ArticleBMC medical informatics and decision making2015
Privacy-preserving GWAS analysis on federated genomic datasets.
Article in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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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.
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Who cites it
28 citing papers in PubMed.
- PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies.Nature communications · 2025Article
- FedGMMAT: Federated generalized linear mixed model association tests.PLoS computational biology · 2024Article
- Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibility.Genome biology · 2024Review
- PPML-Omics: A privacy-preserving federated machine learning method protects patients' privacy in omic data.Science advances · 2024Article
- Ensuring Trust in Genomics Research.... IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications : (TPS-ISA ...). IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications · 2023Article
- Federated Analysis for Privacy-Preserving Data Sharing: A Technical and Legal Primer.Annual review of genomics and human genetics · 2023Review
- Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics.AMIA ... Annual Symposium proceedings. AMIA Symposium · 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
- Privacy-aware multi-institutional time-to-event studies.PLOS digital health · 2022Article
- Artificial intelligence in the diagnosis of cirrhosis and portal hypertension.Journal of medical ultrasonics (2001) · 2022Review
- Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.Methods of information in medicine · 2022Article
- Privacy-preserving federated neural network learning for disease-associated cell classification.Patterns (New York, N.Y.) · 2022Article
- sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies.Genome biology · 2022Article
- Privacy-preserving approximate GWAS computation based on homomorphic encryption.BMC medical genomics · 2020Article
- Privacy challenges and research opportunities for genomic data sharing.Nature genetics · 2020Review
- Big Data Privacy in Biomedical Research.IEEE transactions on big data · 2020Article
- Towards practical privacy-preserving genome-wide association study.BMC bioinformatics · 2018Article
- Secure genome-wide association analysis using multiparty computation.Nature biotechnology · 2018Article
- PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension.BMC medical genomics · 2017Article
- BLOOM: BLoom filter based oblivious outsourced matchings.BMC medical genomics · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundThe biomedical community benefits from the increasing availability of genomic data to support meaningful scientific research, e.g., Genome-Wide Association Studies (GWAS). However, high quality GWAS usually requires a large amount of samples, which can grow beyond the capability of a single institution. Federated genomic data analysis holds the promise of enabling cross-institution collaboration for effective GWAS, but it raises concerns about patient privacy and medical information confidentiality (as data are being exchanged across institutional boundaries), which becomes an inhibiting factor for the practical use.
methodsWe present a privacy-preserving GWAS framework on federated genomic datasets. Our method is to layer the GWAS computations on top of secure multi-party computation (MPC) systems. This approach allows two parties in a distributed system to mutually perform secure GWAS computations, but without exposing their private data outside.
resultsWe demonstrate our technique by implementing a framework for minor allele frequency counting and χ2 statistics calculation, one of typical computations used in GWAS. For efficient prototyping, we use a state-of-the-art MPC framework, i.e., Portable Circuit Format (PCF) 1. Our experimental results show promise in realizing both efficient and secure cross-institution GWAS computations.
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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.