The story began to unfold in recent years when large-scale genetic studies pointed an unexpected finger at ITSN1. One analysis combining the deCODE, UK Biobank, and Accelerating Medicines Partnership Parkinson’s Disease datasets found that heterozygous loss-of-function variants in the gene, meaning changes that cripple one of the two inherited copies, confer a substantially elevated risk of Parkinson’s disease. A second study described five patients from three families carrying distinct frameshift variants in ITSN1, documenting their clinical pictures in detail. A third effort, drawing on the UK Biobank, the AMP-PD cohort, and the All of Us program, reported a similar increase in risk and even supplied functional evidence from a fruit fly model, showing that the protein encoded by ITSN1, called Intersectin-1, interacts with alpha-synuclein, the sticky protein that clumps in the brains of Parkinson’s patients. What remained unclear was whether patients carrying these variants show a recognizable clinical signature.

To answer that question, the ROPAD team turned to their own trove of data. They analyzed 8,660 reportedly unrelated Parkinson’s disease patients recruited between April 2019 and August 2024, drawn from Europe (68 percent), North America (16 percent), Israel (10 percent), Turkey (4 percent), and South America (2 percent). Crucially, all of these patients had undergone exome or genome sequencing and had no relevant variants in known Parkinson’s genes. That exclusion criterion matters: the researchers removed anyone carrying a pathogenic or likely pathogenic variant in dominant Parkinson’s genes such as LRRK2, SNCA, VPS35, RAB32, or CHCHD2, anyone with two heterozygous or one homozygous pathogenic variant in recessive genes including PARK7, PINK1, and PRKN, and anyone with a pathogenic or risk-factor variant in GBA1, the most common genetic contributor to the disease. By stripping out known genetic explanations, the team could ask whether ITSN1 variants stand on their own as a risk factor.

As a comparison group, the researchers examined 210,657 exome- or genome-sequenced individuals from the CENTOGENE Biodatabank, people referred for routine diagnostic testing, healthy parents in trio analyses, or participants in other observational studies, none of whom had been referred for or diagnosed with Parkinson’s disease. Loss-of-function variants were defined rigorously as start-loss, stop-gain, stop-loss, splice-donor, splice-acceptor, or frameshift changes, identified through a validated in-house bioinformatic pipeline. The team then mined electronic case report forms for clinical details, including year of birth, sex, age at disease onset, age at diagnosis, age at enrollment, family history, and motor scores from a partial Unified Parkinson’s Disease Rating Scale examination.

The result was striking. Thirteen of the 8,660 patients, or 0.15 percent, carried heterozygous loss-of-function variants in ITSN1, compared with just 0.025 percent of the 210,657 comparison individuals. That difference translates into an odds ratio of 6.09, with a 95 percent confidence interval of 3.31 to 11.19 and a p-value of 1.34 times ten to the minus six, a level of statistical significance that is difficult to dismiss. The allele-level comparison tells the same story: the proportion of ITSN1 loss-of-function alleles in the Parkinson’s patients, 0.075 percent, is roughly six times higher than the proportion found in non-Finnish European individuals in the Genome Aggregation Database, the reference catalog that population geneticists use as a baseline for how common variants should be. The enrichment held against every other population in that database as well.

Perhaps most convincing is how neatly these numbers overlap with the earlier studies. The confidence interval from the ROPAD analysis largely overlaps those reported in the two prior association studies, which found odds ratios of 7.3 and 10.5 respectively. The carrier frequency of 0.15 percent in ROPAD matches the 0.15 percent reported in the deCODE-led meta-analysis and the 0.14 percent in the family study. Within the ROPAD cohort itself, ITSN1 loss-of-function variants rank as the sixth most common disease-relevant genetic finding, trailing only variants in GBA1, LRRK2, PRKN, SNCA, and GCH1. For a gene that was essentially invisible to Parkinson’s researchers a few years ago, that is a rapid ascent.

Digging into the variants themselves, the team identified 12 distinct ITSN1 loss-of-function changes among the 13 patients: four nonsense mutations, six frameshift variants, and two splice site changes. Eleven of the twelve appeared only once, and one, a small deletion designated c.2842_2843del, was found in two patients. When the researchers tested whether those two German patients were related, analysis of extended haplotypes found no evidence of close relatedness or a shared founder chromosome, suggesting the variant arose independently in each case. This pattern of largely private, one-off mutations contrasts sharply with the genetics of other dominant Parkinson’s genes, where a handful of recurrent variants, such as the famous LRRK2 G2019S change, account for most cases. It also hints that the mutational spectrum of ITSN1 resembles that of genes where virtually any disruption of the protein is harmful.

