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Attacks on genetic privacy via uploads to genealogical databases

Direct-to-consumer (DTC) genetics services are increasingly popular, with tens of millions of customers. Several DTC genealogy services allow users to upload genetic data to search for relatives, identified as people with genomes that share identical by state (IBS) regions. Here, we describe methods...

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Detalles Bibliográficos
Autores principales: Edge, Michael D, Coop, Graham
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6992384/
https://www.ncbi.nlm.nih.gov/pubmed/31908268
http://dx.doi.org/10.7554/eLife.51810
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author Edge, Michael D
Coop, Graham
author_facet Edge, Michael D
Coop, Graham
author_sort Edge, Michael D
collection PubMed
description Direct-to-consumer (DTC) genetics services are increasingly popular, with tens of millions of customers. Several DTC genealogy services allow users to upload genetic data to search for relatives, identified as people with genomes that share identical by state (IBS) regions. Here, we describe methods by which an adversary can learn database genotypes by uploading multiple datasets. For example, an adversary who uploads approximately 900 genomes could recover at least one allele at SNP sites across up to 82% of the genome of a median person of European ancestries. In databases that detect IBS segments using unphased genotypes, approximately 100 falsified uploads can reveal enough genetic information to allow genome-wide genetic imputation. We provide a proof-of-concept demonstration in the GEDmatch database, and we suggest countermeasures that will prevent the exploits we describe.
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spelling pubmed-69923842020-01-31 Attacks on genetic privacy via uploads to genealogical databases Edge, Michael D Coop, Graham eLife Evolutionary Biology Direct-to-consumer (DTC) genetics services are increasingly popular, with tens of millions of customers. Several DTC genealogy services allow users to upload genetic data to search for relatives, identified as people with genomes that share identical by state (IBS) regions. Here, we describe methods by which an adversary can learn database genotypes by uploading multiple datasets. For example, an adversary who uploads approximately 900 genomes could recover at least one allele at SNP sites across up to 82% of the genome of a median person of European ancestries. In databases that detect IBS segments using unphased genotypes, approximately 100 falsified uploads can reveal enough genetic information to allow genome-wide genetic imputation. We provide a proof-of-concept demonstration in the GEDmatch database, and we suggest countermeasures that will prevent the exploits we describe. eLife Sciences Publications, Ltd 2020-01-07 /pmc/articles/PMC6992384/ /pubmed/31908268 http://dx.doi.org/10.7554/eLife.51810 Text en © 2020, Edge and Coop http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Evolutionary Biology
Edge, Michael D
Coop, Graham
Attacks on genetic privacy via uploads to genealogical databases
title Attacks on genetic privacy via uploads to genealogical databases
title_full Attacks on genetic privacy via uploads to genealogical databases
title_fullStr Attacks on genetic privacy via uploads to genealogical databases
title_full_unstemmed Attacks on genetic privacy via uploads to genealogical databases
title_short Attacks on genetic privacy via uploads to genealogical databases
title_sort attacks on genetic privacy via uploads to genealogical databases
topic Evolutionary Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6992384/
https://www.ncbi.nlm.nih.gov/pubmed/31908268
http://dx.doi.org/10.7554/eLife.51810
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