Locus-specific Mutation Databases Research Articles

Genetic analysis of bleeding disorders.

Molecular genetic analysis of inherited bleeding disorders has been practised for over 30years. Technological changes have enabled advances, from analyses using extragenic linked markers to next-generation DNA sequencing and microarray analysis. Two approaches for genetic analysis are described, each suiting their environment. The Christian Medical Centre in Vellore, India, uses conformation-sensitive gel electrophoresis mutation screening of multiplexed PCR products to identify candidate mutations, followed by Sanger sequencing confirmation of variants identified. Specific analyses for F8 intron 1 and 22 inversions are also undertaken. The MyLifeOurFuture US project between the American Thrombosis and Hemostasis Network, the National Hemophilia Foundation, Bloodworks Northwest and Biogen uses molecular inversion probes (MIP) to capture target exons, splice sites plus 5' and 3' sequences and to detect F8 intron 1 and 22 inversions. This allows screening for all F8 and F9 variants in one sequencing run of multiple samples (196 or 392). Sequence variants identified are subsequently confirmed by a diagnostic laboratory. After having identified variants in genes of interest through these processes, a systematic procedure determining their likely pathogenicity should be applied. Several scientific societies have prepared guidelines. Systematic analysis of the available evidence facilitates reproducible scoring of likely pathogenicity. Documentation of frequency in population databases of variant prevalence and in locus-specific mutation databases can provide initial information on likely pathogenicity. Whereas null mutations are often pathogenic, missense and splice site variants often require in silico analyses to predict likely pathogenicity and using an accepted suite of tools can help standardize their documentation.

Toward a mtDNA locus-specific mutation database using the LOVD platform.

The Human Variome Project (HVP) is a global effort to collect and curate all human genetic variation affecting health. Mutations of mitochondrial DNA (mtDNA) are an important cause of neurogenetic disease in humans; however, identification of the pathogenic mutations responsible can be problematic. In this article, we provide explanations as to why and suggest how such difficulties might be overcome. We put forward a case in support of a new Locus Specific Mutation Database (LSDB) implemented using the Leiden Open-source Variation Database (LOVD) system that will not only list primary mutations, but also present the evidence supporting their role in disease. Critically, we feel that this new database should have the capacity to store information on the observed phenotypes alongside the genetic variation, thereby facilitating our understanding of the complex and variable presentation of mtDNA disease. LOVD supports fast queries of both seen and hidden data and allows storage of sequence variants from high-throughput sequence analysis. The LOVD platform will allow construction of a secure mtDNA database; one that can fully utilize currently available data, as well as that being generated by high-throughput sequencing, to link genotype with phenotype enhancing our understanding of mitochondrial disease, with a view to providing better prognostic information.

Phenotype-optimized sequence ensembles substantially improve prediction of disease-causing mutation in cystic fibrosis

Cystic fibrosis transmembrane conductance regulator (CFTR) mutation is associated with a phenotypic spectrum that includes cystic fibrosis (CF). The disease liability of some common CFTR mutations is known, but rare mutations are seen in too few patients to categorize unequivocally, making genetic diagnosis difficult. Computational methods can predict the impact of mutation, but prediction specificity is often below that required for clinical utility. Here, we present a novel supervised learning approach for predicting CF from CFTR missense mutation. The algorithm begins by constructing custom multiple sequence alignments called phenotype-optimized sequence ensembles (POSEs). POSEs are constructed iteratively, by selecting sequences that optimize predictive performance on a training set of CFTR mutations of known clinical significance. Next, we predict CF disease liability from a different set of CFTR mutations (test-set mutations). This approach achieves improved prediction performance relative to popular methods recently assessed using the same test-set mutations. Of clinical significance, our method achieves 94% prediction specificity. Because databases such as HGMD and locus-specific mutation databases are growing rapidly, methods that automatically tailor their predictions for a specific phenotype may be of immediate utility. If the performance achieved here generalizes to other systems, the approach could be an excellent tool to help establish genetic diagnoses.

