Bibliographic record
Abstract
Studies have shown that many common diseases are influenced by multiple genes and their interactions. There is currently a strong interest in testing for association between combinations of these genes and disease, in particular because genes that affect the risk of disease only in the presence of another genetic variant may not be detected in marginal analysis. In this paper we propose the use of additive main effect and multiplicative interaction (AMMI) models to detect and to quantify gene-gene interaction effects for a quantitative trait. The objective of the present research is to demonstrate the practical advantages of these models to describe complex interaction between two unlinked loci. Although gene-gene interactions have often been defined as a deviance from additive genetic effects, the residual term has generally not been appropriately treated. The AMMI models allow for the analysis of a two way factorial data structure and combine the analysis of variance of the two main genotype effects with a principal component analysis of the residual multiplicative interaction. The AMMI models for gene-gene interaction presented here allow for the testing of non additivity between the two loci, and also describe how their interaction structure fits the existing non-additivity. Moreover, these models can be used to identify the specific two genotypes combinations that contribute to the significant gene-gene interaction. We describe the use of the biplot to display the structure of the interaction and evaluate the performance of the AMMI and the special cases of the AMMI previously described by Tukey and Mandel with simulated data sets. Our simulated study showed that the AMMI model is as powerful as general linear models when the interaction is not modeled in the presence of marginal effects. However, in the presence of pure epitasis, i.e. in the absence of marginal effects, the AMMI method was not found to be superior to other tested regression methods.
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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 | 0.047 | 0.104 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".