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

> **NIH NIH K23** · MASSACHUSETTS EYE AND EAR INFIRMARY · 2022 · $263,101

## Abstract

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 patien...

## Key facts

- **NIH application ID:** 10430101
- **Project number:** 5K23EY032634-02
- **Recipient organization:** MASSACHUSETTS EYE AND EAR INFIRMARY
- **Principal Investigator:** Nazlee Zebardast
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $263,101
- **Award type:** 5
- **Project period:** 2021-07-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10430101

## Citation

> US National Institutes of Health, RePORTER application 10430101, Glaucoma Risk Prediction Using Machine Learning Integration of Image-Based Phenotypes and Genetic Associations (5K23EY032634-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10430101. Licensed CC0.

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