Language
English
Publication Date
5-18-2024
Journal
Human Molecular Genetics
DOI
10.1093/hmg/ddae028
PMID
38453143
PMCID
PMC11102593
PubMedCentral® Posted Date
5-7-2024
PubMedCentral® Full Text Version
Post-print
Abstract
Inherited retinal diseases (IRDs) are a group of rare genetic eye conditions that cause blindness. Despite progress in identifying genes associated with IRDs, improvements are necessary for classifying rare autosomal dominant (AD) disorders. AD diseases are highly heterogenous, with causal variants being restricted to specific amino acid changes within certain protein domains, making AD conditions difficult to classify. Here, we aim to determine the top-performing in-silico tools for predicting the pathogenicity of AD IRD variants. We annotated variants from ClinVar and benchmarked 39 variant classifier tools on IRD genes, split by inheritance pattern. Using area-under-the-curve (AUC) analysis, we determined the top-performing tools and defined thresholds for variant pathogenicity. Top-performing tools were assessed using genome sequencing on a cohort of participants with IRDs of unknown etiology. MutScore achieved the highest accuracy within AD genes, yielding an AUC of 0.969. When filtering for AD gain-of-function and dominant negative variants, BayesDel had the highest accuracy with an AUC of 0.997. Five participants with variants in NR2E3, RHO, GUCA1A, and GUCY2D were confirmed to have dominantly inherited disease based on pedigree, phenotype, and segregation analysis. We identified two uncharacterized variants in GUCA1A (c.428T>A, p.Ile143Thr) and RHO (c.631C>G, p.His211Asp) in three participants. Our findings support using a multi-classifier approach comprised of new missense classifier tools to identify pathogenic variants in participants with AD IRDs. Our results provide a foundation for improved genetic diagnosis for people with IRDs.
Keywords
Humans, Retinal Diseases, Pedigree, Computer Simulation, Female, Male, Mutation, Genes, Dominant, Genetic Predisposition to Disease, Computational Biology, Phenotype, Adult, inherited retinal diseases, autosomal dominant, variant classification, genetic diagnosis, next-generation sequencing
Published Open-Access
yes
Recommended Citation
Brock, Daniel C; Wang, Meng; Hussain, Hafiz Muhammad Jafar; et al., "Comparative Analysis of In-Silico Tools in Identifying Pathogenic Variants in Dominant Inherited Retinal Diseases" (2024). Faculty and Staff Publications. 4362.
https://digitalcommons.library.tmc.edu/baylor_docs/4362