Neuropediatrics 2016; 47 - P04-15
DOI: 10.1055/s-0036-1583660

Multi-Gene Panel Analysis in the Primary Diagnosis of Limb-Girdle Muscular Dystrophy

V. Mayer 1, S. Bulst 1, A. Nissen 1, S. Kleinle 1, K. Becker 1, M. C. Walter 2, A. Benet-Pagès 1, A. Abicht 1, 2
  • 1Medizinisch Genetisches Zentrum, MGZ, Munich, Germany
  • 2Dept. of Neurology, Friedrich-Baur-Institute, Ludwig-Maximilian’s University of Munich, Munich, Germany

Background/Purpose: Limb-girdle muscular dystrophies are characterized by great clinical and genetic heterogeneity. Mutations in several genes lead to overlapping phenotypes that cannot always be differentiated clinically or through muscle biopsy. Molecular genetic testing is therefore becoming increasingly important to the initial diagnosis of potential dystrophinopathies with significant clinical and familial consequences.

Methods: Our cohort comprised more than 80 patients with a suspected differential diagnosis of dystrophinopathy and negative test results for 1) DMD gene dosage analysis by MLPA, and 2) DMD gene point mutation analysis by Next-Generation-Sequencing (NGS). As over 700 neurogenetic/neuromuscular genes were captured during the course of the technical analysis by NGS, we were able to perform a second-tier expanded data analysis of a gene panel including more than 140 genes known to be responsible for congenital muscular dystrophies or myopathies.

Results: More than 30% of patients tested were found to have a disease-causing mutation in another gene. Other patients were found to have sequence variants of unknown clinical significance; the potential relevance of these variants to the disease cannot be assessed with certainty without further testing.

Conclusion: Multigene panels utilizing NGS technologies enable the sensitive, cost-efficient, and simultaneous analysis of multiple disease-relevant genes. However, despite the broad analysis, it is not possible to identify pathogenic mutations in all patients. There is also a risk that the analysis of a large number of genes independent of the clinical phenotype may identify variants that can be assessed only in combination with refined clinical or muscle biopsy data and/or segregation analysis.