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Cis-acting genetic–epigenetic interactions can lead to inter-individual differences in DNA looping, gene expression, and disease susceptibility. Simplified representations of three-dimensional chromatin structure in haplotype blocks containing genome wide association study (GWAS) peaks, highlighting the potential effects of regulatory sequence variants (rSNPs) on DNA methylation, interactions between regulatory elements (insulators, enhancers and promoters), topologically associating domain (TAD) structures, gene expression, and disease susceptibility. a CTCF-mediated chromatin looping leading to formation of “active” and “inactive” TADs. Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and Hi-C have mapped chromatin interactions and have identified TADs as large-scale chromatin structures, with CTCF or cohesin enriched at the TAD boundaries [103]. The chromatin loops promote intra-domain interactions between regulatory elements, such as enhancers and gene promoters (which induce gene expression), while preventing inter-domain contacts in order to minimize promiscuous gene expression. In this model, regulatory variants at TAD boundaries or intra-domain contacts (sub-TAD boundaries) can induce high- or low-order chromatin configuration changes that disrupt the insulated neighborhoods formed by the looping, thereby causing either the abolition of enhancer–promoter interactions (in active TADs) or the formation of ectopic enhancer–promoter interactions (in inactive TADs). Additionally, regulatory variants at active transcription factor (TF)-bound enhancers can directly affect enhancer–promoter interactions. Variants that affect the integrity of TAD structures and chromatin interactions are more likely to have functional effects and to be rSNPs, which can sometimes lead to disease susceptibility. b Chromatin looping leads to active or inactive insulated chromatin neighborhoods, which can vary between individuals because of haplotype-dependent allele-specific DNA methylation (hap-ASM) rSNPs and can therefore influence DNA methylation patterns and disease susceptibility. In this genomic configuration (AA alleles at the enhancer SNP of gene X, AA alleles at the CTCF-binding site SNP of the gene-X-containing loop, and AA alleles at the CTCF-binding site SNP of the gene-Y-containing loop), both of the TAD anchor sites have a high affinity for CTCF. In the chromatin loop associated with gene X, the formation of the loop brings the enhancer and promoter into close proximity. The active enhancer is bound by TFs and RNA polymerase interacts with the gene X promoter to induce transcription [122, 189]. Conversely, the chromatin loop containing gene Y enforces gene silencing by isolating the promoter away from neighboring enhancers. CTCF and TF occupancy is associated with low methylation at the TAD anchor sites and in enhancer sequences, expression of gene X, silencing of gene Y, and no disease susceptibility. c In this configuration (BB at the enhancer SNP of gene X, AA at the CTCF-binding site SNP of the gene-X-containing loop, and AA at the CTCF-binding site SNP of the gene-Y-containing loop), the anchor sites bind CTCF with high affinity. Although the CTCF-anchored loops are not altered, the rSNP at the enhancer of gene X disrupts the binding of the TF and RNAPII complex, resulting in a high methylation level at the enhancer and gene silencing. In this scenario, the silencing of gene X leads to disease susceptibility, associated with the GWAS index SNP allele BB, which is in linkage disequilibrium (LD) with the functional rSNP allele BB at the enhancer of gene X. d In this configuration (AA at the enhancer SNP of gene X, BB at the CTCF-binding site SNP of the gene-X-containing loop, and AA at the CTCF-binding site SNP of the gene-Y-containing loop), allele BB at the CTCF-dependent TAD anchor site associated with gene X leads to a low affinity for CTCF. The loss of CTCF binding disrupts the higher-order chromatin loop, and the promoter–enhancer interaction of gene X is no longer facilitated, although TF binding is not altered at the enhancer. e In this configuration (AA at the enhancer SNP of gene X, AA at the CTCF-binding site SNP of the gene-X-containing loop, BB at the