Myopathies — diseases that primarily affect skeletal muscle — represent one of the most heterogeneous diagnostic challenges in clinical genetics. They can arise from defects in hundreds of different genes, spanning structural muscle proteins, ion channels, metabolic enzymes, mitochondrial machinery and RNA-processing factors. Clinical presentations overlap heavily: a patient with progressive weakness may ultimately turn out to have a dystrophinopathy, a metabolic myopathy, a congenital myopathy or an inflammatory condition with a genetic underpinning. This heterogeneity has long forced diagnostic laboratories to choose between narrowly targeted gene panels, which are cheap but blind to anything outside their curated gene list, and exome sequencing, which captures the protein-coding portion of the genome but systematically misses variants buried in introns, regulatory regions and structural rearrangements.

The study was conducted within PFMG2025, a national program piloted by the French Genomic Medicine Initiative to bring genome sequencing into routine clinical practice for rare diseases. Between July 2020 and October 2024, patients with myopathies who remained genetically undiagnosed after prior testing underwent genome sequencing at two designated clinical laboratories: SeqOIA in Paris and AURAGEN in Lyon. The retrospective analysis drew on clinical, paraclinical and genetic data collected across these platforms, making it one of the largest real-world assessments of genome sequencing for muscle disease conducted at a national scale. Crucially, the sequencing was performed as part of ordinary clinical care, not as a research protocol, which means the results reflect what health systems can realistically achieve rather than what is possible only under ideal laboratory conditions.

The headline result is deceptively simple: among 265 families, genome sequencing produced a conclusive molecular diagnosis — variants classified as likely pathogenic or pathogenic under American College of Medical Genetics and Genomics criteria — in 68 cases, a diagnostic yield of 26 percent. That figure might appear modest until it is placed in context. Every one of these patients had already passed through the existing diagnostic cascade, including, for many, gene panels and exome sequencing specifically designed to catch myopathy genes. A 26 percent yield in this heavily pre-tested population demonstrates that genome sequencing is not merely a marginal upgrade over existing methods; it reaches diagnostic territory that those methods structurally cannot access.

The technical reasons for this advantage lie in the fundamental differences between sequencing strategies. Gene panels and exome approaches rely on capture-based enrichment, which selectively amplifies exons — the roughly one to two percent of the genome that encodes proteins — before sequencing. Deep intronic regions, where pathogenic variants can disrupt splicing by creating cryptic exon insertion sites, are largely invisible to these methods unless specifically probed. Copy-number variants, complex rearrangements and variants in poorly captured exons are also systematically underdetected. Whole-genome sequencing, by contrast, reads across the entire genome uniformly, without capture bias, allowing laboratories to detect deep intronic splice-altering variants, exon-level deletions and duplications, and other non-coding pathogenic mechanisms in a single assay. This breadth proved decisive in the French cohort: the authors report that genome sequencing was necessary for the resolution of 20.5 percent of all cases with conclusive or candidate findings, because the responsible variants would not have been detectable using other established techniques.

Beyond the confirmed diagnoses, the study highlights the more nuanced contribution of variants of uncertain significance. When the researchers considered candidate VUS findings that nonetheless raised a strong diagnostic hypothesis — for example, variants in genes consistent with the patient’s clinical presentation, or variants whose pathogenicity is supported by emerging functional evidence but which do not yet meet formal ACMG criteria for pathogenicity — the effective diagnostic yield rose to 44 percent. In clinical practice, such findings are not endpoint diagnoses, but they can reshape patient management, guide surveillance for known complications, inform reproductive counseling, and direct families toward research studies and, increasingly, toward gene-specific clinical trials. As gene-specific therapies for neuromuscular diseases multiply, the difference between a confirmed diagnosis and a strong hypothesis is shrinking in practical importance, because both can determine eligibility for intervention.

The identification of deep intronic variants deserves particular emphasis, because these discoveries are rewriting the known architecture of neuromuscular disease. Pathogenic changes located thousands of base pairs away from the nearest exon were long considered unfindable at diagnostic scale, yet they are now recognized as substantial contributors to myopathies — most famously in dystrophinopathies, where deep intronic pseud exon activation accounts for a meaningful fraction of patients who previously tested negative on exons-focused assays. In the French cohort, the ability of genome sequencing to elucidate these complex variants, and to uncover previously undescribed pathogenic mechanisms, accounted for a large share of the diagnoses that panel and exome sequencing had missed. The authors argue that this capacity underscores the complex nature of neuromuscular genetic disorders and demonstrates how genome sequencing can deliver a genetic diagnosis within routine clinical practice rather than in specialized research settings.

