An Integrated Biplot Analysis System for Displaying, Interpreting, and Exploring Genotype × Environment Interaction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multienvironment trials (MET) generate two types of two‐way data: genotype × environment data for a target trait and genotype × trait data in individual or across environments. These data can be visually analyzed by a GGE biplot and a genotype × trait biplot, respectively. This paper describes a third type of biplot, the covariate‐effect biplot, and illustrates its tandem use with the other biplots to achieve a fuller understanding of MET data. The covariate‐effect biplot is generated on the basis of an explanatory trait × environment two‐way table consisting of correlation coefficients between the target trait (e.g., yield) and each of the other traits in each of the environments. This biplot displays the yield‐trait relations in individual environments and addresses whether and how the genotype × environment interactions (GE) for yield can be explored by indirect selection for the other traits. These other traits are treated as genetic covariables and can be replaced by other genetic covariables such as genetic markers, QTL, or genes. The biplot methodology was demonstrated by MET data of barley ( Hordeum vulgare L.) conducted across North America. Both the GGE biplot and the covariate‐effect biplot showed that the environments fell into two (eastern vs. western) megaenvironments. The covariate‐effect pattern explained 81% of the GGE pattern, suggesting that the GE pattern for yield can be effectively explored by indirect selection for these traits. Specifically, barley yield can be improved by selecting for larger kernel weight, earlier heading, and better lodging resistance in the eastern megaenvironment. In contrast, the yield–trait relationship in the western megaenvironment was highly variable, and yield improvement can be achieved only by selecting for yield per se across environments. We suggest that the GGE biplot, the genotype × trait biplot, and the covariate‐effect biplot be used jointly to better understand and more fully explore MET data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it