MECP2 variation in Rett syndrome-An overview of current coverage of genetic and phenotype data within existing databases
2018; Wiley; Volume: 39; Issue: 7 Linguagem: Inglês
10.1002/humu.23542
ISSN1098-1004
AutoresGillian S. Townend, Friederike Ehrhart, Henk J. van Kranen, Mark D. Wilkinson, Annika Jacobsen, Marco Roos, Egon Willighagen, David van Enckevort, Chris T. Evelo, Leopold Curfs,
Tópico(s)Congenital heart defects research
ResumoHuman MutationVolume 39, Issue 7 p. 914-924 DATABASESOpen Access MECP2 variation in Rett syndrome—An overview of current coverage of genetic and phenotype data within existing databases Gillian S. Townend, Gillian S. Townend orcid.org/0000-0002-5448-9046 Rett Expertise Centre Netherlands - GKC, Maastricht University Medical Center, Maastricht, The Netherlands Gillian S. Townend and Friederike Ehrhart share first authorship.Search for more papers by this authorFriederike Ehrhart, Corresponding Author Friederike Ehrhart friederike.ehrhart@maastrichtuniversity.nl orcid.org/0000-0002-7770-620X Rett Expertise Centre Netherlands - GKC, Maastricht University Medical Center, Maastricht, The Netherlands Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands Gillian S. Townend and Friederike Ehrhart share first authorship. Correspondence Friederike Ehrhart, Rett Expertise Centre Netherlands - GKC, Maastricht University Medical Center, Maastricht, The Netherlands. Email: friederike.ehrhart@maastrichtuniversity.nlSearch for more papers by this authorHenk J. van Kranen, Henk J. van Kranen orcid.org/0000-0001-7777-3245 Rett Expertise Centre Netherlands - GKC, Maastricht University Medical Center, Maastricht, The Netherlands Institute for Public Health Genomics, Maastricht University, Maastricht, The NetherlandsSearch for more papers by this authorMark Wilkinson, Mark Wilkinson orcid.org/0000-0001-6960-357X Center for Plant Biotechnology and Genomics, Universidad Politécnica de Madrid, Madrid, SpainSearch for more papers by this authorAnnika Jacobsen, Annika Jacobsen orcid.org/0000-0003-4818-2360 Department of Human Genetics, Leiden University Medical Center, Leiden, The NetherlandsSearch for more papers by this authorMarco Roos, Marco Roos orcid.org/0000-0002-8691-772X Department of Human Genetics, Leiden University Medical Center, Leiden, The NetherlandsSearch for more papers by this authorEgon L. Willighagen, Egon L. Willighagen Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The NetherlandsSearch for more papers by this authorDavid van Enckevort, David van Enckevort orcid.org/0000-0002-2440-3993 Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The NetherlandsSearch for more papers by this authorChris T. Evelo, Chris T. Evelo orcid.org/0000-0002-5301-3142 Rett Expertise Centre Netherlands - GKC, Maastricht University Medical Center, Maastricht, The Netherlands Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The NetherlandsSearch for more papers by this authorLeopold M. G. Curfs, Leopold M. G. Curfs orcid.org/0000-0001-9154-1395 Rett Expertise Centre Netherlands - GKC, Maastricht University Medical Center, Maastricht, The NetherlandsSearch for more papers by this author Gillian S. Townend, Gillian S. Townend orcid.org/0000-0002-5448-9046 Rett Expertise Centre Netherlands - GKC, Maastricht University Medical Center, Maastricht, The Netherlands Gillian S. Townend and Friederike Ehrhart share first authorship.Search for more papers by this authorFriederike Ehrhart, Corresponding Author Friederike Ehrhart friederike.ehrhart@maastrichtuniversity.nl orcid.org/0000-0002-7770-620X Rett Expertise Centre Netherlands - GKC, Maastricht University Medical Center, Maastricht, The Netherlands Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands Gillian S. Townend and Friederike Ehrhart share first authorship. Correspondence Friederike Ehrhart, Rett Expertise Centre Netherlands - GKC, Maastricht University Medical Center, Maastricht, The Netherlands. Email: friederike.ehrhart@maastrichtuniversity.nlSearch for more papers by this authorHenk J. van Kranen, Henk J. van Kranen orcid.org/0000-0001-7777-3245 Rett Expertise Centre Netherlands - GKC, Maastricht University Medical Center, Maastricht, The Netherlands Institute for Public Health Genomics, Maastricht University, Maastricht, The NetherlandsSearch for more papers by this authorMark Wilkinson, Mark Wilkinson orcid.org/0000-0001-6960-357X Center for Plant Biotechnology and Genomics, Universidad Politécnica de Madrid, Madrid, SpainSearch for more papers by this authorAnnika Jacobsen, Annika Jacobsen orcid.org/0000-0003-4818-2360 Department of Human Genetics, Leiden University Medical Center, Leiden, The NetherlandsSearch for more papers by this authorMarco Roos, Marco Roos orcid.org/0000-0002-8691-772X Department of Human Genetics, Leiden University Medical Center, Leiden, The NetherlandsSearch for more papers by this authorEgon