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用于精准医学应用的临床和遗传数据共享框架。

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

作者信息

Elhussein Ahmed, Baymuradov Ulugbek, Elhadad Noémie, Natarajan Karthik, Gürsoy Gamze

机构信息

Department of Biomedical Informatics, Columbia University, New York, NY, USA.

New York Genome Center, New York, NY, USA.

出版信息

Nat Med. 2024 Dec;30(12):3578-3589. doi: 10.1038/s41591-024-03239-5. Epub 2024 Sep 3.

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.

摘要

精准医学有潜力通过考虑患者的生物构成来提供更准确的诊断、恰当的治疗和及时的预防策略。然而,如果不在一个能实现大样本量的数据共享框架中整合临床数据和组学数据,这一潜力就无法实现。由于临床数据和基因数据类型不同、存在互操作性、安全性及数据所有权问题,整合来自多个来源的临床和基因数据的系统很匮乏。在此,我们提出一个安全框架,该框架利用区块链技术实现临床和基因数据的不可变存储、查询及分析。我们的平台通过在统一框架下合并临床和基因数据,使其得以协调一致。它支持基因型-表型联合查询与分析,让机构能够控制自己的数据,并提供不可变的用户访问日志,从而提高健康信息使用方式及时间的透明度。我们通过创建基因型-表型队列并研究其中的关系,展示了我们的框架对精准医学的价值。我们表明,使用我们的安全平台跨机构合并数据可提高罕见病分析的统计效能。通过提供一个集成、安全且去中心化的框架,我们旨在提高可重复性,并鼓励社区和患者更广泛地参与数据共享。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f321/11645287/4bae481d36b1/41591_2024_3239_Fig1_HTML.jpg

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