CTCF-binding site SNP of the gene-Y-containing loop), allele BB at the CTCF-mediated TAD anchor site of the gene-Y-containing loop has a low affinity for CTCF. The loss of CTCF binding disrupts the chromatin loop, such that the promoter of gene Y is no longer isolated from the active enhancer of the neighboring expressed gene, which induces an ectopic enhancer–promoter interaction. This loss of CTCF occupancy is associated with a high methylation level at one of the anchor sites of gene-Y-containing TAD, and expression of gene Y. In this scenario, the expression of gene Y leads to a disease phenotype associated with the GWAS peak SNP allele BB, which is in LD with the causal rSNP allele BB at the CTCF-binding site

Cis-acting genetic–epigenetic interactions can lead to inter-individual differences in DNA looping, gene expression, and disease susceptibility. Simplified representations of three-dimensional chromatin structure in haplotype blocks containing genome wide association study (GWAS) peaks, highlighting the potential effects of regulatory sequence variants (rSNPs) on DNA methylation, interactions between regulatory elements (insulators, enhancers and promoters), topologically associating domain (TAD) structures, gene expression, and disease susceptibility. a CTCF-mediated chromatin looping leading to formation of “active” and “inactive” TADs. Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and Hi-C have mapped chromatin interactions and have identified TADs as large-scale chromatin structures, with CTCF or cohesin enriched at the TAD boundaries [103]. The chromatin loops promote intra-domain interactions between regulatory elements, such as enhancers and gene promoters (which induce gene expression), while preventing inter-domain contacts in order to minimize promiscuous gene expression. In this model, regulatory variants at TAD boundaries or intra-domain contacts (sub-TAD boundaries) can induce high- or low-order chromatin configuration changes that disrupt the insulated neighborhoods formed by the looping, thereby causing either the abolition of enhancer–promoter interactions (in active TADs) or the formation of ectopic enhancer–promoter interactions (in inactive TADs). Additionally, regulatory variants at active transcription factor (TF)-bound enhancers can directly affect enhancer–promoter interactions. Variants that affect the integrity of TAD structures and chromatin interactions are more likely to have functional effects and to be rSNPs, which can sometimes lead to disease susceptibility. b Chromatin looping leads to active or inactive insulated chromatin neighborhoods, which can vary between individuals because of haplotype-dependent allele-specific DNA methylation (hap-ASM) rSNPs and can therefore influence DNA methylation patterns and disease susceptibility. In this genomic configuration (AA alleles at the enhancer SNP of gene X, AA alleles at the CTCF-binding site SNP of the gene-X-containing loop, and AA alleles at the CTCF-binding site SNP of the gene-Y-containing loop), both of the TAD anchor sites have a high affinity for CTCF. In the chromatin loop associated with gene X, the formation of the loop brings the enhancer and promoter into close proximity. The active enhancer is bound by TFs and RNA polymerase interacts with the gene X promoter to induce transcription [122, 189]. Conversely, the chromatin loop containing gene Y enforces gene silencing by isolating the promoter away from neighboring enhancers. CTCF and TF occupancy is associated with low methylation at the TAD anchor sites and in enhancer sequences, expression of gene X, silencing of gene Y, and no disease susceptibility. c In this configuration (BB at the enhancer SNP of gene X, AA at the CTCF-binding site SNP of the gene-X-containing loop, and AA at the CTCF-binding site SNP of the gene-Y-containing loop), the anchor sites bind CTCF with high affinity. Although the CTCF-anchored loops are not altered, the rSNP at the enhancer of gene X disrupts the binding of the TF and RNAPII complex, resulting in a high methylation level at the enhancer and gene silencing. In this scenario, the silencing of gene X leads to disease susceptibility, associated with the GWAS index SNP allele BB, which is in linkage disequilibrium (LD) with the functional