Accelerating Genomic Data Sharing and Collaborative Research with Privacy Protection

抽象的。基因组测序的快速进展导致了大量的数据收集。分析 这些数据在回答有关疾病关联和我们的 进化论。然而，由于对参与者敏感信息的隐私担忧日益加剧， 访问研究中使用的基因组数据集，例如全基因组关联研究(GWAS)，是 仅限于有限数量的大团体。另一方面，合作研究结束了 基因组数据集也将导致基因组数据共享的民主化，这需要共享数据 跨合作者。在协作者之间共享此类数据集的一种方式是通过IRB流程 以及机构数据使用协议的使用。目前，由于数据的敏感性，全球环境基金 只有在对所有合作者进行IRB审查之后，才能进行计算。在这项研究中，我们建议 一个沙盒环境，潜在的合作者聚集在一起，获得一个准确的 以高效、可重现(可验证)和隐私的方式预览他们的协作研究- 保存方式。我们建议的框架允许每个合作者共享关于其 在建议的沙箱环境中以保护隐私的方式提供数据集。这将有助于 研究人员(1)纠正来自低质量、有偏见或统计依赖的联合数据集 记录，(2)生成他们的协作GWAS结果的准确预览，以提供证据 IRB审批中的收益与风险权衡，以及(3)确定应共享数据集的哪些部分 在合作者中(一旦他们获得IRB的全部批准)。为了实现这些目标，我们将 开发(1)新算法，实现对联合数据的质量控制，同时 所有权和隐私以及(2)通过开发 验证GWAS计算正确性和共享整体的新技术 在研究数据集的同时保护隐私。我们的初步结果表明，所提出的框架 准确地提供GWAS结果的可重复性的证据，识别低质量(例如， 统计相关性)数据，并保护个人的隐私 合作者的数据集。值得注意的是，我们表明，由于建议的框架而导致的隐私风险更低 而不是NIH基因组数据共享政策所接受的数据。最后，与 ，我们会设计一项先导研究，探讨建议的成效。 框架，并将其纳入目前的内部审查委员会进程。这项研究的结果将提供 基因组数据共享的新策略。

Abstract. The rapid progress in genome sequencing has led to significant data collection. Analyzing this data can be transformative in answering the key questions about disease associations and our evolution. However, due to growing privacy concerns about the sensitive information of participants, access to genomic datasets used in studies, such as genome-wide association studies (GWAS), is restricted to only a limited number of large groups. On the other hand, collaborative research over genomic datasets, which will also lead to democratizing genomic data sharing, requires sharing data across collaborators. One way to share such datasets across collaborators is through the IRB process and the use of institutional data use agreements. Currently, due to the sensitivity of data, the GWAS computation can only be carried out after IRB review for all collaborators. In this research, we propose a sandbox environment in which potential collaborators come together and obtain an accurate "preview" of their collaborative research in an efficient, reproducible (verifiable), and privacy- preserving way. Our proposed framework allows each collaborator to share information about their dataset in a privacy-preserving way within the proposed sandbox environment. This will help the researchers (1) rectify their federated datasets from low-quality, biased, or statistically dependent records, (2) generate an accurate preview of their collaborative GWAS results to provide evidence for benefit versus risk tradeoff in IRB approval, and (3) identify what part of the datasets should be shared among the collaborators (once they obtain the full IRB approval). To achieve these goals, we will develop (1) novel algorithms that enable quality control over federated data while preserving ownership and privacy and (2) algorithms that promote reproducibility of GWAS results by developing novel techniques for verifying the correctness of GWAS computation and for sharing the whole research datasets while preserving privacy. Our preliminary results show that the proposed framework accurately provides evidence of reproducibility of GWAS results, identifies low-quality (e.g., statistically dependent) data in federated datasets, and preserves the privacy of individuals in collaborators' datasets. Notably, we show that privacy risk due to the proposed framework is lower than the one accepted by the NIH Genomic Data Sharing Policy. Finally, working together with the IRB from three institutions, we will design a pilot study to explore the efficacy of the proposed framework and its integration into the current IRB process. The outcomes of this research will provide a new strategy for genomic data sharing.