Abstract:BackgroundDNA methylation is one of the main epigenetic mechanisms for the regulation of gene expression in eukaryotes. In the standard model, methylation in gene promoters has received the most attention since it is generally associated with transcriptional silencing. Nevertheless, recent studies in human tissues reveal that methylation of the region downstream of the transcription start site is highly informative of gene expression. Also, in some cell types and specific genes it has been found that methylati… Show more
“…Therefore, we selected these three regions for our analyses. Importantly, Anastasiadi et al 8 were looking at changes in gene expression associated with changes in DNA methylation distribution within promoters, gene bodies, and gene body sub-regions. Their results showed that in the cases where the association was significant, one could basically use mean or median value instead of looking a the distribution.…”
Section: Discussionmentioning
confidence: 99%
“…Although the methylation of gene promoters can potentially lead to the suppression of gene expression 1 , it has been shown that certain transcription factors actually require their binding sites to be methylated in order to promote binding 6 . In addition to promoters, DNA methylation within gene enhancer regions and gene bodies might also regulate gene expression 1,[7][8][9] . Thus, the function of DNA methylation is highly region-dependent and may vary from gene to gene.…”
Section: Introductionmentioning
confidence: 99%
“…We develop the necessary statistical models to estimate the interactions from triad data and discuss critical assumptions that need to be met to avoid false positives. Moreover, we take into account DNA methylation in regions, not single CpG sites, since it has been shown that CpGs act collectively 8,35 .…”
The genetic code is tightly linked to epigenetic instructions as to what genes to express, and when and where to express them. The most studied epigenetic mark is DNA methylation at CpG dinucleotides. Today's technology enables a rapid assessment of DNA sequence and methylation levels at a single-site resolution for hundreds of thousands of sites in the human genome, in thousands of individuals at a time. Recent years have seen a rapid increase in epigenome-wide association studies (EWAS) searching for the causes of risk for genetic diseases that previous genome-wide association studies (GWAS) could not pinpoint. However, those single-omics data analyses led to even more questions and it has become clear that only by integrating data one can get closer to answers. Here, we propose two new methods within genetic association analyses that treat the level of DNA methylation at a given CpG site as environmental exposure. Our analyses search for statistical interactions between a given allele and DNA methylation (G×M e), and between a parent-of-origin effect and DNA methylation (PoO×Me). The new methods were implemented in the R package Haplin and were tested on a dataset comprising genotype data from mother-father-child triadsm with DNA methylation data from the children only. The phenotype here was orofacial clefts (OFC), a relatively common birth defect in humans, which is known to have a genetic origin and an environmental component possibly mediated by DNA methylation. We found no significant PoO×Me interactions and a few significant G×Me interactions. Our results show that the significance of these interaction effects depends on the genomic region in which the CpGs reside and on the number of strata of methylation level. We demonstrate that, by including the methylation level around the SNP in the analyses, the estimated relative risk of OFC can change significantly. We also discuss the importance of including control data in such analyses. The new methods will be of value for all the researchers who want to explore genome-and epigenome-wide datasets in an integrative manner. Moreover, thanks to the implementation in a popular R package, the methods are easily accessible and enable fast scans of the genome-and epigenome-wide datasets.
