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

by Zhu, Mingfu , Venter, J. Craig , Lu, Tim , Lavrenko, Victor , Sabatini, Riccardo , Maher, M. Cyrus , Harley, Alena , Lippert, Christoph , Arikan, Okan , Garst, Peter , Hicks, Barry , Lee, Seunghak , Och, Franz J. , Ramakrishnan, Smriti , Yang, Wen-Yun , Roby, Rhonda K. , Wong, Theodore , Piper, Jason , Kang, Eun Yong , Yocum, Ken , Chang, Chris , Telenti, Amalio , Lee, Charlie W. H. , Bernal, Axel , Brewerton, Suzanne , Tang, Haibao , Xie, Chao , Turpaz, Yaron

in Adult / African Americans / Age Factors / Algorithms / Biological Sciences / Body Size / Cohort Studies / Confidentiality / Data Anonymization / Demographics / DNA Fingerprinting / Entropy / Ethics / Female / Gene sequencing / Genetics / Genomes / Genomics / Genotype & phenotype / Humans / Male / Mathematical models / Maximum entropy / Maximum entropy method / Middle Aged / Models, Genetic / Phenotype / Phenotyping / Pigmentation - genetics / Precision medicine / Predictions / Privacy / Statistical analysis / Statistical methods / Statistical models / Studies / Whole Genome Sequencing / Young Adult

2017

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2017

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2017

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Journal Article

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

Zhu, Mingfu,

Venter, J. Craig,

Lu, Tim,

Lavrenko, Victor,

Sabatini, Riccardo,

Maher, M. Cyrus,

Harley, Alena,

Lippert, Christoph,

Arikan, Okan,

Garst, Peter,

Hicks, Barry,

Lee, Seunghak,

Och, Franz J.,

Ramakrishnan, Smriti,

Yang, Wen-Yun,

Roby, Rhonda K.,

Wong, Theodore,

Piper, Jason,

Kang, Eun Yong,

Yocum, Ken,

Chang, Chris,

Telenti, Amalio,

Lee, Charlie W. H.,

Bernal, Axel,

Brewerton, Suzanne,

Tang, Haibao,

Xie, Chao,

Turpaz, Yaron

2017

Overview

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.

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Publisher

National Academy of Sciences

Subject

/ Biological Sciences

/ Body Size

/ Cohort Studies