Open AccessA Comparative Study of Five Association Tests Based on CpG Set for Epigenome-Wide Association Studies
Author(s) -
Qiuyi Zhang,
Yang Zhao,
Ruyang Zhang,
Yongyue Wei,
Honggang Yi,
Fang Shao,
Feng Chen
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0156895
Subject(s) - dna methylation , linkage disequilibrium , principal component analysis , biology , genetics , genetic association , epigenetics , computational biology , epigenome , single nucleotide polymorphism , type i and type ii errors , bonferroni correction , cpg site , genome wide association study , statistics , mathematics , genotype , gene , gene expression
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 T 2 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.