ArticleNature biotechnology2018
Secure genome-wide association analysis using multiparty computation.
Article in Nature biotechnology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 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
73 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Flimma: a federated and privacy-aware tool for differential gene expression analysis.Genome biology · 2021Pooled it
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- Unlocking the potential of pedigree genomics via precision governance.Nature reviews. Genetics · 2026Article
- A simple demonstration of a privacy-preserving de-centralised genotype imputation workflow.PloS one · 2026Article
- GENESIS-SHIELD: an interpretable ensemble for anomaly detection in CRISPR genomic-workflow security.Frontiers in artificial intelligence · 2026Article
- NitroGene: privacy-preserving collaborative genomic analysis using AWS nitro enclaves.Bioinformatics advances · 2026Article
- PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies.Nature communications · 2025Article
- KmerCrypt: private k-mer search with homomorphic encryption.Briefings in bioinformatics · 2025Article
- Toward owner governance in genomic data privacy with Governome.Cell reports methods · 2025Article
- Streamline automated biomedical discoveries with agentic bioinformatics.Briefings in bioinformatics · 2025Review
- Making the case for an International Childhood Cancer Data Partnership.Journal of the National Cancer Institute · 2025Article
- Shechi: A Secure Distributed Computation Compiler Based on Multiparty Homomorphic Encryption.Proceedings. UNIX Security Symposium · 2025Article
- Secure and federated genome-wide association studies for biobank-scale datasets.Nature genetics · 2025Article
- Collaborative genome-wide association analysis with cryptography.Nature genetics · 2025Article
- Secure and scalable gene expression quantification with pQuant.Nature communications · 2025Article
- Proxy panels enable privacy-aware outsourcing of genotype imputation.Genome research · 2025Article
- Secure and federated quantitative trait loci mapping with privateQTL.Cell genomics · 2025Article
- Genomic privacy and security in the era of artificial intelligence and quantum computing.Discover computing · 2025Review
- Privacy-friendly evaluation of patient data with secure multiparty computation in a European pilot study.NPJ digital medicine · 2024Article
- Secure discovery of genetic relatives across large-scale and distributed genomic data sets.Genome research · 2024Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Most sequenced genomes are currently stored in strict access-controlled repositories. Free access to these data could improve the power of genome-wide association studies (GWAS) to identify disease-causing genetic variants and aid the discovery of new drug targets. However, concerns over genetic data privacy may deter individuals from contributing their genomes to scientific studies and could prevent researchers from sharing data with the scientific community. Although cryptographic techniques for secure data analysis exist, none scales to computationally intensive analyses, such as GWAS. Here we describe a protocol for large-scale genome-wide analysis that facilitates quality control and population stratification correction in 9K, 13K, and 23K individuals while maintaining the confidentiality of underlying genotypes and phenotypes. We show the protocol could feasibly scale to a million individuals. This approach may help to make currently restricted data available to the scientific community and could potentially enable secure genome crowdsourcing, allowing individuals to contribute their genomes to a study without compromising their privacy.
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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.