Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality
Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality
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DOI：
10.1093/genetics/iyad210
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发表时间：
2023-12-12
期刊：
影响因子：
3.3
通讯作者：
Cheng，Hao
中科院分区：
文献类型：
--
作者：
Zhao，Tianjing;Wang，Fangyi;Cheng，Hao
为了在农业基因组到表型组研究中坚持和利用FAIR（可发现、可访问、可互操作和可重复使用）原则的好处，解决阻止研究和工业中数据共享和重复使用的隐私和知识产权问题至关重要。由于知识产权和隐私问题，基因型和表型数据的直接共享通常被禁止。因此，迫切需要一种加密方法，使数据的机密方面模糊不清，而不影响某些统计分析的结果。提出了一种用于全基因组关联研究（GWAS）中单标记回归的基因型和表型同态加密方法（HEGP），该方法使用具有高斯误差的线性混合模型。这种方法允许基于频率似然的参数估计和推断。在本文中，我们将HEGP扩展到基因组对表型组分析的更广泛应用。我们表明，HEGP适合于常用的线性混合模型的数量性状的遗传分析，包括基因组最佳线性无偏预测（GBLUP）和岭回归最佳线性无偏预测（RR-BLUP），以及贝叶斯变量选择方法（例如，在贝叶斯字母表），遗传参数估计，基因组预测，和GWAS。通过提升HEGP的功能，我们为研究人员和行业专业人士提供了一种安全高效的协作基因组分析方法，同时保护数据机密性。
To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses. A homomorphic encryption method for genotypes and phenotypes (HEGP) has been proposed for single-marker regression in genome-wide association studies (GWAS) using linear mixed models with Gaussian errors. This methodology permits frequentist likelihood-based parameter estimation and inference. In this paper, we extend HEGP to broader applications in genome-to-phenome analyses. We show that HEGP is suited to commonly used linear mixed models for genetic analyses of quantitative traits including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP), as well as Bayesian variable selection methods (e.g. those in Bayesian Alphabet), for genetic parameter estimation, genomic prediction, and GWAS. By advancing the capabilities of HEGP, we offer researchers and industry professionals a secure and efficient approach for collaborative genomic analyses while preserving data confidentiality.