Association of polymorphisms in candidate genes with inflammatory subtypes of asthma and effect of gene-environment interactions on asthma severity in adults

Asthma is a chronic respiratory disease involving complex interactions among genetic, environmental, and lifestyle factors, and manifests itself in different subtypes and severity levels. Taking into account the level of white blood cells, two inflammatory subtypes of the disease can be identified: mixed granulocytic asthma (MGA), which is an example of T helper cell type 2 (Th2) subtype, and paucigranulocytic asthma (PGA), which is an example of non-Th2 subtype. MGA is associated with severe disease, an increased risk of exacerbations, and a reduced responsiveness to corticosteroids, while PGA is considered the most common subtype among patients with stable asthma. Identifying the level of asthma severity is crucial for treatment decisions in clinical practice and for characterising patients in epidemiological studies. However, defining asthma se verity is complex due to its heterogeneity and the lack of global consensus. For epidemiological purposes, continuous scores should be adopted, as any simpler classification of asthma severity is biologically unsatisfactory. Agenetic association study examines the relationships between genetic polymorphisms and specific traits or diseases in order to better understand the underlying biological pathways and improve diagnosis, treatment, and prevention. The study of gene-environment interactions can lead to the identification of novel genes that do not exhibit marginal effects, supporting public health policies related to prevalent environmental exposures and refining prevention programs through the creation of better prognostic models. Therefore, this thesis aims to evaluate the association of single-nucleotide polymorphisms (SNPs) in candidate genes with MGA (versus PGA) (Study 1) and to identify polymorphisms that modify the relationship of environmental air pollutants with a continuous score of disease severity (Study 2) in adult patients with asthma from the general Italian population. To achieve these objectives, data from the Gene Environment Interactions in Respiratory Diseases (GEIRD) survey were used. In Study 1, polymorphisms were tested following a two-step approach. A logistic regression model was used for each SNP to filter out polymorphisms significantly associated with MGA (step 1). Statistically significant SNPs at step 1 were simultaneously included as covariates in a multivariable logistic regression model for significance testing (step 2). Findings were replicated using data from a French survey, the Epidemiological study on the Genetics and Environment of Asthma (EGEA). In Study 2, the interaction effect between each SNP and each environmental pollutant on the severity score was assessed using a six-parameter linear regression model according to Aliev et al (Behav Genet, 2014). This re-parameterisation is necessary because, with a genotype classified into three categories (using the additive genetic model), the nature of the interaction may be misrepresented when the interaction effect is assessed with only one interaction term in the regression model. Findings were replicated using data from an international cohort study, the European Community Respiratory Health Survey (ECRHS). The association of SNP rs2069718 (IFNG) with MGA (Study 1) and the interaction of SNP rs9302242 (SMAD3) with annual concentrations of NO2 and PM2.5 on the severity score (Study 2) were identified in GEIRD and replicated in EGEA or ECRHS, respectively. These genes represent interesting targets for further investigation, as the human IFNG gene encodes interferon (IFN)-γ, a key cytokine in diseases involving the immune system, such as asthma, and the human SMAD3 gene encodes SMAD3, a key protein in the transforming growth factor-beta (TGF-β) signalling pathway. In severe asthma, the TGF-β/SMAD3 signalling pathway mediates both pro-inflammatory responses and airway remodelling.