# Gene Hunting with Knockoffs for Hidden Markov Models

arXiv: https://arxiv.org/abs/1706.04677 · HTML: https://mlsanity.com/paper/1706.04677/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2017.

## Built on by (most cited first, 14 of 14)

- [Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent](https://mlsanity.com/paper/2403.04015/) (2024)
- [Model-X Sequential Testing for Conditional Independence via Testing by Betting](https://mlsanity.com/paper/2210.00354/) (2022)
- [The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control](https://mlsanity.com/paper/2110.06048/) (2021)
- [Conditional Feature Importance for Mixed Data](https://mlsanity.com/paper/2210.03047/) (2022)
- [Cluster Stability Selection](https://mlsanity.com/paper/2201.00494/) (2022)
- [DIET: Conditional independence testing with marginal dependence measures of residual information](https://mlsanity.com/paper/2208.08579/) (2022)
- [High-Dimensional False Discovery Rate Control for Dependent Variables](https://mlsanity.com/paper/2401.15796/) (2024)
- [Fundamental limits for learning hidden Markov model parameters](https://mlsanity.com/paper/2106.12936/) (2021)
- [Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery](https://mlsanity.com/paper/2202.02253/) (2022)
- [DeepDRK: Deep Dependency Regularized Knockoff for Feature Selection](https://mlsanity.com/paper/2402.17176/) (2024)
- [Extending Model-x Framework to Missing Data](https://mlsanity.com/paper/2202.13054/) (2022)
- [Error Controlled Feature Selection for Ultrahigh Dimensional and Highly Correlated Feature Space Using Deep Learning](https://mlsanity.com/paper/2209.07011/) (2022)
- [Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age](https://mlsanity.com/paper/2501.06868/) (2025)
- [Knockoffs-based False Discovery Rate Control and Simplification for Deep Neural Networks](https://mlsanity.com/paper/2606.04404/) (2026)

## Abstract (arXiv)

Modern scientific studies often require the identification of a subset of relevant explanatory variables, in the attempt to understand an interesting phenomenon. Several statistical methods have been developed to automate this task, but only recently has the framework of model-free knockoffs proposed a general solution that can perform variable selection under rigorous type-I error control, without relying on strong modeling assumptions. In this paper, we extend the methodology of model-free knockoffs to a rich family of problems where the distribution of the covariates can be described by a hidden Markov model (HMM). We develop an exact and efficient algorithm to sample knockoff copies of an HMM. We then argue that combined with the knockoffs selective framework, they provide a natural and powerful tool for performing principled inference in genome-wide association studies with guaranteed FDR control. Finally, we apply our methodology to several datasets aimed at studying the Crohn's disease and several continuous phenotypes, e.g. levels of cholesterol.