Genomic analysis of migration and admixture in the Chinese population
Project Reference: NDPH/MT16/043
Mixing and interbreeding between previously separate human populations leaves characteristic signatures in the genomes of subsequent generations, including the presence of discrete regions of shared ancestry between individuals (“admixture”). Analysis of high density genetic data can identify these signatures, enabling detailed inferences about human history (e.g. see doi: 10.1126/science.1243518). Further detail about the movement of men and women can be inferred from chromosome Y and mitochondrial lineages, respectively.
Accurate assessment of and correction for admixture is also an important feature of genetic association studies to identify the inherited determinants of disease.
China has been the scene of repeated large-scale population migrations, such as flight from military conquest by the Mongols under Genghis Khan or displacement by natural disasters such as flood or drought. The China Kadoorie Biobank Study (CKB www.ckbiobank.org) of over 0.5 million adults, from 10 different regions across China, is undergoing genome-wide genotyping (>100,000 individuals) including saturation coverage of both chromosome Y and the mitochondrial genome.
Research Experience, Research Methods and Training
The project will involve experience and training in literature review, study design and planning, novel analytic methods, and statistical analysis, interpretation and reporting of large-scale genomics data. It will include:
- Admixture mapping and analysis in CKB
- Construction of molecular phylogenies using genetic data
- Experience in data QC, genotype imputation and association analysis.
- Integrated analysis of data from multiple sources, potentially including involvement in international collaborations.
Field Work, Secondments, Industry Placements and Training
In-house training in statistical and computational genetics. Attendance at relevant courses including the Wellcome Trust course “Design and Analysis of Genetic-based Association Studies”.
An additional supervisor (to be confirmed) will be appointed prior to the commencement of study for any applicant accepted to this project.
Candidates should have a 2.1 or higher degree in genetics, statistics and/or computational biology, with an interest in population genetics and/or the causes of human disease.