GeMes, clusters of DNA methylation under genetic control, can inform genetic and epigenetic analysis of disease

Analysis of blood methylation quantitative trait loci in East Asians reveals ancestry-specific impacts on complex traits

Methylation quantitative trait loci (mQTLs) are essential for understanding the role of DNA methylation changes in genetic predisposition, yet they have not been fully characterized in East Asians (EAs). Here we identified mQTLs in whole blood from 3,523 Chinese individuals and replicated them in additional 1,858 Chinese individuals from two cohorts. Over 9% of mQTLs displayed specificity to EAs, facilitating the fine-mapping of EA-specific genetic associations, as shown for variants associated with height. Trans-mQTL hotspots revealed biological pathways contributing to EA-specific genetic associations, including an ERG-mediated 233 trans-mCpG network, implicated in hematopoietic cell differentiation, which likely reflects binding efficiency modulation of the ERG protein complex. More than 90% of mQTLs were shared between different blood cell lineages, with a smaller fraction of lineage-specific mQTLs displaying preferential hypomethylation in the respective lineages. Our study provides new insights into the mQTL landscape across genetic ancestries and their downstream effects on cellular processes and diseases/traits.

meQTL mapping in the GENOA study reveals genetic determinants of DNA methylation in African Americans

Identifying genetic variants that are associated with variation in DNA methylation, an analysis commonly referred to as methylation quantitative trait locus (meQTL) mapping, is an important first step towards understanding the genetic architecture underlying epigenetic variation. Most existing meQTL mapping studies have focused on individuals of European ancestry and are underrepresented in other populations, with a particular absence of large studies in populations with African ancestry. We fill this critical knowledge gap by performing a large-scale cis-meQTL mapping study in 961 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We identify a total of 4,565,687 cis-acting meQTLs in 320,965 meCpGs. We find that 45% of meCpGs harbor multiple independent meQTLs, suggesting potential polygenic genetic architecture underlying methylation variation. A large percentage of the cis-meQTLs also colocalize with cis-expression QTLs (eQTLs) in the same population. Importantly, the identified cis-meQTLs explain a substantial proportion (median = 24.6%) of methylation variation. In addition, the cis-meQTL associated CpG sites mediate a substantial proportion (median = 24.9%) of SNP effects underlying gene expression. Overall, our results represent an important step toward revealing the co-regulation of methylation and gene expression, facilitating the functional interpretation of epigenetic and gene regulation underlying common diseases in African Americans.

The X-factor in ART: does the use of assisted reproductive technologies influence DNA methylation on the X chromosome?

BACKGROUND

Assisted reproductive technologies (ART) may perturb DNA methylation (DNAm) in early embryonic development. Although a handful of epigenome-wide association studies of ART have been published, none have investigated CpGs on the X chromosome. To bridge this knowledge gap, we leveraged one of the largest collections of mother-father-newborn trios of ART and non-ART (natural) conceptions to date to investigate sex-specific DNAm differences on the X chromosome. The discovery cohort consisted of 982 ART and 963 non-ART trios from the Norwegian Mother, Father, and Child Cohort Study (MoBa). To verify our results from the MoBa cohort, we used an external cohort of 149 ART and 58 non-ART neonates from the Australian 'Clinical review of the Health of adults conceived following Assisted Reproductive Technologies' (CHART) study. The Illumina EPIC array was used to measure DNAm in both datasets. In the MoBa cohort, we performed a set of X-chromosome-wide association studies ('XWASs' hereafter) to search for sex-specific DNAm differences between ART and non-ART newborns. We tested several models to investigate the influence of various confounders, including parental DNAm. We also searched for differentially methylated regions (DMRs) and regions of co-methylation flanking the most significant CpGs. Additionally, we ran an analogous model to our main model on the external CHART dataset.

RESULTS

In the MoBa cohort, we found more differentially methylated CpGs and DMRs in girls than boys. Most of the associations persisted after controlling for parental DNAm and other confounders. Many of the significant CpGs and DMRs were in gene-promoter regions, and several of the genes linked to these CpGs are expressed in tissues relevant for both ART and sex (testis, placenta, and fallopian tube). We found no support for parental DNAm-dependent features as an explanation for the observed associations in the newborns. The most significant CpG in the boys-only analysis was in UBE2DNL, which is expressed in testes but with unknown function. The most significant CpGs in the girls-only analysis were in EIF2S3 and AMOT. These three loci also displayed differential DNAm in the CHART cohort.

CONCLUSIONS

Genes that co-localized with the significant CpGs and DMRs associated with ART are implicated in several key biological processes (e.g., neurodevelopment) and disorders (e.g., intellectual disability and autism). These connections are particularly compelling in light of previous findings indicating that neurodevelopmental outcomes differ in ART-conceived children compared to those naturally conceived.

An overview of DNA methylation-derived trait score methods and applications

Microarray technology has been used to measure genome-wide DNA methylation in thousands of individuals. These studies typically test the associations between individual DNA methylation sites ("probes") and complex traits or diseases. The results can be used to generate methylation profile scores (MPS) to predict outcomes in independent data sets. Although there are many parallels between MPS and polygenic (risk) scores (PGS), there are key differences. Here, we review motivations, methods, and applications of DNA methylation-based trait prediction, with a focus on common diseases. We contrast MPS with PGS, highlighting where assumptions made in genetic modeling may not hold in epigenetic data.

DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits

Studies of DNA methylation (DNAm) in solid human tissues are relatively scarce; tissue-specific characterization of DNAm is needed to understand its role in gene regulation and its relevance to complex traits. We generated array-based DNAm profiles for 987 human samples from the Genotype-Tissue Expression (GTEx) project, representing 9 tissue types and 424 subjects. We characterized methylome and transcriptome correlations (eQTMs), genetic regulation in cis (mQTLs and eQTLs) across tissues and e/mQTLs links to complex traits. We identified mQTLs for 286,152 CpG sites, many of which (>5%) show tissue specificity, and mQTL colocalizations with 2,254 distinct GWAS hits across 83 traits. For 91% of these loci, a candidate gene link was identified by integration of functional maps, including eQTMs, and/or eQTL colocalization, but only 33% of loci involved an eQTL and mQTL present in the same tissue type. With this DNAm-focused integrative analysis, we contribute to the understanding of molecular regulatory mechanisms in human tissues and their impact on complex traits.

RNA biomarkers for alcohol use disorder

Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.

Gene-environment interactions in Alzheimer disease: the emerging role of epigenetics

With the exception of a few monogenic forms, Alzheimer disease (AD) has a complex aetiology that is likely to involve multiple susceptibility genes and environmental factors. The role of environmental factors is difficult to determine and, until a few years ago, the molecular mechanisms underlying gene-environment (G × E) interactions in AD were largely unknown. Here, we review evidence that has emerged over the past two decades to explain how environmental factors, such as diet, lifestyle, alcohol, smoking and pollutants, might interact with the human genome. In particular, we discuss how various environmental AD risk factors can induce epigenetic modifications of key AD-related genes and pathways and consider how epigenetic mechanisms could contribute to the effects of oxidative stress on AD onset. Studies on early-life exposures are helping to uncover critical time windows of sensitivity to epigenetic influences from environmental factors, thereby laying the foundations for future primary preventative approaches. We conclude that epigenetic modifications need to be considered when assessing G × E interactions in AD.

