PT  - JOURNAL ARTICLE
AU  - Lippert, Christoph
AU  - Sabatini, Riccardo
AU  - Maher, M. Cyrus
AU  - Kang, Eun Yong
AU  - Lee, Seunghak
AU  - Arikan, Okan
AU  - Harley, Alena
AU  - Bernal, Axel
AU  - Garst, Peter
AU  - Lavrenko, Victor
AU  - Yocum, Ken
AU  - Wong, Theodore
AU  - Zhu, Mingfu
AU  - Yang, Wen-Yun
AU  - Chang, Chris
AU  - Lu, Tim
AU  - Lee, Charlie W. H.
AU  - Hicks, Barry
AU  - Ramakrishnan, Smriti
AU  - Tang, Haibao
AU  - Xie, Chao
AU  - Piper, Jason
AU  - Brewerton, Suzanne
AU  - Turpaz, Yaron
AU  - Telenti, Amalio
AU  - Roby, Rhonda K.
AU  - Och, Franz J.
AU  - Venter, J. Craig
TI  - Identification of individuals by trait prediction using whole-genome sequencing data
AID  - 10.1073/pnas.1711125114
DP  - 2017 Sep 19
TA  - Proceedings of the National Academy of Sciences
PG  - 10166--10171
VI  - 114
IP  - 38
4099  - http://www.pnas.org/content/114/38/10166.short
4100  - http://www.pnas.org/content/114/38/10166.full
SO  - Proc Natl Acad Sci USA2017 Sep 19; 114
AB  - By associating deidentified genomic data with phenotypic measurements of the contributor, this work challenges current conceptions of genomic privacy. It has significant ethical and legal implications on personal privacy, the adequacy of informed consent, the viability and value of deidentification of data, the potential for police profiling, and more. We invite commentary and deliberation on the implications of these findings for research in genomics, investigatory practices, and the broader legal and ethical implications for society. Although some scholars and commentators have addressed the implications of DNA phenotyping, this work suggests that a deeper analysis is warranted.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.