Combining genomewide association study and lung <scp>eQTL</scp> analysis provides evidence for novel genes associated with asthma
Bibliographic record
Abstract
BACKGROUND: Genomewide association studies (GWASs) of asthma have identified single-nucleotide polymorphisms (SNPs) that modestly increase the risk for asthma. This could be due to phenotypic heterogeneity of asthma. Bronchial hyperresponsiveness (BHR) is a phenotypic hallmark of asthma. We aim to identify susceptibility genes for asthma combined with BHR and analyse the presence of cis-eQTLs among replicated SNPs. Secondly, we compare the genetic association of SNPs previously associated with (doctor's diagnosed) asthma to our GWAS of asthma with BHR. METHODS: A GWAS was performed in 920 asthmatics with BHR and 980 controls. Top SNPs of our GWAS were analysed in four replication cohorts, and lung cis-eQTL analysis was performed on replicated SNPs. We investigated association of SNPs previously associated with asthma in our data. RESULTS: A total of 368 SNPs were followed up for replication. Six SNPs in genes encoding ABI3BP, NAF1, MICA and the 17q21 locus replicated in one or more cohorts, with one locus (17q21) achieving genomewide significance after meta-analysis. Five of 6 replicated SNPs regulated 35 gene transcripts in whole lung. Eight of 20 asthma-associated SNPs from previous GWAS were significantly associated with asthma and BHR. Three SNPs, in IL-33 and GSDMB, showed larger effect sizes in our data compared to published literature. CONCLUSIONS: Combining GWAS with subsequent lung eQTL analysis revealed disease-associated SNPs regulating lung mRNA expression levels of potential new asthma genes. Adding BHR to the asthma definition does not lead to an overall larger genetic effect size than analysing (doctor's diagnosed) 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".