Paige, Brooks, Bell, James, Bellet, Aurelien et al. (2 more authors) (2020) Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores. In: Schwartz, Russell, (ed.) Lecture Notes in Computer Science:Research in Computational Molecular Biology: 24th Annual International Conference, RECOMB 2020, Padua, Italy, May 10–13, 2020, Proceedings. Lecture Notes in Bioinformatics (LNBI) . Springer Nature Switzerland , pp. 266-268.
Abstract
Some organisations like 23andMe and the UK Biobank have large genomic databases that they re-use for multiple different genome-wide association studies (GWAS). Even research studies that compile smaller genomic databases often utilise these databases to investigate many related traits. It is common for the study to report a genetic risk score (GRS) model for each trait within the publication. Here we show that under some circumstances, these GRS models can be used to recover the genetic variants of individuals in these genomic databases—a reconstruction attack. In particular, if two GRS models are trained using a largely overlapping set of participants, then it is often possible to determine the genotype for each of the individuals who were used to train one GRS model, but not the other. We demonstrate this theoretically and experimentally by analysing the Cornell Dog Genome database. The accuracy of our reconstruction attack depends on how accurately we can estimate the rate of co-occurrence of pairs of SNPs within the private database, so if this aggregate information is ever released, it would drastically reduce the security of a private genomic database. Caution should be applied when using the same database for multiple analysis, especially when a small number of individuals are included or excluded from one part of the study.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Biology (York) |
Depositing User: | Pure (York) |
Date Deposited: | 11 Jun 2020 10:00 |
Last Modified: | 16 Oct 2024 11:08 |
Published Version: | https://doi.org/10.1007/978-3-030-45257-5_32 |
Status: | Published |
Publisher: | Springer Nature Switzerland |
Series Name: | Lecture Notes in Bioinformatics (LNBI) |
Identification Number: | 10.1007/978-3-030-45257-5_32 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161818 |
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