A fast algorithm to optimize SNP prioritization for gene-gene and gene-environment interactions
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
Detection of gene-environment interactions using an exhaustive search necessarily raises the multiple hypothesis problem. While frequently used to control for experiment-wise type I error, Bonferroni correction is overly conservative and results in reduced statistical power. We have previously shown that prioritizing SNPs on the basis of heterogeneity in quantitative trait variance per genotype leads to increased power to detect genetic interactions. Our proposed method, variance prioritization (VP), selects SNPs having significant heterogeneity in variance per genotype using a pre-determined P-value threshold. We now suggest prioritizing SNPs individually such that the optimal heterogeneity of variance P-value is determined for each SNP. The large number of SNPs in genome-wide studies calls for a fast algorithm to output the optimal prioritization threshold for each SNP. In this report, we present such an algorithm, the Gene Environment Wide Interaction Search Threshold (GEWIST), and show that the use of GEWIST will increase power under a variety of interaction scenarios. Furthermore, by integrating over possible interaction effect sizes, we provide a framework to optimize prioritization in situations where interactions are a priori unknown.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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