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Large scale genetic data can provide detailed information about the history of human populations. Numerous factors can leave characteristic signatures in the genomes of subsequent generations, including geographic separation, population mixing, or natural selection. Statistical analyses that identify these signatures can enable detailed inferences about human history. Accurate understanding of the genetic structure of populations is also an important feature of genetic association studies to identify the inherited determinants of disease.

The China Kadoorie Biobank Study ( has completed genome-wide genotyping of 100,000 individuals from 10 different regions across China. Initial analyses have identified substantial genetic differences between these regions, including varying patterns of familial relatedness, and gradients in the allele frequencies of functionally important genetic variants. This offers unique opportunities for large-scale research into the population genetics of Chinese populations.


The project will involve the application of multiple analysis approaches to investigate features of the genetic structure of CKB populations. These may include:

  • Description of large-scale genetic structure, both between and within populations
  • Investigation of the genetic basis for self-reported “Han” ancestry
  • Identification and analysis of the signatures of “admixture” between ancestrally different populations
  • Screens to identify evidence of the action of natural selection
  • Comparison of molecular phylogenies constructed using autosomal, sex chromosome or mitochondrial markers

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” and specialist courses on population genetics.


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.


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.