A Deep Survival EWAS approach estimating risk profile based on pre-diagnostic DNA methylation: An application to breast cancer time to diagnosis

Previous studies for cancer biomarker discovery based on pre-diagnostic blood DNA methylation (DNAm) profiles, either ignore the explicit modeling of the Time To Diagnosis (TTD), or provide inconsistent results. This lack of consistency is likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at CpG sites on TTD independently. In this work, we aim to identify blood DNAm profiles associated with TTD, with the aim to improve the reliability of the results, as well as their biological meaningfulness. We argue that a global approach to estimate CpG sites effect profile should capture the complex (potentially non-linear) relationships interplaying between sites. To prove our concept, we develop a new Deep Learning-based approach assessing the relevance of individual CpG Islands (i.e., assigning a weight to each site) in determining TTD while modeling their combined effect in a survival analysis scenario. The algorithm combines a tailored sampling procedure with DNAm sites agglomeration, deep non-linear survival modeling and SHapley Additive exPlanations (SHAP) values estimation to aid robustness of the derived effects profile. The proposed approach deals with the common complexities arising from epidemiological studies, such as small sample size, noise, and low signal-to-noise ratio of blood-derived DNAm. We apply our approach to a prospective case-control study on breast cancer nested in the EPIC Italy cohort and we perform weighted gene-set enrichment analyses to demonstrate the biological meaningfulness of the obtained results. We compared the results of Deep Survival EWAS with those of a traditional EWAS approach, demonstrating that our method performs better than the standard approach in identifying biologically relevant pathways.

Blood-derived DNAm profiles could be exploited as new biomarkers for cancer risk stratification and possibly, early detection. This is of particular interest since blood is a convenient tissue to assay for constitutional methylation and its collection is non-invasive. Exploiting pre-diagnostic blood DNAm data opens the further opportunity to investigate the association of DNAm at baseline on cancer risk, modeling the relationship between sites’ methylation and the Time to Diagnosis. Previous studies mostly provide inconsistent results likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at CpG sites on TTD independently. In this work we argue that an approach to estimate single CpG sites’ effect while modeling their combined effect on the survival outcome is needed, and we claim that such approach should capture the complex (potentially non-linear) relationships interplaying between sites. We prove this concept by developing a novel approach to analyze a prospective case-control study on breast cancer nested in the EPIC Italy cohort. A weighted gene set enrichment analysis confirms that our approach outperforms standard EWAS in identifying biologically meaningful pathways.

Modeling dependency structures in 450k DNA methylation data

MOTIVATION

DNA methylation has been shown to be spatially dependent across chromosomes. Previous studies have focused on the influence of genomic context on the dependency structure, while not considering differences in dependency structure between individuals.

RESULTS

We modeled spatial dependency with a flexible framework to quantify the dependency structure, focusing on inter-individual differences by exploring the association between dependency parameters and technical and biological variables. The model was applied to a subset of the Finnish Twin Cohort study (N = 1611 individuals). The estimates of the dependency parameters varied considerably across individuals, but were generally consistent across chromosomes within individuals. The variation in dependency parameters was associated with bisulfite conversion plate, zygosity, sex and age. The age differences presumably reflect accumulated environmental exposures and/or accumulated small methylation differences caused by stochastic mitotic events, establishing recognizable, individual patterns more strongly seen in older individuals.

AVAILABILITY AND IMPLEMENTATION

The twin dataset used in the current study are located in the Biobank of the National Institute for Health and Welfare, Finland. All the biobanked data are publicly available for use by qualified researchers following a standardized application procedure (https://thl.fi/en/web/thl-biobank/for-researchers). A R-script for fitting the dependency structure to publicly available DNA methylation data with the software used in this article is provided in supplementary data.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genotype-dependent epigenetic regulation of DLGAP2 in alcohol use and dependence

Alcohol misuse is a major public health problem originating from genetic and environmental risk factors. Alterations in the brain epigenome may orchestrate changes in gene expression that lead to alcohol misuse and dependence. Through epigenome-wide association analysis of DNA methylation from human brain tissues, we identified a differentially methylated region, DMR-DLGAP2, associated with alcohol dependence. Methylation within DMR-DLGAP2 was found to be genotype-dependent, allele-specific and associated with reward processing in brain. Methylation at the DMR-DLGAP2 regulated expression of DLGAP2 in vitro, and Dlgap2-deficient mice showed reduced alcohol consumption compared with wild-type controls. These results suggest that DLGAP2 may be an interface for genetic and epigenetic factors controlling alcohol use and dependence.

Epigenetic and Transcriptional Control of the Opioid Prodynorphine Gene: In-Depth Analysis in the Human Brain

Neuropeptides serve as neurohormones and local paracrine regulators that control neural networks regulating behavior, endocrine system and sensorimotor functions. Their expression is characterized by exceptionally restricted profiles. Circuit-specific and adaptive expression of neuropeptide genes may be defined by transcriptional and epigenetic mechanisms controlled by cell type and subtype sequence-specific transcription factors, insulators and silencers. The opioid peptide dynorphins play a critical role in neurological and psychiatric disorders, pain processing and stress, while their mutations cause profound neurodegeneration in the human brain. In this review, we focus on the prodynorphin gene as a model for the in-depth epigenetic and transcriptional analysis of expression of the neuropeptide genes. Prodynorphin studies may provide a framework for analysis of mechanisms relevant for regulation of neuropeptide genes in normal and pathological human brain.

Bipartite graph-based approach for clustering of cell lines by gene expression-drug response associations

MOTIVATION

In pharmacogenomic studies, the biological context of cell lines influences the predictive ability of drug-response models and the discovery of biomarkers. Thus, similar cell lines are often studied together based on prior knowledge of biological annotations. However, this selection approach is not scalable with the number of annotations, and the relationship between gene-drug association patterns and biological context may not be obvious.

RESULTS

We present a procedure to compare cell lines based on their gene-drug association patterns. Starting with a grouping of cell lines from biological annotation, we model gene-drug association patterns for each group as a bipartite graph between genes and drugs. This is accomplished by applying sparse canonical correlation analysis (SCCA) to extract the gene-drug associations, and using the canonical vectors to construct the edge weights. Then, we introduce a nuclear norm-based dissimilarity measure to compare the bipartite graphs. Accompanying our procedure is a permutation test to evaluate the significance of similarity of cell line groups in terms of gene-drug associations. In the pharmacogenomics datasets CTRP2, GDSC2, and CCLE, hierarchical clustering of carcinoma groups based on this dissimilarity measure uniquely reveals clustering patterns driven by carcinoma subtype rather than primary site. Next, we show that the top associated drugs or genes from SCCA can be used to characterize the clustering patterns of haematopoietic and lymphoid malignancies. Finally, we confirm by simulation that when drug responses are linearly-dependent on expression, our approach is the only one that can effectively infer the true hierarchy compared to existing approaches.

AVAILABILITY

Bipartite graph-based hierarchical clustering is implemented in R and can be obtained from CRAN: https://CRAN.R-project.org/package=hierBipartite. The source code is available at https://github.com/CalvinTChi/hierBipartite.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predicting lung adenocarcinoma disease progression using methylation-correlated blocks and ensemble machine learning classifiers

Applying the knowledge that methyltransferases and demethylases can modify adjacent cytosine-phosphorothioate-guanine (CpG) sites in the same DNA strand, we found that combining multiple CpGs into a single block may improve cancer diagnosis. However, survival prediction remains a challenge. In this study, we developed a pipeline named "stacked ensemble of machine learning models for methylation-correlated blocks" (EnMCB) that combined Cox regression, support vector regression (SVR), and elastic-net models to construct signatures based on DNA methylation-correlated blocks for lung adenocarcinoma (LUAD) survival prediction. We used methylation profiles from the Cancer Genome Atlas (TCGA) as the training set, and profiles from the Gene Expression Omnibus (GEO) as validation and testing sets. First, we partitioned the genome into blocks of tightly co-methylated CpG sites, which we termed methylation-correlated blocks (MCBs). After partitioning and feature selection, we observed different diagnostic capacities for predicting patient survival across the models. We combined the multiple models into a single stacking ensemble model. The stacking ensemble model based on the top-ranked block had the area under the receiver operating characteristic curve of 0.622 in the TCGA training set, 0.773 in the validation set, and 0.698 in the testing set. When stratified by clinicopathological risk factors, the risk score predicted by the top-ranked MCB was an independent prognostic factor. Our results showed that our pipeline was a reliable tool that may facilitate MCB selection and survival prediction.