There is an intriguing twist. More than 40 distinct ITSN1 loss-of-function variants have already been reported in individuals with autism and neurodevelopmental disorders, and four of the variants found in the ROPAD patients had appeared in that literature. This overlap raises the possibility of pleiotropy, the phenomenon in which a single gene produces different outcomes depending on context. Intersectin-1 is a scaffolding protein involved in clathrin-mediated endocytosis, synaptic vesicle recycling, and actin and CDC42-related signaling, processes essential both for building neural circuits during development and for maintaining them across a lifetime. Losing one functional copy, a state called haploinsufficiency, might disturb neurodevelopmental circuitry in childhood or undermine neuronal maintenance later in life. Which outcome prevails may depend on genetic modifiers, the residual expression produced by splice-altering variants, environmental stressors, and other factors scientists have yet to untangle.

On the clinical side, the ROPAD analysis found that patients carrying ITSN1 variants look, in most respects, like ordinary Parkinson’s patients. The male-to-female ratio, the proportion with a positive family history, the age at diagnosis, the age at enrollment, and the disease duration all failed to differ significantly between carriers and non-carriers. A multiple linear regression of motor scores adjusted for disease duration also found no significant difference, though the p-value of 0.075 leaves open the possibility of a subtle effect. Interestingly, the two earlier large-cohort studies did report significantly younger ages at onset among carriers, with mean or median onsets in the early sixties versus around seventy in non-carriers. The ROPAD cohort as a whole skews young, with a mean and median onset of 55 years, which likely explains why the same signal did not emerge here. Notably, the median onset of 53 years among ROPAD carriers is comparable to the 47-year average onset in the five family-study patients, and one ROPAD participant carried both an ITSN1 deletion and a mild GBA1 variant, a combination the researchers excluded from their main analysis because the GBA1 change may have modified her disease risk, echoing what has been documented in people carrying both LRRK2 and GBA1 variants.

Taken together, the findings cement ITSN1 as a genuine Parkinson’s disease gene, one that produces a form of the illness clinically indistinguishable from idiopathic disease. The authors propose the designation PARK-ITSN1 for this genetic entity and call for further research into its natural history and treatment responses, questions that will matter as genetic testing becomes routine and as pharmaceutical companies design trials stratified by genotype. The study also carries a broader lesson about the power of deeply curated clinical and genetic datasets: when a diagnostic laboratory pairs sequencing with systematic phenotyping across thousands of patients, even vanishingly rare risk factors, present in fewer than two people per thousand, can be detected, validated, and connected to the biology of one of the world’s most common neurodegenerative diseases.

Subject of Research: Association between ITSN1 loss-of-function variants and Parkinson's disease risk in the ROPAD cohort

Article Title: Data from the ROPAD study corroborate an association between ITSN1 loss-of-function variants and Parkinson’s disease

Article References: Ganoza, C. A., Westenberger, A., Paul, J. J., Curado, F., Somerville, E. N., Rennecke, J., Bauer, P., & Beetz, C. (2026). Data from the ROPAD study corroborate an association between ITSN1 loss-of-function variants and Parkinson’s disease. npj Parkinson's Disease, 12(1), Article 237. https://doi.org/10.1038/s41531-026-01562-x

Image Credits: AI Generated

DOI: 10.1038/s41531-026-01562-x

Keywords: Parkinson's disease, ITSN1, genetics, loss-of-function variants, ROPAD study, haploinsufficiency, Intersectin-1, neurodegeneration, exome sequencing, age at onset, GBA1, pleiotropy

The motivation behind the work lies in what geneticists call the missing heritability problem. Genome-wide association studies have catalogued thousands of common genetic variants linked to common diseases, yet together these variants explain only a portion of the heritability estimated from family studies. Rare variants, defined here as those carried by fewer than one percent of the population, have long been suspected of hiding much of the missing signal. Population-scale sequencing efforts, including the exome sequencing of nearly half a million UK Biobank participants, have uncovered millions of these rare missense variants. The difficulty is that the overwhelming majority of them are evolutionarily young, present in only one or a few individuals, and statistically almost impossible to test for association one at a time. Distinguishing the functional few from the neutral many has remained one of the field’s central challenges.

