Comparison of novel and existing methods for detecting differentially methylated regions |
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Authors: | Lent,Samantha,Xu,Hanfei,Wang,Lan,Wang,Zhe,Sarnowski,Chloé ,Hivert,Marie-France,Dupuis, José e |
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Affiliation: | 1.Department of Biostatistics,Boston University School of Public Health,Boston,USA;2.Bioinformatics Program,Boston University,Boston,USA;3.Obesity Prevention Program, Department of Population Medicine,Harvard Medical School and Harvard Pilgrim Health Care Institute,Boston,USA;4.Diabetes Unit,Massachusetts General Hospital,Boston,USA |
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Abstract: | BackgroundSingle-probe analyses in epigenome-wide association studies (EWAS) have identified associations between DNA methylation and many phenotypes, but do not take into account information from neighboring probes. Methods to detect differentially methylated regions (DMRs) (clusters of neighboring probes associated with a phenotype) may provide more power to detect associations between DNA methylation and diseases or phenotypes of interest.ResultsWe proposed a novel approach, GlobalP, and perform comparisons with 3 methods—DMRcate, Bumphunter, and comb-p—to identify DMRs associated with log triglycerides (TGs) in real GAW20 data before and after fenofibrate treatment. We applied these methods to the summary statistics from an EWAS performed on the methylation data. Comb-p, DMRcate, and GlobalP detected very similar DMRs near the gene CPT1A on chromosome 11 in both the pre- and posttreatment data. In addition, GlobalP detected 2 DMRs before fenofibrate treatment in the genes ETV6 and ABCG1. Bumphunter identified several DMRs on chromosomes 1 and 20, which did not overlap with DMRs detected by other methods.ConclusionsOur novel method detected the same DMR identified by two existing methods and detected two additional DMRs not identified by any of the existing methods we compared. |
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