ArticleCell systems2016
Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations.
Article in Cell systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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
23 citing papers in PubMed.
- GENESIS-SHIELD: an interpretable ensemble for anomaly detection in CRISPR genomic-workflow security.Frontiers in artificial intelligence · 2026Article
- Secure and federated genome-wide association studies for biobank-scale datasets.Nature genetics · 2025Article
- Privacy-friendly evaluation of patient data with secure multiparty computation in a European pilot study.NPJ digital medicine · 2024Article
- Privacy-Enhancing Technologies in Biomedical Data Science.Annual review of biomedical data science · 2024Review
- Future-proofing genomic data and consent management: a comprehensive review of technology innovations.GigaScience · 2024Review
- The Future of Precision Medicine in the Cure of Alzheimer's Disease.Biomedicines · 2023Review
- Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges.Frontiers in genetics · 2023Article
- Sociotechnical safeguards for genomic data privacy.Nature reviews. Genetics · 2022Review
- Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.Methods of information in medicine · 2022Article
- Mechanisms for Hiding Sensitive Genotypes with Information-Theoretic Privacy.IEEE transactions on information theory · 2022Article
- Using game theory to thwart multistage privacy intrusions when sharing data.Science advances · 2021Article
- Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption.Nature communications · 2021Article
- Privacy challenges and research opportunities for genomic data sharing.Nature genetics · 2020Review
- Privacy-preserving techniques of genomic data-a survey.Briefings in bioinformatics · 2019Review
- A secure SNP panel scheme using homomorphically encrypted K-mers without SNP calling on the user side.BMC genomics · 2019Article
- Protecting Genomic Data Privacy with Probabilistic Modeling.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Realizing private and practical pharmacological collaboration.Science (New York, N.Y.) · 2018Article
- Secure genome-wide association analysis using multiparty computation.Nature biotechnology · 2018Article
- Privacy Policy and Technology in Biomedical Data Science.Annual review of biomedical data science · 2018Article
- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
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
The proliferation of large genomic databases offers the potential to perform increasingly larger-scale genome-wide association studies (GWASs). Due to privacy concerns, however, access to these data is limited, greatly reducing their usefulness for research. Here, we introduce a computational framework for performing GWASs that adapts principles of differential privacy-a cryptographic theory that facilitates secure analysis of sensitive data-to both protect private phenotype information (e.g., disease status) and correct for population stratification. This framework enables us to produce privacy-preserving GWAS results based on EIGENSTRAT and linear mixed model (LMM)-based statistics, both of which correct for population stratification. We test our differentially private statistics, PrivSTRAT and PrivLMM, on simulated and real GWAS datasets and find they are able to protect privacy while returning meaningful results. Our framework can be used to securely query private genomic datasets to discover which specific genomic alterations may be associated with a disease, thus increasing the availability of these valuable datasets.
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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.