ArticlePloS one2016
A Comparative Study of Five Association Tests Based on CpG Set for Epigenome-Wide Association Studies.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- MPCI: A novel metric for quantifying DNA methylation patterns in NGS data.PLoS computational biology · 2026Article
- A novel principal component based method for identifying differentially methylated regions in Illumina Infinium MethylationEPIC BeadChip data.Epigenetics · 2023Article
- Genetic and environment effects on structural neuroimaging endophenotype for bipolar disorder: a novel molecular approach.Translational psychiatry · 2022Article
- Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine.Biochimica et biophysica acta. Reviews on cancer · 2021Review
- Associations between an integrated component of maternal glycemic regulation in pregnancy and cord blood DNA methylation.Epigenomics · 2021Article
- PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi-Omics Data.Proteomics · 2020Article
- Integrative Omics Approach to Identifying Genes Associated With Atrial Fibrillation.Circulation research · 2020Article
- An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays.Briefings in bioinformatics · 2019Review
- coMethDMR: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes.Nucleic acids research · 2019Article
- Application of novel and existing methods to identify genes with evidence of epigenetic association: results from GAW20.BMC genetics · 2018Article
- Comparison of novel and existing methods for detecting differentially methylated regions.BMC genetics · 2018Article
- Integrative methylation score to identify epigenetic modifications associated with lipid changes resulting from fenofibrate treatment in families.BMC proceedings · 2018Article
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Authors and funding
7 authors.
Funding
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Abstract
An epigenome-wide association study (EWAS) is a large-scale study of human disease-associated epigenetic variation, specifically variation in DNA methylation. High throughput technologies enable simultaneous epigenetic profiling of DNA methylation at hundreds of thousands of CpGs across the genome. The clustering of correlated DNA methylation at CpGs is reportedly similar to that of linkage-disequilibrium (LD) correlation in genetic single nucleotide polymorphisms (SNP) variation. However, current analysis methods, such as the t-test and rank-sum test, may be underpowered to detect differentially methylated markers. We propose to test the association between the outcome (e.g case or control) and a set of CpG sites jointly. Here, we compared the performance of five CpG set analysis approaches: principal component analysis (PCA), supervised principal component analysis (SPCA), kernel principal component analysis (KPCA), sequence kernel association test (SKAT), and sliced inverse regression (SIR) with Hotelling's T2 test and t-test using Bonferroni correction. The simulation results revealed that the first six methods can control the type I error at the significance level, while the t-test is conservative. SPCA and SKAT performed better than other approaches when the correlation among CpG sites was strong. For illustration, these methods were also applied to a real methylation dataset.
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