QTL Identification, Mega-Environment Classification, and Strategy Development for Marker-Based Selection Using Biplots
Bibliographic record
Abstract
SUMMARY This paper describes a biplot approach to QTL identification based on phenotypic data from multiple environments, and demonstrates its use in the investigation of QTL-by-environment patterns. The effects of each marker on the target trait were estimated for each environment, leading to a marker-by-environment two-way table. This table was then visually investigated in a marker-by-environment biplot. In the biplot, markers with short vectors should have little or no associations with the trait and can be deleted. The remaining markers would fall into clusters, each suggesting the existence of one or more QTL with similar QTL-by-environment patterns. Within each cluster, the marker with the longest vector should be the one located closest to the QTL. When each QTL is represented by its closest marker, the marker-by-environment biplot is referred to as a QTL-by-environment (QQE) biplot. It can help visualize (1) groups of QTL with similar environmental responses; (2) major vs. minor QTL; (3) the average effect of a QTL and its stability across environments; (4) groups of environments with similar expressions of QTL effects, and (5) QTL allele combinations for maximizing/minimizing the expression of the trait for each mega-environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".