The presence of methylation quantitative trait loci indicates a direct genetic influence on the level of DNA methylation in adipose tissue

Potentially causal associations between placental DNA methylation and schizophrenia and other neuropsychiatric disorders

Increasing evidence supports the role of placenta in neurodevelopment and potentially, in the later onset of neuropsychiatric disorders. Recently, methylation quantitative trait loci (mQTL) and interaction QTL (iQTL) maps have proven useful to understand SNP-genome wide association study (GWAS) relationships, otherwise missed by conventional expression QTLs. In this context, we propose that part of the genetic predisposition to complex neuropsychiatric disorders acts through placental DNA methylation (DNAm). We constructed the first public placental cis-mQTL database including nearly eight million mQTLs calculated in 368 fetal placenta DNA samples from the INMA project, ran cell type- and gestational age-imQTL models and combined those data with the summary statistics of the largest GWAS on 10 neuropsychiatric disorders using Summary-based Mendelian Randomization (SMR) and colocalization. Finally, we evaluated the influence of the DNAm sites identified on placental gene expression in the RICHS cohort. We found that placental cis-mQTLs are highly enriched in placenta-specific active chromatin regions, and useful to map the etiology of neuropsychiatric disorders at prenatal stages. Specifically, part of the genetic burden for schizophrenia, bipolar disorder and major depressive disorder confers risk through placental DNAm. The potential causality of several of the observed associations is reinforced by secondary association signals identified in conditional analyses, regional pleiotropic methylation signals associated to the same disorder, and cell type-imQTLs, additionally associated to the expression levels of relevant immune genes in placenta. In conclusion, the genetic risk of several neuropsychiatric disorders could operate, at least in part, through DNAm and associated gene expression in placenta.

Epigenetics of type 2 diabetes mellitus and weight change - a tool for precision medicine?

Pioneering studies performed over the past few decades demonstrate links between epigenetics and type 2 diabetes mellitus (T2DM), the metabolic disorder with the most rapidly increasing prevalence in the world. Importantly, these studies identified epigenetic modifications, including altered DNA methylation, in pancreatic islets, adipose tissue, skeletal muscle and the liver from individuals with T2DM. As non-genetic factors that affect the risk of T2DM, such as obesity, unhealthy diet, physical inactivity, ageing and the intrauterine environment, have been associated with epigenetic modifications in healthy individuals, epigenetics probably also contributes to T2DM development. In addition, genetic factors associated with T2DM and obesity affect the epigenome in human tissues. Notably, causal mediation analyses found DNA methylation to be a potential mediator of genetic associations with metabolic traits and disease. In the past few years, translational studies have identified blood-based epigenetic markers that might be further developed and used for precision medicine to help patients with T2DM receive optimal therapy and to identify patients at risk of complications. This Review focuses on epigenetic mechanisms in the development of T2DM and the regulation of body weight in humans, with a special focus on precision medicine.

Rare genetic variation at transcription factor binding sites modulates local DNA methylation profiles

Although DNA methylation is the best characterized epigenetic mark, the mechanism by which it is targeted to specific regions in the genome remains unclear. Recent studies have revealed that local DNA methylation profiles might be dictated by cis-regulatory DNA sequences that mainly operate via DNA-binding factors. Consistent with this finding, we have recently shown that disruption of CTCF-binding sites by rare single nucleotide variants (SNVs) can underlie cis-linked DNA methylation changes in patients with congenital anomalies. These data raise the hypothesis that rare genetic variation at transcription factor binding sites (TFBSs) might contribute to local DNA methylation patterning. In this work, by combining blood genome-wide DNA methylation profiles, whole genome sequencing-derived SNVs from 247 unrelated individuals along with 133 predicted TFBS motifs derived from ENCODE ChIP-Seq data, we observed an association between the disruption of binding sites for multiple TFs by rare SNVs and extreme DNA methylation values at both local and, to a lesser extent, distant CpGs. While the majority of these changes affected only single CpGs, 24% were associated with multiple outlier CpGs within ±1kb of the disrupted TFBS. Interestingly, disruption of functionally constrained sites within TF motifs lead to larger DNA methylation changes at nearby CpG sites. Altogether, these findings suggest that rare SNVs at TFBS negatively influence TF-DNA binding, which can lead to an altered local DNA methylation profile. Furthermore, subsequent integration of DNA methylation and RNA-Seq profiles from cardiac tissues enabled us to observe an association between rare SNV-directed DNA methylation and outlier expression of nearby genes. In conclusion, our findings not only provide insights into the effect of rare genetic variation at TFBS on shaping local DNA methylation and its consequences on genome regulation, but also provide a rationale to incorporate DNA methylation data to interpret the functional role of rare variants.

Insights into the multifactorial causation of obesity by integrated genetic and epigenetic analysis

Obesity is a highly heritable multifactorial disease that places an enormous burden on human health. Its increasing prevalence and the concomitant-reduced life expectancy has intensified the search for new analytical methods that can reduce the knowledge gap between genetic susceptibility and functional consequences of the disease pathology. Although the influence of genetics and epigenetics has been studied independently in the past, there is increasing evidence that genetic variants interact with environmental factors through epigenetic regulation. This suggests that a combined analysis of genetic and epigenetic variation may be more effective in characterizing the obesity phenotype. To date, limited genome-wide integrative analyses have been performed. In this review, we provide an overview of the latest findings, advantages, and challenges and discuss future perspectives.

Sex Chromosomes and Sex Phenotype Contribute to Biased DNA Methylation in Mouse Liver

Sex biases in the genome-wide distribution of DNA methylation and gene expression levels are some of the manifestations of sexual dimorphism in mammals. To advance our understanding of the mechanisms that contribute to sex biases in DNA methylation and gene expression, we conducted whole genome bisulfite sequencing (WGBS) as well as RNA-seq on liver samples from mice with different combinations of sex phenotype and sex-chromosome complement. We compared groups of animals with different sex phenotypes, but the same genetic sexes, and vice versa, same sex phenotypes, but different sex-chromosome complements. We also compared sex-biased DNA methylation in mouse and human livers. Our data show that sex phenotype, X-chromosome dosage, and the presence of Y chromosome shape the differences in DNA methylation between males and females. We also demonstrate that sex bias in autosomal methylation is associated with sex bias in gene expression, whereas X-chromosome dosage-dependent methylation differences are not, as expected for a dosage-compensation mechanism. Furthermore, we find partial conservation between the repertoires of mouse and human genes that are associated with sex-biased methylation, an indication that gene function is likely to be an important factor in this phenomenon.

