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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., Sabatini, R., Maher, M.C., Kang, E.Y., Lee, S., Arikan, O., Harley, A., Bernal, A., Garst, P., Lavrenko, V., Yocum, K., Wong, T.M., Zhu, M., Yang, W.Y.n, Chang, C., Hicks, B., Ramakrishnan, S., Tang, H., Xie, C., Brewerton, S., Turpaz, Y., Telenti, A., Roby, R.K., 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. |
| Source: | bioRxiv |
| 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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