Genetic improvement in the presence of genotype by environment interaction
Bibliographic record
Abstract
ABSTRACT Although the underlying mechanisms for triggering genotype–environment (GE) interaction are poorly understood, the potential impacts of GE interaction on genetic improvement are well recognized. Genotype–environment interaction may be classified into three levels: breed, individual and gene–environment interactions. Three measures of GE interaction (genetic correlation, interaction correlation, and commonality of individuals selected between environments) are discussed. Three options are currently available to deal with GE interaction: environmental, breeding and marker‐assisted approaches. Three possible selection strategies for improving global net merit were outlined: (i) selection of a specific genotype for each environment; (ii) selection in a single environment alone for overall response across environments; and (iii) global optimum index selection for high stability and average performance across environments. Global optimum index should be the method of choice from the standpoint of global marketing. Because of the complexity of GE interaction, it is impossible to develop a general strategy to deal with different types of GE interaction. Each type of interaction requires its own solution, depending upon a combination of the following six factors: (i) the intensity of GE interaction; (ii) relative economic weights among environments; (iii) the size of environments; (iv) the nature of environments; (v) the nature of GE interaction; and (vi) selection intensity. Profitability is a major concern in animal production. Extra genetic gain does not necessarily mean extra profit. Does additional genetic gain justify the associated costs of dealing with GE interaction? This is a fundamental issue that needs to be considered before a specific breeding strategy for GE interaction is developed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".