&NA; Modern scientific studies often require the identification of a subset of explanatory variables. Several statistical methods have been developed to automate this task, and the framework of knockoffs has been proposed as a general solution for variable selection under rigorous Type I error control, without relying on strong modelling assumptions. In this paper, we extend the methodology of knockoffs to problems where the distribution of the covariates can be described by a hidden Markov model. We develop an exact and efficient algorithm to sample knockoff variables in this setting and then argue that, combined with the existing selective framework, this provides a natural and powerful tool for inference in genome‐wide association studies with guaranteed false discovery rate control. We apply our method to datasets on Crohn's disease and some continuous phenotypes.

中文翻译：

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使用隐藏的马尔可夫模型仿冒品进行基因搜索

&NA; 现代科学研究通常需要识别解释变量的子集。已经开发了几种统计方法来自动化这项任务，并且已经提出仿冒框架作为在严格的 I 类错误控制下进行变量选择的通用解决方案，而不依赖于强建模假设。在本文中，我们将仿冒方法扩展到协变量的分布可以用隐马尔可夫模型描述的问题。我们开发了一种精确有效的算法来在这种情况下对仿冒变量进行采样，然后认为，结合现有的选择性框架，这为全基因组关联研究中的推理提供了一种自然而强大的工具，并保证了错误发现率控制。我们将我们的方法应用于 Crohn 上的数据集