ArticleGenome research2014
Identifying genetic relatives without compromising privacy.
Article in Genome research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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.
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Who cites it
22 citing papers in PubMed.
- Learning-augmented sketching offers improved performance for privacy preserving and secure GWAS.iScience · 2025Article
- Private detection of relatives in forensic genomics using homomorphic encryption.BMC medical genomics · 2024Article
- Secure discovery of genetic relatives across large-scale and distributed genomic data sets.Genome research · 2024Article
- Learning-Augmented Sketching Offers Improved Performance for Privacy Preserving and Secure GWAS.bioRxiv : the preprint server for biology · 2024Article
- Privacy-aware estimation of relatedness in admixed populations.Briefings in bioinformatics · 2022Article
- Sociotechnical safeguards for genomic data privacy.Nature reviews. Genetics · 2022Review
- Functional genomics data: privacy risk assessment and technological mitigation.Nature reviews. Genetics · 2022Review
- Privately computing set-maximal matches in genomic data.BMC medical genomics · 2020Article
- Privacy challenges and research opportunities for genomic data sharing.Nature genetics · 2020Review
- Article
- Genomic GPS: using genetic distance from individuals to public data for genomic analysis without disclosing personal genomes.Genome biology · 2019Article
- FOLD: a method to optimize power in meta-analysis of genetic association studies with overlapping subjects.Bioinformatics (Oxford, England) · 2017Article
- PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS.Bioinformatics (Oxford, England) · 2017Article
- An overview of human genetic privacy.Annals of the New York Academy of Sciences · 2017Review
- Reconsidering Anonymization-Related Concepts and the Term "Identification" Against the Backdrop of the European Legal Framework.Biopreservation and biobanking · 2016Article
- Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations.Cell systems · 2016Article
- Efficient privacy-preserving string search and an application in genomics.Bioinformatics (Oxford, England) · 2016Article
- GIW and InCoB are advancing bioinformatics in the Asia-Pacific.BMC bioinformatics · 2015Article
- Privacy in the Genomic Era.ACM computing surveys · 2015Article
- Privacy preserving protocol for detecting genetic relatives using rare variants.Bioinformatics (Oxford, England) · 2014Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The development of high-throughput genomic technologies has impacted many areas of genetic research. While many applications of these technologies focus on the discovery of genes involved in disease from population samples, applications of genomic technologies to an individual's genome or personal genomics have recently gained much interest. One such application is the identification of relatives from genetic data. In this application, genetic information from a set of individuals is collected in a database, and each pair of individuals is compared in order to identify genetic relatives. An inherent issue that arises in the identification of relatives is privacy. In this article, we propose a method for identifying genetic relatives without compromising privacy by taking advantage of novel cryptographic techniques customized for secure and private comparison of genetic information. We demonstrate the utility of these techniques by allowing a pair of individuals to discover whether or not they are related without compromising their genetic information or revealing it to a third party. The idea is that individuals only share enough special-purpose cryptographically protected information with each other to identify whether or not they are relatives, but not enough to expose any information about their genomes. We show in HapMap and 1000 Genomes data that our method can recover first- and second-order genetic relationships and, through simulations, show that our method can identify relationships as distant as third cousins while preserving privacy.
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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.