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Approved research

Classification of asthma patients and identification of group-specific genetic variants

Principal Investigator: Dr Han-Kyul Kim
Approved Research ID: 56987
Approval date: January 14th 2020

Lay summary

Asthma is a common respiratory disease with a prevalence of more than 5 percent of adults worldwide, requiring huge public healthcare cost burdens. Recently it has been known that asthma is a kind of clinical syndrome that shows heterogeneity of clinical phenotypes. This study aims to identify asthma group-specific genetic markers by the classification analysis of asthma patients and follow-up genome-wide association studies. Though most asthma can be treated by conventional bronchodilators, some need particular treatment such as long-acting !2 agonists or inhaled corticosteroids and even the expensive antibody-based drugs such as Anti-IgE or Anti-IL5 are required in severe asthma for improvement in symptoms. To make it worse, effective drug control is often complicated by asthma heterogeneity in clinical phenotypes. Indeed, asthma is a heterogeneous disease with individually distinct clinical features. Therefore, asthma classification is important before the selection of the right clinical care. Previously classification of asthma patients was successfully carried out according to clinical phenotypes. However, this classification does not provide molecular information about the heterogeneity, and also underlying biology of each group. In order to understand the molecular mechanism, we will classify over 60,000 asthma patients of UK Biobank into groups using the clinical variables related to asthma and then identify group-specific genetic markers. We will use diverse clinical measurements such as lung function, onset age, blood cell counts, smoking, and atopy status for the classification. Genetic markers unique to asthma group may help classify patients in addition to the classification of asthma patients by clinical phenotypes. Moreover, these genetic markers can be useful for delineating molecular pathways of asthma group that will help severe asthma patients select the right drug based on biological markers such as Anti-IgE and Anti-IL5. Ultimately, this study will provide tools for the modeling of prediction of asthma grouping as well as the precision medicine through tailored health care to alleviate patients' economic and physical burdens. This study may take three years.