A novel computational strategy for DNA methylation imputation using mixture regression model (MRM)

BACKGROUND

DNA methylation is an important heritable epigenetic mark that plays a crucial role in transcriptional regulation and the pathogenesis of various human disorders. The commonly used DNA methylation measurement approaches, e.g., Illumina Infinium HumanMethylation-27 and -450 BeadChip arrays (27 K and 450 K arrays) and reduced representation bisulfite sequencing (RRBS), only cover a small proportion of the total CpG sites in the human genome, which considerably limited the scope of the DNA methylation analysis in those studies.

RESULTS

We proposed a new computational strategy to impute the methylation value at the unmeasured CpG sites using the mixture of regression model (MRM) of radial basis functions, integrating information of neighboring CpGs and the similarities in local methylation patterns across subjects and across multiple genomic regions. Our method achieved a better imputation accuracy over a set of competing methods on both simulated and empirical data, particularly when the missing rate is high. By applying MRM to an RRBS dataset from subjects with low versus high bone mineral density (BMD), we recovered methylation values of ~ 300 K CpGs in the promoter regions of chromosome 17 and identified some novel differentially methylated CpGs that are significantly associated with BMD.

CONCLUSIONS

Our method is well applicable to the numerous methylation studies. By expanding the coverage of the methylation dataset to unmeasured sites, it can significantly enhance the discovery of novel differential methylation signals and thus reveal the mechanisms underlying various human disorders/traits.

Detection of haplotype-dependent allele-specific DNA methylation in WGBS data

In heterozygous genomes, allele-specific measurements can reveal biologically significant differences in DNA methylation between homologous alleles associated with local changes in genetic sequence. Current approaches for detecting such events from whole-genome bisulfite sequencing (WGBS) data perform statistically independent marginal analysis at individual cytosine-phosphate-guanine (CpG) sites, thus ignoring correlations in the methylation state, or carry-out a joint statistical analysis of methylation patterns at four CpG sites producing unreliable statistical evidence. Here, we employ the one-dimensional Ising model of statistical physics and develop a method for detecting allele-specific methylation (ASM) events within segments of DNA containing clusters of linked single-nucleotide polymorphisms (SNPs), called haplotypes. Comparisons with existing approaches using simulated and real WGBS data show that our method provides an improved fit to data, especially when considering large haplotypes. Importantly, the method employs robust hypothesis testing for detecting statistically significant imbalances in mean methylation level and methylation entropy, as well as for identifying haplotypes for which the genetic variant carries significant information about the methylation state. As such, our ASM analysis approach can potentially lead to biological discoveries with important implications for the genetics of complex human diseases.

Genome-wide DNA methylation profiling in human breast tissue by Illumina TruSeq methyl capture EPIC sequencing and infinium methylationEPIC beadchip microarray

A newly-developed platform, the Illumina TruSeq Methyl Capture EPIC library prep (TruSeq EPIC), builds on the content of the Infinium MethylationEPIC Beadchip Microarray (EPIC-array) and leverages the power of next-generation sequencing for targeted bisulphite sequencing. We empirically examined the performance of TruSeq EPIC and EPIC-array in assessing genome-wide DNA methylation in breast tissue samples. TruSeq EPIC provided data with a much higher density in the regions when compared to EPIC-array (~2.74 million CpGs with at least 10X coverage vs ~752 K CpGs, respectively). Approximately 398 K CpGs were common and measured across the two platforms in every sample. Overall, there was high concordance in methylation levels between the two platforms (Pearson correlation r = 0.98, P < 0.0001). However, we observed that TruSeq EPIC measurements provided a wider dynamic range and likely a higher quantitative sensitivity for CpGs that were either hypo- or hyper-methylated (β close to 0 or 1, respectively). In addition, when comparing different breast tissue types TruSeq EPIC identified more differentially methylated CpGs than EPIC-array, not only out of additional sites interrogated by TruSeq EPIC alone, but also out of common sites interrogated by both platforms. Our results suggest that both platforms show high reproducibility and reliability in genome-wide DNA methylation profiling, while TruSeq EPIC had a significant improvement over EPIC-array regarding genomic resolution and coverage. The wider dynamic range and likely higher precision of the estimates by the TruSeq EPIC may lead to the identification of novel differentially methylated markers that are associated with disease risk.

CoMeBack: DNA methylation array data analysis for co-methylated regions

Motivation

High-dimensional DNA methylation (DNAm) array coverage, while sparse in the context of the entire DNA methylome, still constitutes a very large number of CpG probes. The ensuing multiple-test corrections affect the statistical power to detect associations, likely contributing to prevalent limited reproducibility. Array probes measuring proximal CpG sites often have correlated levels of DNAm that may not only be biologically meaningful but also imply statistical dependence and redundancy. New methods that account for such correlations between adjacent probes may enable improved specificity, discovery and interpretation of statistical associations in DNAm array data.

Results

We developed a method named Co-Methylation with genomic CpG Background (CoMeBack) that estimates DNA co-methylation, defined as proximal CpG probes with correlated DNAm across individuals. CoMeBack outputs co-methylated regions (CMRs), spanning sets of array probes constructed based on all genomic CpG sites, including those not measured on the array, and without any phenotypic variable inputs. This approach can reduce the multiple-test correction burden, while enhancing the discovery and specificity of statistical associations. We constructed and validated CMRs in whole blood, using publicly available Illumina Infinium 450 K array data from over 5000 individuals. These CMRs were enriched for enhancer chromatin states, and binding site motifs for several transcription factors involved in blood physiology. We illustrated how CMR-based epigenome-wide association studies can improve discovery and reduce false positives for associations with chronological age.

Availability and implementation

https://bitbucket.org/flopflip/comeback.

Supplementary information

Supplementary data are available at Bioinformatics online.

Neuronal Expression of Opioid Gene is Controlled by Dual Epigenetic and Transcriptional Mechanism in Human Brain

Molecular mechanisms that define patterns of neuropeptide expression are essential for the formation and rewiring of neural circuits. The prodynorphin gene (PDYN) gives rise to dynorphin opioid peptides mediating depression and substance dependence. We here demonstrated that PDYN is expressed in neurons in human dorsolateral prefrontal cortex (dlPFC), and identified neuronal differentially methylated region in PDYN locus framed by CCCTC-binding factor binding sites. A short, nucleosome size human-specific promoter CpG island (CGI), a core of this region may serve as a regulatory module, which is hypomethylated in neurons, enriched in 5-hydroxymethylcytosine, and targeted by USF2, a methylation-sensitive E-box transcription factor (TF). USF2 activates PDYN transcription in model systems, and binds to nonmethylated CGI in dlPFC. USF2 and PDYN expression is correlated, and USF2 and PDYN proteins are co-localized in dlPFC. Segregation of activatory TF and repressive CGI methylation may ensure contrasting PDYN expression in neurons and glia in human brain.

A genomic atlas of systemic interindividual epigenetic variation in humans

BACKGROUND

DNA methylation is thought to be an important determinant of human phenotypic variation, but its inherent cell type specificity has impeded progress on this question. At exceptional genomic regions, interindividual variation in DNA methylation occurs systemically. Like genetic variants, systemic interindividual epigenetic variants are stable, can influence phenotype, and can be assessed in any easily biopsiable DNA sample. We describe an unbiased screen for human genomic regions at which interindividual variation in DNA methylation is not tissue-specific.

RESULTS

For each of 10 donors from the NIH Genotype-Tissue Expression (GTEx) program, CpG methylation is measured by deep whole-genome bisulfite sequencing of genomic DNA from tissues representing the three germ layer lineages: thyroid (endoderm), heart (mesoderm), and brain (ectoderm). We develop a computational algorithm to identify genomic regions at which interindividual variation in DNA methylation is consistent across all three lineages. This approach identifies 9926 correlated regions of systemic interindividual variation (CoRSIVs). These regions, comprising just 0.1% of the human genome, are inter-correlated over long genomic distances, associated with transposable elements and subtelomeric regions, conserved across diverse human ethnic groups, sensitive to periconceptional environment, and associated with genes implicated in a broad range of human disorders and phenotypes. CoRSIV methylation in one tissue can predict expression of associated genes in other tissues.

