Identifying disease-causing mutations with privacy protection

Abstract

Motivation The use of genome data for diagnosis and treatment is becoming increasingly common. Researchers need access to as many genomes as possible to interpret the patient genome, to obtain some statistical patterns, and to reveal disease-gene relationships. The sensitive information contained in the genome data and the high risk of re-identification increase the privacy and security concerns associated with sharing such data. In this paper, we present an approach to identify disease-associated variants and genes while ensuring patient privacy. The proposed method uses secure multi-party computation to find disease-causing mutations under specific inheritance models without sacrificing the privacy of individuals. It discloses only variants or genes obtained as a result of the analysis. Thus, the vast majority of patient data can be kept private. Results Our prototype implementation performs analyses on thousands of genomic data in milliseconds, and the runtime scales logarithmically with the number of patients. We present the first inheritance model (recessive, dominant, compound heterozygous) based privacy-preserving analyses of genomic data in order to find disease-causing mutations. Furthermore, we reimplement the privacy-preserving methods (MAX, SETDIFF, and INTERSECTION) proposed in a previous study. Our MAX, SETDIFF, and INTERSECTION implementations are 2.5, 1122, and 341 times faster than the corresponding operations of the state-of-the-art protocol, respectively. Availability https://gitlab.com/DIFUTURE/privacy-preserving-genomic-diagnosis Supplementary information Supplementary data are available at Bioinformatics online.

Genetic data protection as an indispensable element of genomic medicine development

“…Database of Single Nucleotide Polymorphisms (dbSNP Build 155) contains human SNVs, microsatellites, and small scale indels along with publication, population frequency and molecular consequence [27,28] DECIPHER [32], COSMIC [33] and OMIM [34]. As a consequence of "dramatic increase in the amount of genetic information generated, analyzed, shared, and stored by diverse individuals and entities" [1], an issue of ensuring relevant protection of these data becomes even more urgent [35,36].…”

Privacy-preserving Polygenic Risk Scoring using Homomorphic Encryption

“…Jagadeesh et al proposed to use SMC to identify diagnosis for monogenic rare diseases while preserving privacy for the remaining variants [11]. Later Akgun et al improved the performance of the protocol [2]. Cui et al proposed to use secure function evaluation to implement a privacy-preserving Human Leukocyte Antigen (HLA) matching [6].…”

Digital DNA lifecycle security and privacy: an overview

“…Akgün et al [99] produced a privacy-preserving multiparty computation approach to identify disease-associated variants and genes based on a combination of arithmetic and boolean sharing in the same computation. The researchers' approach was faster and more accurate than the previous solution, and It could also allow cross-institution collaborations which were very useful in the case of rare diseases.…”