Novel Genetic Susceptibility Loci for FEV1 in the Context of Occupational Exposure in Never-Smokers
Bibliographic record
Abstract
Recently, we identified several novel and plausible genetic susceptibility loci for impaired lung function levels in the context of occupational exposure in a sample, including both never-and ever-smokers (1).Previous studies suggest that effects of genetic variants (2), occupational exposures (3), and their interactions (1) may be different in never-smokers and ever-smokers.Yet never-smokers generally make up a smaller proportion of subjects in general population studies (including current, former, and never-smokers), and effects solely present in never-smokers may therefore not be detected.Hence, to unravel why and how never-smokers develop impaired lung function levels and chronic respiratory diseases such as chronic obstructive pulmonary disease, it is important to study the effects of nonsmoking-related exposures without potential interference of tobacco smoke exposure.With the current genomewide interaction study, we aimed to identify novel genetic susceptibility loci for impaired levels of FEV 1 in the context of occupational exposure to biological dust, mineral dust, and gases/fumes in a sample including never-smokers only.We included never-smokers from two Dutch general population-based cohorts: LifeLines (N = 5,070) and Vlagtwedde-Vlaardingen (N = 431).First, in each cohort separately, genome-wide single-nucleotide polymorphism (SNP)-by-exposure interactions were assessed, using linear regression models specified as follows: FEV 1 = SNP (additive effect) 1 low exposure 1 high exposure 1 SNP 3 low exposure 1 SNP 3 high exposure 1 sex 1 age 1 height.To have a clear exposure contrast, we focused on the SNP-byhigh exposure interaction only.Subsequently, the SNP-by-high exposure interactions from both cohorts were metaanalyzed using effects estimates weighted by the SEs.SNPs with interaction P values ,5 3 10 28 and with the same direction of interaction in both cohorts were taken further for cis-acting expression quantitative trait loci (cis-eQTL) analysis in lung tissue of 1,087 subjects (4).Finally, we performed pathway analyses using all SNPs (5).More detailed information about the cohorts, phenotyping, genotyping, occupational exposure assessment, cis-eQTL, and pathway analysis can be found elsewhere (1).Subjects included from the LifeLines study had a median age of 46 years (range, 18-90 yr), with a mean FEV 1 of 104% predicted and mean FEV 1 /FVC of 78%.Subjects from the Vlagtwedde-Vlaardingen study had a median age of 54 years (range, 36-79 yr), with a mean FEV 1 of 98% predicted and mean FEV 1 /FVC of 76%.We identified four significant SNP-by-high exposure interactions, one with mineral dust and three with gases/fumes exposure (Table 1).No significant interactions were found with high exposure to biological dust.For all four SNPs, highly exposed subjects had substantially lower FEV 1 levels compared with subjects without exposure, yet only when carrying at least one copy of the risk allele and not when carrying the wild-type genotype (Figures 1A-1D).None of the four identified SNPs was a cis-eQTL in lung tissue.Finally, the Biocarta pathways patched 1 and the natural killer cells were suggestively associated (false discovery rate P value , 0.25) with FEV 1 in the context of mineral dust and gases/fumes exposure, respectively.The most significant interaction identified was between gases/fumes exposure and SNP rs10223081 located nearby the gene NMUR2, a G coupled-protein receptor for neuromedin U (NMU) (6).NMU can induce mast cell degranulation leading to, for example, early-phase inflammation, such as neutrophil infiltration in inflamed sites (7), and can induce eosinophil infiltration in allergic inflammatory sites in an antigen-induced asthma model.We found modest expression of NMUR2 in lung tissue (data not shown), yet this expression was not associated with the identified SNP.Importantly, effects of high exposure on gases/fumes were large and of
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".