Homomorphic Encryption of Genotypes and Phenotypes for Quantitative Genetics

为了确定与人类疾病或作物产量提高等重要特征相关的基因，有必要对非常大的个体样本进行分析。这通常涉及共享在不同研究中收集的基因和其他数据，如果这些数据以明文形式共享，会对个人的基因隐私构成风险。同态加密是指一种数据加密类型，它通过用密文替换原始明文数据来模糊原始明文数据，但该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，从而在保持遗传隐私的同时增加发现的能力。我们之前开发了一种基于数据的随机高维旋转的基因型和表型数据的同态加密方法。在这项提案中，我们将把我们的方法发展成一种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。

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.