CONCLUSIONS

In addition to charting a previously unexplored molecular level of human individuality, this atlas of human CoRSIVs provides a resource for future population-based investigations into how interindividual epigenetic variation modulates risk of disease.

Epigenome-wide association study of DNA methylation in narcolepsy: an integrated genetic and epigenetic approach

Narcolepsy with cataplexy, which is a hypersomnia characterized by excessive daytime sleepiness and cataplexy, is a multifactorial disease caused by both genetic and environmental factors. Several genetic factors including HLA-DQB1*06:02 have been identified; however, the disease etiology is still unclear. Epigenetic modifications, such as DNA methylation, have been suggested to play an important role in the pathogenesis of complex diseases. Here, we examined DNA methylation profiles of blood samples from narcolepsy and healthy control individuals and performed an epigenome-wide association study (EWAS) to investigate methylation loci associated with narcolepsy. Moreover, data from the EWAS and a previously performed narcolepsy genome-wide association study were integrated to search for methylation loci with causal links to the disease. We found that (1) genes annotated to the top-ranked differentially methylated positions (DMPs) in narcolepsy were associated with pathways of hormone secretion and monocarboxylic acid metabolism. (2) Top-ranked narcolepsy-associated DMPs were significantly more abundant in non-CpG island regions and more than 95 per cent of such sites were hypomethylated in narcolepsy patients. (3) The integrative analysis identified the CCR3 region where both a single methylation site and multiple single-nucleotide polymorphisms were found to be associated with the disease as a candidate region responsible for narcolepsy. The findings of this study suggest the importance of future replication studies, using methylation technologies with wider genome coverage and/or larger number of samples, to confirm and expand on these results.

Combining evidence from four immune cell types identifies DNA methylation patterns that implicate functionally distinct pathways during Multiple Sclerosis progression

Background

Multiple Sclerosis (MS) is a chronic inflammatory disease and a leading cause of progressive neurological disability among young adults. DNA methylation, which intersects genes and environment to control cellular functions on a molecular level, may provide insights into MS pathogenesis.

Methods

We measured DNA methylation in CD4⁺ T cells (n = 31), CD8⁺ T cells (n = 28), CD14⁺ monocytes (n = 35) and CD19⁺ B cells (n = 27) from relapsing-remitting (RRMS), secondary progressive (SPMS) patients and healthy controls (HC) using Infinium HumanMethylation450 arrays. Monocyte (n = 25) and whole blood (n = 275) cohorts were used for validations.

Findings

B cells from MS patients displayed most significant differentially methylated positions (DMPs), followed by monocytes, while only few DMPs were detected in T cells. We implemented a non-parametric combination framework (omicsNPC) to increase discovery power by combining evidence from all four cell types. Identified shared DMPs co-localized at MS risk loci and clustered into distinct groups. Functional exploration of changes discriminating RRMS and SPMS from HC implicated lymphocyte signaling, T cell activation and migration. SPMS-specific changes, on the other hand, implicated myeloid cell functions and metabolism. Interestingly, neuronal and neurodegenerative genes and pathways were also specifically enriched in the SPMS cluster.

Interpretation

We utilized a statistical framework (omicsNPC) that combines multiple layers of evidence to identify DNA methylation changes that provide new insights into MS pathogenesis in general, and disease progression, in particular.

Fund

This work was supported by the Swedish Research Council, Stockholm County Council, AstraZeneca, European Research Council, Karolinska Institutet and Margaretha af Ugglas Foundation.

Epigenetic Alterations Associated With Early Prenatal Dexamethasone Treatment

Prenatal treatment with dexamethasone (DEX) reduces virilization in girls with congenital adrenal hyperplasia (CAH). It has potential short- and long-term risks and has been shown to affect cognitive functions. Here, we investigate whether epigenetic modification of DNA during early developmental stages may be a key mediating mechanism by which prenatal DEX treatment could result in poor outcomes in the offspring. We analyzed genome-wide CD4+ T cell DNA methylation, assessed using the Infinium HumanMethylation450 BeadChip array in 29 individuals (mean age = 16.4 ± 5.9 years) at risk for CAH and treated with DEX during the first trimester and 37 population controls (mean age = 17.0 years, SD = 6.1 years). We identified 9672 differentially methylated probes (DMPs) associated with DEX treatment and 7393 DMPs associated with a DEX × sex interaction. DMPs were enriched in intergenic regions located near epigenetic markers for active enhancers. Functional enrichment of DMPs was mostly associated with immune functioning and inflammation but also with nonimmune-related functions. DEX-associated DMPs enriched near single nucleotide polymorphisms (SNPs) associated with inflammatory bowel disease, and DEX × sex-associated DMPs enriched near SNPs associated with asthma. DMPs in genes involved in the regulation and maintenance of methylation and steroidogenesis were identified as well. Methylation in the BDNF, FKBP5, and NR3C1 genes were associated with the performance on several Wechsler Adult Intelligence Scale-Fourth Edition subscales. In conclusion, this study indicates that DNA methylation is altered after prenatal DEX treatment. This finding may have implications for the future health of the exposed individual.

Association of maternal prenatal smoking GFI1-locus and cardio-metabolic phenotypes in 18,212 adults

BACKGROUND

DNA methylation at the GFI1-locus has been repeatedly associated with exposure to smoking from the foetal period onwards. We explored whether DNA methylation may be a mechanism that links exposure to maternal prenatal smoking with offspring's adult cardio-metabolic health.

METHODS

We meta-analysed the association between DNA methylation at GFI1-locus with maternal prenatal smoking, adult own smoking, and cardio-metabolic phenotypes in 22 population-based studies from Europe, Australia, and USA (n = 18,212). DNA methylation at the GFI1-locus was measured in whole-blood. Multivariable regression models were fitted to examine its association with exposure to prenatal and own adult smoking. DNA methylation levels were analysed in relation to body mass index (BMI), waist circumference (WC), fasting glucose (FG), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), diastolic, and systolic blood pressure (BP).

FINDINGS

Lower DNA methylation at three out of eight GFI1-CpGs was associated with exposure to maternal prenatal smoking, whereas, all eight CpGs were associated with adult own smoking. Lower DNA methylation at cg14179389, the strongest maternal prenatal smoking locus, was associated with increased WC and BP when adjusted for sex, age, and adult smoking with Bonferroni-corrected P < 0·012. In contrast, lower DNA methylation at cg09935388, the strongest adult own smoking locus, was associated with decreased BMI, WC, and BP (adjusted 1 × 10-7 < P < 0.01). Similarly, lower DNA methylation at cg12876356, cg18316974, cg09662411, and cg18146737 was associated with decreased BMI and WC (5 × 10-8 < P < 0.001). Lower DNA methylation at all the CpGs was consistently associated with higher TG levels.

INTERPRETATION

Epigenetic changes at the GFI1 were linked to smoking exposure in-utero/in-adulthood and robustly associated with cardio-metabolic risk factors. FUND: European Union's Horizon 2020 research and innovation programme under grant agreement no. 633595 DynaHEALTH.

Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship between Methylomic Variation, Gene Expression, and Complex Traits

Characterizing the complex relationship between genetic, epigenetic, and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. We undertook a comprehensive analysis of common genetic variation on DNA methylation (DNAm) by using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (p < 6.52 × 10-14) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified by previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits by using summary-data-based Mendelian randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression and thereby identify 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database as a resource to the research community.

