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      The Human Gene Mutation Database (HGMD ®): optimizing its use in a clinical diagnostic or research setting

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          Abstract

          The Human Gene Mutation Database (HGMD ®) constitutes a comprehensive collection of published germline mutations in nuclear genes that are thought to underlie, or are closely associated with human inherited disease. At the time of writing (June 2020), the database contains in excess of 289,000 different gene lesions identified in over 11,100 genes manually curated from 72,987 articles published in over 3100 peer-reviewed journals. There are primarily two main groups of users who utilise HGMD on a regular basis; research scientists and clinical diagnosticians. This review aims to highlight how to make the most out of HGMD data in each setting.

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          dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Nonsynonymous and Splice-Site SNVs.

          Xiaoming Liu, Chunlei Wu, Chang Li … (2016)
          The purpose of the dbNSFP is to provide a one-stop resource for functional predictions and annotations for human nonsynonymous single-nucleotide variants (nsSNVs) and splice-site variants (ssSNVs), and to facilitate the steps of filtering and prioritizing SNVs from a large list of SNVs discovered in an exome-sequencing study. A list of all potential nsSNVs and ssSNVs based on the human reference sequence were created and functional predictions and annotations were curated and compiled for each SNV. Here, we report a recent major update of the database to version 3.0. The SNV list has been rebuilt based on GENCODE 22 and currently the database includes 82,832,027 nsSNVs and ssSNVs. An attached database dbscSNV, which compiled all potential human SNVs within splicing consensus regions and their deleteriousness predictions, add another 15,030,459 potentially functional SNVs. Eleven prediction scores (MetaSVM, MetaLR, CADD, VEST3, PROVEAN, 4× fitCons, fathmm-MKL, and DANN) and allele frequencies from the UK10K cohorts and the Exome Aggregation Consortium (ExAC), among others, have been added. The original seven prediction scores in v2.0 (SIFT, 2× Polyphen2, LRT, MutationTaster, MutationAssessor, and FATHMM) as well as many SNV and gene functional annotations have been updated. dbNSFP v3.0 is freely available at http://sites.google.com/site/jpopgen/dbNSFP.
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            De novo mutations in human genetic disease.

            Joris A. Veltman, Han Brunner (2012)
            New mutations have long been known to cause genetic disease, but their true contribution to the disease burden can only now be determined using family-based whole-genome or whole-exome sequencing approaches. In this Review we discuss recent findings suggesting that de novo mutations play a prominent part in rare and common forms of neurodevelopmental diseases, including intellectual disability, autism and schizophrenia. De novo mutations provide a mechanism by which early-onset reproductively lethal diseases remain frequent in the population. These mutations, although individually rare, may capture a significant part of the heritability for complex genetic diseases that is not detectable by genome-wide association studies.
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              Automated inference of molecular mechanisms of disease from amino acid substitutions.

              Biao Li, Vidhya G Krishnan, Matthew Mort … (2009)
              Advances in high-throughput genotyping and next generation sequencing have generated a vast amount of human genetic variation data. Single nucleotide substitutions within protein coding regions are of particular importance owing to their potential to give rise to amino acid substitutions that affect protein structure and function which may ultimately lead to a disease state. Over the last decade, a number of computational methods have been developed to predict whether such amino acid substitutions result in an altered phenotype. Although these methods are useful in practice, and accurate for their intended purpose, they are not well suited for providing probabilistic estimates of the underlying disease mechanism. We have developed a new computational model, MutPred, that is based upon protein sequence, and which models changes of structural features and functional sites between wild-type and mutant sequences. These changes, expressed as probabilities of gain or loss of structure and function, can provide insight into the specific molecular mechanism responsible for the disease state. MutPred also builds on the established SIFT method but offers improved classification accuracy with respect to human disease mutations. Given conservative thresholds on the predicted disruption of molecular function, we propose that MutPred can generate accurate and reliable hypotheses on the molecular basis of disease for approximately 11% of known inherited disease-causing mutations. We also note that the proportion of changes of functionally relevant residues in the sets of cancer-associated somatic mutations is higher than for the inherited lesions in the Human Gene Mutation Database which are instead predicted to be characterized by disruptions of protein structure. http://mutdb.org/mutpred predrag@indiana.edu; smooney@buckinstitute.org.
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                Author and article information

                Contributors
                Peter D. Stenson: stensonPD@cardiff.ac.uk
                Journal
                Journal ID (nlm-ta): Hum Genet
                Journal ID (iso-abbrev): Hum. Genet
                Title: Human Genetics
                Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )
                ISSN (Print): 0340-6717
                ISSN (Electronic): 1432-1203
                Publication date (Electronic): 28 June 2020
                Publication date PMC-release: 28 June 2020
                Publication date (Print): 2020
                Volume: 139
                Issue: 10
                Pages: 1197-1207
                Affiliations
                [1 ]GRID grid.5600.3, ISNI 0000 0001 0807 5670, Institute of Medical Genetics, School of Medicine, , Cardiff University, ; Heath Park, Cardiff, CF14 4XN UK
                [2 ]GRID grid.5808.5, ISNI 0000 0001 1503 7226, i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, ; 4200-135 Porto, Portugal
                Article
                Publisher ID: 2199
                DOI: 10.1007/s00439-020-02199-3
                PMC ID: 7497289
                PubMed ID: 32596782
                SO-VID: 01c4efdf-bc15-44f3-8974-a4256a9a7fab
                Copyright © © The Author(s) 2020
                License:

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 18 May 2020
                Date accepted : 19 June 2020
                Categories
                Subject: Review
                Custom metadata
                issue-copyright-statement © Springer-Verlag GmbH Germany, part of Springer Nature 2020

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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