Privacy-preserving genomic medicine at scale

1项目概要 2 3高通量测序、生物医学成像和电子健康记录技术是 4.生成规模空前的健康相关数据集。综合分析这些 5资源承诺揭示新的生物学，并推动个人和精准医疗。然而 这些数据的敏感性通常要求将其保存在隔离的孤岛中，从而限制了其 7科学的用处该项目的目标是开发创新的隐私保护 8种算法实现数据共享并推动基因组医学。关键是，我们将利用我们的 9过去在安全基因组分析和计算生物学算法专业知识方面的成功， 10解决了安全和大规模执行复杂综合分析的迫切需要。 11目前的隐私保护工具过于昂贵，无法执行复杂的 基因组分析需要12次计算。我们以前利用了高度结构化的性质， 13生物数据和新的优化策略，以实现安全的高效管道 14项全基因组关联研究（GWAS）和药物相互作用预测， 1500万个样本在这个项目中，我们将进一步利用生物医学数据的独特属性 16：（一）为基因组医学开发安全的综合分析方法;（二）开发一种易于使用的 17使用具有高级自动优化的编程环境，以促进采用 18隐私保护分析;以及（iii）促进使用我们的隐私技术，以获得新颖的 19通过多种族群体的大规模协作遗传研究获得的生物学见解。 20与我公司的Amarasinghe（麻省理工学院）和Cho（布罗德研究所）合作，我们的目标是将这些工具应用于 21与我们的合作伙伴一起，在2015年实现第一个多机构，多国安全遗传研究。 22 Swiss Personalized Health Network，UK Biobank，FinnGen，All of Us，NIH NCBI，Broad 23和巴塞罗那超级计算中心（支持信）。我们也会利用我们的隐私- 24种保存方法来研究疾病多基因性状的基因组起源， 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转换为社区标准（MPEG-G，GA 4GH）。 27.这些目标的成功实现将产生计算方法和开源， 28个易于使用的生产级实施方案，为安全集成打开了大门， 29分析大量敏感的基因组和临床数据。通过我们的合作， 我们将建立这些工具，并应用它们来更好地了解人类疾病的分子原因。 31健康和它的翻译到诊所。

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.