Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations
Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations
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DOI：
10.1016/j.cels.2016.04.013
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发表时间：
2016-07-27
期刊：
影响因子：
9.3
通讯作者：
Berger, Bonnie
中科院分区：
文献类型：
--
作者：
Simmons, Sean;Sahinalp, Cenk;Berger, Bonnie
大型基因组数据库的激增为进行越来越大规模的全基因组关联研究（GWAS）提供了可能。然而，由于隐私问题，对这些数据的访问受到限制，大大降低了它们对研究的有用性。在这里，我们介绍了一个用于执行GWAS的计算框架，该框架适应差分隐私的原则-一种促进敏感数据安全分析的密码学理论-既保护私有表型信息（例如，疾病状态），并对人群分层进行校正。该框架使我们能够基于EIGENSTRAT和基于线性混合模型（LMM）的统计数据生成保护隐私的GWAS结果，这两种方法都可以校正人口分层。我们在模拟和真实的GWAS数据集上测试了我们的差异隐私统计，PrivSTRAT和PrivLMM，发现它们能够保护隐私，同时返回有意义的结果。我们的框架可用于安全地查询私人基因组数据集，以发现哪些特定的基因组改变可能与疾病相关，从而增加这些有价值的数据集的可用性。
The proliferation of large genomic databases offers the potential to perform increasingly larger-scale genome-wide association studies (GWASs). Due to privacy concerns, however, access to these data is limited, greatly reducing their usefulness for research. Here, we introduce a computational framework for performing GWASs that adapts principles of differential privacy-a cryptographic theory that facilitates secure analysis of sensitive data-to both protect private phenotype information (e.g., disease status) and correct for population stratification. This framework enables us to produce privacy-preserving GWAS results based on EIGENSTRAT and linear mixed model (LMM)-based statistics, both of which correct for population stratification. We test our differentially private statistics, PrivSTRAT and PrivLMM, on simulated and real GWAS datasets and find they are able to protect privacy while returning meaningful results. Our framework can be used to securely query private genomic datasets to discover which specific genomic alterations may be associated with a disease, thus increasing the availability of these valuable datasets.