Matchmaker Exchange

Nara L. M. Sobreira1, Harindra Arachchi2, Orion J. Buske3, Jessica X. Chong4, Ben Hutton5, Julia Foreman5, François Schiettecatte6, Tudor Groza7, Julius O.B. Jacobsen8, Melissa A. Haendel9, Kym M. Boycott10, Ada Hamosh11, Heidi L. Rehm2, null null12

1 McKusick‐Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, 2 The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, 3 Centre for Computational Medicine, Hospital for Sick Children, Toronto, Ontario, 4 Department of Pediatrics, University of Washington, Seattle, 5 Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, 6 FS Consulting LLC, Seattle, Washington, 7 St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Darlinghurst, New South Wales, 8 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, 9 Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, 10 Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, Ontario, 11 McKusick‐Nathans Institute of Genetic Medicine (IGM), Clinical Director, IGM. Scientific Director, OMIM. Johns Hopkins University. Baltimore, Maryland, 12 null
Publication Name:  Current Protocols in Human Genetics
Unit Number:  Unit 9.31
DOI:  10.1002/cphg.50
Online Posting Date:  October, 2017
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Abstract

In well over half of the individuals with rare disease who undergo clinical or research next‐generation sequencing, the responsible gene cannot be determined. Some reasons for this relatively low yield include unappreciated phenotypic heterogeneity; locus heterogeneity; somatic and germline mosaicism; variants of uncertain functional significance; technically inaccessible areas of the genome; incorrect mode of inheritance investigated; and inadequate communication between clinicians and basic scientists with knowledge of particular genes, proteins, or biological systems. To facilitate such communication and improve the search for patients or model organisms with similar phenotypes and variants in specific candidate genes, we have developed the Matchmaker Exchange (MME). MME was created to establish a federated network connecting databases of genomic and phenotypic data using a common application programming interface (API). To date, seven databases can exchange data using the API (GeneMatcher, PhenomeCentral, DECIPHER, MyGene2, matchbox, Australian Genomics Health Alliance Patient Archive, and Monarch Initiative; the latter included for model organism matching). This article guides usage of the MME for rare disease gene discovery. © 2017 by John Wiley & Sons, Inc.

Keywords: Australian Genomics Health Alliance Patient Archive; candidate genes; DECIPHER; GeneMatcher; PhenomeCentral; matchbox; matchmaker exchange; monarch initiative; MyGene2

     
 
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Table of Contents

  • Introduction
  • Basic Protocol 1: Using an Existing Matchmaker Database to Search for a Match
  • Basic Protocol 2: Connecting a Database to Matchmaker Exchange
  • Commentary
  • Literature Cited
  • Figures
     
 
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Materials

Basic Protocol 1: Using an Existing Matchmaker Database to Search for a Match

  Necessary Resources
  • The minimal resources required for matchmaking are an Internet‐connected device and a gene candidate symbol, a set of Human Phenotype Ontology (HPO) terms to describe the patient's phenotype, or both

Basic Protocol 2: Connecting a Database to Matchmaker Exchange

  Necessary Resources
  • The only resource required is an Internet‐connected computer
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Figures

