LOVD v.2.0: The next generation in gene variant databases

Center of Human and Clinical Genetics, Department of Human Genetics, Leiden University Medical Center, Leiden, Nederland.
Human Mutation (Impact Factor: 5.14). 05/2011; 32(5):557-63. DOI: 10.1002/humu.21438
Source: PubMed


Locus-Specific DataBases (LSDBs) store information on gene sequence variation associated with human phenotypes and are frequently used as a reference by researchers and clinicians. We developed the Leiden Open-source Variation Database (LOVD) as a platform-independent Web-based LSDB-in-a-Box package. LOVD was designed to be easy to set up and maintain and follows the Human Genome Variation Society (HGVS) recommendations. Here we describe LOVD v.2.0, which adds enhanced flexibility and functionality and has the capacity to store sequence variants in multiple genes per patient. To reduce redundancy, patient and sequence variant data are stored in separate tables. Tables are linked to generate connections between sequence variant data for each gene and every patient. The dynamic structure allows database managers to add custom columns. The database structure supports fast queries and allows storage of sequence variants from high-throughput sequence analysis, as demonstrated by the X-chromosomal Mental Retardation LOVD installation. LOVD contains measures to ensure database security from unauthorized access. Currently, the LOVD Website ( lists 71 public LOVD installations hosting 3,294 gene variant databases with 199,000 variants in 84,000 patients. To promote LSDB standardization and thereby database interoperability, we offer free server space and help to establish an LSDB on our Leiden server.
Hum Mutat 32:1–7, 2011.

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    • "Classifications for LOVD are defined as follows: B+^ is pathogenic, B+?^ is probably pathogenic, B?^ is effect unknown, B-^ is no known pathogenicity, and B-?^ is probably no pathogenicity. All classifications are listed in the format BReported/Concluded^, although for all variants in this data set, the Concluded classification was B?^ (Fokkema et al. 2011). A VUS classification in UMD is defined as B3—UV^, referring to Buncertain variant^ (Beroud et al. 2000). "
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    ABSTRACT: Genetic variants of uncertain clinical significance (VUSs) are a common outcome of clinical genetic testing. Locus-specific variant databases (LSDBs) have been established for numerous disease-associated genes as a research tool for the interpretation of genetic sequence variants to facilitate variant interpretation via aggregated data. If LSDBs are to be used for clinical practice, consistent and transparent criteria regarding the deposition and interpretation of variants are vital, as variant classifications are often used to make important and irreversible clinical decisions. In this study, we performed a retrospective analysis of 2017 consecutive BRCA1 and BRCA2 genetic variants identified from 24,650 consecutive patient samples referred to our laboratory to establish an unbiased dataset representative of the types of variants seen in the US patient population, submitted by clinicians and researchers for BRCA1 and BRCA2 testing. We compared the clinical classifications of these variants among five publicly accessible BRCA1 and BRCA2 variant databases: BIC, ClinVar, HGMD (paid version), LOVD, and the UMD databases. Our results show substantial disparity of variant classifications among publicly accessible databases. Furthermore, it appears that discrepant classifications are not the result of a single outlier but widespread disagreement among databases. This study also shows that databases sometimes favor a clinical classification when current best practice guidelines (ACMG/AMP/CAP) would suggest an uncertain classification. Although LSDBs have been well established for research applications, our results suggest several challenges preclude their wider use in clinical practice. Electronic supplementary material The online version of this article (doi:10.1007/s12687-015-0220-x) contains supplementary material, which is available to authorized users.
    Journal of community genetics 03/2015; 6(4). DOI:10.1007/s12687-015-0220-x
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    • "Locus specific databases (see for a comprehensive list) and " whole-genome " mutation databases such as HGMD [33], ClinVar [34], LOVD [35], and OMIM [36] are very informative resources for this task. Finding no previously identified variants indicates a novel variant in the proband analysed. "
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    ABSTRACT: Recent technological advances have created challenges for geneticists and a need to adapt to a wide range of new bioinformatics tools and an expanding wealth of publicly available data (e.g., mutation databases, and software). This wide range of methods and a diversity of file formats used in sequence analysis is a significant issue, with a considerable amount of time spent before anyone can even attempt to analyse the genetic basis of human disorders. Another point to consider that is although many possess " just enough " knowledge to analyse their data, they do not make full use of the tools and databases that are available and also do not fully understand how their data was created. The primary aim of this review is to document some of the key approaches and provide an analysis schema to make the analysis process more efficient and reliable in the context of discovering highly penetrant causal mutations/genes. This review will also compare the methods used to identify highly penetrant variants when data is obtained from consanguineous individuals as opposed to nonconsanguineous; and when Mendelian disorders are analysed as opposed to common-complex disorders.
    BioMed Research International 03/2015; DOI:10.1155/2015/923491 · 3.17 Impact Factor
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    • "The search in disease-specific databases, HGMD (Stenson et al., 2009) and LOVD (Fokkema et al., 2011) (http: //, and dbSNP database or Exome Variant Server (EVS) (URL: "
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    ABSTRACT: The PCDH19 gene encodes protocadherin-19, a transmembrane protein with six cadherin (EC) domains, containing adhesive interfaces likely to be involved in neuronal connection. Over a hundred mostly private mutations have been identified in girls with epilepsy, with or without intellectual disability (ID). Furthermore, transmitting hemizygous males are devoid of seizures or ID, making it difficult to establish the pathogenic nature of newly identified variants. Here, we describe an integrated approach to evaluate the pathogenicity of four novel PCDH19 mutations. Segregation analysis has been complemented with an in silico analysis of mutation effects at the protein level. Using sequence information, we compared different computational prediction methods. We used homology modeling to build structural models of two PCDH19 EC-domains, and compared wild-type and mutant models to identify differences in residue interactions or biochemical properties of the model surfaces. Our analysis suggests different molecular effects of the novel mutations in exerting their pathogenic role. Two of them interfere with or alter functional residues predicted to mediate ligand or protein binding, one alters the EC-domain folding stability; the frame-shift mutation produces a truncated protein lacking the intracellular domain. Interestingly, the girl carrying the putative loss of function mutation presents the most severe phenotype.
    Annals of Human Genetics 09/2014; 78(6). DOI:10.1111/ahg.12082 · 2.21 Impact Factor
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