SNP–SNP Interaction Analysis of Soybean Protein Content under Multiple Environments
Bibliographic record
Abstract
Soybean protein content is a valuable quantitative trait controlled by multiple genes. The epistatic interaction of these genes can increase protein content observably. In this study, we used the multifactor dimensionality reduction method and a soybean high-density genetic map including 5308 markers to identify stable loci controlling protein content in soybean across 23 environments. In total, 31 897 046 single nucleotide polymorphism (SNP) – protein interaction pairs were detected. Among these, 46 stable SNP interaction pair associations with soybean protein content were identified under multiple environments, with 2 and 44 SNP pairs stably detected across four and three environments, respectively. Hot spot regions for interaction pairs were detected on linkage groups Gm17, Gm06, and Gm03, consistent with previous quantitative trait locus mapping. The epistatic effects and contributions of the stable interaction pairs ranged from 0.0008 to 0.5483 and 0.0003 to 0.5126, respectively. Eight SNP epistatic interaction subnets were constructed. Ten candidate genes from these interaction subnets showed a relationship with seed protein storage or amino acid biosynthesis and metabolism. The results of this study provide insights into the genetic architecture of soybean protein content and can serve as a basis for marker-assisted selection breeding.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".