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Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data.

Fang Xie1, Johannes Lederer1

  • 1Department of Mathematics, Ruhr-University Bochum, Universitätsstraße 150, 44801 Bochum, Germany.

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Aggregating knockoffs enhances the power of microbiome analysis, controlling false discoveries. This method identifies novel gut bacteria phyla linked to obesity, offering new health insights.

Keywords:
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Area of Science:

  • Microbiome research
  • Statistical genetics
  • Bioinformatics

Background:

  • The gut microbiome significantly impacts human health and well-being.
  • High-dimensional microbiome data present analytical challenges, including the risk of false discoveries with traditional methods like the lasso estimator.
  • Knockoffs offer a recent statistical approach to control false discoveries.

Purpose of the Study:

  • To develop and validate an aggregated knockoff method for enhanced power in high-dimensional microbiome data analysis.
  • To rigorously control the number of false discoveries in microbiome association studies.
  • To identify novel microbial associations with health conditions, specifically obesity.

Main Methods:

  • Development of an aggregated knockoff procedure for high-dimensional data.
  • Theoretical validation of the proposed method.
  • Simulation studies to assess performance and power.
  • Application to real-world microbiome data from the American Gut Project.

Main Results:

  • The aggregated knockoff method increases statistical power while maintaining strict control over false discoveries.
  • Simulations confirm the theoretical guarantees and practical utility of the approach.
  • Analysis of American Gut Project data revealed significant associations between previously overlooked gut phyla and obesity.

Conclusions:

  • Aggregated knockoffs provide a powerful and reliable tool for analyzing complex microbiome data.
  • This method facilitates the discovery of novel microbial biomarkers for health and disease.
  • The findings highlight potential new targets for understanding and managing obesity through gut microbiome modulation.