ArticleBMC medical genomics2020
Achieving GWAS with homomorphic encryption.
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 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Safeguarding biomedical AI: a critical scoping review of privacy-enhancing technologies, hybrid approaches, and deployment models.Frontiers in digital health · 2026Pooled it
- Secure bioinformatics: privacy-preserving federated analytics using homomorphic encryption.Bioinformatics (Oxford, England) · 2026Article
- Article
- PROVGEN: A Privacy-Preserving Approach for Outcome Validation in Genomic Research.Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium · 2026Article
- Secure and scalable gene expression quantification with pQuant.Nature communications · 2025Article
- SQUiD: ultra-secure storage and analysis of genetic data for the advancement of precision medicine.Genome biology · 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
- Ultra-secure storage and analysis of genetic data for the advancement of precision medicine.bioRxiv : the preprint server for biology · 2024Article
- Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality.Genetics · 2024Article
- Federated generalized linear mixed models for collaborative genome-wide association studies.iScience · 2023Article
- 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
- Secure tumor classification by shallow neural network using homomorphic encryption.BMC genomics · 2022Article
- Blockchains as a means to promote privacy protecting, access availing, incentive increasing, ELSI lessening DNA databases.Frontiers in digital health · 2022Review
- iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching.BMC medical genomics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOne way of investigating how genes affect human traits would be with a genome-wide association study (GWAS). Genetic markers, known as single-nucleotide polymorphism (SNP), are used in GWAS. This raises privacy and security concerns as these genetic markers can be used to identify individuals uniquely. This problem is further exacerbated by a large number of SNPs needed, which produce reliable results at a higher risk of compromising the privacy of participants.
methodsWe describe a method using homomorphic encryption (HE) to perform GWAS in a secure and private setting. This work is based on a proposed algorithm. Our solution mainly involves homomorphically encrypted matrix operations and suitable approximations that adapts the semi-parallel GWAS algorithm for HE. We leverage upon the complex space of the CKKS encryption scheme to increase the number of SNPs that can be packed within a ciphertext. We have also developed a cache module that manages ciphertexts, reducing the memory footprint.
resultsWe have implemented our solution over two HE open source libraries, HEAAN and SEAL. Our best implementation took 24.70 minutes for a dataset with 245 samples, over 4 covariates and 10643 SNPs.
conclusionsWe demonstrate that it is possible to achieve GWAS with homomorphic encryption with suitable approximations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.