rSNP allele BB at the enhancer of gene X. d In this configuration (AA at the enhancer SNP of gene X, BB at the CTCF-binding site SNP of the gene-X-containing loop, and AA at the CTCF-binding site SNP of the gene-Y-containing loop), allele BB at the CTCF-dependent TAD anchor site associated with gene X leads to a low affinity for CTCF. The loss of CTCF binding disrupts the higher-order chromatin loop, and the promoter–enhancer interaction of gene X is no longer facilitated, although TF binding is not altered at the enhancer. e In this configuration (AA at the enhancer SNP of gene X, AA at the CTCF-binding site SNP of the gene-X-containing loop, BB at the CTCF-binding site SNP of the gene-Y-containing loop), allele BB at the CTCF-mediated TAD anchor site of the gene-Y-containing loop has a low affinity for CTCF. The loss of CTCF binding disrupts the chromatin loop, such that the promoter of gene Y is no longer isolated from the active enhancer of the neighboring expressed gene, which induces an ectopic enhancer–promoter interaction. This loss of CTCF occupancy is associated with a high methylation level at one of the anchor sites of gene-Y-containing TAD, and expression of gene Y. In this scenario, the expression of gene Y leads to a disease phenotype associated with the GWAS peak SNP allele BB, which is in LD with the causal rSNP allele BB at the CTCF-binding site

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Studies on genetic–epigenetic interactions, including the mapping of methylation quantitative trait loci (mQTLs) and haplotype-dependent allele-specific DNA methylation (hap-ASM), have become a major focus in the post-genome-wide-association-study (GWAS) era. Such maps can nominate regulatory sequence variants that underlie GWAS signals for common...

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... GWAS involve large populations, but often the effect of variants found in a study does not apply to other populations. The lack of transferability between populations is due to failures in sample stratification, small samples with low statistical power, epigenetic factors, and different linkage disequilibrium pattern, which affects the interaction between genes [10,21]. Using epigenetic factors, the variants can interact with the environment and be heterogeneously associated with the phenotypes, which may explain the difference in the impact of some variants on height according to age, pubertal stage, and sex [9,10]. ...
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Objectives Our study evaluated the association of the polymorphism rs724016 in the ZBTB38 gene, previously associated with height in other populations, with predictors of height, clinical outcomes, and laboratory parameters in sickle cell anemia (SCA). Methods Cross-sectional study with individuals with SCA and aged between 3 and 20 years. Clinical, laboratory, molecular, and bone age (BA) data were evaluated. Levels of IGF-1 and IGFBP-3 were adjusted for BA, target height (TH) was calculated as the mean parental height standard deviation score (SDS), and predicted adult height (PAH) SDS was calculated using BA. Results We evaluated 80 individuals with SCA. The homozygous genotype of the G allele of rs724016 was associated with a lower height SDS (p < 0.001) and, in a additive genetic model, was negatively associated with HbF levels (p = 0.016). Lower adjusted IGF-1 levels were associated with co-inheritance of alpha-thalassemia and with the absence of HU therapy. Elevated HbF levels were associated with a lower deficit in adjusted growth potential (TH minus PAH). Conclusion Our analysis shows that SNP rs724016 in the ZBTB38 is associated with shorter height and lower HbF levels, an important modifier of SCA.
... In addition, amrfinder is part of MethPipe [5], a pipeline of tools for methylation analysis that is highly referenced in the literature. This made it the choice to perform interesting biological studies in fields such as early human development [6], genetic-epigenetic interactions [7] or the building of allele-specific epigenome maps [8]. On top of that, amrfinder has been successfully utilized in several recent studies, demonstrating its continued relevance and effectiveness in the field of epigenetics [9][10][11]. ...
