Together with Professor Hingorani and Dr Finan, we have pioneered using human genetics to inform drug development. Our work has provided the underpinnings for scaled cis Mendelian randomisation, which we have used, in combination with other computational genetics tools, to conduct applied research on drug target identification.

Key contributions:

• Drug target Mendelian randomisation
• Assessing the viability of CETP as a cardiovascular drug target
• Multi-omics prioritisation of coronary heart disease drug targets
• Identifying druggable concordant plasma protein and metabolomics pathways for heart failure and atrial fibrillation
• Develop and externally validate a proteomics based classification model for heart failure patients

In collaboration with Professor Chaturvedi and Asselbergs, we have highlighted the challenges of developing accurate cardiovascular (CV) prediction models in people with established diseases such as type 2 diabetes. To ameliorate this we employed scalable machine learning methods to identify novel non-traditional risk factors, as well as explore model repurposing (i.e. transfer learning), and de novo model development.

Key contributions:

• Systematic evaluation of CV prediction models in people with diabetes
• Identifying non-traditional risk factors for cardiovascular disease, atrial fibrillation and heart failure
• Employing multivariable machine learning to prioritise familiar hypercholesterolemia carriers for cascade screening
• Repurposing cardiovascular prediction models to identify people at risk of future cancers

Through strong collaboration with Dr Sudre and Dr Paliwal we have sought to combine evidence from cardiac MRI with information from brain MRI, proteomics, or rare genetic carriership to identify novel pathways and risk factors of (shared) disease.

Key contributions:

• Genome wide association study and plasma proteomics analysis of 16 cardiac MRI traits
• Identifying the pre-clinical cardiac MRI phenotype of cardiomyopathy genetic carriership
• Linking cardiac indices of structure and function to regional cerebral white matter hyperintensity volumes: [PLACE HOLDER]