Homomorphic Encryption of Genotypes and Phenotypes for Quantitative Genetics

定量遗传学基因型和表型的同态加密

• 批准号: BB/V00767X/1
• 负责人: Richard Mott
• 金额: $ 51.35万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FV00767X%2F1
• 关键词: Homomorphic; Encryption; Genotypes; Phenotypes; Quantitative

In order to identify genes that are associated with important traits like disease in humans, or improved yield in crops, it is necessary to analyse very large samples of individuals. Often this involves sharing genetic and other data collected in different studies, and there are risks to individuals' genetic privacy if these data are shared as plaintext. Homomorphic encryption refers to a type of data encryption that obscures the original plaintext data by replacing it with a ciphertext which nonetheless contains sufficient structure that it is still possible to perform the same data analyses as with the plaintext, thereby increasing the power to make discoveries whilst maintaining genetic privacy.We have previously developed a method for homomorphic encryption of genotype and phenotype data, based on random high-dimensional rotations of data. In this proposal we will develop our method into a practical tool that can be used by geneticists and other scientists. This will involve writing a software implementation that can operate on very large datasets, and working closely with stakeholders to ensure the code is as useful as possible.

为了识别与人类疾病或农作物产量提高等重要特征相关的基因，有必要分析非常大的个体样本。这通常涉及共享在不同研究中收集的遗传数据和其他数据，如果这些数据以明文形式共享，那么个人的遗传隐私就会面临风险。同态加密是指一种数据加密，通过用密文替换原始明文数据来掩盖原始明文数据，但密文仍然包含足够的结构，仍然可以执行与明文相同的数据分析，从而增加发现的能力，同时维护遗传隐私。我们之前开发了一种基于数据随机高维旋转的基因型和表型数据同态加密方法。在本提案中，我们将把我们的方法发展成为可供遗传学家和其他科学家使用的实用工具。这将涉及编写一个可以在非常大的数据集上运行的软件实现，并与利益相关者密切合作，以确保代码尽可能有用。

项目成果

Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality

使用加密的基因型和表型进行协作基因组分析以维护数据机密性

• DOI: http://dx.10.1093/genetics/iyad210
• 发表时间: 2023
• 期刊: GENETICS
• 影响因子: 3.3
• 作者: Zhao T