Home Department: Undeclared
Mentor: Euan Ashley (Medicine - Cardiovascular Medicine, Genetics, and Biomedical Data Science)

“Development of a Polygenic Risk Score of the Left Ventricular End Diastolic Volume and Mass using a Deep Learning Approach”

An accurate understanding of how the genome contributes to observable characteristics is becoming crucial for personalized therapy in cardiovascular medicine. Cardiomyopathies are known to have a strong genetic background. Shriya’s project aims to develop a polygenic risk score of left ventricular end diastolic volume and mass, using Magnetic Resonance images and whole-genome sequencing data from the UK Biobank. Firstly, a machine learning model will be trained to automatically label the MRI dataset. Secondly, the obtained volume and mass data will be used for the genome-wide association (GWA) study, using a machine learning model. Lastly, a polygenic risk score will be developed using Stanford University patient data. Cardiomyopathy risk prediction, contingent on an individual’s genomic data, is a powerful tool to develop for both preventative medicine and personalized treatment.