2,518,518 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
Solving unsolved rare neurological diseases-a Solve-RD viewpoint
No full textRare genetic neurological disorders (RND; ORPHA:71859) are a heterogeneous group of disorders comprising >1700 distinct genetic disease entities. However, genetic discoveries have not yet translated into dramatic increases of diagnostic yield and indeed rates of molecular genetic diagnoses have been stuck at about 30–50% across NGS modalities and RND phenotypes [1, 2]. Existence of yet unknown disease genes as well as shortcomings of commonly employed NGS technologies and analysis pipelines in detecting certain variant types are typically cited to explain the low diagnosis rates. To increase the diagnostic yield in RNDs - one of the four focus disease groups in Solve-RD - we follow two major approaches, that we will here present and exemplify: (i) systematic state-of the art re-analysis of large cohorts of unsolved whole-exome/genome sequencing (WES/WGS) RND datasets; and (ii) novel-omics approaches. Based on the way Solve-RD systematically organizes researchers’ expertise to channel this approach [3], the European Reference Network for Rare Neurological Diseases (ERN-RND) has established its own Data Interpretation Task Force (DITF) within SOLVE-RD, which is currently composed of clinical and genetic experts from 29 sites in 15 European countries.The Solve-RD project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 779257. Data were analysed using the RD‐Connect Genome‐Phenome Analysis Platform, which received funding from EU projects RD‐Connect, Solve-RD and EJP-RD (Grant Numbers FP7 305444, H2020 779257, H2020 825575), Instituto de Salud Carlos III (Grant Numbers PT13/0001/0044, PT17/0009/0019; Instituto Nacional de Bioinformática, INB) and ELIXIR Implementation Studies. The study was further funded by the Federal Ministry of Education and Research, Germany, through the TreatHSP network (01GM1905 to RS and LS), the National Institute of Neurological Diseases and Stroke (R01NS072248 to SZ and RS), the European Joint Program on Rare Diseases-EJP-RD COFUND-EJP N° 825575 through funding for the PROSPAX consortium (441409627 to MS, RS and BvW). CW was supported by the PATE program of the Medical Faculty, University of Tübingen. CEE received support from the Dutch Princess Beatrix Muscle Fund and the Dutch Spieren voor Spieren Muscle fund. Authors on this paper are members of the European Reference Network for Rare Neurological Diseases (ERN-RND, Project ID 739510
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
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