Recommendations for the design and analysis of epigenome-wide association studies

An empirically driven data reduction method on the human 450K methylation array to remove tissue specific non-variable CpGs

Background

Population based epigenetic association studies of disease and exposures are becoming more common with the availability of economical genome-wide technologies for interrogation of the methylome, such as the Illumina 450K Human Methylation Array (450K). Often, the expected small number of differentially methylated cytosine-guanine pairs (CpGs) in studies of the human methylome presents a statistical challenge, as the large number of CpGs measured on the 450K necessitates careful multiple test correction. While the 450K is a highly useful tool for population epigenetic studies, many of the CpGs tested are not variable and thus of limited information content in the context of the study and tissue. CpGs with observed lack of variability in the tissue under study could be removed to reduce the data dimensionality, limit the severity of multiple test correction and allow for improved detection of differential DNA methylation.

Methods

Here, we performed a meta-analysis of 450K data from three commonly studied human tissues, namely blood (605 samples), buccal epithelial cells (121 samples) and placenta (157 samples). We developed lists of CpGs that are non-variable in each tissue.

Results

These lists are surprisingly large (blood 114,204 CpGs, buccal epithelial cells 120,009 CpGs and placenta 101,367 CpGs) and thus will be valuable filters for epigenetic association studies, considerably reducing the dimensionality of the 450K and subsequently the multiple testing correction severity.

Conclusions

We propose this empirically derived method for data reduction to allow for more power in detecting differential DNA methylation associated with exposures in studies on the human methylome.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-017-0320-z) contains supplementary material, which is available to authorized users.

Placental mitochondrial DNA and CYP1A1 gene methylation as molecular signatures for tobacco smoke exposure in pregnant women and the relevance for birth weight

BACKGROUND

Maternal smoking during pregnancy results in an increased risk of low birth weight through perturbations in the utero-placental exchange. Epigenetics and mitochondrial function in fetal tissues might be molecular signatures responsive to in utero tobacco smoke exposure.

METHODS

In the framework of the ENVIRONAGE birth cohort, we investigated the effect of self-reported tobacco smoke exposure during pregnancy on birth weight and the relation with placental tissue markers such as, (1) relative mitochondrial DNA (mtDNA) content as determined by real-time quantitative PCR, (2) DNA methylation of specific loci of mtDNA (D-loop and MT-RNR1), and (3) DNA methylation of the biotransformation gene CYP1A1 (the last two determined by bisulfite-pyrosequencing). The total pregnant mother sample included 255 non-smokers, 65 former-smokers who had quit smoking before pregnancy, and 62 smokers who continued smoking during pregnancy.

RESULTS

Smokers delivered newborns with a birth weight on average 208 g lower [95% confidence interval (CI) -318 to -99, p = 0.0002] than mothers who did not smoke during pregnancy. In the smoker group, the relative mtDNA content was lower (-21.6%, 95% CI -35.4 to -4.9%, p = 0.01) than in the non-smoker group; whereas, absolute mtDNA methylation levels of MT-RNR1 were higher (+0.62%, 95% CI 0.21 to 1.02%, p = 0.003). Lower CpG-specific methylation of CYP1A1 in placental tissue (-4.57%, 95% CI -7.15 to -1.98%, p < 0.0001) were observed in smokers compared with non-smokers. Nevertheless, no mediation of CYP1A1 methylation nor any other investigated molecular signature was observed for the association between tobacco smoke exposure and birth weight.

CONCLUSIONS

mtDNA content, methylation of specific loci of mtDNA, and CYP1A1 methylation in placental tissue may serve as molecular signatures for the association between gestational tobacco smoke exposure and low birth weight.

Differential methylation between ethnic sub-groups reflects the effect of genetic ancestry and environmental exposures

Populations are often divided categorically into distinct racial/ethnic groups based on social rather than biological constructs. Genetic ancestry has been suggested as an alternative to this categorization. Herein, we typed over 450,000 CpG sites in whole blood of 573 individuals of diverse Hispanic origin who also had high-density genotype data. We found that both self-identified ethnicity and genetically determined ancestry were each significantly associated with methylation levels at 916 and 194 CpGs, respectively, and that shared genomic ancestry accounted for a median of 75.7% (IQR 45.8% to 92%) of the variance in methylation associated with ethnicity. There was a significant enrichment (p=4.2×10-64) of ethnicity-associated sites amongst loci previously associated environmental exposures, particularly maternal smoking during pregnancy. We conclude that differential methylation between ethnic groups is partially explained by the shared genetic ancestry but that environmental factors not captured by ancestry significantly contribute to variation in methylation.

Prenatal Maternal Stress Predicts Methylation of Genes Regulating the Hypothalamic-Pituitary-Adrenocortical System in Mothers and Newborns in the Democratic Republic of Congo

Exposure to stress early in life permanently shapes activity of the hypothalamic-pituitary-adrenocortical (HPA) axis and the brain. Prenatally, glucocorticoids pass through the placenta to the fetus with postnatal impacts on brain development, birth weight (BW), and HPA axis functioning. Little is known about the biological mechanisms by which prenatal stress affects postnatal functioning. This study addresses this gap by examining the effect of chronic stress and traumatic war-related stress on epigenetic changes in four key genes regulating the HPA axis in neonatal cord blood, placenta, and maternal blood: CRH, CRHBP, NR3C1, and FKBP5. Participants were 24 mother-newborn dyads in the conflict-ridden region of the eastern Democratic Republic of Congo. BW data were collected at delivery and maternal interviews were conducted to assess culturally relevant chronic and war-related stressors. Chronic stress and war trauma had widespread effects on HPA axis gene methylation, with significant effects observed at transcription factor binding (TFB) sites in all target genes tested. Some changes in methylation were unique to chronic or war stress, whereas others were observed across both stressor types. Moreover, stress exposures impacted maternal and fetal tissues differently, supporting theoretical models that stress impacts vary according to life phase. Methylation in several NR3C1 and CRH CpG sites, all located at TFB sites, was associated with BW. These findings suggest that prenatal stress exposure impacts development via epigenetic changes in HPA axis genes.

