Increase the Power of Epigenome-Wide Association Testing Using ICC-Based Hypothesis Weighting

Bowen Cui, Shuya Cui, Jinyan Huang, Jun Chen

Research output: Chapter in Book/Report/Conference proceedingChapter


For large-scale hypothesis testing such as epigenome-wide association testing, adaptively focusing power on the more promising hypotheses can lead to a much more powerful multiple testing procedure. In this chapter, we introduce a multiple testing procedure that weights each hypothesis based on the intraclass correlation coefficient (ICC), a measure of “noisiness” of CpG methylation measurement, to increase the power of epigenome-wide association testing. Compared to the traditional multiple testing procedure on a filtered CpG set, the proposed procedure circumvents the difficulty to determine the optimal ICC cutoff value and is overall more powerful. We illustrate the procedure and compare the power to classical multiple testing procedures using an example data.

Original languageEnglish (US)
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Number of pages10
StatePublished - 2022

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029


  • Epigenome-wide association studies
  • False discovery rate control
  • Independent hypothesis weighting
  • Intraclass correlation coefficient
  • Multiple testing

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics


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