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Boycott KM, Azzariti DR, Hamosh A, Rehm HL. Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking. Hum Mutat 2022; 43:659-667. [PMID: 35537081 PMCID: PMC9133175 DOI: 10.1002/humu.24373] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 03/22/2022] [Indexed: 11/09/2022]
Abstract
The Matchmaker Exchange (MME) was launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. It operates as a federated network connecting databases holding relevant data using a common application programming interface, where two or more users are looking for a match for the same gene (two-sided matchmaking). Seven years from its launch, it is clear that the MME is making outstanding contributions to understanding the morbid anatomy of the genome. The number of unique genes present across the MME has steadily increased over time; there are currently >13,520 unique genes (~68% of all protein-coding genes) connected across the MME's eight genomic matchmaking nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The collective data set accessible across the MME currently includes more than 120,000 cases from over 12,000 contributors in 98 countries. The discovery of potential new disease-gene relationships is happening daily and international collaborative teams are moving these advances forward to publication, now numbering well over 500. Expansion of data sharing into routine clinical practice by clinicians, genetic counselors, and clinical laboratories has ensured access to discovery for even more individuals with undiagnosed rare genetic diseases. Tens of thousands of patients and their family members have been directly or indirectly impacted by the discoveries facilitated by two-sided genomic matchmaking. MME supports further connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher). Efforts are now underway to explore additional approaches to matchmaking at the gene or variant level where there is only one querier (one-sided matchmaking). Genomic matchmaking has proven its utility over the past 7 years and will continue to facilitate discoveries in the years to come.
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Affiliation(s)
- Kym M. Boycott
- Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Danielle R. Azzariti
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Ada Hamosh
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Heidi L. Rehm
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
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2
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Osmond M, Hartley T, Johnstone B, Andjic S, Girdea M, Gillespie M, Buske O, Dumitriu S, Koltunova V, Ramani A, Boycott KM, Brudno M. PhenomeCentral: 7 years of rare disease matchmaking. Hum Mutat 2022; 43:674-681. [PMID: 35165961 DOI: 10.1002/humu.24348] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/04/2022] [Accepted: 02/08/2022] [Indexed: 11/08/2022]
Abstract
A major challenge in validating genetic causes for patients with rare diseases (RDs) is the difficulty in identifying other RD patients with overlapping phenotypes and variants in the same candidate gene. This process, known as matchmaking, requires robust data sharing solutions in order to be effective. In 2014 we launched PhenomeCentral, a RD data repository capable of collecting computer-readable genotypic and phenotypic data for the purposes of RD matchmaking. Over the past 7 years PhenomeCentral's features have been expanded and its dataset has consistently grown. There are currently 1,615 users registered on PhenomeCentral, which have contributed over 12,000 patient cases. Most of these cases contain detailed phenotypic terms, with a significant portion also providing genomic sequence data or other forms of clinical information. Matchmaking within PhenomeCentral, and with connections to other data repositories in the Matchmaker Exchange, have collectively resulted in over 60,000 matches, which have facilitated multiple gene discoveries. The collection of deep phenotypic and genotypic data has also positioned PhenomeCentral well to support next generation of matchmaking initiatives that utilize genome sequencing data, ensuring that PhenomeCentral will remain a useful tool in solving undiagnosed RD cases in the years to come. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Matthew Osmond
- Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, ON, Canada
| | - Taila Hartley
- Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, ON, Canada
| | - Brittney Johnstone
- Cancer Genetics and High Risk Program, Sunnybrook Health Sciences Centre and Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Sasha Andjic
- DATA Team and Techna Institute, University Health Network, Toronto, ON, Canada
| | - Marta Girdea
- DATA Team and Techna Institute, University Health Network, Toronto, ON, Canada
| | - Meredith Gillespie
- Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, ON, Canada
| | | | - Sergiu Dumitriu
- DATA Team and Techna Institute, University Health Network, Toronto, ON, Canada
| | - Veronika Koltunova
- DATA Team and Techna Institute, University Health Network, Toronto, ON, Canada
| | - Arun Ramani
- Hospital for Sick Children, Toronto, ON, Canada
| | - Kym M Boycott
- Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, ON, Canada.,Department of Genetics, Children's Hospital of Eastern Ontario, ON, Canada
| | - Michael Brudno
- DATA Team and Techna Institute, University Health Network, Toronto, ON, Canada.,Department of Computer Science, University of Toronto, ON, Canada.,Hospital for Sick Children, Toronto, ON, Canada
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3
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Towne MC, Rossi M, Wayburn B, Huang JM, Radtke K, Alcaraz W, Farwell Hagman KD, Shinde DN. Diagnostic testing laboratories are valuable partners for disease gene discovery: 5-year experience with GeneMatcher. Hum Mutat 2022; 43:772-781. [PMID: 35143109 PMCID: PMC9313781 DOI: 10.1002/humu.24342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 02/01/2022] [Accepted: 02/07/2022] [Indexed: 12/01/2022]
Abstract
Although the rates of disease gene discovery have steadily increased with the expanding use of genome and exome sequencing by clinical and research laboratories, only ~16% of genes in the genome have confirmed disease associations. Here we describe our clinical laboratory's experience utilizing GeneMatcher, an online portal designed to promote disease gene discovery and data sharing. Since 2016, we submitted 246 candidates from 243 unique genes to GeneMatcher, of which 111 (45%) are now clinically characterized. Submissions meeting our candidate gene‐reporting criteria based on a scoring system using patient and molecular‐weighted evidence were significantly more likely to be characterized as of October 2021 versus genes that did not meet our clinical‐reporting criteria (p = 0.025). We reported relevant findings related to these newly characterized gene–disease associations in 477 probands. In 218 (46%) instances, we issued reclassifications after an initial negative or candidate gene (uncertain) report. We coauthored 104 publications delineating gene–disease relationships, including descriptions of new associations (60%), additional supportive evidence (13%), subsequent descriptive cohorts (23%), and phenotypic expansions (4%). Clinical laboratories are pivotal for disease gene discovery efforts and can screen phenotypes based on genotype matches, contact clinicians of relevant cases, and issue proactive reclassification reports.
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Affiliation(s)
| | - Mari Rossi
- Ambry Genetics, Enterprise, Aliso Viejo, CA, USA
| | - Bess Wayburn
- Ambry Genetics, Enterprise, Aliso Viejo, CA, USA
| | | | - Kelly Radtke
- Ambry Genetics, Enterprise, Aliso Viejo, CA, USA
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4
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Fujiwara T, Shin JM, Yamaguchi A. Advances in the development of PubCaseFinder, including the new application programming interface and matching algorithm. Hum Mutat 2022; 43:734-742. [PMID: 35143083 PMCID: PMC9305291 DOI: 10.1002/humu.24341] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/17/2022] [Accepted: 02/07/2022] [Indexed: 11/11/2022]
Abstract
Over 10,000 rare genetic diseases have been identified, and millions of newborns are affected by severe rare genetic diseases each year. A variety of Human Phenotype Ontology (HPO)-based clinical decision support systems (CDSS) and patient repositories have been developed to support clinicians in diagnosing patients with suspected rare genetic diseases. In September 2017, we released PubCaseFinder (https://pubcasefinder.dbcls.jp), a web-based CDSS that provides ranked lists of genetic and rare diseases using HPO-based phenotypic similarities, where top-listed diseases represent the most likely differential diagnosis. We also developed a Matchmaker Exchange (MME) application programming interface (API) to query PubCaseFinder, which has been adopted by several patient repositories. In this paper, we describe notable updates regarding PubCaseFinder, the GeneYenta matching algorithm implemented in PubCaseFinder, and the PubCaseFinder API. The updated GeneYenta matching algorithm improves the performance of the CDSS automated differential diagnosis function. Moreover, the updated PubCaseFinder and new API empower patient repositories participating in MME and medical professionals to actively use HPO-based resources. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Toyofumi Fujiwara
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Kashiwa-shi, Chiba-ken, 277-0871, Japan
| | - Jae-Moon Shin
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Kashiwa-shi, Chiba-ken, 277-0871, Japan
| | - Atsuko Yamaguchi
- Graduate School of Integrative Science and Engineering, Tokyo City University, Setagaya-ku, Tokyo, 158-8557, Japan
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5
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Abstract
In the last decade, exome and/or genome sequencing has become a common test in the diagnosis of individuals with features of a rare Mendelian disorder. Despite its success, this test leaves the majority of tested individuals undiagnosed. This review describes the Matchmaker Exchange (MME), a federated network established to facilitate the solving of undiagnosed rare-disease cases through data sharing. MME supports genomic matchmaking, the act of connecting two or more parties looking for cases with similar phenotypes and variants in the same candidate genes. An application programming interface currently connects six matchmaker nodes-the Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER), GeneMatcher, PhenomeCentral, seqr, MyGene2, and the Initiative on Rare and Undiagnosed Diseases (IRUD) Exchange-resulting in a collective data set spanning more than 150,000 cases from more than 11,000 contributors in 88 countries. Here, we describe the successes and challenges of MME, its individual matchmaking nodes, plans for growing the network, and considerations for future directions.
