| Title: | Comprehensive gene-environment interaction analysis in chronic diseases and cancers |
| Lead Institution: | Korea University College of Medicine |
| Principal investigator: |
Application 100359
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About
Extensive research within the last several decades has revealed that the major risk factors for most chronic diseases including cancers are infections, obesity, alcohol, tobacco, radiation, environmental pollutants, and diet. It is now well established that these factors induce chronic diseases through induction of inflammation. Moreover, Inflammation is often associated with the development and progression of cancer. The chronically inflammation can activate signaling pathways that cross-talk between inflammation and carcinogenesis. Therefore, environmental factors are increasingly being studied for their associations with chronic diseases and cancers. Genetic factors also underlie the differential vulnerability to environmental risk factors of susceptible individuals. Currently the way in which environmental risk factors interact with genetic factors to increase the incidence of chronic diseases and cancers is not well understood. In this study, we aim to conduct a wide range of interaction studies on genetic variations and environmental factors including lifestyle and occupations related to developing inflammatory diseases and cancers. In addition, by restricting various operational definitions, researchers intend to conduct gene-environment interactions for chronic diseases including cancers and environmental factors. Especially, for air pollutants, which are major environmental exposure, we plan to perform gene-environment interaction analysis for chronic disease and cancer susceptibility and genetic variants by linking air pollutants measurement data in the UK. We plan to conduct these comprehensive analyses by running this project for 3 years (2023-2026). Findings regarding gene-environment interactions have a significant public health implication in disease prevention, as they can help identify groups of individuals who are more likely to benefit from lifestyle or environmental modifications.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 18725 | PGVDA: a pathway-aggregated genetic dosage framework for interpretable disease classification using machine learning | Sanghyun Shon (+4) | 2026 | Briefings in Bioinformatics |
Enabling scientific discoveries that improve human health