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Updated: Feb 24, 2026

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia
Published on: June 15, 2011
A novel non-sense variant in GSDME causing exon skipping associated with DFNA5 in a large Chinese family
Bingqian Yang1, Mingwan Zhu1, Xicui Long2
1Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
Background:
Hereditary hearing loss demonstrates significant genetic heterogeneity, involving diverse genes and variation types. Autosomal dominant forms present particular challenges in variant interpretation due to variable expressivity.
Objectives:
This study aimed to clinically and molecularly characterize a multi-generational family with autosomal dominant hereditary hearing loss, and to functionally validate the pathogenicity of an identified novel variant.
Methods:
Comprehensive clinical evaluations included audiometric testing and medical history review. Genetic analysis employed whole-exome sequencing followed by Sanger validation. Functional characterization involved minigene splicing assays and transcript analysis to assess the impact on splicing mechanisms.
Results:
Affected individuals exhibited post-lingual, bilateral, symmetric, progressive sensorineural hearing loss, initially affecting high frequencies. We identified a novel GSDME mutation (NM_001127453.2:c.1123G>T; p.Glu375Ter) that disrupts an exon splicing enhancer, causing exon 8 skipping and frameshift alterations. Functional assays confirmed reduced enhancer activity and aberrant splicing. Literature review of 20 reported mutations revealed substantial phenotypic variability and highlighted limitations of splice prediction algorithms.
Conclusions:
Our findings expand the GSDME mutation spectrum and provide functional evidence supporting a pathogenic role for non-sense variants through splicing disruption mechanisms. This study reinforces the potential gain-of-function hypothesis for GSDME-associated hearing loss and emphasizes the necessity of functional validation for accurate variant interpretation.
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