Xie Wei, Kantarcioglu Murat, Bush William S, Crawford Dana, Denny Joshua C, Heatherly Raymond, Malin Bradley A
Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA.
Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA.
Bioinformatics. 2014 Dec 1;30(23):3334-41. doi: 10.1093/bioinformatics/btu561. Epub 2014 Aug 21.
Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies. However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data.
We introduce a novel cryptographic strategy to securely perform meta-analysis for genetic association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where privacy or confidentiality is of concern. We validate our method using three multisite association studies. Our research shows that genetic associations can be analyzed efficiently and accurately across substudy sites, without leaking information on individual participants and site-level association summaries.
Our software for secure meta-analysis of genetic association studies, SecureMA, is publicly available at http://github.com/XieConnect/SecureMA. Our customized secure computation framework is also publicly available at http://github.com/XieConnect/CircuitService.
共享基因组数据对于支持全基因组关联研究等科学调查至关重要。然而，最近的调查表明，这些研究中个体参与者的隐私可能会受到侵犯，从而引发严重的担忧和后果，例如对数据的访问过度受限。
我们引入了一种新颖的加密策略，用于在大型联盟中安全地进行基因关联研究的荟萃分析。我们的方法对于支持不同数据站点之间的联合研究很有用，在这些研究中，隐私或保密性是令人关注的问题。我们使用三项多站点关联研究对我们的方法进行了验证。我们的研究表明，可以在子研究站点之间高效且准确地分析基因关联，而不会泄露个体参与者的信息和站点级关联总结。
我们用于基因关联研究安全荟萃分析的软件SecureMA可在http://github.com/XieConnect/SecureMA上公开获取。我们定制的安全计算框架也可在http://github.com/XieConnect/CircuitService上公开获取。
