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coMethDMR: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes.
Gomez, Lissette; Odom, Gabriel J; Young, Juan I; Martin, Eden R; Liu, Lizhong; Chen, Xi; Griswold, Anthony J; Gao, Zhen; Zhang, Lanyu; Wang, Lily.
Affiliation
  • Gomez L; John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Odom GJ; Division of Biostatistics, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Young JI; John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Martin ER; Dr. John T. Macdonald Foundation, Department of Human Genetics, University of Miami, Miami, FL 33136, USA.
  • Liu L; John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Chen X; Dr. John T. Macdonald Foundation, Department of Human Genetics, University of Miami, Miami, FL 33136, USA.
  • Griswold AJ; Division of Biostatistics, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Gao Z; Division of Biostatistics, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Zhang L; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
  • Wang L; John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Nucleic Acids Res ; 47(17): e98, 2019 09 26.
Article in En | MEDLINE | ID: mdl-31291459
Recent technology has made it possible to measure DNA methylation profiles in a cost-effective and comprehensive genome-wide manner using array-based technology for epigenome-wide association studies. However, identifying differentially methylated regions (DMRs) remains a challenging task because of the complexities in DNA methylation data. Supervised methods typically focus on the regions that contain consecutive highly significantly differentially methylated CpGs in the genome, but may lack power for detecting small but consistent changes when few CpGs pass stringent significance threshold after multiple comparison. Unsupervised methods group CpGs based on genomic annotations first and then test them against phenotype, but may lack specificity because the regional boundaries of methylation are often not well defined. We present coMethDMR, a flexible, powerful, and accurate tool for identifying DMRs. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first. Next, coMethDMR tests association between methylation levels within the sub-region and phenotype via a random coefficient mixed effects model that models both variations between CpG sites within the region and differential methylation simultaneously. coMethDMR offers well-controlled Type I error rate, improved specificity, focused testing of targeted genomic regions, and is available as an open-source R package.
Subject(s)

Full text: 1 Database: MEDLINE Main subject: Software / CpG Islands / DNA Methylation / Epigenesis, Genetic / Epigenomics Type of study: Diagnostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Nucleic Acids Res Year: 2019 Type: Article Affiliation country: United States

Full text: 1 Database: MEDLINE Main subject: Software / CpG Islands / DNA Methylation / Epigenesis, Genetic / Epigenomics Type of study: Diagnostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Nucleic Acids Res Year: 2019 Type: Article Affiliation country: United States