Optimized homomorphic encryption solution for secure genome-wide association studies
© 2020 The Author(s). Background: Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterati...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/135463 |
| _version_ | 1826208619025137664 |
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| author | Blatt, Marcelo Gusev, Alexander Polyakov, Yuriy Rohloff, Kurt Vaikuntanathan, Vinod |
| author_facet | Blatt, Marcelo Gusev, Alexander Polyakov, Yuriy Rohloff, Kurt Vaikuntanathan, Vinod |
| author_sort | Blatt, Marcelo |
| collection | MIT |
| description | © 2020 The Author(s). Background: Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns. Methods: We present a solution for secure GWAS using homomorphic encryption (HE) that keeps all individual data encrypted throughout the association study. Our solution is based on an optimized semi-parallel GWAS compute model, a new Residue-Number-System (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) HE scheme, novel techniques to switch between data encodings, and more than a dozen crypto-engineering optimizations. Results: Our prototype can perform the full GWAS computation for 1,000 individuals, 131,071 SNPs, and 3 covariates in about 10 minutes on a modern server computing node (with 28 cores). Our solution for a smaller dataset was awarded co-first place in iDASH'18 Track 2: "Secure Parallel Genome Wide Association Studies using HE". Conclusions: Many of the HE optimizations presented in our paper are general-purpose, and can be used in solving challenging problems with large datasets in other application domains. |
| first_indexed | 2024-09-23T14:08:29Z |
| format | Article |
| id | mit-1721.1/135463 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:08:29Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1354632021-10-28T03:41:49Z Optimized homomorphic encryption solution for secure genome-wide association studies Blatt, Marcelo Gusev, Alexander Polyakov, Yuriy Rohloff, Kurt Vaikuntanathan, Vinod © 2020 The Author(s). Background: Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns. Methods: We present a solution for secure GWAS using homomorphic encryption (HE) that keeps all individual data encrypted throughout the association study. Our solution is based on an optimized semi-parallel GWAS compute model, a new Residue-Number-System (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) HE scheme, novel techniques to switch between data encodings, and more than a dozen crypto-engineering optimizations. Results: Our prototype can perform the full GWAS computation for 1,000 individuals, 131,071 SNPs, and 3 covariates in about 10 minutes on a modern server computing node (with 28 cores). Our solution for a smaller dataset was awarded co-first place in iDASH'18 Track 2: "Secure Parallel Genome Wide Association Studies using HE". Conclusions: Many of the HE optimizations presented in our paper are general-purpose, and can be used in solving challenging problems with large datasets in other application domains. 2021-10-27T20:23:33Z 2021-10-27T20:23:33Z 2020 2021-03-19T15:40:15Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135463 en 10.1186/S12920-020-0719-9 BMC Medical Genomics Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC BMC |
| spellingShingle | Blatt, Marcelo Gusev, Alexander Polyakov, Yuriy Rohloff, Kurt Vaikuntanathan, Vinod Optimized homomorphic encryption solution for secure genome-wide association studies |
| title | Optimized homomorphic encryption solution for secure genome-wide association studies |
| title_full | Optimized homomorphic encryption solution for secure genome-wide association studies |
| title_fullStr | Optimized homomorphic encryption solution for secure genome-wide association studies |
| title_full_unstemmed | Optimized homomorphic encryption solution for secure genome-wide association studies |
| title_short | Optimized homomorphic encryption solution for secure genome-wide association studies |
| title_sort | optimized homomorphic encryption solution for secure genome wide association studies |
| url | https://hdl.handle.net/1721.1/135463 |
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