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Affiliation(s)
- Danielle R Azzariti
- The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA;
| | - Ada Hamosh
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland 21287, USA;
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6
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Arachchi H, Wojcik MH, Weisburd B, Jacobsen JOB, Valkanas E, Baxter S, Byrne AB, O'Donnell-Luria AH, Haendel M, Smedley D, MacArthur DG, Philippakis AA, Rehm HL. matchbox: An open-source tool for patient matching via the Matchmaker Exchange. Hum Mutat 2018; 39:1827-1834. [PMID: 30240502 DOI: 10.1002/humu.23655] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2018] [Revised: 08/29/2018] [Accepted: 09/18/2018] [Indexed: 12/11/2022]
Abstract
Rare disease investigators constantly face challenges in identifying additional cases to build evidence for gene-disease causality. The Matchmaker Exchange (MME) addresses this limitation by providing a mechanism for matching patients across genomic centers via a federated network. The MME has revolutionized searching for additional cases by making it possible to query across institutional boundaries, so that what was once a laborious and manual process of contacting researchers is now automated and computable. However, while the MME network is beginning to scale, the growth of additional nodes is limited by the lack of easy-to-use solutions that can be implemented by any rare disease database owner, even one without significant software engineering resources. Here, we describe matchbox, which is an open-source, platform-independent, portable bridge between any given rare disease genomic center and the MME network, which has already led to novel gene discoveries. We also describe how matchbox greatly reduces the barrier to participation by overcoming challenges for new databases to join the MME.
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Affiliation(s)
- Harindra Arachchi
- Center for Mendelian Genomics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Monica H Wojcik
- Center for Mendelian Genomics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Benjamin Weisburd
- Center for Mendelian Genomics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Julius O B Jacobsen
- William Harvey Research Institute, Barts & The London School of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Elise Valkanas
- Center for Mendelian Genomics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Samantha Baxter
- Center for Mendelian Genomics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Alicia B Byrne
- Center for Mendelian Genomics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Department of Genetics and Molecular Pathology, Centre for Cancer Biology, SA Pathology, Adelaide, Australia.,School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, Australia
| | - Anne H O'Donnell-Luria
- Center for Mendelian Genomics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Melissa Haendel
- Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, Oregon.,Linus Pauling Institute, Oregon State University, Corvallis, Oregon
| | - Damian Smedley
- William Harvey Research Institute, Barts & The London School of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Daniel G MacArthur
- Center for Mendelian Genomics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | | | - Heidi L Rehm
- Center for Mendelian Genomics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
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7
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Boycott KM, Rath A, Chong JX, Hartley T, Alkuraya FS, Baynam G, Brookes AJ, Brudno M, Carracedo A, den Dunnen JT, Dyke SOM, Estivill X, Goldblatt J, Gonthier C, Groft SC, Gut I, Hamosh A, Hieter P, Höhn S, Hurles ME, Kaufmann P, Knoppers BM, Krischer JP, Macek M, Matthijs G, Olry A, Parker S, Paschall J, Philippakis AA, Rehm HL, Robinson PN, Sham PC, Stefanov R, Taruscio D, Unni D, Vanstone MR, Zhang F, Brunner H, Bamshad MJ, Lochmüller H. International Cooperation to Enable the Diagnosis of All Rare Genetic Diseases. Am J Hum Genet 2017; 100:695-705. [PMID: 28475856 PMCID: PMC5420351 DOI: 10.1016/j.ajhg.2017.04.003] [Citation(s) in RCA: 245] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Provision of a molecularly confirmed diagnosis in a timely manner for children and adults with rare genetic diseases shortens their "diagnostic odyssey," improves disease management, and fosters genetic counseling with respect to recurrence risks while assuring reproductive choices. In a general clinical genetics setting, the current diagnostic rate is approximately 50%, but for those who do not receive a molecular diagnosis after the initial genetics evaluation, that rate is much lower. Diagnostic success for these more challenging affected individuals depends to a large extent on progress in the discovery of genes associated with, and mechanisms underlying, rare diseases. Thus, continued research is required for moving toward a more complete catalog of disease-related genes and variants. The International Rare Diseases Research Consortium (IRDiRC) was established in 2011 to bring together researchers and organizations invested in rare disease research to develop a means of achieving molecular diagnosis for all rare diseases. Here, we review the current and future bottlenecks to gene discovery and suggest strategies for enabling progress in this regard. Each successful discovery will define potential diagnostic, preventive, and therapeutic opportunities for the corresponding rare disease, enabling precision medicine for this patient population.
