Item Type: | Article |
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Title: | Identification of individuals by trait prediction using whole-genome sequencing data |
Creators Name: | Lippert, C. and Sabatini, R. and Maher, M.C. and Kang, E.Y. and Lee, S. and Arikan, O. and Harley, A. and Bernal, A. and Garst, P. and Lavrenko, V. and Yocum, K. and Wong, T. and Zhu, M. and Yang, W.Y. and Chang, C. and Lu, T. and Lee, C.W.H. and Hicks, B. and Ramakrishnan, S. and Tang, H. and Xie, C. and Piper, J. and Brewerton, S. and Turpaz, Y. and Telenti, A. and Roby, R.K. and 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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