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Curr Protoc Hum Genet. 2017 Oct 18;95:9.31.1-9.31.15. doi: 10.1002/cphg.50.

Matchmaker Exchange.

Author information

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, Canada.
4
Department of Pediatrics, University of Washington, Seattle, Washington.
5
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom.
6
FS Consulting LLC, Seattle, Washington.
7
Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, New South Wales, Australia.
8
St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Darlinghurst, New South Wales, Australia.
9
William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.
10
Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon.
11
Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, Ontario, Canada.
12
McKusick-Nathans Institute of Genetic Medicine (IGM), Clinical Director, IGM. Scientific Director, OMIM. Johns Hopkins University. Baltimore, Maryland.

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.

KEYWORDS:

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

PMID:
29044468
PMCID:
PMC6016856
DOI:
10.1002/cphg.50
[Indexed for MEDLINE]
Free PMC Article

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