Increased DNA methylation variability in type 1 diabetes across three immune effector cell types

The incidence of type 1 diabetes (T1D) has substantially increased over the past decade, suggesting a role for non-genetic factors such as epigenetic mechanisms in disease development. Here we present an epigenome-wide association study across 406,365 CpGs in 52 monozygotic twin pairs discordant for T1D in three immune effector cell types. We observe a substantial enrichment of differentially variable CpG positions (DVPs) in T1D twins when compared with their healthy co-twins and when compared with healthy, unrelated individuals. These T1D-associated DVPs are found to be temporally stable and enriched at gene regulatory elements. Integration with cell type-specific gene regulatory circuits highlight pathways involved in immune cell metabolism and the cell cycle, including mTOR signalling. Evidence from cord blood of newborns who progress to overt T1D suggests that the DVPs likely emerge after birth. Our findings, based on 772 methylomes, implicate epigenetic changes that could contribute to disease pathogenesis in T1D.

Genome-wide epigenomic profiling for biomarker discovery

A myriad of diseases is caused or characterized by alteration of epigenetic patterns, including changes in DNA methylation, post-translational histone modifications, or chromatin structure. These changes of the epigenome represent a highly interesting layer of information for disease stratification and for personalized medicine. Traditionally, epigenomic profiling required large amounts of cells, which are rarely available with clinical samples. Also, the cellular heterogeneity complicates analysis when profiling clinical samples for unbiased genome-wide biomarker discovery. Recent years saw great progress in miniaturization of genome-wide epigenomic profiling, enabling large-scale epigenetic biomarker screens for disease diagnosis, prognosis, and stratification on patient-derived samples. All main genome-wide profiling technologies have now been scaled down and/or are compatible with single-cell readout, including: (i) Bisulfite sequencing to determine DNA methylation at base-pair resolution, (ii) ChIP-Seq to identify protein binding sites on the genome, (iii) DNaseI-Seq/ATAC-Seq to profile open chromatin, and (iv) 4C-Seq and HiC-Seq to determine the spatial organization of chromosomes. In this review we provide an overview of current genome-wide epigenomic profiling technologies and main technological advances that allowed miniaturization of these assays down to single-cell level. For each of these technologies we evaluate their application for future biomarker discovery. We will focus on (i) compatibility of these technologies with methods used for clinical sample preservation, including methods used by biobanks that store large numbers of patient samples, and (ii) automation of these technologies for robust sample preparation and increased throughput.

Factors associated with heterogeneity in microarray gene expression in peripheral blood mononuclear cells from large pedigrees

BACKGROUND

Genome-wide microarray expression is a rich source of functional genomic data. We examined evidence for differences in expression from peripheral blood mononuclear cells between individuals, examined some of factors that may be responsible and provide recommendations for analysis.

METHODS

A total of 643 individuals from 17 large Mexican American pedigrees had microarray gene expression data generated from peripheral blood mononuclear cells. This data has previously been used to map cis- and trans-expression quantitative trait loci using genome-wide linkage analysis. We estimated both principal components and cell proportions in these data, and tested them for association with clinical factors to provide insight into causes of variation in gene expression between individuals.

RESULTS

We identified that there were highly significant differences in the second principal component of gene expression between pedigrees, with 3 pedigrees being outliers. The estimated cell proportions identified 1 individual who was a gross outlier, as well as pedigrees that differed from others in their estimated proportions of helper and cytotoxic T cells.

CONCLUSIONS

These phenomena could be from either pedigree-specific genetic variation, technical artefacts, or clinical factors. Incorporating factors that influence gene expression into genetic analysis, and exclusion of outliers could improve the power of genetic mapping of expression traits.

RETRACTION: Illuminating the dark road from schizophrenia genetic associations to disease mechanisms

Recent large-scale genome-wide association studies (GWAS) have enabled the discovery of common genetic variations contributing to risk architectures of schizophrenia in human populations; however, the majority of GWAS-identified variants are located in large genomic regions spanning multiple genes, and recognizing the precise targets and mechanisms of these clinical associations is now the major challenge. Here, we review recent progress in schizophrenia genetics, functional genomics and related neuroscience research, and propose a functional pipeline to translate schizophrenia GWAS risk loci into disease biology and information for drug discovery. The pipeline includes identification of underlying molecular mechanisms using transcriptomic data in human brain, prioritization of putative functional causative variants by the integration of genetic epidemiological and bioinformatics methods as well as molecular approaches, and in vitro and in vivo experimental characterizations of the identified targeted species and causative variants to dissect the relevant disease biology. These approaches will accelerate progress from schizophrenia genetic studies to biological mechanisms and ultimately guide the development of prognostic, preventive and therapeutic measures.

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.

Opportunities and challenges of big data for the social sciences: The case of genomic data

In this paper, we draw attention to one unique and valuable source of big data, genomic data, by demonstrating the opportunities they provide to social scientists. We discuss different types of large-scale genomic data and recent advances in statistical methods and computational infrastructure used to address challenges in managing and analyzing such data. We highlight how these data and methods can be used to benefit social science research.

Enlarged leukocyte referent libraries can explain additional variance in blood-based epigenome-wide association studies

AIM

We examined whether variation in blood-based epigenome-wide association studies could be more completely explained by augmenting existing reference DNA methylation libraries.

MATERIALS & METHODS

We compared existing and enhanced libraries in predicting variability in three publicly available 450K methylation datasets that collected whole-blood samples. Models were fit separately to each CpG site and used to estimate the additional variability when adjustments for cell composition were made with each library.

RESULTS

Calculation of the mean difference in the CpG-specific residual sums of squares error between models for an arthritis, aging and metabolic syndrome dataset, indicated that an enhanced library explained significantly more variation across all three datasets (p < 10(-3)).

CONCLUSION

Pathologically important immune cell subtypes can explain important variability in epigenome-wide association studies done in blood.

DNA hypermethylation of CD3(+) T cells from cord blood of infants exposed to intrauterine growth restriction

AIMS/HYPOTHESIS

Intrauterine growth restriction (IUGR) is associated with increased susceptibility to obesity, metabolic syndrome and type 2 diabetes. Although the mechanisms underlying the developmental origins of metabolic disease are poorly understood, evidence suggests that epigenomic alterations play a critical role. We sought to identify changes in DNA methylation patterns that are associated with IUGR in CD3(+) T cells purified from umbilical cord blood obtained from male newborns who were appropriate for gestational age (AGA) or who had been exposed to IUGR.

METHODS

CD3(+) T cells were isolated from cord blood obtained from IUGR and AGA infants. The genome-wide methylation profile in eight AGA and seven IUGR samples was determined using the HELP tagging assay. Validation analysis using targeted bisulfite sequencing and bisulfite massARRAY was performed on the original cohort as well as biological replicates consisting of two AGA and four IUGR infants. The Segway algorithm was used to identify methylation changes within regulatory regions of the genome.