DNA methylation and cis-regulation of gene expression by prostate cancer risk SNPs

Genome-wide association studies have identified more than 100 SNPs that increase the risk of prostate cancer (PrCa). We identify and compare expression quantitative trait loci (eQTLs) and CpG methylation quantitative trait loci (meQTLs) among 147 established PrCa risk SNPs in primary prostate tumors (n = 355 from a Seattle-based study and n = 495 from The Cancer Genome Atlas, TCGA) and tumor-adjacent, histologically benign samples (n = 471 from a Mayo Clinic study). The role of DNA methylation in eQTL regulation of gene expression was investigated by data triangulation using several causal inference approaches, including a proposed adaptation of the Causal Inference Test (CIT) for causal direction. Comparing eQTLs between tumors and benign samples, we show that 98 of the 147 risk SNPs were identified as eQTLs in the tumor-adjacent benign samples, and almost all 34 eQTL identified in tumor sets were also eQTLs in the benign samples. Three lines of results support the causal role of DNA methylation. First, nearly 100 of the 147 risk SNPs were identified as meQTLs in one tumor set, and almost all eQTLs in tumors were meQTLs. Second, the loss of eQTLs in tumors relative to benign samples was associated with altered DNA methylation. Third, among risk SNPs identified as both eQTLs and meQTLs, mediation analyses suggest that over two-thirds have evidence of a causal role for DNA methylation, mostly mediating genetic influence on gene expression. In summary, we provide a comprehensive catalog of eQTLs, meQTLs and putative cancer genes for known PrCa risk SNPs. We observe that a substantial portion of germline eQTL regulatory mechanisms are maintained in the tumor development, despite somatic alterations in tumor genome. Finally, our mediation analyses illuminate the likely intermediary role of CpG methylation in eQTL regulation of gene expression.

A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine

Quantitative trait loci (QTL) analysis is an important approach to investigate the effects of genetic variants identified through an increasing number of large-scale, multidimensional 'omics data sets. In this 'big data' era, the research community has identified a significant number of molecular QTLs (molQTLs) and increased our understanding of their effects. Herein, we review multiple categories of molQTLs, including those associated with transcriptome, post-transcriptional regulation, epigenetics, proteomics, metabolomics, and the microbiome. We summarize approaches to identify molQTLs and to infer their causal effects. We further discuss the integrative analysis of molQTLs through a multi-omics perspective. Our review highlights future opportunities to better understand the functional significance of genetic variants and to utilize the discovery of molQTLs in precision medicine.

Epigenetic and Transcriptomic Characterization of Pure Adipocyte Fractions From Obese Pigs Identifies Candidate Pathways Controlling Metabolism

Reprogramming of adipocyte function in obesity is implicated in metabolic disorders like type 2 diabetes. Here, we used the pig, an animal model sharing many physiological and pathophysiological similarities with humans, to perform in-depth epigenomic and transcriptomic characterization of pure adipocyte fractions. Using a combined DNA methylation capture sequencing and Reduced Representation bisulfite sequencing (RRBS) strategy in 11 lean and 12 obese pigs, we identified in 3529 differentially methylated regions (DMRs) located at close proximity to-, or within genes in the adipocytes. By sequencing of the transcriptome from the same fraction of isolated adipocytes, we identified 276 differentially expressed transcripts with at least one or more DMR. These transcripts were over-represented in gene pathways related to MAPK, metabolic and insulin signaling. Using a candidate gene approach, we further characterized 13 genes potentially regulated by DNA methylation and identified putative transcription factor binding sites that could be affected by the differential methylation in obesity. Our data constitute a valuable resource for further investigations aiming to delineate the epigenetic etiology of metabolic disorders.

Early-life determinants of hypoxia-inducible factor 3A gene (HIF3A) methylation: a birth cohort study

Background

Methylation of the hypoxia-inducible factor 3α gene (HIF3A) has been linked to pregnancy exposures, infant adiposity and later BMI. Genetic variation influences HIF3A methylation levels and may modify these relationships. However, data in very early life are limited, particularly in association with adverse pregnancy outcomes. We investigated the relationship between maternal and gestational factors, infant anthropometry, genetic variation and HIF3A DNA methylation in the Barwon Infant Study, a population-based birth cohort. Methylation of two previously studied regions of HIF3A were tested in the cord blood mononuclear cells of 938 infants.

Results

No compelling evidence was found of an association between birth weight, adiposity or maternal gestational diabetes with methylation at the most widely studied HIF3A region. Male sex (− 4.3%, p < 0.001) and pre-eclampsia (− 5.4%, p = 0.02) negatively associated with methylation at a second region of HIF3A; while positive associations were identified for gestational diabetes (4.8%, p = 0.01) and gestational age (1.2% increase per week, p < 0.001). HIF3A genetic variation also associated strongly with methylation at this region (p < 0.001).

Conclusions

Pre- and perinatal factors impact HIF3A methylation, including pre-eclampsia. This provides evidence that specific pregnancy complications, previously linked to adverse outcomes for both mother and child, impact the infant epigenome in a molecular pathway critical to several vascular and metabolic conditions. Further work is required to understand the mechanisms and clinical relevance, particularly the differing effects of in utero exposure to gestational diabetes or pre-eclampsia.

Electronic supplementary material

The online version of this article (10.1186/s13148-019-0687-0) contains supplementary material, which is available to authorized users.

Identification of 55,000 Replicated DNA Methylation QTL

DNA methylation plays an important role in the regulation of transcription. Genetic control of DNA methylation is a potential candidate for explaining the many identified SNP associations with disease that are not found in coding regions. We replicated 52,916 cis and 2,025 trans DNA methylation quantitative trait loci (mQTL) using methylation from whole blood measured on Illumina HumanMethylation450 arrays in the Brisbane Systems Genetics Study (n = 614 from 177 families) and the Lothian Birth Cohorts of 1921 and 1936 (combined n = 1366). The trans mQTL SNPs were found to be over-represented in 1 Mbp subtelomeric regions, and on chromosomes 16 and 19. There was a significant increase in trans mQTL DNA methylation sites in upstream and 5′ UTR regions. The genetic heritability of a number of complex traits and diseases was partitioned into components due to mQTL and the remainder of the genome. Significant enrichment was observed for height (p = 2.1 × 10⁻¹⁰), ulcerative colitis (p = 2 × 10⁻⁵), Crohn’s disease (p = 6 × 10⁻⁸) and coronary artery disease (p = 5.5 × 10⁻⁶) when compared to a random sample of SNPs with matched minor allele frequency, although this enrichment is explained by the genomic location of the mQTL SNPs.

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.

Genome-wide analysis of DNA methylation in buccal cells: a study of monozygotic twins and mQTLs

BACKGROUND

DNA methylation arrays are widely used in epigenome-wide association studies and methylation quantitative trait locus (mQTL) studies. Here, we performed the first genome-wide analysis of monozygotic (MZ) twin correlations and mQTLs on data obtained with the Illumina MethylationEPIC BeadChip (EPIC array) and compared the performance of the EPIC array to the Illumina HumanMethylation450 BeadChip (HM450 array) for buccal-derived DNA.

