Assessing the roles of chronic infections, host immunity, and chronic inflammation in disease aetiology

background

Several infectious pathogens (e.g. H. pylori, HBV, HPV) are established carcinogenic agents for certain cancers. However, evidence is still limited about their causal relevance for non-neoplastic chronic diseases, about the long-term health consequences of other chronic infections (e.g. certain types of herpes viruses), and about the complex interplays of host immunity, chronic inflammation, co-infections, lifestyle factors and pathogen subtypes in disease aetiology. Using available and emerging serological data on 20 infectious pathogens in the prospective China Kadoorie Biobank (CKB) and UK Biobank (UKB), the project will assess the genetic determinants and non-genetic correlates of multiple pathogen infections and the associated long-term health consequences in Chinese and UK populations. The information generated will inform early detection, risk prediction and development of new preventive and treatments for many diseases. 

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The DPhil project will be developed according to the student’s interests and aptitude, and may cover some of the following objectives:

• to examine the genetic determinants and non-genetic correlates of chronic infections in Chinese and UK populations;
• to assess the associations of chronic infections with risks of specific diseases and to estimate the infection-attributable disease burden in both populations;
• to clarify the roles of host genetics (e.g. HLA variants) and chronic inflammation (e.g. inflammatory protein markers) in development of chronic infection-associated diseases;
• to develop and validate serology-based prediction models, in combination with other lifestyle and genetic factors, and to assess their utilities in predicting disease risks in Chinese and UK populations.

The student will work within a multi-disciplinary team, and will gain research experience and in-house training in a range of epidemiological methods. By the end of the DPhil, the student will be competent to plan, undertake and interpret analyses of large datasets, and to report research findings, including publications in peer-reviewed journals as lead author and presentation at national and international conferences.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

The student will be based at Big Data Institutes, with excellent facilities and a world-class community of population health, data science, infectious disease, and genomic medicine researchers. There will be opportunities to work with external research institutes.

PROSPECTIVE  STUDENT

The ideal candidate should have a good first degree (2.1) and MSc in epidemiology, statistics, genetics, biomedical science, or a related discipline, with a strong interest in infection-related population health.