RESULTS

A global shift towards hypermethylation in IUGR was seen compared with AGA (89.8% of 4,425 differentially methylated loci), targeted to regulatory regions of the genome, specifically promoters and enhancers. Pathway analysis identified dysregulation of pathways involved in metabolic disease (type 2 diabetes mellitus, insulin signalling, mitogen-activated protein kinase signalling) and T cell development, regulation and activation (T cell receptor signalling), as well as transcription factors (TCF3, LEF1 and NFATC) that regulate T cells. Furthermore, bump-hunting analysis revealed differentially methylated regions in PRDM16 and HLA-DPB1, genes important for adipose tissue differentiation, stem cell maintenance and function and T cell activation.

CONCLUSIONS/INTERPRETATION

Our findings suggest that the alterations in methylation patterns observed in IUGR CD3(+) T cells may have functional consequences in targeted genes, regulatory regions and transcription factors. These may serve as biomarkers to identify those at 'high risk' for diminished attainment of full health potential who can benefit from early interventions.

ACCESS TO RESEARCH MATERIALS

HELP tagging data: Gene Expression Omnibus database (GSE77268), scheduled to be released on 25 January 2019.

How to make DNA methylome wide association studies more powerful

Genome-wide association studies had a troublesome adolescence, while researchers increased statistical power, in part by increasing subject numbers. Interrogating the interaction of genetic and environmental influences raised new challenges of statistical power, which were not easily bested by the addition of subjects. Screening the DNA methylome offers an attractive alternative as methylation can be thought of as a proxy for the combined influences of genetics and environment. There are statistical challenges unique to DNA methylome data and also multiple features, which can be exploited to increase power. We anticipate the development of DNA methylome association study designs and new analytical methods, together with integration of data from other molecular species and other studies, which will boost statistical power and tackle causality. In this way, the molecular trajectories that underlie disease development will be uncovered.

Genomics, Telomere Length, Epigenetics, and Metabolomics in the Nurses' Health Studies

OBJECTIVES

To review the contribution of the Nurses' Health Study (NHS) and NHS II to genomics, epigenetics, and metabolomics research.

METHODS

We performed a narrative review of the publications of the NHS and NHS II between 1990 and 2016 based on biospecimens, including blood and tumor tissue, collected from participants.

RESULTS

The NHS has contributed to the discovery of genetic loci influencing more than 45 complex human phenotypes, including cancers, diabetes, cardiovascular disease, reproductive characteristics, and anthropometric traits. The combination of genomewide genotype data with extensive exposure and lifestyle data has enabled the evaluation of gene-environment interactions. Furthermore, data suggest that longer telomere length increases risk of cancers not related to smoking, and that modifiable factors (e.g., diet) may have an impact on telomere length. "Omics" research in the NHS continues to expand, with epigenetics and metabolomics becoming greater areas of focus.

CONCLUSIONS

The combination of prospective biomarker data and broad exposure information has enabled the NHS to participate in a variety of "omics" research, contributing to understanding of the epidemiology and biology of multiple complex diseases.

Uncovering the Role of the Methylome in Dementia and Neurodegeneration

Our understanding of the epigenome has advanced dramatically over the past decade, particularly in terms of DNA methylation, a modification found throughout the genome. Studies of the brain and neurons have outlined an increasingly complex architecture involving not just CG dinucleotide methylation but also methylation of other dinucleotides, and modifications of methylated bases such as 5-hydroxymethylcytosine. Different modifications may play an important role in brain development, function and decline; recent descriptions of the effects of aging and neurodegenerative processes such as Alzheimer disease on methylation profiles have ushered in an era of DNA methylome-wide association studies. Rapidly improving technologies and study designs are returning robust results, and investigations of the human brain's epigenome are increasingly feasible, complementing insights gained from genetic studies.

Functional genomics bridges the gap between quantitative genetics and molecular biology

Deep characterization of molecular function of genetic variants in the human genome is becoming increasingly important for understanding genetic associations to disease and for learning to read the regulatory code of the genome. In this paper, I discuss how recent advances in both quantitative genetics and molecular biology have contributed to understanding functional effects of genetic variants, lessons learned from eQTL studies, and future challenges in this field.

The role of global and regional DNA methylation and histone modifications in glycemic traits and type 2 diabetes: A systematic review

BACKGROUND

New evidence suggests the potential involvement of epigenetic mechanisms in type 2 diabetes (T2D) as a crucial interface between the effects of genetic predisposition and environmental influences.

AIM

To systematically review studies investigating the association between epigenetic marks (DNA methylation and histone modifications) with T2D and glycemic traits (glucose and insulin levels, insulin resistance measured by HOMA-IR).

METHOD AND RESULTS

Six bibliographic databases (Embase.com, Medline (Ovid), Web-of-Science, PubMed, Cochrane Central and Google Scholar) were screened until 28th August 2015. We included randomized controlled trials, cohort, case-control and cross-sectional studies in humans that examined the association between epigenetic marks (global, candidate or genome-wide methylation of DNA and histone modifications) with T2D, glucose and insulin levels and insulin metabolism. Of the initially identified 3879 references, 53 articles, based on 47 unique studies met our inclusion criteria. Overall, data were available on 10,823 participants, with a total of 3358 T2D cases. There was no consistent evidence for an association between global DNA-methylation with T2D, glucose, insulin and insulin resistance. The studies reported epigenetic regulation of several candidate genes for diabetes susceptibility in blood cells, muscle, adipose tissue and placenta to be related with T2D without any general overlap between them. Histone modifications in relation to T2D were reported only in 3 observational studies.

CONCLUSIONS AND RELEVANCE

Current evidence supports an association between epigenetic marks and T2D. However, overall evidence is limited, highlighting the need for further larger-scale and prospective investigations to establish whether epigenetic marks may influence the risk of developing T2D.

Quantitative comparison of DNA methylation assays for biomarker development and clinical applications

DNA methylation patterns are altered in numerous diseases and often correlate with clinically relevant information such as disease subtypes, prognosis and drug response. With suitable assays and after validation in large cohorts, such associations can be exploited for clinical diagnostics and personalized treatment decisions. Here we describe the results of a community-wide benchmarking study comparing the performance of all widely used methods for DNA methylation analysis that are compatible with routine clinical use. We shipped 32 reference samples to 18 laboratories in seven different countries. Researchers in those laboratories collectively contributed 21 locus-specific assays for an average of 27 predefined genomic regions, as well as six global assays. We evaluated assay sensitivity on low-input samples and assessed the assays' ability to discriminate between cell types. Good agreement was observed across all tested methods, with amplicon bisulfite sequencing and bisulfite pyrosequencing showing the best all-round performance. Our technology comparison can inform the selection, optimization and use of DNA methylation assays in large-scale validation studies, biomarker development and clinical diagnostics.