RovHer attacks the problem from an unusual angle. Instead of asking whether a variant is deleterious to a protein in the abstract, the method asks whether a variant contributes to the variance of a measurable trait in a real population. The researchers trained their model using exome-wide association statistics covering 4,927,334 rare variants, treating the statistical evidence of association as a noisy label of functionality. On top of that foundation, they used a machine-learning technique known as multivariate adaptive regression splines, or MARS, to integrate 75 different features describing each variant and the gene it sits in. These features ranged from conservation measures and protein-domain information to gene-level annotations such as tolerance to loss-of-function mutations. Crucially, the MARS framework imposes no a priori constraints on how these features should be combined, allowing the data to reveal nonlinear interactions, for example cases where a variant’s importance depends on the properties of the gene it inhabits.

The benchmark results are dramatic. Across 21 quantitative traits measured in up to 357,086 individuals of European ancestry in the UK Biobank, the top one percent of variants prioritized by RovHer, a set of just 13,410 missense variants, explained on average 16.1 percent of the total heritability attributable to rare missense variation. Seven widely used alternative methods, including established pathogenicity predictors such as CADD, REVEL and AlphaMissense, managed an average of only 1.5 percent, with the best performer reaching 2.3 percent. In other words, RovHer achieved roughly a tenfold gain over the existing state of the art. Because the evaluation metric was heritability explained rather than agreement with laboratory assays or clinical classifications, the comparison directly measured what matters most for complex-trait genetics: how much inherited variation a prioritized variant set actually captures.

The authors were careful to rule out trivial explanations for this performance. RovHer’s predictions proved independent of allele frequency, meaning the method is not simply rewarding variants that happen to be slightly more common and therefore easier to detect statistically. The scores also generalized beyond the population in which they were trained. In a replication analysis using whole-genome sequencing data from the National Institutes of Health’s All of Us Research Program, RovHer outperformed competing methods across European, African and Admixed American ancestry groups, with sample sizes of 172,108, 61,620 and 52,645 respectively. This multi-ancestry validation matters because rare variant landscapes differ substantially between populations, and tools trained exclusively on European data have historically underperformed elsewhere.

Beyond population-level statistics, the team tested whether RovHer could identify individual carriers at elevated risk of severe clinical outcomes. Across 17 monogenic gene-trait pairs, where a single gene is known to exert a strong effect on a specific condition, high-scoring variants effectively flagged carriers with increased disease risk. Examples included variants in LDLR, the low-density lipoprotein receptor gene whose pathogenic mutations cause familial hypercholesterolemia, and variants in SLC34A3, a sodium-dependent phosphate transporter gene linked to kidney stone disease and altered estimated glomerular filtration rates. The analysis showed that carriers of top-ranked missense variants in these genes had measurably worse kidney function and higher chronic kidney disease risk than non-carriers, suggesting the method can bridge the gap between statistical genetics and clinical risk stratification.

Technically, the choice of MARS proved central to the method’s success. Unlike deep neural networks, which can be difficult to interpret, multivariate adaptive regression splines fit piecewise linear functions with hinge points, producing a model whose behavior can be inspected through partial dependence plots and variable importance measures. This allowed the researchers to see which of the 75 input features drove predictions and where thresholds lay. The team also demonstrated robustness to training choices: models trained on height, body mass index, creatinine or apolipoprotein B each performed well when tested on other traits, and adding more training traits provided incremental gains rather than overfitting. This suggests the method captures general principles of variant functionality rather than quirks of any single phenotype.

Perhaps the most practical aspect of the study is its accessibility. The researchers have released RovHer scores for all 4,927,334 rare variants observed in the UK Biobank, along with precomputed scores for approximately 80 million potential single-nucleotide rare variants across the human exome, hosted on Zenodo. The software package runs offline on all major operating systems, requires only a simple tab-delimited file of variant identifiers as input, and is free for noncommercial use, with a version stripped of commercially licensed prediction methods also provided. Tutorials and accompanying code for rare variant heritability estimation are available on GitHub. For biobank-scale analyses the authors recommend Unix-based hardware with at least 300 gigabytes of memory, but individual variant scoring is far lighter.

The broader significance of the work lies in its reframing of rare variation’s role in common disease. Rather than treating rare missense variants as an undifferentiated mass too sparse to analyze, RovHer shows that a small, computationally identifiable subset carries a disproportionate share of trait-relevant signal. This opens the door to incorporating rare variant information into polygenic risk scores, which currently rely almost entirely on common variants, and to prioritizing which variants of uncertain significance in clinical exomes deserve the closest scrutiny. It also complements recent work showing that rare penetrant mutations confer severe risks for common diseases, and that the genetic architecture of complex traits differs in the tails of their distributions.

