Characterizing the associations of heart disease genetic variants with metabolomics and CVD risk in the China Kadoorie Biobank study
Project Reference: NDPH/MT16/033
Cardiovascular disease is the major cause of death and disability worldwide. While genome wide association studies (GWAS) have identified associations of many SNPs with CHD, the mechanism by which SNPs associate with CHD remains elusive. A deeper understanding of the association of CHD SNPs with blood-based biomarkers may help shed light on potential pathways of disease from SNP through to CHD. Metabolomics is a method of quantifying blood-based markers that provides information on hundreds of cardiovascular traits at high fidelity.
The China Kadoorie Biobank (CKB) study of over 0.5 million adults was set up to investigate genetic and environmental causes of chronic diseases in the Chinese population. Individuals have extensive questionnaire-based health and lifestyle data at baseline and prospective follow-up for incident events from hospital registries and health insurance data. The cohort is undergoing genotyping using a GWAS array (~800K SNPs) specifically designed for individuals of Asian ancestry to identify genetic determinants of disease, and facilitate causal analyses of biomarkers using genetic epidemiological techniques such as Mendelian randomization.
From the 0.5 million CKB adults, we have selected a nested case-control study of 5000 adults (1000 healthy controls and 4000 with CVD) that have been genotyped, and are in the process of quantifying metabolomics in these individuals.
Research Experience, Research Methods and Training
The student will conduct a series of analyses of the relationship of CHD SNPs (derived from CARDIoGRAMplusC4D) with metabolomics, carotid intima medial thickness and vascular disease events in the China Kadoorie Biobank study. Self-organizing maps will be generated to characterize the association of CHD SNPs with metabolites. SNPs will be clustered according to association on metabolomics to investigate potential shared pathways. The student will then use information on genetic variants to conduct multivariate Mendelian randomization analyses to identify whether particular metabolites are causally related to CVD. These findings are expected to greatly increase our understanding of mechanisms by which CHD-related SNPs influence disease risk.
Field Work, Secondments, Industry Placements and Training
The student will work in collaboration with Mika Ala-Korporela’s group in University of Oulu with potential to visit for a week or longer. There will be in-house training and students will be encouraged to attend training courses on Mendelian randomization.
Candidates should have a 2.1 or higher degree in Medicine or medical sciences and an MSc in epidemiology, statistics, genetics or public health would be an advantage. The project will involve large-scale data and statistical analyses and, therefore, requires some previous statistical and programming training/experience and an aptitude and interest in extending these skills. Training in the use and interpretation of such analyses will be provided. Candidates should also have a strong interest in cardiovascular epidemiology.