Quantitative Trait Loci in the Ogle/TAM O-301 Oat Mapping Population Controlling Resistance to <i>Puccinia coronata</i> in the Field
Bibliographic record
Abstract
Crown rust is the most damaging disease of cultivated oat (Avena sativa) and genetic resistance is the primary means of controlling the disease. Quantitative trait loci (QTL) with major and minor effects have been identified in Ogle1040 and TAM O-301 (most notably, Pc58 and PcNQMG/LGCG from TAM O-301 and OT-27 from Ogle1040) through single-isolate greenhouse and field tests. To map loci and determine the effectiveness of previously identified QTL against naturally occurring pathogen populations in highly disease-conducive environments, the Ogle/TAM O-301 (OT) recombinant inbred line (RIL) population was grown in Texas and Louisiana over 2 years and in Manitoba, Canada. The genetic region characterized by the Pc58 resistance gene complex, particularly Pc58a, accounted for most of the diseased leaf area (DLA) and infection type (IT) variance in all five experiments. Additionally, the genetic region characterized by PcNQMG/LGCG accounted for a portion of the IT variance in three experiments. Although no QTL was detected on OT-27 in this study, all the markers on this linkage group were associated (P < 0.0001) with reducing both IT and DLA using single-marker analysis. Screening with 25 Puccinia coronata isolates from six different states indicated that Pc58abc and Pc58a were highly effective, while characterization using F(2) populations derived from OT RILs containing the two main genetic regions responsible for crown rust resistance in TAM O-301 (Pc58 and PcNQMG/LGCG) and a minor QTL in Ogle (OT-27) indicated that Pc58a, in combination with a locus in Ogle1040, provided high levels of resistance to natural races in Texas. This study provides new information and key loci in OT mapping population and may be useful for effective control of crown rust in North America.
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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 | 0.000 | 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) | 0.001 | 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".