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The discovery of Allele-Specific Methylation (ASM) is an important research field in biology as it regulates genomic imprinting, which has been identified as the cause of some genetic diseases. Nevertheless, the high computational cost of the bioinformatic tools developed for this purpose prevents their application to large-scale datasets. Hence, much faster tools are required to further progress in this research field. In this work we present PARamrfinder, a parallel tool that applies a statistical model to identify ASM in data from high-throughput short-read bisulfite sequencing. It is based on the state-of-the-art sequential tool amrfinder, which is able to detect ASM at regional level from Bisulfite Sequencing (BS-Seq) experiments in the absence of Single Nucleotide Polymorphism information. PARamrfinder provides the same Allelically Methylated Regions as amrfinder but at significantly reduced runtime thanks to exploiting the compute capabilities of common multicore CPU clusters and MPI RMA operations to attain an efficient dynamic workload balance. As an example, our tool is up to 567 times faster for real data experiments on a cluster with 8 nodes, each one containing two 16-core processors. The source code of PARamrfinder, as well as a reference manual, is available at https://github.com/UDC-GAC/PARamrfinder.
... Specifically, we tested (i) whether exposure to conspecific cues triggers changes in DNA methylation patterns in cane toad tadpoles that might then induce developmental plasticity, and (ii) whether both cannibal cues and alarm cues trigger the same epigenetic modifications. We focused on DNA methylation, because this epigenetic mechanism can influence transcriptional activity on the one hand (Jaenisch & Bird, 2003), and can be affected by environmental factors on the other (Dowen et al., 2012;Radford et al., 2014), and because studies of cane toads have shown that exposure to alarm cues changes DNA methylation patterns in tadpoles (Sarma et al., 2020(Sarma et al., , 2021. Changes in DNA methylation are thus a plausible molecular mechanism that might underlie developmental plasticity in cane toads. ...
... The influence of genotypic variation on DNA methylation marks appears ubiquitous. Mounting evidence shows that, although epigenetic marks can be modified by environmental exposure, in many cases they do so under genetic control(Do et al., 2017;Gaunt et al., 2016;Hannon et al., 2018;Kerkel et al., 2008;Min et al., 2021;Tycko, 2010;Villicaña & Bell, 2021). These results stress the importance of controlling ...
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Many organisms can adjust their development according to environmental conditions, including the presence of conspecifics. Although this developmental plasticity is common in amphibians, its underlying molecular mechanisms remain largely unknown. Exposure during development to either ‘cannibal cues’ from older conspecifics, or ‘alarm cues’ from injured conspecifics, causes reduced growth and survival in cane toad (Rhinella marina) tadpoles. Epigenetic modifications, such as changes in DNA methylation patterns, are a plausible mechanism underlying these developmental plastic responses. Here we tested this hypothesis, and asked whether cannibal cues and alarm cues trigger the same DNA methylation changes in developing cane toads. We found that exposure to both cannibal cues and alarm cues was associated with local changes in DNA methylation patterns. These DNA methylation changes affected genes putatively involved in developmental processes, but in different genomic regions for different conspecific-derived cues. Genetic background explains most of the epigenetic variation among individuals. Overall, the molecular mechanisms triggered by exposure to cannibal cues seem to differ from those triggered by alarm cues. Studies linking epigenetic modifications to transcriptional activity are needed to clarify the proximate mechanisms that regulate developmental plasticity in cane toads.
... a. La génétique influence l'épigénétique Il est désormais bien établi que les polymorphismes de l'ADN peuvent affecter les marques épigénétiques (Bell et al., 2011 ;Do et al., 2017). Par exemple, chez la poule, il a été montré récemment dans un tissu, le foie, qu'environ 30 % des ARN longs non-codants (lncRNA) annotés chez la poule avaient leur expression significativement associée à au moins un variant génétique (Jehl et al., 2021). ...
... Une étude chez l'homme a démontré que la méthylation CpG était parfois significativement associée avec des polymorphismes génétiques distants des sites régulés (Lemire et al., 2015). Do et al. (2017) ont passé en revue de nombreuses études révélant l'existence d'une grande variabilité épigénétique d'origine génétique. Un mécanisme expliquant l'influence des génotypes sur l'état épigénétique est le fait que la séquence d'ADN, à des sites de liaison spécifiques, peut affecter la liaison des facteurs de transcription, certains de ces facteurs de transcription étant capables de modifier le niveau de méthylation de l'ADN à proximité (Feldmann et al., 2013). ...
