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迈向安全港中使用基因组和表型数据进行研究的风险效用数据治理框架:多方面综述

Toward a Risk-Utility Data Governance Framework for Research Using Genomic and Phenotypic Data in Safe Havens: Multifaceted Review.

作者信息

Jones Kerina, Daniels Helen, Heys Sharon, Lacey Arron, Ford David V

机构信息

Population Data Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom.

出版信息

J Med Internet Res. 2020 May 15;22(5):e16346. doi: 10.2196/16346.

Abstract

BACKGROUND

Research using genomic data opens up new insights into health and disease. Being able to use the data in association with health and administrative record data held in safe havens can multiply the benefits. However, there is much discussion about the use of genomic data with perceptions of particular challenges in doing so safely and effectively.

OBJECTIVE

This study aimed to work toward a risk-utility data governance framework for research using genomic and phenotypic data in an anonymized form for research in safe havens.

METHODS

We carried out a multifaceted review drawing upon data governance arrangements in published research, case studies of organizations working with genomic and phenotypic data, public views and expectations, and example studies using genomic and phenotypic data in combination. The findings were contextualized against a backdrop of legislative and regulatory requirements and used to create recommendations.

RESULTS

We proposed recommendations toward a risk-utility model with a flexible suite of controls to safeguard privacy and retain data utility for research. These were presented as overarching principles aligned to the core elements in the data sharing framework produced by the Global Alliance for Genomics and Health and as practical control measures distilled from published literature and case studies of operational safe havens to be applied as required at a project-specific level.

CONCLUSIONS

The recommendations presented can be used to contribute toward a proportionate data governance framework to promote the safe, socially acceptable use of genomic and phenotypic data in safe havens. They do not purport to eradicate risk but propose case-by-case assessment with transparency and accountability. If the risks are adequately understood and mitigated, there should be no reason that linked genomic and phenotypic data should not be used in an anonymized form for research in safe havens.

摘要

背景

利用基因组数据开展的研究为洞察健康与疾病开辟了新途径。若能将这些数据与安全港中保存的健康及行政记录数据相结合使用,效益将成倍增加。然而,对于基因组数据的使用存在诸多讨论,人们认为在安全且有效地使用这些数据方面存在特殊挑战。

目的

本研究旨在努力构建一个风险效用数据治理框架,用于在安全港中以匿名形式使用基因组和表型数据进行研究。

方法

我们进行了多方面的综述,参考了已发表研究中的数据治理安排、处理基因组和表型数据的组织的案例研究、公众的观点和期望,以及结合使用基因组和表型数据的实例研究。研究结果结合立法和监管要求的背景进行分析,并用于提出建议。

结果

我们针对风险效用模型提出了建议,该模型具有一套灵活的控制措施,以保护隐私并保留数据用于研究的效用。这些建议以与全球基因组与健康联盟制定的数据共享框架的核心要素相一致的总体原则形式呈现,并作为从已发表文献和实际安全港案例研究中提炼出的实际控制措施,可根据具体项目的要求加以应用。

结论

所提出的建议可用于推动建立一个适度的数据治理框架,以促进在安全港中安全、社会可接受地使用基因组和表型数据。这些建议并非旨在消除风险,而是提议进行逐案评估,确保透明度和问责制。如果能够充分理解并减轻风险,那么没有理由不以匿名形式在安全港中使用关联的基因组和表型数据进行研究。

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