The framework for population epigenetic study

At present, understanding of DNA methylation at the population level is still limited. Here, we first extended the classical framework of population genetics, such as single nucleotide polymorphism allele frequency, linkage disequilibrium (LD), LD block and haplotype, to epigenetics. Then, as an example, we compared the DNA methylation disequilibrium (MD) maps between HapMap CEU (Caucasian residents of European ancestry from Utah) population and YRI (Yoruba people from Ibadan) population (lymphoblastoid cell lines). We analyzed the differences and similarities between CEU and YRI from the following aspects: SMP (single methylation polymorphism) allele frequency, SMP allele association, MD, MD block and methylation haplotype (meplotype) frequency. The results showed that CEU and YRI had similar distribution of SMP allele frequency, and shared many MD block region. We believe that the framework of population genetics can be used in the population epigenetics. The population epigenetic framework also has potential prospects in the study of complex diseases, such as epigenome-wide association study.

DNA methylation yields epigenetic clues into the diabetic nephropathy of Pima Indians

Environmental factors drive epigenetic programming. DNA methylation is the best studied modification transmitting epigenetic information. A study by Qiu et al. examined potential epigenetic roots for the decline of renal function in Pima Indians. A genomewide survey of blood leukocytes uncovered differentially methylated DNA sites in regulatory regions of genes associated with chronic kidney disease. This longitudinal study provides the first clues on epigenetic links between environmental factors and a high prevalence of diabetic kidney disease in Pima Indians.

DNA methylation as a mediator of HLA-DRB1*15:01 and a protective variant in multiple sclerosis

The human leukocyte antigen (HLA) haplotype DRB1*15:01 is the major risk factor for multiple sclerosis (MS). Here, we find that DRB1*15:01 is hypomethylated and predominantly expressed in monocytes among carriers of DRB1*15:01. A differentially methylated region (DMR) encompassing HLA-DRB1 exon 2 is particularly affected and displays methylation-sensitive regulatory properties in vitro. Causal inference and Mendelian randomization provide evidence that HLA variants mediate risk for MS via changes in the HLA-DRB1 DMR that modify HLA-DRB1 expression. Meta-analysis of 14,259 cases and 171,347 controls confirms that these variants confer risk from DRB1*15:01 and also identifies a protective variant (rs9267649, p < 3.32 × 10^-8, odds ratio = 0.86) after conditioning for all MS-associated variants in the region. rs9267649 is associated with increased DNA methylation at the HLA-DRB1 DMR and reduced expression of HLA-DRB1, suggesting a modulation of the DRB1*15:01 effect. Our integrative approach provides insights into the molecular mechanisms of MS susceptibility and suggests putative therapeutic strategies targeting a methylation-mediated regulation of the major risk gene.

Epigenetics applied to psychiatry: Clinical opportunities and future challenges

Psychiatric disorders are clinically heterogeneous and debilitating chronic diseases resulting from a complex interplay between gene variants and environmental factors. Epigenetic processes, such as DNA methylation and histone posttranslational modifications, instruct the cell/tissue to correctly interpret external signals and adjust its functions accordingly. Given that epigenetic modifications are sensitive to environment, stable, and reversible, epigenetic studies in psychiatry could represent a promising approach to better understanding and treating disease. In the present review, we aim to discuss the clinical opportunities and challenges arising from the epigenetic research in psychiatry. Using selected examples, we first recapitulate key findings supporting the role of adverse life events, alone or in combination with genetic risk, in epigenetic programming of neuropsychiatric systems. Epigenetic studies further report encouraging findings about the use of methylation changes as diagnostic markers of disease phenotype and predictive tools of progression and response to treatment. Then we discuss the potential of using targeted epigenetic pharmacotherapy, combined with psychosocial interventions, for future personalized medicine for patients. Finally, we review the methodological limitations that could hinder interpretation of epigenetic data in psychiatry. They mainly arise from heterogeneity at the individual and tissue level and require future strategies in order to reinforce the biological relevance of epigenetic data and its translational use in psychiatry. Overall, we suggest that epigenetics could provide new insights into a more comprehensive interpretation of mental illness and might eventually improve the nosology, treatment, and prevention of psychiatric disorders.

An information-theoretic approach to the modeling and analysis of whole-genome bisulfite sequencing data

BACKGROUND

DNA methylation is a stable form of epigenetic memory used by cells to control gene expression. Whole genome bisulfite sequencing (WGBS) has emerged as a gold-standard experimental technique for studying DNA methylation by producing high resolution genome-wide methylation profiles. Statistical modeling and analysis is employed to computationally extract and quantify information from these profiles in an effort to identify regions of the genome that demonstrate crucial or aberrant epigenetic behavior. However, the performance of most currently available methods for methylation analysis is hampered by their inability to directly account for statistical dependencies between neighboring methylation sites, thus ignoring significant information available in WGBS reads.

RESULTS

We present a powerful information-theoretic approach for genome-wide modeling and analysis of WGBS data based on the 1D Ising model of statistical physics. This approach takes into account correlations in methylation by utilizing a joint probability model that encapsulates all information available in WGBS methylation reads and produces accurate results even when applied on single WGBS samples with low coverage. Using the Shannon entropy, our approach provides a rigorous quantification of methylation stochasticity in individual WGBS samples genome-wide. Furthermore, it utilizes the Jensen-Shannon distance to evaluate differences in methylation distributions between a test and a reference sample. Differential performance assessment using simulated and real human lung normal/cancer data demonstrate a clear superiority of our approach over DSS, a recently proposed method for WGBS data analysis. Critically, these results demonstrate that marginal methods become statistically invalid when correlations are present in the data.

CONCLUSIONS

This contribution demonstrates clear benefits and the necessity of modeling joint probability distributions of methylation using the 1D Ising model of statistical physics and of quantifying methylation stochasticity using concepts from information theory. By employing this methodology, substantial improvement of DNA methylation analysis can be achieved by effectively taking into account the massive amount of statistical information available in WGBS data, which is largely ignored by existing methods.

Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms

Inherited genetic variation affects local gene expression and DNA methylation in humans. Most expression quantitative trait loci (cis-eQTLs) occur at the same genomic location as a methylation QTL (cis-meQTL), suggesting a common causal variant and shared mechanism. Using DNA and RNA from peripheral blood of Bangladeshi individuals, here we use co-localization methods to identify eQTL-meQTL pairs likely to share a causal variant. We use partial correlation and mediation analyses to identify >400 of these pairs showing evidence of a causal relationship between expression and methylation (i.e., shared mechanism) with many additional pairs we are underpowered to detect. These co-localized pairs are enriched for SNPs showing opposite associations with expression and methylation, although many SNPs affect multiple CpGs in opposite directions. This work demonstrates the pervasiveness of co-regulated expression and methylation in the human genome. Applying this approach to other types of molecular QTLs can enhance our understanding of regulatory mechanisms.

A DNA methylation biomarker of alcohol consumption

The lack of reliable measures of alcohol intake is a major obstacle to the diagnosis and treatment of alcohol-related diseases. Epigenetic modifications such as DNA methylation may provide novel biomarkers of alcohol use. To examine this possibility, we performed an epigenome-wide association study of methylation of cytosine-phosphate-guanine dinucleotide (CpG) sites in relation to alcohol intake in 13 population-based cohorts (n_total=13 317; 54% women; mean age across cohorts 42-76 years) using whole blood (9643 European and 2423 African ancestries) or monocyte-derived DNA (588 European, 263 African and 400 Hispanic ancestry) samples. We performed meta-analysis and variable selection in whole-blood samples of people of European ancestry (n=6926) and identified 144 CpGs that provided substantial discrimination (area under the curve=0.90-0.99) for current heavy alcohol intake (⩾42 g per day in men and ⩾28 g per day in women) in four replication cohorts. The ancestry-stratified meta-analysis in whole blood identified 328 (9643 European ancestry samples) and 165 (2423 African ancestry samples) alcohol-related CpGs at Bonferroni-adjusted P<1 × 10^-7. Analysis of the monocyte-derived DNA (n=1251) identified 62 alcohol-related CpGs at P<1 × 10^-7. In whole-blood samples of people of European ancestry, we detected differential methylation in two neurotransmitter receptor genes, the γ-Aminobutyric acid-A receptor delta and γ-aminobutyric acid B receptor subunit 1; their differential methylation was associated with expression levels of a number of genes involved in immune function. In conclusion, we have identified a robust alcohol-related DNA methylation signature and shown the potential utility of DNA methylation as a clinically useful diagnostic test to detect current heavy alcohol consumption.

