On the Use of Allelic Transmission Rates for Assessing Gene‐by‐Environment Interaction in Case‐Parent Trios
Bibliographic record
Abstract
Allelic transmission rates from parents to cases are frequently stratified by an environmental risk factor E and compared, with heterogeneity interpreted as gene-environment interaction or GxE. Though generally invalid, such analyses continue to appear. We revisit why heterogeneity is not equivalent to GxE in a range of settings not considered previously. The objective is a fuller understanding of the bias in transmission rates and what is driving it. Extending previously published findings, we derive parental mating-type probabilities in cases and use them to obtain transmission rates, which we then compare to GxE. Through simulation, we investigate the practical implications of the bias for a transmission-based test of GxE. We find that general population characteristics distort the picture of GxE obtained from transmission rates: the stratum-specific mating-type probabilities under G - E dependence and the allele frequency under independence. Furthermore, the transmission-based test has inflated error rates relative to a likelihood-based test. Our investigation provides further insight into how and why transmission-based tests and descriptive summaries can mislead about GxE. For exploring GxE, we suggest graphical displays of the transmission rates within parental mating types, as they are robust to population stratification and the penetrance model.
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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 | 0.128 | 0.446 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".