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Area of Science:

  • Statistics
  • Genetics
  • Computational Biology

Background:

  • Variable selection is crucial in modern scientific studies.
  • Knockoffs offer a robust framework for variable selection with Type I error control.
  • Existing methods may rely on strong modeling assumptions.

Purpose of the Study:

  • To extend the knockoff methodology to settings with hidden Markov models (HMMs).
  • To develop an efficient algorithm for sampling knockoff variables under HMMs.
  • To provide a powerful tool for inference in genome-wide association studies (GWAS) with guaranteed false discovery rate (FDR) control.

Main Methods:

  • Developed an exact and efficient algorithm for sampling knockoff variables within an HMM framework.
  • Integrated the new knockoff sampling with the existing selective inference framework.
  • Applied the method to real-world GWAS datasets.

Main Results:

  • Successfully generated knockoff variables for HMM-distributed covariates.
  • Demonstrated the utility of the method for FDR-controlled inference in GWAS.
  • Achieved robust results on Crohn's disease and continuous phenotype datasets.

Conclusions:

  • The proposed knockoff extension for HMMs is a powerful and natural tool for variable selection in GWAS.
  • The method provides rigorous FDR control without strong modeling assumptions.
  • This approach facilitates reliable genetic association studies.