ArticleBMC medical genomics2020
Semi-Parallel logistic regression for GWAS on encrypted data.
Article in BMC medical genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Secure and scalable gene expression quantification with pQuant.Nature communications · 2025Article
- Private detection of relatives in forensic genomics using homomorphic encryption.BMC medical genomics · 2024Article
- Privacy-preserving model evaluation for logistic and linear regression using homomorphically encrypted genotype data.Journal of biomedical informatics · 2024Article
- Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision.Briefings in bioinformatics · 2024Review
- The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- SVAT: Secure outsourcing of variant annotation and genotype aggregation.BMC bioinformatics · 2022Article
- Secure human action recognition by encrypted neural network inference.Nature communications · 2022Article
- Secure tumor classification by shallow neural network using homomorphic encryption.BMC genomics · 2022Article
- A secure system for genomics clinical decision support.Journal of biomedical informatics · 2020Article
- iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching.BMC medical genomics · 2020Article
- Optimized homomorphic encryption solution for secure genome-wide association studies.BMC medical genomics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundThe 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.
methodsThis 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.
resultsWe 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.
conclusionsWe demonstrate the feasibility and scalability of our solution.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.