# Genetic Variation in AMD Progression  and Treatment Response

> **NIH EY K23** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2026 · $244,379

## Abstract

Summary/Abstract
This project aims to explore how genetic variation influences disease progression and therapeutic response in
age-related macular degeneration (AMD) by integrating advanced optical coherence tomography (OCT)
imaging with machine learning (ML) and artificial intelligence (AI). AMD, a leading cause of vision loss in older
adults, presents with highly variable progression, ranging from slow decline to rapid vision loss. This variability
remains poorly understood, largely due to the genetic and phenotypic diversity of AMD. This project leverages
ML for deep phenotyping of OCT data to refine the classification of AMD subtypes, combined with genetic
analysis for a deeper understanding of disease progression. The hypothesis is that genetic variations influence
distinct AMD subtypes and stages, shaping both disease progression and therapeutic response. To test this
hypothesis, the following aims are proposed:
Specific Aim 1: Determine how genetic variation influences AMD progression using AI-driven analysis of OCT
biomarkers and ML to classify patients as slow or rapid progressors. Three-dimensional ML models, such as
SLIViT and retina-specific models like RETFound, will enable detailed phenotype analysis, revealing high-risk
features and novel subtypes correlated with progression rates.
Specific Aim 2: Investigate how genetic variation affects functional and therapeutic outcomes in AMD by
integrating OCT data with patient treatment responses through ML-based models, exploring genetic factors
linked to visual acuity outcomes and treatment efficacy. The project will refine polygenic risk scores (PRS) by
combining these insights with genome-wide association study (GWAS) data to improve predictions of rapid
progression and treatment responsiveness.
Datasets from the UK Biobank and UCLA Biobank will be utilized, applying ML-based imaging analysis,
transfer learning for 3D data, ML-based deep phenotyping, and traditional and post-GWAS analysis.
Techniques includ

## Key facts

- **NIH application ID:** 11413792
- **Project number:** 5K23EY037861-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Adrian C Au
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** EY
- **Fiscal year:** 2026
- **Award amount:** $244,379
- **Award type:** 5
- **Project period:** 2025-09-15T00:00:00 → 2030-03-31T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11413792

## Citation

> US National Institutes of Health, RePORTER application 11413792, Genetic Variation in AMD Progression  and Treatment Response (5K23EY037861-02). Retrieved via AI Analytics 2026-06-26 from https://api.ai-analytics.org/grant/nih/11413792. Licensed CC0.

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