@article {722, title = {Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative.}, journal = {Front Genet}, volume = {10}, year = {2019}, month = {2019}, pages = {611}, abstract = {

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.

}, issn = {1664-8021}, doi = {10.3389/fgene.2019.00611}, author = {Nellaker, Christoffer and Alkuraya, Fowzan S and Baynam, Gareth and Bernier, Raphael A and Bernier, Francois P J and Boulanger, Vanessa and Brudno, Michael and Brunner, Han G and Clayton-Smith, Jill and Cogn{\'e}, Benjamin and Dawkins, Hugh J S and deVries, Bert B A and Douzgou, Sofia and Dudding-Byth, Tracy and Eichler, Evan E and Ferlaino, Michael and Fieggen, Karen and Firth, Helen V and FitzPatrick, David R and Gration, Dylan and Groza, Tudor and Haendel, Melissa and Hallowell, Nina and Hamosh, Ada and Hehir-Kwa, Jayne and Hitz, Marc-Phillip and Hughes, Mark and Kini, Usha and Kleefstra, Tjitske and Kooy, R Frank and Krawitz, Peter and K{\"u}ry, S{\'e}bastien and Lees, Melissa and Lyon, Gholson J and Lyonnet, Stanislas and Marcadier, Julien L and Meyn, Stephen and Moslerov{\'a}, Veronika and Politei, Juan M and Poulton, Cathryn C and Raymond, F Lucy and Reijnders, Margot R F and Robinson, Peter N and Romano, Corrado and Rose, Catherine M and Sainsbury, David C G and Schofield, Lyn and Sutton, Vernon R and Turnovec, Marek and Van Dijck, Anke and Van Esch, Hilde and Wilkie, Andrew O M} } @article {363, title = {International Cooperation to Enable the Diagnosis of All Rare Genetic Diseases.}, journal = {Am J Hum Genet}, volume = {100}, year = {2017}, month = {2017 May 04}, pages = {695-705}, 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.

}, keywords = {Databases, Factual, Exome, Genome, Human, Humans, International Cooperation, Rare Diseases}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2017.04.003}, author = {Boycott, Kym M and Rath, Ana and Chong, Jessica X and Hartley, Taila and Alkuraya, Fowzan S and Baynam, Gareth and Brookes, Anthony J and Brudno, Michael and Carracedo, Angel and den Dunnen, Johan T and Dyke, Stephanie O M and Estivill, Xavier and Goldblatt, Jack and Gonthier, Catherine and Groft, Stephen C and Gut, Ivo and Hamosh, Ada and Hieter, Philip and H{\"o}hn, Sophie and Hurles, Matthew E and Kaufmann, Petra and Knoppers, Bartha M and Krischer, Jeffrey P and Macek, Milan and Matthijs, Gert and Olry, Annie and Parker, Samantha and Paschall, Justin and Philippakis, Anthony A and Rehm, Heidi L and Robinson, Peter N and Sham, Pak-Chung and Stefanov, Rumen and Taruscio, Domenica and Unni, Divya and Vanstone, Megan R and Zhang, Feng and Brunner, Han and Bamshad, Michael J and Lochm{\"u}ller, Hanns} } @article {428, title = {"Matching" consent to purpose: The example of the Matchmaker Exchange.}, journal = {Hum Mutat}, volume = {38}, year = {2017}, month = {2017 10}, pages = {1281-1285}, abstract = {

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.

}, keywords = {Databases, Genetic, Genetic Association Studies, Genetic Diseases, Inborn, Genomics, Humans, Information Dissemination, Patient Selection, Physicians, Rare Diseases, Research Personnel, Translational Medical Research}, issn = {1098-1004}, doi = {10.1002/humu.23278}, author = {Dyke, Stephanie O M and Knoppers, Bartha M and Hamosh, Ada and Firth, Helen V and Hurles, Matthew and Brudno, Michael and Boycott, Kym M and Philippakis, Anthony A and Rehm, Heidi L} } @article {131, title = {The Matchmaker Exchange: a platform for rare disease gene discovery.}, journal = {Hum Mutat}, volume = {36}, year = {2015}, month = {2015 Oct}, pages = {915-21}, 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.

}, keywords = {Database Management Systems, Databases, Genetic, Genetic Association Studies, Genetic Predisposition to Disease, Humans, Information Dissemination, Rare Diseases, Software}, issn = {1098-1004}, doi = {10.1002/humu.22858}, author = {Philippakis, Anthony A and Azzariti, Danielle R and Beltran, Sergi and Brookes, Anthony J and Brownstein, Catherine A and Brudno, Michael and Brunner, Han G and Buske, Orion J and Carey, Knox and Doll, Cassie and Dumitriu, Sergiu and Dyke, Stephanie O M and den Dunnen, Johan T and Firth, Helen V and Gibbs, Richard A and Girdea, Marta and Gonzalez, Michael and Haendel, Melissa A and Hamosh, Ada and Holm, Ingrid A and Huang, Lijia and Hurles, Matthew E and Hutton, Ben and Krier, Joel B and Misyura, Andriy and Mungall, Christopher J and Paschall, Justin and Paten, Benedict and Robinson, Peter N and Schiettecatte, Fran{\c c}ois and Sobreira, Nara L and Swaminathan, Ganesh J and Taschner, Peter E and Terry, Sharon F and Washington, Nicole L and Z{\"u}chner, Stephan and Boycott, Kym M and Rehm, Heidi L} } @article {136, title = {The Matchmaker Exchange API: automating patient matching through the exchange of structured phenotypic and genotypic profiles.}, journal = {Hum Mutat}, volume = {36}, year = {2015}, month = {2015 Oct}, pages = {922-7}, 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.

}, keywords = {Algorithms, Computational Biology, Databases, Genetic, Genetic Predisposition to Disease, Genotype, Humans, Information Dissemination, Phenotype, Rare Diseases, Web Browser}, issn = {1098-1004}, doi = {10.1002/humu.22850}, author = {Buske, Orion J and Schiettecatte, Fran{\c c}ois and Hutton, Benjamin and Dumitriu, Sergiu and Misyura, Andriy and Huang, Lijia and Hartley, Taila and Girdea, Marta and Sobreira, Nara and Mungall, Chris and Brudno, Michael} }