RESULTS

Good-quality EPIC data were obtained for 102 buccal-derived DNA samples from 49 MZ twin pairs (mean age = 7.5 years, range = 1-10). Differences between MZ twins in the cellular content of buccal swabs were a major driver for differences in their DNA methylation profiles, highlighting the importance to adjust for cellular composition in DNA methylation studies of buccal-derived DNA. After adjusting for cellular composition, the genome-wide mean correlation (r) between MZ twins was 0.21 for the EPIC array, and cis mQTL analysis in 84 twins identified 1,296,323 significant associations (FDR 5%), encompassing 33,749 methylation sites and 616,029 genetic variants. MZ twin correlations were slightly larger (p < 2.2 × 10-16) for novel EPIC probes (N = 383,066, mean r = 0.22) compared to probes that are also present on HM450 (N = 406,822, mean r = 0.20). In line with this observation, a larger percentage of novel EPIC probes was associated with genetic variants (novel EPIC probes with significant mQTL 4.7%, HM450 probes with mQTL 3.9%, p < 2.2 × 10-16). Methylation sites with a large MZ correlation and sites associated with mQTLs were most strongly enriched in epithelial cell DNase I hypersensitive sites (DHSs), enhancers, and histone mark H3K4me3.

CONCLUSIONS

We conclude that the contribution of familial factors to individual differences in DNA methylation and the effect of mQTLs are larger for novel EPIC probes, especially those within regulatory elements connected to active regions specific to the investigated tissue.

Characterizing genetic and environmental influences on variable DNA methylation using monozygotic and dizygotic twins

Variation in DNA methylation is being increasingly associated with health and disease outcomes. Although DNA methylation is hypothesized to be a mechanism by which both genetic and non-genetic factors can influence the regulation of gene expression, little is known about the extent to which DNA methylation at specific sites is influenced by heritable as well as environmental factors. We quantified DNA methylation in whole blood at age 18 in a birth cohort of 1,464 individuals comprising 426 monozygotic (MZ) and 306 same-sex dizygotic (DZ) twin pairs. Site-specific levels of DNA methylation were more strongly correlated across the genome between MZ than DZ twins. Structural equation models revealed that although the average contribution of additive genetic influences on DNA methylation across the genome was relatively low, it was notably elevated at the highly variable sites characterized by intermediate levels of DNAm that are most relevant for epigenetic epidemiology. Sites at which variable DNA methylation was most influenced by genetic factors were significantly enriched for DNA methylation quantitative trait loci (mQTL) effects, and overlapped with sites where inter-individual variation correlates across tissues. Finally, we show that DNA methylation at sites robustly associated with environmental exposures such as tobacco smoking and obesity is also influenced by additive genetic effects, highlighting the need to control for genetic background in analyses of exposure-associated DNA methylation differences. Estimates of the contribution of genetic and environmental influences to DNA methylation at all sites profiled in this study are available as a resource for the research community (http://www.epigenomicslab.com/online-data-resources).

The study of monozygotic (MZ) and dizygotic (DZ) twins provides an opportunity for exploring the extent to which heritable and environmental factors contribute to phenotypic variation in human populations. We exploit the twin study design to explore the factors influencing epigenetic variation between individuals, focussing on DNA methylation, the best-characterized and most stable epigenetic modification. We find that site-specific levels of DNA methylation are more strongly correlated across the genome between MZ than DZ twins. While the average contribution of additive genetic influences on DNA methylation is relatively low, it is notably elevated at sites that are highly variable and have intermediate levels of DNAm, which are most relevant for epigenetic epidemiology. Sites at which variable DNA methylation is strongly influenced by genetic factors are enriched for DNA methylation quantitative trait loci (mQTL) effects, and overlap with sites where inter-individual variation correlates across tissues. Importantly, we show that DNA methylation at sites robustly associated with environmental exposures such as smoking and obesity is also influenced by genetic effects, highlighting the need to control for genetic background in analyses of exposure-associated DNA methylation differences. Finally, we present a searchable database cataloguing the genetic and environmental contributions to variable DNA methylation across the genome (http://www.epigenomicslab.com/online-data-resources).

Identification of epigenetic modulators in human breast cancer by integrated analysis of DNA methylation and RNA-Seq data

Human tumors undergo massive changes in DNA methylation. Recent studies showed that site-specific methylation of CpG sites is determined by the DNA sequence context surrounding the CpG site, which alludes to a possible mechanism for site-specific aberrant DNA methylation in cancer through DNA-binding proteins. In this paper, DNA methylation data and RNA-Seq data of breast tumors and normal tissues in the database of The Cancer Genome Atlas (TCGA) were integrated with information of DNA motifs in seven databases to find DNA-binding proteins and their binding motifs that were involved in aberrant DNA methylation in breast cancer. A total of 42,850 differentially methylated regions (DMRs) that include 77,298 CpG sites were detected in breast cancer. One hundred eight DNA motifs were found to be enriched in DMRs, and 109 genes encoding proteins binding to these motifs were determined. Based on these motifs and genes, 63 methylation modulator genes were identified to regulate differentially methylated CpG sites in breast cancer. A network of these 63 modulator genes and 645 transcription factors was constructed, and 20 network modules were determined. A number of pathways and gene sets related to breast cancer were found to be enriched in these network modules. The 63 methylation modulator genes identified may play an important role in aberrant methylation of CpG sites in breast cancer. They may help to understand site-specific dysregulation of DNA methylation and provide epigenetic markers for breast cancer.

Transcriptional differences between smokers and non-smokers and variance by obesity as a risk factor for human sensitivity to environmental exposures

BACKGROUND

Obesity has been shown to alter response to air pollution and smoking but underlying biological mechanisms are largely unknown and few studies have explored mechanisms by which obesity increases human sensitivity to environmental exposures.

OBJECTIVE

Overall study goals were to investigate whole blood gene expression in smokers and non-smokers to examine associations between cigarette smoke and changes in gene expression by obesity status and test for effect modification.

METHODS

Relative fold-change in mRNA expression levels of 84 genes were analyzed using a Toxicity and Stress PCR array among 50 21-54 year old adults. Data on smoking status was confirmed using urinary cotinine levels. Adjusted models included age, gender, white blood cell count and body-mass index.

RESULTS

Models comparing gene expression of smokers vs. non-smokers identified six differentially expressed genes associated with smoking after adjustments for covariates. Obesity was associated with 29 genes differentially expressed compared to non-obese. We also identified 9 genes with significant smoking/obesity interactions influencing mRNA levels in adjusted models comparing expression between smokers vs non-smokers for four DNA damage related genes (GADD45A, DDB2, RAD51 and P53), two oxidative stress genes (FTH1, TXN), two hypoxia response genes (BN1P3lL, ARNT), and one gene associated with unfolded protein response (ATF6B).

CONCLUSIONS

Findings suggest that obesity alters human sensitivity to smoke exposures through several biological pathways by modifying gene expression. Additional studies are needed to fully understand the clinical impact of these effects, but risk assessments should consider underlying phenotypes, such as obesity, that may modulate sensitivity of vulnerable populations to environmental exposures.

SMYD2 promoter DNA methylation is associated with abdominal aortic aneurysm (AAA) and SMYD2 expression in vascular smooth muscle cells

Background

Abdominal aortic aneurysm (AAA) is a deadly cardiovascular disease characterised by the gradual, irreversible dilation of the abdominal aorta. AAA is a complex genetic disease but little is known about the role of epigenetics. Our objective was to determine if global DNA methylation and CpG-specific methylation at known AAA risk loci is associated with AAA, and the functional effects of methylation changes.

