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Computational Methods for Detection of Differentially Methylated Regions Using Kernel Distance and Scan Statistics

1
Genome Research Center, AbbVie, North Chicago, IL 60064, USA
2
Department of Population Health Sciences, Augusta University, Augusta, GA 30912, USA
3
Division of Statistics, Northern Illinois University, DeKalb, IL 60115, USA
4
Georgia Cancer Center, Augusta University, Augusta, GA 30912, USA
*
Author to whom correspondence should be addressed.
Genes 2019, 10(4), 298; https://doi.org/10.3390/genes10040298
Received: 28 February 2019 / Revised: 29 March 2019 / Accepted: 8 April 2019 / Published: 12 April 2019
(This article belongs to the Special Issue Bioinformatic Analysis for Rare Diseases)
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Abstract

Motivation: Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation because they play an important role in regulating gene expression without changes in the sequence of DNA. Abnormal DNA methylation is associated with many human diseases. Results: We propose two different approaches to test for differentially methylated regions (DMRs) associated with complex traits, while accounting for correlations among CpG sites in the DMRs. The first approach is a nonparametric method using a kernel distance statistic and the second one is a likelihood-based method using a binomial spatial scan statistic. The kernel distance method uses the kernel function, while the binomial scan statistic approach uses a mixed-effects model to incorporate correlations among CpG sites. Extensive simulations show that both approaches have excellent control of type I error, and both have reasonable statistical power. The binomial scan statistic approach appears to have higher power, while the kernel distance method is computationally faster. The proposed methods are demonstrated using data from a chronic lymphocytic leukemia (CLL) study. View Full-Text
Keywords: binomial scan statistic; CpG sites; DNA methylation; kernel distance statistic; mixed-effects model binomial scan statistic; CpG sites; DNA methylation; kernel distance statistic; mixed-effects model
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Dunbar, F.; Xu, H.; Ryu, D.; Ghosh, S.; Shi, H.; George, V. Computational Methods for Detection of Differentially Methylated Regions Using Kernel Distance and Scan Statistics. Genes 2019, 10, 298.

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