Epigenome-wide Association Studies and the Interpretation of Disease -Omics

Epigenome-wide association studies represent one means of applying genome-wide assays to identify molecular events that could be associated with human phenotypes. The epigenome is especially intriguing as a target for study, as epigenetic regulatory processes are, by definition, heritable from parent to daughter cells and are found to have transcriptional regulatory properties. As such, the epigenome is an attractive candidate for mediating long-term responses to cellular stimuli, such as environmental effects modifying disease risk. Such epigenomic studies represent a broader category of disease -omics, which suffer from multiple problems in design and execution that severely limit their interpretability. Here we define many of the problems with current epigenomic studies and propose solutions that can be applied to allow this and other disease -omics studies to achieve their potential for generating valuable insights.

Amnion as a surrogate tissue reporter of the effects of maternal preeclampsia on the fetus

BACKGROUND

Preeclampsia, traditionally characterized by high blood pressure and proteinuria, is a common pregnancy complication, which affects 2-8 % of all pregnancies. Although children born to women with preeclampsia have a higher risk of hypertension in later life, the mechanism of this increased risk is unknown. DNA methylation is an epigenetic modification that has been studied as a mediator of cellular memory of adverse exposures in utero. Since each cell type in the body has a unique DNA profile, cell subtype composition is a major confounding factor in studies of tissues with heterogeneous cell types. The best way to avoid this confounding effect is by using purified cell types. However, using purified cell types in large cohort translational studies is difficult. The amnion, the inner layer of the fetal membranes of the placenta, is derived from the epiblast and consists of two cell types, which are easy to isolate from the delivered placenta. In this study, we demonstrate the value of using amnion samples for DNA methylation studies, revealing distinctive patterns between fetuses exposed to proteinuria or hypertension and fetuses from normal pregnancies.

RESULTS

We performed a genome-wide DNA methylation analysis, HpaII tiny fragment Enrichment by Ligation-mediated PCR (HELP)-tagging, on 62 amnion samples from the placentas of uncomplicated, normal pregnancies and from those with complications of preeclampsia or hypertension. Using a regression model approach, we found 123, 85, and 99 loci with high-confidence hypertension-associated, proteinuria-associated, and hypertension- and proteinuria-associated DNA methylation changes, respectively. A gene ontology analysis showed DNA methylation changes to be selecting genes with different biological processes in exposure status. We also found that these differentially methylated regions overlap loci previously reported as differentially methylated regions in preeclampsia.

CONCLUSIONS

Our findings support prior observations that preeclampsia is associated with changes of DNA methylation near genes that have previously been found to be dysregulated in preeclampsia. We propose that amniotic membranes represent a valuable surrogate fetal tissue on which to perform epigenome-wide association studies of adverse intrauterine conditions.

Differential methylation tests of regulatory regions

Differential methylation of regulatory elements is critical in epigenetic researches and can be statistically tested. We developed a new statistical test, the generalized integrated functional test (GIFT), that tests for regional differences in methylation based on the methylation percent at each CpG site within a genomic region. The GIFT uses estimated subject-specific profiles with smoothing methods, specifically wavelet smoothing, and calculates an ANOVA-like test to compare the average profile of groups. In this way, possibly correlated CpG sites within the regulatory region are compared all together. Simulations and analyses of data obtained from patients with chronic lymphocytic leukemia indicate that GIFT has good statistical properties and is able to identify promising genomic regions. Further, GIFT is likely to work with multiple different types of experiments since different smoothing methods can be used to estimate the profiles of data without noise. Matlab code for GIFT and sample data are available at http://www.augusta.edu/mcg/biostatepi/people/software/gift.html.

Next-generation sequencing identifies major DNA methylation changes during progression of Ph+ chronic myeloid leukemia

Little is known about the impact of DNA methylation on the evolution/progression of Ph+ chronic myeloid leukemia (CML). We investigated the methylome of CML patients in chronic phase (CP-CML), accelerated phase (AP-CML) and blast crisis (BC-CML) as well as in controls by reduced representation bisulfite sequencing. Although only ~600 differentially methylated CpG sites were identified in samples obtained from CP-CML patients compared with controls, ~6500 differentially methylated CpG sites were found in samples from BC-CML patients. In the majority of affected CpG sites, methylation was increased. In CP-CML patients who progressed to AP-CML/BC-CML, we identified up to 897 genes that were methylated at the time of progression but not at the time of diagnosis. Using RNA-sequencing, we observed downregulated expression of many of these genes in BC-CML compared with CP-CML samples. Several of them are well-known tumor-suppressor genes or regulators of cell proliferation, and gene re-expression was observed by the use of epigenetic active drugs. Together, our results demonstrate that CpG site methylation clearly increases during CML progression and that it may provide a useful basis for revealing new targets of therapy in advanced CML.

Epigenetic regulation of neurodevelopmental genes in response to in utero exposure to phthalate plastic chemicals: How can we delineate causal effects?

Accumulating evidence, from animal models and human observational studies, implicates the in utero (and early postnatal) environment in the 'programming' of risk for a variety of adverse outcomes and health trajectories. The modern environment is replete with man-made compounds such as plastic product chemicals (PPC), including phenols and phthalates. Evidence from several human cohorts implicates exposure to these chemicals in adverse offspring neurodevelopment, though a direct causal relationship has not been firmly established. In this review we consider a potential causal pathway that encompasses epigenetic human variation, and how we might test this mechanistic hypothesis in human studies. In the first part of this report we outline how PPCs induce epigenetic change, focusing on the brain derived neurotrophic factor (BDNF) gene, a key regulator of neurodevelopment. Further, we discuss the role of the epigenetics of BDNF and other genes in neurodevelopment and the emerging human evidence of an association between phthalate exposure and adverse offspring neurodevelopment. We discuss aspects of epidemiological and molecular study design and analysis that could be employed to strengthen the level of human evidence to infer causality. We undertake this using an exemplar recent research example: maternal prenatal smoking, linked to methylation change at the aryl hydrocarbon receptor repressor (AHRR) gene at birth, now shown to mediate some of the effects of maternal smoking on birth weight. Characterizing the relationship between the modern environment and the human molecular pathways underpinning its impact on early development is paramount to understanding the public health significance of modern day chemical exposures.

