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other supervisors

Wei Gan, Wellcome Trust Centre for Human Genetics


Large prospective biobanks, including UK Biobank (UKB) and China Kadoorie Biobank (CKB), are accumulating detailed and complex data including questionnaire-based health and lifestyle data; detailed physical and biomarkers measurements; and prospective follow-up for disease events. The recent availability of corresponding genome-wide genotyping datasets (0.5M for UKB, 100,000 for CKB) provides unprecedented opportunities for investigating the genetic architecture underlying disease risk and disease risk factors.

However, many interesting behaviours or phenotypes are within highly complex data structures, and investigation of these requires development and/or application of novel approaches to genetic association analysis.


This project will involve the development, testing and application of new methods for investigating the contribution of genetic variation to phenotypes of interest. Depending on the student’s interests and capabilities, the project will involve one or more of the following:

  • Development and testing of efficient analytical pipelines for large-scale genome wide association studies of individual and/or multiple phenotypes
  • Formulation and coding/programming of novel analytical approaches
  • Application of new and existing methods to investigate disease and phenotype heritability in UKB and CKB datasets
  • Integration of association results with expression, pathway or other external datasets, to elucidate the functional basis for the observed associations
  • Investigation of differences between UK and Chinese populations

There will be in-house training in epidemiology and in statistical and computational genetics, and attendance at relevant courses including the Wellcome Trust course “Design and Analysis of Genetic-based Association Studies”.


The project will be based within the China Kadoorie Biobank research group, part of the Nuffield Department of Population Health and based in the Big Data Institute. There are excellent facilities and a world-class community of genomics and population health scientists. There will be opportunities to collaborate across scientific disciplines and potential for involvement in international collaborations and/or consortia, depending on the direction of the project.

Prospective candidate

The candidate should have a 2.1 or higher degree, with a strong background in genetics, statistics and/or computational biology. The project will involve large-scale data and statistical analyses and, therefore, requires some previous statistical and programming training/experience, and aptitude for and interest in extending these skills.