Privacy-preserving genomic medicine at scale

大规模保护隐私的基因组医学

• 批准号: 10266081
• 负责人: BONNIE BERGER
• 金额: $ 67.49万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-18 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10266081
• 关键词: Address; Adoption; Algorithmic Analysis; Algorithms; Automobile Driving; Biological; Biology; Clinic; Clinical Data; Collaborations; Communities; Complex; Complex Analysis; Computational Biology; Computer software; Computing Methodologies; Consumption; Data; Data Analyses; Data Pooling; Data Security; Data Set; Disease; Drug Interactions; Electronic Health Record; Engineering; Environment; Genetic; Genetic study; Genome; Genomic medicine; Genomics; Goals; Health; High-Throughput Nucleotide Sequencing; Human; Individual; Institutes; Institution; Knowledge; Letters; Machine Learning; Mainstreaming; Measures; Medical Imaging; Medical Records; Medicine; Methods; Modernization; Molecular; Nature; Patients; Performance; Pharmacology; Polygenic Traits; Privacy; Process; Production; Property; Research Personnel; Resources; Risk; Sampling; Science; Secure; Security; Software Engineering; Software Tools; Standardization; Stream; Structure; Supercomputing; Techniques; Technology; Time; Translations; United States National Institutes of Health; Work; analysis pipeline; base; biobank; bioimaging; clinical development; clinical phenotype; clinically relevant; cohort; computer framework; cost; cryptography; data analysis pipeline; data handling; data integration; data repository; data sharing; disorder risk; epidemiology study; experimental study; genome analysis; genome wide association study; genomic data; health data; innovation; insight; monomethoxypolyethylene glycol; multi-ethnic; neuroimaging; novel; open source; polygenic risk score; precision medicine; preservation; privacy preservation; statistics; success; task analysis; theories; tool

1 Project Summary 2 3 High-throughput sequencing, biomedical imaging, and electronic health record technologies are 4 generating health-related datasets of unprecedented scale. Integrative analysis of these 5 resources promises to reveal new biology and drive personal and precision medicine. Yet, the 6 sensitive nature of these data often requires that they be kept in isolated silos, limiting their 7 usefulness to science. The goal of this project is to develop innovative privacy-preserving 8 algorithms to enable data sharing and drive genomic medicine. Crucially, we will draw upon our 9 past success in secure genome analysis and algorithmic expertise in computational biology to 10 address the imminent need to perform complex integrative analyses securely and at scale. 11 Current privacy-preserving tools are prohibitively too costly to perform the complex 12 calculations required in genomic analysis. We previously leveraged the highly structured nature 13 of biological data and novel optimization strategies to implement efficient pipelines for secure 14 genome-wide association studies (GWAS) and drug interaction predictions which scaled to 15 millions of samples. In this project, we will further exploit the unique properties of biomedical data 16 to: (i) develop secure integrative analysis methods for genomic medicine; (ii) develop an easy-to- 17 use programming environment with advanced automated optimizations to facilitate the adoption 18 of privacy-preserving analyses; and (iii) promote the use of our privacy techniques to gain novel 19 biological insights through large-scale collaborative genetic studies of multi-ethnic cohorts. 20 With co-I’s Amarasinghe (MIT) and Cho (Broad Institute), we aim to apply these tools to 21 realize the first multi-institution, multi-national secure genetic studies with our partners at the 22 Swiss Personalized Health Network, UK Biobank, Finnish FinnGen, All of Us, NIH NCBI, Broad 23 and Barcelona Supercomputing Center (Letters of Support). We will also use our privacy- 24 preserving approaches to study genomic origins of polygenic traits for disease as well as 25 neuroimaging and other clinical phenotypes. We will continue to actively integrate our methods 26 into community standards (MPEG-G, GA4GH). 27 Successful completion of these aims will result in computational methods and open-source, 28 easy-to-use, production-grade implementations that open the door to secure integration and 29 analysis of massive sets of sensitive genomic and clinical data. With input from our collaborations, 30 we will build these tools and apply them to better understand the molecular causes of human 31 health and its translation to the clinic.

1个项目摘要 2 3个高通量测序，生物医学成像和电子健康记录技术是 4生成前所未有的与健康相关的数据集。这些综合分析 5资源有望揭示新的生物学，并推动个人和精密医学。但是， 6这些数据的敏感性通常要求它们保持孤立的孤岛，从而限制它们 7对科学的有用性。该项目的目的是开发具有创新的隐私性 8算法启用数据共享并驱动基因组医学。至关重要的是，我们将借鉴我们的 9在确保基因组分析和计算生物学方面的算法专业知识方面的成功成功 10应对安全和大规模执行复杂的集成分析的直接需求。 11当前保存隐私的工具被禁止过于昂贵，无法执行该综合体 基因组分析中需要的12个计算。我们以前利用了高度结构化的性质 生物数据和新型优化策略的13个以实施安全管道 14个全基因组关联研究（GWAS）和药物相互作用预测，缩放为 1500万样品。在这个项目中，我们将进一步探讨生物医学数据的独特属性 16至：（i）为基因组医学开发安全的整合分析方法； （ii）开发易于 - 17使用具有高级自动优化的编程环境来促进采用 保存隐私分析的18个； （iii）促进使用我们的隐私技术来获得新颖 19通过大规模的多种族群体协作遗传学研究的生物学见解。 20与Co-I的Amarasinghe（MIT）和Cho（Broad Institute），我们的目标是将这些工具应用于 21与我们的合作伙伴在 22瑞士个性化健康网络，英国生物银行，芬兰芬兰，我们所有人，NIH NCBI，Broad 23和巴塞罗那超级计算中心（支持信）。我们还将使用我们的隐私 - 24保存研究疾病多基因性状的基因组起源的方法 25神经影像学和其他临床表型。我们将继续积极整合我们的方法 26符合社区标准（MPEG-G，GA4GH）。 27这些目标的成功完成将导致计算方法和开源方法， 28易于使用的生产级实施，打开了确保集成和 29大量敏感基因组和临床数据的分析。在我们的合作中的意见下， 30我们将构建这些工具并应用它们以更好地了解人类的分子原因 31健康及其转化为诊所。