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Bayesian variable selection using Knockoffs with applications to genomics.

Jurel K Yap1,2, Iris Ivy M Gauran3

  • 1School of Statistics, University of the Philippines Diliman, Quezon City, Philippines.

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|September 26, 2022
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Summary
This summary is machine-generated.

This study introduces a new statistical method to accurately identify genetic markers for HIV drug resistance, improving true positive rates (TPR) and minimizing false discoveries (FDR) in costly drug therapy research.

Keywords:
Bayesian variable selectionDrug resistant HIV-1False discovery controlModel-free Knockoffs

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Area of Science:

  • Computational Statistics
  • Genetics
  • Bioinformatics

Background:

  • HIV drug resistance research is expensive.
  • Accurate identification of genetic markers for drug resistance is crucial.
  • Minimizing false discoveries (FDR) alongside maximizing true positives (TPR) is essential.

Purpose of the Study:

  • To propose a novel multiple testing procedure for identifying genetic markers associated with HIV drug resistance.
  • To unify computational statistics concepts: Model-free Knockoffs, Bayesian variable selection, and local false discovery rate.
  • To develop an algorithm for signal identification and compare its performance against existing methods.

Main Methods:

  • Developed an algorithm using an augmented data-Knockoff matrix and Bayesian Lasso.
  • Utilized Markov Chain Monte Carlo (MCMC) outputs and local false discovery rate for signal identification.
  • Compared the proposed method against non-Bayesian methods like Benjamini-Hochberg (BHq) and Lasso regression.

Main Results:

  • The proposed method demonstrated a lower false discovery rate (FDR) compared to BHq and Lasso in specific scenarios, including low and equi-dimensional cases.
  • Numerical studies validated the effectiveness of the unified statistical approach.
  • The method shows promise for analyzing genetic markers linked to drug-resistant HIV.

Conclusions:

  • The proposed unified statistical procedure offers an effective approach to identifying genetic markers for HIV drug resistance.
  • It provides a valuable tool for reducing costs in drug therapy research by improving accuracy.
  • Future applications include analyzing genetic markers for drug-resistant HIV-1 in the Philippines.