Department of Ophthalmology

Baylor College of Medicine
Houston, TX

BASIC RESEARCH PROJECT

Mapping the epigenetic and regulatory landscape to decode genetic heterogeneity in IRDs

Research Interests

Despite major advances in genetic testing, current methods like whole exome and targeted sequencing can only explain about 50–70% of inherited retinal disease (IRD) cases. These methods focus mostly on the protein-coding parts of our DNA and often miss changes in the non-coding regions, which play a critical role in regulating how genes are turned on and off. A growing body of research shows that these non coding regions—especially parts called cis-regulatory elements (CREs), such as enhancers and promoters—may hold key insights into the causes of IRDs. New genomic technologies and large datasets now allow us to study these hidden areas of the genome more effectively. This project aims to identify important non-coding variants and cell specific regulatory elements in the human retina by combining epigenetic data, machine learning tools, and gene regulation analysis (eQTLs).

Plans for 2026

Dr. Ratnapriya’s lab is uniquely equipped to investigate non-coding variation in IRDs, having generated a high-resolution epigenomic atlas of the human retina, including a retina-specific eQTL map from over 500 individuals and comprehensive maps of epigenome using ATAC-seq data from 18 donors. the lab has developed and implemented machine learning models (e.g., gkm-SVM) to predict the regulatory impact of variants and validated CRE function through enhancer reporter assays, establishing a powerful pipeline for identifying disease-relevant cis-regulatory elements. In addition, the lab’s extensive experience with whole-exome sequencing across diverse IRD cohorts has provided a strong foundation for integrating coding and non-coding data to uncover the genetic basis of unsolved IRD cases. The proposed research is significant as harnessing the susceptibility regulatory networks and defining their role for IRD gene will offer a mechanistic understanding of non-coding variants in IRDs.

The purpose of this study is to systematically identify and characterize functional non-coding elements that may be perturbed in IRDs.  Dr. Ratnapriya hypothesizes that integration of ATAC-seq data with machine learning approaches to predict enhancers and then integrating the regulatory information eQTLs in the retina can prioritize putative functional non-coding variants contributing to IRD pathogenesis.