Mapping Quantitative Trait Loci Underlining Tolerance to Phytophthora Root Rot in Soybean
Bibliographic record
Abstract
Phytophthora root rot(PRR) caused by Phytophthora sojae M.J.Kaufmann J.W.Gerdemann is a serious disease of soybeaan world wide.The objective of this study was to identify the location of quantitative trait loci(QTL)in ‘Conrad’,a soybean cultivar with broad tolerance to many races of P.sojae,and ‘Hefeng 25’,a soybean cultivar tolerant to P.sojae in China.The 140 individual of F2∶5 RIL population derived from Conrad×Hefeng 25 were adopted,and field and green hourse identification were conducted at Woodslee,Canada and Jiamusi,China in 2007 year,respectively.470 pair of SSR markers were used and 88 of them showed polymorphism.The genetic map was constructed with Mapmaker/EXP3.0b and QTLs analysis was done by CIM method of WinQTL 2.0.Four markers Satt428,Satt600,Satt325,Satt233,from three linkage group MLG D1b+W,MLG F and MLG A2,were significantly associated with PRR.Each marker explained 5.47% to 27.89% of phenotypic variance.Satt428 and Satt600 were mapped to MLG D1b+W,the genetic distance was 10.9 cM;Satt325 and Satt233 were mapped to MLG F and MLG A2 respectively.Quantitative trait loci on the linkage groups which are associated with PRR was discovered firstly from Heifeng 25,and was mapped to MLG A2.The identified QTLs would be beneficial for marker assistant selection of PRR tolerance varieties against China P.sojae races.
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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) | 0.000 | 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 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".