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Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis.

William R P Denault1,2,3, Astanand Jugessur4,5,6

  • 1Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway. william.denault@fhi.no.

BMC Bioinformatics
|February 11, 2021
PubMed
Summary

We developed fast functional wavelet (FFW), a computational shortcut to accelerate DNA methylation association analysis. This method significantly improves speed and maintains accuracy for identifying differentially methylated regions.

Keywords:
Association analysisDNA methylationEWASEpigeneticsWavelets

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • The WaveQTL method, based on wavelets, was developed to identify DNase I hypersensitivity quantitative trait loci (dsQTL).
  • WaveQTL utilizes permutations for statistical significance evaluation, which can be computationally intensive.

Purpose of the Study:

  • To present a computational shortcut to enhance the WaveQTL method.
  • To improve the efficiency of identifying associations between functional data and phenotypes.

Main Methods:

  • Applied a simulation-based approach (Zhou and Guan, 2017) to estimate significance using Bayes factors, replacing permutations.
  • This simulation-based approach was termed fast functional wavelet (FFW).
  • Tested FFW on a public DNA methylation dataset from colorectal cancer patients.

Main Results:

  • FFW demonstrated a substantial increase in computational speed compared to the original permutation-based WaveQTL.
  • The FFW method effectively controls type I errors.
  • FFW exhibits good statistical power for detecting differentially methylated regions.

Conclusions:

  • The FFW approach offers broad applicability for detecting associations between various functional data types and phenotypes.
  • FFW can be utilized to re-analyze large public DNA methylation datasets, potentially uncovering novel associations.
  • The FFW R package is publicly available on GitHub for research use.