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.

Mechanisms and Disease Associations of Haplotype-Dependent Allele-Specific DNA Methylation

Haplotype-dependent allele-specific methylation (hap-ASM) can impact disease susceptibility, but maps of this phenomenon using stringent criteria in disease-relevant tissues remain sparse. Here we apply array-based and Methyl-Seq approaches to multiple human tissues and cell types, including brain, purified neurons and glia, T lymphocytes, and placenta, and identify 795 hap-ASM differentially methylated regions (DMRs) and 3,082 strong methylation quantitative trait loci (mQTLs), most not previously reported. More than half of these DMRs have cell type-restricted ASM, and among them are 188 hap-ASM DMRs and 933 mQTLs located near GWAS signals for immune and neurological disorders. Targeted bis-seq confirmed hap-ASM in 12/13 loci tested, including CCDC155, CD69, FRMD1, IRF1, KBTBD11, and S100A(∗)-ILF2, associated with immune phenotypes, MYT1L, PTPRN2, CMTM8 and CELF2, associated with neurological disorders, NGFR and HLA-DRB6, associated with both immunological and brain disorders, and ZFP57, a trans-acting regulator of genomic imprinting. Polymorphic CTCF and transcription factor (TF) binding sites were over-represented among hap-ASM DMRs and mQTLs, and analysis of the human data, supplemented by cross-species comparisons to macaques, indicated that CTCF and TF binding likelihood predicts the strength and direction of the allelic methylation asymmetry. These results show that hap-ASM is highly tissue specific; an important trans-acting regulator of genomic imprinting is regulated by this phenomenon; and variation in CTCF and TF binding sites is an underlying mechanism, and maps of hap-ASM and mQTLs reveal regulatory sequences underlying supra- and sub-threshold GWAS peaks in immunological and neurological disorders.

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.

An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies

BACKGROUND

Many different methods exist to adjust for variability in cell-type mixture proportions when analyzing DNA methylation studies. Here we present the result of an extensive simulation study, built on cell-separated DNA methylation profiles from Illumina Infinium 450K methylation data, to compare the performance of eight methods including the most commonly used approaches.

RESULTS

We designed a rich multi-layered simulation containing a set of probes with true associations with either binary or continuous phenotypes, confounding by cell type, variability in means and standard deviations for population parameters, additional variability at the level of an individual cell-type-specific sample, and variability in the mixture proportions across samples. Performance varied quite substantially across methods and simulations. In particular, the number of false positives was sometimes unrealistically high, indicating limited ability to discriminate the true signals from those appearing significant through confounding. Methods that filtered probes had consequently poor power. QQ plots of p values across all tested probes showed that adjustments did not always improve the distribution. The same methods were used to examine associations between smoking and methylation data from a case-control study of colorectal cancer, and we also explored the effect of cell-type adjustments on associations between rheumatoid arthritis cases and controls.

CONCLUSIONS

We recommend surrogate variable analysis for cell-type mixture adjustment since performance was stable under all our simulated scenarios.

Establishing an analytic pipeline for genome-wide DNA methylation

The need for research investigating DNA methylation (DNAm) in clinical studies has increased, leading to the evolution of new analytic methods to improve accuracy and reproducibility of the interpretation of results from these studies. The purpose of this article is to provide clinical researchers with a summary of the major data processing steps routinely applied in clinical studies investigating genome-wide DNAm using the Illumina HumanMethylation 450K BeadChip. In most studies, the primary goal of employing DNAm analysis is to identify differential methylation at CpG sites among phenotypic groups. Experimental design considerations are crucial at the onset to minimize bias from factors related to sample processing and avoid confounding experimental variables with non-biological batch effects. Although there are currently no de facto standard methods for analyzing these data, we review the major steps in processing DNAm data recommended by several research studies. We describe several variations available for clinical researchers to process, analyze, and interpret DNAm data. These insights are applicable to most types of genome-wide DNAm array platforms and will be applicable for the next generation of DNAm array technologies (e.g., the 850K array). Selection of the DNAm analytic pipeline followed by investigators should be guided by the research question and supported by recently published methods.

Whole Genome DNA Methylation Analysis of Obstructive Sleep Apnea: IL1R2, NPR2, AR, SP140 Methylation and Clinical Phenotype

STUDY OBJECTIVES

We hypothesized that DNA methylation patterns may contribute to disease severity or the development of hypertension and excessive daytime sleepiness (EDS) in patients with obstructive sleep apnea (OSA).

METHODS

Illumina's (San Diego, CA, USA) DNA methylation 27-K assay was used to identify differentially methylated loci (DML). DNA methylation levels were validated by pyrosequencing. A discovery cohort of 15 patients with OSA and 6 healthy subjects, and a validation cohort of 72 patients with sleep disordered breathing (SDB).

RESULTS

Microarray analysis identified 636 DMLs in patients with OSA versus healthy subjects, and 327 DMLs in patients with OSA and hypertension versus those without hypertension. In the validation cohort, no significant difference in DNA methylation levels of six selected genes was found between the primary snoring subjects and OSA patients (primary outcome). However, a secondary outcome analysis showed that interleukin-1 receptor 2 (IL1R2) promoter methylation (-114 cytosine followed by guanine dinucleotide sequence [CpG] site) was decreased and IL1R2 protein levels were increased in the patients with SDB with an oxygen desaturation index > 30. Androgen receptor (AR) promoter methylation (-531 CpG site) and AR protein levels were both increased in the patients with SDB with an oxygen desaturation index > 30. Natriuretic peptide receptor 2 (NPR2) promoter methylation (-608/-618 CpG sites) were decreased, whereas levels of both NPR2 and serum C type natriuretic peptide protein were increased in the SDB patients with EDS. Speckled protein 140 (SP140) promoter methylation (-194 CpG site) was increased, and SP140 protein levels were decreased in the patients with SDB and EDS.

CONCLUSIONS

IL1R2 hypomethylation and AR hypermethylation may constitute an important determinant of disease severity, whereas NPR2 hypomethylation and SP140 hypermethylation may provide a biomarker for vulnerability to EDS in OSA.

COMMENTARY

A commentary on this article appears in this issue on page 723.

Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL)

Background

Confounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the cellular mixture of heterogenous biospecimens offer a promising solution, however the performance of such methods depends entirely on the library of methylation markers being used for deconvolution. Here, we introduce a novel algorithm for Identifying Optimal Libraries (IDOL) that dynamically scans a candidate set of cell-specific methylation markers to find libraries that optimize the accuracy of cell fraction estimates obtained from cell mixture deconvolution.

