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

BMC Bioinformatics

William R. P. Denault¹  Astanand Jugessur¹ 

[1] Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway;Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway;Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway;

关键词: Wavelets;    DNA methylation;    EWAS;    Association analysis;    Epigenetics;   

DOI  :  10.1186/s12859-021-03979-y

来源: Springer

【 摘 要 】

BackgroundWe 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).ResultsWaveQTL 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.ConclusionsOur 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.

【 授权许可】

CC BY