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Affiliation(s)
- Kym M Boycott
- Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON K1H 8L1, Canada.
| | - Ana Rath
- Orphanet, Institut National de la Santé et de la Recherche Médicale US14, 75014 Paris, France
| | - Jessica X Chong
- Department of Pediatrics, University of Washington, Seattle, WA 98195, USA
| | - Taila Hartley
- Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON K1H 8L1, Canada
| | - Fowzan S Alkuraya
- Department of Genetics, King Faisal Research Center, Riyadh 11211, Saudi Arabia; Saudi Human Genome Program, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi Arabia
| | - Gareth Baynam
- Genetic Services of Western Australia, Perth, WA 6008, Australia
| | - Anthony J Brookes
- Department of Genetics, University of Leicester, Leicester LE1 7RH, UK
| | - Michael Brudno
- Department of Computer Science, University of Toronto, Toronto M5S 1A1, Canada
| | - Angel Carracedo
- Genomic Medicine Group, Galician Foundation of Genomic Medicine and University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Johan T den Dunnen
- Departments of Human Genetics and Clinical Genetics, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands
| | - Stephanie O M Dyke
- Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, QC H3A 1A4, Canada
| | - Xavier Estivill
- Experimental Division, Sidra Medical and Research Center, PO Box 26999, Doha, Qatar; Genetics Unit, Dexeus Woman's Health, 08028 Barcelona, Spain
| | - Jack Goldblatt
- Genetic Services of Western Australia, Perth, WA 6008, Australia
| | - Catherine Gonthier
- Orphanet, Institut National de la Santé et de la Recherche Médicale US14, 75014 Paris, France
| | - Stephen C Groft
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892-4874, USA
| | - Ivo Gut
- Centre Nacional d'Anàlisi Genòmica, Center for Genomic Regulation, Barcelona Institute of Science and Technology, Universitat Pompeu Fabra, 08028 Barcelona, Spain
| | - Ada Hamosh
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21286, USA
| | - Philip Hieter
- Michael Smith Laboratories, Department of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Sophie Höhn
- Orphanet, Institut National de la Santé et de la Recherche Médicale US14, 75014 Paris, France
| | - Matthew E Hurles
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Petra Kaufmann
- Office of Rare Diseases Research, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892-4874, USA
| | - Bartha M Knoppers
- Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, QC H3A 1A4, Canada
| | - Jeffrey P Krischer
- University of South Florida Health Informatics Institute, Tampa, FL 33620, USA
| | - Milan Macek
- Department of Biology and Medical Genetics, Second Faculty of Medicine, Charles University and University Hospital Motol, 150 06 Prague 5, Czech Republic
| | - Gert Matthijs
- Center for Human Genetics, University of Leuven, 3000 Leuven, Belgium
| | - Annie Olry
- Orphanet, Institut National de la Santé et de la Recherche Médicale US14, 75014 Paris, France
| | | | - Justin Paschall
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | | | - Heidi L Rehm
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Peter N Robinson
- Institut für Medizinische Genetik und Humangenetik, Charité Universitätsmdizin Berlin, 13353 Berlin, Germany; Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Pak-Chung Sham
- Centre for Genomic Sciences, University of Hong Kong, Hong Kong, China
| | - Rumen Stefanov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv 4002, Bulgaria
| | - Domenica Taruscio
- National Centre for Rare Diseases, Istituto Superiore di Sanità, Rome 299-00161, Italy
| | - Divya Unni
- Orphanet, Institut National de la Santé et de la Recherche Médicale US14, 75014 Paris, France
| | - Megan R Vanstone
- Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON K1H 8L1, Canada
| | - Feng Zhang
- WuXi AppTec, Waigaoqiao Free Trade Zone, Shanghai 200131, China; WuXi NextCODE, Cambridge, MA 02142, USA
| | - Han Brunner
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands; Maastricht University Medical Center, Department of Clinical Genetics, 6229 GT Maastricht, the Netherlands
| | - Michael J Bamshad
- Department of Pediatrics, University of Washington, Seattle, WA 98195, USA; Division of Genetic Medicine, Seattle Children's Hospital, Seattle, WA 98105, USA
| | - Hanns Lochmüller
- John Walton Muscular Dystrophy Research Centre, MRC Centre for Neuromuscular Diseases, Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne NE1 3BZ, UK
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8
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Akle S, Chun S, Jordan DM, Cassa CA. Mitigating false-positive associations in rare disease gene discovery. Hum Mutat 2016; 36:998-1003. [PMID: 26378430 DOI: 10.1002/humu.22847] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Accepted: 07/19/2015] [Indexed: 11/09/2022]
Abstract
Clinical sequencing is expanding, but causal variants are still not identified in the majority of cases. These unsolved cases can aid in gene discovery when individuals with similar phenotypes are identified in systems such as the Matchmaker Exchange. We describe risks for gene discovery in this growing set of unsolved cases. In a set of rare disease cases with the same phenotype, it is not difficult to find two individuals with the same phenotype that carry variants in the same gene. We quantify the risk of false-positive association in a cohort of individuals with the same phenotype, using the prior probability of observing a variant in each gene from over 60,000 individuals (Exome Aggregation Consortium). Based on the number of individuals with a genic variant, cohort size, specific gene, and mode of inheritance, we calculate a P value that the match represents a true association. A match in two of 10 patients in MECP2 is statistically significant (P = 0.0014), whereas a match in TTN would not reach significance, as expected (P > 0.999). Finally, we analyze the probability of matching in clinical exome cases to estimate the number of cases needed to identify genes related to different disorders. We offer Rare Disease Match, an online tool to mitigate the uncertainty of false-positive associations.
