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While many modifiable risk factors for cardiovascular disease (CVD) are well established, other risk factors remain to be discovered. Genome-wide association studies (GWAS) have identified several hundred common genetic variants for CVD types, but the mechanisms underlying these associations remain to be elucidated. Advances in proteomics now enable quantification of plasma levels of several thousand proteins and integrated analyses of proteomics and genomics in prospective studies have great potential to discover novel treatment targets for CVD and CVD types.

This proposal will use available and emerging proteomic data in the China Kadoorie Biobank (CKB; ~10,000 proteins in 4000 participants) and UK Biobank (UKB; ~3000 proteins in 50,000 participants) with detailed genetic and other CVD risk factors linked to a wide range of CVD outcomes and surrogate measures of CVD (cIMT, carotid plaque and ECG). 


The specific aims of this project will be developed according to the candidate’s interests and aptitude but could involve: 

  1. to identify, using conventional and machine learning approaches, proteins associated with CVD risk factors in different populations;
  2. to assess, using genetic approaches (e.g. two-sample MR analyses with cis-pQTLs for proteins identified in CKB and UKB and global GWAS consortia datasets), the causal relevance of proteins for CVD risk factors, CVD and CVD types (e.g. IHD, stroke);
  3. to explore, using a range of downstream investigations (e.g. enrichment analyses, colocalization, tissue expression and knockout mouse models), the biological mechanisms linking proteins with CVD traits and clinical outcomes, and their potential as possible novel drug targets. 

Students will conduct literature reviews, perform analyses on large-scale datasets and publicly available resources using a range of state-of-the-art analytic methods, and write academic papers for peer-reviewed publications. In doing so they will work closely with a strong interdisciplinary team of researchers with expertise in epidemiology, CVD, biomarker and molecular epidemiology, statistics and Mendelian randomisation.


Training in advanced statistics, epidemiological methods, programming, and scientific writing will be provided. There may be opportunities to collaborate with industry partners and laboratory research groups. Attendance at seminars, workshops and courses provided by the Department and University will be encouraged. There will be opportunity to present research work at relevant international/national conferences. 

prospective student

This project will suit someone with an interest in CVD genetic epidemiology. Candidates should have a good first degree (2.1) and postgraduate training (e.g. MSc) in epidemiology, genomic medicine, biomedical science, or a related discipline.