L. Willighagen, Egon L. Willighagen Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The NetherlandsSearch for more papers by this authorDavid van Enckevort, David van Enckevort orcid.org/0000-0002-2440-3993 Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The NetherlandsSearch for more papers by this authorChris T. Evelo, Chris T. Evelo orcid.org/0000-0002-5301-3142 Rett Expertise Centre Netherlands - GKC, Maastricht University Medical Center, Maastricht, The Netherlands Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The NetherlandsSearch for more papers by this authorLeopold M. G. Curfs, Leopold M. G. Curfs orcid.org/0000-0001-9154-1395 Rett Expertise Centre Netherlands - GKC, Maastricht University Medical Center, Maastricht, The NetherlandsSearch for more papers by this author First published: 27 April 2018 https://doi.org/10.1002/humu.23542Citations: 11 Communicated by Alastair F. Brown AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Rett syndrome (RTT) is a monogenic rare disorder that causes severe neurological problems. In most cases, it results from a loss-of-function mutation in the gene encoding methyl-CPG-binding protein 2 (MECP2). Currently, about 900 unique MECP2 variations (benign and pathogenic) have been identified and it is suspected that the different mutations contribute to different levels of disease severity. For researchers and clinicians, it is important that genotype–phenotype information is available to identify disease-causing mutations for diagnosis, to aid in clinical management of the disorder, and to provide counseling for parents. In this study, 13 genotype–phenotype databases were surveyed for their general functionality and availability of RTT-specific MECP2 variation data. For each database, we investigated findability and interoperability alongside practical user functionality, and type and amount of genetic and phenotype data. The main conclusions are that, as well as being challenging to find these databases and specific MECP2 variants held within, interoperability is as yet poorly developed and requires effort to search across databases. Nevertheless, we found several thousand online database entries for MECP2 variations and their associated phenotypes, diagnosis, or predicted variant effects, which is a good starting point for researchers and clinicians who want to provide, annotate, and use the data. 1 INTRODUCTION Rett syndrome (RTT; MIM# 312750) is one of 5,000–8,000 known rare diseases that together have been identified as affecting 6%–8% of the world's population. Approximately 80% of these diseases have a genetic origin (Council Recommendation on an action in the field of rare diseases (2009/C 151/02), Recital 5). Most of these diseases are caused by pathological variants in one single, disease-specific gene. In the case of RTT, this is in MECP2, an important regulator of neuronal development and function (Ehrhart et al., 2016; Lyst & Bird, 2015). At the present time, around 900 unique variations in MECP2 have been identified (Gold, Krishnarajy, Ellaway, & Christodoulou, 2018). To help distinguish between pathological and neutral genetic variants (Hunter et al., 2016), scientists and clinicians collect genetic data and corresponding phenotypic information and make this information available in databases, which can be used for research and prognostic purposes. In this respect, RTT serves as an example for any monogenic rare disease where, due to the limited number of individuals, a better understanding of the disease can be reached through combining data from different databases that may be housed at different institutions and in different countries. In recent years, the European Union's policy on rare diseases (e.g., Directive 2011/24/EU) has recognized the value of sharing information, knowledge and expertise, and has generated a number of initiatives to encourage pan-European collaboration, for example, through the creation of European Reference Networks (ERNs) such as Intellectual disability TeleHealth And Congenital Anomalies (ITHACA), the ERN focused on rare congenital malformations and rare intellectual disability in which RTT is placed (https://ec.europa.eu/health/sites/health/files/ern/docs/ernithaca_factsheet_en.pdf). Generally, there are different types of databases for rare diseases: (1) Patient registries, containing i.a. patient data, genetic data, phenotype descriptions and information on medication. These are not normally open to the public. There are several data platforms, for example, RD-connect, which host patient registries with controlled access. (2) Genetic data repositories, for example, EGA (European Genome-Phenome Archive). These have been increasing in number since next-generation sequencing (NGS), and especially whole exome sequencing (WES), has been used as a clinical standard for the diagnosis of rare