Results

Application of IDOL to training set consisting of samples with both whole-blood DNA methylation data (Illumina HumanMethylation450 BeadArray (HM450)) and flow cytometry measurements of cell composition revealed an optimized library comprised of 300 CpG sites. When compared existing libraries, the library identified by IDOL demonstrated significantly better overall discrimination of the entire immune cell landscape (p = 0.038), and resulted in improved discrimination of 14 out of the 15 pairs of leukocyte subtypes. Estimates of cell composition across the samples in the training set using the IDOL library were highly correlated with their respective flow cytometry measurements, with all cell-specific R²>0.99 and root mean square errors (RMSEs) ranging from [0.97 % to 1.33 %] across leukocyte subtypes. Independent validation of the optimized IDOL library using two additional HM450 data sets showed similarly strong prediction performance, with all cell-specific R²>0.90 and RMSE<4.00 %. In simulation studies, adjustments for cell composition using the IDOL library resulted in uniformly lower false positive rates compared to competing libraries, while also demonstrating an improved capacity to explain epigenome-wide variation in DNA methylation within two large publicly available HM450 data sets.

Conclusions

Despite consisting of half as many CpGs compared to existing libraries for whole blood mixture deconvolution, the optimized IDOL library identified herein resulted in outstanding prediction performance across all considered data sets and demonstrated potential to improve the operating characteristics of EWAS involving adjustments for cell distribution. In addition to providing the EWAS community with an optimized library for whole blood mixture deconvolution, our work establishes a systematic and generalizable framework for the assembly of libraries that improve the accuracy of cell mixture deconvolution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0943-7) contains supplementary material, which is available to authorized users.

Profiling placental and fetal DNA methylation in human neural tube defects

Background

The incidence of neural tube defects (NTDs) declined by about 40 % in Canada with the introduction of a national folic acid (FA) fortification program. Despite the fact that few Canadians currently exhibit folate deficiency, NTDs are still the second most common congenital abnormality. FA fortification may have aided in reducing the incidence of NTDs by overcoming abnormal one carbon metabolism cycling, the process which provides one carbon units for methylation of DNA. We considered that NTDs persisting in a folate-replete population may also occur in the context of FA-independent compromised one carbon metabolism, and that this might manifest as abnormal DNA methylation (DNAm). Second trimester human placental chorionic villi, kidney, spinal cord, brain, and muscle were collected from 19 control, 22 spina bifida, and 15 anencephalic fetuses in British Columbia, Canada. DNA was extracted, assessed for methylenetetrahydrofolate reductase (MTHFR) genotype and for genome-wide DNAm using repetitive elements, in addition to the Illumina Infinium HumanMethylation450 (450k) array.

Results

No difference in repetitive element DNAm was noted between NTD status groups. Using a false discovery rate <0.05 and average group difference in DNAm ≥0.05, differentially methylated array sites were identified only in (1) the comparison of anencephaly to controls in chorionic villi (n = 4 sites) and (2) the comparison of spina bifida to controls in kidney (n = 3342 sites).

Conclusions

We suggest that the distinctive DNAm of spina bifida kidneys may be consequent to the neural tube defect or reflective of a common etiology for abnormal neural tube and renal development. Though there were some small shifts in DNAm in the other tested tissues, our data do not support the long-standing hypothesis of generalized altered genome-wide DNAm in NTDs. This finding may be related to the fact that most Canadians are not folate deficient, but it importantly opens the field to the investigation of other epigenetic and non-epigenetic mechanisms in the etiology of NTDs.

Electronic supplementary material

The online version of this article (doi:10.1186/s13072-016-0054-8) contains supplementary material, which is available to authorized users.

The Epigenetic Effects of Prenatal Cadmium Exposure

Prenatal exposure to the highly toxic and common pollutant cadmium has been associated with adverse effects on child health and development. However, the underlying biological mechanisms of cadmium toxicity remain partially unsolved. Epigenetic disruption due to early cadmium exposure has gained attention as a plausible mode of action, since epigenetic signatures respond to environmental stimuli and the fetus undergoes drastic epigenomic rearrangements during embryogenesis. In the current review, we provide a critical examination of the literature addressing prenatal cadmium exposure and epigenetic effects in human, animal, and in vitro studies. We conducted a PubMed search and obtained eight recent studies addressing this topic, focusing almost exclusively on DNA methylation. These studies provide evidence that cadmium alters epigenetic signatures in the DNA of the placenta and of the newborns, and some studies indicated marked sexual differences for cadmium-related DNA methylation changes. Associations between early cadmium exposure and DNA methylation might reflect interference with de novo DNA methyltransferases. More studies, especially those including environmentally relevant doses, are needed to confirm the toxicoepigenomic effects of prenatal cadmium exposure and how that relates to the observed health effects of cadmium in childhood and later life.

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.

Genome-Wide Epigenetic Studies in Human Disease: A Primer on -Omic Technologies

Epigenetic information encoded in covalent modifications of DNA and histone proteins regulates fundamental biological processes through the action of chromatin regulators, transcription factors, and noncoding RNA species. Epigenetic plasticity enables an organism to respond to developmental and environmental signals without genetic changes. However, aberrant epigenetic control plays a key role in pathogenesis of disease. Normal epigenetic states could be disrupted by detrimental mutations and expression alteration of chromatin regulators or by environmental factors. In this primer, we briefly review the epigenetic basis of human disease and discuss how recent discoveries in this field could be translated into clinical diagnosis, prevention, and treatment. We introduce platforms for mapping genome-wide chromatin accessibility, nucleosome occupancy, DNA-binding proteins, and DNA methylation, primarily focusing on the integration of DNA methylation and chromatin immunoprecipitation-sequencing technologies into disease association studies. We highlight practical considerations in applying high-throughput epigenetic assays and formulating analytical strategies. Finally, we summarize current challenges in sample acquisition, experimental procedures, data analysis, and interpretation and make recommendations on further refinement in these areas. Incorporating epigenomic testing into the clinical research arsenal will greatly facilitate our understanding of the epigenetic basis of disease and help identify novel therapeutic targets.

