Mendelian randomisation (MR) is an established statistical tool for causal inference using data about human genetic variation to estimate casual effects. MR has been applied successfully to demonstrate the causal contribution of risk factors (e.g. body mass index, blood cholesterol levels, interleukin-6 signalling) to complex diseases such as type 2 diabetes, coronary heart disease and inflammatory bowel disease. A specialised application of MR is in the identification and validation of potential drug targets. In this scenario, common genetic variants encoding a drug target are used as proxies for pharmacological modulation. Using the MR paradigm, the association of these variants with disease can be used to infer a causal role for the target in the disease of interest. By extension, the same variants can model the potential consequences of modulating that target with a drug. MR analyses, including several published by researchers at UCL, have been successfully shown to recapitulate the effects of established drug targets and to provide valuable insights into the utility of novel targets.
Applications of MR to date have largely been restricted to single or small numbers of targets, and have considered comparatively limited combinations of target-encoding genes, biomarkers and disease outcomes. Given the power of the approach, scaling up its deployment to large numbers of targets and diseases would substantially increase its utility. Machine learning on genome-wide data could also be applied to account for some known limitations of the MR method and to increase the scope of its utility. A major increase in scale brings methodological challenges that are best addressed by formulating meaningful hypotheses and testing them with real and simulated data. This PhD project has three core components:
These questions will be addressed using large volumes of summary statistics from published genome-wide association studies (GWAS) and individual level data from population studies (e.g. UK Biobank).
Funding Availability and Award
This full time PhD studentship is funded by BenevolentAI. Funding covers university course fees, an annual maintenance stipend and research expenses.