在大规模和分布式基因组数据集中安全地发现遗传亲属.
Matthew M Hong1, David Froelicher1,2, Ricky Magner2
1MIT, Cambridge, 02139, USA.
概括
SF-Relate是一个新的联合算法,用于在分布式数据集中安全地找到遗传亲属. 它使用保护隐私的方法来准确识别家庭联系,而无需共享敏感数据.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 保护隐私的技术 保护隐私的技术
背景情况:
- 在基因组研究中识别亲属至关重要,但由于数据孤岛和隐私问题而受到阻碍.
- 由于数据共享限制和计算负担,现有的方法在分布式队列方面遇到了困难.
- 对于跨数据集的亲属关系估计,需要一个安全有效的解决方案.
研究的目的:
- 引入SF-Relate,一种实用且安全的联合算法,用于在分布式数据中识别遗传亲属.
- 为了能够准确地检测亲属关系,同时保持个人隐私.
- 克服大规模基因组研究中数据共享限制的局限性.
主要方法:
- 开发了SF-Relate,这是一个联合算法,利用本地敏感散列来将可能相关的个体分组到桶中.
- 实现了一种新的哈希函数,捕获身份按后裔 (IBD) 分段,以实现高效和准确的分类.
- 采用多方同型加密 (MHE) 进行安全,协作计算的相关系数,而无需共享数据.
主要成果:
- SF-Relate显著减少了亲属关系分析所需的对对比的数量.
- 在2000个个别数据集中,SF-Relate实现了94.9%的第三级亲属检测,99.9%的第二级或更近的亲属检测.
- 该算法展示了实际的运行时间,在15小时内完成了分析.
结论:
- SF-Relate提供了一个准确,实用和保护隐私的解决方案,用于在分布式数据集中识别遗传亲属.
- 新的哈希和MHE方法使得安全的亲属关系估计,促进大规模的基因组研究.
- 这项工作克服了合作基因组研究中的重大障碍,通过实现安全的跨实体关系发现.
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