Genetics of early onset retinal diseases

Abstract
The goal of this project is to improve the molecular diagnosis rate and better understand the molecular
mechanisms of early onset cone inherited retinal degeneration (eocIRD) diseases. As human heavily relies on
cone vision for the activities, degeneration of cone photoreceptors has significant impact on their ability to
perform daily routine. Unfortunately, the current molecular diagnosis rate of eocIRD, such as cone rod dystrophy
(COD) and cone rod dystrophy (CRD), is significantly lower that of rod degeneration IRDs such as retinitis
pigmentosa. More than 50% COD and CRD remain unassigned even upon screening of all IRD associated
genes, highlighting a significant gap in our knowledge of the disease. To overcome this challenge, we propose
to systematically identify novel genes and mutant alleles that are missed by current screen process through a
combination of short and long read whole genome sequencing and functional validation experiments. To achieve
this goal, we have established a large collection of over 1,500 well-characterized, unrelated COD, CRD, and
LCA patient families. Screens for mutations in known inherited retinal disease genes led to the identification of
causal mutations in about 900 probands, leaving about 570 patient families unsolved. Patients from these
families are likely to due to mutations missed by current technology, representing a well characterized, rich
resource for identifying new mutations and disease associated genes. Whole exome sequencing has been
performed for all unsolved probands, including 300 with whole genome sequencing. Building on this work, our
Specific Aims are:
Specific Aim 1. Characterize the novel eocIRD associated gene TLCD3B
Specific Aim 2. Investigate the full spectrum of mutations in unsolved patients
Specific Aim 3. Identify and perform functional studies of novel candidate disease genes
Progress toward these goals is likely to lead to new insights into disease mechanisms through studies of
novel eocIRD disease genes and lay the foundation for developing new diagnoses and treatment methods.
Importantly, the protocols and software tools developed from these aims, particularly noncoding mutation
identification, will be applicable to other human diseases as well.

Grant Number: 5R01EY018571-17
NIH Institute/Center: NIH
Principal Investigator: RUI CHEN