Population structure analysis and association mapping of seed antioxidant content in USDA cowpea (Vigna unguiculata L. Walp.) core collection using SNPs
Bibliographic record
Abstract
Cowpea (Vigna unguiculata L. Walp.) is an important legume, and the antioxidant content in cowpea seeds has been recognized as a health-promoting compound for humans. The objectives of this study were to analyze the population structure of cowpea collections and to identify single nucleotide polymorphism (SNP) markers associated with the seed antioxidant content and seed coat colour. A set of 1047 SNPs were used to analyze a 369 cowpea core collection from 47 countries. Results indicated that: (1) there were three clusters in the 369 entries; and the germplasm collected from India, South Africa, and the US showed broader genetic diversity; (2) Scaffold7139_14363 and Scaffold29110_4657 were strongly associated with antioxidant content, and C35063613_1497, Scaffold81493_886, and Scaffold84620_6785 were strongly associated with seed coat colour across three models; (3) significant correlations were detected between the seed antioxidant content and black seed colour (r = 0.45) and between seed antioxidant content and red seed coat colour (r = 0.50); and (4) Scaffold42008_191 and C35082838_2258 were associated with both seed antioxidant content and seed coat colour. The SNP markers identified could potentially be used in marker-assisted breeding to accelerate genetic improvement of cowpea for higher seed antioxidant content.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".