TY - JOUR T1 - No major flaws in “Identification of individuals by trait prediction using whole-genome sequencing data” JF - bioRxiv DO - 10.1101/187542 SP - 187542 AU - Christoph Lippert AU - Riccardo Sabatini AU - M. Cyrus Maher AU - Eun Yong Kang AU - Seunghak Lee AU - Okan Arikan AU - Alena Harley AU - Axel Bernal AU - Peter Garst AU - Victor Lavrenko AU - Ken Yocum AU - Theodore M. Wong AU - Mingfu Zhu AU - Wen-Yun Yang AU - Chris Chang AU - Barry Hicks AU - Smriti Ramakrishnan AU - Haibao Tang AU - Chao Xie AU - Suzanne Brewerton AU - Yaron Turpaz AU - Amalio Telenti AU - Rhonda K. Roby AU - Franz Och AU - J. Craig Venter Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/10/19/187542.abstract N2 - 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. ER -