Privacy-preserving GWAS analysis on federated genomic datasets.
Privacy-preserving GWAS analysis on federated genomic datasets.
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DOI：
10.1186/1472-6947-15-s5-s2
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发表时间：
2015
影响因子：
3.5
通讯作者：
Chapin S
中科院分区：
文献类型：
--
作者：
Constable SD;Tang Y;Wang S;Jiang X;Chapin S
生物医学界受益于越来越多的基因组数据，以支持有意义的科学研究，例如全基因组联合研究(GWAS)。然而，高质量的GWAS通常需要大量的样本，这可能会超出单个机构的能力。联合基因组数据分析有望实现跨机构合作，以实现有效的GWAS，但它引发了对患者隐私和医疗信息机密性的担忧(因为数据正在跨机构边界交换)，这成为实际使用的制约因素。我们在联合基因组数据集上提出了一个隐私保护的GWA框架。我们的方法是在安全多方计算(MPC)系统的基础上进行GWAS计算。这种方法允许分布式系统中的双方相互执行安全的GWAS计算，但不会将他们的私有数据暴露在外部。我们通过实现一个用于次要等位基因频率计数和χ2统计计算的框架来演示我们的技术，这是GWA中使用的典型计算之一。为了高效地进行原型设计，我们使用了最先进的MPC框架，即可移植电路格式(PCF)。我们的实验结果表明，我们在实现高效和安全的跨机构GWA计算方面是有希望的。
The biomedical community benefits from the increasing availability of genomic data to support meaningful scientific research, e.g., Genome-Wide Association Studies (GWAS). However, high quality GWAS usually requires a large amount of samples, which can grow beyond the capability of a single institution. Federated genomic data analysis holds the promise of enabling cross-institution collaboration for effective GWAS, but it raises concerns about patient privacy and medical information confidentiality (as data are being exchanged across institutional boundaries), which becomes an inhibiting factor for the practical use. We present a privacy-preserving GWAS framework on federated genomic datasets. Our method is to layer the GWAS computations on top of secure multi-party computation (MPC) systems. This approach allows two parties in a distributed system to mutually perform secure GWAS computations, but without exposing their private data outside. We demonstrate our technique by implementing a framework for minor allele frequency counting and χ2 statistics calculation, one of typical computations used in GWAS. For efficient prototyping, we use a state-of-the-art MPC framework, i.e., Portable Circuit Format (PCF). Our experimental results show promise in realizing both efficient and secure cross-institution GWAS computations.