The PFMG2025 program itself is a case study in how to operationalize genomic medicine at national scale. Funded by the French government through the National Research Agency under the Programme d’Investissements d’Avenir, the initiative connects a dense network of neuromuscular reference centers — spanning hospitals from Paris to Marseille, Lyon, Bordeaux, Grenoble and beyond — with centralized sequencing and analysis platforms. Clinicians across the FILNEMUS network prescribed genome sequencing for patients with suggestive phenotypes, encoded clinical information in standardized HPO terms, and returned findings through structured counseling pathways. This architecture allowed the study to capture the full diversity of myopathy subtypes and patient ages, from infants with congenital presentations to adults with slowly progressive weakness, and to measure performance in the messy reality of clinical medicine rather than in a curated cohort.

For patients and families, the practical consequences of a molecular diagnosis are difficult to overstate. A confirmed genetic cause ends the diagnostic odyssey — often a decade or more of investigations — and replaces uncertainty with a named condition that carries a defined natural history, evidence-based surveillance recommendations and, increasingly, targeted treatment options. Genetic diagnoses also ripple across families, enabling cascade testing of relatives, precise reproductive risk assessment and prenatal or preimplantation diagnostic options. In neuromuscular medicine specifically, the therapeutic landscape is changing rapidly: therapies for spinal muscular atrophy have transformed that disease’s trajectory, and gene-targeted approaches for Duchenne muscular dystrophy, limb-girdle muscular dystrophies and metabolic myopathies are advancing through trials. Every one of these interventions depends on knowing the exact gene involved — which makes the diagnostic yield of genome sequencing a direct determinant of who can access emerging treatments.

The French results add to a growing international consensus that the era of sequential, gene-limited testing is ending. Health systems that still route patients through panels first, then exomes, then genomes only after years of delay, are paying for repeated negative tests while withholding the one assay capable of detecting the full spectrum of pathogenic variation. The PFMG2025 findings — a 26 percent conclusive yield in a pre-tested myopathy population, rising to 44 percent with strong candidate findings, and more than one in five resolved cases achievable only through genome sequencing — make a data-driven argument for moving genome sequencing forward in the diagnostic pathway for muscle disease. As sequencing costs continue to fall and analysis pipelines for non-coding and structural variants mature, the question facing clinicians and payers is shifting from whether to adopt genome sequencing to how quickly the diagnostic cascade can be reorganized around it.

Subject of Research: Diagnostic yield of whole-genome sequencing compared with gene-panel and exome sequencing in genetically unresolved myopathies

Article Title: Genome sequencing improves diagnostic outcomes over panel and exome sequencings in myopathies: findings from the French PFMG2025 initiative

Article References: Verebi, C., Maino, A., Metay, C., Nectoux, J., Leturcq, F., Michel, L., Menassa, R., Pion, E., Cossee, M., Gorokhova, S., Krahn, M., Ramond, F., Fauré, J., Hersent, C., Malfatti, E., FILNEMUS PFMG2025 Consortium, Audic, F., Authier, F.-J., Barnerias, C., … Stojkovic, T. (2026). Genome sequencing improves diagnostic outcomes over panel and exome sequencings in myopathies: findings from the French PFMG2025 initiative. Genome Medicine. https://doi.org/10.1186/s13073-026-01769-w

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01769-w

Keywords: genome sequencing, myopathy, diagnostic yield, PFMG2025, exome sequencing, gene panel, neuromuscular disorders, deep intronic variants, genomic medicine, France, rare disease diagnosis, variants of uncertain significance

The challenge of diagnosing ultra-rare monogenic diseases stems from their extreme genetic heterogeneity and clinical variability. Conventional diagnostic techniques often fall short due to limited understanding of pathogenic variants and their biochemical or phenotypic manifestations. The new method embraces a holistic view by integrating multiple genomic dimensions—ranging from sequence variants and copy number alterations to structural rearrangements—within a unified analytic pipeline. Such comprehensive scrutiny offers unprecedented resolution in detecting subtle, yet clinically crucial, genetic anomalies that would otherwise evade standard diagnostic protocols.

At the heart of this paradigm is joint data analysis, a statistical and computational strategy that combines various genomic signatures and patterns to increase the sensitivity and specificity of variant detection. Unlike isolated approaches, joint analysis leverages correlated information across genetic features, ancestry background, and clinical phenotypes. This permits the identification of candidate variants with higher confidence, reducing false positives and illuminating genotype-phenotype correlations that can guide clinical interpretation. By capitalizing on these interdependencies, the study demonstrates that even the rarest monogenic conditions can be genetically elucidated with remarkable clarity.

An integral component of the framework involves leveraging next-generation sequencing data, both whole-exome and whole-genome, to capture the full spectrum of genetic variation. Deep sequencing coverage ensures that low-frequency variants and mosaicism are detected, while advanced computational algorithms parse through the enormous volume of data to prioritize likely pathogenic changes. The researchers have developed sophisticated bioinformatic tools that efficiently model variant impact, gene constraint, population allele frequency, and functional annotation, providing an exhaustive assessment of each mutation’s potential relevance to disease.

