{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zheng Y"],"funding":["RRD VA","NIMH NIH HHS","Biomedical Laboratory Research and Development, VA Office of Research and Development","National Institute of Mental Health"],"pagination":["2241-2258"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9665129"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["17(13)"],"pubmed_abstract":["Differentially methylated regions (DMRs) are genomic regions with specific methylation patterns across multiple loci that are associated with a phenotype. We examined the genome-wide false positive (GFP) rates of five widely used DMR methods: comb-p, Bumphunter, DMRcate, mCSEA and coMethDMR using both Illumina HumanMethylation450 (450 K) and MethylationEPIC (EPIC) data and simulated continuous and dichotomous null phenotypes (i.e., generated independently of methylation data). coMethDMR provided well-controlled GFP rates (~5%) except when analysing skewed continuous phenotypes. DMRcate generally had well-controlled GFP rates when applied to 450 K data except for the skewed continuous phenotype and EPIC data only for the normally distributed continuous phenotype. GFP rates for mCSEA were at"],"journal":["Epigenetics"],"pubmed_title":["An evaluation of the genome-wide false positive rates of common methods for identifying differentially methylated regions using illumina methylation arrays."],"pmcid":["PMC9665129"],"funding_grant_id":["R21 MH102834","R21MH102834","1R01MH108826","R01 MH108826","I50 RX003001","I01BX003477"],"pubmed_authors":["Miller MW","Zheng Y","Katrinli S","Logue MW","Liu C","Lunetta KL","Smith AK"],"additional_accession":[]},"is_claimable":false,"name":"An evaluation of the genome-wide false positive rates of common methods for identifying differentially methylated regions using illumina methylation arrays.","description":"Differentially methylated regions (DMRs) are genomic regions with specific methylation patterns across multiple loci that are associated with a phenotype. We examined the genome-wide false positive (GFP) rates of five widely used DMR methods: comb-p, Bumphunter, DMRcate, mCSEA and coMethDMR using both Illumina HumanMethylation450 (450 K) and MethylationEPIC (EPIC) data and simulated continuous and dichotomous null phenotypes (i.e., generated independently of methylation data). coMethDMR provided well-controlled GFP rates (~5%) except when analysing skewed continuous phenotypes. DMRcate generally had well-controlled GFP rates when applied to 450 K data except for the skewed continuous phenotype and EPIC data only for the normally distributed continuous phenotype. GFP rates for mCSEA were at","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2026-05-28T01:31:40.956Z","creation":"2025-02-19T01:18:01.749Z"},"accession":"S-EPMC9665129","cross_references":{"pubmed":["36047742"],"doi":["10.1080/15592294.2022.2115600"]}}