The Role of DNA Methylation in Cardiovascular Risk and Disease: Methodological Aspects, Study Design, and Data Analysis for Epidemiological Studies

Epidemiological studies have demonstrated that genetic, environmental, behavioral, and clinical factors contribute to cardiovascular disease development. How these risk factors interact at the cellular level to cause cardiovascular disease is not well known. Epigenetic epidemiology enables researchers to explore critical links between genomic coding, modifiable exposures, and manifestation of disease phenotype. One epigenetic link, DNA methylation, is potentially an important mechanism underlying these associations. In the past decade, there has been a significant increase in the number of epidemiological studies investigating cardiovascular risk factors and outcomes in relation to DNA methylation, but many gaps remain in our understanding of the underlying cause and biological implications. In this review, we provide a brief overview of the biology and mechanisms of DNA methylation and its role in cardiovascular disease. In addition, we summarize the current evidence base in epigenetic epidemiology studies relevant to cardiovascular health and disease and discuss the limitations, challenges, and future directions of the field. Finally, we provide guidelines for well-designed epigenetic epidemiology studies, with particular focus on methodological aspects, study design, and analytical challenges.

DNA methylation, early life environment, and health outcomes

Epigenetics, and especially DNA methylation, have recently become provocative biological explanations for early-life environmental effects on later health. Despite the large increase in papers on the topic over the last few years, many questions remain with regards to the biological feasibility of this mechanism and the strength of the evidence to date. In this review, we examine the literature on early-life effects on epigenetic patterns, with special emphasis on social environmental influences. First, we review the basic biology of epigenetic modification of DNA and debate the role of early-life stressful, protective, and positive environments on gene-specific, system-specific, and whole-genome epigenetic patterns later in life. Second, we compare the epigenetic literatures of both humans and other animals and review the research linking epigenetic patterns to health in order to complete the mechanistic pathway. Third, we discuss physical environmental and social environmental effects, which have to date, generally not been jointly considered. Finally, we close with a discussion of the current state of the area's research, its future direction, and its potential use in pediatric health.

Genomic responses to hepatitis B virus (HBV) infection in primary human hepatocytes

Viral infections are able to modify the host's cellular programs, with DNA methylation being a biological intermediate in this process. The extent to which viral infections deregulate gene expression and DNA methylation is not fully understood. In the case of Hepatitis B virus (HBV), there is evidence for an interaction between viral proteins and the host DNA methylation machinery. We studied the ability of HBV to modify the host transcriptome and methylome, using naturally infected primary human hepatocytes to better mimic the clinical setting.Gene expression was especially sensitive to culture conditions, independently of HBV infection. However, we identified non-random changes in gene expression and DNA methylation occurring specifically upon HBV infection. There was little correlation between expression and methylation changes, with transcriptome being a more sensitive marker of time-dependent changes induced by HBV. In contrast, a set of differentially methylated sites appeared early and were stable across the time course experiment. Finally, HBV-induced DNA methylation changes were defined by a specific chromatin context characterized by CpG-poor regions outside of gene promoters.These data support the ability of HBV to modulate host cell expression and methylation programs. In addition, it may serve as a reference for studies addressing the genome-wide consequences of HBV infection in human hepatocytes.

Differential DNA Methylation Analysis without a Reference Genome

Genome-wide DNA methylation mapping uncovers epigenetic changes associated with animal development, environmental adaptation, and species evolution. To address the lack of high-throughput methods for DNA methylation analysis in non-model organisms, we developed an integrated approach for studying DNA methylation differences independent of a reference genome. Experimentally, our method relies on an optimized 96-well protocol for reduced representation bisulfite sequencing (RRBS), which we have validated in nine species (human, mouse, rat, cow, dog, chicken, carp, sea bass, and zebrafish). Bioinformatically, we developed the RefFreeDMA software to deduce ad hoc genomes directly from RRBS reads and to pinpoint differentially methylated regions between samples or groups of individuals (http://RefFreeDMA.computational-epigenetics.org). The identified regions are interpreted using motif enrichment analysis and/or cross-mapping to annotated genomes. We validated our method by reference-free analysis of cell-type-specific DNA methylation in the blood of human, cow, and carp. In summary, we present a cost-effective method for epigenome analysis in ecology and evolution, which enables epigenome-wide association studies in natural populations and species without a reference genome.

DNA methylation markers for oral pre-cancer progression: A critical review

Although oral cancers are generally preceded by a well-established pre-cancerous stage, there is a lack of well-defined clinical and morphological criteria to detect and signal progression from pre-cancer to malignant tumours. We conducted a critical review to summarize the evidence regarding aberrant DNA methylation patterns as a potential diagnostic biomarker predicting progression. We identified all relevant human studies published in English prior to 30th April 2015 that examined DNA methylation (%) in oral pre-cancer by searching PubMed, Web-of-Science and Embase databases using combined key-searches. Twenty-one studies (18-cross-sectional; 3-longitudinal) were eligible for inclusion in the review, with sample sizes ranging from 4 to 156 affected cases. Eligible studies examined promoter region hyper-methylation of tumour suppressor genes in pathways including cell-cycle-control (n=15), DNA-repair (n=7), cell-cycle-signalling (n=4) and apoptosis (n=3). Hyper-methylated loci reported in three or more studies included p16, p14, MGMT and DAPK. Two longitudinal studies reported greater p16 hyper-methylation in pre-cancerous lesions transformed to malignancy compared to lesions that regressed (57-63.6% versus 8-32.1%; p<0.01). The one study that explored epigenome-wide methylation patterns reported three novel hyper-methylated loci (TRHDE; ZNF454; KCNAB3). The majority of reviewed studies were small, cross-sectional studies with poorly defined control groups and lacking validation. Whilst limitations in sample size and study design preclude definitive conclusions, current evidence suggests a potential utility of DNA methylation patterns as a diagnostic biomarker for oral pre-cancer progression. Robust studies such as large epigenome-wide methylation explorations of oral pre-cancer with longitudinal tracking are needed to validate the currently reported signals and identify new risk-loci and the biological pathways of disease progression.

Postmortem human brain genomics in neuropsychiatric disorders--how far can we go?

Large-scale collection of postmortem human brain tissue and subsequent genomic data generation has become a useful approach for better identifying etiological factors contributing to neuropsychiatric disorders. In particular, studying genetic risk variants in non-psychiatric controls can identify biological mechanisms of risk free from confounding factors related to epiphenomena of illness. While the field has begun moving towards cell type-specific analyses, homogenate brain tissue with accompanying cellular profiles, can still identify useful hypotheses for more focused experiments, particularly when the dysregulated cell types are unknown. Technological advances, larger sample sizes, and focused research questions can continue to further leverage postmortem human brain research to better identify and understand the molecular etiology of neuropsychiatric disorders.

