Using metabolomics to identify causal biomarkers in the development of diabetes
Lipid fractions play a causal role in the development of diabetes. However, the major lipid fractions only crudely quantify the lipidome. Metabolomics can quantify hundreds of circulating lipid-related traits, together with fatty acids and amino acids.
Research Experience, Research Methods and Training
- Analyses of omics data to investigate observational associations of metabolites with incident diabetes and progression of diabetes (including diabetes complications) using the DIRECT study and UK Biobank.
- Characterize GWAS hits for diabetes for their association with metabolites (PheWAS scan)
- Conduct Mendelian randomization analyses of individual lipoproteins, lipids, fatty acids and amino acids for their causal role in diabetes
- Handling of multidimensional datasets and ‘big data’
Working with world leaders in translational genetics
Field Work, Secondments, Industry Placements and Training
- Mendelian randomization course in Cambridge
- Potential secondment at University of Bristol with Prof George Davey Smith’s group, that are pioneers in development of novel methodology for Mendelian randomization as applied to ‘omics’ data
In addition to the above supervisor, Associate Professor Cecilia Lindgren of the Big Data Institute and Professor Mark McCarthy of the Nuffield Department of Medicine will also be supervisors for this project.
MSc in Epidemiology, Biostatistics, Genetics or related field.
Prospective candidates should be interested in identifying causes of disease, seek experience handling large multidimensional datasets including ‘omics’ and want to publish high-impact Science with the aim to becoming a translational genetic epidemiologist.