No major flaws in “Identification of individuals by trait prediction using whole-genome sequencing data”-猫眼课题宝

DOI：

10.1101/187542

发表时间：

2017-09

期刊：

bioRxiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

C. Lippert;Riccardo Sabatini;M. C. Maher;Eun Yong Kang;Seunghak Lee;Okan Arikan;Alena S. Harley;Axel Bernal;P. Garst;V. Lavrenko;K. Yocum;Theodore M. Wong;Mingfu Zhu;Wen-Yun Yang;Chris Chang;Tim Lu;Charlie W. H. Lee;B. Hicks;Smriti R. Ramakrishnan;Haibao Tang;C. Xie;Jason Piper;S. Brewerton;Y. Turpaz;A. Telenti;R. Roby;F. Och;J. Venter

在最近发表的PNAS文章中，我们使用机器学习方法研究了基因组样本的可识别性[Lippert et al.，2017年]。在回应中，Erlich [2017]认为我们的工作存在重大缺陷。Erlich [2017]的主要技术批评建立在一个模拟实验的基础上，该实验表明，我们提出的算法只使用基因组样本进行识别，其表现并不比使用人口统计变量的策略更好。下面，我们将说明为什么这种比较具有误导性，并详细讨论Erlich [2017]和媒体提出的我们分析中的关键关键点。此外，不仅面部可以从DNA中导出，而且广泛的表型和人口统计变量也可以从DNA中导出。在这种情况下，Lippert等人的主要贡献。[2017]是一种算法，通过结合多种基于DNA的预测模型来识别个体的基因组。

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.

No major flaws in “Identification of individuals by trait prediction using whole-genome sequencing data”

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