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Updated: Jan 23, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
Correction for multiple testing in candidate-gene methylation studies
Zhenwei Zhou1,2, Kathryn L Lunetta2, Alicia K Smith3,4
1National Center for PTSD, VA Boston Healthcare System, Boston, MA 02130, USA.
Abstract:
Aim: We compared the performance of multiple testing corrections for candidate gene methylation studies, namely Sidak (accurate Bonferroni), false-discovery rate and three adjustments that incorporate the correlation between CpGs: extreme tail theory (ETT), Gao et al. (GEA), and Li and Ji methods. Materials & methods: The experiment-wide type 1 error rate was examined in simulations based on Illumina EPIC and 450K data. Results: For high-correlation genes, Sidak and false-discovery rate corrections were conservative while the Li and Ji method was liberal. The GEA method tended to be conservative unless a threshold parameter was adjusted. The ETT yielded an appropriate type 1 error rate. Conclusion: For genes with substantial correlation across measured CpGs, GEA and ETT can appropriately correct for multiple testing in candidate gene methylation studies.
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