.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".