Recently, I had the opportunity to sit down with Dr. Satomi Mitsuhashi, the Section Chief of the Department of Neuromuscular Research at the National Center of Neurology and Psychiatry in Japan and discuss her research in the field of neuromuscular genetics. Over the past two years, Dr. Mitsuhashi and her team have adopted and evaluated a next-generation sequencing (NGS) approach for the clinical research of muscle disorders. During our discussion, she described in detail how her lab uses targeted gene panels for identifying different mutation types.
The pathology of inherited muscle disorders
Genetic skeletal muscle diseases are a group of inherited disorders characterized by progressive weakness and degeneration of skeletal muscles. There are two main categories of skeletal muscle disorders – myopathies and muscular dystrophies. While inherited myopathies are a result of genetic defects in the contractile apparatus of the muscle, muscular dystrophies are diseases of the muscle membrane or supporting proteins1.
Neuromuscular disorders are genetically heterogeneous and more than 200 genes have been implicated in the pathogenesis of these disorders2, 3. There are significant phenotypic and genotypic overlaps between different disease categories of inherited muscle disorders and a lot of these genes are quite large in size4. All these factors contribute to the complexity of testing for these genes via a sequential, single-gene testing approach.
The decision to move to Next-Generation Sequencing
Traditionally, Dr. Mitsuhashi’s team used Sanger sequencing or Multiplex Ligation-dependent Probe Amplification (MLPA) as a first line tool for characterizing these diseases. However, in September 2014, the team followed the recommendation of a collaborator and bought the Ion PGM™ System to implement a next-generation sequencing approach for the study of neuromuscular disorders. The goal was to speed up the discovery of disease-causing mutations in a cost-effective manner.
Applying panels as a first-line mutation discovery tool
The team realized that applying whole-exome sequencing to all samples as a first-line discovery tool would be cost prohibitive so they decided to evaluate the variant yield of targeted gene panels as the first tool for analyzing incoming samples. The team used Ion AmpliSeq™ designer to design four custom gene panels for identifying mutations that cause muscular dystrophy, congenital myopathy, metabolic myopathy and myofibrillar myopathy. The details of the designs are shown in Table 1.
Table 1 Details of four custom Ion AmpliSeq panels
|Custom Ion AmpliSeq™ Panel||Genes||Targets||Target size||Amplicons||Pool||Coverage (%)|
|Muscular Dystrophy||61||1,483||435.34 kb||2,527||2||96.79|
|Congenital Myopathy||41||1,196||312.67 kb||1,742||2||97.22|
|Metabolic Myopathy||41||918||211.73 kb||1,153||2||96.69|
|Myofibrillar Myopathy||36||1,250||381.22 kb||2,062||2||97.8|
In total, the team sequenced about 809 samples and managed to detect known disease-causing mutations in about 227 samples, which translates to a total variant yield of about 28% for all four panels. The variants identified with NGS were orthogonally confirmed with Sanger sequencing. More details of the study methodology and the individual panel results will be available when the team publishes their results in coming months. With the ability to sequence 4 to 8 samples per Ion 318™ chip and a variant yield close to 30%, the team concluded that the total cost of using targeted gene panels is less than applying whole-exome sequencing as a first-choice discovery tool.
The rest of the 582 samples went through follow-up whole-exome sequencing and the data from these exomes has been added to the ncnpGenDB, a database that currently has data from 1,022 exomes including 198 family members. According to Dr. Mitsuhashi, this constitutes the academic and basic research aspect of their research since they can review this data on a periodic basis in light of new findings. For example, in 2015 alone, about 44 new genes were linked to the pathology of neuromuscular disorders3.
Automated library preparation
Dr. Mitsuhashi also elaborated on her team’s use of the automated library preparation functionality of the Ion Chef™ system. “We tried the new AmpliSeq on Chef workflow and managed to sequence 86 samples within 14 working days. It’s pretty fast for identifying mutations and has minimal hands-on steps. The technicians in the lab really like it.”
Improving CNV detection capabilities using NGS
In addition to the results from the study involving the aforementioned panels, Dr. Mitsuhashi also described the lab’s workflow for identifying both copy number variations (CNVs) and small mutations using the Ion Torrent™ NGS workflow. Taking the example of Duchenne Muscular Dystrophy (DMD), she explained the frequency of different mutation types observed in DMD samples (Table 2). In the future, identifying the precise mutation type for a specific DMD sample will be important for the selection of the right molecular therapy. For example, a deletion mutation will require exon skipping therapy whereas a small mutation will require a read-through compound that can ignore the premature stop signals.
Previously, the lab employed MLPA as a first-line discovery tool for all incoming DMD samples since copy number variations (Deletions/Duplications) constitute about 70% of all DMD mutations. This is followed up by Sanger sequencing for identifying the small mutations that constitute the remaining 30% of samples.
Table 2 DMD mutation frequency and analysis methods
|Mutation analysis technique||MLPA||MLPA||Sanger sequencing|
The team was interested in evaluating NGS as a first-line discovery tool to identify both CNVs and small mutations. They developed a custom Ion AmpliSeq panel for DMD genes (~161 amplicons) and evaluated the variant yield for the Ion PGM workflow in comparison to the traditional workflow. They found that the variant yield for the NGS workflow was ~92%, about 22% higher than using MLPA as a first-line discovery tool5.
With the ability to sequence up to 48 samples per Ion 318™ chip, the team found that the cost of using the Ion PGM workflow as the first-line discovery tool is roughly 30% lower than using MLPA as the first-line mutation identification tool. Moving forward, the team intends to use the Ion AmpliSeq panel and Ion PGM system workflow for analyzing DMD samples and will reflex to Sanger sequencing or MLPA in cases where known mutations can’t be identified through NGS.
“Muscle disorders are complex with many genes involved. Adopting targeted NGS panels as a first-line tool in our lab has helped make our workflow both rapid and cost-effective” concluded Dr. Mitsuhashi.
The dilemma of panels vs exomes
There is a constant dilemma in the minds of clinical researchers – should we just sequence the whole exome or should we focus on targeted gene panels? There are several factors to consider when making this decision – the phenotypic and genotypic complexity of the disease in question, the cost of the assay, the cost of bioinformatics analysis and data storage, the implications of incidental findings, the coverage for the genes of interest, to name a few.
The answer to this dilemma is usually “It depends…”; however, each clinical research lab should carefully consider the above-mentioned factors in the context of their infrastructure and decide whether a targeted gene panel approach or a whole-exome sequencing approach is the right choice as a first-line mutation discovery tool.
1. Cardamone M, Darras BT, Ryan MM. Inherited myopathies and muscular dystrophies. Semin Neurol. 2008;28(2):250-9.
2. Karpati G, Hilton-Jones D, Bushby K, and Griggs R, editors. Disorders of Voluntary Muscle. Cambridge: Cambridge University Press; 2010.
3. Kaplan JC, Hamroun D. Corrigendum to “The 2016 version of the gene table of monogenic neuromuscular disorders (nuclear genome)”: [Neuromuscular Disorders Volume 25 (2015) 991-1020]. Neuromuscul Disord. 2016;26(4-5):330.
4. Flanigan KM, Niederhausern A von, Dunn DM, Alder J, Mendell JR, Weiss RB. Rapid Direct Sequence Analysis of the Dystrophin Gene. American Journal of Human Genetics. 2003; 72(4):931-939.
5. Okubo M, Minami N, Goto K et al. Genetic diagnosis of Duchenne/Becker muscular dystrophy using next-generation sequencing: validation analysis of DMD mutations. Journal of Human Genetics. 2016. doi:10.1038/jhg.2016.7.
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