Genome-wide association mapping in exotic × Canadian elite crosses: mining beneficial alleles for agronomic and seed composition traits in soybean
Bibliographic record
Abstract
Given the narrow genetic base of North American soybean germplasm, which originates from approximately 35 ancestral lines, discovering and introducing useful diversity for key traits in exotic germplasm could potentially enhance diversity in the current elite gene pool. This study explores the potential of exotic germplasm to enhance yield and agronomic traits in the University of Guelph soybean germplasm. We utilized a nested association mapping (NAM) design to develop a population (n = 294) composed of crosses of high-yielding Canadian elite cultivar, OAC Bruton, with four high-yielding exotic lines developed at USDA (Urbana, IL), and we mapped the genetic architecture of agronomic and seed composition traits using association mapping methods. The analysis across three Southwestern Ontario environments revealed seven unique genomic regions underlying agronomic traits and four for seed composition traits, with both desirable and undesirable alleles from the exotic parents. Notably, a region on chromosome 10, co-locating to the E2 maturity locus, was found to be associated with seed yield and maturity. The allele that increased yield by 166 kg/ha was contributed by all exotic parents and was absent in the Canadian-adapted parent. The study underscores the potential of using exotic germplasm to introduce novel genetic diversity into the Canadian elite soybean breeding pool. By identifying exotic-derived beneficial alleles, our findings offer a pathway for enhancing agronomic traits in Canadian soybeans with novel exotic diversity.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".