“…Therefore, we selected these three regions for our analyses. Importantly, Anastasiadi et al 8 were looking at changes in gene expression associated with changes in DNA methylation distribution within promoters, gene bodies, and gene body sub-regions. Their results showed that in the cases where the association was significant, one could basically use mean or median value instead of looking a the distribution.…”
Section: Discussionmentioning
confidence: 99%
“…Although the methylation of gene promoters can potentially lead to the suppression of gene expression 1 , it has been shown that certain transcription factors actually require their binding sites to be methylated in order to promote binding 6 . In addition to promoters, DNA methylation within gene enhancer regions and gene bodies might also regulate gene expression 1,[7][8][9] . Thus, the function of DNA methylation is highly region-dependent and may vary from gene to gene.…”
Section: Introductionmentioning
confidence: 99%
“…We develop the necessary statistical models to estimate the interactions from triad data and discuss critical assumptions that need to be met to avoid false positives. Moreover, we take into account DNA methylation in regions, not single CpG sites, since it has been shown that CpGs act collectively 8,35 .…”
The genetic code is tightly linked to epigenetic instructions as to what genes to express, and when and where to express them. The most studied epigenetic mark is DNA methylation at CpG dinucleotides. Today's technology enables a rapid assessment of DNA sequence and methylation levels at a single-site resolution for hundreds of thousands of sites in the human genome, in thousands of individuals at a time. Recent years have seen a rapid increase in epigenome-wide association studies (EWAS) searching for the causes of risk for genetic diseases that previous genome-wide association studies (GWAS) could not pinpoint. However, those single-omics data analyses led to even more questions and it has become clear that only by integrating data one can get closer to answers. Here, we propose two new methods within genetic association analyses that treat the level of DNA methylation at a given CpG site as environmental exposure. Our analyses search for statistical interactions between a given allele and DNA methylation (G×M e), and between a parent-of-origin effect and DNA methylation (PoO×Me). The new methods were implemented in the R package Haplin and were tested on a dataset comprising genotype data from mother-father-child triadsm with DNA methylation data from the children only. The phenotype here was orofacial clefts (OFC), a relatively common birth defect in humans, which is known to have a genetic origin and an environmental component possibly mediated by DNA methylation. We found no significant PoO×Me interactions and a few significant G×Me interactions. Our results show that the significance of these interaction effects depends on the genomic region in which the CpGs reside and on the number of strata of methylation level. We demonstrate that, by including the methylation level around the SNP in the analyses, the estimated relative risk of OFC can change significantly. We also discuss the importance of including control data in such analyses. The new methods will be of value for all the researchers who want to explore genome-and epigenome-wide datasets in an integrative manner. Moreover, thanks to the implementation in a popular R package, the methods are easily accessible and enable fast scans of the genome-and epigenome-wide datasets.
“…Conventionally, higher methylation levels in the gene promoters have been associated with transcriptional silencing (Illingworth and Bird, 2009; Straussman et al, 2009). However, gene expression inhibition have been related to other genomic features like the first exon and the first intron of the gene body not only in human (Blattler et al, 2014) but also in fish (Anastasiadi et al, 2018a). Further, the approach here performed for studying the methylation levels is limited to amplicons about ∼500 bp, that even with the intention of include promotor, first intron and first exon in the targeted regions, the entire gene body and possible enhancers involved in the gene regulation were not fully studied.…”
“…Methylation of cytosine residues within CpG dinucleotides is an important mechanism of variation and regulation in the genome 29–32 . Cytosine methylation, particularly in the promoter region of genes, is often associated with a decrease in transcription 33 , and DNA methylation in the first intron and gene expression is correlated and conserved across tissues and vertebrate species 34 . Furthermore, modulation of methylation at CpG sites within the human genome can result in an epigenetic pattern that is specific to individual environmental exposures, and these may contribute to disease 26; 35–37 .…”
ABSTRACTCannabis use is of increasing public health interest globally. Here we examined the effect of cannabis use, with and without tobacco, on genome-wide DNA methylation in a longitudinal birth cohort (Christchurch Health and Development Study). We found the most differentially methylated sites in cannabis with tobacco users were in the AHRR and F2RL3 genes, replicating previous studies on the effects of tobacco. Cannabis-only users had no evidence of differential methylation in these genes, or at any other loci at the epigenomewide significance level (P<10−7). However, there were 521 sites differentially methylated at P<0.001 which were enriched for genes involved in cardiomyopathy and neuronal signalling. Further, the most differentially methylated loci were associated with genes with reported roles in brain function (e.g. TMEM190, MUC3L, CDC20 and SP9). We conclude that the effects of cannabis use on the mature human blood methylome differ from, and are less pronounced than, the effects of tobacco use, and that larger sample sizes are required to investigate this further.
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