Recent advances in the usage of DNA as incriminating forensic evidence to solve high-profile cases bring many new ethical questions that may cause concerns for these individuals. While there is a strong urge to share the data for curing diseases, privacy issues are generally not coherently addressed. These risks may extend to the relatives of the individuals. This makes it even harder to provide privacy as only a few rare variants among millions can easily re-identify an individual. The sequencing of millions of samples provides genetic variants with allele frequencies spanning a very large spectrum. Among these, genomic privacy has recently become an important facet of genomic data sharing because the genetic variants are shown to be strong re-identifiers even from de-identified genomic datasets. In particular, the high prevalence of genetic data in clinical, recreational, and research areas makes interpretation and management of genomic data challenging. These genomes can provide great insight for developing new therapies and drugs for diseases, prenatal genetic testing, and more advanced methods for disease risk prediction. Starting with the initial population-wide genotyping projects such as The HapMap Consortium, The 1000 Genomes Project, Genomics England, The Cancer Genome Atlas (TCGA), Trans-omics for precision medicine (TOPMed), The Genotype-Tissue Expression (GTEx) Project, and the Precision Medicine Initiative, there are now millions of genomes that are deposited in research, clinical, or recreational database. Īs the cost of next-generation sequencing is decreasing, the number of personal genomes and associated personal information is rapidly increasing. SVAT is publicly available for download from. Overall, SVAT provides a secure, flexible, and practical framework for privacy-aware outsourcing of annotation, filtering, and aggregation of genetic variants. Also, SVAT utilizes a secure re-encryption approach so that multiple disparate genotype datasets can be combined for federated aggregation and secure computation of allele frequencies on the aggregated dataset. SVAT makes use of a vectorized data representation to convert annotation and aggregation into efficient vectorized operations in a single framework. The data always stays encrypted while it is stored, in-transit, and most importantly while it is analyzed. SVAT uses homomorphic encryption to encrypt the data at the client-side. We present SVAT, a method for secure outsourcing of variant annotation and aggregation, which are two basic steps in variant interpretation and detection of causal variants. In certain cases, there are policy barriers against sharing genetic data from indigenous populations and stigmatizing conditions. Protecting the genetic privacy of participants is challenging as only a few rare variants can easily re-identify an individual among millions. Sequencing of thousands of samples provides genetic variants with allele frequencies spanning a very large spectrum and gives invaluable insight into genetic determinants of diseases.
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