2,010 research outputs found
Genomic reanalysis of a pan-European rare-disease resource yields new diagnoses
No full textAbstract: Genetic diagnosis of rare diseases requires accurate identification and interpretation of genomic variants. Clinical and molecular scientists from 37 expert centers across Europe created the Solve-Rare Diseases Consortium (Solve-RD) resource, encompassing clinical, pedigree and genomic rare-disease data (94.5% exomes, 5.5% genomes), and performed systematic reanalysis for 6,447 individuals (3,592 male, 2,855 female) with previously undiagnosed rare diseases from 6,004 families. We established a collaborative, two-level expert review infrastructure that allowed a genetic diagnosis in 506 (8.4%) families. Of 552disease-causing variants identified, 464 (84.1%) were single-nucleotide variants or short insertions/deletions. These variants were either located in recently published novel disease genes (n=67), recently reclassified in ClinVar (n=187) or reclassified by consensus expert decision within Solve-RD (n=210). Bespoke bioinformatics analyses identified the remaining 15.9% of causative variants (n=88). Ad hoc expert review, parallel to the systematic reanalysis, diagnosed 249 (4.1%) additional families for an overall diagnostic yield of 12.6%. The infrastructure and collaborative networks set up by Solve-RD can serve as a blueprint for future further scalable international efforts. The resource is open to the global rare-disease community, allowing phenotype, variant and gene queries, as well as genome-wide discoveries
Unraveling undiagnosed rare disease cases by HiFi long-read genome sequencing
No full textAbstract: Solve-RD is a pan-European rare disease (RD) research program that aims to identify disease-causing genetic variants in previously undiagnosed RD families. We utilized 10-fold coverage HiFi long-read sequencing (LRS) for detecting causative structural variants (SVs), single-nucleotide variants (SNVs), insertion-deletions (indels), and short tandem repeat (STR) expansions in previously studied RD families without a clear molecular diagnosis. Our cohort includes 293 individuals from 114 genetically undiagnosed RD families selected by European Reference Network (ERN) experts. Of these, 21 families were affected by so-called \u201cunsolvable\u201d syndromes for which genetic causes remain unknown and for which prior testing was not a prerequisite. The remaining 93 families had at least one individual affected by a rare neurological, neuromuscular, or epilepsy disorder without a genetic diagnosis despite extensive prior testing. Clinical interpretation and orthogonal validation of variants in known disease genes yielded 12 novel genetic diagnoses due to de novo and rare inherited SNVs, indels, SVs, and STR expansions. In an additional five families, we identified a candidate disease-causing variant, including an MCF2/FGF13 fusion and a PSMA3 deletion. However, no common genetic cause was identified in any of the \u201cunsolvable\u201d syndromes. Taken together, we found (likely) disease-causing genetic variants in 11.8% of previously unsolved families and additional candidate disease-causing SVs in another 5.4% of these families. In conclusion, our results demonstrate the potential added value of HiFi long-read genome sequencing in undiagnosed rare diseases
Correction to: The Solve-RD Solvathons as a pan-European interdisciplinary collaboration to diagnose patients with rare disease (Nature Genetics, (2025), 57, 10, (2361-2370), 10.1038/s41588-025-02290-3)
No full text\ua9 Springer Nature America, Inc. 2026.Correction to: Nature Geneticshttps://doi.org/10.1038/s41588-025-02290-3, published online 9 September 2025. In the version of the article initially published, the Supplementary Information did not include the members of the Solve-RD DITF-EPICARE, Solve-RD DITF-ITHACA, Solve-RD DITF-EURO-NMD, Solve-RD DITF-RITA, Solve-RD DITF-RND and Solve-RD consortia. The Supplementary Information has now been corrected to include these lists
Solve-RD : systematic pan-European data sharing and collaborative analysis to solve rare diseases
No full textFor the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient’s data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together >300 clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs; ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of >19,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be shared with controlled access. The first preliminary data re-analysis has already diagnosed 255 cases form 8393 exomes/genome datasets. This unprecedented degree of collaboration focused on sharing of data and expertise shall identify many new disease genes and enable diagnosis of many so far undiagnosed patients from all over Europe.Peer reviewe
Correction to: Genomic reanalysis of a pan-European rare-disease resource yields new diagnoses (Nature Medicine, (2025), 31, 2, (478-489), 10.1038/s41591-024-03420-w)
No full text\ua9 The Author(s) 2025. Correction to: Nature Medicinehttps://doi.org/10.1038/s41591-024-03420-w, published online 17 January 2025. In the version of this article initially published, collaborators in the Solve-RD DITF-GENTURIS, Solve-RD DITF-ITHACA, Solve-RD DITF-EURO-NMD, Solve-RD DITF-RND and Solve-RD consortia were not listed correctly as authors in the metadata associated with this paper, as is now amended in the HTML and PDF versions of the article
Solving patients with rare diseases through programmatic reanalysis of genome-phenome data
No full textReanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP’s Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics.Peer reviewe
Publisher Correction: Genomic reanalysis of a pan-European rare-disease resource yields new diagnoses
No full textIn the version of this article initially published, collaborators in the Solve-RD DITF-GENTURIS, Solve-RD DITF-ITHACA, Solve-RD DITF-EURO-NMD, Solve-RD DITF-RND and Solve-RD consortia were not listed correctly as authors in the metadata associated with this paper, as is now amended in the HTML and PDF versions of the article
Publisher Correction: Genomic reanalysis of a pan-European rare-disease resource yields new diagnoses
No full textCorrection to: Nature Medicinehttps://doi.org/10.1038/s41591-024-03420-w, published online 17 January 2025. In the version of this article initially published, collaborators in the Solve-RD DITF-GENTURIS, Solve-RD DITF-ITHACA, Solve-RD DITF-EURO-NMD, Solve-RD DITF-RND and Solve-RD consortia were not listed correctly as authors in the metadata associated with this paper, as is now amended in the HTML and PDF versions of the article.</p
A Solve-RD ClinVar-based reanalysis of 1,522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing
No full textPurpose: Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods: Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results: We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion: The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock.</p
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