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L'épigénétique est communément définie comme l’étude de l'ensemble des mécanismes moléculaires impliqués dans la régulation de l’expression des gènes qui sont réversibles et transmissibles au cours du développement et parfois entre générations, sans altérer la séquence de l'ADN. Plusieurs mécanismes épigénétiques sont maintenant bien connus, comme la méthylation de l'ADN, les variants et modifications post-traductionnelles des histones, ainsi que certains ARN non codants. Grâce au développement technologique du séquençage tout-génome, ces « marques » épigénétiques peuvent être étudiées à l'échelle du génome entier. Il est aujourd’hui clairement établi que l’épigénome, c'est-à-dire l'ensemble des marques épigénétiques d'un tissu, est sensible aux fluctuations de l’environnement, notamment la température ou l’alimentation. Des stratégies de programmation précoce des phénotypes reposant sur ces mécanismes épigénétiques sont ainsi envisagées comme levier pour adapter le phénotype ultérieur des individus à leurs conditions de vie. Par ailleurs, au cours des dernières décennies, la sélection génétique a contribué à l’amélioration considérable des performances des animaux. Bien que la composante génétique puisse être estimée avec précision, une grande partie de la variabilité phénotypique n'est pas directement accessible par les approches actuelles. Dans un contexte de diversification des environnements de production (changement climatique, modes de production plus respectueux du bien-être et de l'environnement), il est nécessaire de comprendre l'impact de l'environnement sur la variabilité phénotypique via les marques épigénétiques, pour optimiser les systèmes d'élevage et mieux prédire le phénotype d'un animal. Comme la sélection génomique il y a quelques années, l'apport de la recherche en épigénétique pourrait contribuer à rendre les systèmes de production avicole plus efficaces et plus durables.
... Single nucleotide polymorphisms (SNPs), the most common type of genetic variant, contribute to the diversity in human disease susceptibility ( 7 ). Genome-wide association studies (GWAS) have identified thousands of SNPs that are associated with complex human traits and diseases ( 8 ). However, the underlying biological mechanisms behind these associations are not yet fully understood, and most studies currently focus on statistical significance ( 9 ). ...
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Altered promoter activity has been generally observed in diverse biological processes, including tumorigenesis. Accumulating evidence suggests that employing a quantitative trait locus mapping approach is effective in comprehending the genetic basis of promoter activity. By utilizing genotype data from The Cancer Genome Atlas and calculating corresponding promoter activity values using proActiv, we systematically evaluated the impact of genetic variants on promoter activity and identified >1.0 million promoter activity quantitative trait loci (paQTLs) as both cis- and trans-acting. Additionally, leveraging data from the genome-wide association study (GWAS) catalog, we discovered >1.3 million paQTLs that overlap with known GWAS linkage disequilibrium regions. Remarkably, ∼9324 paQTLs exhibited significant associations with patient prognosis. Moreover, investigating the impact of promoter activity on >1000 imputed antitumor therapy responses among pan-cancer patients revealed >43 000 million significant associations. Furthermore, ∼25 000 significant associations were identified between promoter activity and immune cell abundance. Finally, a user-friendly data portal, Pancan-paQTL (https://www.hbpding.com/PancanPaQTL/), was constructed for users to browse, search and download data of interest. Pancan-paQTL serves as a comprehensive multidimensional database, enabling functional and clinical investigations into genetic variants associated with promoter activity, drug responses and immune infiltration across multiple cancer types.
... Since the CCR5/CCR2 gene cluster acts as a central regulatory region, it might be a useful model for studying disease-associated epigenetic alternations and genetic variants controlling chemokine expression and function to identify cell-specific enhancers buried in intergenic regions [207,211]. As mentioned, dissection Data source: clinicaltrials.gov ...
