Homomorphic Encryption of Genotypes and Phenotypes for Quantitative Genetics

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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.