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No major flaws in "Identification of individuals by trait prediction using whole-genome sequencing data"

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Item Type:Preprint
Title:No major flaws in "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.M., Zhu, M., Yang, W.Y.n, Chang, C., Hicks, B., Ramakrishnan, S., Tang, H., Xie, C., Brewerton, S., Turpaz, Y., Telenti, A., Roby, R.K., Och, F. and Venter, J.C.
Abstract:In a recently published PNAS article, we studied the identifiability of genomic samples using machine learning methods [Lippert et al., 2017]. In a response, Erlich [2017] argued that our work contained major flaws. The main technical critique of Erlich [2017] builds on a simulation experiment that shows that our proposed algorithm, which uses only a genomic sample for identification, performed no better than a strategy that uses demographic variables. Below, we show why this comparison is misleading and provide a detailed discussion of the key critical points in our analyses that have been brought up in Erlich [2017] and in the media. Further, not only faces may be derived from DNA, but a wide range of phenotypes and demographic variables. In this light, the main contribution of Lippert et al. [2017] is an algorithm that identifies genomes of individuals by combining multiple DNA-based predictive models for a myriad of traits.
Publisher:Cold Spring Harbor Laboratory Press
Article Number:187542
Date:19 October 2017
Official Publication:https://doi.org/10.1101/187542

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