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Over the past thirty years, the importance of chemokines and their seven-transmembrane G protein-coupled receptors (GPCRs) has been increasingly recognized. Chemokine interactions with receptors trigger signaling pathway activity to form a network fundamental to diverse immune processes, including host homeostasis and responses to disease. Genetic and nongenetic regulation of both the expression and structure of chemokines and receptors conveys chemokine functional heterogeneity. Imbalances and defects in the system contribute to the pathogenesis of a variety of diseases, including cancer, immune and inflammatory diseases, and metabolic and neurological disorders, which render the system a focus of studies aiming to discover therapies and important biomarkers. The integrated view of chemokine biology underpinning divergence and plasticity has provided insights into immune dysfunction in disease states, including, among others, coronavirus disease 2019 (COVID-19). In this review, by reporting the latest advances in chemokine biology and results from analyses of a plethora of sequencing-based datasets, we outline recent advances in the understanding of the genetic variations and nongenetic heterogeneity of chemokines and receptors and provide an updated view of their contribution to the pathophysiological network, focusing on chemokine-mediated inflammation and cancer. Clarification of the molecular basis of dynamic chemokine-receptor interactions will help advance the understanding of chemokine biology to achieve precision medicine application in the clinic.
... However, almost two decades later, most of the human phenome remains largely unexplained by solely genetic variants. Moreover, discovered genetic associations, which frequently map to the non-coding genome, remain to be rationalized by mechanistic models (Do et al. 2017;Brandes et al. 2022). One level higher, the epigenome lies in the interphase between the genome and the environment. ...
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In the post-GWAS era, great interest has arisen in the mapping of epigenetic inter-individual variation towards investigating the emergence of phenotype in health and disease. Relevant DNA methylation methodologies – epigenome-wide association studies (EWAS), methylation quantitative trait loci (mQTL) mapping and allele-specific methylation (ASM) analysis – can each map certain sources of epigenetic variation and all depend on matching phenotypic/genotypic data. Here, to avoid these requirements, we developed Binokulars, a novel randomization test that identifies signatures of joint CpG regulation from reads spanning multiple CpGs. We tested and benchmarked our novel approach against EWAS and ASM on pooled whole-genome bisulfite sequencing (WGBS) data from whole blood, sperm and combined. As a result, Binokulars simultaneously discovered regions associated with imprinting, cell type- and tissue-specific regulation, mQTL, ageing and other (still unknown) epigenetic processes. To verify examples of mQTL and polymorphic imprinting, we developed JRC_sorter, another novel tool that classifies regions based on epigenotype models, which we deployed on non-pooled WGBS data from cord blood. In the future, this approach can be applied on larger pools to simultaneously map and characterise inter-haplotype, inter-cell and inter-individual variation in DNA methylation in a cost-effective fashion, a relevant pursuit towards phenome-mapping in the post-GWAS era.
... Allele-specific methylation is a wide-spread phenomenon in the human genome whereby DNA methylation is differential between alleles. It can identify regulatory sequence variants that underlie genome-wide association studies signals for common diseases 36,37 . Reads covering a polymorphism were interrogated for methylation levels that differed between alleles. ...
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DNA comprises molecular information stored in genetic and epigenetic bases, both of which are vital to our understanding of biology. Most DNA sequencing approaches address either genetics or epigenetics and thus capture incomplete information. Methods widely used to detect epigenetic DNA bases fail to capture common C-to-T mutations or distinguish 5-methylcytosine from 5-hydroxymethylcytosine. We present a single base-resolution sequencing methodology that sequences complete genetics and the two most common cytosine modifications in a single workflow. DNA is copied and bases are enzymatically converted. Coupled decoding of bases across the original and copy strand provides a phased digital readout. Methods are demonstrated on human genomic DNA and cell-free DNA from a blood sample of a patient with cancer. The approach is accurate, requires low DNA input and has a simple workflow and analysis pipeline. Simultaneous, phased reading of genetic and epigenetic bases provides a more complete picture of the information stored in genomes and has applications throughout biomedicine.
