Single and multiple resistance QTL delay symptom appearance and slow down root colonization by Aphanomyces euteiches in pea near isogenic lines
Bibliographic record
Abstract
BACKGROUND: Understanding the effects of resistance QTL on pathogen development cycle is an important issue for the creation of QTL combination strategies to durably increase disease resistance in plants. The oomycete pathogen Aphanomyces euteiches, causing root rot disease, is one of the major factors limiting the pea crop in the main producing countries. No commercial resistant varieties are currently available in Europe. Resistance alleles at seven main QTL were recently identified and introgressed into pea agronomic lines, resulting in the creation of Near Isogenic Lines (NILs) at the QTL. This study aimed to determine the effect of main A. euteiches resistance QTL in NILs on different steps of the pathogen life cycle. RESULTS: NILs carrying resistance alleles at main QTL in susceptible genetic backgrounds were evaluated in a destructive test under controlled conditions. The development of root rot disease severity and pathogen DNA levels in the roots was measured during ten days after inoculation. Significant effects of several resistance alleles at the two major QTL Ae-Ps7.6 and Ae-Ps4.5 were observed on symptom appearance and root colonization by A. euteiches. Some resistance alleles at three other minor-effect QTL (Ae-Ps2.2, Ae-Ps3.1 and Ae-Ps5.1) significantly decreased root colonization. The combination of resistance alleles at two or three QTL including the major QTL Ae-Ps7.6 (Ae-Ps5.1/Ae-Ps7.6 or Ae-Ps2.2/Ae-Ps3.1/Ae-Ps7.6) had an increased effect on delaying symptom appearance and/or slowing down root colonization by A. euteiches and on plant resistance levels, compared to the effects of individual or no resistance alleles. CONCLUSIONS: This study demonstrated the effects of single or multiple resistance QTL on delaying symptom appearance and/or slowing down colonization by A. euteiches in pea roots, using original plant material and a precise pathogen quantification method. Our findings suggest that single resistance QTL can act on multiple or specific steps of the disease development cycle and that their actions could be pyramided to increase partial resistance in future pea varieties. Further studies are needed to investigate QTL effects on different steps of the pathogen life cycle, as well as the efficiency and durability of pyramiding strategies using QTL which appear to act on the same stage of the pathogen cycle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".