Results

We assessed global methylation in peripheral blood mononuclear cell DNA from 92 individuals with AAA and 93 controls using enzyme-linked immunosorbent assays, identifying hyper-methylation in those with large AAA and a positive linear association with AAA diameter (P < 0.0001, R² = 0.3175).We then determined CpG methylation status of regulatory regions in genes located at AAA risk loci identified in genome-wide association studies, using bisulphite next-generation sequencing (NGS) in vascular smooth muscle cells (VSMCs) taken from aortic tissues of 44 individuals (24 AAAs and 20 controls). In IL6R, 2 CpGs were hyper-methylated (P = 0.0145); in ERG, 13 CpGs were hyper-methylated (P = 0.0005); in SERPINB9, 6 CpGs were hypo-methylated (P = 0.0037) and 1 CpG was hyper-methylated (P = 0.0098); and in SMYD2, 4 CpGs were hypo-methylated (P = 0.0012).RT-qPCR was performed for each differentially methylated gene on mRNA from the same VSMCs and compared with methylation. This analysis revealed downregulation of SMYD2 and SERPINB9 in AAA, and a direct linear relationship between SMYD2 promoter methylation and SMYD2 expression (P = 0.038). Furthermore, downregulation of SMYD2 at the site of aneurysm in the aortic wall was further corroborated in 6 of the same samples used for methylation and gene expression analysis with immunohistochemistry.

Conclusions

This study is the first to assess DNA methylation in VSMCs from individuals with AAA using NGS, and provides further evidence there is an epigenetic basis to AAA. Our study shows that methylation status of the SMYD2 promoter may be linked with decreased SMYD2 expression in disease pathobiology. In support of our work, downregulated SMYD2 has previously been associated with adverse cardiovascular physiology and inflammation, which are both hallmarks of AAA. The identification of such adverse epigenetic modifications could potentially contribute towards the development of epigenetic treatment strategies in the future.

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.

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.

Epigenomics of Major Depressive Disorders and Schizophrenia: Early Life Decides

Brain development is guided by the interactions between the genetic blueprint and the environment. Epigenetic mechanisms, especially DNA methylation, can mediate these interactions and may also trigger long-lasting adaptations in developmental programs that increase the risk of major depressive disorders (MDD) and schizophrenia (SCZ). Early life adversity is a major risk factor for MDD/SCZ and can trigger persistent genome-wide changes in DNA methylation at genes important to early, but also to mature, brain function, including neural proliferation, differentiation, and synaptic plasticity, among others. Moreover, genetic variations controlling dynamic DNA methylation in early life are thought to influence later epigenomic changes in SCZ. This finding corroborates the high genetic load and a neurodevelopmental origin of SCZ and shows that epigenetic responses to the environment are, at least in part, genetically controlled. Interestingly, genetic variants influencing DNA methylation are also enriched in risk variants from genome-wide association studies (GWAS) on SCZ supporting a role in neurodevelopment. Overall, epigenomic responses to early life adversity appear to be controlled to different degrees by genetics in MDD/SCZ, even though the potential reversibility of epigenomic processes may offer new hope for timely therapeutic interventions in MDD/SCZ.

Genetic regulation of differentially methylated genes in visceral adipose tissue of severely obese men discordant for the metabolic syndrome

A genetic influence on methylation levels has been reported and methylation quantitative trait loci (meQTL) have been identified in various tissues. The contribution of genetic and epigenetic factors in the development of the metabolic syndrome (MetS) has also been noted. To pinpoint candidate genes for testing the association of SNPs with MetS and its components, we aimed to evaluate the contribution of genetic variations to differentially methylated CpG sites in severely obese men discordant for MetS. A genome-wide differential methylation analysis was conducted in visceral adipose tissue (VAT) of 31 severely obese men discordant for MetS (16 with and 15 without MetS) and identified ∼17,800 variable CpG sites. The genome-wide association study conducted to identify the SNPs (meQTL) associated with methylation levels at variable CpG sites revealed 2292 significant associations (P < 2.22 × 10^-11) involving 2182 unique meQTLs regulating the methylation levels of 174 variable CpG sites. Two meQTLs disrupting CpG sites located within the collagen-encoding COL11A2 gene were tested for associations with MetS and its components in a cohort of 3021 obese individuals. Rare alleles of these meQTLs showed association with plasma fasting glucose levels. Further analysis conducted on these meQTL suggested a biological impact mediated through the disruption of transcription factor (TF)-binding sites based on the prediction of TF-binding affinities. The current study identified meQTL in the VAT of severely obese men and revealed associations of two COL11A2 meQTL with fasting glucose levels.

Recent Advances in Human Genetics and Epigenetics of Adiposity: Pathway to Precision Medicine?

Obesity is a heritable trait that contributes to substantial global morbidity and mortality. Here, we summarize findings from the past decade of genetic and epigenetic research focused on unravelling the underpinnings of adiposity. More than 140 genetic regions now are known to influence adiposity traits. The genetics of general adiposity, as measured by body mass index, and that of abdominal obesity, as measured by waist-to-hip ratio, have distinct biological backgrounds. Gene expression associated with general adiposity is enriched in the nervous system. In contrast, genes associated with abdominal adiposity function in adipose tissue. Recent population-based epigenetic analyses have highlighted additional distinct loci. We discuss how associated genetic variants can lead to understanding causal mechanisms, and to disentangling reverse causation in epigenetic analyses. Discoveries emerging from population genomics are identifying new disease markers and potential novel drug targets to better define and combat obesity and related diseases.

Population Epigenetics

The field of epigenetics is maturing, with increased interest in understanding the normal regulation of the genome and the possibility that it becomes reprogrammed aberrantly as part of the cause of disease phenotypes. Applying the current technologies and insights to the study of human populations is potentially a way of understanding mechanisms and consequences of these diseases. When extended to encompass health care disparities, understanding why certain populations are unusually prone to specific conditions, there is certainly some potential for gaining new and valuable insights, but these studies are likely to be unusually prone to the effects of confounding influences and need to be designed, executed and interpreted with extra care.

