Next Article in Journal / Special Issue
Foundations of Programmable Secure Computation
Previous Article in Journal
Complementing Privacy and Utility Trade-Off with Self-Organising Maps
Previous Article in Special Issue
Fair and Secure Multi-Party Computation with Cheater Detection
Article

Implementing Privacy-Preserving Genotype Analysis with Consideration for Population Stratification

1
Cybernetica AS, 12618 Tallinn, Estonia
2
Institute of Mathematics and Statistics, University of Tartu, 51009 Tartu, Estonia
3
Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
*
Author to whom correspondence should be addressed.
Academic Editor: Josef Pieprzyk
Cryptography 2021, 5(3), 21; https://doi.org/10.3390/cryptography5030021
Received: 29 April 2021 / Revised: 12 August 2021 / Accepted: 18 August 2021 / Published: 20 August 2021
(This article belongs to the Special Issue Secure Multiparty Computation)
In bioinformatics, genome-wide association studies (GWAS) are used to detect associations between single-nucleotide polymorphisms (SNPs) and phenotypic traits such as diseases. Significant differences in SNP counts between case and control groups can signal association between variants and phenotypic traits. Most traits are affected by multiple genetic locations. To detect these subtle associations, bioinformaticians need access to more heterogeneous data. Regulatory restrictions in cross-border health data exchange have created a surge in research on privacy-preserving solutions, including secure computing techniques. However, in studies of such scale, one must account for population stratification, as under- and over-representation of sub-populations can lead to spurious associations. We improve on the state of the art of privacy-preserving GWAS methods by showing how to adapt principal component analysis (PCA) with stratification control (EIGENSTRAT), FastPCA, EMMAX and the genomic control algorithm for secure computing. We implement these methods using secure computing techniques—secure multi-party computation (MPC) and trusted execution environments (TEE). Our algorithms are the most complex ones at this scale implemented with MPC. We present performance benchmarks and a security and feasibility trade-off discussion for both techniques. View Full-Text
Keywords: privacy-preserving GWAS; secure multi-party computation; privacy-preserving statistics; trusted execution environments privacy-preserving GWAS; secure multi-party computation; privacy-preserving statistics; trusted execution environments
MDPI and ACS Style

Ostrak, A.; Randmets, J.; Sokk, V.; Laur, S.; Kamm, L. Implementing Privacy-Preserving Genotype Analysis with Consideration for Population Stratification. Cryptography 2021, 5, 21. https://doi.org/10.3390/cryptography5030021

AMA Style

Ostrak A, Randmets J, Sokk V, Laur S, Kamm L. Implementing Privacy-Preserving Genotype Analysis with Consideration for Population Stratification. Cryptography. 2021; 5(3):21. https://doi.org/10.3390/cryptography5030021

Chicago/Turabian Style

Ostrak, Andre, Jaak Randmets, Ville Sokk, Sven Laur, and Liina Kamm. 2021. "Implementing Privacy-Preserving Genotype Analysis with Consideration for Population Stratification" Cryptography 5, no. 3: 21. https://doi.org/10.3390/cryptography5030021

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop