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

Dunbar Dunbar,Xu Xu,Ghosh Ghosh,Ryu Ryu,Shi Shi,George George

doi:10.3390/genes10040298

Dunbar Dunbar, Xu Xu + Show 4 more

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https://doi.org/10.3390/genes10040298

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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.

Highlights

Genetic variations from genome-wide association studies can explain only a small proportion of the phenotypic variation for most diseases [1]
We propose two methods for differentially methylated regions (DMRs) detections, one based on a kernel distance statistic (KDM) and the other based on a binomial scan statistic method (SSM)
The methods are applied to a bisulfitesequenced data from a chronic lymphocytic leukemia (CLL) study [19], with the results presented in Analysis of CLL Data, followed by conclusions and discussions presented in Discussion

Summary

Introduction

Genetic variations from genome-wide association studies can explain only a small proportion of the phenotypic variation for most diseases [1]. It has been established that most diseases are caused by both genetic factors and non-genetic factors such as environmental factors, contributing to epigenetic changes, especially changes in DNA methylation at CpG sites. Research has found that aberrant DNA methylation of multiple promoter-associated CpG islands can suppress gene expression by inactivating the function of tumor suppressor genes, eventually causing cancer [2]. NGS coupled with bisulphite treatment of DNA converts unmethylated cytosines to uracils and leaves methylated cytosines intact. This results in counts of uracils (unmethylated) and cytosines (methylated) at each CpG site for every sample. The total counts of uracils and cytosines are the sequencing coverage at each CpG site, which could be different for each sample.

Methods

Results

Conclusion