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dc.contributor.authorCassa, Christopher A.en_US
dc.contributor.authorSchmidt, Brianen_US
dc.contributor.authorKohane, Isaacen_US
dc.contributor.authorMandl, Kenneth D.en_US
dc.date.accessioned2009-10-19T13:35:32Z
dc.date.available2009-10-19T13:35:32Z
dc.date.issued2008-07en_US
dc.date.submitted2007-11en_US
dc.identifier.issn1755-8794en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/49471
dc.description.abstractBackground: Genomic sequencing of SNPs is increasingly prevalent, though the amount of familial information these data contain has not been quantified. Methods: We provide a framework for measuring the risk to siblings of a patient's SNP genotype disclosure, and demonstrate that sibling SNP genotypes can be inferred with substantial accuracy. Results: Extending this inference technique, we determine that a very low number of matches at commonly varying SNPs is sufficient to confirm sib-ship, demonstrating that published sequence data can reliably be used to derive sibling identities. Using HapMap trio data, at SNPs where one child is homozygotic major, with a minor allele frequency ≤ 0.20, (N = 452684, 65.1%) we achieve 91.9% inference accuracy for sibling genotypes. Conclusion: These findings demonstrate that substantial discrimination and privacy risks arise from use of inferred familial genomic data.en_US
dc.language.isoen_USen_US
dc.publisherBioMed Central Ltd.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1186/1755-8794-1-32en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en_US
dc.sourcePublisheren_US
dc.titleMy sister's keeper?: genomic research and the identifiability of siblingsen_US
dc.typeArticleen_US
dc.identifier.citationCassa, Christopher, Brian Schmidt, Isaac Kohane, and Kenneth Mandl. 2008. My sister's keeper?: genomic research and the identifiability of siblings. BMC Medical Genomics 1, no. 1: 32.en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technologyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineeringen_US
dc.contributor.approverCassa, Christopher A.en_US
dc.contributor.mitauthorCassa, Christopher A.en_US
dc.contributor.mitauthorSchmidt, Brianen_US
dc.contributor.mitauthorKohane, Isaacen_US
dc.contributor.mitauthorMandl, Kenneth D.en_US
dc.relation.journalBMC Medical Genomicsen_US
dc.eprint.versionFinal published versionen_US
dc.identifier.pmid18655711en_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsCassa, Christopher A; Schmidt, Brian; Kohane, Isaac S; Mandl, Kenneth Den
mit.licensePUBLISHER_CCen_US


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