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ABSTRACT: Background
Due to large genetic and phenotypic heterogeneity, the conventional workup for Charcot-Marie-Tooth (CMT) diagnosis is often underpowered, leading to diagnostic delay or even lack of diagnosis. In the present study, we explored how bioinformatics analysis on whole-exome sequencing (WES) data can be used to diagnose patients with CMT disease efficiently.Case presentation
The proband is a 29-year-old female presented with a severe amyotrophy and distal skeletal deformity that plagued her family for over 20 years since she was 5-year-old. No other aberrant symptoms were detected in her speaking, hearing, vision, and intelligence. Similar symptoms manifested in her younger brother, while her parents and her older brother showed normal. To uncover the genetic causes of this disease, we performed exome sequencing for the proband and her parents. Subsequent bioinformatics analysis on the KGGSeq platform and further Sanger sequencing identified a novel homozygous GDAP1 nonsense mutation (c.218C?>?G, p.Ser73*) that responsible for the family. This genetic finding then led to a quick diagnosis of CMT type 4A (CMT4A), confirmed by nerve conduction velocity and electromyography examination of the patients.Conclusions
The patients with severe muscle atrophy and distal skeletal deformity were caused by a novel homozygous nonsense mutation in GDAP1 (c.218C?>?G, p.Ser73*), and were diagnosed as CMT4A finally. This study expanded the mutation spectrum of CMT disease and demonstrated how affordable WES could be effectively employed for the clinical diagnosis of unexplained phenotypes.
SUBMITTER: Jiang H
PROVIDER: S-EPMC7923504 | biostudies-literature | 2021 Mar
REPOSITORIES: biostudies-literature
BMC neurology 20210302 1
<h4>Background</h4>Due to large genetic and phenotypic heterogeneity, the conventional workup for Charcot-Marie-Tooth (CMT) diagnosis is often underpowered, leading to diagnostic delay or even lack of diagnosis. In the present study, we explored how bioinformatics analysis on whole-exome sequencing (WES) data can be used to diagnose patients with CMT disease efficiently.<h4>Case presentation</h4>The proband is a 29-year-old female presented with a severe amyotrophy and distal skeletal deformity ...[more]