Dataset Information

MethylCC: technology-independent estimation of cell type composition using differentially methylated regions.

ABSTRACT: A major challenge in the analysis of DNA methylation (DNAm) data is variability introduced from intra-sample cellular heterogeneity, such as whole blood which is a convolution of DNAm profiles across a unique cell type. When this source of variability is confounded with an outcome of interest, if unaccounted for, false positives ensue. Current methods to estimate the cell type proportions in whole blood DNAm samples are only appropriate for one technology and lead to technology-specific biases if applied to data generated from other technologies. Here, we propose the technology-independent alternative: methylCC, which is available at https://github.com/stephaniehicks/methylCC.

SUBMITTER: Hicks SC

PROVIDER: S-EPMC6883691 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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methylCC: technology-independent estimation of cell type composition using differentially methylated regions.

Hicks Stephanie C SC Irizarry Rafael A RA

Genome biology 20191129 1

A major challenge in the analysis of DNA methylation (DNAm) data is variability introduced from intra-sample cellular heterogeneity, such as whole blood which is a convolution of DNAm profiles across a unique cell type. When this source of variability is confounded with an outcome of interest, if unaccounted for, false positives ensue. Current methods to estimate the cell type proportions in whole blood DNAm samples are only appropriate for one technology and lead to technology-specific biases i ...[more]

PMID: 31783894

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Project description:BACKGROUND:Cigarette smoke is a causal factor in cancers and cardiovascular disease. Smoking-associated differentially methylated regions (SM-DMRs) have been observed in disease studies, but the causal link between altered DNA methylation and transcriptional change is obscure. OBJECTIVE:Our objectives were to finely resolve SM-DMRs and to interrogate the mechanistic link between SM-DMRs and altered transcription of enhancer noncoding RNA (eRNA) and mRNA in human circulating monocytes. METHOD:We integrated SM-DMRs identified by reduced representation bisulfite sequencing (RRBS) of circulating CD14+ monocyte DNA collected from two independent human studies [n=38 from Clinical Research Unit (CRU) and n=55 from the Multi-Ethnic Study of Atherosclerosis (MESA), about half of whom were active smokers] with gene expression for protein-coding genes and noncoding RNAs measured by RT-PCR or RNA sequencing. Candidate SM-DMRs were compared with RRBS of purified CD4+ T cells, CD8+ T cells, CD15+ granulocytes, CD19+ B cells, and CD56+ NK cells (n=19 females, CRU). DMRs were validated using pyrosequencing or bisulfite amplicon sequencing in up to 85 CRU volunteers, who also provided saliva DNA. RESULTS:RRBS identified monocyte SM-DMRs frequently located in putative gene regulatory regions. The most significant monocyte DMR occurred at a poised enhancer in the aryl-hydrocarbon receptor repressor gene (AHRR) and it was also detected in both granulocytes and saliva DNA. To our knowledge, we identify for the first time that SM-DMRs in or near AHRR, C5orf55-EXOC-AS, and SASH1 were associated with increased noncoding eRNA as well as mRNA in monocytes. Functionally, the AHRR SM-DMR appeared to up-regulate AHRR mRNA through activating the AHRR enhancer, as suggested by increased eRNA in the monocytes, but not granulocytes, from smokers compared with nonsmokers. CONCLUSIONS:Our findings suggest that AHRR SM-DMR up-regulates AHRR mRNA in a monocyte-specific manner by activating the AHRR enhancer. Cell type-specific activation of enhancers at SM-DMRs may represent a mechanism driving smoking-related disease. https://doi.org/10.1289/EHP2395.

Project description:BackgroundGrowing evidence shows that epigenetic modifications play a role in Alzheimer's disease (AD). We performed an epigenome-wide association study (EWAS) to evaluate the DNA methylation differences using postmortem superior temporal gyrus (STG) and inferior frontal gyrus (IFG) samples.ResultsSamples from 72 AD patients and 62 age-matched cognitively normal controls were assayed using Illumina© Infinium MethylationEPIC BeadChip. Five and 14 differentially methylated positions (DMPs) associated with pathology (i.e., Braak stage) with p value less than Bonferroni correction threshold of 6.79 × 10-8 in the STG and IFG were identified, respectively. These cytosine-phosphate-guanine (CpG) sites included promoter associated cg26263477 annotated to ABCA7 in the STG (p = 1.21 × 10-11), and cg14058329 annotated to the HOXA5/HOXA3/HOXA-AS3 gene cluster (p = 1.62 × 10-9) and cg09448088 (p = 3.95 × 10-9) annotated to MCF2L in the IFG. These genes were previously reported to harbor DMPs and/or differentially methylated regions (DMRs). Previously reported DMPs annotated to RMGA, GNG7, HOXA3, GPR56, SPG7, PCNT, RP11-961A15.1, MCF2L, RHBDF2, ANK1, PCNT, TPRG1, and RASGEF1C were replicated (p < 0.0001). One hundred twenty-one and 173 DMRs associated with pathology in the STG and IFG, respectively, were additionally identified. Of these, DMRs annotated to 30 unique genes were also identified as significant DMRs in the same brain region in a recent meta-analysis, while additional DMRs annotated to 12 genes were reported as DMRs in a different brain region or in a cross-cortex meta-analysis. The significant DMRs were enriched in promoters, CpG islands, and exons in the genome. Gene set enrichment analysis of DMPs and DMRs showed that gene sets involved in neuroinflammation (e.g., microglia differentiation), neurogenesis, and cognition were enriched (false discovery rate (FDR) < 0.05).ConclusionsTwenty-two DMPs and 30 DMRs associated with pathology were replicated, and novel DMPs and DMRs were discovered.

Dataset Information

MethylCC: technology-independent estimation of cell type composition using differentially methylated regions.

Publications

methylCC: technology-independent estimation of cell type composition using differentially methylated regions.

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