Matchmaker Exchange.

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.

• Authors: Nara L M Sobreira, Harindra Arachchi, Orion J Buske, Jessica X Chong, Ben Hutton, Julia Foreman, François Schiettecatte, Tudor Groza, Julius O B Jacobsen, Melissa A Haendel, Kym M Boycott, Ada Hamosh, Heidi L Rehm, Matchmaker Exchange Consortium
• Journal: Current protocols in human genetics (Curr Protoc Hum Genet) Vol. 95 Pg. 9.31.1-9.31.15 (10 18 2017) ISSN: 1934-8258 [Electronic] United States
• PMID: 29044468 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2017 John Wiley and Sons, Inc.

Topics

• Animals
• Computational Biology (methods)
• Databases, Genetic
• Genetic Association Studies (methods)
• Genetic Predisposition to Disease
• Genomics (methods)
• Humans
• Rare Diseases (genetics)
• Software
• Web Browser