Suchergebnisse

For 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. ; The Solve-RD project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 779257. This research is supported (not financially) by four ERNs: (1) The ERN for Intellectual Disability, Telehealth and Congenital Anomalies (ERN-ITHACA)—Project ID No 869189; (2) The ERN on Rare Neurological Diseases (ERN-RND)—Project ID No 739510; (3) The ERN for Neuromuscular Diseases (ERN Euro-NMD)—Project ID No 870177; (4) The ERN on Genetic Tumour Risk Syndromes (ERN GENTURIS)—Project ID No 739547. The ERNs are co-funded by the European Union within the framework of the Third Health Programme.

Funding Information: Funding 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). Funding Information: Conflict of interest HG receives/has received research support from the Deutsche Forschungsgemeinschaft (DFG), the Bundesministerium für Bildung und Forschung (BMBF), the Bundesministerium für Gesundheit (BMG) and the European Union (EU). He has received consulting fees from Roche. He has received a speaker honorarium from Takeda. The authors declare no competing interests. ; Peer reviewed

Data de publicació elecrònica: 10-05-2021 ; Rare 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)

Reanalysis 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. ; 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.