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Affiliation(s)
- Sebastian Akle
- Department of Organismic and Evolutionary Biology, Harvard University, Boston, MA.,Division of Genetics, Brigham and Women's Hospital, Boston, MA
| | - Sung Chun
- Division of Genetics, Brigham and Women's Hospital, Boston, MA.,Department of Medicine, Harvard Medical School, Boston, MA
| | - Daniel M Jordan
- Division of Genetics, Brigham and Women's Hospital, Boston, MA.,Department of Medicine, Harvard Medical School, Boston, MA
| | - Christopher A Cassa
- Division of Genetics, Brigham and Women's Hospital, Boston, MA.,Department of Medicine, Harvard Medical School, Boston, MA
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9
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Philippakis AA, Azzariti DR, Beltran S, Brookes AJ, Brownstein CA, Brudno M, Brunner HG, Buske OJ, Carey K, Doll C, Dumitriu S, Dyke SOM, den Dunnen JT, Firth HV, Gibbs RA, Girdea M, Gonzalez M, Haendel MA, Hamosh A, Holm IA, Huang L, Hurles ME, Hutton B, Krier JB, Misyura A, Mungall CJ, Paschall J, Paten B, Robinson PN, Schiettecatte F, Sobreira NL, Swaminathan GJ, Taschner PE, Terry SF, Washington NL, Züchner S, Boycott KM, Rehm HL. The Matchmaker Exchange: a platform for rare disease gene discovery. Hum Mutat 2016; 36:915-21. [PMID: 26295439 DOI: 10.1002/humu.22858] [Citation(s) in RCA: 340] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2015] [Accepted: 07/21/2015] [Indexed: 12/21/2022]
Abstract
There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.
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Affiliation(s)
- Anthony A Philippakis
- The Broad Institute of Harvard and MIT, Cambridge, Massachusetts.,Department of Cardiology, Brigham & Women's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Danielle R Azzariti
- Laboratory for Molecular Medicine, Partners Personalized Medicine, Boston, Massachusetts
| | - Sergi Beltran
- Centro Nacional de Análisis Genómico, Barcelona, Spain
| | | | - Catherine A Brownstein
- Harvard Medical School, Boston, Massachusetts.,Division of Genetics and Genomics and the Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, Massachusetts
| | - Michael Brudno
- Department of Computer Science, University of Toronto, Toronto, Canada.,Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada.,Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Han G Brunner
- Radboud University Medical Center, Department of Human Genetics, Nijmegen 6500 HB, The Netherlands.,Maastricht University Medical Center, Department of Clinical Genetics, Maastricht 6202AZ, The Netherlands
| | - Orion J Buske
- Department of Computer Science, University of Toronto, Toronto, Canada.,Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada.,Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | | | | | - Sergiu Dumitriu
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Stephanie O M Dyke
- Centre of Genomics and Policy, Faculty of Medicine, McGill University, Quebec, Canada
| | - Johan T den Dunnen
- Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Helen V Firth
- East Anglian Medical Genetics Service, Box 134, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, 77030
| | - Marta Girdea
- Department of Computer Science, University of Toronto, Toronto, Canada.,Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | | | - Melissa A Haendel
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
| | - Ada Hamosh
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Ingrid A Holm
- Harvard Medical School, Boston, Massachusetts.,Division of Genetics and Genomics and the Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, Massachusetts
| | - Lijia Huang
- The Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Matthew E Hurles
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Ben Hutton
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Joel B Krier
- Harvard Medical School, Boston, Massachusetts.,Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, 02115
| | - Andriy Misyura
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | | | - Justin Paschall
- European Molecular Biology Laboratory European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, Santa Cruz, California
| | - Peter N Robinson
- Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin 13353, Germany.,Max Planck Institute for Molecular Genetics, Berlin 14195, Germany.,Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin 14195, Germany.,Berlin Brandenburg Center for Regenerative Therapies, Berlin 13353, Germany
| | | | - Nara L Sobreira
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Ganesh J Swaminathan
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Peter E Taschner
- Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands.,Generade Center of Expertise Genomics, University of Applied Sciences Leiden, Leiden, The Netherlands
| | | | | | - Stephan Züchner
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida
| | - Kym M Boycott
- Department of Genetics, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada
| | - Heidi L Rehm
- The Broad Institute of Harvard and MIT, Cambridge, Massachusetts.,Harvard Medical School, Boston, Massachusetts.,Laboratory for Molecular Medicine, Partners Personalized Medicine, Boston, Massachusetts.,Department of Pathology, Brigham & Women's Hospital, Boston, Massachusetts
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10
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Buske OJ, Schiettecatte F, Hutton B, Dumitriu S, Misyura A, Huang L, Hartley T, Girdea M, Sobreira N, Mungall C, Brudno M. The Matchmaker Exchange API: automating patient matching through the exchange of structured phenotypic and genotypic profiles. Hum Mutat 2016; 36:922-7. [PMID: 26255989 DOI: 10.1002/humu.22850] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 07/24/2015] [Indexed: 01/28/2023]
Abstract