Tissue-specific methylation profile in obese patients with type 2 diabetes before and after Roux-en-Y gastric bypass

Eating habits, lifestyles, and exposure to specific environmental factors can greatly impact the risk of developing type 2 diabetes (T2D), influence the genome epigenetically, and affect the expression of genes, including genes related to glycemic control, at any stage of life. The epigenetic mechanism underlying obesity and T2D pathogenesis remains poorly understood. Conventional strategies for the treatment of obesity and its comorbidities often have poor long-term adherence, and pharmacological interventions are limited. Bariatric surgery is the most effective current option to treat severe obesity, and Roux-en-Y gastric bypass (RYGB) is the most applied technique worldwide. Epigenetic changes differ depending on the approach used to treat obesity and its associated comorbidities (clinical or surgical). Compared to primary clinical care, bariatric surgery leads to much greater loss of body weight and higher remission rates of T2D and metabolic syndrome, with methylation profiles in promoter regions of genes in obese individuals becoming similar to those of normal-weight individuals. Bariatric surgery can influence DNA methylation in parallel with changes in gene expression pattern. Changes in clinical biomarkers that reflect improvements in glucose and lipid metabolism after RYGB often occur before major weight loss and are coordinated by surgery-induced changes in intestinal hormones. Therefore, the intestine methylation profile would assist in understanding the mechanisms involved in improved glycemic control after bariatric surgery. The main objectives in this area for the future are to identify epigenetic marks that could be used as early indicators of metabolic risk, and to develop treatments able to delay or even reverse these epigenetic changes. Studies that provide the "human epigenetic profile" will be of considerable value to identify tissue-specific epigenetic signatures and their role in the development of chronic diseases. Further studies should apply methods based on global analysis of the genome to identify methylated sites associated with disease and epigenetic marks associated with the remodeling response to bariatric surgery. This review describes the main epigenetic alterations associated with obesity and T2D and the potential role of RYGB in remodeling these changes.

The Future is The Past: Methylation QTLs in Schizophrenia

Genome-wide association studies (GWAS) have remarkably advanced insight into the genetic basis of schizophrenia (SCZ). Still, most of the functional variance in disease risk remains unexplained. Hence, there is a growing need to map genetic variability-to-genes-to-functions for understanding the pathophysiology of SCZ and the development of better treatments. Genetic variation can regulate various cellular functions including DNA methylation, an epigenetic mark with important roles in transcription and the mediation of environmental influences. Methylation quantitative trait loci (meQTLs) are derived by mapping levels of DNA methylation in genetically different, genotyped individuals and define loci at which DNA methylation is influenced by genetic variation. Recent evidence points to an abundance of meQTLs in brain tissues whose functional contributions to development and mental diseases are still poorly understood. Interestingly, fetal meQTLs reside in regulatory domains affecting methylome reconfiguration during early brain development and are enriched in loci identified by GWAS for SCZ. Moreover, fetal meQTLs are preserved in the adult brain and could trace early epigenomic deregulation during vulnerable periods. Overall, these findings highlight the role of fetal meQTLs in the genetic risk for and in the possible neurodevelopmental origin of SCZ.

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.

Familial resemblances in blood leukocyte DNA methylation levels

Epigenetic factors such as DNA methylation are DNA alterations affecting gene expression that can convey environmental information through generations. Only a few studies have demonstrated epigenetic inheritance in humans. Our objective is to quantify genetic and common environmental determinants of familial resemblances in DNA methylation levels, using a family based sample. DNA methylation was measured in 48 French Canadians from 16 families as part of the GENERATION Study. We used the Illumina HumanMethylation450 BeadChip array to measure DNA methylation levels in blood leukocytes on 485,577 CpG sites. Heritability was assessed using the variance components method implemented in the QTDT software, which partitions the variance into polygenic (G), common environmental (C), and non-shared environmental (E) effects. We computed maximal heritability, genetic heritability, and common environmental effect for all probes (12.7%, 8.2%, and 4.5%, respectively) and for statistically significant probes (81.8%, 26.9%, and 54.9%, respectively). Higher maximal heritability was observed in the Major Histocompatibility Complex region on chromosome 6. In conclusion, familial resemblances in DNA methylation levels are mainly attributable to genetic factors when considering the average across the genome, but common environmental effect plays an important role when considering statistically significant probes. Further epigenome-wide studies on larger samples combined with genome-wide genotyping studies are needed to better understand the underlying mechanisms of DNA methylation heritability.

Disease-specific classification using deconvoluted whole blood gene expression

Blood-based biomarker assays have an advantage in being minimally invasive. Diagnostic and prognostic models built on peripheral blood gene expression have been reported for various types of disease. However, most of these studies focused on only one disease type, and failed to address whether the identified gene expression signature is disease-specific or more widely applicable across diseases. We conducted a meta-analysis of 46 whole blood gene expression datasets covering a wide range of diseases and physiological conditions. Our analysis uncovered a striking overlap of signature genes shared by multiple diseases, driven by an underlying common pattern of cell component change, specifically an increase in myeloid cells and decrease in lymphocytes. These observations reveal the necessity of building disease-specific classifiers that can distinguish different disease types as well as normal controls, and highlight the importance of cell component change in deriving blood gene expression based models. We developed a new strategy to develop blood-based disease-specific models by leveraging both cell component changes and cell molecular state changes, and demonstrate its superiority using independent datasets.

Comparison of pre-processing methodologies for Illumina 450k methylation array data in familial analyses

Background

Human methylome mapping in health and disease states has largely relied on Illumina Human Methylation 450k array (450k array) technology. Accompanying this has been the necessary evolution of analysis pipelines to facilitate data processing. The majority of these pipelines, however, cater for experimental designs where matched ‘controls’ or ‘normal’ samples are available. Experimental designs where no appropriate ‘reference’ exists remain challenging. Herein, we use data generated from our study of the inheritance of methylome profiles in families to evaluate the performance of eight normalisation pre-processing methods. Fifty individual samples representing four families were interrogated on five 450k array BeadChips. Eight normalisation methods were tested using qualitative and quantitative metrics, to assess efficacy and suitability.

Results

Stratified quantile normalisation combined with ComBat were consistently found to be the most appropriate when assessed using density, MDS and cluster plots. This was supported quantitatively by ANOVA on the first principal component where the effect of batch dropped from p < 0.01 to p = 0.97 after stratified QN and ComBat. Median absolute differences between replicated samples were the lowest after stratified QN and ComBat as were the standard error measures on known imprinted regions. Biological information was preserved after normalisation as indicated by the maintenance of a significant association between a known mQTL and methylation (p = 1.05e-05).

Conclusions

A strategy combining stratified QN with ComBat is appropriate for use in the analyses when no reference sample is available but preservation of biological variation is paramount. There is great potential for use of 450k array data to further our understanding of the methylome in a variety of similar settings. Such advances will be reliant on the determination of appropriate methodologies for processing these data such as established here.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-016-0241-2) contains supplementary material, which is available to authorized users.

