Hunting for the perfect test: Neuromuscular diagnosis in the age of genomic bounty

The differential diagnosis of a child with a suspected neuromuscular disease is vast, given the continued proliferation of disease entities, particularly those that are inherited. Correspondingly, clinical genetic testing has become both increasingly sophisticated and simultaneously more accessible, which has led to clear diagnoses for many patients but confusion for others. Variants of unknown significance (abbreviated VUS or VOUS) on genetic test reports have become notorious for generating diagnostic uncertainty, along with other findings such as a single pathogenic heterozygous variant in a gene that is associated with a recessive disease. The practical implications of correct and prompt diagnoses of inherited neuromuscular diseases have become more apparent in recent years, as the US Food and Drug Administration has approved three new treatments for 5q spinal muscular atrophy (SMA) and four new treatments for Duchenne muscular dystrophy (DMD), all since 2016. Numerous other therapies for inherited neuromuscular diseases are in the pipeline. Is there a single clinical genetic test that can diagnose all inherited neuromuscular diseases? The answer is not yet. The study by Herman et al in this issue of the journal describes the diagnostic outcomes of 79 children with suspected neuromuscular disease who underwent exome sequencing from a total cohort of 106. Exome sequencing detected definitive genetic diagnoses in 36 of the 79 children. Others in the cohort of 106 patients were diagnosed using chromosomal microarray (9%) and candidate gene testing (20%). A significant number of individuals, however, remained undiagnosed after various combinations of these genetic evaluations. The results agree with findings from targeted sequence capture panels used in clinical diagnostic laboratories for neuromuscular diseases in general (diagnostic yield 46%), and for limb-girdle muscular dystrophy (LGMD) in particular (diagnostic yield 27%). For exome sequencing in research settings the numbers skew the other way, with a diagnostic yield of 12.9% for neuromuscular diseases in general, and 40% to 45% for LGMD. In general, whether looking at neuromuscular diseases overall vs specific disease categories such as LGMD, clinical vs research settings, or targeted sequence capture vs exome technologies, it is difficult for short-read next-generation sequencing approaches to yield diagnoses in over 50% of any given neuromuscular cohort, in part due to the ever-proliferating numbers of genes associated with these diseases. In contrast to the commonly used forms of next-generation sequencing, which involve short sequence reads and are sometimes referred to as second-generation sequencing, the generation of much longer raw sequence reads is now possible with the implementation of thirdgeneration techniques. How can we crack this 50% diagnostic ceiling? One key task is to resolve the endless stream of VUS/VOUSs that emerge from genetic test reports. Before any computational analyses are considered, a clinician should think about whether a gene affected by a VUS/VOUS is associated with the patient's phenotype, or at least a similar phenotype. In rare cases, a new phenotype for a gene may be emerging, but in most cases pathogenic variants are found in genes that have previously been associated with the phenotype in question. In cases of suspected neuromuscular disease without a clear-cut genetic diagnosis after the initial round of clinical genetic testing, it is important for the neuromuscular physician to perform a comprehensive diagnostic/ phenotypic analysis, which, aside from a thorough history, threegeneration family history and physical examination may include a combination of a serum creatine kinase level and other serum/urine markers, electrodiagnostic studies, muscle imaging via MRI or ultrasound, and muscle biopsy. For computational analyses of VUS/VOUSs, a key statistic to look up is the allele frequency of single-nucleotide variants, which can be found in a number of publicly accessible databases, one of the largest being the genome aggregation database (gnomAD). Pathogenic variants for Mendelian diseases are typically quite rare, barring founder effects in sequestered communities. Conservation of affected amino acid residues across species also supports pathogenicity; this can be assessed by first by looking up amino acid sequences of interest in a database such as UniProt and then checking sequence alignment using an online tool such as Clustal Omega. A third variable that is frequently assessed is the predicted damaging effect, if any, on the protein structure. There are several commonly used online programs, each with a different algorithm, such as SIFT, PolyPhen2, FATHMM, PROVEAN, and MutationTaster. In many cases these programs will yield conflicting results, so this analysis is most useful when all or most of the prediction programs are in agreement. In silico visualizations of protein crystal structures using programs such as PyMOL help analyze potential consequences of amino acid substitutions, although these visualizations can only be generated for proteins with solved crystal structures. An online tool that aggregates multiple pathogenic predictive factors, the combined annotation-dependent depletion score (CADD), is also increasingly used. ClinVar and ClinGen are complementary US National Institutes of Health −funded resources that can help clinicians and researchers who are seeking assessments of pathogenicity (or lack thereof) for particular variants, along with genotype-phenotype correlations. ClinVar is a publicly accessible online database that accepts submissions on Received: 11 December 2020 Revised: 26 December 2020 Accepted: 28 December 2020