Regional Differences in Soybean Protein and Amino Acid Profiles: A Genetic Exploration Using a Novel GWAS Panel
Bibliographic record
Abstract
ABSTRACT Regional differences in soybean seed protein and amino acid content in Canada present significant challenges for crop improvement and the market value of high‐protein livestock feed. This study employed genome‐wide association studies (GWAS) using a novel panel of 206 cultivars to investigate the genetic basis of regional variations. Field trials were conducted across six site years in Eastern and Western Canada in 2021 and 2022. Phenotypic analysis revealed lower protein and amino acid content in Western regions, with an average decrease of 0.9% in protein compared with Eastern regions. Using 31,362 SNPs, we identified 370 significant marker trait associations (MTAs), consolidated into 175 quantitative trait loci (QTL), 27 of which are novel. Differences in reporting methodology for amino acid content, whether on a dry matter or protein basis, resulted in different results in phenotypic correlation and detected MTAs. Gene ontology analysis of novel QTL revealed pathways related to amino acid metabolism, cold stress response, and auxin biosynthesis. Previously reported QTL on Chromosomes 14, 15, and 20 were validated through detection in this panel. Stable critical amino acid values (CAAVs) across regions and only one detected MTA suggest that an amino acid–specific and not CAAV‐targeted approach should be used in breeding strategies. The novel association panel assembled here will be a resource for crop improvement efforts. This study provides valuable insights into the genetic architecture of regional protein and amino acid variation in Canadian soybean and identifies promising targets for breeding programs aimed at improving seed protein content and amino acid profiles in specific growing regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".