mCSEA: Detecting subtle differentially methylated regions

Motivation The identification of differentially methylated regions (DMRs) among phenotypes is one of the main goals of epigenetic analysis. Although there are several methods developed to detect DMRs, most of them are focused on detecting relatively large differences in methylation levels and fail to detect moderate, but consistent, methylation changes that might be associated to complex disorders. Results We present mCSEA, an R package that implements a Gene Set Enrichment Analysis method to identify differentially methylated regions from Illumina450K and EPIC array data. It is especially useful for detecting subtle, but consistent, methylation differences in complex phenotypes. mCSEA also implements functions to integrate gene expression data and to detect genes with significant correlations among methylation and gene expression patterns. Using simulated datasets we show that mCSEA outperforms other tools in detecting DMRs. In addition, we applied mCSEA to a previously published dataset of sibling pairs discordant for intrauterine hyperglycemia exposure. We found several differentially methylated promoters in genes related to metabolic disorders like obesity and diabetes, demonstrating the potential of mCSEA to identify differentially methylated regions not detected by other methods. Availability mCSEA is freely available from the Bioconductor repository. Supplementary information Supplementary data are available at Bioinformatics online.

[1]  Stephan Beck,et al.  Probe Lasso: A novel method to rope in differentially methylated regions with 450K DNA methylation data , 2015, Methods.

[2]  Dan Wang,et al.  IMA: an R package for high-throughput analysis of Illumina's 450K Infinium methylation data , 2012, Bioinform..

[3]  T. Lappalainen,et al.  Associating cellular epigenetic models with human phenotypes , 2017, Nature Reviews Genetics.

[4]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[5]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[6]  C. Marsit Influence of environmental exposure on human epigenetic regulation , 2015, Journal of Experimental Biology.

[7]  Peter L Molloy,et al.  De novo identification of differentially methylated regions in the human genome , 2015, Epigenetics & Chromatin.

[8]  Peter A. Jones,et al.  The fundamental role of epigenetic events in cancer , 2002, Nature Reviews Genetics.

[9]  Sung Hee Choi,et al.  DNA methylation profiles in sibling pairs discordant for intrauterine exposure to maternal gestational diabetes , 2017, Epigenetics.

[10]  Morten Mattingsdal,et al.  DNA Methylation and Gene Expression Changes in Monozygotic Twins Discordant for Psoriasis: Identification of Epigenetically Dysregulated Genes , 2012, PLoS genetics.

[11]  Douglas A. Wolfe,et al.  Nonparametric Statistical Methods , 1973 .

[12]  M. Esteller,et al.  Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome , 2011, Epigenetics.

[13]  Insulin promoter DNA methylation correlates negatively with insulin gene expression and positively with HbA1c levels in human pancreatic islets , 2010, Diabetologia.

[14]  V. Levenson,et al.  DNA methylation as a universal biomarker , 2010, Expert review of molecular diagnostics.

[15]  Jonathan D. Turner,et al.  DNA methylation: conducting the orchestra from exposure to phenotype? , 2016, Clinical Epigenetics.

[16]  Z. Pan,et al.  Infants born large-for-gestational-age display slower growth in early infancy, but no epigenetic changes at birth , 2015, Scientific Reports.

[17]  J. Flanagan,et al.  Epigenome-wide association studies (EWAS): past, present, and future. , 2015, Methods in molecular biology.

[18]  Jeffrey T Leek,et al.  Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. , 2012, International journal of epidemiology.

[19]  M. Skinner,et al.  Identification of Genomic Features in Environmentally Induced Epigenetic Transgenerational Inherited Sperm Epimutations , 2014, PloS one.

[20]  S. London,et al.  Effect of maternal gestational weight gain on offspring DNA methylation: a follow-up to the ALSPAC cohort study , 2015, BMC Research Notes.

[21]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[22]  Alexey Sergushichev,et al.  An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation , 2016 .

[23]  D. Boomsma,et al.  Epigenome-Wide Association Study of Aggressive Behavior , 2015, Twin Research and Human Genetics.

[24]  Thierry Boon,et al.  DNA Methylation Is the Primary Silencing Mechanism for a Set of Germ Line- and Tumor-Specific Genes with a CpG-Rich Promoter , 1999, Molecular and Cellular Biology.

[25]  J. Issa,et al.  The epigenome of AML stem and progenitor cells , 2013, Epigenetics.

[26]  Thomas Mikeska,et al.  DNA Methylation Biomarkers: Cancer and Beyond , 2014, Genes.

[27]  Thomas Lengauer,et al.  Comprehensive Analysis of DNA Methylation Data with RnBeads , 2014, Nature Methods.

[28]  Avi Ma'ayan,et al.  Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool , 2013, BMC Bioinformatics.

[29]  Alexey Sergushichev,et al.  Fast gene set enrichment analysis , 2019, bioRxiv.

[30]  D. Aran,et al.  Replication timing-related and gene body-specific methylation of active human genes. , 2011, Human molecular genetics.

[31]  Hua Yu,et al.  COHCAP: an integrative genomic pipeline for single-nucleotide resolution DNA methylation analysis , 2013, Nucleic acids research.

[32]  P. Gluckman,et al.  Comparison of Methyl-capture Sequencing vs. Infinium 450K methylation array for methylome analysis in clinical samples , 2016, Epigenetics.