Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Identifying Disease-causing Mutations with Privacy Protection


Kohlbacher,  O
Research Group Biomolecular Interactions, Max Planck Institute for Developmental Biology, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Akgün, M., Ünal, A., Ergüner, B., Pfeifer, N., & Kohlbacher, O. (2021). Identifying Disease-causing Mutations with Privacy Protection. Bioinformatics, 36(21), 5205-5213. doi:10.1093/bioinformatics/btaa641.

Cite as: https://hdl.handle.net/21.11116/0000-000A-56CB-1
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 article, 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 and compound heterozygous) based privacy-preserving analyses of genomic data to find disease-causing mutations. Furthermore, we re-implement 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.