Potential Application of Genomic Technologies in Breeding for Fungal and Oomycete Disease Resistance in Pea
Bibliographic record
Abstract
Growth and yield of pea crops are severely affected by various fungal diseases, including root rot, Ascochyta blight, powdery mildew, and rust, in different parts of the world. Conventional breeding methods have led to enhancement of host plant resistance against these diseases in adapted cultivars, which is the primary option to minimize the yield losses. To support the breeding programs for marker-assisted selection, several successful attempts have been made to detect the genetic loci associated with disease resistance, based on SSR and SNP markers. In recent years, advances in next-generation sequencing platforms, and resulting improvements in high-throughput and economical genotyping methods, have been used to make rapid progress in identification of these loci. The first reference genome sequence of pea was published in 2019 and provides insights on the distribution and architecture of gene families associated with disease resistance. Furthermore, the genome sequence is a resource for anchoring genetic linkage maps, markers identified in multiple studies, identification of candidate genes, and functional genomics studies. The available pea genomic resources and the potential application of genomic technologies for development of disease-resistant cultivars with improved agronomic profile will be discussed, along with the current status of the arising improved pea germplasm.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".