Influence de la taille de la population et de la quantité de données phénotypiques sur les résultats d'analyses GWAS chez une collection de cultivars de soja canadien (Glycine max (L.) Merr)
Bibliographic record
Abstract
Understanding the genetic architecture of complex traits in soybean, such as maturity and yield, requires representative panels and suitable GWAS models. We investigated how sample size, statistical model, and phenotypic data quality jointly affect QTL detection. Three panels (315, 1,315, and 2,057 accessions) were analyzed with three multilocus models (MLMM, BLINK, and FarmCPU). Increasing panel size enhanced detection power, revealing additional loci including major genes E1, E2, E3, Dt1, and Dt2. FarmCPU showed the highest sensitivity, identifying robust QTLs even in smaller populations, and its substantial overlap with BLINK highlights model complementarity. High-quality phenotypic data (BLUPs) reduced noise, eliminated secondary signals, increased significance of key loci, and improved reproducibility across models. Co-localizations between maturity and yield QTLs further revealed shared genetic bases, notably around E2. These results underscore that precise phenotypes, sufficiently large panels, and complementary GWAS models are crucial to maximize both the statistical power and biological relevance of QTL mapping for complex traits in soybean.
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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 | 0.027 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".