Allele-specific expression reveals interactions between genetic variation and environment
Bibliographic record
Abstract
The impact of environment on human health is dramatic, with major risk factors including substance use, diet and exercise. However, identifying interactions between the environment and an individual's genetic background (GxE) has been hampered by statistical and computational challenges. By combining RNA sequencing of whole blood and extensive environmental annotations collected from 922 individuals, we have evaluated GxE interactions at a cellular level. We have developed EAGLE, a hierarchical Bayesian model for identifying GxE interactions based on association between environment and allele-specific expression (ASE). EAGLE increases power by leveraging the controlled, within-sample comparison of environmental impact on different genetic backgrounds provided by ASE, while also taking into account technical covariates and over-dispersion of sequencing read counts. EAGLE identifies 35 GxE interactions, a substantial increase over standard GxE testing. Among EAGLE hits are variants that modulate response to smoking, exercise and blood pressure medication. Further, application of EAGLE identifies GxE interactions to infection response that replicate results reported in vitro, demonstrating the power of EAGLE to accurately identify GxE candidates from large RNA sequencing studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".