Estimation of a significance threshold for epigenome-wide association studies

Epigenome-wide association studies (EWAS) are designed to characterise population-level epigenetic differences across the genome and link them to disease. Most commonly, they assess DNA-methylation status at cytosine-guanine dinucleotide (CpG) sites, using platforms such as the Illumina 450k array that profile a subset of CpGs genome wide. An important challenge in the context of EWAS is determining a significance threshold for declaring a CpG site as differentially methylated, taking multiple testing into account. We used a permutation method to estimate a significance threshold specifically for the 450k array and a simulation extrapolation approach to estimate a genome-wide threshold. These methods were applied to five different EWAS datasets derived from a variety of populations and tissue types. We obtained an estimate of α=2.4×10-7 for the 450k array, and a genome-wide estimate of α=3.6×10-8. We further demonstrate the importance of these results by showing that previously recommended sample sizes for EWAS should be adjusted upwards, requiring samples between ∼10% and ∼20% larger in order to maintain type-1 errors at the desired level.

DNA hypomethylation of a transcription factor binding site within the promoter of a gout risk gene NRBP1 upregulates its expression by inhibition of TFAP2A binding

BACKGROUND

Genome-wide association studies (GWASs) have identified dozens of loci associated with gout, but for most cases, the risk genes and the underlying molecular mechanisms contributing to these associations are unknown. This study sought to understand the molecular mechanism of a common genetic variant, rs780093, in the development of gout, both in vitro and in vivo.

RESULTS

Nuclear receptor binding protein 1 (NRBP1), as a gout risk gene, and its regulatory region, 72 bp upstream of the transcription start site, designated as B1, were identified through integrative analyses of genome-wide genotype and DNA methylation data. We observed elevated NRBP1 expression in human peripheral blood mononuclear cells (PBMCs) from gout patients. In vitro luciferase reporter and protein pulldown assay results showed that DNA methylation could increase the binding of the transcription factor TFAP2A to B1, leading to suppressed gene expression. There results were further confirmed by in vivo bisulfite pyrosequencing showing that hypomethylation on B1 is associated with increased NRBP1 expression in gout patients.

CONCLUSIONS

Hypomethylation at the promoter region of NRBP1 reduces the binding of TFAP2A and thus leads to elevated NRBP1 expression, which might contribute to the development of gout.

The Epigenomic Analysis of Human Obesity

OBJECTIVE

Analysis of the epigenome-the chemical modifications and packaging of the genome that can influence or indicate its activity-enables molecular insight into cell type-specific machinery. It can, therefore, reveal the pathophysiological mechanisms at work in disease. Detected changes can also represent physiological responses to adverse environmental exposures, thus enabling the epigenetic mark of DNA methylation to act as an epidemiological biomarker, even in surrogate tissue. This makes epigenomic analysis an attractive prospect to further understand the pathobiology and epidemiological aspects of obesity. Furthermore, integrating epigenomic data with known obesity-associated common genetic variation can aid in deciphering their molecular mechanisms.

METHODS AND CONCLUSIONS

This review primarily examines epidemiological or population-based studies of epigenetic modifications in relation to adiposity traits, as opposed to animal or cell models. It discusses recent work exploring the epigenome with respect to human obesity, which to date has predominately consisted of array-based studies of DNA methylation in peripheral blood. It is of note that highly replicated BMI DNA methylation associations are not causal, but strongly driven by coassociations for more precisely measured intertwined outcomes and factors, such as hyperlipidemia, hyperglycemia, and inflammation. Finally, the potential for the future exploration of the epigenome in obesity and related disorders is considered.

Epigenetic Changes During Food-Specific Immunotherapy

PURPOSE OF REVIEW

The prevalence and severity of IgE-mediated food allergy has increased dramatically over the last 15 years and is becoming a global health problem. Multiple lines of evidence suggest that epigenetic modifications of the genome resulting from gene-environment interactions have a key role in the increased prevalence of atopic disease. In this review, we describe the recent evidence suggesting how epigenetic changes mediate susceptibility to food allergies, and discuss how immunotherapy (IT) may reverse these effects. We discuss the areas of the epigenome as yet unexplored in terms of food allergy and IT such as histone modification and chromatin accessibility, and new techniques that may be utilized in future studies.

RECENT FINDINGS

Recent findings provide strong evidence that DNA methylation of certain promoter regions such as Forkhead box protein 3 is associated with clinical reactivity, and further, can be changed during IT treatment. Reports on other epigenetic changes are limited but also show evidence of significant change based on both disease status and treatment. In comparison to epigenetic studies focusing on asthma and allergic rhinitis, food allergy remains understudied. However, within the next decade, it is likely that epigenetic modifications may be used as biomarkers to aid in diagnosis and treatment of food-allergic patients. DNA methylation at specific loci has shown associations between food challenge outcomes, successful desensitization treatment, and overall phenotype compared to healthy controls.

Epigenetic research in multiple sclerosis: progress, challenges, and opportunities

Multiple sclerosis (MS) is a chronic inflammatory and demyelinating disease of the central nervous system. MS likely results from a complex interplay between predisposing causal gene variants (the strongest influence coming from HLA class II locus) and environmental risk factors such as smoking, infectious mononucleosis, and lack of sun exposure/vitamin D. However, little is known about the mechanisms underlying MS development and progression. Moreover, the clinical heterogeneity and variable response to treatment represent additional challenges to a comprehensive understanding and efficient treatment of disease. Epigenetic processes, such as DNA methylation and histone posttranslational modifications, integrate influences from the genes and the environment to regulate gene expression accordingly. Studying epigenetic modifications, which are stable and reversible, may provide an alternative approach to better understand and manage disease. We here aim to review findings from epigenetic studies in MS and further discuss the challenges and clinical opportunities arising from epigenetic research, many of which apply to other diseases with similar complex etiology. A growing body of evidence supports a role of epigenetic processes in the mechanisms underlying immune pathogenesis and nervous system dysfunction in MS. However, disparities between studies shed light on the need to consider possible confounders and methodological limitations for a better interpretation of the data. Nevertheless, translational use of epigenetics might offer new opportunities in epigenetic-based diagnostics and therapeutic tools for a personalized care of MS patients.

Prenatal phthalate exposure and altered patterns of DNA methylation in cord blood

Epigenetic changes such as DNA methylation may be a molecular mechanism through which environmental exposures affect health. Phthalates are known endocrine disruptors with ubiquitous exposures in the general population including pregnant women, and they have been linked with a number of adverse health outcomes. We examined the association between in utero phthalate exposure and altered patterns of cord blood DNA methylation in 336 Mexican-American newborns. Concentrations of 11 phthalate metabolites were analyzed in maternal urine samples collected at 13 and 26 weeks gestation as a measure of fetal exposure. DNA methylation was assessed using the Infinium HumanMethylation 450K BeadChip adjusting for cord blood cell composition. To identify differentially methylated regions (DMRs) that may be more informative than individual CpG sites, we used two different approaches, DMRcate and comb-p. Regional assessment by both methods identified 27 distinct DMRs, the majority of which were in relation to multiple phthalate metabolites. Most of the significant DMRs (67%) were observed for later pregnancy (26 weeks gestation). Further, 51% of the significant DMRs were associated with the di-(2-ethylhexyl) phthalate metabolites. Five individual CpG sites were associated with phthalate metabolite concentrations after multiple comparisons adjustment (FDR), all showing hypermethylation. Genes with DMRs were involved in inflammatory response (IRAK4 and ESM1), cancer (BRCA1 and LASP1), endocrine function (CNPY1), and male fertility (IFT140, TESC, and PRDM8). These results on differential DNA methylation in newborns with prenatal phthalate exposure provide new insights and targets to explore mechanism of adverse effects of phthalates on human health. Environ. Mol. Mutagen. 58:398-410, 2017. © 2017 Wiley Periodicals, Inc.

