Asthma exacerbations and eosinophilia in the UK Biobank: a genome-wide association study.

BackgroundAsthma exacerbations reflect disease severity, affect morbidity and mortality, and may lead to declining lung function. Inflammatory endotypes (e.g. T2-high (eosinophilic)) may play a key role in asthma exacerbations. We aimed to assess whether genetic susceptibility underlies asthma exacerbation risk and additionally tested for an interaction between genetic variants and eosinophilia on exacerbation risk.MethodsUK Biobank data were used to perform a genome-wide association study of individuals with asthma and at least one exacerbation compared to individuals with asthma and no history of exacerbations. Individuals with asthma were identified using self-reported data, hospitalisation data and general practitioner records. Exacerbations were identified as either asthma-related hospitalisation, general practitioner record of asthma exacerbation or an oral corticosteroid burst prescription. A logistic regression model adjusted for age, sex, smoking status and genetic ancestry via principal components was used to assess the association between genetic variants and asthma exacerbations. We sought replication for suggestive associations (p-6) in the GERA cohort.ResultsIn the UK Biobank, we identified 11 604 cases and 37 890 controls. While no variants reached genome-wide significance (p-8) in the primary analysis, 116 signals were suggestively significant (p-6). In GERA, two single nucleotide polymorphisms (rs34643691 and rs149721630) replicated (pConclusionsOur study has identified reproducible associations with asthma exacerbations in the UK Biobank and GERA cohorts. Confirmation of these findings in different asthma subphenotypes in diverse ancestries and functional investigation will be required to understand their mechanisms of action and potentially inform therapeutic development

Power curves for RITSS1, RITSS2, GESAT, and GAMsv over increasing signal density <i>p</i>_<i>XE</i>.

Significance level α = 0.005, μXE = 0.1, and results based on 1,000 replicates. Power was simulated for pXE = 0.05, 0.1, 0.2, 0.3, 0.4, 0.5.</p

Density plots for standardized residuals for all four traits in the analysis.

FEV1: forced expiratory volume in 1 second, FVC: forced vital capacity. (TIF)</p

Analysis configurations and number of genetic variants incorporated.

Analysis configurations and number of genetic variants incorporated.</p

Type 1 error: Quantile-quantile-plots for RITSS1, RITSS2, GESAT, and GAMsv for scenarios 1–5 with SELECT:yes.

All results based on 10,000 replicates. P-values with p−10 were set to p = 10−10. SELECT:yes refers to the scenario where the simulated genetic variants are selected into the analysis based on marginal association p-values.</p

Power curves for RITSS1, RITSS2, GESAT, and GAMsv over increasing signal density <i>p</i>_<i>XE</i>.

Significance level α = 0.005, μXE = 0.05, and results based on 1,000 replicates. Power was simulated for pXE = 0.05, 0.1, 0.2, 0.3, 0.4, 0.5.</p

Additional information regarding genetic variants tested in the UK Biobank analysis.

These tables contain all genetic variants as well as the m4/m3 information for the sex-interaction analysis in the UK Biobank. (XLSX)</p

Type 1 error: Quantile-quantile-plots for RITSS1, RITSS2, GESAT, and GAMsv for scenarios 1–5 with SELECT:no.

All results based on 10,000 replicates. P-values with p−10 were set to p = 10−10. SELECT:no refers to the scenario where all simulated genetic variants are included in the analysis.</p

Results of the interaction testing using the two approaches RITSS1 and RITSS2 in the UK Biobank.

The environmental factor tested for interaction is denoted by Eit. |m| is the number of total SNPs in the analysis, |m4| and |m3| are the number of SNPs that are shared by all four and exactly three interaction scores, respectively. P-Y-S: pack-years of smoking, E-S: ever-smoking.</p

Visualization of the RITSS algorithm.

Visualization of the RITSS algorithm.</p