# Deep-Learning-Derived Endophenotypes  from Retina Images

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2024 · $544,593

## Abstract

Abstract
The goal of this proposal is to establish a novel artificial intelligence-based strategy of genome
wide association studies (GWAS) to identify new genetic loci associated with common human
disorders. Identification of the genetic factors underlying these diseases will provide not only
mechanistic insights into the diseases but also form the basis for developing novel preventative,
diagnostic, and targeted therapeutic methods. While GWAS has achieved great successes in
the past 1.5 decades, only a small portion of common diseases' heritability can be currently
explained by loci identified from traditional GWAS. Leveraging on the public genetics and
clinical imaging data, we propose a novel approach, termed image based GWAS (iGWAS),
where GWAS will be performed on endophenotypes derived from images using cutting edge
deep learning (DL) algorithms. By creating a more objective and quantitative output from the
images, with less information loss, many new disease-associated genetic loci are expected to
be identified. To test the efficacy of this novel approach, the human visual system will be used
as an example. A DL-phenotyper, Multi-modal multi-view Self-Supervised deep Learning
Encoder for Retinal images (MuSSLER), will be developed to extract quantitative output from
optical coherence tomography scans and fundus images from both normal eyes and patients
with diabetic retinopathy (DR). GWAS will be performed on normal eye endophenotypes to
elucidate genes relevant to retina development and physiology. Also, GWAS will be performed
on DR-specific endophenotypes to identify DR associated genetic loci and candidate genes. If
successful, our approach represents a general framework that can be readily extended to the
study of other retinal diseases and other common diseases with imaging data. Furthermore, the
phenotyping neural network architecture established in this project can be readily adopted to
develop an automated grading tool for heterogeneous clinical data and applied to many other
common diseases.

## Key facts

- **NIH application ID:** 10877005
- **Project number:** 5R01EY032768-03
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** RUI CHEN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $544,593
- **Award type:** 5
- **Project period:** 2022-09-30 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10877005

## Citation

> US National Institutes of Health, RePORTER application 10877005, Deep-Learning-Derived Endophenotypes  from Retina Images (5R01EY032768-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10877005. Licensed CC0.

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