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EpipwR: efficient power analysis for EWAS with continuous outcomes.

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  • 1Department of Statistical Science, Baylor University, Waco, TX 76798, United States.

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EpipwR is a new R-package for estimating statistical power in epigenome-wide association studies (EWAS). It offers improved accuracy for various study designs, aiding researchers in planning robust EWAS.

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Area of Science:

  • Genomics
  • Biostatistics
  • Computational Biology

Background:

  • Epigenome-wide association studies (EWAS) are crucial for understanding complex diseases.
  • Existing power estimation tools for EWAS are limited, especially for non-case-control designs.
  • There is a need for user-friendly tools that support diverse EWAS study designs.

Purpose of the Study:

  • To introduce EpipwR, an open-source R-package for efficient EWAS power estimation.
  • To expand power calculation capabilities beyond traditional case-control studies.
  • To provide researchers with a tool for planning more accurate and comprehensive EWAS.

Main Methods:

  • EpipwR utilizes a quasi-simulated approach for power estimation.
  • It generates data for relevant CpG sites and calculates P-values directly for non-associated sites.
  • The package leverages empirical EWAS datasets to guide data generation.

Main Results:

  • EpipwR demonstrates efficient power estimation for EWAS with continuous or binary outcomes.
  • Numerical studies confirm the influence of empirical datasets on correlation and power.
  • EpipwR outperforms existing power calculation alternatives on both simulated and real EWAS data.

Conclusions:

  • EpipwR provides a valuable and accurate tool for EWAS power analysis.
  • The R-package supports a wider range of study designs than previously available.
  • EpipwR is accessible on Bioconductor and GitHub, facilitating its adoption by researchers.