Abstract
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.
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