Asthma, Rhinitis and Chronic Obstructive Pulmonary Disease (COPD) are common respiratory diseases characterised by systemic and local chronic inflammation of the airways, that contribute substantially to morbidity and mortality in adults. Airway inflammation and oxidative stress causing epithelial damage and increasing bronchial responsiveness are the principal features in the pathophysiology and exacerbations of these diseases, and are common traits of all the three conditions. They are considered genetic complex diseases due to the complicated relations between genetic and environmental components. Several genes with rather small effects are likely involved in the development of these diseases, and might contribute to the variability of the phenotypes, according to environmental exposure and genetic background of the population.
Many genes have been reported in several linkage and association with one or more of these diseases Pleiotropy in immunomediated and/or inflammatory diseases is not unusual, and there is increasing evidence that many of these diseases have common aetiologies). However, these studies have been replicated with difficulty in the different populations. Limitation in study design, insufficient power, poorly matched control groups, poor definition of the phenotypes, absence of correction for multiple testing, and not taking environmental exposure sufficiently into account have been brought forward to explain the inconsistencies.
This study aims to determine the genetic involvement in the susceptibility to asthma, rhinitis and COPD, by candidate gene association analysis, in a large and accurately defined, population based, series of subjects, even considering exposure or no-exposure to some environmetal contexts. A DNA bank, of well characterised subjects with asthma, rhinitis, or COPD and controls without airways diseases, will be created. Single Nucleotide Polymorphisms (SNP) in candidate genes or chromosomal regions will be analysed in patients and controls. The choice of the candidate genes for the study will be done on the basis of literature data and considering genes involved in biological patways possibly related to the diseases, such as genes involved in inflammation, innate-immunity and immunoregulation, oxidative stress and xenobiotic metabolism, protease-antiprotease imbalance, tissue remodelling. Tag-SNPs representative of each gene or chromosome region of interest will be selected considering both literature data and consultation of specific databases (HapMap, dbSNP, etc). SNP genotyping will be done by the GoldenGate Genotyping assay combined with the VeraCode technology (Illumina). A customized multiplexed genotyping assay will be devolepd. Allele and genotype frequency and the Hardy-Weinberg equilibrium will be calculated for each polymorphism. A case/control association study of the candidate gene polymorphisms will be done for the susceptibility to one or more of the studied phenotypes. The relevance of single polymorphisms and of multiple genotypes in the increased disease risk calculation will be determined. Probabilistic reconstruction of molecular haplotypes based on maximum likelihood will be done for the identification of association to the disease. The significance of the results will be ascertained by the use of appropriate statistical tests based on permutation tests, corrected for the number of the statistical tests performed. Multiple logistic regression models will be used to evaluate significant association with continuous or categorical variables. Association analysis will be performed by generalized linear model regression of a trait on haplotype effects, allowing for ambiguous haplotypes. Methods able to identify the gene/gene and gene/environment interaction, such as second locus conditional analysisand two loci analysis, will be developed.
In the postgenomic era, searching and identification of genes associated with complex diseases are still one of the great challenges for dissecting human complex diseases. Association analysis is expected to be the more suitable approach for the detection of a common mild-risk allele which accounts for a substantial population attributable fraction in common complex diseases. Large scale case-control studies, where cases are based on an accurate characterization of the basic phenotypes are needed to make reliable to study the role of the genetic component and of gene-gene, gene-environment and genome-drug interactions in common diseases. Identification of susceptibility genes and understanding of the aetiology of complex diseases will lead to the real potential of diagnosis, treatment, and prevention.