Genome-wide prediction and association studies offer a powerful approach to connecting genotype to phenotype at a large scale, but performing genomic analyses in humans invokes genomic privacy concerns that complicate the sharing of data. In a study published in the March issue of GENETICS, [researcher Tianjing] Zhao and colleagues expand an existing encryption approach, offering a secure avenue to perform genomic analysis without compromising confidentiality.
Because of the inherent privacy and intellectual property concerns, direct sharing of raw genotype and phenotype data is often prohibited, for example in human research; researchers first anonymize sensitive information like individual ID numbers, sex, disease status, family relations between individuals, and other covariates before performing any calculations.
So then, in a research landscape that values open-access data principles like FAIR (findable, accessible, interoperable, and reusable), how can population geneticists make their data widely available without compromising the privacy of the individuals in question?
Several data encryption approaches that obscure sensitive information have been developed; the homomorphic encryption method for genotype and phenotype (HEGP) methodology encrypts genotype, phenotype, and covariate data in a way that cannot be linked back to original identifiers, thus maintaining data privacy.
Share this article
…
In conclusion, geneticists have an encryption method available for genomic analyses that allows them to perform necessary statistical analyses without disclosing sensitive information, thereby avoiding privacy concerns altogether.
