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The levels of individual metabolites in blood, urine and other biofluids can be used to predict current and future disease risk. High-throughput methods for analysing large numbers of metabolites provide new opportunities for understanding the contribution of metabolism to the causal pathways leading to diseases such as diabetes, stroke and cancer. Integrated analysis of metabolomics and genome-wide genotyping in European populations has identified genetic loci associated with changes in metabolism and, therefore, potentially with disease risk (see doi:10.1038/ng.1073). Extending these studies to other ethnicities will reveal additional loci associated with changes in metabolism and, potentially, with altered disease risk.

The China Kadoorie Biobank Study ( of over 0.5 million adults is undergoing genome-wide genotyping (>100,000 individuals), and NMR metabolomics and LC-MS metabolomics of blood serum (5,000 samples). Additional NMR metabolomics of blood serum and urine will be produced using a new NMR facility at CTSU.

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 multi-dimensional  ‘omics data. It will include:

Genome-wide association analysis of metabolomics data: experience in all stages of data QC, genotype imputation and association analysis.

Meta-analysis of association data from multiple studies, potentially including involvement in international collaborations.

Bioinformatic analysis of identified variants and/or metabolites to identify potential causal roles for genes/pathways in disease.

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”.

Prospective Candidate

Candidates should have a 2.1 or higher degree in genetics, statistics and/or computational biology, with an interest in the causes of human disease.