Glaucoma Risk Prediction Using Machine Learning Integration of Image-Based Phenotypes and Genetic Associations

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PROJECT SUMMARY/ ABSTRACT This proposal describes a 5-year training program to develop an academic career focused on improving glaucoma risk prediction through a combination of genomic and phenotypic risk. I will use supervised, semi- supervised and unsupervised machine learning methods to define novel structural and longitudinal image based endophenotypes for POAG aligned with disease subtype and progression. These endophenotypes will be used to discover new disease associated genomic loci. By including longitudinal data, we aim to identify genetic markers for progressive disease. We will use known POAG risk variants and novel genetic variants identified in these analyses to create several candidate genome wide polygenic risk scores (PRS) for POAG. Each candidate PRS with and without addition of demographic and image features will be tested for its utility to predict glaucoma risk is independent NEIGHBORHOOD and LIFE cohorts. We hypothesize that a PRS based on genetic variants associated with our endophenotypes will have improved POAG case predictive power compared to PRS based on cross-sectional genome wide association studies. The proposed studies have the potential to provide insight into disease pathogenesis and improve predictive power of genetic testing I am well positioned to conduct this research and undertake the training proposed here. I have a strong quantitative science background with a degree in engineering, statistical training and established track records of large database research. Additionally, I have proposed a detailed career development plan that will allow me to 1) learn the fundamentals, applications and limitations of machine learning based approaches for automated fundus image analysis and 2) understand computational biology and statistical approaches to handle large genomics datasets. My training plan includes an MPH in quantitative methods at the HSPH with concentration in computational biology and statistical learning. Additionally, I am supported by a multidisciplinary team of committed mentors dedicated to my academic growth and progression into an independent clinician scientist. I will work with glaucoma genetics experts, Drs Wiggs and Segre, and leaders in statistical and machine learning, Drs Elze and Kalpathy-Cramer. I will have full access to the extensive resources at MEE, Partners Healthcare and the Harvard system for this work and my career development. The research outlined here will improve our understanding of glaucoma pathogenesis and lay the foundation for development of multimodal precision medicine approaches for glaucoma screening and diagnosis. This research is cutting edge and prepares me well for my career as an independent NIH funded investigator with the aim to use longitudinal multi-modal clinical, imaging, testing and multi-omics data in multi- ethnic glaucoma patients to 1) understand pathways of vision loss, 2) develop precision medicine approaches to pre-symptomatically identify patients at high risk of functional vision loss and progression and 3) make these technologies a clinical reality in order to reduce the burden of unnecessary blindness.