Facilitating Collaboration in Rare Genetic Disorders Through Effective Matchmaking in DECIPHER

Chatzimichali, Eleni A.; Brent, Simon; Hutton, Benjamin; Perrett, Daniel; Wright, Caroline F.; Bevan, A. Paul; Hurles, Matthew E.; Firth, Helen V.; Swaminathan, Ganesh

doi:10.1002/humu.22842

Cited by 40 publications

(31 citation statements)

References 34 publications

(38 reference statements)

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“…The MME API is currently implemented at the DECIPHER (Chatzimichali et al., , this issue), GeneMatcher (Sobreira et al., , this issue), and PhenomeCentral (Buske et al., , this issue) portals. We have validated the API through two means.…”

Section: Methods and Resultsmentioning

confidence: 99%

The Matchmaker Exchange API: Automating Patient Matching Through the Exchange of Structured Phenotypic and Genotypic Profiles

et al. 2015

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Despite the increasing prevalence of clinical sequencing, the difficulty of identifying additional affected families is a key obstacle to solving many rare diseases. There may only be a handful of similar patients worldwide, and their data may be stored in diverse clinical and research databases. Computational methods are necessary to enable finding similar patients across the growing number of patient repositories and registries. We present the Matchmaker Exchange Application Programming Interface (MME API), a protocol and data format for exchanging phenotype and genotype profiles to enable matchmaking among patient databases, facilitate the identification of additional cohorts, and increase the rate with which rare diseases can be researched and diagnosed. We designed the API to be straightforward and flexible in order to simplify its adoption on a large number of data types and workflows. We also provide a public test data set, curated from the literature, to facilitate implementation of the API and development of new matching algorithms. The initial version of the API has been successfully implemented by three members of the Matchmaker Exchange and was immediately able to reproduce previously-identified matches and generate several new leads currently being validated. The API is available at https://github.com/ga4gh/mme-apis.

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Section: Methods and Resultsmentioning

confidence: 99%

The Matchmaker Exchange API: Automating Patient Matching Through the Exchange of Structured Phenotypic and Genotypic Profiles

et al. 2015

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“…Matchmaking platforms are being developed by Universities, medical centres, and research institutions that are creating repositories of undiagnosed cases through the collection and comparison of genotypic and phenotypic data based on the HPO or other ontologies [35,36,37].…”

Section: Getting Accurate Diagnosis Through Integrated Genotype and Pmentioning

confidence: 99%

Is There a Right to Get a Right Diagnosis Whenever Possible? Ethical and Social Issues in Dealing with Undiagnosed Rare Diseases

Gainotti¹,

Mascalzoni²,

Facer³

et al. 2018

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The time required to reach a correct diagnosis is one of the most important problems for rare disease (RD) patients.Diagnostic delay can be intolerably long, to the point that it is usually described as a "diagnostic odyssey" and, sometimes, a diagnosis might never occur.The International Rare Disease Research Consortium proposed, as ultimate goal for 2017-2027, to enable all people with a suspected RD to be diagnosed within one year if the disorder is known, and to enter a globally coordinated diagnostic and research pipeline for the unsolved cases.In-depth analysis of the genotype through next generation sequencing, together with a standardized indepth phenotype description and sophisticated high-throughput approaches, have been applied as diagnostic tools to increase the chance of a timely and accurate diagnosis.This approach is very fruitful as, according to the Orphanet database, from 2010 to March 2017 more than 600 new RDs have been described and about 3600 genes linked to RDs have been identified.However, combination of -omics and phenotype data and international sharing of this information raise ethical concerns. Values to be assessed include not only patient autonomy but also family implications, beneficence, non-maleficence, justice, solidarity and reciprocity, which must be respected and promoted and, at the same time, balanced among each other.In this work we suggest that, to maximise patients involvement in the search for a diagnosis and identification of new causative genes, undiagnosed patients should have the possibility to: 1) actively participate in the description of their phenotype; 2) choose the level of visibility of their profile in matchmaking databases; 3) express their preferences regarding return of new findings, in particular which level of Variant of Unknown Significance (VUS) significance should be considered relevant to them.The quality of the relation between individual patients and physicians, and between the patient community and the scientific community is critically important for making the best use of available data and combining efforts in the search for matches with similar cases worldwide that will help to solve unsolved cases.The contribution of patients to collecting and coding data comprehensively is critical for efficient use of data downstream of data collection.

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“…In this context, we sought to provide practical guidance to assist the large data sharing initiatives participating in MME, which span countries with different regulatory requirements, in order to produce a consent policy for MME (Philippakis et al., ). The matchmakers currently connected through the MME API are located in four countries: GeneMatcher (Baylor‐Hopkins, USA)(Sobreira, Schiettecatte, Valle, & Hamosh, ), PhenomeCentral (Hospital for Sick Children, Canada)(Buske et al., ), DECIPHER (Sanger Institute, UK)(Chatzimichali et al., ), MyGene2 (University of Washington, USA)(Chong et al., ), Patient Archive (Australia)(patientarchive.org), and matchbox (Broad Institute, USA). All of the connected nodes handle data from multiple countries and jurisdictions.…”

Section: The Matchmaker Exchangementioning

confidence: 99%

“Matching” consent to purpose: The example of the Matchmaker Exchange

et al. 2017

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The Matchmaker Exchange (MME) connects rare disease clinicians and researchers to facilitate the sharing of data from undiagnosed patients for the purpose of novel gene discovery. Such sharing raises the odds that two or more similar patients with candidate genes in common may be found, thereby allowing their condition to be more readily studied and understood. Consent considerations for data sharing in MME included both the ethical and legal differences between clinical and research settings and the level of privacy risk involved in sharing varying amounts of rare disease patient data to enable patient matches. In this Commentary, we discuss these consent considerations and the resulting MME Consent Policy as they may be relevant to other international data sharing initiatives.

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Facilitating Collaboration in Rare Genetic Disorders Through Effective Matchmaking in DECIPHER

Cited by 40 publications

References 34 publications

The Matchmaker Exchange API: Automating Patient Matching Through the Exchange of Structured Phenotypic and Genotypic Profiles

The Matchmaker Exchange API: Automating Patient Matching Through the Exchange of Structured Phenotypic and Genotypic Profiles

Is There a Right to Get a Right Diagnosis Whenever Possible? Ethical and Social Issues in Dealing with Undiagnosed Rare Diseases

“Matching” consent to purpose: The example of the Matchmaker Exchange

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