Publication Date

3-5-2024

Journal

Investigative Ophthalmology & Visual Science

DOI

10.1167/iovs.65.3.25

PMID

38502138

PMCID

PMC10959191

PubMedCentral® Posted Date

3-19-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Animals, Humans, Anophthalmos, Coloboma, Exome Sequencing, Microphthalmos, Algorithms, DNA Helicases, Nuclear Proteins, Transcription Factors, Histone Acetyltransferases, exome sequencing, microphthalmia, anophthalmia, machine learning, candidate genes

Abstract

PURPOSE: A molecular diagnosis is only made in a subset of individuals with nonisolated microphthalmia, anophthalmia, and coloboma (MAC). This may be due to underutilization of clinical (whole) exome sequencing (cES) and an incomplete understanding of the genes that cause MAC. The purpose of this study is to determine the efficacy of cES in cases of nonisolated MAC and to identify new MAC phenotypic expansions.

METHODS: We determined the efficacy of cES in 189 individuals with nonisolated MAC. We then used cES data, a validated machine learning algorithm, and previously published expression data, case reports, and animal models to determine which candidate genes were most likely to contribute to the development of MAC.

RESULTS: We found the efficacy of cES in nonisolated MAC to be between 32.3% (61/189) and 48.1% (91/189). Most genes affected in our cohort were not among genes currently screened in clinically available ophthalmologic gene panels. A subset of the genes implicated in our cohort had not been clearly associated with MAC. Our analyses revealed sufficient evidence to support low-penetrance MAC phenotypic expansions involving nine of these human disease genes.

CONCLUSIONS: We conclude that cES is an effective means of identifying a molecular diagnosis in individuals with nonisolated MAC and may identify putatively damaging variants that would be missed if only a clinically available ophthalmologic gene panel was obtained. Our data also suggest that deleterious variants in BRCA2, BRIP1, KAT6A, KAT6B, NSF, RAC1, SMARCA4, SMC1A, and TUBA1A can contribute to the development of MAC.

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