The androgen receptor gene mutations database: 2012 update

The current version of the androgen receptor gene (AR) mutations database is described. A major change to the database is that the nomenclature and numbering scheme now conforms to all Human Genome Variation Society norms. The total number of reported mutations has risen from 605 to 1,029 since 2004. The database now contains a number of mutations that are associated with prostate cancer (CaP) treatment regimens, while the number of AR mutations found in CaP tissues has more than doubled from 76 to 159. In addition, in a number of androgen insensitivity syndrome (AIS) and CaP cases, multiple mutations have been found within the same tissue samples. For the first time, we report on a disconnect within the AIS phenotype-genotype relationship among our own patient database, in that over 40% of our patients with a classic complete AIS or partial AIS phenotypes did not appear to have a mutation in their AR gene. The implications of this phenomenon on future locus-specific mutation database (LSDB) development are discussed, together with the concept that mutations can be associated with both loss- and gain-of-function, and the effect of multiple AR mutations within individuals. The database is available on the internet (http://androgendb.mcgill.ca), and a web-based LSDB with the variants using the Leiden Open Variation Database platform is available at http://www.lovd.nl/AR.

Integration of molecular and clinical data of 40 unrelated von Willebrand Disease families in a Spanish locus-specific mutation database: first release including 58 mutations

The VWF gene ( VWF ) extends along 178 kb in the genome and comprises a total of 52 exons, whose sizes range from 40 to 1400 bp.[1][1] Systematic identification of the responsible mutations has been hampered by the large size and complex genomic organization of VWF , which is highly polymorphic and

CEP290, a gene with many faces: mutation overview and presentation of CEP290base

Ciliopathies are an emerging group of disorders, caused by mutations in ciliary genes. One of the most intriguing disease genes associated with ciliopathies is CEP290, in which mutations cause a wide variety of distinct phenotypes, ranging from isolated blindness over Senior-Loken syndrome (SLS), nephronophthisis (NPHP), Joubert syndrome (related disorders) (JS[RD]), Bardet-Biedl syndrome (BBS), to the lethal Meckel-Grüber syndrome (MKS). Despite the identification of over 100 unique CEP290 mutations, no clear genotype-phenotype correlations could yet be established, and consequently the predictive power of a CEP290-related genotype remains limited. One of the challenges is a better understanding of second-site modifiers. In this respect, there is a growing interest in the potential modifying effects of variations in genes encoding other members of the ciliary proteome that interact with CEP290. Here, we provide an overview of all CEP290 mutations identified so far, with their associated phenotypes. To this end, we developed CEP290base, a locus-specific mutation database that links mutations with patients and their phenotypes (medgen.ugent.be/cep290base).

161st ENMC International Workshop on nemaline myopathy and related disorders, Newcastle upon Tyne, 2008

Twenty doctors and scientists from 7 countries gathered in Newcastle upon Tyne on the 28th and 29th September 2008, for the seventh ENMC Workshop on nemaline myopathy (NM) and related disorders. A representative of the patient group ‘‘A Foundation Building Strength” gave a presentation at the start of theWorkshop. The main themes of the Workshop were continued gene discovery; better definition of clinical entities; animal and tissue culture models of NM; preliminary experimental investigation in model systems of possible therapeutic strategies; locus-specific mutation databases; establishing databases in preparation for anticipated future clinical trials and finally discussion on better methods for molecular analysis of the known NM and related disorders disease genes, especially nebulin. Expected major efforts in the near future include further analysis of possible therapies, development of additional animal models in which to investigate therapies and the implementation of the databases in order to be ready for any future clinical trials. The Workshop participants recognise that a multinational approach to these rare disorders is essential, especially in relation to enrolling patients in any future clinical trials.

Human Variome Project: progress and plans.

