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Chronic infections with viruses and bacteria are associated with many chronic non-communicable diseases (NCDs) and responsible for >2 million new cancer cases globally each year. In many low- and middle-income Countries (LMICs), including China, the burden of cancer is particularly high due partly to a high prevalence of chronic infections (e.g. HBV/HCV, HPV, H. pylori and EBV) and partly to delayed diagnosis and inadequate treatment. Even when infected, the individual risk of developing specific cancers may be affected strongly by host immunity, pathogen subtypes, and other risk factors and their complex interactions. As well as immunisation programs to reduce the burden of infections in the general population, eradication of certain common infections (e.g. H. Pylori) among infected individuals also represents a potentially effective strategy to reduce the infection-related cancer burden in LMICs.

The China Kadoorie Biobank (CKB) including 0.5 million Chinese adults who were recruited during 2004-08 from 5 urban and 5 rural areas across China, with follow-up through linkage to death registries and hospital records. To date, there are ~50,000 deaths and ~1 million ICD-10 coded episodes of hospitalisation of >1500 different disease types (including ~30,000 incident cancers) recorded among participants. Genome-wide SNP data are available for 100,000 participants, and whole genome sequencing of the first 10,000 participants will be complete by the end of 2020. Funded by CRUK, assays of serology markers (43 antigens from 19 pathogens including HBV, HCV, EBV and H. pylori), using a custom-designed multiplex serology panel, are being conducted among 45,000 participants, including all incident cancer cases of different types and 15,000 randomly selected controls. Moreover, HBV viral genomes will be sequenced among 4,000 HBV-infected participants (2,000 who developed hepatocellular cancer during follow-up, and 2,000 who did not) in order to investigate host-virus interactions in liver cancer development.

This DPhil project will comprehensively assess the influence of multiple pathogens, and their interactions with host genetic and other factors on risk of different cancers.


The specific lines of investigation (e.g. specific types of pathogen and cancer) covered by this DPhil project will be subject to student’s personal interest and further discussion, but may include the following elements:

  • Examining the burden and pattern of chronic infections by demographic and socioeconomic factors;
  • Assessing prospectively the associations of HBV, HCV, H. pylori, EBV and other pathogens with risks of cancers;
  • Investigating the role of host immune system genetics (e.g. HLA) in determining susceptibility to chronic infection and development of infection-related cancers;
  • Estimating the predictive value of serological markers, in combination with other lifestyle and genetic risk factors, in risk of infection-related cancers.

There will be in-house training opportunities in epidemiology, genetics, bioinformatics and statistical analysis and attendance at relevant courses. By the end of the DPhil, the student will be competent to plan, undertake and interpret analyses of large-scale epidemiological and genetic data, and to report research findings, including publications in peer-reviewed journals as the lead author and presentation at national/international conferences.


The project will be based within the CKB research group, part of the Nuffield Department of Population Health and based in the Big Data Institute (BDI) building. There are excellent facilities and a world-class community of population health, laboratory, genetic and data scientists. There will be opportunities to collaborate across scientific disciplines and participate in international collaborations. 

Prospective candidate

The candidate should have a degree in a biomedical or quantitative science and an MSc in epidemiology, biology, genetics, statistics or a related subject, with a strong background and interest in epidemiology, genetics and/or infection-related disease research. The project will requires some previous statistical and programming training/experience.