Quantitative trait loci underlying resistance to the soybean cyst nematode in PI 507354
Bibliographic record
Abstract
Due to the current prevalence and shift in virulence of Heterodera glycines, the soybean cyst nematode (SCN), there is a pressing need to find sustainable alternatives or complements to the commonly found Rhg1 and Rhg4 resistance loci. The objective of this study was to find novel quantitative trait loci (QTL) to expand the toolbox for SCN resistance. To do so, a population of recombinant inbred lines named QS13073 was generated from a biparental cross between an SCN-susceptible cultivar (S12-A5) and an SCN-resistant accession (PI 507354, also named Tokei 421). Using four mapping algorithms (SMA, ICIM, GCIM, and R/qtl2), seven loci, including rhg1a and Rhg4-a, involved in the resistance to SCN Hg type 5.7 were identified. To the best of our knowledge, two QTL (Chr.10-QTL and Chr.19-QTL) were novel, while three others (Chr.07-QTL, Chr.14-QTL, and Chr.20-QTL) were previously identified by other researchers. Based on statistical analyses, two loci, Chr.10-QTL and Chr.20-QTL, seemed to be partially viable alternatives to the rhg1a and Rhg4-a loci, as they were demonstrated to be effective in the Rhg1 and/or Rhg4 susceptible backgrounds. Using a four-step prediction pipeline, seven candidate genes ( Glyma.07G196800, Glyma.07G199700, Glyma.07G203300, Glyma.07G206200, Glyma.14G019600, Glyma.14G025800, and Glyma.20G197600) were identified in the narrow regions of Chr.07-QTL, Chr.14-QTL, and Chr.20-QTL. Based on these predictions and a thorough literature review, we consider that Glyma.07G196800, Glyma.07G203300, and Glyma.07G206200 are the best candidates for Chr.07-QTL. In conclusion, our study strengthens our understanding of the genetic loci underlying SCN resistance and diversifies the single nucleotide polymorphism marker catalog currently available to breeders.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".