Background The Human Variome Project (HVP) [101] was officially launched in Melbourne, Australia in June 2006 [1] and was devised to collect and curate all genetic variation, its phenotype and associated disease(s). The project’s data are to be made accessible to researchers, diagnosticians, clinicians and all those involved in healthcare. Lack of complete, correctly curated information can lead to misdiagnosis and waste of valuable healthcare funds. This project was suggested as an activity that would focus the complete collection and curation of mutations and their phenotypes in an attempt to coordinate current activities. Work in this field had begun more than a decade ago with the Human Genome Variation Society (HGVS) promoting the collection and display of variants, and whose members have already produced recommendations and software. Their efforts can be seen at the HGVS website [102] and a list of 683 geneor locus-specific databases, most encouraged by the HGVS, can be found at the HGVS Locus Specific Mutation Databases [103]. By naming the project, it was hoped the HVP would attract major funding bodies and that they would get involved at the international level and, in general, improve the funding prospects of this and related projects. At the launch of the project, 96 recommendations were established [2] and will be implemented in the future. As the HVP aims to collect, at least initially, all human variation causing human disease and its specific effects in all 20,000 genes from all countries, thousands or even more people will need to be involved in a coordinated manner with specially developed tools and protocols. This project is needed so that other projects aiming to sequence 1000 individuals or companies aiming to sequence individuals for disease prediction can draw useful conclusions from the data.

The Iranian human mutation database.

Introduction ore than 20 years have elapsed since the first single base pair substitution underlying an inherited disease in humans was characterized at the DNA level. Disease-associated gene lesions are currently collected and publicized by the Human Gene Mutation Database (HGMD) in Cardiff, locusspecific mutation databases, and to some extent by the Genome Database (GDB) and Online Mendelian Inheritance in Man (OMIM) as comprehensive and up-to-date resources for information on genetic disorders and mutations causing them. – 4 The mutation spectrum observed for any gene or disorder will often vary not only between population groups but also between distinct ethnic groups within a geographical region. This is an important extra-dimension reflected in the recent emergence of a new type of mutation depository, namely the National Mutation Databases (NMDBs) that serve to enhance awareness among health care professionals, bioscientists, patients, and the general public about the range of common genetic disorders, and can provide essential reference information for the detection of mutations for clinical laboratory settings. Information on inherited disorders in Iran is not extensive. There are only two detailed studies on the incidence of beta-thalassemia 7 and nonsyndromic sensorineural deafness. But a huge amount of sporadic data are available for more than 100 genetic diseases, such as G6PD, alphathalassemia, sporadic breast cancer, infertility, mental retardation, cystic fibrosis, familial Mediterranean fever, etc. By studying the multiethnic Iranian population (Figure 1), researchers have access to a valuable genetic pool as it has been shown that the mutation spectrum in each region of Iran corresponds to the mutation spectrum in neighboring countries (Figure 2). Because of its very rich gene pool, Iran has a valuable source of material for the identification of the genes involved in different conditions. In order to promote the human genome study in a collaborative worldwide manner, the Iranian Human Mutation Gene Bank has been established and through this DNA bank many collaborative projects have been started. Presentation of the latest information of such joint-projects will help scientists to follow the progress of such joint projects. Report

Interpreting missense variants: comparing computational methods in human disease genesCDKN2A,MLH1,MSH2,MECP2, and tyrosinase (TYR)

The human genome contains frequent single-basepair variants that may or may not cause genetic disease. To characterize benign vs. pathogenic missense variants, numerous computational algorithms have been developed based on comparative sequence and/or protein structure analysis. We compared computational methods that use evolutionary conservation alone, amino acid (AA) change alone, and a combination of conservation and AA change in predicting the consequences of 254 missense variants in the CDKN2A (n = 92), MLH1 (n = 28), MSH2 (n = 14), MECP2 (n = 30), and tyrosinase (TYR) (n = 90) genes. Variants were validated as either neutral or deleterious by curated locus-specific mutation databases and published functional data. All methods that use evolutionary sequence analysis have comparable overall prediction accuracy (72.9-82.0%). Mutations at codons where the AA is absolutely conserved over a sufficient evolutionary distance (about one-third of variants) had a 91.6 to 96.8% likelihood of being deleterious. Three algorithms (SIFT, PolyPhen, and A-GVGD) that differentiate one variant from another at a given codon did not significantly improve predictive value over conservation score alone using the BLOSUM62 matrix. However, when all four methods were in agreement (62.7% of variants), predictive value improved to 88.1%. These results confirm a high predictive value for methods that use evolutionary sequence conservation, with or without considering protein structural change, to predict the clinical consequences of missense variants. The methods can be generalized across genes that cause different types of genetic disease. The results support the clinical use of computational methods as one tool to help interpret missense variants in genes associated with human genetic disease.

