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
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
Genome research
|August 7, 2024
概括
SF-Relate是一个新的联合算法,可以在分布式数据集中安全地识别遗传亲属. 它使用隐私保护技术有效地找到家庭联系,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.
- 计算生物学 计算生物学
背景情况:
- 在基因组研究中识别亲属至关重要,但因机构间数据共享限制而受到阻碍.
- 在分布式数据集中估计亲属关系是计算密集的,并带来隐私挑战.
研究的目的:
- 开发一种保护隐私的联合算法,用于识别跨数据孤岛的遗传亲属.
- 克服大规模基因组研究中数据共享和计算负担的局限性.
主要方法:
- 引入了SF-Relate,这是一个使用局部敏感哈希 (LSH) 组合潜在相关个体的联合算法.
- 开发了一种新的哈希函数,可以捕获身份按后裔 (IBD) 分段,并制定了用于高效比较的分类策略.
- 实现多方同型加密 (MHE) 以安全计算相关系数而无需数据暴露.
主要成果:
- SF-Relate显著减少了用于亲属关系估计所需的对对比的数量.
- 该算法准确地检测遗传亲属,在200,000个个体的数据集中实现了97%的第三度或更近的亲属检测.
- 经过实践运行时间的证明,分析在15小时内完成,数据集分为两方.
结论:
- SF-Relate提供了一种实用且安全的解决方案,用于在分布式基因组数据集中识别遗传亲属.
- 通过在数据共享约束下促进安全的亲属关系分析,使大规模的基因组研究成为可能.
- 在生物信息学和人口遗传学中推进保护隐私的计算方法.
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