Gene–Environment Interaction Affects Risk of Atopic Eczema: Population and In Vitro Studies
Bibliographic record
Abstract
ABSTRACT Background Multiple environmental and genetic factors play a role in the pathogenesis of atopic eczema ( AE ). We aimed to investigate gene–environment interactions (G × E) to improve understanding of the pathophysiology. Methods We analysed data from 16 European studies to test for interaction between the 24 most significant AE‐associated loci identified from genome‐wide association studies and 18 early‐life environmental factors. We tested for replication using a further 10 studies and in vitro modeling to independently assess findings. Results The discovery analysis (including 25,339 individuals) showed suggestive evidence for interaction ( p < 0.05) between seven environmental factors (antibiotic use, cat ownership, dog ownership, breastfeeding, elder sibling, smoking and washing practices) and at least one established variant for AE, 14 interactions in total. In the replication analysis (254,532 individuals) dog exposure × rs10214237 (on chromosome 5p13.2 near IL7R ) was nominally significant (OR interaction = 0.91 [0.83–0.99] p = 0.025), with a risk effect of the T allele observed only in those not exposed to dogs. A similar interaction with rs10214237 was observed for siblings in the discovery analysis (OR interaction = 0.84 [0.75–0.94] p = 0.003), but replication analysis was under‐powered (OR interaction = 1.09 [0.82–1.46]). rs10214237 homozygous risk genotype is associated with lower IL‐7R expression in human keratinocytes, and dog exposure modelled in vitro showed a differential response according to rs10214237 genotype. Conclusion Interaction analysis and functional assessment provide preliminary evidence that early‐life dog exposure may modify the genetic effect of rs10214237 on AE via IL7R , supporting observational epidemiology showing a protective effect for dog ownership. The lack of evidence for other G × E studied here implies only weak effects are likely to occur.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".