Improving sequence variant descriptions in mutation databases and literature using the Mutalyzer sequence variation nomenclature checker

Unambiguous and correct sequence variant descriptions are of utmost importance, not in the least since mistakes and uncertainties may lead to undesired errors in clinical diagnosis. We developed the Mutation Analyzer (Mutalyzer) sequence variation nomenclature checker (www.lovd.nl/mutalyzer; last accessed 13 September 2007) for automated analysis and correction of sequence variant descriptions using reference sequences from any organism. Mutalyzer handles most variation types: substitution, deletion, duplication, insertion, indel, and splice-site changes following current recommendations of the Human Genome Variation Society (HGVS). Input is a GenBank accession number or an uploaded reference sequence file in GenBank format with user-modified annotation, an HGNC gene symbol, and the variant (single or in a batch file). Mutalyzer generates variant descriptions at DNA level, the level of all annotated transcripts and the deduced outcome at protein level. To validate Mutalyzer's performance and to investigate the sequence variant description quality in locus-specific mutation databases (LSDBs), more than 11,000 variants in the PAH, BIC BRCA2, and HbVar databases were analyzed, showing that 87%, 25%, and 38%, respectively, were error-free and following the recommendations. Low recognition rates in BIC and HbVar (38% and 51%, respectively) were due to lack of a well-annotated genomic reference sequence (HbVar) or noncompliance to the guidelines (BRCA2). Provided with well-annotated genomic reference sequences, Mutalyzer is very effective for the curation of newly discovered sequence variation descriptions and existing LSDB data. Mutalyzer will be linked to the Leiden Open source Variation Database (LOVD) (www.LOVD.nl; last accessed 13 September 2007) and is the first module of a sequence variant effect prediction package.

ThePAH gene, phenylketonuria, and a paradigm shift

"Inborn errors of metabolism," first recognized 100 years ago by Garrod, were seen as transforming evidence for chemical and biological individuality. Phenylketonuria (PKU), a Mendelian autosomal recessive phenotype, was identified in 1934 by Asbjörn Fölling. It is a disease with impaired postnatal cognitive development resulting from a neurotoxic effect of hyperphenylalaninemia (HPA). Its metabolic phenotype is accountable to multifactorial origins both in nurture, where the normal nutritional experience introduces L-phenylalanine, and in nature, where mutations (>500 alleles) occur in the phenylalanine hydroxylase gene (PAH) on chromosome 12q23.2 encoding the L-phenylalanine hydroxylase enzyme (EC 1.14.16.1). The PAH enzyme converts phenylalanine to tyrosine in the presence of molecular oxygen and catalytic amounts of tetrahydrobiopterin (BH4), its nonprotein cofactor. PKU is among the first of the human genetic diseases to enter, through newborn screening, the domain of public health, and to show a treatment effect. This effect caused a paradigm shift in attitudes about genetic disease. The PKU story contains many messages, including: a framework on which to appreciate the complexity of PKU in which phenotype reflects both locus-specific and genomic components; what the human PAH gene tells us about human population genetics and evolution of modern humans; and how our interest in PKU is served by a locus-specific mutation database (http://www.pahdb.mcgill.ca; last accessed 20 March 2007). The individual Mendelian PKU phenotype has no "simple" or single explanation; every patient has her/his own complex PKU phenotype and will be treated accordingly. Knowledge about PKU reveals genomic components of both disease and health.

