| Title: | Deciphering chronic disease - association studies of genome, exposome and phenome |
| Lead Institution: | Fudan University |
| Principal investigator: |
Application 92718
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About
Most chronic disorders are classified as multifactorial diseases (cardiovascular and cerebrovascular diseases, neurodegenerative diseases, metabolic diseases, and cancer, etc.). Better characterizing the interactions between environmental and genetic factors and investigating relationships between health-related outcomes is an important issue in understanding the biological mechanisms underlying these diseases. In this study, we aim to identify causal variants underlying disease risk, assess novel genetic associations across a wide spectrum of phenotypes, evaluate the phenotypic associations and genetic associations between chronic diseases, determine critical paths of disease connectivity through disease trajectory analysis, and identify novel biomarkers (derived from body fluids and imaging) linking to the genotype and phenotype. Our findings may have the potential to explore the pathogenic mechanisms of chronic diseases, identify biomarkers for early detection and possible targets for intervention, and provide solutions for precision medicine. The rolling period of the proposed program is three years, yet it might be prolonged due to methodological updates and novel findings which need to be further validated. Our objectives are aligned with UK Biobank's goal to improve the prevention, diagnosis, and treatment of a wide range of serious and life-threatening illnesses.2 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 9871 | Machine learning-based plasma metabolomic profiles for predicting long-term complications of cirrhosis. | Chengnan Guo (+10) | 2024 | Hepatology |
| 8659 | Synergistic effect of genetic predisposition and lifestyle for coronary heart disease | Linyao Lu (+7) | 2024 | European Journal of Preventive Cardiology |
Enabling scientific discoveries that improve human health