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


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 (, 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 quickly, accurately and in a privacy-preserving manner.

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Fig. 1: Overview of the SkSES pipeline.
Fig. 2: The impact of increasing cohort size on the runtime of SkSES, with a fixed sketch size.
Fig. 3: The fraction of true top k significant SNPs (according to χ2 statistic) included in the query of top l SNPs returned by SkSES (accuracy) as a function of k.
Fig. 4: The impact of normalizing genotype matrix A and multiplying the sketching matrix (\(\hat{{\it{X}}}={\it{XT}}\)) for reducing the space needed for PCA on the left singular vectors \({\hat{{\rm{U}}}}_{L}\) as well as the ranks of Cochran–Armitage trend χ2 statistics across all unique SNPs in the iDASH2017-chr1 dataset.

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Data availability

The VCF files from the original benchmarking dataset for the iDASH-2017 competition (iDASH2017-chr1) can be found at The VCF files consisting of whole-genome data (iDASH2017-wg) and the synthetic VCF files are available from the corresponding author upon request. The AMD dataset (dbGaP phs001039.v1.p1) from ref. 44 can be obtained via dbGaP authorized access.

Code availability

The source code for SkSES, under MIT license, is available for download at GitHub:


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N.K. was partially supported by NSF CCF-1844234, NSF CCF-1525024 and IIS-1633215. M.O.K. was partially supported by TUBITAK grant 114E293. Part of the work was done while D.P.W. was visiting the Simons Institute for the Theory of Computing. S.C.S. was supported in part by NSF CCF-1619081, NIH GM108348, NIH HG010798 and the Indiana University Grand Challenges Program, Precision Health Initiative, before moving to his current post at NCI.

We thank S. Simmons and H. Cho from the Computer Science and Artificial Intelligence Laboratory at MIT (now at the Broad Institute) for useful discussions and their help in benchmarking SkSES on the AMD dataset44 against the SMC tool. We also thank L. Wang and D. Bu at Indiana University for providing the iDASH2017-wg dataset. We finally thank the Linux team and B. Shei from University Information Technology Services at Indiana University for useful instructions on software installation and preparation.

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C.K., N.D., M.O.K. and S.C.S. initially participated in the iDASH-2017 competition. K.Z., N.K. and S.C.S. formulated the problem with limited memory. K.Z., S.C.S. and D.P.W. further formulated the problem to correct for population stratification. C.K., K.Z. and N.D. implemented the proposed solution. C.K., K.Z., N.D., N.K. and S.C.S. co-wrote the manuscript. M.O.K., D.P.W. and S.C.S. supervised the study.

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Correspondence to S. Cenk Sahinalp.

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Kockan, C., Zhu, K., Dokmai, N. et al. Sketching algorithms for genomic data analysis and querying in a secure enclave. Nat Methods 17, 295–301 (2020).

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