Genome-wide interaction study of gene-by-occupational exposures on respiratory symptoms
Bibliographic record
Abstract
Respiratory symptoms are important indicators of respiratory diseases. Both genetic and environmental factors contribute to respiratory symptoms development but less is known about gene-environment interactions. We aimed to assess interactions between single nucleotide polymorphisms (SNPs) and occupational exposures on respiratory symptoms cough, dyspnea and phlegm. As identification cohort LifeLines I (n = 7976 subjects) was used. Job-specific exposure was estimated using the ALOHA + job exposure matrix. SNP-by-occupational exposure interactions on respiratory symptoms were tested using logistic regression adjusted for gender, age, and current smoking. SNP-by-exposure interactions with a p-value <10−4 were tested for replication in two independent cohorts: LifeLines II (n = 5260) and the Vlagtwedde-Vlaardingen cohort (n = 1529). The interaction estimates of the replication cohorts were meta-analyzed using PLINK. Replication was achieved when the meta-analysis p-value was <0.05 and the interaction effect had the same direction as in the identification cohort. Additionally, we assessed whether replicated SNPs associated with gene expression by analyzing if they were cis-acting expression quantitative trait loci (eQTL) in lung tissue. In the replication meta-analysis, sixteen out of 477 identified SNP-by-occupational exposure interactions had a p-value <0.05 and 9 of these interactions had the same direction as in the identification cohort. Several identified loci were plausible candidates for respiratory symptoms, such as TMPRSS9, SERPINH1, TOX3, and ARHGAP18. Three replicated SNPs were cis-eQTLs for FCER1A, CHN1, and TIMM13 in lung tissue. Taken together, this genome-wide SNP-by-occupational exposure interaction study in relation to cough, dyspnea, and phlegm identified several suggestive susceptibility genes. Further research should determine if these genes are true susceptibility loci for respiratory symptoms in relation to occupational exposures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 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; both teacher heads agree on what is shown here.
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".