+ Site Statistics
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Identification of differentially methylated loci using wavelet-based functional mixed models

Identification of differentially methylated loci using wavelet-based functional mixed models

Bioinformatics 32(5): 664-672

DNA methylation is a key epigenetic modification that can modulate gene expression. Over the past decade, a lot of studies have focused on profiling DNA methylation and investigating its alterations in complex diseases such as cancer. While early studies were mostly restricted to CpG islands or promoter regions, recent findings indicate that many of important DNA methylation changes can occur in other regions and DNA methylation needs to be examined on a genome-wide scale. In this article, we apply the wavelet-based functional mixed model methodology to analyze the high-throughput methylation data for identifying differentially methylated loci across the genome. Contrary to many commonly-used methods that model probes independently, this framework accommodates spatial correlations across the genome through basis function modeling as well as correlations between samples through functional random effects, which allows it to be applied to many different settings and potentially leads to more power in detection of differential methylation. We applied this framework to three different high-dimensional methylation data sets (CpG Shore data, THREE data and NIH Roadmap Epigenomics data), studied previously in other works. A simulation study based on CpG Shore data suggested that in terms of detection of differentially methylated loci, this modeling approach using wavelets outperforms analogous approaches modeling the loci as independent. For the THREE data, the method suggests newly detected regions of differential methylation, which were not reported in the original study. Automated software called WFMM is available at https://biostatistics.mdanderson.org/SoftwareDownload CpG Shore data is available at http://rafalab.dfci.harvard.edu NIH Roadmap Epigenomics data is available at http://compbio.mit.edu/roadmap Supplementary data are available at Bioinformatics online. jefmorris@mdanderson.org.

(PDF emailed within 0-6 h: $19.90)

Accession: 058039103

Download citation: RISBibTeXText

PMID: 26559505

DOI: 10.1093/bioinformatics/btv659

Related references

Quantitative identification of differentially methylated loci based on relative entropy for matched case-control data. Epigenomics 5(6): 631-643, 2014

Wavelet-based functional mixed models. Journal of the Royal Statistical Society. Series B, Statistical Methodology 68(2): 179-199, 2006

Wavelet-based functional linear mixed models: an application to measurement error-corrected distributed lag models. Biostatistics 11(3): 432-452, 2010

Identification and quantification of differentially methylated loci by the pyrosequencing technology. Methods in Molecular Biology 507: 189-205, 2008

Wavelet-based clustering for mixed-effects functional models in high dimension. Biometrics 69(1): 31-40, 2013

Automatic quantitative analysis of ultrasound tongue contours via wavelet-based functional mixed models. Journal of the Acoustical Society of America 137(2): El178-El183, 2016

Bayesian analysis of mass spectrometry proteomic data using wavelet-based functional mixed models. Biometrics 64(2): 479-489, 2007

Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study. Journal of the American Statistical Association 101(476): 1352-1364, 2006

Identifying differentially methylated genes using mixed effect and generalized least square models. Bmc Bioinformatics 10: 404, 2010

Detecting differentially methylated loci for multiple treatments based on high-throughput methylation data. Bmc Bioinformatics 15: 142, 2014

Identification of new differentially methylated genes that have potential functional consequences in prostate cancer. Plos One 7(10): E48455, 2013

Identification of differentially methylated sequences in gastric cancer by methylated CpG island amplification. Proceedings of the American Association for Cancer Research Annual Meeting 44: 432-433, 2003

Identification of differentially methylated sequences in colorectal cancer by methylated CpG island amplification. Cancer Research 59(10): 2307-2312, 1999

Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data. Bmc Medical Genomics 6 Suppl 1: S9, 2013

HOME: a histogram based machine learning approach for effective identification of differentially methylated regions. Bmc Bioinformatics 20(1): 253, 2019