Genetic Analysis and Mapping of QTLs for Soybean Biological Nitrogen Fixation Traits Under Varied Field Conditions
Bibliographic record
Abstract
Soybean is an important economic and green manure crop that is widely used in intercropping and rotation systems due to its high biological nitrogen fixation (BNF) capacity and the resulting reduction in N fertilization. However, the genetic mechanisms underlying soybean BNF are largely unknown. Here, two soybean parent genotypes contrasting in BNF traits and 168 F9:11 recombinant inbred lines (RILs) were evaluated under four conditions in the field. The parent FC1 always produced more big nodules, yet fewer nodules in total than the parent FC2 in the field. Furthermore, nodulation in FC1 was more responsive to environmental changes than that in FC2. Broad-sense heritability (h2b) for all BNF traits varied from 0.48 to 0.87, which suggests that variation in the observed BNF traits was primarily determined by genotype. Moreover, two new QTLs for BNF traits, qBNF-16 and qBNF-17, were identified in this study. The qBNF-16 locus was detected under all of the four tested conditions, where it explained 15.9%-59.0% of phenotypic variation with LOD values of 6.31-32.5. Meanwhile qBNF-17 explained 12.6-18.6% of observed variation with LOD values of 4.93-7.51. Genotype group analysis indicated that the FC1 genotype of qBNF-16 primarily affected nodule size (NS), while the FC2 genotype of qBNF-16 promoted nodule number (NN). On the other hand, the FC1 genotype of qBNF-17 influenced NN and the FC2 genotype of qBNF-17 impacted NS. The results on the whole suggest that these two QTLs might be valuable markers for breeding elite soybean varieties with high BNF capacities.
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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".