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Front Genet. 2019 Jul 29;10:611. doi: 10.3389/fgene.2019.00611. eCollection 2019.

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

Author information

1
Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom.
2
Big Data Institute, University of Oxford, Oxford, United Kingdom.
3
Institute for Biomedical Engineering, University of Oxford, Oxford, United Kingdom.
4
Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.
5
Western Australian Register of Developmental Anomalies, and Genetic Services of Western Australia, King Edward Memorial, Subiaco, WA, Australia.
6
Telethon Kids Institute and School of Paediatrics and Child Health, University of Western Australia, Perth, WA, Australia.
7
Spatial Sciences, Science and Engineering, Curtin University, Perth, WA, Australia.
8
Department of Psychiatry & Behavioral Science, University of Washington School of Medicine, Seattle, WA, United States.
9
Alberta Children's Hospital Research Institute, Calgary, AB, Canada.
10
National Organization for Rare Disorders, Danbury, CT, United States.
11
Department of Computer Science, University of Toronto and the Hospital for Sick Children, Toronto, Canada.
12
Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands.
13
Manchester Centre for Genomic Medicine, Central Manchester University Hospitals NHS Foundation Trust, MAHSC, Saint Mary's Hospital, Manchester, United Kingdom.
14
CHU Nantes, Service de Génétique Médicale, Nantes, France.
15
Office of Population Health Genomics, Public and Aboriginal Health Division, Department of Health Government of Western Australia, Perth, WA, Australia.
16
Sir Walter Murdoch School of Policy and International Affairs, Murdoch University.
17
Centre for Population Health Research, Curtin University of Technology, Perth, WA, Australia.
18
Hunter Genetics, Waratah, NSW, Australia.
19
Department of Genome Science, University of Washington School of Medicine, Seattle, WA, United States.
20
Howard Hughes Medical Institute, University of Washington, Seattle, WA, United States.
21
Division of Human Genetics, Level 3, Wernher and Beit North, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Observatory, South Africa.
22
Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom.
23
MRC Human Genetics Unit, IGMM, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom.
24
Genetic Services of Western Australia, King Edward Memorial Hospital, Subiaco, WA, Australia.
25
The Garvan Institute, Sydney, NSW, Australia.
26
Oregon Health & Science University, Portland, OR, United States.
27
Wellcome Centre for Ethics and Humanities, University of Oxford, Oxford, United Kingdom.
28
Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
29
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, United States.
30
Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands.
31
Department of Congenital Heart Disease and Pediatric Cardiology, University Hospital of Schleswig-Holstein-Campus Kiel, Kiel, Germany.
32
Department of Clinical Neurosciences, Western General Hospital, Edinburgh, United Kingdom.
33
Oxford Centre for Genomic Medicine, Oxford, United Kingdom.
34
Department of Medical Genetics, University of Antwerp, Antwerp, Belgium.
35
Institut für Genomische Statistik und Bioinformatik, Universitätsklinikum Bonn, Rheinische-Friedrich-Wilhelms-Universität, Bonn, Germany.
36
Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom.
37
George A. Jervis Clinic and Institute for Basic Research in Developmental Disabilities (IBR), Staten Island, NY, United States.
38
Imagine Institute, Paris, France.
39
Department of Biology and Medical Genetics, 2nd Faculty of Medicine, Charles University and University Hospital, Prague, Czechia.
40
Laboratorio Chamoles, Errores Congénitos del Metabolismo, Buenos Aires, Argentina.
41
Department of Paediatrics and Neonates, Fiona Stanley Hospital, Perth, WA, Australia.
42
CIMR (Wellcome Trust/MRC Building), Cambridge, United Kingdom.
43
Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, Netherlands.
44
The Jackson Laboratory, Farmington, CT, United States.
45
Oasi Research Institute-IRCCS, Troina, Italy.
46
Victorian Clinical Genetics Service and Murdoch Childrens Research Institute, The Royal Children's Hospital, Parkville, VIC, Australia.
47
Northern & Yorkshire Cleft Lip and Palate Service, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom.
48
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States.
49
Department of Medical Genetics, University and University Hospital Antwerp, Antwerp, Belgium.
50
Center for Human Genetics, University Hospitals Leuven, University of Leuven, Leuven, Belgium.
51
Clinical Genetics Group, MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford, United Kingdom.

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.

KEYWORDS:

Faces; data protection; data sharing; patient information; phenotyping; rare disease

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