SCI

7 September 2024

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

(Nature Medicine, IF: 58.7)

  • Ahmed Elhussein, Ulugbek Baymuradov, NYGC ALS Consortium, Noémie Elhadad, Karthik Natarajan & Gamze Gürsoy

  • CORRESPONDENCE TO: gamze.gursoy@columbia.edu

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.

精准医学通过考虑患者的生物学构成,有可能提供更准确的诊断、更恰当的治疗和更及时的预防策略。然而,如果不将临床数据和组学数据整合到一个存储大样本量的数据共享框架中,就无法实现这一点。由于不同的数据类型、数据间操作性、安全性和数据所有权问题,将多个来源的临床和遗传数据的整合的系统很少。在这里,我们提出了一个安全的框架,使用区块链技术对临床和遗传数据进行不可变的存储、查询和分析。我们的平台允许通过在统一的框架下组合临床和遗传数据来协调它们。它支持组合的基因型-表型查询和分析,使相关机构能够控制其数据,并提供不可变的用户访问日志,提高了健康信息如何使用以及何时使用的透明度。我们通过创建基因型-表型队列并检查其中的关系来证明我们的精准医学框架的价值。我们发现,应用我们的安全平台将各机构的数据结合起来,可以提高罕见病的分析统计能力。通过提供一个集成、安全和分散的框架,我们的目标是提高可重复性,并鼓励社区和患者更广泛地参与数据共享。


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