Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis
Abstract Background We present here a computational shortcut to improve a powerful wavelet-based method by Shim and Stephens (Ann Appl Stat 9(2):665–686, 2015. https://doi.org/10.1214/14-AOAS776 ) called WaveQTL that was originally designed to identify DNase I hypersensitivity quantitative trait loc...
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doaj-b21da84162cf445f868bb68929b8a7a02021-02-14T12:50:58ZengBMCBMC Bioinformatics1471-21052021-02-0122111510.1186/s12859-021-03979-yDetecting differentially methylated regions using a fast wavelet-based approach to functional association analysisWilliam R. P. Denault0Astanand Jugessur1Department of Genetics and Bioinformatics, Norwegian Institute of Public HealthDepartment of Genetics and Bioinformatics, Norwegian Institute of Public HealthAbstract Background We present here a computational shortcut to improve a powerful wavelet-based method by Shim and Stephens (Ann Appl Stat 9(2):665–686, 2015. https://doi.org/10.1214/14-AOAS776 ) called WaveQTL that was originally designed to identify DNase I hypersensitivity quantitative trait loci (dsQTL). Results WaveQTL relies on permutations to evaluate the significance of an association. We applied a recent method by Zhou and Guan (J Am Stat Assoc 113(523):1362–1371, 2017. https://doi.org/10.1080/01621459.2017.1328361 ) to boost computational speed, which involves calculating the distribution of Bayes factors and estimating the significance of an association by simulations rather than permutations. We called this simulation-based approach “fast functional wavelet” (FFW), and tested it on a publicly available DNA methylation (DNAm) dataset on colorectal cancer. The simulations confirmed a substantial gain in computational speed compared to the permutation-based approach in WaveQTL. Furthermore, we show that FFW controls the type I error satisfactorily and has good power for detecting differentially methylated regions. Conclusions Our approach has broad utility and can be applied to detect associations between different types of functions and phenotypes. As more and more DNAm datasets are being made available through public repositories, an attractive application of FFW would be to re-analyze these data and identify associations that might have been missed by previous efforts. The full R package for FFW is freely available at GitHub https://github.com/william-denault/ffw .https://doi.org/10.1186/s12859-021-03979-yWaveletsDNA methylationEWASAssociation analysisEpigenetics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
William R. P. Denault Astanand Jugessur |
spellingShingle |
William R. P. Denault Astanand Jugessur Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis BMC Bioinformatics Wavelets DNA methylation EWAS Association analysis Epigenetics |
author_facet |
William R. P. Denault Astanand Jugessur |
author_sort |
William R. P. Denault |
title |
Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis |
title_short |
Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis |
title_full |
Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis |
title_fullStr |
Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis |
title_full_unstemmed |
Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis |
title_sort |
detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2021-02-01 |
description |
Abstract Background We present here a computational shortcut to improve a powerful wavelet-based method by Shim and Stephens (Ann Appl Stat 9(2):665–686, 2015. https://doi.org/10.1214/14-AOAS776 ) called WaveQTL that was originally designed to identify DNase I hypersensitivity quantitative trait loci (dsQTL). Results WaveQTL relies on permutations to evaluate the significance of an association. We applied a recent method by Zhou and Guan (J Am Stat Assoc 113(523):1362–1371, 2017. https://doi.org/10.1080/01621459.2017.1328361 ) to boost computational speed, which involves calculating the distribution of Bayes factors and estimating the significance of an association by simulations rather than permutations. We called this simulation-based approach “fast functional wavelet” (FFW), and tested it on a publicly available DNA methylation (DNAm) dataset on colorectal cancer. The simulations confirmed a substantial gain in computational speed compared to the permutation-based approach in WaveQTL. Furthermore, we show that FFW controls the type I error satisfactorily and has good power for detecting differentially methylated regions. Conclusions Our approach has broad utility and can be applied to detect associations between different types of functions and phenotypes. As more and more DNAm datasets are being made available through public repositories, an attractive application of FFW would be to re-analyze these data and identify associations that might have been missed by previous efforts. The full R package for FFW is freely available at GitHub https://github.com/william-denault/ffw . |
topic |
Wavelets DNA methylation EWAS Association analysis Epigenetics |
url |
https://doi.org/10.1186/s12859-021-03979-y |
work_keys_str_mv |
AT williamrpdenault detectingdifferentiallymethylatedregionsusingafastwaveletbasedapproachtofunctionalassociationanalysis AT astanandjugessur detectingdifferentiallymethylatedregionsusingafastwaveletbasedapproachtofunctionalassociationanalysis |
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1724269989952749568 |