An integrative genomics approach identifies new asthma pathways related to air pollution exposure
Bibliographic record
Abstract
The evidence on traffic-related air pollution exposure (TRAP) and incident childhood asthma is inconsistent, and may depend on genetic factors. We aimed to identify mechanisms of childhood asthma using genome-wide SNP data and individual TRAP exposure, and to evaluate the effect of susceptibility SNPs and TRAP on DNA-methylation and gene expression. We used LUR models to estimate individual outdoor NO2 levels at the birth address and performed a genome-wide interaction study for doctor9s diagnosis of asthma up to 8 years in three European birth cohorts with replication in two North American cohorts (n=3,322 subjects). The top GWIS and replicated SNPs were assessed for methQTL effects in peripheral blood cells and eQTL effects in human lung specimens. Short- and long-term TRAP associations with methylation patterns and TRAP-induced differential gene expression in blood cells were also assessed. The novel loci MAGI1, B4GALT5, MOCOS and DLG2, and the previously lung disease linked locus ADCY2 showed strong evidence for interaction with TRAP (genome-wide significance or replication). The top replication SNP rs686237 was identified as an eQTL for B4GALT5 (p=1.18x10-17) and affected TRAP-induced gene expression (p=0.03). Differential methylation following TRAP exposure was seen for DLG2, ADCY2, MAGI1 and MOCOS. Identified genes belong to the guanylate kinase, sphingolipid and calcium signaling pathways, suggesting involvement in asthma pathogenesis. Our results indicate that gene-environment interactions are important for asthma development and that functional genomics analyses in conjunction with environmental exposures may give valuable insights about pathophysiologic mechanisms.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".