Sketching algorithms for genomic data analysis and querying in a secure enclave.

Nature Methods
Can KockanS Cenk Sahinalp

Abstract

Genome-wide association studies (GWAS), especially on rare diseases, may necessitate exchange of sensitive genomic data between multiple institutions. Since genomic data sharing is often infeasible due to privacy concerns, cryptographic methods, such as secure multiparty computation (SMC) protocols, have been developed with the aim of offering privacy-preserving collaborative GWAS. Unfortunately, the computational overhead of these methods remain prohibitive for human-genome-scale data. Here we introduce SkSES (https://github.com/ndokmai/sgx-genome-variants-search), a hardware-software hybrid approach for privacy-preserving collaborative GWAS, which improves the running time of the most advanced cryptographic protocols by two orders of magnitude. The SkSES approach is based on trusted execution environments (TEEs) offered by current-generation microprocessors-in particular, Intel's SGX. To overcome the severe memory limitation of the TEEs, SkSES employs novel 'sketching' algorithms that maintain essential statistical information on genomic variants in input VCF files. By additionally incorporating efficient data compression and population stratification reduction methods, SkSES identifies the top k genomic variants in a cohort ...Continue Reading

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Citations

Sep 16, 2020·Nature Genetics·Amalio Telenti, Xiaoqian Jiang
Aug 28, 2021·Cell Systems·Natnatee DokmaiHyunghoon Cho

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Methods Mentioned

BETA
PCA

Software Mentioned

gzip
HElib
SGX
EIGENSTRAT
Linux
Software Guard Extensions ( SGX )
Windows
SkSES
Omega

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