Integrative pathway genomics of lung function and airflow obstruction

Chronic respiratory disorders are important contributors to the global burden of disease. Genome-wide association studies (GWASs) of lung function measures have identified several trait-associated loci, but explain only a modest portion of the phenotypic variability. We postulated that integrating pathway-based methods with GWASs of pulmonary function and airflow obstruction would identify a broader repertoire of genes and processes influencing these traits. We performed two independent GWASs of lung function and applied gene set enrichment analysis to one of the studies and validated the results using the second GWAS. We identified 131 significantly enriched gene sets associated with lung function and clustered them into larger biological modules involved in diverse processes including development, immunity, cell signalling, proliferation and arachidonic acid. We found that enrichment of gene sets was not driven by GWAS-significant variants or loci, but instead by those with less stringent association P-values. Next, we applied pathway enrichment analysis to a meta-analysed GWAS of airflow obstruction. We identified several biologic modules that functionally overlapped with those associated with pulmonary function. However, differences were also noted, including enrichment of extracellular matrix (ECM) processes specifically in the airflow obstruction study. Network analysis of the ECM module implicated a candidate gene, matrix metalloproteinase 10 (MMP10), as a putative disease target. We used a knockout mouse model to functionally validate MMP10’s role in influencing lung’s susceptibility to cigarette smoke-induced emphysema. By integrating pathway analysis with population-based genomics, we unravelled biologic processes underlying pulmonary function traits and identified a candidate gene for obstructive lung disease

Evidence for Large-Scale Gene-by-Smoking Interaction Effects on Pulmonary Function

Background: Smoking is the strongest environmental risk factor for reduced pulmonary function. The genetic component of various pulmonary traits has also been demonstrated, and at least 26 loci have been reproducibly associated with either FEV1 (forced expiratory volume in 1 second) or FEV1/FVC (FEV1/forced vital capacity). Although the main effects of smoking and genetic loci are well established, the question of potential gene-by-smoking interaction effect remains unanswered. The aim of the present study was to assess, using a genetic risk score approach, whether the effect of these 26 loci on pulmonary function is influenced by smoking. Methods: We evaluated the interaction between smoking exposure, considered as either ever vs. never or pack-years, and a 26 SNPs genetic risk score in relation to FEV1 or FEV1/FVC in 50 047 participants of European ancestry from the CHARGE and SpiroMeta consortia. Results: We identified an interaction ( = −0.036, 95% confidence interval, -0.040 – -0.032, P=0.00057) between an unweighted 26 SNPs genetic risk score and smoking status (ever/never) on the FEV1/FVC ratio. In interpreting this interaction, we showed that the genetic risk of falling below the FEV1/FVC threshold used to diagnose chronic obstructive pulmonary disease is higher among ever smokers than among never smokers. Conclusions: This study highlights the benefit of using genetic risk scores for identifying interactions missed when studying individual SNPs, and shows for the first time that persons with the highest genetic risk for low FEV1/FVC may be more susceptible to the deleterious effects of smoking

Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets

Chronic Obstructive Pulmonary Disease (COPD) is characterised by reduced lung function and is the third leading cause of death globally. Through genome-wide association discovery in 48,943 individuals, selected from extremes of the lung function distribution in UK Biobank, and follow-up in 95,375 individuals, we increased the yield of independent signals for lung function from 54 to 97. A genetic risk score was associated with COPD susceptibility (odds ratios per standard deviation of the risk score (~6 alleles) (95% confidence interval) 1.24 (1.20-1.27), P=5.05x10^-49) and we observed a 3.7 fold difference in COPD risk between highest and lowest genetic risk score deciles in UK Biobank. The 97 signals show enrichment in development, elastic fibres and epigenetic regulation pathways. We highlight targets for drugs and compounds in development for COPD and asthma (genes in the inositol phosphate metabolism pathway and CHRM3) and describe targets for potential drug repositioning from other clinical indications