Application of market basket analisys data mining method to genetic of allergy

Asthma and allergic rhinitis are among the commonest causes of chronic illness (World Allergy Organization, 2013). The pathophysiology of these allergic disorders is complex and caused by largely uncharacterized interaction between genes and environmental factors. The genetic components of allergy, however, are still currently under study with the use of genomewide or candidate gene linkage analysis and association, interaction and functional studies. Risk assessment with the use of a single candidate gene variant might not show the actual effect on the disease outcome given the multifactorial nature of chronic diseases. NonMendelian forms of inheritance, the ubiquitous characteristic of biomolecular communication and the inconsistent results of several single-variant, single-gene studies all support the idea that gene-gene interactions are common and can best describe the disease symptoms1. Several tools are being used to detect significant association of gene-gene interaction with the disease phenotype. In this study, we utilized a frequent itemset mining methods to identify genetic and clinical elements that often co-occur in a dataset involving allergic patients and healthy subjects. Originally the model of this method was the Market Basket Analysis (MBA) and was applied to analyse buying habits of consumers. Nowadays MBA is used in bioinformatics to identify biologically relevant patterns that can be interpreted in a biological context as the associations between allelic combinations with diseases. We applied this method to enlight the genetic and biological complexities of asthma and allergic rhinitis, frequently associated in atopic subjects and generally considered as two different features of the same airway disease in which the presence of the rhinitis increases the chance of development of asthma by about three times.