Genetic heterogeneity of asthma phenotypes identified by a clustering approach
Bibliographic record
Abstract
The aim of the study was to identify genetic variants associated with refined asthma phenotypes enabling multiple features of the disease to be taken into account. Latent class analysis (LCA) was applied in 3001 adults ever having asthma recruited in the frame of three epidemiological surveys (the European Community Respiratory Health Survey (ECRHS), the Swiss Study on Air Pollution and Lung Disease in Adults (SAPALDIA) and the Epidemiological Study on the Genetics and Environment of Asthma (EGEA)). 14 personal and phenotypic characteristics, gathered from questionnaires and clinical examination, were used. A genome-wide association study was conducted for each LCA-derived asthma phenotype, compared to subjects without asthma (n=3474). The LCA identified four adult asthma phenotypes, mainly characterised by disease activity, age of asthma onset and atopic status. Associations of genome-wide significance (p<1.25 × 10(-7)) were observed between "active adult-onset nonallergic asthma" and rs9851461 flanking CD200 (3q13.2) and between "inactive/mild nonallergic asthma" and rs2579931 flanking GRIK2 (6q16.3). Borderline significant results (2.5 × 10(-7) < p <8.2 × 10(-7)) were observed between three single nucleotide polymorphisms (SNPs) in the ALCAM region (3q13.11) and "active adult-onset nonallergic asthma". These results were consistent across studies. 15 SNPs identified in previous genome-wide association studies of asthma have been replicated with at least one asthma phenotype, most of them with the "active allergic asthma" phenotype. Our results provide evidence that a better understanding of asthma phenotypic heterogeneity helps to disentangle the genetic heterogeneity of asthma.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".