PT  - JOURNAL ARTICLE
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  - Amalio Telenti
AU  - Franz Och
AU  - J. Craig Venter
TI  - No major flaws in “Identification of individuals by trait prediction using whole-genome sequencing data”
AID  - 10.1101/187542
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 187542
4099  - http://biorxiv.org/content/early/2017/09/11/187542.1.short
4100  - http://biorxiv.org/content/early/2017/09/11/187542.1.full
AB  - 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.