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Major flaws in “Identification of individuals by trait prediction using whole-genome sequencing data”

Yaniv Erlich
doi: https://doi.org/10.1101/185330
Yaniv Erlich
Department of Computer Science, Fu School of Engineering, Columbia University New York Genome Center
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Summary

Genetic privacy is an area of active research. While it is important to identify new risks, it is equally crucial to supply policymakers with accurate information based on scientific evidence. Recently, Lippert et al. (PNAS, 2017) investigated the status of genetic privacy using trait-predictions from whole genome sequencing. The authors sequenced a cohort of about 1000 individuals and collected a range of demographic, visible, and digital traits such as age, sex, height, face morphology, and a voice signature. They attempted to use the genetic features in order to predict those traits and re-identify the individuals from small pool using the trait predictions. Here, I report major flaws in the Lippert et al. manuscript. In short, the authors’ technique performs similarly to a simple baseline procedure, does not utilize the power of whole genome markers, uses technically wrong metrics, and finally does not really identify anyone.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 07, 2017.
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Major flaws in “Identification of individuals by trait prediction using whole-genome sequencing data”
Yaniv Erlich
bioRxiv 185330; doi: https://doi.org/10.1101/185330
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Major flaws in “Identification of individuals by trait prediction using whole-genome sequencing data”
Yaniv Erlich
bioRxiv 185330; doi: https://doi.org/10.1101/185330

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