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A general framework for powerful confounder adjustment in omics association studies.

Asmita Roy1, Jun Chen2, Xianyang Zhang1

  • 1epartment of Statistics, Texas A&M University, 155 Ireland Street, College Station, TX 77840, United States.

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Summary

This study introduces a new method, 2DFDR+, to improve the analysis of genomic data by enhancing statistical power in identifying genomic features associated with specific variables while accounting for confounding factors.

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genomic data analysis often faces confounding variables like demographics and batch effects.
  • Traditional methods for identifying genomic associations with confounders are suboptimal.
  • Accurate identification of genomic features requires robust statistical approaches.

Purpose of the Study:

  • To propose a novel statistical framework, 2DFDR+, for analyzing genomic data with confounding variables.
  • To enhance the power of detecting genomic associations compared to conventional methods.
  • To provide a flexible method applicable across diverse genomic data settings.

Main Methods:

  • Developed a two-dimensional false discovery rate control framework (2DFDR+).
  • Utilizes marginal independence test statistics for feature filtering.
  • Employs conditional independence test statistics for false discovery rate control.
  • Designed for settings with unknown conditional distributions of genomic variables.

Main Results:

  • The proposed 2DFDR+ framework significantly improves statistical power over traditional methods.
  • Demonstrated robust performance through extensive simulations.
  • Validated the approach with real-world genomic data applications.
  • Provides asymptotically valid inference even with unknown conditional distributions.

Conclusions:

  • 2DFDR+ offers a powerful and versatile approach for genomic association studies with confounders.
  • The method addresses limitations of traditional regression-based analyses.
  • Available R codes facilitate implementation and further research.