Limitations remain, as with any predictive model. RovHer was trained on European-ancestry UK Biobank data, and while its multi-ancestry replication is encouraging, further calibration across diverse populations will be needed as adoption spreads. The method also inherits the limitations of its input features, since variants in poorly characterized genes or genomic regions with sparse annotations may be scored less reliably. Nonetheless, the tenfold improvement in heritability capture, the clinical validation across monogenic gene-trait pairs and the open availability of scores for essentially every possible exomic variant make RovHer a landmark addition to the geneticist’s toolkit. As sequencing continues to expand across global populations, heritability-optimized approaches of this kind are likely to become standard instruments for separating the functional signal from the neutral noise in the rare variant universe.

Subject of Research: Heritability-optimized functional prioritization of rare coding variants in complex traits

Article Title: A heritability-optimized method for functional prioritization of rare coding variants in complex traits

Article References: Pang, K., Pathan, N., Le, A., Man, A., Graf, J., Li, Y., da Rocha, G. L., Shemesh, E., Grafodatskaya, D., Chong, M. R., & Paré, G. (2026). A heritability-optimized method for functional prioritization of rare coding variants in complex traits. Nature Genetics. https://doi.org/10.1038/s41588-026-02766-w

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02766-w

Keywords: rare variants, missense variants, heritability, complex traits, UK Biobank, RovHer, variant effect prediction, machine learning, genetic risk, population genetics, monogenic disease, exome sequencing

The study, known as the BeSolveRD trial and published in Genome Medicine, enrolled 567 individuals with unexplained developmental disorders between June 2021 and July 2024. Participants were randomized using blocked, center-specific lists into two arms: 283 received the standard diagnostic pathway, while 284 received whole genome sequencing. The trial was designed to answer a question that has divided clinical genetics for years — whether the extra information contained in the roughly 90 percent of the genome beyond the exome, the protein-coding fraction, translates into real diagnostic gains in routine hospital settings rather than in centralized reference laboratories.

The headline result was clear. Genome sequencing achieved a diagnostic yield of 39.8 percent, identifying a certain molecular diagnosis in 113 of 284 probands, compared with 30 percent — 85 of 283 — for the standard of care, a difference that was statistically significant (p = 0.015). When the researchers accounted for inherited variants in genes associated with autosomal dominant conditions, the gap widened further, with genome sequencing outperforming standard care by 9.8 percentage points, equivalent to a relative increase in diagnostic yield of 32.7 percent. Most of the added value came from better detection of single nucleotide variants and small insertions or deletions, which contributed an additional 8.7 percent of diagnoses in the genome sequencing arm.

The technical story behind those numbers is instructive. Genome sequencing offers more uniform coverage across the genome than exome sequencing, which relies on capture probes that can miss regions with poor sequence homology to the reference genome. In this trial, the researchers found that the exome data actually covered the genomic loci of nearly all genome-sequencing-detected variants at more than 20-fold depth, suggesting the advantage was not simply a matter of coverage drops. Instead, the most striking difference emerged in the detection of out-of-frame indels: 26 frameshift variants were identified in the genome sequencing arm versus only 10 in the standard care arm, a difference of 5.6 percent that reached statistical significance. The authors hypothesize that variants with lower sequence similarity to the reference genome may be less effectively captured by exome probes, leading to their underrepresentation in sequencing data.

Genome sequencing also uncovered diagnoses that exome-based approaches structurally cannot reach. Three non-coding or otherwise elusive pathogenic variants were detected, including a de novo variant in the 5′ untranslated region of MEF2C, a gene whose regulatory disruption causes severe developmental disorder; a short tandem repeat expansion in the promoter of FMR1, the cause of Fragile-X syndrome, one of the most prevalent forms of inherited intellectual disability in males; and two pathogenic SHANK3 frameshift variants that had been filtered out in routine analysis because of inflated frequency estimates in population databases. A small 5′ untranslated region deletion potentially downregulating ANKRD11, associated with KBG syndrome, was also identified only through genome sequencing. These findings illustrate the growing recognition that the non-coding genome, repeat expansions, and structural variation harbor a meaningful share of disease-causing variants that exome-centric pipelines miss.