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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Affiliation(s)
- Orion J Buske
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada.,Department of Computer Science, University of Toronto, Toronto, Canada.,Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | | | | | - Sergiu Dumitriu
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Andriy Misyura
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Lijia Huang
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Taila Hartley
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Marta Girdea
- Department of Computer Science, University of Toronto, Toronto, Canada.,Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Nara Sobreira
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Chris Mungall
- Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California
| | - Michael Brudno
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada.,Department of Computer Science, University of Toronto, Toronto, Canada.,Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
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11
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Mungall CJ, Washington NL, Nguyen-Xuan J, Condit C, Smedley D, Köhler S, Groza T, Shefchek K, Hochheiser H, Robinson PN, Lewis SE, Haendel MA. Use of model organism and disease databases to support matchmaking for human disease gene discovery. Hum Mutat 2015; 36:979-84. [PMID: 26269093 PMCID: PMC5473253 DOI: 10.1002/humu.22857] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Accepted: 07/22/2015] [Indexed: 11/10/2022]
Abstract
The Matchmaker Exchange application programming interface (API) allows searching a patient's genotypic or phenotypic profiles across clinical sites, for the purposes of cohort discovery and variant disease causal validation. This API can be used not only to search for matching patients, but also to match against public disease and model organism data. This public disease data enable matching known diseases and variant-phenotype associations using phenotype semantic similarity algorithms developed by the Monarch Initiative. The model data can provide additional evidence to aid diagnosis, suggest relevant models for disease mechanism and treatment exploration, and identify collaborators across the translational divide. The Monarch Initiative provides an implementation of this API for searching multiple integrated sources of data that contextualize the knowledge about any given patient or patient family into the greater biomedical knowledge landscape. While this corpus of data can aid diagnosis, it is also the beginning of research to improve understanding of rare human diseases.
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Affiliation(s)
| | - Nicole L. Washington
- Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Jeremy Nguyen-Xuan
- Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Christopher Condit
- San Diego Supercomputing Center, UC San Diego, La Jolla, California, USA
| | - Damian Smedley
- Wellcome Trust Sanger Institute, Mouse Informatics group, Hinxton, UK
| | - Sebastian Köhler
- Charité - Universitätsmedizin Berlin, Institute for Medical and Human Genetics, Berlin, Germany
| | - Tudor Groza
- Garvan Institute, Kinghorn Centre for Clinical Genomics, Sydney, Australia
| | - Kent Shefchek
- Department of Biomedical Informatics and Clinical Epidemiology, Oregon Health and Science University
| | - Harry Hochheiser
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Peter N. Robinson
- Charité - Universitätsmedizin Berlin, Institute for Medical and Human Genetics, Berlin, Germany
| | - Suzanna E. Lewis
- Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Melissa A. Haendel
- Department of Biomedical Informatics and Clinical Epidemiology, Oregon Health and Science University
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12
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Buske OJ, Girdea M, Dumitriu S, Gallinger B, Hartley T, Trang H, Misyura A, Friedman T, Beaulieu C, Bone WP, Links AE, Washington NL, Haendel MA, Robinson PN, Boerkoel CF, Adams D, Gahl WA, Boycott KM, Brudno M. PhenomeCentral: a portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases. Hum Mutat 2015; 36:931-40. [PMID: 26251998 DOI: 10.1002/humu.22851] [Citation(s) in RCA: 101] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Accepted: 07/28/2015] [Indexed: 01/18/2023]
Abstract
The discovery of disease-causing mutations typically requires confirmation of the variant or gene in multiple unrelated individuals, and a large number of rare genetic diseases remain unsolved due to difficulty identifying second families. To enable the secure sharing of case records by clinicians and rare disease scientists, we have developed the PhenomeCentral portal (https://phenomecentral.org). Each record includes a phenotypic description and relevant genetic information (exome or candidate genes). PhenomeCentral identifies similar patients in the database based on semantic similarity between clinical features, automatically prioritized genes from whole-exome data, and candidate genes entered by the users, enabling both hypothesis-free and hypothesis-driven matchmaking. Users can then contact other submitters to follow up on promising matches. PhenomeCentral incorporates data for over 1,000 patients with rare genetic diseases, contributed by the FORGE and Care4Rare Canada projects, the US NIH Undiagnosed Diseases Program, the EU Neuromics and ANDDIrare projects, as well as numerous independent clinicians and scientists. Though the majority of these records have associated exome data, most lack a molecular diagnosis. PhenomeCentral has already been used to identify causative mutations for several patients, and its ability to find matching patients and diagnose these diseases will grow with each additional patient that is entered.
