在54,092个GWAS样本中通过加密的基因型回归搜索跨队列的亲属
Qi-Xin Zhang1,2, Tianzi Liu3,4, Xinxin Guo5
1Institute of Bioinformatics, Zhejiang University, Hangzhou, Zhejiang, China.
PLoS genetics
|January 11, 2024
概括
我们开发了encG-reg,这是一种使用加密的基因组数据检测亲属的新方法,克服了隐私障碍. 这种方法确保了准确的关系识别,而不影响敏感的遗传信息.
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:
- 分享个体级别的基因组数据提供了诸如增强的统计能力和基因组广泛关联研究 (GWAS) 中严格的质量控制等优势.
- 然而,隐私问题和道德约束往往限制了这种敏感数据的直接共享.
- 现有的关系推断方法通常需要访问未加密的基因型数据,这对协作研究构成了重大障碍.
研究的目的:
- 开发和验证一种新的回归方法,encG-reg,用于使用加密的基因组数据检测亲属.
- 为了证明encG-reg可以准确地识别关系,而不会损害个人隐私或道德标准.
- 评估encG-reg在各种数据集和多中心协作中的计算效率和性能.
主要方法:
- 开发了encG-reg,一种基于回归的方法,利用随机矩阵理论来加密基因型数据,同时保持精度.
- 建立了随机矩阵尺寸和加密基因型数据分析所需的精度之间的关系.
- 将encG-reg应用于加密的英国生物库数据,并在中国的9个队列中进行了大规模的多中心合作 (54,092个样本).
主要成果:
- 通过encG-reg处理的加密基因组数据产生了虚假阳性和虚假阴性率,相当于使用原始,未加密的个人级数据.
- 使用encG-reg进行的关系估计与使用KING获得的数据一样准确,KING是一种广泛使用的软件,需要未加密的数据.
- 在一项复杂的多中心研究中,EncG-reg成功地在多个群体中识别了真亲,这些群体具有不同的种族背景和基因型特质.
结论:
- 加密的基因组数据可以有效地共享用于关系检测,而不会丢失关键信息.
- EncG-reg提供了一个保护隐私和计算效率高的解决方案,以克服基因组学中的数据共享障碍.
- 这种方法促进了强大的多中心合作,提高了基因组数据在研究中的实用性,同时维护了隐私和道德标准.
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