Multi-ancestry fine-mapping of the chromosome 17q12-q21 asthma locus identifies independent associations implicating lymphocyte and eosinophil levels in the causal pathway
Bibliographic record
Abstract
Abstract Background Asthma pathophysiology varies by age-of-onset and involves diverse immune processes reflected in white blood cell (WBC) subsets. To investigate the genetic architecture of asthma and potential endophenotypes, we analyzed the chr17q12-q21 locus, a robustly replicated asthma locus, across European (EUR), African (AFR), East Asian (EAS), and South Asian (SAS) ancestry groups from the UK Biobank (UKB) and Biobank Japan (BBJ). The largest EUR sample was further stratified by age-ofonset as a proxy for etiological heterogeneity. Results Eight independent asthma signals were identified in UKB-EUR, including two novel associations (Signal 2—rs72832915 and Signal 6—rs507671). Signal 4, corresponding to the originally identified pediatric signal, showed the strongest cross-ancestry evidence, with asthma risk and diminished lymphocyte count co-localizing in three populations. Signal 8 was distinguished by multiple lines of evidence converging on rs112401631 as a likely causal variant, including fine-mapping, colocalization with eosinophil and lymphocyte counts, and mediation of asthma risk through eosinophil count. Signal 6 implicated RARA expression, suggesting vitamin A metabolism impacts on late-onset asthma, the only associated stratum. Notably, integrating WBC traits and Bayesian fine-mapping enabled leveraging non-European ancestry groups to strengthen causal inference despite smaller sample sizes. Conclusion These findings illustrate how combining age-of-onset stratification, quantitative endophenotypes, and multi-ancestry analyses can reveal mechanistic heterogeneity and prioritize specific variants and path-ways for functional validation. This framework is broadly applicable to complex diseases with measurable quantitative endophenotypes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".