Semi-Parallel logistic regression for GWAS on encrypted data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Miran | - |
dc.contributor.author | Song, Yongsoo | - |
dc.contributor.author | Li, Baiyu | - |
dc.contributor.author | Micciancio, Daniele | - |
dc.date.accessioned | 2023-09-04T08:03:55Z | - |
dc.date.available | 2023-09-04T08:03:55Z | - |
dc.date.created | 2023-07-19 | - |
dc.date.issued | 2020-07 | - |
dc.identifier.issn | 17558794 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/190064 | - |
dc.description.abstract | Background The sharing of biomedical data is crucial to enable scientific discoveries across institutions and improve health care. For example, genome-wide association studies (GWAS) based on a large number of samples can identify disease-causing genetic variants. The privacy concern, however, has become a major hurdle for data management and utilization. Homomorphic encryption is one of the most powerful cryptographic primitives which can address the privacy and security issues. It supports the computation on encrypted data, so that we can aggregate data and perform an arbitrary computation on an untrusted cloud environment without the leakage of sensitive information. Methods This paper presents a secure outsourcing solution to assess logistic regression models for quantitative traits to test their associations with genotypes. We adapt the semi-parallel training method by Sikorska et al., which builds a logistic regression model for covariates, followed by one-step parallelizable regressions on all individual single nucleotide polymorphisms (SNPs). In addition, we modify our underlying approximate homomorphic encryption scheme for performance improvement. Results We evaluated the performance of our solution through experiments on real-world dataset. It achieves the best performance of homomorphic encryption system for GWAS analysis in terms of both complexity and accuracy. For example, given a dataset consisting of 245 samples, each of which has 10643 SNPs and 3 covariates, our algorithm takes about 43 seconds to perform logistic regression based genome wide association analysis over encryption. Conclusions We demonstrate the feasibility and scalability of our solution. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | BMC | - |
dc.title | Semi-Parallel logistic regression for GWAS on encrypted data | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Miran | - |
dc.identifier.doi | 10.1186/s12920-020-0724-z | - |
dc.identifier.scopusid | 2-s2.0-85088509481 | - |
dc.identifier.wosid | 000553597700008 | - |
dc.identifier.bibliographicCitation | BMC MEDICAL GENOMICS, v.13, pp.1 - 13 | - |
dc.relation.isPartOf | BMC MEDICAL GENOMICS | - |
dc.citation.title | BMC MEDICAL GENOMICS | - |
dc.citation.volume | 13 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 13 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Genetics & Heredity | - |
dc.relation.journalWebOfScienceCategory | Genetics & Heredity | - |
dc.subject.keywordPlus | SEARCH-AND-COMPUTE | - |
dc.subject.keywordAuthor | Genome-wide association studies | - |
dc.subject.keywordAuthor | Homomorphic encryption | - |
dc.subject.keywordAuthor | Logistic regression | - |
dc.identifier.url | https://bmcmedgenomics.biomedcentral.com/articles/10.1186/s12920-020-0724-z | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.