FINDbase: a relational database recording frequencies of genetic defects leading to inherited disorders worldwide

Frequency of INherited Disorders database (FINDbase) () is a relational database, derived from the ETHNOS software, recording frequencies of causative mutations leading to inherited disorders worldwide. Database records include the population and ethnic group, the disorder name and the related gene, accompanied by links to any corresponding locus-specific mutation database, to the respective Online Mendelian Inheritance in Man entries and the mutation together with its frequency in that population. The initial information is derived from the published literature, locus-specific databases and genetic disease consortia. FINDbase offers a user-friendly query interface, providing instant access to the list and frequencies of the different mutations. Query outputs can be either in a table or graphical format, accompanied by reference(s) on the data source. Registered users from three different groups, namely administrator, national coordinator and curator, are responsible for database curation and/or data entry/correction online via a password-protected interface. Databaseaccess is free of charge and there are no registration requirements for data querying. FINDbase provides a simple, web-based system for population-based mutation data collection and retrieval and can serve not only as a valuable online tool for molecular genetic testing of inherited disorders but also as a non-profit model for sustainable database funding, in the form of a ‘database-journal’.

Locus-specific mutation databases: pitfalls and good practice based on the p53 experience

Between 50,000 and 60,000 mutations have been described in various genes that are associated with a wide variety of diseases. Reporting, storing and analysing these data is an important challenge as such data provide invaluable information for both clinical medicine and basic science. Locus-specific databases have been developed to exploit this huge volume of data. The p53 mutation database is a paradigm, as it constitutes the largest collection of somatic mutations (22,000). However, there are several biases in this database that can lead to serious erroneous interpretations. We describe several rules for mutation database management that could benefit the entire scientific community.

The Human Gene Mutation Database (HGMD) and Its Exploitation in the Study of Mutational Mechanisms

The Human Gene Mutation Database (HGMD) constitutes a comprehensive core collection of data on germ-line mutations in nuclear genes underlying or associated with human inherited disease (http://www.hgmd.org). Data cataloged include single base-pair substitutions in coding, regulatory, and splicing-relevant regions, microdeletions and microinsertions, indels, and triplet repeat expansions, as well as gross gene deletions, insertions, duplications, and complex rearrangements. Each mutation is entered into HGMD only once, in order to avoid confusion between recurrent and identical-by-descent lesions. By June 2005, the database contained in excess of 53,000 different lesions detected in 2029 different nuclear genes, with new entries currently accumulating at a rate in excess of 5000 per annum. HGMD includes cDNA reference sequences, now provided for more than 90% of the listed genes, splice junction data, disease-associated and functional polymorphisms, and links to data present in publicly available online locus-specific mutation databases.