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Affiliation(s)
- Orion J Buske
- Department of Computer Science, University of Toronto, Toronto, Canada.,Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada.,Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Marta Girdea
- Department of Computer Science, University of Toronto, Toronto, Canada.,Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada.,Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Sergiu Dumitriu
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Bailey Gallinger
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada.,Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Taila Hartley
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Heather Trang
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada.,Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Andriy Misyura
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Tal Friedman
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - Chandree Beaulieu
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - William P Bone
- Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, Maryland
| | - Amanda E Links
- Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, Maryland
| | - Nicole L Washington
- Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California
| | - Melissa A Haendel
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
| | - Peter N Robinson
- Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Cornelius F Boerkoel
- Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, Maryland
| | - David Adams
- Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, Maryland
| | - William A Gahl
- Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, Maryland
| | - Kym M Boycott
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Michael Brudno
- Department of Computer Science, University of Toronto, Toronto, Canada.,Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada.,Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
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13
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Lambertson KF, Damiani SA, Might M, Shelton R, Terry SF. Participant-driven matchmaking in the genomic era. Hum Mutat 2015; 36:965-73. [PMID: 26252162 DOI: 10.1002/humu.22852] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Accepted: 07/15/2015] [Indexed: 01/16/2023]
Abstract
Whole-genome and whole-exome sequencing are increasingly useful diagnostic tools for novel monogenic conditions. In order to confirm diagnoses made using these technologies, genomic matchmaking-the matching of cases with similar phenotypic and/or genotypic profiles, to narrow the number of candidate genes or ascertain a condition's etiology with greater certainty-is essential. Yet, due to current limitations on the size of matchmaking networks and data sets available to support them, identifying a match can be difficult. We argue that matchmaking efforts led by affected individuals and their families-participant-led efforts-offer a twofold solution to this need, in that participants both have the capacity to access larger networks and to provide more detailed sets of phenotypic and genotypic data. These features of participant-led efforts have the potential to increase the value of matchmaking networks, both in terms of number of matches and in terms of the overall energy of the network. We provide two examples of participant-led matchmaking, and propose a model for scaling these efforts.
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Affiliation(s)
| | - Stephen A Damiani
- Mission Massimo Foundation, Inc., Elsternwick, Victoria, Australia.,Mission Massimo Foundation, Inc., Westlake Village, California
| | - Matthew Might
- NGLY1.org, Salt Lake City, Utah.,University of Utah, Salt Lake City, Utah, United States
| | | | - Sharon F Terry
- Genetic Alliance, Washington, District of Columbia.,PXE International, Inc, Washington, District of Columbia
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14
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Abstract
The Undiagnosed Diseases Network (UDN) builds on the successes of the Undiagnosed Diseases Program at the National Institutes of Health (NIH UDP). Through support from the NIH Common Fund, a coordinating center, six additional clinical sites, and two sequencing cores comprise the UDN. The objectives of the UDN are to: (1) improve the level of diagnosis and care for patients with undiagnosed diseases through the development of common protocols designed by an enlarged community of investigators across the network; (2) facilitate research into the etiology of undiagnosed diseases, by collecting and sharing standardized, high-quality clinical and laboratory data including genotyping, phenotyping, and environmental exposure data; and (3) create an integrated and collaborative research community across multiple clinical sites, and among laboratory and clinical investigators, to investigate the pathophysiology of these rare diseases and to identify options for patient management. Broad-based data sharing is at the core of achieving these objectives, and the UDN is establishing the policies and governance structure to support broad data sharing.
