Airway Epithelial Expression Quantitative Trait Loci Reveal Genes Underlying Asthma and Other Airway Diseases
Bibliographic record
Abstract
Genome-wide association studies (GWASs) have identified loci that are robustly associated with asthma and related phenotypes; however, the molecular mechanisms underlying these associations need to be explored. The most relevant tissues to study the functional consequences of asthma are the airways. We used publically available data to derive expression quantitative trait loci (eQTLs) for human epithelial cells from small and large airways and applied the eQTLs in the interpretation of GWAS results of asthma and related phenotypes. For the small airways (n = 105), we discovered 660 eQTLs at a 10% false discovery rate (FDR), among which 315 eQTLs were not previously reported in a large-scale eQTL study of whole lung tissue. A large fraction of the identified eQTLs is supported by data from Encyclopedia of DNA Elements (ENCODE) showing that the eQTLs reside in regulatory elements (57.5 and 67.6% of cis- and trans-eQTLs, respectively). Published pulmonary GWAS hits were enriched as airway epithelial eQTLs (9.2-fold). Further, genes regulated by asthma GWAS loci in epithelium are significantly enriched in immune response pathways, such as IL-4 signaling (FDR, 5.2 × 10(-4)). The airway epithelial eQTLs described in this study are complementary to previously reported lung eQTLs and represent a powerful resource to link GWAS-associated variants to their regulatory function and thus elucidate the molecular mechanisms underlying asthma and airway-related conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".