Genetic variation at 16q24.2 is associated with small vessel stroke

Objective

Genome‐wide association studies (GWAS) have been successful at identifying associations with stroke and stroke subtypes, but have not yet identified any associations solely with small vessel stroke (SVS). SVS comprises one quarter of all ischemic stroke and is a major manifestation of cerebral small vessel disease, the primary cause of vascular cognitive impairment. Studies across neurological traits have shown that younger‐onset cases have an increased genetic burden. We leveraged this increased genetic burden by performing an age‐at‐onset informed GWAS meta‐analysis, including a large younger‐onset SVS population, to identify novel associations with stroke.

Methods

We used a three‐stage age‐at‐onset informed GWAS to identify novel genetic variants associated with stroke. On identifying a novel locus associated with SVS, we assessed its influence on other small vessel disease phenotypes, as well as on messenger RNA (mRNA) expression of nearby genes, and on DNA methylation of nearby CpG sites in whole blood and in the fetal brain.

Results

We identified an association with SVS in 4,203 cases and 50,728 controls on chromosome 16q24.2 (odds ratio [OR; 95% confidence interval {CI}] = 1.16 [1.10–1.22]; p = 3.2 × 10⁻⁹). The lead single‐nucleotide polymorphism (rs12445022) was also associated with cerebral white matter hyperintensities (OR [95% CI] = 1.10 [1.05–1.16]; p = 5.3 × 10⁻⁵; N = 3,670), but not intracerebral hemorrhage (OR [95% CI] = 0.97 [0.84–1.12]; p = 0.71; 1,545 cases, 1,481 controls). rs12445022 is associated with mRNA expression of ZCCHC14 in arterial tissues (p = 9.4 × 10⁻⁷) and DNA methylation at probe cg16596957 in whole blood (p = 5.3 × 10⁻⁶).

Interpretation

16q24.2 is associated with SVS. Associations of the locus with expression of ZCCHC14 and DNA methylation suggest the locus acts through changes to regulatory elements. Ann Neurol 2017;81:383–394

The Changing Epidemiology of Autism Spectrum Disorders

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with lifelong impacts. Genetic and environmental factors contribute to ASD etiology, which remains incompletely understood. Research on ASD epidemiology has made significant advances in the past decade. Current prevalence is estimated to be at least 1.5% in developed countries, with recent increases primarily among those without comorbid intellectual disability. Genetic studies have identified a number of rare de novo mutations and gained footing in the areas of polygenic risk, epigenetics, and gene-by-environment interaction. Epidemiologic investigations focused on nongenetic factors have established advanced parental age and preterm birth as ASD risk factors, indicated that prenatal exposure to air pollution and short interpregnancy interval are potential risk factors, and suggested the need for further exploration of certain prenatal nutrients, metabolic conditions, and exposure to endocrine-disrupting chemicals. We discuss future challenges and goals for ASD epidemiology as well as public health implications.

Constitutional Epi/Genetic Conditions: Genetic, Epigenetic, and Environmental Factors

There are more than 4,000 phenotypes for which the molecular basis is at least partly known. Though defects in primary DNA structure constitute a major cause of these disorders, epigenetic disruption is emerging as an important alternative mechanism in the etiology of a broad range of congenital and developmental conditions. These include epigenetic defects caused by either localized (in cis) genetic alterations or more distant (in trans) genetic events but can also include environmental effects. Emerging evidence suggests interplay between genetic and environmental factors in the epigenetic etiology of several constitutional "epi/genetic" conditions. This review summarizes our broadening understanding of how epigenetics contributes to pediatric disease by exploring different classes of epigenomic disorders. It further challenges the simplistic dogma of "DNA encodes RNA encodes protein" to best understand the spectrum of factors that can influence genetic traits in a pediatric population.

CpG Methylation across the adipogenic PPARγ gene and its relationship with birthweight and child BMI at 9 years

Background

To examine methylation of the peroxisome proliferator-activated receptor γ (PPARγ) gene and its relationship with child weight status, at birth and 9 years.

Methods

We measured PPARγ methylation across 23 CpG sites using the Infinium Illumina 450 k array for children from the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) cohort at birth (N = 373) and 9 years (N = 245).

Results

Methylation level correlation patterns across the 23 PPARγ CpG sites were conserved between birth and 9-year ages. We found high inter-CpG correlations between sites 1–3 (methylation block 1) and also between sites 18–23 (methylation block 2) for both time points, although these patterns were less pronounced at 9 years. Additionally, sites 1–3 (north shore) had the highest intra-CpG correlations over time (r = 0.24, 0.42, and 0.3; P = 0.002, P < 0.001, P < 0.001, respectively). PPARγ methylation levels tended to increase with age, and the largest differences were observed for north shore sites (7.4%). Adjusting for sex, both site 1 and site 20 (gene body) methylation at birth was significantly and inversely associated with birth weight (β = −0.13, P = 0.033; β = −0.09, P = 0.025, respectively). Similarly, we found that site 1 and site 20 methylation at 9 years was significantly and inversely associated with 9-year BMI z-score (β = −0.41, P = 0.015; β = −0.23, P = 0.045, respectively).

Conclusion

Our results indicate that PPARγ methylation is highly organized and conserved over time, and highlight the potential functional importance of north shore sites, adding to a better understanding of regional human methylome patterns. Overall, our results suggest that PPARγ methylation may be associated with child body size.

Electronic supplementary material

The online version of this article (doi:10.1186/s12881-016-0365-4) contains supplementary material, which is available to authorized users.

Interactions between genetic, lifestyle and environmental risk factors for multiple sclerosis

Genetic predisposition to multiple sclerosis (MS) only explains a fraction of the disease risk; lifestyle and environmental factors are key contributors to the risk of MS. Importantly, these nongenetic factors can influence pathogenetic pathways, and some of them can be modified. Besides established MS-associated risk factors - high latitude, female sex, smoking, low vitamin D levels caused by insufficient sun exposure and/or dietary intake, and Epstein-Barr virus (EBV) infection - strong evidence now supports obesity during adolescence as a factor increasing MS risk. Organic solvents and shift work have also been reported to confer increased risk of the disease, whereas factors such as use of nicotine or alcohol, cytomegalovirus infection and a high coffee consumption are associated with a reduced risk. Certain factors - smoking, EBV infection and obesity - interact with HLA risk genes, pointing at a pathogenetic pathway involving adaptive immunity. All of the described risk factors for MS can influence adaptive and/or innate immunity, which is thought to be the main pathway modulated by MS risk alleles. Unlike genetic risk factors, many environmental and lifestyle factors can be modified, with potential for prevention, particularly for people at the greatest risk, such as relatives of individuals with MS. Here, we review recent data on environmental and lifestyle factors, with a focus on gene-environment interactions.

EWAS: epigenome-wide association studies software 1.0 - identifying the association between combinations of methylation levels and diseases

Similar to the SNP (single nucleotide polymorphism) data, there is non-random association of the DNA methylation level (we call it methylation disequilibrium, MD) between neighboring methylation loci. For the case-control study of complex diseases, it is important to identify the association between methylation levels combination types (we call it methylecomtype) and diseases/phenotypes. We extended the classical framework of SNP haplotype-based association study in population genetics to DNA methylation level data, and developed a software EWAS to identify the disease-related methylecomtypes. EWAS can provide the following basic functions: (1) calculating the DNA methylation disequilibrium coefficient between two CpG loci; (2) identifying the MD blocks across the whole genome; (3) carrying out case-control association study of methylecomtypes and identifying the disease-related methylecomtypes. For a DNA methylation level data set including 689 samples (354 cases and 335 controls) and 473864 CpG loci, it takes only about 25 min to complete the full scan. EWAS v1.0 can rapidly identify the association between combinations of methylation levels (methylecomtypes) and diseases. EWAS v1.0 is freely available at: http://www.ewas.org.cn or http://www.bioapp.org/ewas.

