Accelerating Genomic Data Sharing and Collaborative Research with Privacy Protection

通过隐私保护加速基因组数据共享和协作研究

• 批准号: 10735407
• 负责人: Erman Ayday
• 金额: $ 67.3万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10735407
• 关键词: Acceleration; Address; Agreement; Algorithms; Collaborations; Computer software; Data; Data Collection; Data Pooling; Data Protection; Data Set; Democracy; Disease; Ensure; Environment; Evolution; Generations; Genetic study; Genome; Genomic approach; Genomics; Goals; Health; Heterogeneity; Individual; Institution; Institutional Review Boards; Lead; Meta-Analysis; Metadata; Methods; Nature; Notification; Outcome; Outcomes Research; Ownership; Participant; Personal Satisfaction; Pilot Projects; Policies; Population; Population Heterogeneity; Preparation; Preservation Technique; Privacy; Privatization; Process; Publishing; Quality Control; Records; Reproducibility; Research; Research Personnel; Risk; Sampling; Secure; Site; Societies; Techniques; United States National Institutes of Health; Work; computer studies; data access; data sharing; design; effective therapy; federated data; genome sciences; genome sequencing; genome wide association study; genomic data; improved; insight; interest; novel; preservation; privacy preservation; privacy protection; software development; statistics; success; tool

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.

抽象的。基因组测序的快速进展带来了大量的数据收集。分析中 这些数据对于回答有关疾病关联和我们的疾病的关键问题具有革命性的意义。 进化。然而，由于对参与者敏感信息的隐私担忧日益增加， 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） 仅限于数量有限的大型团体。另一方面，合作研究 基因组数据集也将导致基因组数据共享民主化，需要共享数据 跨合作者。在合作者之间共享此类数据集的一种方法是通过 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，GWAS 所有合作者必须经过IRB审核后才能进行计算。在这项研究中，我们提出 沙盒环境，潜在合作者聚集在一起并获得准确的 以高效、可重复（可验证）和隐私的方式“预览”他们的合作研究 保存方式。我们提出的框架允许每个合作者共享有关他们的信息 在建议的沙箱环境中以保护隐私的方式存储数据集。这将有助于 研究人员 (1) 纠正低质量、有偏见或统计依赖性的联合数据集 记录，(2) 生成其协作 GWAS 结果的准确预览，为以下方面提供证据： IRB 批准中的收益与风险权衡，以及 (3) 确定应共享数据集的哪一部分 合作者之间（一旦获得 IRB 的完全批准）。为了实现这些目标，我们将 开发 (1) 新颖的算法，能够对联合数据进行质量控制，同时保留 所有权和隐私，以及 (2) 通过开发提高 GWAS 结果可重复性的算法 验证 GWAS 计算正确性和共享整体的新技术 研究数据集，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 结果可重复性的证据，识别低质量（例如， 统计上相关的）联邦数据集中的数据，并保护个人的隐私 合作者的数据集。值得注意的是，我们表明所提出的框架带来的隐私风险较低 比 NIH 基因组数据共享政策所接受的数据更重要。最后，与 来自三个机构的IRB，我们将设计一项试点研究来探索所提议的功效 框架及其与当前 IRB 流程的整合。这项研究的成果将提供 基因组数据共享的新策略。