A Genome-Wide mQTL Analysis in Human Adipose Tissue Identifies Genetic Variants Associated with DNA Methylation, Gene Expression and Metabolic Traits

Little is known about the extent to which interactions between genetics and epigenetics may affect the risk of complex metabolic diseases and/or their intermediary phenotypes. We performed a genome-wide DNA methylation quantitative trait locus (mQTL) analysis in human adipose tissue of 119 men, where 592,794 single nucleotide polymorphisms (SNPs) were related to DNA methylation of 477,891 CpG sites, covering 99% of RefSeq genes. SNPs in significant mQTLs were further related to gene expression in adipose tissue and obesity related traits. We found 101,911 SNP-CpG pairs (mQTLs) in cis and 5,342 SNP-CpG pairs in trans showing significant associations between genotype and DNA methylation in adipose tissue after correction for multiple testing, where cis is defined as distance less than 500 kb between a SNP and CpG site. These mQTLs include reported obesity, lipid and type 2 diabetes loci, e.g. ADCY3/POMC, APOA5, CETP, FADS2, GCKR, SORT1 and LEPR. Significant mQTLs were overrepresented in intergenic regions meanwhile underrepresented in promoter regions and CpG islands. We further identified 635 SNPs in significant cis-mQTLs associated with expression of 86 genes in adipose tissue including CHRNA5, G6PC2, GPX7, RPL27A, THNSL2 and ZFP57. SNPs in significant mQTLs were also associated with body mass index (BMI), lipid traits and glucose and insulin levels in our study cohort and public available consortia data. Importantly, the Causal Inference Test (CIT) demonstrates how genetic variants mediate their effects on metabolic traits (e.g. BMI, cholesterol, high-density lipoprotein (HDL), hemoglobin A1c (HbA1c) and homeostatic model assessment of insulin resistance (HOMA-IR)) via altered DNA methylation in human adipose tissue. This study identifies genome-wide interactions between genetic and epigenetic variation in both cis and trans positions influencing gene expression in adipose tissue and in vivo (dys)metabolic traits associated with the development of obesity and diabetes.

Differential DNA methylation identified in the blood and retina of AMD patients

Age-related macular degeneration (AMD) is a major cause of blindness in the western world. While genetic studies have linked both common and rare variants in genes involved in regulation of the complement system to increased risk of development of AMD, environmental factors, such as smoking and nutrition, can also significantly affect the risk of developing the disease and the rate of disease progression. Since epigenetics has been implicated in mediating, in part, the disease risk associated with some environmental factors, we investigated a possible epigenetic contribution to AMD. We performed genome-wide DNA methylation profiling of blood from AMD patients and controls. No differential methylation site reached genome-wide significance; however, when epigenetic changes in and around known GWAS-defined AMD risk loci were explored, we found small but significant DNA methylation differences in the blood of neovascular AMD patients near age-related maculopathy susceptibility 2 (ARMS2), a top-ranked GWAS locus preferentially associated with neovascular AMD. The methylation level of one of the CpG sites significantly correlated with the genotype of the risk SNP rs10490924, suggesting a possible epigenetic mechanism of risk. Integrating genome-wide DNA methylation analysis of retina samples with and without AMD together with blood samples, we further identified a consistent, replicable change in DNA methylation in the promoter region of protease serine 50 (PRSS50). These methylation changes may identify sites in novel genes that are susceptible to non-genetic factors known to contribute to AMD development and progression.

How to interpret epigenetic association studies: a guide for clinicians

Epigenetic mechanisms are able to alter gene expression, without altering DNA sequence, in a stable manner through cell divisions. They include, among others, the methylation of DNA cytosines and microRNAs and allow the cells to adapt to changing environmental conditions. In recent years, epigenetic association studies are providing new insights into the pathogenesis of complex disorders including prevalent skeletal disorders. Unlike the genome, the epigenome is cell and tissue specific and may change with age and a number of acquired factors. This poses particular difficulties for the design and interpretation of epigenetic studies, particularly those exploring the association of genome-wide epigenetic marks with disease phenotypes. In this report, we propose a framework to help in the critical appraisal of epigenetic association studies. In line with previous suggestions, we focus on the questions critical to appraise the validity of the study, to interpret the results and to assess the generalizability and relevance of the information.

Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (eg, transcriptomics for RNA transcripts). However, a single layer of "omics" can only provide limited insights into the biological mechanisms of a disease. In the case of genome-wide association studies, although thousands of single nucleotide polymorphisms have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.

Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci

We characterized DNA methylation quantitative trait loci (mQTLs) in a large collection (n = 166) of human fetal brain samples spanning 56-166 d post-conception, identifying >16,000 fetal brain mQTLs. Fetal brain mQTLs were primarily cis-acting, enriched in regulatory chromatin domains and transcription factor binding sites, and showed substantial overlap with genetic variants that were also associated with gene expression in the brain. Using tissue from three distinct regions of the adult brain (prefrontal cortex, striatum and cerebellum), we found that most fetal brain mQTLs were developmentally stable, although a subset was characterized by fetal-specific effects. Fetal brain mQTLs were enriched amongst risk loci identified in a recent large-scale genome-wide association study (GWAS) of schizophrenia, a severe psychiatric disorder with a hypothesized neurodevelopmental component. Finally, we found that mQTLs can be used to refine GWAS loci through the identification of discrete sites of variable fetal brain methylation associated with schizophrenia risk variants.

Presence of an epigenetic signature of prenatal cigarette smoke exposure in childhood

Prenatal exposure to tobacco smoke has lifelong health consequences. Epigenetic signatures such as differences in DNA methylation (DNAm) may be a biomarker of exposure and, further, might have functional significance for how in utero tobacco exposure may influence disease risk. Differences in infant DNAm associated with maternal smoking during pregnancy have been identified. Here we assessed whether these infant DNAm patterns are detectible in early childhood, whether they are specific to smoking, and whether childhood DNAm can classify prenatal smoke exposure status. Using the Infinium 450K array, we measured methylation at 26 CpG loci that were previously associated with prenatal smoking in infant cord blood from 572 children, aged 3-5, with differing prenatal exposure to cigarette smoke in the Study to Explore Early Development (SEED). Striking concordance was found between the pattern of prenatal smoking associated DNAm among preschool aged children in SEED and those observed at birth in other studies. These DNAm changes appear to be tobacco-specific. Support vector machine classification models and 10-fold cross-validation were applied to show classification accuracy for childhood DNAm at these 26 sites as a biomarker of prenatal smoking exposure. Classification models showed prenatal exposure to smoking can be assigned with 81% accuracy using childhood DNAm patterns at these 26 loci. These findings support the potential for blood-derived DNAm measurements to serve as biomarkers for prenatal exposure.

Methylation quantitative trait loci within the TOMM20 gene are associated with metabolic syndrome-related lipid alterations in severely obese subjects

BACKGROUND

The TOMM20 gene was previously identified as differentially expressed and methylated between severely obese subjects with and without metabolic syndrome (MS). Since metabolic complications do not affect all obese patients to the same extent, the aim of this study was to identify methylation quantitative trait loci (meQTL) potentially associated with MS-related complications within the TOMM20 locus.

METHODS

Methylation profiling, SNP genotyping and meQTL association tests (general linear models) were performed in a population of 48 severely obese subjects. Genotyping was extended to a larger population of 1720 severely obese subjects with or without MS, where genotype- and diplotype-based association tests were assessed by logistic regression. In silico analyses were performed using TRAP.

