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使用加密的基因型和表型进行合作基因组分析,以保持数据保密性

Tianjing Zhao^1,2, Fangyi Wang³, Richard Mott⁴

¹Department of Animal Science, University of California, Davis, CA 95616, USA.

|December 12, 2023

概括

对基因型和表型 (HEGP) 的同型加密使得安全的农业基因组研究成为可能. 这种方法允许共享基因组对基因组分析的数据,同时保护隐私和知识产权.

科学领域:

农业基因组学农业基因组学
生物信息学是一种生物信息学.
数据安全数据安全

背景情况:

公平原则促进农业基因组对现象研究中的数据共享.
隐私和知识产权问题阻碍了数据共享.
现有的方法缺乏安全的基因组数据分析的强大解决方案.

研究的目的:

扩展对基因型和表型 (HEGP) 的同型加密,用于更广泛的基因组到现象应用.
为了实现安全的合作基因组分析,同时保持数据保密性.
促进在农业研究中采用FAIR原则.

主要方法:

扩展对基因型和表型 (HEGP) 的同型加密方法.
将HEGP应用于线性混合模型,包括基因组最佳线性无偏预测 (GBLUP) 和回归最佳线性无偏预测 (RR-BLUP).
整合HEGP与贝叶斯变量选择方法进行基因分析.

主要成果:

HEGP适用于各种遗传分析,包括参数估计,基因组预测和全基因组关联研究 (GWAS).
扩展的HEGP方法在统计分析期间保持数据保密.
已证明适用于常用于定量特征遗传学的复杂遗传模型.

结论:

先进的HEGP方法为合作的农业基因组研究提供了安全有效的方法.
在基因组对现象研究中,HEGP有效地解决了隐私和知识产权障碍.
这有利于更大的数据共享和可重复使用性,与FAIR原则保持一致.

关键词:

在GWAS中,GWAS就是GWAS.基因组预测基因组预测同型型的加密方式.联合分析联合分析这是一个混合模型混合模型.

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