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Affiliation(s)
- Catherine A Brownstein
- Division of Genetics and Genomics and the Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, Massachusetts
| | - Ingrid A Holm
- Division of Genetics and Genomics and the Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, Massachusetts
| | - Rachel Ramoni
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - David B Goldstein
- Columbia Institute for Genomic Medicine, Genetics and Development, New York, New York
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15
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Lancaster O, Beck T, Atlan D, Swertz M, Thangavelu D, Veal C, Dalgleish R, Brookes AJ. Cafe Variome: general-purpose software for making genotype-phenotype data discoverable in restricted or open access contexts. Hum Mutat 2015. [PMID: 26224250 DOI: 10.1002/humu.22841] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Biomedical data sharing is desirable, but problematic. Data "discovery" approaches-which establish the existence rather than the substance of data-precisely connect data owners with data seekers, and thereby promote data sharing. Cafe Variome (http://www.cafevariome.org) was therefore designed to provide a general-purpose, Web-based, data discovery tool that can be quickly installed by any genotype-phenotype data owner, or network of data owners, to make safe or sensitive content appropriately discoverable. Data fields or content of any type can be accommodated, from simple ID and label fields through to extensive genotype and phenotype details based on ontologies. The system provides a "shop window" in front of data, with main interfaces being a simple search box and a powerful "query-builder" that enable very elaborate queries to be formulated. After a successful search, counts of records are reported grouped by "openAccess" (data may be directly accessed), "linkedAccess" (a source link is provided), and "restrictedAccess" (facilitated data requests and subsequent provision of approved records). An administrator interface provides a wide range of options for system configuration, enabling highly customized single-site or federated networks to be established. Current uses include rare disease data discovery, patient matchmaking, and a Beacon Web service.
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Affiliation(s)
- Owen Lancaster
- Department of Genetics, University of Leicester, Leicester, UK
| | - Tim Beck
- Department of Genetics, University of Leicester, Leicester, UK
| | | | - Morris Swertz
- University Medical Center Groningen, The Netherlands
| | | | - Colin Veal
- Department of Genetics, University of Leicester, Leicester, UK
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16
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Loucks CM, Parboosingh JS, Shaheen R, Bernier FP, McLeod DR, Seidahmed MZ, Puffenberger EG, Ober C, Hegele RA, Boycott KM, Alkuraya FS, Innes AM. Matching two independent cohorts validates DPH1 as a gene responsible for autosomal recessive intellectual disability with short stature, craniofacial, and ectodermal anomalies. Hum Mutat 2015. [PMID: 26220823 DOI: 10.1002/humu.22843] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Recently, Alazami et al. (2015) identified 33 putative candidate disease genes for neurogenetic disorders. One such gene was DPH1, in which a homozygous missense mutation was associated with a 3C syndrome-like phenotype in four patients from a single extended family. Here, we report a second homozygous missense variant in DPH1, seen in four members of a founder population, and associated with a phenotype initially reminiscent of Sensenbrenner syndrome. This postpublication "match" validates DPH1 as a gene underlying syndromic intellectual disability with short stature and craniofacial and ectodermal anomalies, reminiscent of, but distinct from, 3C and Sensenbrenner syndromes. This validation took several years after the independent discoveries due to the absence of effective methods for sharing both candidate phenotype and genotype data between investigators. Sharing of data via Web-based anonymous data exchange servers will play an increasingly important role toward more efficient identification of the molecular basis for rare Mendelian disorders.
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Affiliation(s)
- Catrina M Loucks
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Jillian S Parboosingh
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute for Child and Maternal Health, University of Calgary, Calgary, Alberta, Canada
| | - Ranad Shaheen
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyhadh, 11211, Saudi Arabia
| | - Francois P Bernier
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute for Child and Maternal Health, University of Calgary, Calgary, Alberta, Canada
| | - D Ross McLeod
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | | | | | - Carole Ober
- Department of Human Genetics, and Department of Obstetrics and Gynecology, The University of Chicago, Chicago, Illinois
| | - Robert A Hegele
- Department of Paediatrics, University of Western Ontario, London, Ontario, Canada
| | - Kym M Boycott
- Department of Genetics, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada
| | - Fowzan S Alkuraya
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyhadh, 11211, Saudi Arabia.,Department of Anatomy and Cell Biology, College of Medicine, Alfaisal University, Riyadh, 11533, Saudi Arabia.,Saudi Human Genome Program, King Abdulaziz City for Science and Technology, Riyadh, 11442, Saudi Arabia
| | - A Micheil Innes
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute for Child and Maternal Health, University of Calgary, Calgary, Alberta, Canada
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17
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Abstract
Here, we describe an overview and update on GeneMatcher (http://www.genematcher.org), a freely accessible Web-based tool developed as part of the Baylor-Hopkins Center for Mendelian Genomics. We created GeneMatcher with the goal of identifying additional individuals with rare phenotypes who had variants in the same candidate disease gene. We also wanted to facilitate connections to basic scientists working on orthologous genes in model systems with the goal of connecting their work to human Mendelian phenotypes. Meeting these goals will enhance the identification of novel Mendelian genes. Launched in September, 2013, GeneMatcher now has 2,178 candidate genes from 486 submitters spread across 38 countries entered in the database (June 1, 2015). GeneMatcher is also part of the Matchmaker Exchange (http://matchmakerexchange.org/) with an Application Programing Interface enabling submitters to query other databases of genetic variants and phenotypes without having to create accounts and data entries in multiple systems.
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Affiliation(s)
- Nara Sobreira
- Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | - David Valle
- Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Ada Hamosh
- Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
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