Abstract
The molecular diagnosis of muscle disorders is challenging: genetic heterogeneity (>100 causal genes for skeletal and cardiac muscle disease) precludes exhaustive clinical testing, prioritizing sequencing of specific genes is difficult due to the similarity of clinical presentation, and the number of variants returned through exome sequencing can make the identification of the disease-causing variant difficult. We have filtered variants found through exome sequencing by prioritizing variants in genes known to be involved in muscle disease while examining the quality and depth of coverage of those genes. We ascertained two families with autosomal dominant limb-girdle muscular dystrophy of unknown etiology. To identify the causal mutations in these families, we performed exome sequencing on five affected individuals using the Agilent SureSelect Human All Exon 50 Mb kit and the Illumina HiSeq 2000 (2×100 bp). We identified causative mutations in desmin (IVS3+3A>G) and filamin C (p.W2710X), and augmented the phenotype data for individuals with muscular dystrophy due to these mutations. We also discuss challenges encountered due to depth of coverage variability at specific sites and the annotation of a functionally proven splice site variant as an intronic variant.
Highlights
Muscular dystrophies and cardiomyopathies are devastating diseases for which no cures or preventative treatments are currently available
I:3 and I:4 were initially diagnosed with Charcot-Marie-Tooth disease (CMT) because both patients exhibited peripheral neuropathy, and I:4 had pes cavus
No members of the family have presented with elevated serum creatine kinase (CK)
Summary
Muscular dystrophies and cardiomyopathies are devastating diseases for which no cures or preventative treatments are currently available. There has been great progress in the identification of genetic mutations that cause some forms of muscle disease; genetic heterogeneity is the rule rather than the exception Molecular diagnosis of these disorders is challenging because the large number of known causative genes (more than 100) makes exhaustive clinical testing very expensive and the similarity of clinical presentation makes selection of likely candidate genes difficult [1]. When a known causative mutation is not identified for a family, researchers have the opportunity to examine novel candidate variants using the same data As this is a relatively new and continually developing technology, there are some challenges associated with the analysis of this data. It is necessary to perform Sanger sequencing to confirm that an identified variant is present and not a sequencing error [6]
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