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Updated: Sep 18, 2025

Utility of Dissociated Intrinsic Hand Muscle Atrophy in the Diagnosis of Amyotrophic Lateral Sclerosis
Published on: March 4, 2014
Diagnosis of Australasian Patients with Neuromuscular Disease: Insights from a Comprehensive Panel Approach
Carolin K Scriba1, Fathimath Faiz2, Michael Black2
1Harry Perkins Institute of Medical Research, Queen Elizabeth II Medical Centre, Nedlands, Western Australia, Australia; Centre for Medical Research, University of Western Australia, Nedlands, Western Australia, Australia; Department of Diagnostic Genomics, PathWest Laboratory Medicine, Nedlands, Western Australia, Australia.
Abstract:
Neurogenetic disorders are a large group of genetic and phenotypically heterogeneous diseases, making diagnosis challenging. Sequencing hundreds of disease genes concurrently using massively parallel sequencing is, therefore, invaluable for diagnosis of these disorders. The PathWest neuromuscular disease gene panels include all known genes associated with neurologic and muscle disorders. Initially implemented in 2013, covering 336 genes, the gene panel has undergone various updates in chemistries and seen the addition of many newly described neurogenic disease genes. The results from versions 3 and 5 of the panel are reported, which included 644 and 830 genes, respectively. In total, 3961 patients were tested across 20 phenotypic subpanels: 2740 on version 3 and 1221 on version 5. Overall diagnostic success was 23.0%, with 8.4% of diagnoses attributed to newly added genes. Diagnostic success varied greatly between phenotypic subpanels, from 63.4% for the congenital muscular dystrophy subpanel to 2.6% for the Alzheimer disease/frontotemporal dementia subpanel. The five most frequently reported genes, DMD, RYR1, SPG7, PMP22, and NOTCH3, accounted for 22% of all diagnoses. Changing chemistries improved coverage of regions that were previously not well resolved. This enabled improved copy number variant calling, with 10.5% of diagnoses from version 5 attributed to copy number variants. The data generated have enabled identification of factors broadly affecting diagnosis of neuromuscular disorders and potential limitations hampering diagnostic success.
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