Videos

Literature Cited

  Boycott, K. M., Rath, A., Chong, J. X., Hartley, T., Alkuraya, F. S., Baynam, G., … Lochmüller, H. (2017). International cooperation to enable the diagnosis of all rare genetic diseases. American Journal of Human Genetics, 100(5)695–705. doi: 10.1016/j.ajhg.2017.04.003.
  Buske, O. J., Girdea, M., Dumitriu, S., Gallinger, B., Hartley, T., Trang, H., … Brudno, M. (2015). PhenomeCentral: A portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases. Human Mutation, 36(10), 931–940. doi: 10.1002/humu.22851.
  Buske, O. J., Schiettecatte, F., Hutton, B., Dumitriu, S., Misyura, A., Huang, … Brudno, M. (2015). The Matchmaker Exchange API: Automating patient matching through the exchange of structured phenotypic and genotypic profiles. Human Mutation, 36(10), 922–927.
  Chong, J. X., Buckingham, K. J., Jhangiani, S. N., Boehm, C., Sobreira, N., Smith, J. D., … Bamshad, M. J. (2015). The genetic basis of Mendelian phenotypes: Discoveries, challenges, and opportunities. American Journal of Human Genetics, 97, 199–215. doi: 10.1016/j.ajhg.2015.06.009.
  Dyke, S. O. M., Knoppers, B. M., Hamosh, A., Firth, H. V., Hurles, M., Brudno, M., … Rehm, H. L. (2017). “Matching” consent to purpose: The example of the Matchmaker Exchange. Human Mutation, 38, 1281–1285. doi: https://doi.org/10.1002/humu.23278.
  Gonzalez, M. A., Lebrigio, R. F. A., Van Booven, D., Ulloa, R. H., Powell, E., Speziani, F., … Zuchner, S. (2013). GEnomes Management Application (GEM.app): A new software tool for large‐scale collaborative genome analysis. Human Mutation, 34(6), 842–846. doi: 10.1002/humu.22305.
  Gonzalez, M. A., Van Booven, D., Hulme, W., Ulloa, R. H., Lebrigio, R. F., Osterloh, J., … Zuchner, S. (2012). Whole genome sequencing and a new bioinformatics platform allow for rapid gene identification in D. melanogaster EMS screens. Biology, 1(3), 766–777. doi: 10.3390/biology1030766.
  Lancaster, O., Beck, T., Atlan, D., Swertz, M., Dagleish, R., & Brookes, A. J. (2015). Cafe Variome: General‐purpose software for making genotype‐phenotype data discoverable in restricted or open access contexts. Human Mutation, 36(10), 957–964. doi: 10.1002/humu.22841.
  Philippakis, A. A., Azzariti, D. R., Beltran, S., Brookes, A. J., Brownstein, C. A., Brudno, M., … Rehm, H. L. (2015). The Matchmaker Exchange: A platform for rare disease gene discovery. Human Mutation, 36(10), 915–921. doi: 10.1002/humu.22858.
  Retterer, K., Juusola, J., Cho, M. T., Vitazka, P., Millan, F., Gibellini, F., Chung, W. K. & Bale, S. (2015). Clinical application of whole‐exome sequencing across clinical indications. Genetics in Medicine, 18, 696–704. doi: 10.1038/gim.2015.148.
  Robinson, P. N., Köhler, S., Oellrich, A., Sanger Mouse Genetics Project, Wang, K., Mungall, C. J., … Smedley, D. (2014). Improved exome prioritization of disease genes through cross‐species phenotype comparison. Genome Research, 24(2), 340–348. doi: 10.1101/gr.160325.113.
  Sobreira, N., Schiettecatte, F., Boehm, C., Valle, D., & Hamosh, A. (2015a). New tools for Mendelian disease gene identification: PhenoDB variant analysis module; and GeneMatcher, a web‐based tool for linking investigators with an interest in the same gene. Human Mutation, 36(4), 425–431. doi: 10.1002/humu.22769.
  Swaminathan, G. J., Bragin, E., Chatzimichali, E. A., Corpas, M., Bevan, A. P., Wright, C. F., … Firth, H. V. (2012). DECIPHER: Web‐based, community resource for clinical interpretation of rare variants in developmental disorders. Human Molecular Genetics, 21(R1), R37–R44. doi: 10.1093/hmg/dds362.
  Washington, N. L., Haendel, M. A., Mungall, C. J., Ashburner, M., Westerfield, M., & Lewis, S. E. (2009). Linking human diseases to animal models using ontology‐based phenotype annotation. Plos Biology, 7(11), e1000247. doi: 10.1371/journal.pbio.1000247.
  Yang, Y., Muzny, D. M., Xia, F., Niu, Z., Person, R., Ding, Y., … Eng, C. M. (2014). Molecular findings among patients referred for clinical whole‐exome sequencing. The Journal of the American Medical Association, 312, 1870–1879. doi: 10.1001/jama.2014.14601.
  Zemojtel, T., Köhler, S., Mackenroth, L., Jäger, M., Hecht, J., Krawitz, P., … Robinson, P. N. (2014). Effective diagnosis of genetic disease by computational phenotype analysis of the disease‐associated genome. Science Translational Medicine, 6(252), 252ra123. doi: 10.1126/scitranslmed.3009262.
Internet Resources
  http://www.matchmakerexchange.org/
  Matchmaker Exchange.
  http://genematcher.org/
  GeneMatcher.
  http://phenomecentral.org/
  PhenomeCentral.
  http://decipher.sanger.ac.uk/
  DECIPHER.
  http://www.mygene2.org/
  MyGene2.
  http://seqr.broadinstitute.org/
  Matchbox.
  http://mme.australiangenomics.org.au/#/home
  Australian Genomics Health Alliance Patient Archive.
  http://monarchinitiative.org/
  Monarch Initiative.
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