scholarly journals Calling differential DNA methylation at cell-type resolution: avoiding misconceptions and promoting best practices

2021 ◽  
Author(s):  
Han Jing ◽  
Shijie C. Zheng ◽  
Charles E. Breeze ◽  
Stephan Beck ◽  
Andrew E. Teschendorff

AbstractThe accurate detection of cell-type specific DNA methylation alterations in the context of general epigenome studies is an important task to improve our understanding of epigenomics in disease development. Although a number of statistical algorithms designed to address this problem have emerged, the task remains challenging. Here we show that a recent commentary by Rahmani et al, that aims to address misconceptions and best practices in the field, continues to suffer from critical misconceptions in how statistical algorithms should be compared and evaluated. In addition, we report contradictory results on real EWAS datasets.

2021 ◽  
Author(s):  
Elior Rahmani ◽  
Brandon Jew ◽  
Regev Schweiger ◽  
Brooke Rhead ◽  
Lindsey A. Criswell ◽  
...  

AbstractWe benchmarked two approaches for the detection of cell-type-specific differential DNA methylation: Tensor Composition Analysis (TCA) and a regression model with interaction terms (CellDMC). Our experiments alongside rigorous mathematical explanations show that TCA is superior over CellDMC, thus resolving recent criticisms suggested by Jing et al. Following misconceptions by Jing and colleagues with modelling cell-type-specificity and the application of TCA, we further discuss best practices for performing association studies at cell-type resolution. The scripts for reproducing all of our results and figures are publicly available at github.com/cozygene/CellTypeSpecificMethylationAnalysis.


2018 ◽  
Author(s):  
Meaghan J Jones ◽  
Louie Dinh ◽  
Hamid Reza Razzaghian ◽  
Olivia de Goede ◽  
Julia L MacIsaac ◽  
...  

AbstractBackgroundDNA methylation profiling of peripheral blood leukocytes has many research applications, and characterizing the changes in DNA methylation of specific white blood cell types between newborn and adult could add insight into the maturation of the immune system. As a consequence of developmental changes, DNA methylation profiles derived from adult white blood cells are poor references for prediction of cord blood cell types from DNA methylation data. We thus examined cell-type specific differences in DNA methylation in leukocyte subsets between cord and adult blood, and assessed the impact of these differences on prediction of cell types in cord blood.ResultsThough all cell types showed differences between cord and adult blood, some specific patterns stood out that reflected how the immune system changes after birth. In cord blood, lymphoid cells showed less variability than in adult, potentially demonstrating their naïve status. In fact, cord CD4 and CD8 T cells were so similar that genetic effects on DNA methylation were greater than cell type effects in our analysis, and CD8 T cell frequencies remained difficult to predict, even after optimizing the library used for cord blood composition estimation. Myeloid cells showed fewer changes between cord and adult and also less variability, with monocytes showing the fewest sites of DNA methylation change between cord and adult. Finally, including nucleated red blood cells in the reference library was necessary for accurate cell type predictions in cord blood.ConclusionChanges in DNA methylation with age were highly cell type specific, and those differences paralleled what is known about the maturation of the postnatal immune system.


Author(s):  
Xiangyu Luo ◽  
Joel Schwartz ◽  
Andrea Baccarelli ◽  
Zhonghua Liu

Abstract Epigenome-wide mediation analysis aims to identify DNA methylation CpG sites that mediate the causal effects of genetic/environmental exposures on health outcomes. However, DNA methylations in the peripheral blood tissues are usually measured at the bulk level based on a heterogeneous population of white blood cells. Using the bulk level DNA methylation data in mediation analysis might cause confounding bias and reduce study power. Therefore, it is crucial to get fine-grained results by detecting mediation CpG sites in a cell-type-specific way. However, there is a lack of methods and software to achieve this goal. We propose a novel method (Mediation In a Cell-type-Specific fashion, MICS) to identify cell-type-specific mediation effects in genome-wide epigenetic studies using only the bulk-level DNA methylation data. MICS follows the standard mediation analysis paradigm and consists of three key steps. In step1, we assess the exposure-mediator association for each cell type; in step 2, we assess the mediator-outcome association for each cell type; in step 3, we combine the cell-type-specific exposure-mediator and mediator-outcome associations using a multiple testing procedure named MultiMed [Sampson JN, Boca SM, Moore SC, et al. FWER and FDR control when testing multiple mediators. Bioinformatics 2018;34:2418–24] to identify significant CpGs with cell-type-specific mediation effects. We conduct simulation studies to demonstrate that our method has correct FDR control. We also apply the MICS procedure to the Normative Aging Study and identify nine DNA methylation CpG sites in the lymphocytes that might mediate the effect of cigarette smoking on the lung function.


2012 ◽  
Vol 31 (2) ◽  
pp. 89-95 ◽  
Author(s):  
Masaki Nishioka ◽  
Takafumi Shimada ◽  
Miki Bundo ◽  
Wataru Ukai ◽  
Eri Hashimoto ◽  
...  

