Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative. - Nellaker, Christoffer; Alkuraya, Fowzan S; Baynam, Gareth; Bernier, Raphael A; Bernier, Francois P J; Boulanger, Vanessa; Brudno, Michael; Brunner, Han G; Clayton-Smith, Jill; Cogné, Benjamin; Dawkins, Hugh J S; deVries, Bert B A; et al. (Front Genet, 2019)
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.

Plain-language medical vocabulary for precision diagnosis. - Vasilevsky, Nicole A; Foster, Erin D; Engelstad, Mark E; Carmody, Leigh; Might, Matt; Chambers, Chip; Dawkins, Hugh J S; Lewis, Janine; Della Rocca, Maria G; Snyder, Michelle; Boerkoel, Cornelius F; Rath, Ana; et al. (Nat Genet, 2018)

Matchmaker Exchange. - Sobreira, Nara L M; Arachchi, Harindra; Buske, Orion J; Chong, Jessica X; Hutton, Ben; Foreman, Julia; Schiettecatte, François; Groza, Tudor; Jacobsen, Julius O B; Haendel, Melissa A; Boycott, Kym M; Hamosh, Ada; et al. (Curr Protoc Hum Genet, 2017)
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.
Keywords: Animals; Computational Biology; Databases, Genetic; Genetic Association Studies; Genetic Predisposition to Disease; Genomics; Humans; Rare Diseases; Software; Web Browser