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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. 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.M. and Zhu, M. and Yang, W.Y.n and Chang, C. and Hicks, B. and Ramakrishnan, S. and Tang, H. and Xie, C. and Brewerton, S. and Turpaz, Y. and Telenti, A. and Roby, R.K. and 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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