Age-adjusted nonparametric detection of differential DNA methylation with case–control designs

BMC Bioinformatics

Hanwen Huang¹  Zhongxue Chen²  Xudong Huang³ 

[1] Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA 30605, USA

[2] Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, 1025 E. 7th Street, Bloomington, IN 47405, USA

[3] Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA

关键词: Combining p-value;    One-sided test;    Nonparametric method;   

Others  :  1087949
DOI  :  10.1186/1471-2105-14-86

 received in 2012-08-13, accepted in 2013-02-20,  发布年份 2013

【 摘 要 】

Background

DNA methylation profiles differ among disease types and, therefore, can be used in disease diagnosis. In addition, large-scale whole genome DNA methylation data offer tremendous potential in understanding the role of DNA methylation in normal development and function. However, due to the unique feature of the methylation data, powerful and robust statistical methods are very limited in this area.

Results

In this paper, we proposed and examined a new statistical method to detect differentially methylated loci for case control designs that is fully nonparametric and does not depend on any assumption for the underlying distribution of the data. Moreover, the proposed method adjusts for the age effect that has been shown to be highly correlated with DNA methylation profiles. Using simulation studies and a real data application, we have demonstrated the advantages of our method over existing commonly used methods.

Conclusions

Compared to existing methods, our method improved the detection power for differentially methylated loci for case control designs and controlled the type I error well. Its applications are not limited to methylation data; it can be extended to many other case–control studies.

【 授权许可】

   
2013 Huang et al; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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