DNA Methylation Signatures of Early Childhood Malnutrition Associated With Impairments in Attention and Cognition

BACKGROUND

Early childhood malnutrition affects 113 million children worldwide, impacting health and increasing vulnerability for cognitive and behavioral disorders later in life. Molecular signatures after childhood malnutrition, including the potential for intergenerational transmission, remain unexplored.

METHODS

We surveyed blood DNA methylomes (~483,000 individual CpG sites) in 168 subjects across two generations, including 50 generation 1 individuals hospitalized during the first year of life for moderate to severe protein-energy malnutrition, then followed up to 48 years in the Barbados Nutrition Study. Attention deficits and cognitive performance were evaluated with the Connors Adult Attention Rating Scale and Wechsler Abbreviated Scale of Intelligence. Expression of nutrition-sensitive genes was explored by quantitative reverse transcriptase polymerase chain reaction in rat prefrontal cortex.

RESULTS

We identified 134 nutrition-sensitive, differentially methylated genomic regions, with most (87%) specific for generation 1. Multiple neuropsychiatric risk genes, including COMT, IFNG, MIR200B, SYNGAP1, and VIPR2 showed associations of specific methyl-CpGs with attention and IQ. IFNG expression was decreased in prefrontal cortex of rats showing attention deficits after developmental malnutrition.

CONCLUSIONS

Early childhood malnutrition entails long-lasting epigenetic signatures associated with liability for attention and cognition, and limited potential for intergenerational transmission.

The impact of genetic variation and cigarette smoke on DNA methylation in current and former smokers from the COPDGene study

DNA methylation can be affected by systemic exposures, such as cigarette smoking and genetic sequence variation; however, the relative impact of each on the epigenome is unknown. We aimed to assess if cigarette smoking and genetic variation are associated with overlapping or distinct sets of DNA methylation marks and pathways. We selected 85 Caucasian current and former smokers with genome-wide single nucleotide polymorphism (SNP) genotyping available from the COPDGene study. Genome-wide methylation was obtained on DNA from whole blood using the Illumina HumanMethylation27 platform. To determine the impact of local sequence variation on DNA methylation (mQTL), we examined the association between methylation and SNPs within 50 kb of each CpG site. To examine the impact of cigarette smoking on DNA methylation, we examined the differences in methylation by current cigarette smoking status. We detected 770 CpG sites annotated to 708 genes associated at an FDR < 0.05 in the cis-mQTL analysis and 1,287 CpG sites annotated to 1,242 genes, which were nominally associated in the smoking-CpG association analysis (P(unadjusted) < 0.05). Forty-three CpG sites annotated to 40 genes were associated with both SNP variation and current smoking; this overlap was not greater than that expected by chance. Our results suggest that cigarette smoking and genetic variants impact distinct sets of DNA methylation marks, the further elucidation of which may partially explain the variable susceptibility to the health effects of cigarette smoking. Ascertaining how genetic variation and systemic exposures differentially impact the human epigenome has relevance for both biomarker identification and therapeutic target development for smoking-related diseases.

Polymorphisms involving gain or loss of CpG sites are significantly enriched in trait-associated SNPs

Some single nucleotide polymorphisms (SNPs) influence the existence of CpG sites, the basis of DNA modification such as methylation and hydroxymethylation. These polymorphisms can lead to gain or loss of CpG sites and were defined as CpG site related SNPs (cgSNPs) in this study. The cgSNPs change DNA sequence and might potentially affect DNA modification such as methylation. However, the functional consequence of cgSNPs is poorly understood. We observed that a considerable proportion (23.0%) of common variants were cgSNPs in human genome. Mutations involving loss of CpG sites were associated with reduced levels of methylation (~20.2%) using The Cancer Genome Atlas (TCGA) data. Using public databases (SCAN and seeQTL) of expression quantitative trait loci (eQTLs), we found that the cgSNPs were significantly enriched in eQTLs via logistic regression and simulation test. Furthermore, we observed that cgSNPs were more likely to be trait-associated loci especially cancers using a catalog of published genome-wide association studies (GWAS) recorded by National Human Genome Research Institute (NHGRI). Our results indicated that cgSNP might be meaningful as annotation either in SNP functional prediction or in screening for trait-associated SNPs.

DNA methylation: conducting the orchestra from exposure to phenotype?

DNA methylation, through 5-methyl- and 5-hydroxymethylcytosine (5mC and 5hmC), is considered to be one of the principal interfaces between the genome and our environment, and it helps explain phenotypic variations in human populations. Initial reports of large differences in methylation level in genomic regulatory regions, coupled with clear gene expression data in both imprinted genes and malignant diseases, provided easily dissected molecular mechanisms for switching genes on or off. However, a more subtle process is becoming evident, where small (<10 %) changes to intermediate methylation levels are associated with complex disease phenotypes. This has resulted in two clear methylation paradigms. The latter “subtle change” paradigm is rapidly becoming the epigenetic hallmark of complex disease phenotypes, although we are currently hampered by a lack of data addressing the true biological significance and meaning of these small differences.

Our initial expectation of rapidly identifying mechanisms linking environmental exposure to a disease phenotype led to numerous observational/association studies being performed. Although this expectation remains unmet, there is now a growing body of literature on specific genes, suggesting wide ranging transcriptional and translational consequences of such subtle methylation changes. Data from the glucocorticoid receptor (NR3C1) has shown that a complex interplay between DNA methylation, extensive 5′UTR splicing, and microvariability gives rise to the overall level and relative distribution of total and N-terminal protein isoforms generated. Additionally, the presence of multiple AUG translation initiation codons throughout the complete, processed mRNA enables translation variability, hereby enhancing the translational isoforms and the resulting protein isoform diversity, providing a clear link between small changes in DNA methylation and significant changes in protein isoforms and cellular locations. Methylation changes in the NR3C1 CpG island alters the NR3C1 transcription and eventually protein isoforms in the tissues, resulting in subtle but visible physiological variability.

This review addresses the current pathophysiological and clinical associations of such characteristically small DNA methylation changes, the ever-growing roles of DNA methylation and the evidence available, particularly from the glucocorticoid receptor of the cascade of events initiated by such subtle methylation changes, as well as addressing the underlying question as to what represents a genuine biologically significant difference in methylation.

Leveraging gene-environment interactions and endotypes for asthma gene discovery

Asthma is a heterogeneous clinical syndrome that includes subtypes of disease with different underlying causes and disease mechanisms. Asthma is caused by a complex interaction between genes and environmental exposures; early-life exposures in particular play an important role. Asthma is also heritable, and a number of susceptibility variants have been discovered in genome-wide association studies, although the known risk alleles explain only a small proportion of the heritability. In this review, we present evidence supporting the hypothesis that focusing on more specific asthma phenotypes, such as childhood asthma with severe exacerbations, and on relevant exposures that are involved in gene-environment interactions (GEIs), such as rhinovirus infections, will improve detection of asthma genes and our understanding of the underlying mechanisms. We will discuss the challenges of considering GEIs and the advantages of studying responses to asthma-associated exposures in clinical birth cohorts, as well as in cell models of GEIs, to dissect the context-specific nature of genotypic risks, to prioritize variants in genome-wide association studies, and to identify pathways involved in pathogenesis in subgroups of patients. We propose that such approaches, in spite of their many challenges, present great opportunities for better understanding of asthma pathogenesis and heterogeneity and, ultimately, for improving prevention and treatment of disease.