disorders and other suspected genetic disorders. (3) Genotype–phenotype databases that combine genetic data (e.g., DNA sequences, variants, genotypes) with phenotypic data. (4) Databases that store general information about genes, proteins, metabolites, their interactions and their mutation specific aberrations. It is within this context that rare disease registries and databases have also been recognized by the European Union as "key instruments to develop clinical research in the field of rare diseases, to improve patient care and healthcare planning" (https://ec.europa.eu/health/rare_diseases/policy/registries_en). This study focusses on the genotype–phenotype databases. Several such databases have been developed and will be discussed here. The fundamental goal of these databases is to collect and provide access to data and knowledge to promote research into the functional and pathogenic significance of genetic variants (Brookes & Robinson, 2015; Johnston & Biesecker, 2013). Critical for accurate analysis is the ability to distinguish between the disease-causing alleles and the abundance of benign variants or less important functional variants that co-occur in both normal and disease-affected individuals. One consequence of the increased power of NGS—often used for gene panels, WES, and whole genome sequencing (WGS)—is the increased danger of incorrect assignment of pathogenicity, when compared with single gene analysis. For instance, a typical WES (e.g., in the context of suspected diagnosis of a rare monogenic disorder) may uncover up to 25,000 variants (Gilissen, Hoischen, Brunner, & Veltman, 2012). Elucidation of just a handful of pathogenic variants from the resulting thousands continues to be a major challenge in spite of the availability of standardized software solutions. The most effective way to start distinguishing benign from pathogenic variants is based on population frequencies of variants. In this approach, all variants occurring in the population at higher frequencies than the disease prevalence are considered benign. From the many recent initiatives to collect exome variants of individuals without clear disease phenotypes, the Exome Aggregation Consortium (ExAC) is the largest, containing more than 60,000 exomes (Exome_Aggregation_Consortium, Lek, & MacArthur, 2015). In general, the population frequency information will reduce the number of candidate (pathogenic) mutations to a couple of hundred (Gilissen et al., 2012). Further prioritization can then take place by employing tools such as PolyPhen and SIFT (Sorting Intolerant From Tolerant). Ensembl's Variant Effect Predictor tool (Lelieveld, Veltman, & Gilissen, 2016) makes these aforementioned classic approaches available; it also includes a number of newer methods to distinguish between pathogenic, implicated, associated, damaging, and deleterious variants, and/or those of unknown significance among the remaining variants. These next steps in the prioritization process are summarized by Lelieveld et al. (2016). The challenge of distinguishing disease-causing sequence variants from the many potentially functional variants in any human genome recently prompted MacArthur et al. (2014) to propose guidelines for investigating causality of sequence variants in human disease. The proper setup and use of databases is one of the key issues they identified in order to be able to upload, store and find pathogenic and benign variants. The results of the analysis of disease-causing variants also provides vital information, not just for scientists and researchers who are seeking to further knowledge and understanding of certain diseases, but for clinicians to make the correct diagnosis and provide genetic counseling and patient care. State of the art genotype–phenotype databases are of particular value, and among these, the so-called locus-specific mutation databases (LSDBs) (e.g., LOVD (Fokkema et al., 2011)) have served diagnosticians for many years by facilitating the interpretations of genetic variants (Brookes & Robinson, 2015; Johnston & Biesecker, 2013). In addition to the LSDBs, a variety of other (clinically relevant) databases with a focus on genotype–phenotype relationships has emerged in recent years (Lelieveld et al., 2016) and the need to integrate information from these databases has also generated many initiatives. The RD-Connect project provides a platform for the rare disease community to find and share data and tools (Thompson et al., 2014). It includes a pipeline to harmonize variant annotation of rare disease genomes (Laurie et al., 2016), registries of rare disease registries and biobanks (Gainotti et al., submitted), and bioinformatics tools. It is developed in collaboration with infrastructures such as ELIXIR (https://www.elixir-europe.org/), BBMRI-ERIC (https://www.bbmri-eric.eu/ (Mayrhofer, Holub, Wutte, & Litton, 