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BACKGROUND

Genetic analyses provide powerful tools for understanding the causes of disease, and for providing improved disease prediction, prevention, and treatment. The power of these investigations can be enhanced by identifying similarities and differences between different populations. Large prospective biobanks in diverse populations, such as China Kadoorie Biobank (CKB) and UK Biobank (UKB), are well-positioned to fulfil these goals (www.ckbiobank.org/achievements/genetic collaborations).

Projects will use genetics data from both CKB and UKB, each involving 0.5 million adults, have extensive data from questionnaires; detailed physical measurements; a wide range of biochemical and imaging measurements; and prospective follow-up for incident disease events. Genome-wide genotyping is available for the full UKB cohort and for 102,000 CKB participants, with whole genome sequencing of both biobanks underway. Together, these provide unprecedented opportunities for comparing the genetic architecture of disease in Europeans and East Asians, and for using genetics to investigate the contribution of genetic and other risk factors to a wide range of diseases and traits.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

A wide range of genetics and genomics projects are available. The specific areas of research will be developed in discussion with the student according to their interests and aptitudes, and will potentially include one or more of:

  • Genome-wide association analysis of relevant traits
  • Trans-ancestry meta-analysis
  • Impact of genomic structural variants on disease and risk factors
  • Construction and application of genetic and/or polygenic scores
  • Formulation and coding/programming of novel analytical approaches
  • Mendelian randomisation, including traditional and genetic epidemiology

Examples of possible project areas include:

  • Stroke and stroke subtypes
  • Reproductive traits
  • Metabolomics and proteomics
  • Educational attainment
  • Infection and immunity

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

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

The project will be based within the CKB research group in the Big Data Institute Building. There are excellent facilities and a world-class community of genomics and population health scientists. There may be opportunities for involvement in international collaborations/consortia.

PROSPECTIVE STUDENT

Candidates should have a relevant degree (2.1 or higher), with a strong background in one or more of genetics, statistics, computational biology, or epidemiology. The project will involve large-scale data and statistical analyses so, requires some aptitude for data handling and programming. Prior experience is not essential, as training will be provided.

Supervisors