Perhaps the most surprising finding of the trial concerned inherited variants. Clinical laboratories analyzing trio data typically prioritize de novo variants, X-linked variants, and autosomal recessive inheritance. But when the researchers systematically examined inherited variants in genes associated with autosomal dominant conditions, they found 22 additional diagnoses across both arms — 3.9 percent of the entire cohort — representing 11 percent of all certain diagnoses. For 18 of these 22 families, there had been no prior suspicion of a dominantly inherited disorder, and half of the carrier parents were reported to be asymptomatic, consistent with the variable expression and reduced penetrance described for these genes. The contribution of inherited dominant variants (3.9 percent) exceeded that of X-linked variants (1.9 percent) and matched that of autosomal recessive variants (4.1 percent). The authors argue this strongly favors including autosomal dominant inherited variants in the standard work-up for developmental disorders, even when parents appear unaffected — a practice with real clinical consequences, as illustrated by two maternally inherited pathogenic PTEN variants that carry implications for lifelong cancer surveillance.

The trial also revealed a striking and persistent sex difference. Females obtained a diagnosis in 45.5 percent of cases, compared with only 28.5 percent of males — a 17 percentage-point gap that remained significant across indication categories and even among probands with moderate to profound intellectual disability. Part of this difference may reflect the higher prevalence of autism and autistic behavior among males, since autism was associated with lower diagnostic yields in this cohort. But the pattern aligns with previous large exome analyses from the United Kingdom and Ireland showing lower odds of diagnosis in males, and with the hypothesis that females with neurodevelopmental disorders carry a higher burden of rare, high-impact variants, consistent with a female protective model. After adjusting for sex distribution and the additional analyses performed in the genome sequencing arm, the overall yield difference between the two strategies narrowed to 7.3 percent, which no longer reached conventional statistical significance (p = 0.069) — a nuance the authors report transparently.

What sets this trial apart from earlier comparisons is its setting. Most previous studies of genome sequencing relied on centralized reference or research laboratories, raising questions about whether the technology could deliver comparable performance in the decentralized, hospital-based laboratories where most patients are actually seen. The Belgian consortium implemented clinical-grade genome sequencing across five sequencing sites and eight clinical interpretation centers, each using its own validated wet-lab and bioinformatic protocols. Before recruitment, all centers participated in an inter-laboratory ring trial using Genome-In-A-Bottle reference samples, achieving consistently high precision and recall for variant calling despite differences in library preparation kits, fragmentation methods, and reference genome versions. This demonstrated that nationwide, multi-site implementation of clinical genome sequencing is feasible — and that its diagnostic utility in hospital settings may actually exceed that reported in external reference laboratories.

The study was not without limitations. Recruitment fell short of the initial target of 800 participants, reducing statistical power, and the trial was retrospectively registered. Over two-thirds of the cohort had already undergone negative copy-number analysis before enrollment, meaning the CNV detection advantage of genome sequencing was likely underestimated; in a true first-tier setting, where pathogenic copy-number variants are found in roughly 12 percent of patients, the gap in favor of genome sequencing could be wider. Short-read sequencing also remains blind to certain challenges: low-complexity repetitive regions, some structural variants, inversions, complex rearrangements, mosaic variants, and epigenetic abnormalities all persist as blind spots, though long-read technologies promise to address some of these constraints as costs fall.

The implications for clinical practice are substantial. Beyond raw diagnostic yield, genome sequencing consolidates what is currently a fragmented, sequential testing cascade — microarray, exome, and follow-up assays — into a single test that simultaneously detects single nucleotide variants, indels, copy-number variants, structural variants, and repeat expansions. That streamlining reduces turnaround time, a critical factor for critically ill newborns, and preserves the option of reanalysis as knowledge grows: a post hoc targeted look at the recently discovered RNU4-2 gene, implicated in roughly 0.5 percent of neurodevelopmental disorders, immediately yielded one additional diagnosis among the trial’s unsolved genome sequencing samples. With artificial intelligence-driven variant prioritization tools already contributing to the yield gains observed here, and with non-coding variant interpretation maturing rapidly, the authors anticipate the advantage of genome sequencing will only grow — strengthening the case for making it the first-line molecular test for children with unexplained developmental disorders.

Subject of Research: Genome sequencing versus standard of care for diagnosing developmental disorders

Article Title: A nationwide prospective randomized trial for diagnosing developmental disorders demonstrates genome sequencing outperforms standard of care

Article References: A nationwide prospective randomized trial for diagnosing developmental disorders demonstrates genome sequencing outperforms standard of care. (n.d.). https://doi.org/10.1186/s13073-026-01771-2

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01771-2

Keywords: genome sequencing, exome sequencing, developmental disorders, intellectual disability, congenital anomalies, diagnostic yield, randomized controlled trial, clinical genetics, non-coding variants, autosomal dominant inheritance, BeSolveRD trial, Genome Medicine

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