Solving patients with rare diseases through programmatic reanalysis of genome-phenome data

Publisher Copyright: © 2021, The Author(s).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.Peer reviewe

Solving patients with rare diseases through programmatic reanalysis of genome-phenome data

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

Solving unsolved rare neurological diseases—a Solve-RD viewpoint

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 reviewe

Genome-wide variant calling in reanalysis of exome sequencing data uncovered a pathogenic TUBB3 variant.

Almost half of all individuals affected by intellectual disability (ID) remain undiagnosed. In the Solve-RD project, exome sequencing (ES) datasets from unresolved individuals with (syndromic) ID (n = 1,472 probands) are systematically reanalyzed, starting from raw sequencing files, followed by genome-wide variant calling and new data interpretation. This strategy led to the identification of a disease-causing de novo missense variant in TUBB3 in a girl with severe developmental delay, secondary microcephaly, brain imaging abnormalities, high hypermetropia, strabismus and short stature. Interestingly, the TUBB3 variant could only be identified through reanalysis of ES data using a genome-wide variant calling approach, despite being located in protein coding sequence. More detailed analysis revealed that the position of the variant within exon 5 of TUBB3 was not targeted by the enrichment kit, although consistent high-quality coverage was obtained at this position, resulting from nearby targets that provide off-target coverage. In the initial analysis, variant calling was restricted to the exon targets ± 200 bases, allowing the variant to escape detection by the variant calling algorithm. This phenomenon may potentially occur more often, as we determined that 36 established ID genes have robust off-target coverage in coding sequence. Moreover, within these regions, for 17 genes (likely) pathogenic variants have been identified before. Therefore, this clinical report highlights that, although compute-intensive, performing genome-wide variant calling instead of target-based calling may lead to the detection of diagnostically relevant variants that would otherwise remain unnoticed

Solve-RD : systematic pan-European data sharing and collaborative analysis to solve rare diseases

Publisher Copyright: © 2021, The Author(s).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.Peer reviewe

A MT-TL1 variant identified by whole exome sequencing in an individual with intellectual disability, epilepsy, and spastic tetraparesis.

Funder: The Solve-RD project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779257The genetic etiology of intellectual disability remains elusive in almost half of all affected individuals. Within the Solve-RD consortium, systematic re-analysis of whole exome sequencing (WES) data from unresolved cases with (syndromic) intellectual disability (n = 1,472 probands) was performed. This re-analysis included variant calling of mitochondrial DNA (mtDNA) variants, although mtDNA is not specifically targeted in WES. We identified a functionally relevant mtDNA variant in MT-TL1 (NC_012920.1:m.3291T > C; NC_012920.1:n.62T > C), at a heteroplasmy level of 22% in whole blood, in a 23-year-old male with severe intellectual disability, epilepsy, episodic headaches with emesis, spastic tetraparesis, brain abnormalities, and feeding difficulties. Targeted validation in blood and urine supported pathogenicity, with heteroplasmy levels of 23% and 58% in index, and 4% and 17% in mother, respectively. Interestingly, not all phenotypic features observed in the index have been previously linked to this MT-TL1 variant, suggesting either broadening of the m.3291T > C-associated phenotype, or presence of a co-occurring disorder. Hence, our case highlights the importance of underappreciated mtDNA variants identifiable from WES data, especially for cases with atypical mitochondrial phenotypes and their relatives in the maternal line

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose: 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.The Solve-RD project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement number 779257. Data were analyzed using the RD-Connect Genome-Phenome Analysis Platform, which received funding from the EU projects RD-Connect, Solve-RD, and European Joint Programme on Rare Diseases (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), and ELIXIR Implementation Studies. The collaborations in this study were facilitated by the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies, one of the 24 European Reference Networks approved by the European Reference Network Board of Member States, cofunded by the European Commission. This project was supported by the Czech Ministry of Health (number 00064203) and by the Czech Ministry of Education, Youth and Sports (number - LM2018132) to M.M.S

A mosaic PIK3CA variant in a young adult with diffuse gastric cancer: case report.

Funder: Data was reanalysed 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.Funder: the European Regional Development Fund (ERDF) through COMPETE 2020 - Operacional Programme for Competitiveness and Internationalisation (POCI), Portugal 2020, and by Portuguese funds through FCT/ Ministério da Ciência, Tecnologia e Inovação in the framework of the project "Institute for Research and Innovation in Health Sciences" (POCI-01-0145-FEDER-007274) and Project Ref. POCI-01-0145-FEDER-030164Hereditary diffuse gastric cancer (HDGC) is associated with germline deleterious variants in CDH1 and CTNNA1. The majority of HDGC-suspected patients are still genetically unresolved, raising the need for identification of novel HDGC predisposing genes. Under the collaborative environment of the SOLVE-RD consortium, re-analysis of whole-exome sequencing data from unresolved gastric cancer cases (n = 83) identified a mosaic missense variant in PIK3CA in a 25-year-old female with diffuse gastric cancer (DGC) without familial history for cancer. The variant, c.3140A>G p.(His1047Arg), a known cancer-related somatic hotspot, was present at a low variant allele frequency (18%) in leukocyte-derived DNA. Somatic variants in PIK3CA are usually associated with overgrowth, a phenotype that was not observed in this patient. This report highlights mosaicism as a potential, and understudied, mechanism in the etiology of DGC

Comprehensive reanalysis for CNVs in ES data from unsolved rare disease cases results in new diagnoses

We report the diagnostic results of a comprehensive copy number variant (CNV) reanalysis of 9,171 exome sequencing (ES) datasets from 5,757 families, including 6,143 individuals affected by a rare disease (RD). The data analysed was extremely heterogeneous, having been generated using 28 different exome enrichment kits, and sequenced on multiple short-read sequencing platforms, by 42 different research groups across Europe partnering in the Solve-RD project. Each of these research groups had previously undertaken their own analysis of the ES data but had failed to identify disease-causing variants.We applied three CNV calling algorithms to maximise sensitivity: ClinCNV, Conifer, and ExomeDepth. Rare CNVs overlapping genes of interest in custom lists provided by one of four partner European Reference Networks (ERN) were identified and taken forward for interpretation by clinical experts in RD. To facilitate interpretation, Integrative Genomics Viewer (IGV) screenshots incorporating a variety of custom-made tracks were generated for all prioritised CNVs.These analyses have resulted in a molecular diagnosis being provided for 51 families in this sample, with ClinCNV performing the best of the three algorithms in identifying disease-causing CNVs. We also identified pathogenic CNVs that are partially explanatory of the proband’s phenotype in a further 34 individuals. This work illustrates the value of reanalysing EScold casesfor CNVs even where analyses had been undertaken previously. Crucially, identification of these previously undetected CNVs has resulted in the conclusion of the diagnostic odyssey for these RD families, some of which had endured decades.3. Good health and well-bein