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Open Access Methodology article

Detecting differentially methylated loci for multiple treatments based on high-throughput methylation data

Zhongxue Chen1*, Hanwen Huang2 and Qingzhong Liu3

Author Affiliations

1 Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, 1025 E. 7th street, PH C104, Bloomington, IN 47405, USA

2 Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA 30602, USA

3 Department of Computer Science, Sam Houston State University, Huntsville, TX 77341, USA

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BMC Bioinformatics 2014, 15:142  doi:10.1186/1471-2105-15-142

Published: 15 May 2014



Because of its important effects, as an epigenetic factor, on gene expression and disease development, DNA methylation has drawn much attention from researchers. Detecting differentially methylated loci is an important but challenging step in studying the regulatory roles of DNA methylation in a broad range of biological processes and diseases. Several statistical approaches have been proposed to detect significant methylated loci; however, most of them were designed specifically for case-control studies.


Noticing that the age is associated with methylation level and the methylation data are not normally distributed, in this paper, we propose a nonparametric method to detect differentially methylated loci under multiple conditions with trend for Illumina Array Methylation data. The nonparametric method, Cuzick test is used to detect the differences among treatment groups with trend for each age group; then an overall p-value is calculated based on the method of combining those independent p-values each from one age group.


We compare the new approach with other methods using simulated and real data. Our study shows that the proposed method outperforms other methods considered in this paper in term of power: it detected more biological meaningful differentially methylated loci than others.

Cuzick test; Nonparametric test; Trend test