2016)), the infrastructure consortium for biobanks, and the Global Alliance for Genomics and Health (GA4GH, https://genomicsandhealth.org). The creation of GA4GH in 2013 represents one of the most prominent large-scale initiatives in this area. The goals and progress of this group were published recently (GA4GH, 2016) To support both clinicians and researchers, we present in this article an overview of a number of current genotype–phenotype databases. We evaluate their general structure and function for use in biomedical research, especially for researchers/clinicians who want to find "their" mutation or intend to find a database in which to store their genotype–phenotype data. We give an indication of the findability and interoperability, the practical user functionality (up and download functions), the type and quantity of genotype and phenotype data available, and provide suggestions for future improvement. 2 MATERIALS AND METHODS 2.1 Selection of databases The databases and meta/integrated databases in this survey were selected according to the following criteria: The database contains genetic variation and associated phenotypic information (genotype–phenotype databases); The genetic data are available in a processed form to enable a direct search for variations in a specific gene, region, or disease (e.g., in the HGVS or reference SNP (rs) format, an identifier given by the database dbSNP); The database is available online (with or without prior registration); The database is available in English. We do not claim complete coverage of all available databases; we focus on those which were findable online using search engines (e.g., Google) or listed in FairSharing.org (formerly known as BioSharing.org) or other meta-databases (RD-connect, bioCADDIE). We evaluated as a separate category certain meta or integrated databases, which in themselves contain no new or unique information, but instead try to integrate information from others. However, a number of RTT-specific databases, akin to patient registries, were not included in our evaluation as they require membership of the consortium and an agreement to input data to the database, or they grant permission on a case-by-case basis when the request to access data is part of a specific research project with prior approval from a medical ethical board. In some instances, a minimal level of data is accessible to qualified researchers through already existing data-sharing rules. These include the database associated with the longitudinal, population-based Australian Rett Syndrome Study (AussieRett) (https://rett.telethonkids.org.au/about/aussierett/, (Downs & Leonard, 2013)), the International Rett Syndrome Database (InterRett) (https://rett.telethonkids.org.au/about/interrett/, https://interrett.ichr.uwa.edu.au//output/index.php, (Louise et al., 2009)), the Rett Database Network (https://www.rettdatabasenetwork.org, (Grillo et al., 2012)), and the database generated by the US Rett Syndrome Natural History Study (NHS) (https://www.rettsyndrome.org/research/clinical-trials/natural-history-study) (Neul et al., 2014). These databases generally contain cross-sectional and longitudinal natural history data that has been directly acquired from or input by individuals and their families, either by families completing a questionnaire or through direct examination of the individual by a clinician experienced in RTT. Such methods of data collection differ from the genotype–phenotype databases of interest in this article. 2.2 Assessment of database properties and functions 2.2.1 Aspects of FAIR The FAIR metrics are not yet fully developed (Schultes et al., in preparation) but as several of these aspects are interesting for the purposes of our evaluation we checked whether each database meets the basic FAIR principles described by Wilkinson et al. (2016). These principles define that data is: (i) *findable* if data or meta data are assigned unique identifiers, described with rich metadata, and registered or indexed in a searchable resource; (ii) *accessible* if the data are retrievable by their identifiers via a standardized communication protocol, the protocol itself is open, free, universally implementable and allows authentication and authorization, whilst, to prevent data being lost, metadata continues to be accessible even when the data is no longer available; (iii) *interoperable* if a suitable language for knowledge presentation and an established vocabulary (e.g., ontologies) are used, and, ideally, the (meta)data include references to other data; and (iv) *reusable* if a clear and accessible data usage license is available, the data are correctly and sufficiently described using domain-relevant community standards, and data origin and history are included. 