| Title: | Using molecular and genetic epidemiological approach to dissect the mechanism of complex diseases in the UK Biobank |
| Lead Institution: | City University of Hong Kong |
| Principal investigator: | Dr Kei Hang Katie Chan |
Application 45788
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About
Many complex traits such as diabetes, cardiovascular diseases, and neurodegenerative disorders encompass the interplay between genes and the environment. We will conduct an integrative genomics, modification assessment, and mendelian randomization studies that leverage genetic, genomic, clinical, medication, and phenotypic data from several large-scale multi-ethnic cohorts including the Women's Health Initiative, Framingham study, Jackson Heart Study, and Cardiovascular Health Study, and UK Biobank to dissect the mechanisms of various complex diseases. In particular, we will use epidemiological, statistical, and bioinformatics to dissect the molecular mechanism of complex diseases using genetic, clinical and medication information from the UK Biobank. For example, how statin plays a role in regulating the association between the risk of cardiovascular diseases (CVD) and genetic variants related to some CVD related biomarkers such as low-density lipoprotein (LDL) levels. We will also ferret out the potential commonly shared pathways and gene networks between complex diseases such as diabetes and Alzheimer's disease. We will investigate the potential causal relations between biomarkers and the development of complex diseases. By building disease risk prediction model using the key genes found together with clinical data, we will be able to provide high-risk patients with preventive measures, for example, lifestyle recommendation and better clinical management such as closely monitoring their disease related biomarkers. We plan to complete the analyses of the mentioned specific aims in about 36 months. Using this molecular and genetic epidemiological approach to dissect the underlying mechanisms of complex diseases may enable us to predict, prevent and treat these diseases with an innovative and interdisciplinary strategy by potentially identifying novel therapeutic targets for these diseases.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 7158 | Identifying Predictors of COVID-19 Mortality Using Machine Learning | Tsz-Kin Wan (+6) | 2022 | Life |
Enabling scientific discoveries that improve human health