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Gene hunting with hidden Markov model knockoffs

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  • M Sesia
  • C Sabatti
  • E J Candès

Abstract

SUMMARY 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.

Suggested Citation

  • M Sesia & C Sabatti & E J Candès, 2019. "Gene hunting with hidden Markov model knockoffs," Biometrika, Biometrika Trust, vol. 106(1), pages 1-18.
    • Handle: RePEc:oup:biomet:v:106:y:2019:i:1:p:1-18.
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    File URL: http://hdl.handle.net/10.1093/biomet/asy033
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    Citations

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    Cited by:

    1. Panxu Yuan & Yinfei Kong & Gaorong Li, 2024. "FDR control and power analysis for high-dimensional logistic regression via StabKoff," Statistical Papers, Springer, vol. 65(5), pages 2719-2749, July.
    2. Zihuai He & Linxi Liu & Michael E. Belloy & Yann Guen & Aaron Sossin & Xiaoxia Liu & Xinran Qi & Shiyang Ma & Prashnna K. Gyawali & Tony Wyss-Coray & Hua Tang & Chiara Sabatti & Emmanuel Candès & Mich, 2022. "GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    3. Nikolaos Ignatiadis & Wolfgang Huber, 2021. "Covariate powered cross‐weighted multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 720-751, September.
    4. Emmanuel Candès & Chiara Sabatti, 2020. "Discussion of the Paper “Prediction, Estimation, and Attribution” by B. Efron," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 60-63, December.
    5. L Bottolo & S Richardson, 2019. "Discussion of ‘Gene hunting with hidden Markov model knockoffs’," Biometrika, Biometrika Trust, vol. 106(1), pages 19-22.
    6. Ruth Heller, 2020. "Comments on: Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 51-55, March.

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