No major flaws in “Identification of individuals by trait prediction using whole-genome sequencing data”
By Christoph Lippert, Riccardo Sabatini, M. C. Maher, E. Kang, Seunghak Lee, Okan Arikan, Alena S. Harley, Axel Bernal, P. Garst, Victor Lavrenko and 18 more. bioRxiv, 2017
112 citations recorded by the source corpus.
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.
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/fba25052d6c4f8a7239479fb0ffc3e28df81a5eb
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Citations in the corpus
- Machine learning and genomics: precision medicine versus patient privacy (2018, background): “More recently, the work of [23] on re-identification based on the combination of low-quality predictions of physical traits initiated a heated controversy [24, 25] on genomic data sharing.”
- Deep Audio-visual Learning: A Survey (2020, background): “, gender and nationality) [43, 44] to bridge the relation between voice and face information.”