Toward a Human Variome Project

Human MutationVolume 26, Issue 6 p. 499-499 EditorialFree Access Toward a Human Variome Project Richard G.H. Cotton, Corresponding Author Richard G.H. Cotton [email protected] Genomic Disorders Research Centre, Melbourne, Victoria, AustraliaGenomic Disorders Research Centre, 7th Floor Daly Wing, St. Vincent's Hospital Melbourne, 35 Victoria Pde, Fitzroy VIC 3065, AustraliaSearch for more papers by this authorHaig H. Kazazian Jr., Haig H. Kazazian Jr. Department of Genetics, University of Pennsylvania, Philadelphia, PennsylvaniaSearch for more papers by this author Richard G.H. Cotton, Corresponding Author Richard G.H. Cotton [email protected] Genomic Disorders Research Centre, Melbourne, Victoria, AustraliaGenomic Disorders Research Centre, 7th Floor Daly Wing, St. Vincent's Hospital Melbourne, 35 Victoria Pde, Fitzroy VIC 3065, AustraliaSearch for more papers by this authorHaig H. Kazazian Jr., Haig H. Kazazian Jr. Department of Genetics, University of Pennsylvania, Philadelphia, PennsylvaniaSearch for more papers by this author First published: 24 October 2005 https://doi.org/10.1002/humu.20272Citations: 5AboutPDF 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 No abstract is available for this article. REFERENCES Antonarakis SE, the Nomenclature Working Group. 1998. Recommendations for a nomenclature system for human gene mutations. Hum Mutat 11: 1– 3. Claustres M, Horaitis O, Vanevski M, Cotton RGH. 2002. Time for a unified system of mutation description and reporting: a review of locus specific mutation databases. Genome Res 12: 680– 688. Cotton RGH, Horaitis O. 2000. Quality control in the discovery, reporting, and recording of genomic variation. Hum Mutat 15: 16– 21. Cotton RGH. 2002. The Human Genome Project and genome variation. Intern Med J 32: 285– 288. Czeizel A, Sankaranarayanan K. 1984. The load of genetic and partially genetic disorders in man. Mutat Res 128: 73– 103. den Dunnen JT, Antonarakis SE. 2000. Mutation nomenclature extensions and suggestions to describe complex mutations: a discussion. Hum Mutat 15: 7– 12. Fokkema IFAC, den Dunnen JT, Taschner PEM. 2005. LOVD: easy creation of a locus-specific sequence variation database using an “LSDB-in-a-Box” approach. Hum Mutat 26: 63– 68. Horaitis O, Cotton RGH. 2004. The challenge of documenting mutation across the genome: the Human Genome Variation Society approach. Hum Mutat 23: 447– 452. Scriver CR, Nowacki PM, Lehväslaiho H. 1999. Guidelines and recommendations for content, structure and deployment of mutation databases. Hum Mutat 13: 344– 350. Scriver CR, Nowacki PM, Lehväslaiho H. 2000. Guidelines and recommendations for content, structure, and deployment of mutation databases: II. Journey in progress. Hum Mutat 15: 13– 15. Stenson PD, Ball EV, Mort M, Phillips AD, Shiel JA, Thomas NS, Abeysinghe S, Krawczak M, Cooper DN. 2003. Human Gene Mutation Database (HGMD®): 2003 update. Hum Mutat 21: 577– 581. Citing Literature Volume26, Issue6December 2005Pages 499-499 ReferencesRelatedInformation

Human Mutation Databases

The first part of this unit compares general and locus-specific mutation databases. The second section deals with submitting data. The third part provides guidance for accessing mutation data. The final section offers advice on database construction.

Human Mutation Databases

The first part of this unit compares general and locus-specific mutation databases. The second section deals with submitting data. The third part provides guidance for accessing mutation data. The final section offers advice on database construction.

'A variant of uncertain significance' and the proliferation of human disease gene databases

The rapid accumulation of mutation data has led to the creation of nearly 300 locus-specific mutation databases. These sites may contain a few dozen to almost 20,000 mutations for a given gene. Many of the mutations are uncharacterised and have no known effects on the gene product, the 'variant of uncertain significance'. Here, the statistics of mutation distribution are examined for six different gene databases: BRCA1 and BRCA2, haemoglobin-beta (HBB), HPRT1, CFTR and TP53. The percentage of all possible point mutations for a protein (the mutation space) is calculated for each gene and the question 'How much mutation data is enough?' is raised.

Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders

Online Mendelian Inheritance in Man (OMIM™) is a comprehensive, authoritative and timely knowledgebase of human genes and genetic disorders compiled to support human genetics research and education and the practice of clinical genetics. Started by Dr Victor A. McKusick as the definitive reference Mendelian Inheritance in Man, OMIM (http://www.ncbi.nlm.nih.gov/omim/) is now distributed electronically by the National Center for Biotechnology Information, where it is integrated with the Entrez suite of databases. Derived from the biomedical literature, OMIM is written and edited at Johns Hopkins University with input from scientists and physicians around the world. Each OMIM entry has a full-text summary of a genetically determined phenotype and/or gene and has numerous links to other genetic databases such as DNA and protein sequence, PubMed references, general and locus-specific mutation databases, HUGO nomenclature, MapViewer, GeneTests, patient support groups and many others. OMIM is an easy and straightforward portal to the burgeoning information in human genetics.