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Investigation of GSDME results in the identification of the first pathogenic synonymous variants and genotype-phenotype correlations
Journal article   Open access   Peer reviewed

Investigation of GSDME results in the identification of the first pathogenic synonymous variants and genotype-phenotype correlations

Joseph J Chin, W Daniel Walls, Kai Wang, Amanda M Odell, Diana L Kolbe, Kevin T A Booth, Hela Azaiez and Richard J H Smith
Human genetics, Vol.144(11-12), pp.1127-1138
12/2025
DOI: 10.1007/s00439-025-02782-6
PMCID: PMC12689683
PMID: 41020988
url
https://doi.org/10.1007/s00439-025-02782-6View
Published (Version of record) Open Access

Abstract

Despite advances in the genetic diagnosis of hearing loss, there remains room for improvement. One way to improve the genetic diagnostic rate is the proper assessment of synonymous variants that are often bioinformatically filtered out. We used GSDME as a model to demonstrate the importance of assessing synonymous variants. Variants in the gene GSDME (also known as DFNA5) are associated with autosomal dominant nonsyndromic hearing loss. The hearing loss is typically progressive and downsloping. All reported causative variants of GSDME-related hearing loss involve the skipping of exon 8, which results in the expression of a constitutively active, but truncated protein that induces apoptosis of cochlear hair cells. A retrospective search of previously tested patients identified 3 novel pathogenic synonymous GSDME variants. The functional impact of these variants was confirmed in vitro via a minigene splicing assay. We also observed variant-dependent differences in the levels of aberrant splicing, leading us to hypothesize that partial loss of splicing will result in a less severe hearing loss phenotype as compared to complete loss of splicing. Audiometric analysis found an association between complete loss of splicing and greater initial and/or more quickly progressing hearing loss as compared to partial loss of splicing. Over the course of the study, we also found limited correlation between in silico prediction and in vitro observed effects of a variant on splicing, indicating the need to cautiously apply in silico prediction tools in the context of genetic diagnosis.
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