Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative

Christoffer Nellaker*, Fowzan S. Alkuraya, Gareth Baynam, Raphael A. Bernier, Francois P. J. Bernier, Vanessa Boulangerw, Michael Brudno, Han G. Brunner, Jill Clayton-Smith, Benjamin Cogne, Hugh J. S. Dawkins, Bert B. A. deVries, Sofia Douzgou, Tracy Dudding-Byth, Evan E. Eichler, Michael Ferlaino, Karen Fieggen, Helen Firth, David R. FitzPatrick, Dylan GrationTudor Groza, Melissa Haende, Nina Hallowel, Ada Hamosh, Jayne Hehir-Kwa, Marc-Phillip Hitz, Mark Hughes, Usha Kini, Tjitske Kleefstra, R. Frank Kooy, Peter Krawitz, Sebastien Kury, Melissa Lees, Gholson J. Lyon, Stanislas Lyonnet, Julien L. Marcadier, Stephen Meyn, Veronika Moslerova, Juan M. Politei, Cathryn C. Poulton, F. Lucy Raymond, Margot R. F. Reijnders, Peter N. Robinson, Corrado Romano, Catherine M. Rose, David C. G. Sainsbury, Lyn Schofield, Vernon R. Sutton, Marek Tumovec, Anke Van Dijck, Hilde Van Esch, Andrew O.M. Wilkie, Minerva Consortium

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Article number611
Number of pages9
JournalFrontiers in Genetics
Publication statusPublished - 29 Jul 2019


  • data sharing
  • phenotyping
  • patient information
  • data protection
  • rare disease
  • Faces
  • RARE

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