ArticleBMC bioinformatics2013
Age-adjusted nonparametric detection of differential DNA methylation with case-control designs.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- Whole-Genome Bisulfite Sequencing Identifies Blood-Based DNA Methylation Biomarker for Hepatocellular Carcinoma.Molecular carcinogenesis · 2026Article
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- Integrative Analysis of Epigenome and Transcriptome Data Reveals Aberrantly Methylated Promoters and Enhancers in Hepatocellular Carcinoma.Frontiers in oncology · 2021Article
- Data-Driven-Based Approach to Identifying Differentially Methylated Regions Using Modified 1D Ising Model.BioMed research international · 2018Article
- Review: DNA methylation and alcohol use disorders: Progress and challenges.The American journal on addictions · 2017Review
- PLMET: A Novel Pseudolikelihood-Based EM Test for Homogeneity in Generalilzed Exponential Tilt Mixture Models.Journal of the American Statistical Association · 2017Article
- Simultaneous statistical inference for epigenetic data.PloS one · 2015Article
- Identification of methylation quantitative trait loci (mQTLs) influencing promoter DNA methylation of alcohol dependence risk genes.Human genetics · 2014Article
- Detecting differentially methylated loci for multiple treatments based on high-throughput methylation data.BMC bioinformatics · 2014Article
- A new statistical approach to combining p-values using gamma distribution and its application to genome-wide association study.BMC bioinformatics · 2014Article
- Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data.BMC medical genomics · 2013Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDNA 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.
resultsIn 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.
conclusionsCompared 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.