The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine

By Peter D. Stenson, Matthew E. Mort, Edward V. Ball, Katy Shaw, A. Phillips, David N. Cooper. 2013

1,335 citations recorded by the source corpus.

The Human Gene Mutation Database (HGMD®) is a comprehensive collection of germline mutations in nuclear genes that underlie, or are associated with, human inherited disease. By June 2013, the database contained over 141,000 different lesions detected in over 5,700 different genes, with new mutation entries currently accumulating at a rate exceeding 10,000 per annum. HGMD was originally established in 1996 for the scientific study of mutational mechanisms in human genes. However, it has since acquired a much broader utility as a central unified disease-oriented mutation repository utilized by human molecular geneticists, genome scientists, molecular biologists, clinicians and genetic counsellors as well as by those specializing in biopharmaceuticals, bioinformatics and personalized genomics. The public version of HGMD (http://www.hgmd.org) is freely available to registered users from academic institutions/non-profit organizations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via BIOBASE GmbH.

Subfields: Cancer Research, Genetics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Genetics

Citations in the corpus

Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, background): “Whenever assessing variant pathogenicity, start by looking at known variants in databases including LSDBs, ClinVar and HGMD (especially if you have the commercial license), and others (Table 1).”
The Evaluation of Tools Used to Predict the Impact of Missense Variants Is Hindered by Two Types of Circularity (2015, methodology): “, 2014] because MT2 was trained on private data (a large collection of disease variants from HGMD Professional [Stenson et al., 2014]).”
The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies (2017, neutral): “HGMD was originally established in 1996 for the scientific study of mutational mechanisms in human genes believed to cause inherited disease (Cooper et al. 2010; Stenson et al. 2014).”
The human gene damage index as a gene-level approach to prioritizing exome variants (2015, methodology): “We used the sets of all known Mendelian PID genes (for which information about AD and AR inheritance was available) to estimate gene-level GDI upper cutoff values, whereas we used HGMD (41) to extract all known human disease-causing genes.”
High-throughput discovery of novel developmental phenotypes (2016, positive): “We established orthology between genes in the mouse and human, and used the Human Genome Mutation Database (HGMD) to annotate human disease associations 31,32 .”
Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST‐Indel) (2015, methodology): “20 base-pairs in length, annotated as being pathogenic from publications in the biomedical literature, was downloaded from Human Gene Mutation Database [Stenson et al., 2014 ] ( 2014v. 3 ).”
Variable population prevalence estimates of germline TP53 variants: a gnomAD-based analysis (2018, methodology): “2 database, which compiles research-based evidence on genetic variants (Stenson et al., 2014).”
Quantifying penetrance in a dominant disease gene using large population control cohorts (2016, neutral): “More than 100,000 genetic variants have been reported to cause Mendelian disease in humans (56, 57).”
InterVar: Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines. (2017, background): “The HGMD is a comprehensive collection of germline mutations in nuclear genes that underlie, or are associated with, human inherited disease and is compiled primarily from the published literature.(19) ClinVar(20) archives the clinical significance of variants reported directly from submitters.”
dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Non-synonymous and Splice Site SNVs (2016, methodology): “0. dbNSFP has gained popularity among human geneticists and has been adopted by mainstream annotation tools/resources, including the UCSC Genome Browser’s Variant Annotation Integrator (http://genome.ucsc.edu/cgi-bin/hgVai), Ensembl’s Variant Effect Predictor [McLaren et al., 2010], ANNOVAR [Wang et”
Deep learning in bioinformatics (2016, background): “It shows that in a human gene mutation database (HGMD), single nucleotide variants affect an SP1 transcription binding site in LDL-R promoter, causing familial hypercholesterolemia [139, 140].”