Identification of individuals by trait prediction using whole-genome sequencing data

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Item Type:	Article
Title:	Identification of individuals by trait prediction using whole-genome sequencing data
Creators Name:	Lippert, C., Sabatini, R., Maher, M.C., Kang, E.Y., Lee, S., Arikan, O., Harley, A., Bernal, A., Garst, P., Lavrenko, V., Yocum, K., Wong, T., Zhu, M., Yang, W.Y., Chang, C., Lu, T., Lee, C.W.H., Hicks, B., Ramakrishnan, S., Tang, H., Xie, C., Piper, J., Brewerton, S., Turpaz, Y., Telenti, A., Roby, R.K., Och, F.J. and Venter, J.C.
Abstract:	Prediction of human physical traits and demographic information from genomic data challenges privacy and data deidentification in personalized medicine. To explore the current capabilities of phenotype-based genomic identification, we applied whole-genome sequencing, detailed phenotyping, and statistical modeling to predict biometric traits in a cohort of 1,061 participants of diverse ancestry. Individually, for a large fraction of the traits, their predictive accuracy beyond ancestry and demographic information is limited. However, we have developed a maximum entropy algorithm that integrates multiple predictions to determine which genomic samples and phenotype measurements originate from the same person. Using this algorithm, we have reidentified an average of >8 of 10 held-out individuals in an ethnically mixed cohort and an average of 5 of either 10 African Americans or 10 Europeans. This work challenges current conceptions of personal privacy and may have far-reaching ethical and legal implications.
Keywords:	Genomic Privacy, Genome Sequencing, DNA Phenotyping, Phenotype Prediction, Reidentification
Source:	Proceedings of the National Academy of Sciences of the United States of America
ISSN:	0027-8424
Publisher:	National Academy of Sciences
Volume:	114
Number:	38
Page Range:	10166-10171
Date:	19 September 2017
Additional Information:	Erratum in: PNAS 114(41): E8800.
Official Publication:	https://doi.org/10.1073/pnas.1711125114
PubMed:	View item in PubMed

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