2.2.2 Upload and download functions To investigate user functionality, we looked especially at the upload and download functions of each database. The upload functions were typically found in separate "submit" pages or information was given on how or to whom the data should be sent. For download functionality we checked whether we could manually download search results, for example, a list of MECP2 variants, and which formats were possible for this. Additionally, we looked for the API description (if available). 2.2.3 Form of genetic and phenotypic data Each database was investigated for the form in which genetic variation (e.g., HGVS or rs) and phenotype information (e.g., diagnosis, predicted pathogenicity scores, HPO terms etc.) is stored. 2.3 Assessment of RTT/MECP2 specific content 2.3.1 Total numbers of MECP2 variants in the database The total number of entries for (unique) MECP2 variants, or variants which are associated with RTT, was assessed in each database (status March 2018). 2.3.2 Availability of five selected test variants To examine the coverage of MECP2 variants in more detail, five MECP2 mutations were selected and used to perform test searches within each database (Table 1). We decided upon three "classical" variants: first, a well-known and well-described mutation—an MBD hotspot mutation—published by Zappella, Meloni, Longo, Hayek, & Renieri (2001) and reviewed by Lyst & Bird (2015); and, second and third respectively, two of the most frequently reported nonsense and missense mutations. Finally, two mutations that were discovered more recently by WES and WGS: a 23 bp deletion in the C-terminus of MECP2, reported by Rauch et al. (2012) after performing WES in a girl displaying a RTT-phenotype; and, an intra-exonic deletion, taken from Gilissen et al. (2014), after WGS in a person described as having severe intellectual disability (IQ < 50), a commonly reported clinical phenotype of RTT (Zoghbi, 2016). The appearance of each of these five mutations in the selected databases was investigated. Table 1. MECP2 mutations selected for test database searches Variant 1 Variant 2 Variant 3 Variant 4 Variant 5 Source MBD hotspot mutation from (Zappella et al., 2001) Frequently reported nonsense mutation Frequently reported missense mutation WES variant from (Rauch et al., 2012) WGS variant from (Gilissen et al., 2014) Genomic level (GRCh37)a g.153296882G>A g.153296777G>A g.153296363G>A g.153296093_153296115del g.153295929_153296514del RNA level (NM_004992.3) c.397C>T c.502C>T c.916C>T c.1200_1222del c.765_1350del Protein level p.(Arg133Cys) p.(Arg168*) p.(Arg306Cys) p.(Pro401Argfs*8) p.(Lys256Asnfs*31) a The current genome build at the time of writing this article is GRCh38, but most databases were using GRCh37. For MECP2, there is a difference ranging from735 to 659 kbp. 3 RESULTS We identified nine standalone databases and four meta/integrated databases for evaluation (Table 2) and collected information by exploring their content. We checked for general database features and RTT-specific entries. In detail, we analyzed (a) the FAIR status, (b) the upload and download possibilities, (c) form of phenotype and genotype information, (d) the total number of entries relating to the MECP2 gene or RTT, and (e) the coverage for the chosen MECP2 mutations. Table 2. Overview of databases included in the review Database and link Contact How to cite (literature reference for database) Short description RTT-specific database RettBase Prof. John Christodoulou and Rahul Krishnaraj Christodoulou et al. (2003) Specific focus on RTT. Database of genetic information about RTT patients. Contains mutation information about MECP2 as well as CDKL5 and FOXG1 which cause different syndromes (formerly named Rett-like syndromes). Databases for genetic variations and phenotype information for diseases in general KMD KMD Rett Syndrome (Korean Mutation Database) Contact via KCDC (Korea Centre for Disease Control and Prevention) – Genotype-disease database. Collection of disease-causing variants in genes. ClinVar ClinVar (MECP2) Mail Landrum et al. (2016) Genotype–phenotype database. Focus on disease-causing variants in genes. HGMD "professional" Contact (via public Website) Stenson et al. (2017) Commercial genotype–phenotype database Databases for all kinds of genetic variations and phenotype information LOVD LOVD3.0 MECP2 (Leiden Open Variation Database) MECP2 curator: Henk van Kranen Fokkema et al. (2011) Genetic variants database. Locus/gene specific, all genes. DECIPHER (DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources) Mail Firth et al. (2009) Genotype–phenotype database. All genes. EVS EVS (MECP2) (Exome Variant Server) Mail – Genetic variants database. Originally those which contribute to heart, lung and blood disorders. Now open to all genes, linked to dbSNP and dbGAP. ExAC Browser (Exome Aggregation Consortium) Github Mail Lek et al. (2016) Database/project to collect and harmonize whole exome