RESULTS

Four SNPs were identified as significant meQTLs for the differentially methylated site cg16490124. Individuals carrying rare alleles of rs4567344 (A > G) (P = 4.9 × 10(-2)) and rs11301 (T > C) (P = 5.9 × 10(-3)) showed decreased methylation levels at this site, whereas those carrying rare alleles of rs4551650 (T > C) (P = 3.5 × 10(-15)) and rs17523127 (C > G) (P = 3.5 × 10(-15)) exhibited a significant increase in methylation. rs4567344 and rs11301 were associated with increased susceptibility to exhibit high plasma triglycerides (TG ≥ 1.69 mmol/L), while rare alleles of rs4551650 and rs17523127 were significantly more represented in the low plasma total-C group (total-C ≤ 6.2 mmol/L). Haplotype reconstruction with the four meQTLs (rs4567344, rs11301, rs4551650, rs17523127) led to the identification of ten different diplotypes, with H1/H2 (GCGG/ACGG) exhibiting a nearly absence of methylation at cg16490124, and showing the highest risk of elevated plasma TG levels [OR = 2.03 (1.59-3.59)], a novel association with elevated LDL-cholesterol [OR = 1.86 (1.06-3.27)] and the complete inversion of the protective effect on total-C levels [OR = 2.03 (1.59-3.59)], especially in men. In silico analyses revealed that rs17523127 overlapped the CpG site cg16490124 and encompassed the core binding sites of the transcription factors Egr 1, 2 and 3, located within the TOMM20 promoter region.

CONCLUSION

This study demonstrates that TOMM20 SNPs associated with MS-related lipid alterations are meQTLs potentially exerting their action through a CpG methylation-dependent effect. The strength of the diplotype-based associations may denote a novel meQTL additive action and point to this locus as particularly relevant in the inter-individual variability observed in the metabolic profiles of obese subjects.

High density methylation QTL analysis in human blood via next-generation sequencing of the methylated genomic DNA fraction

BACKGROUND

Genetic influence on DNA methylation is potentially an important mechanism affecting individual differences in humans. We use next-generation sequencing to assay blood DNA methylation at approximately 4.5 million loci, each comprising 2.9 CpGs on average, in 697 normal subjects. Methylation measures at each locus are tested for association with approximately 4.5 million single nucleotide polymorphisms (SNPs) to exhaustively screen for methylation quantitative trait loci (meQTLs).

RESULTS

Using stringent false discovery rate control, 15 % of methylation sites show genetic influence. Most meQTLs are local, where the associated SNP and methylation site are in close genomic proximity. Distant meQTLs and those spanning different chromosomes are less common. Most local meQTLs encompass common SNPs that alter CpG sites (CpG-SNPs). Local meQTLs encompassing CpG-SNPs are enriched in regions of inactive chromatin in blood cells. In contrast, local meQTLs lacking CpG-SNPs are enriched in regions of active chromatin and transcription factor binding sites. Of 393 local meQTLs that overlap disease-associated regions from genome-wide studies, a high percentage encompass common CpG-SNPs. These meQTLs overlap active enhancers, differentiating them from CpG-SNP meQTLs in inactive chromatin.

CONCLUSIONS

Genetic influence on the human blood methylome is common, involves several heterogeneous processes and is predominantly dependent on local sequence context at the meQTL site. Most meQTLs involve CpG-SNPs, while sequence-dependent effects on chromatin binding are also important in regions of active chromatin. An abundance of local meQTLs resulting from methylation of CpG-SNPs in inactive chromatin suggests that many meQTLs lack functional consequence. Integrating meQTL and Roadmap Epigenomics data could assist fine-mapping efforts.

Genetic and environmental impacts on DNA methylation levels in twins

Epigenetics describes the study of cellular modifications that can modify the expression of genes without changing the DNA sequence. DNA methylation is one of the most stable and prevalent epigenetic mechanisms. Twin studies have been a valuable model for unraveling the genetic and epigenetic epidemiology of complex traits, and now offer a potential to dissect the factors that impact DNA methylation variability and its biomedical significance. The twin design specifically allows for the study of genetic, environmental and lifestyle factors, and their potential interactions, on epigenetic profiles. Furthermore, genetically identical twins offer a unique opportunity to assess nongenetic impacts on epigenetic profiles. Here, we summarize recent findings from twin studies of DNA methylation profiles across tissues, to define current knowledge regarding the genetic and nongenetic factors that influence epigenetic variation.

Insights into genetic susceptibility in the etiology of spontaneous preterm birth

Preterm birth (PTB; <37 weeks of gestation) is a complex disorder, whose etiology is influenced by a variety of factors. A greater understanding of the biological mechanisms that contribute to PTB will facilitate identification of those at increased risk and may inform new treatments. To accomplish this, it is vital to elucidate the heritability patterns of this condition as well as the environment and lifestyle factors that increase risk for PTB. Identifying individual genes that contribute to the etiology of PTB presents particular challenges, and there has been little agreement among candidate gene and genome-wide studies performed to date. In this review we will evaluate recent genetic studies of spontaneous PTB, discuss common themes among their findings, and suggest approaches for future studies of PTB.

Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer

Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whose expression levels were significantly associated with both SNPs and CPGs. Importantly, 36 (75%) of them were replicated in an independent data set (TCGA) and the performance of the proposed method was checked with a simulation study. We further support our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexible and easy to implement when analyzing several types of omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complex genetic mechanisms involved in disease conditions.

At present, it is already possible to generate different type of omics–high throughput–data in the same individuals. However, we lack methodology to adequately combine them. Many challenges arise while the amount of data increases and we need to find the way to identify and understand the complex relationships when integrating data. In this regard, new statistical approaches are needed, such as the ones we propose and apply here to integrate three types of omics data (genomics, epigenomics, and transcriptomics) generated using bladder cancer tumor samples. These innovative approaches (LASSO and ENET combined with a permutation-based MaxT method) allowed us to find 48 genes whose expression levels were significantly associated with genomics and epigenomics markers. The adequacy of this approach was confirmed by the use of an independent data set from The Cancer Genome Atlas Consortium: 75% of the genes were replicated. Previous sound biological evidences further support the results obtained.

The role of epigenetics in genetic and environmental epidemiology

Epidemiology is the branch of science that investigates the causes and distribution of disease in populations in order to provide preventative measures and promote human health. The fields of genetic and environmental epidemiology primarily seek to identify genetic and environmental risk factors for disease, respectively. Epigenetics is emerging as an important piece of molecular data to include in these studies because it can provide mechanistic insights into genetic and environmental risk factors for disease, identify potential intervention targets, provide biomarkers of exposure, illuminate gene-environment interactions and help localize disease-relevant genomic regions. Here, we describe the importance of including epigenetics in genetic and environmental epidemiology studies, provide a conceptual framework when considering epigenetic data in population-based studies and touch upon the many challenges that lie ahead.

Many obesity-associated SNPs strongly associate with DNA methylation changes at proximal promoters and enhancers

BACKGROUND

The mechanisms by which genetic variants, such as single nucleotide polymorphisms (SNPs), identified in genome-wide association studies act to influence body mass remain unknown for most of these SNPs, which continue to puzzle the scientific community. Recent evidence points to the epigenetic and chromatin states of the genome as having important roles.

