Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Science. 2017 Aug 18;357(6352):692-695. doi: 10.1126/science.aam9710.
Patient genomes are interpretable only in the context of other genomes; however, genome sharing enables discrimination. Thousands of monogenic diseases have yielded definitive genomic diagnoses and potential gene therapy targets. Here we show how to provide such diagnoses while preserving participant privacy through the use of secure multiparty computation. In multiple real scenarios (small patient cohorts, trio analysis, two-hospital collaboration), we used our methods to identify the causal variant and discover previously unrecognized disease genes and variants while keeping up to 99.7% of all participants' most sensitive genomic information private.
患者基因组只有在与其他基因组结合的情况下才能进行解读；然而，基因组共享可以实现区分。数千种单基因疾病已经获得了明确的基因组诊断和潜在的基因治疗靶点。在这里，我们展示了如何通过使用安全多方计算在保护参与者隐私的同时提供这种诊断。在多个真实场景（小患者队列、三亲分析、两医院合作）中，我们使用我们的方法来识别因果变异，并发现以前未被识别的疾病基因和变异，同时保持高达 99.7%的所有参与者的最敏感基因组信息隐私。
