Analysis of GWAS-nominated loci for lung cancer and COPD revealed a new asthma locus
Bibliographic record
Abstract
BACKGROUND: Asthma, lung cancer (LC) and chronic obstructive pulmonary disease (COPD) are three respiratory diseases characterized by complex mechanisms underlying and genetic predispositions, with asthma having the highest calculated heritability. Despite efforts deployed in the last decades, only a small part of its heritability has been elucidated. It was hypothesized that shared genetic factors by these three diseases could help identify new asthma loci. METHODS: GWAS-nominated LC and COPD loci were selected among studies performed in Caucasian cohorts using the GWAS Catalog. Genetic analyses were carried out for these loci in the Saguenay-Lac-Saint-Jean (SLSJ) asthma familial cohort and then replicated in two independent cohorts (the Canadian Cohort Obstructive Lung Disease [CanCOLD] and the Epidemiological Study of the Genetics and Environment of Asthma [EGEA]). RESULTS: Analyses in the SLSJ cohort identified 2851 and 4702 genetic variants to be replicated in the CanCOLD and EGEA cohorts for LC and COPD loci respectively. Replication and meta-analyses allowed the association of one new locus with asthma, 2p24.3, from COPD studies. None was associated from LC studies reported. CONCLUSIONS: The approach used in this study contributed to better understand the heritability of asthma with shared genetic backgrounds of respiratory diseases.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.002 | 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".