Principal Investigator: Professor Sungho Won
Seoul National University (South Korea)Tags: 41056, COPD, Gene-smoking interaction, GWISs, Lung function
Chronic obstructive pulmonary disease is a major cause of mortality and morbidity worldwide, and smoking is the most important risk factors. However, a minority smokers develop chronic obstructive pulmonary disease, and the susceptibility to smoking varies considerably from person to person. Theses implies the effect of genes and their interactions with smoking. Today, many genome-wide association studies have been conducted to investigate the loci associated with lung function, but it is difficult to replicate the identified SNPs in multiple populations. Thus, identifying effect of gene-smoking interaction on lung function in several populations is remaining task.
From our previous papers, we have identified the importance of statistical models that should consider heteroscedasticity among smoking status for gene-smoking interaction analysis, and the heterogeneity among different studied data (Park et al, Sci Rep 2018). Thus, we provided more robust and powerful statistical models for detect gene-smoking interactions across all populations under heteroscedasticity. With those models, we aim to identifying the interacting SNPs with smoking on lung function in multiple ethnics using genome-wide interaction studies and meta-analysis. Furthermore, we will develop the prediction models by including novel SNPs effects.
We expect that our finding provides significant insights into people’s understanding about lung disease prevention and control, reduce social burdens, and emphasize the importance of personalized medicine. This is consistent with UK Biobank’s mission of health-related research in the public interest.
For this research, we will use data from UK Biobank cohort, Korea Associated Resource (KARE) cohort, Gene-Environment of Interaction and Phenotype (GENIE) cohort, COPDGene study, and Multi-Ethnic Study of Atherosclerosis-Lung (MESA-Lung) and expect to take three years to complete this projects. One years for data cleaning, analyzing and validating identified SNPs, and additional two years for the development and application of prediction models.