... Allele-specific methylation is a wide-spread phenomenon in the human genome whereby DNA methylation is differential between alleles. It can identify regulatory sequence variants that underlie genome-wide association studies signals for common diseases 36,37 . Reads covering a polymorphism were interrogated for methylation levels that differed between alleles. ...
Article
Full-text available
DNA comprises molecular information stored in genetic and epigenetic bases, both of which are vital to our understanding of biology. Most DNA sequencing approaches address either genetics or epigenetics and thus capture incomplete information. Methods widely used to detect epigenetic DNA bases fail to capture common C-to-T mutations or distinguish 5-methylcytosine from 5-hydroxymethylcytosine. We present a single base-resolution sequencing methodology that sequences complete genetics and the two most common cytosine modifications in a single workflow. DNA is copied and bases are enzymatically converted. Coupled decoding of bases across the original and copy strand provides a phased digital readout. Methods are demonstrated on human genomic DNA and cell-free DNA from a blood sample of a patient with cancer. The approach is accurate, requires low DNA input and has a simple workflow and analysis pipeline. Simultaneous, phased reading of genetic and epigenetic bases provides a more complete picture of the information stored in genomes and has applications throughout biomedicine.
... Analysis of steady-state DNA methylation patterns in human populations has found differences in the methylation levels of individual loci between people that associate with sequence polymorphisms. These have been characterized as allele-specific methylation or methylation quantitative trait loci (meth-QTLs) [24,25]. This suggests that DNA sequence can program local DNA methylation levels, a hypothesis supported by the inheritance pattern of allele-specific methylation in families [26] and analysis of the methylation patterns of ectopic DNA sequences integrated into cell lines [27]. ...
... For plotting, CpG density is binned into equally sized groups. Lines = median; Box = 25th-75th percentile; whiskers = 1.5 × interquartile range from box Cis-meth-QTLs have previously been shown to be enriched at enhancers and associated with SNPs altering local transcription factor (TF) binding sites [24,25]. We therefore asked whether this might also be the case for slope-QTLs. ...
... Genetic effects on steady-state DNA methylation levels have been widely documented in human populations as allele-specific methylation and meth-QTLs [24]. These are hypothesized to reflect the alteration of TF binding by sequence polymorphisms with downstream effects on DNA methylation particularly at enhancers. ...
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Background DNA methylation is an epigenetic mark associated with the repression of gene promoters. Its pattern in the genome is disrupted with age and these changes can be used to statistically predict age with epigenetic clocks. Altered rates of aging inferred from these clocks are observed in human disease. However, the molecular mechanisms underpinning age-associated DNA methylation changes remain unknown. Local DNA sequence can program steady-state DNA methylation levels, but how it influences age-associated methylation changes is unknown. Results We analyze longitudinal human DNA methylation trajectories at 345,895 CpGs from 600 individuals aged between 67 and 80 to understand the factors responsible for age-associated epigenetic changes at individual CpGs. We show that changes in methylation with age occur at 182,760 loci largely independently of variation in cell type proportions. These changes are especially apparent at 8322 low CpG density loci. Using SNP data from the same individuals, we demonstrate that methylation trajectories are affected by local sequence polymorphisms at 1487 low CpG density loci. More generally, we find that low CpG density regions are particularly prone to change and do so variably between individuals in people aged over 65. This differs from the behavior of these regions in younger individuals where they predominantly lose methylation. Conclusions Our results, which we reproduce in two independent groups of individuals, demonstrate that local DNA sequence influences age-associated DNA methylation changes in humans in vivo. We suggest that this occurs because interactions between CpGs reinforce maintenance of methylation patterns in CpG dense regions.