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Updated: Jan 23, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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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.

Epigenomics
|June 27, 2019
PubMed
Summary
This summary is machine-generated.

For candidate gene methylation studies with correlated CpGs, the Extreme Tail Theory (ETT) and Gao et al. (GEA) methods offer appropriate multiple testing corrections. These methods outperform Sidak and false-discovery rate corrections in simulations.

Keywords:
450KEPICcandidate genemethylationmultiple testing correction

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Area of Science:

  • Epigenetics and Genomics
  • Statistical Bioinformatics

Background:

  • Candidate gene methylation studies are crucial for understanding disease mechanisms.
  • Accurate multiple testing correction is essential for identifying significant CpG sites.
  • CpG correlations within genes complicate standard correction methods.

Purpose of the Study:

  • To evaluate multiple testing correction methods for candidate gene methylation studies.
  • To compare the performance of standard corrections (Sidak, FDR) with correlation-aware methods (ETT, GEA, Li and Ji).

Main Methods:

  • Simulations were conducted using Illumina EPIC and 450K methylation data.
  • Experiment-wide type 1 error rates were assessed for each correction method.
  • Performance was analyzed under conditions of high CpG correlation within genes.

Main Results:

  • Sidak and false-discovery rate (FDR) corrections were conservative for highly correlated genes.
  • The Li and Ji method showed liberal type 1 error rates.
  • Extreme Tail Theory (ETT) and Gao et al. (GEA) methods demonstrated appropriate type 1 error rates, with GEA requiring threshold adjustment.

Conclusions:

  • For candidate gene methylation studies with substantial CpG correlation, ETT and GEA provide robust multiple testing correction.
  • These correlation-aware methods are recommended over standard Sidak or FDR for such genomic regions.