METHODS

We genotyped 355 healthy young individuals for 52 known obesity-associated SNPs and obtained DNA methylation levels in their blood using the Illumina 450 K BeadChip. Associations between alleles and methylation at proximal cytosine residues were tested using a linear model adjusted for age, sex, weight category, and a proxy for blood cell type counts. For replication in other tissues, we used two open-access datasets (skin fibroblasts, n = 62; four brain regions, n = 121-133) and an additional dataset in subcutaneous and visceral fat (n = 149).

RESULTS

We found that alleles at 28 of these obesity-associated SNPs associate with methylation levels at 107 proximal CpG sites. Out of 107 CpG sites, 38 are located in gene promoters, including genes strongly implicated in obesity (MIR148A, BDNF, PTPMT1, NR1H3, MGAT1, SCGB3A1, HOXC12, PMAIP1, PSIP1, RPS10-NUDT3, RPS10, SKOR1, MAP2K5, SIX5, AGRN, IMMP1L, ELP4, ITIH4, SEMA3G, POMC, ADCY3, SSPN, LGR4, TUFM, MIR4721, SULT1A1, SULT1A2, APOBR, CLN3, SPNS1, SH2B1, ATXN2L, and IL27). Interestingly, the associated SNPs are in known eQTLs for some of these genes. We also found that the 107 CpGs are enriched in enhancers in peripheral blood mononuclear cells. Finally, our results indicate that some of these associations are not blood-specific as we successfully replicated four associations in skin fibroblasts.

CONCLUSIONS

Our results strongly suggest that many obesity-associated SNPs are associated with proximal gene regulation, which was reflected by association of obesity risk allele genotypes with differential DNA methylation. This study highlights the importance of DNA methylation and other chromatin marks as a way to understand the molecular basis of genetic variants associated with human diseases and traits.

Methylation of the FGFR2 gene is associated with high birth weight centile in humans

AIMS

This study examined links between DNA methylation and birth weight centile (BWC), and explored the impact of genetic variation.

MATERIALS & METHODS

Using HumanMethylation450 arrays, we examined candidate gene-associated CpGs in cord blood from newborns with low (<15th centile), medium (40-60th centile) and high (>85th centile) BWC (n = 12). Candidates were examined in an investigation cohort (n = 110) using pyrosequencing and genotyping for putative methylation-associated polymorphisms performed using standard PCR.

RESULTS

Array analysis identified 314 candidate genes associated with BWC extremes, four of which showed ≥ 4 BWC-linked CpGs. Of these, PM20D1 and MI886 suggested genetically determined methylation levels. However, methylation at three CpGs in FGFR2 remained significantly associated with high BWC (p = 0.004-0.027).

CONCLUSION

We identified a novel biologically plausible candidate (FGFR2) for with BWC that merits further study.

DNA methylation profiling: comparison of genome-wide sequencing methods and the Infinium Human Methylation 450 Bead Chip

AIMS

To compare the performance of four sequence-based and one microarray methods for DNA methylation profiling.

METHODS

DNA from two cell lines were profiled by reduced representation bisulfite sequencing, methyl capture sequencing (SS-Meth Seq), NimbleGen SeqCapEpi CpGiant(Nimblegen MethSeq), methylated DNA immunoprecipitation (MeDIP) and the Human Methylation 450 Bead Chip (Meth450K).

RESULTS & CONCLUSION

Despite differences in genome-wide coverage, high correlation and concordance were observed between different methods. Significant overlap of differentially methylated regions was identified between sequenced-based platforms. MeDIP provided the best coverage for the whole genome and gene body regions, while RRBS and Nimblegen MethSeq were superior for CpGs in CpG islands and promoters. Methylation analyses can be achieved by any of the five methods but understanding their differences may better address the research question being posed.

Implication of a Chromosome 15q15.2 Locus in Regulating UBR1 and Predisposing Smokers to MGMT Methylation in Lung

O(6)-Methylguanine-DNA methyltransferase (MGMT) is a DNA repair enzyme that protects cells from carcinogenic effects of alkylating agents; however, MGMT is silenced by promoter hypermethylation during carcinogenesis. A single-nucleotide polymorphism (SNP) in an enhancer in the MGMT promoter was previously identified to be highly significantly associated with risk for MGMT methylation in lung cancer and sputum from smokers. To further genetic investigations, a genome-wide association and replication study was conducted in two smoker cohorts to identify novel loci for MGMT methylation in sputum that were independent of the MGMT enhancer polymorphism. Two novel trans-acting loci (15q15.2 and 17q24.3) that were identified acted together with the enhancer SNP to empower risk prediction for MGMT methylation. We found that the predisposition to MGMT methylation arising from the 15q15.2 locus involved regulation of the ubiquitin protein ligase E3 component UBR1. UBR1 attenuation reduced turnover of MGMT protein and increased repair of O6-methylguanine in nitrosomethylurea-treated human bronchial epithelial cells, while also reducing MGMT promoter activity and abolishing MGMT induction. Overall, our results substantiate reduced gene transcription as a major mechanism for predisposition to MGMT methylation in the lungs of smokers, and support the importance of UBR1 in regulating MGMT homeostasis and DNA repair of alkylated DNA adducts in cells.

The role of epigenetic mediation and the future of food allergy research

IgE-mediated food allergy is a developing global health problem with prevalence rising at alarmingly fast rates. In this review, we discuss the interplay between genetics, epigenetics, and environmental exposures in the pathogenesis of food allergies. We aim to highlight the most recent evidence that suggests how epigenetic control may mediate genetic susceptibility of food allergies. We also examine how epigenetic modifications may be the key in explaining how environmental factors modulate and modify gene expression, leading to the dysregulation of immune tolerance and consequently, the development of food allergies. The emerging epigenetic paradigm in food allergies is likely to provide new mechanistic insight into food allergy risk and development as well as shape our therapeutic and preventive strategies.

A pooling-based approach to mapping genetic variants associated with DNA methylation

DNA methylation is an epigenetic modification that plays a key role in gene regulation. Previous studies have investigated its genetic basis by mapping genetic variants that are associated with DNA methylation at specific sites, but these have been limited to microarrays that cover <2% of the genome and cannot account for allele-specific methylation (ASM). Other studies have performed whole-genome bisulfite sequencing on a few individuals, but these lack statistical power to identify variants associated with DNA methylation. We present a novel approach in which bisulfite-treated DNA from many individuals is sequenced together in a single pool, resulting in a truly genome-wide map of DNA methylation. Compared to methods that do not account for ASM, our approach increases statistical power to detect associations while sharply reducing cost, effort, and experimental variability. As a proof of concept, we generated deep sequencing data from a pool of 60 human cell lines; we evaluated almost twice as many CpGs as the largest microarray studies and identified more than 2000 genetic variants associated with DNA methylation. We found that these variants are highly enriched for associations with chromatin accessibility and CTCF binding but are less likely to be associated with traits indirectly linked to DNA, such as gene expression and disease phenotypes. In summary, our approach allows genome-wide mapping of genetic variants associated with DNA methylation in any tissue of any species, without the need for individual-level genotype or methylation data.