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A framework for sharing of clinical and genetic data for precision medicine applications

  • NYGC ALS Consortium
  • Columbia University
  • New York Genome Center
  • Northwell Health System
  • Massachusetts General Hospital
  • Broad Institute
  • Uppsala University
  • Translational Genomics Research Institute
  • St. Joseph's Hospital and Medical Center, Phoenix
  • Hadassah University Medical Centre
  • University of California at San Francisco
  • University of Pennsylvania
  • University of California at San Diego
  • Tel Aviv University
  • Georgetown University
  • Icahn School of Medicine at Mount Sinai
  • University of Edinburgh
  • University of Pennsylvania
  • Spaulding Rehabilitation Hospital
  • Board of Governors Regenerative Medicine Institute
  • Cedars-Sinai Medical Center
  • Academy of Athens
  • National and Kapodistrian University of Athens
  • Atlantic Health
  • Overlook Medical Center
  • Hospital for Special Surgery - New York
  • Pennsylvania State University
  • University of Amsterdam
  • National Institutes of Health
  • Brigham and Women’s Hospital
  • Cold Spring Harbor Laboratory
  • Temple University
  • Weizmann Institute of Science
  • Washington University St. Louis
  • University of Thessaly
  • Gladstone Institutes
  • University of California at Irvine
  • Jackson Laboratory
  • University College London

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Precision medicine has the potential to provide more accurate diagnosis, appropriate treatment and timely prevention strategies by considering patients’ biological makeup. However, this cannot be realized without integrating clinical and omics data in a data-sharing framework that achieves large sample sizes. Systems that integrate clinical and genetic data from multiple sources are scarce due to their distinct data types, interoperability, security and data ownership issues. Here we present a secure framework that allows immutable storage, querying and analysis of clinical and genetic data using blockchain technology. Our platform allows clinical and genetic data to be harmonized by combining them under a unified framework. It supports combined genotype–phenotype queries and analysis, gives institutions control of their data and provides immutable user access logs, improving transparency into how and when health information is used. We demonstrate the value of our framework for precision medicine by creating genotype–phenotype cohorts and examining relationships within them. We show that combining data across institutions using our secure platform increases statistical power for rare disease analysis. By offering an integrated, secure and decentralized framework, we aim to enhance reproducibility and encourage broader participation from communities and patients in data sharing.

Original languageEnglish
Article number134
Pages (from-to)3578-3589
Number of pages12
JournalNature Medicine
Volume30
Issue number12
DOIs
StatePublished - Dec 2024

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