ArticleProceedings of the National Academy of Sciences of the United States of America2020
Secure large-scale genome-wide association studies using homomorphic encryption.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
What it found
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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
39 citing papers in PubMed, 1 synthesis or guideline pooled it, 137 citations in OpenAlex.
- Flimma: a federated and privacy-aware tool for differential gene expression analysis.Genome biology · 2021Pooled it
- bioETH-PRS: confidential polygenic risk scoring with smart contracts on an FHE-enabled blockchain.Briefings in bioinformatics · 2026Article
- Article
- Homomorphic encryption enables privacy preserving polygenic risk scores.Cell reports methods · 2026Article
- Securing gait recognition with homomorphic encryption.Scientific reports · 2025Article
- Shechi: A Secure Distributed Computation Compiler Based on Multiparty Homomorphic Encryption.Proceedings. UNIX Security Symposium · 2025Article
- Making the case for an International Childhood Cancer Data Partnership.Journal of the National Cancer Institute · 2025Article
- Secure and federated genome-wide association studies for biobank-scale datasets.Nature genetics · 2025Article
- Secure and scalable gene expression quantification with pQuant.Nature communications · 2025Article
- Article
- Secondary use under the European Health Data Space: setting the scene and towards a research agenda on privacy-enhancing technologies.Frontiers in digital health · 2025Article
- SQUiD: ultra-secure storage and analysis of genetic data for the advancement of precision medicine.Genome biology · 2024Article
- Privacy-preserving biological age prediction over federated human methylation data using fully homomorphic encryption.Genome research · 2024Article
- Secure discovery of genetic relatives across large-scale and distributed genomic data sets.Genome research · 2024Article
- Private pathological assessment via machine learning and homomorphic encryption.BioData mining · 2024Article
- Privacy-preserving model evaluation for logistic and linear regression using homomorphically encrypted genotype data.Journal of biomedical informatics · 2024Article
- Privacy-Enhancing Technologies in Biomedical Data Science.Annual review of biomedical data science · 2024Review
- Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision.Briefings in bioinformatics · 2024Review
- Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibility.Genome biology · 2024Review
- Sharing sensitive data in life sciences: an overview of centralized and federated approaches.Briefings in bioinformatics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Genome-wide association studies (GWASs) seek to identify genetic variants associated with a trait, and have been a powerful approach for understanding complex diseases. A critical challenge for GWASs has been the dependence on individual-level data that typically have strict privacy requirements, creating an urgent need for methods that preserve the individual-level privacy of participants. Here, we present a privacy-preserving framework based on several advances in homomorphic encryption and demonstrate that it can perform an accurate GWAS analysis for a real dataset of more than 25,000 individuals, keeping all individual data encrypted and requiring no user interactions. Our extrapolations show that it can evaluate GWASs of 100,000 individuals and 500,000 single-nucleotide polymorphisms (SNPs) in 5.6 h on a single server node (or in 11 min on 31 server nodes running in parallel). Our performance results are more than one order of magnitude faster than prior state-of-the-art results using secure multiparty computation, which requires continuous user interactions, with the accuracy of both solutions being similar. Our homomorphic encryption advances can also be applied to other domains where large-scale statistical analyses over encrypted data are needed.
Indexed as
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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.