Identification of microsatellite markers linked to quantitative trait loci controlling resistance to Fusarium root rot in field pea
Bibliographic record
Abstract
Feng, J., Hwang, R., Chang, K. F., Conner, R. L., Hwang, S. F., Strelkov, S. E., Gossen, B. D., McLaren, D. L. and Xue, A. G. 2011. Identification of microsatellite markers linked to quantitative trait loci controlling resistance to Fusarium root rot in field pea. Can. J. Plant Sci. 91: 199-204. Fusarium root rot, caused by Fusarium solani (Mart.) Sacc. f. sp. pisi (F. R. Jones) W. C. Snyder & H. N. Hans, is the most common root disease of field pea (Pisum sativum L.) in western Canada. In this study, a recombinant inbred line (RIL) population (n=71) of field pea, derived from crosses between a resistant cultivar Carman, and a susceptible cultivar Reward, was evaluated to identify quantitative trait loci (QTL) controlling resistance to Fusarium root rot. The parental genotypes and RILs were evaluated for resistance to root rot following inoculation with F. solani in field experiments during 2007 and 2008. The frequency distribution of disease severities among the RILs was continuous. Transgressive segregation for resistance was observed among the RILs, with five lines more resistant than Carman, but no lines were more susceptible than Reward. To identify DNA markers linked with the resistance, 213 microsatellite markers were screened with genomic DNA from the two parental cultivars. Only 14 markers were polymorphic between the two parents and were used to genotype each of the RILs. Quantitative trait loci analysis based on the mean disease severity data from 2007 and 2008 identified a QTL that explained 39.0% of the phenotypic variance in the RIL population. This QTL is flanked by markers AA416 and AB60 on linkage group VII. The microsatellite markers that are closely linked to this QTL may be useful for marker assisted selection to develop cultivars with superior Fusarium root rot resistance.
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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".