%0 Journal Article %A Christoph Lippert %A Riccardo Sabatini %A M. Cyrus Maher %A Eun Yong Kang %A Seunghak Lee %A Okan Arikan %A Alena Harley %A Axel Bernal %A Peter Garst %A Victor Lavrenko %A Ken Yocum %A Theodore M. Wong %A Mingfu Zhu %A Wen-Yun Yang %A Chris Chang %A Barry Hicks %A Smriti Ramakrishnan %A Haibao Tang %A Amalio Telenti %A Franz Och %A J. Craig Venter %T No major flaws in “Identification of individuals by trait prediction using whole-genome sequencing data” %D 2017 %R 10.1101/187542 %J bioRxiv %P 187542 %X 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 analysis that have been brought up in Erlich [2017] and in the media. We also want to point out that it is not only faces that 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 DNA-based predictive models for multiple traits. %U https://www.biorxiv.org/content/biorxiv/early/2017/09/11/187542.full.pdf