A particularly innovative aspect of the study is its ability to resolve complex structural variants, such as duplications, deletions, inversions, and translocations, which traditionally pose formidable challenges to clinical genetic testing. By integrating multiple orthogonal data streams, including read-depth signals, split-read alignments, and paired-end mapping anomalies, the framework reconstructs intricate genomic rearrangements with high accuracy. This capability is critical because structural variants can disrupt gene function or regulatory elements in ways that single-nucleotide variants cannot, often manifesting as severe or atypical clinical syndromes.

Beyond genomic data, the research incorporates phenotypic information and family pedigree analysis to contextualize genetic findings. By cross-referencing observed symptoms and inheritance patterns, the model refines variant prioritization and disease association, enhancing diagnostic precision. This multidimensional approach recognizes that a genetic variant’s effect cannot be fully understood in isolation but must be interpreted within the broader biological and clinical milieu of the patient.

Importantly, the study’s cohort encompasses a diverse population set, reflecting the necessity to account for ancestral genetic variation when diagnosing rare diseases. This inclusivity addresses a long-standing bias within genomic medicine where underrepresented populations are often disadvantaged by reference genomes and allele frequency databases skewed toward European ancestries. The authors emphasize that this broad representation improves variant classification accuracy across demographic lines, thereby supporting equitable healthcare outcomes.

The clinical implications of this research are profound. By enabling timely and accurate identification of ultra-rare monogenic disorders, the method empowers personalized management strategies, prognosis assessment, and potential therapeutic interventions. Patients who have endured lengthy diagnostic delays may finally receive definitive diagnoses, allowing for targeted treatments or participation in clinical trials that would otherwise be inaccessible. Moreover, genetic counseling can be better informed, providing families with clearer insights into inheritance risk and reproductive options.

Looking forward, the integration of this joint genomic approach into routine clinical workflows has the potential to revolutionize diagnostic services. While computational demands and interpretive complexities present challenges, ongoing advancements in bioinformatics infrastructure and clinician training promise to bridge these gaps. The study lays a strong foundation for the development of standardized pipelines that can be implemented in genetic diagnostic laboratories worldwide.

The research also opens avenues for discovery beyond diagnosis. The rich genomic and phenotypic datasets generated through this methodology can illuminate novel disease mechanisms and genetic pathways. This knowledge expands our understanding of human biology and disease etiology, potentially informing the development of new drugs and therapeutic targets. Additionally, the framework may serve as a model for dissecting other genetic complexities, including multifactorial diseases and gene-environment interactions that have remained elusive.

In terms of methodology, the study exemplifies the power of collaborative, interdisciplinary efforts. Geneticists, bioinformaticians, clinicians, and computational scientists have converged to design and validate this multifaceted analytic framework. Their efforts underscore the importance of integrating expertise across domains to tackle the intricate challenges posed by rare genetic disorders. Such synergy is essential to harness the full potential of genomic medicine.

Moreover, the use of open data and transparent algorithm development ensures that the scientific community can replicate, validate, and extend the work. The study highlights the value of shared resources and collaborative networks in accelerating innovation and translating research findings into clinical impact. This communal approach fosters collective progress in an era where genomic data generation far outpaces our capacity to interpret it.

Ethical considerations also accompany these advances. The ability to diagnose rare genetic conditions raises questions about data privacy, informed consent, and the psychological impact of uncertain or incidental findings. The authors advocate for responsible stewardship of genetic information and the incorporation of ethical frameworks into diagnostic practices. Patient engagement and education are crucial to mitigate potential harms and maximize benefits.

The study’s findings resonate within the broader context of precision medicine, where tailoring interventions based on individual genetic makeup represents the future of healthcare. Rare diseases, despite their low prevalence individually, collectively affect millions worldwide and represent a significant burden on health systems and families. By elucidating the genetic underpinnings of these conditions, this research contributes to the democratization of precision medicine, ensuring that even the most elusive diagnoses are within reach.

In conclusion, this pioneering work offers a transformative vision for genomic diagnostics. Through joint, multifaceted genomic analysis, the once seemingly insurmountable challenge of diagnosing ultra-rare monogenic diseases inches closer to resolution. The integration of cutting-edge sequencing technologies, computational ingenuity, and clinical insight heralds a new era in genetic medicine—one where every patient’s genetic puzzle can be decoded with speed, accuracy, and compassion.

Subject of Research: Diagnosis of ultra-rare monogenic diseases through integrated genomic analysis

Article Title: Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations

Article References:
Kobren, S.N., Moldovan, M.A., Reimers, R. et al. Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations. Nat Commun 16, 7267 (2025). https://doi.org/10.1038/s41467-025-61712-2

Image Credits: AI Generated