Children's Hospital Boston, Boston, MA 02115, USA.
Bioinformatics. 2012 Mar 15;28(6):886-8. doi: 10.1093/bioinformatics/bts045. Epub 2012 Feb 1.
Meta-analysis across genome-wide association studies is a common approach for discovering genetic associations. However, in some meta-analysis efforts, individual-level data cannot be broadly shared by study investigators due to privacy and Institutional Review Board concerns. In such cases, researchers cannot confirm that each study represents a unique group of people, leading to potentially inflated test statistics and false positives. To resolve this problem, we created a software tool, Gencrypt, which utilizes a security protocol known as one-way cryptographic hashes to allow overlapping participants to be identified without sharing individual-level data.
跨全基因组关联研究的荟萃分析是发现遗传关联的常用方法。然而，在一些荟萃分析工作中，由于隐私和机构审查委员会的担忧，研究人员无法广泛共享个体水平的数据。在这种情况下，研究人员无法确认每个研究都代表了一组独特的人群，从而导致潜在的检验统计量膨胀和假阳性。为了解决这个问题，我们创建了一个名为 Gencrypt 的软件工具，它利用一种称为单向密码哈希的安全协议来允许识别重叠的参与者，而无需共享个体水平的数据。