sequencing data. Allows search for variants at certain locations or single genes, and direct search for variants. dbSNP (NCBI Short Genetic Variations database) dbSNP (MECP2) Mail Kitts et al. (2013) Genetic variation database. Collection of single nucleotide polymorphism (SNP) and an effect predictor score. Integrated/meta-databases and genome browsers dbVAR dbVAR (MECP2) Mail Lappalainen et al. (2013), Phan et al. (2016) Database for genomic structural variations, including indels, mobile element insertions, duplications, inversions, translocations, and complex chromosomal arrangements. EVA (European Variation Archive) Mail – Variant browser. Allows search for variants of specific locations or genes. Cafe Variome Mail Lancaster et al. (2015) Meta-database for genetic variants, genotype-phenotype databases. Links to 1000 Genomes Project, dbSNP, Diagnostic Variants, Diagnostic Mutation Database, The Frequency of Inherited Disorders Database, Finnish Disease, FORGE Canada Consortium, PhenCode, UniProt, Human Gene Mutation Database, Locus-specific Databases. Freely available, but some of the linked databases content is only available after registration. DisGeNET Mail Pinero et al. (2015, 2017) Database for gene-disease and variant-disease associations. Imports data from curated databases like Uniprot, ClinVar, GWAS Catalog, and so on. 3.1 Database properties 3.1.1 Aspects of FAIR In general, the genetic variation or location databases were easier to find than the RTT-specific ones. Using Google as the search engine for "Rett syndrome database" only RettBase (Christodoulou, Grimm, Maher, & Bennetts, 2003; Krishnaraj, Ho, & Christodoulou, 2017) or excluded databases such as InterRett and the Rett Syndrome Database Network (both of which do not allow direct online access to genotype–phenotype information) were immediately findable—and several publications about RTT databases (e.g., about the Italian Rett database and biobank (Sampieri et al., 2007)). Using more generic terms like "genotype phenotype database" dbGAP (which is an archive for genotype–phenotype studies), DECIPHER and DisGeNET were found. A more specific search result was yielded using meta-databases for biomedical databases. Seven of the databases were findable on FairSharing.org using the tags "rare disease", "genetic variation", or "phenotype". Others were mentioned in previous publications (Lelieveld et al., 2016) or found through personal recommendation within the scientific community. Considering findability of variants within the database, most offered the possibility to search for variants using at least one of the nomenclatures recommended by the guidelines of the HGVS for genome, RNA or protein changes, or by rs identifiers. The Korean Mutation Database provided no option to search for specific variants, only searches by disease (or disease identifier) were possible. In most cases the databases investigated were publicly accessible; several, however, restricted access to members only (e.g., parts of Café Variome) or were commercial databases with pay to view content (HGMD) (Table 3). Table 3. Description of database structure and information types Database ↑ Up- and↓ Download of dataAPI (if available) Phenotype information available Genotype information available RettBase ↑ Submission of data by mail possible ↓No download function, Web interface No API or similar Information on whether RTT or not, distinguishes between classical, atypical, preserved speech, and forme fruste RTT, mental retardation (not Rett), Autism According to HGVS change on the mRNA/cDNA level, RefSeq NM_004992 unless stated otherwise KMD ↑ Submission of data by registered users ↓No download function, Web interface No API or similar Diagnosed with RTT using the OMIM identifier (= RTT/RTT preserved speech variant) According to HGVS change on the mRNA/cDNA level and RefSeq ClinVara ↑ Possible, detailed submission templates and instructions available ↓ Download/export of search results in form of text files or UI lists possible API available here Information on whether Pathogenic or not, Diagnosis, for example, RTT, Autism, X-linked mental retardation According to HGVS change on the mRNA/cDNA level (mostly) and RefSeq HGMD "professional" ↑ Not possible, HGMD has its own data acquisition resources ↓ Download and export possible (for registered paying users) UMLS (ontology) HPO (ontology) OMIM SNOMED CT MeSH Descriptive: e.g. 11 kb deletion in exon 1–2, HGVS format in the detailed description LOVDa ↑ Upload possible after registration with Submitter clearance ↓ Download of complete database possible, not for specific genes/search results, API available for LOVD 2.0, for LOVD 3.0 under construction Variant effect predictor: "+" indicating the variant affects function, "+?" probably affects function, "-" does not affect
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