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 211-211
Author(s):  
Amber Hogart ◽  
Jens Lichtenberg ◽  
Subramanian Ajay ◽  
Elliott Margulies ◽  
David M. Bodine

Abstract Abstract 211 The hematopoietic system is ideal for the study of epigenetic changes in primary cells because hematopoietic cells representing distinct stages of hematopoiesis can be enriched and isolated by differences in surface marker expression. DNA methylation is an essential epigenetic mark that is required for normal development. Conditional knockout of the DNA methyltransferase enzymes in the mouse hematopoietic compartment have revealed that methylation is critical for long-term renewal and lineage differentiation of hematopoietic stem cells (Broske et al 2009, Trowbridge el al 2009). To better understand the role of DNA methylation in self-renewal and differentiation of hematopoietic cells, we characterized genome-wide DNA methylation in primary cells representing three distinct stages of hematopoiesis. We isolated mouse hematopoietic stem cells (HSC; Lin- Sca-1+ c-kit+), common myeloid progenitor cells (CMP; Lin- Sca-1- c-kit+), and erythroblasts (ERY; CD71+ Ter119+). Methyl Binding Domain Protein 2 (MBD2) is an endogenous reader of DNA methylation that recognizes DNA with a high concentration of methylated CpG residues. Recombinant MBD2 enrichment of DNA followed by massively-parallel sequencing was used to map and compare genome-wide DNA methylation patterns in HSC, CMP and ERY. Two biological replicates were sequenced for each cell type with total read counts ranging from 32,309,435–46,763,977. Model-based analysis of ChIP Seq (MACS) with a significance cutoff of p<10−5 was used to determine statistically significant peaks of methylation in each replicate. Globally, the number of methylation peaks was highest in HSC (85,797peaks), lower in CMP (50,638 peaks), and lowest in ERY (27,839 peaks). Comparison of the peaks in HSC, CMP and ERY revealed that only 2% of the peaks in CMP or ERY are absent in HSC indicating that the vast majority of methylation in HSC is lost during differentiation. Comparison of methylation with genomic features revealed that CpG islands associated with promoters are hypomethylated, while many non-promoter CpG islands are methylated. Furthermore, methylation of non-promoter associated CpG islands occurs infrequently in cell-type specific peaks but is more abundant in common methylation peaks. When the DNA methylation patterns were compared to mRNA expression, we found that as expected, proximal promoter sequences of expressed genes were hypomethylated in all three cell types, while methylation in the gene body positively correlated with gene expression in HSC and CMP. Utilizing de novo motif discovery we found a subset of transcription factor consensus binding motifs that were overrepresented in methylated sequences. Motifs for several ETS transcription factors, including GABPalpha and ELF1 were found to be overrepresented in cell-type specific as well as common methylated regions. Other transcription factor consensus sites, such as the NFAT factors involved in T-cell activation, were specifically overrepresented in the methylated promoter regions of CMP and ERY. Comparison of our methylation data with the occupancy of hematopoietic transcription factors in the HPC7 cell line, which is similar to CMP (Wilson et al 2010), revealed a significant anti-correlation between DNA methylation and the binding of Fli1, Lmo2, Lyl1, Runx1, and Scl. Our genome-wide survey provides new insights into the role of DNA methylation in hematopoiesis. Firstly, the methylation of CpG islands is associated with the most primitive hematopoietic cells and is unlikely to drive hematopoietic differentiation. We feel that the elevated genome-wide DNA methylation in HSC compared to CMP and ERY, combined with the positive association between gene body methylation and gene expression demonstrates that DNA methylation is a mark of cellular plasticity in HSC. Finally, the finding that transcription factor binding sites are over represented in the methylated sequences of the genome leads us to conclude that DNA methylation modulates key hematopoietic transcription factor programs that regulate hematopoiesis. Disclosures: No relevant conflicts of interest to declare.


2021 ◽  
Author(s):  
Michael Scherer ◽  
Gilles Gasparoni ◽  
Souad Rahmouni ◽  
Tatiana Shashkova ◽  
Marion Arnoux ◽  
...  

Background: Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL) but also for discriminating general from cell-type-specific effects. Results: Here, we present a two-step computational framework MAGAR, which fully supports identification of methQTLs from matched genotyping and DNA methylation data, and additionally the identification of quantitative cell-type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T-cells, B-cells) from healthy individuals and demonstrate the discrimination of common from cell-type-specific methQTLs. We experimentally validate both types of methQTLs in an independent dataset comprising additional cell types and tissues. Finally, we validate selected methQTLs (PON1, ZNF155, NRG2) by ultra-deep local sequencing. In line with previous reports, we find cell-type-specific methQTLs to be preferentially located in enhancer elements. Conclusions: Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell-type-specific epigenomic variation.


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