CpG island methylation profile in non-invasive oral rinse samples is predictive of oral and pharyngeal carcinoma

BACKGROUND

There are currently no screening tests in routine use for oral and pharyngeal cancer beyond visual inspection and palpation, which are provided on an opportunistic basis, indicating a need for development of novel methods for early detection, particularly in high-risk populations. We sought to address this need through comprehensive interrogation of CpG island methylation in oral rinse samples.

METHODS

We used the Infinium HumanMethylation450 BeadArray to interrogate DNA methylation in oral rinse samples collected from 154 patients with incident oral or pharyngeal carcinoma prior to treatment and 72 cancer-free control subjects. Subjects were randomly allocated to either a training or a testing set. For each subject, average methylation was calculated for each CpG island represented on the array. We applied a semi-supervised recursively partitioned mixture model to the CpG island methylation data to identify a classifier for prediction of case status in the training set. We then applied the resultant classifier to the testing set for validation and to assess the predictive accuracy.

RESULTS

We identified a methylation classifier comprised of 22 CpG islands, which predicted oral and pharyngeal carcinoma with a high degree of accuracy (AUC = 0.92, 95 % CI 0.86, 0.98).

CONCLUSIONS

This novel methylation panel is a strong predictor of oral and pharyngeal carcinoma case status in oral rinse samples and may have utility in early detection and post-treatment follow-up.

ADMIRE: analysis and visualization of differential methylation in genomic regions using the Infinium HumanMethylation450 Assay

Background

DNA methylation at cytosine nucleotides constitutes epigenetic gene regulation impacting cellular development and a wide range of diseases. Cytosine bases of the DNA are converted to 5-methylcytosine by the methyltransferase enzyme, acting as a reversible regulator of gene expression. Due to its outstanding importance in the epigenetic field, a number of lab techniques were developed to interrogate DNA methylation on a global range. Besides whole-genome bisulfite sequencing, the Infinium HumanMethylation450 Assay represents a versatile and cost-effective tool to investigate genome-wide changes of methylation patterns.

Results

Analysis of DNA Methylation In genomic REgions (ADMIRE) is an open source, semi-automatic analysis pipeline and visualization tool for Infinium HumanMethylation450 Assays with a special focus on ease of use. It features flexible experimental settings, quality control, automatic filtering, normalization, multiple testing, and differential analyses on arbitrary genomic regions. Publication-ready graphics, genome browser tracks, and table outputs include summary data and statistics, permitting instant comparison of methylation profiles between sample groups and the exploration of methylation patterns along the whole genome. ADMIREs statistical approach permits simultaneous large-scale analyses of hundreds of assays with little impact on algorithm runtimes.

Conclusions

The web-based version of ADMIRE provides a simple interface to researchers with limited programming skills, whereas the offline version is suitable for integration into custom pipelines. ADMIRE may be used via our freely available web service at https://bioinformatics.mpi-bn.mpg.de without any limitations concerning the size of a project. An offline version for local execution is available from our website or GitHub (https://github.molgen.mpg.de/loosolab/admire).

Electronic supplementary material

The online version of this article (doi:10.1186/s13072-015-0045-1) contains supplementary material, which is available to authorized users.

Epigenetics of Osteoporosis: Critical Analysis of Epigenetic Epidemiology Studies

Osteoarthritis (OA) is an age-related disease with poorly understood pathogenesis. Recent studies have demonstrated that miRNA might play a key role in OA initiation and development. We reviewed recent publications and elucidated the connection between miRNA and OA cartilage anabolic and catabolic signals, including four signaling pathways: TGF-β/Smads and BMPs signaling, associated with cartilage anabolism; and MAPK and NF-KB signaling, associated with cartilage catabolism. We also explored the relationships with MMP, ADAMTS and NOS (NitricOxide Synthases) families, as well as with the catabolic cytokines IL-1 and TNF-α. The potential role of miRNAs in biological processes such as cartilage degeneration, chondrocyte proliferation, and differentiation is discussed. Collective evidence indicates that miRNAs play a critical role in cartilage degeneration. These findings will aid in understanding the molecular network that governs articular cartilage homeostasis and in to elucidate the role of miRNA in the pathogenesis of OA.

Estimation of blood cellular heterogeneity in newborns and children for epigenome-wide association studies

Confounding by cellular heterogeneity has become a major concern for epigenome-wide association studies (EWAS) in peripheral blood samples from population and clinical studies. Adjusting for white blood cell percentage estimates produced by the minfi implementation of the Houseman algorithm (minfi) during statistical analysis is now an established method to account for this bias in adults. However, minfi has not been benchmarked against white blood cell counts in children that may differ substantially from the reference dataset used in its estimation. We compared estimates of white blood cell type percentages produced by two methods, minfi and differential cell count (DCC), in a birth cohort at two time points (birth and 12 years of age). We found that both minfi and DCC had similar trends as children aged, and neither count method differed by sex among newborns (P > 0.10). However, minfi estimates did not correlate well with DCC in samples from newborns (ρ = -0.05 for granulocytes; ρ = -0.03 for lymphocytes). In older children, correlation improved substantially (ρ = 0.77 for granulocytes; ρ = 0.75 for lymphocytes), likely due to increasing similarity with minfi's adult reference data as children aged. Our findings suggest that the minfi method may provide suitable estimates of white blood cell composition for samples from adults and older children, but may not currently be appropriate for EWAS involving newborns or young children.

Epigenetic Control of Smooth Muscle Cell Identity and Lineage Memory

Vascular smooth muscle cells (SMCs), like all cells, acquire a cell-specific epigenetic signature during development that includes acquisition of a unique repertoire of histone and DNA modifications. These changes are postulated to induce an open chromatin state (referred to as euchromatin) on the repertoire of genes that are expressed in differentiated SMC, including SMC-selective marker genes like Acta2 and Myh11, as well as housekeeping genes expressed by most cell types. In contrast, genes that are silenced in differentiated SMC acquire modifications associated with a closed chromatin state (ie, heterochromatin) and transcriptional silencing. Herein, we review mechanisms that regulate epigenetic control of the differentiated state of SMC. In addition, we identify some of the major limitations in the field and future challenges, including development of innovative new tools and approaches, for performing single-cell epigenetic assays and locus-selective editing of the epigenome that will allow direct studies of the functional role of specific epigenetic controls during development, injury repair, and disease, including major cardiovascular diseases, such as atherosclerosis, hypertension, and microvascular disease, associated with diabetes mellitus.