Identifying disease-causing mutations with privacy protection.

Bioinformatics
Mete AkgünOliver Kohlbacher

Abstract

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. 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 (MA...Continue Reading

References

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Methods Mentioned

BETA
exome sequencing
genotyping

Software Mentioned

gnomAD
ABY
VEP
VCF Explorer
RECESSIVE
INTERSECTION
SETDIFF
DOMINANT
Esembl
gcc

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