Principal Investigator: Dr Kei Hang Katie Chan
City University of Hong Kong, Hong Kong
Lead Collaborators – Professor Simin Liu, Brown University, USA
Dr Kenneth Lo – The Hong Kong Polytechnic University, Hung Hom, Hong Kong
Tags: 45788, complex diseases, genetic epidemiology, molecular epidemiology, molecular mechanism
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.
Project extension – February 2020
With the requested data from the UK Biobank, we aim to (including but not limited to the following):
1) identify shared and ethnic-specific biological pathways and gene networks between complex traits e.g. diabetes and Alzheimer’s diseases
2) evaluate the modification of medication use on biomarker-related genetic variants with risk of complex traits
3) assess the causal association between biomarkers and risk of complex traits using mendelian randomization approach
4) identify shared and ethnic-specific biological pathways and gene networks that are perturbed by genetic risks of complex traits between ethnic groups
5) identify shared and ethnic-specific gene network x environmental risk factors interactions that contribute to risk of complex traits between ethnic groups
6) identify shared and ethnic-specific biological pathways and gene networks that are perturbed by genetic effect of medication use between ethnic groups.
7) assess the association between potential biomarkers targeted by medication use and risk of complex traits using Mendelian Randomization with medication related genetic loci as instruments
8) build diseases risk prediction models
9) identify omics variants related to demographic, lifestyle, biomarkers, and environmental risk factors of complex traits
10) assess the association between early life factors with risk of complex traits
11) apply various methodologies e.g. mendelian randomization, mediation, polygenic score, etc. to investigate the molecular and physiologic mechanism of complex traits
12) incorporate various types of data e.g. genomics, demographic, lifestyle, biomarkers, questionnaire as well as imaging data to improve the understanding of complex traits
Last updated Feb 17, 2020