Genetic analysis of complex cardiovascular traits

The heart is a dynamic biomechanical phenotype, and discovering how genetic and environmental factors shape its structure and function is crucial for understanding the transition from health to disease. While conventional image-derived phenotypes have relied on simple measures of volumes and mass, we have shown the transformative potential for modelling complex traits that embed rich information on the dynamic physiology of the heart. For instance, our group has shown that deep-learning cardiac motion analysis can efficiently predict human survival and that fractal analysis reveals how structural complexity affects susceptibility to heart failure.

The aim of this project is to use high dimensional image-derived phenotypes collected in large biobanked-populations to identify the role of common and rare variants on the 3D geometry and motion of the heart in both health and disease. This project will exploit developments in dimensionality reduction and multi-trait analyses for genetic association, classification and prediction tasks. This work is at the intersection of multiple disciplines that include bioinformatics, data science and machine learning – and involves collaborating closely with clinicians in cardiology and radiology.

The project will involve training opportunities in translational medicine within a vibrant multidisciplinary biomedical research group. The ideal candidate will have an enthusiasm for combining cutting edge bioinformatics and data science to answer important questions in cardiovascular health.

To Apply please visit the LMS website - https://lms.mrc.ac.uk/study-here/phd-studentships/lms-3-5yr-studentships/