Methylomic profiling of human brain tissue supports a neurodevelopmental origin for schizophrenia

Sex effects on DNA methylation affect discovery in epigenome-wide association study of schizophrenia

Sex differences in the epidemiology and clinical characteristics of schizophrenia are well-known; however, the molecular mechanisms underlying these differences remain unclear. Further, the potential advantages of sex-stratified meta-analyses of epigenome-wide association studies (EWAS) of schizophrenia have not been investigated. Here, we performed sex-stratified EWAS meta-analyses to investigate whether sex stratification improves discovery, and to identify differentially methylated regions (DMRs) in schizophrenia. Peripheral blood-derived DNA methylation data from 1519 cases of schizophrenia (male n = 989, female n = 530) and 1723 controls (male n = 997, female n = 726) from three publicly available datasets, and the TOP cohort were meta-analyzed to compare sex-specific, sex-stratified, and sex-adjusted EWAS. The predictive power of each model was assessed by polymethylation score (PMS). The number of schizophrenia-associated differentially methylated positions identified was higher for the sex-stratified model than for the sex-adjusted one. We identified 20 schizophrenia-associated DMRs in the sex-stratified analysis. PMS from sex-stratified analysis outperformed that from sex-adjusted analysis in predicting schizophrenia. Notably, PMSs from the sex-stratified and female-only analyses, but not those from sex-adjusted or the male-only analyses, significantly predicted schizophrenia in males. The findings suggest that sex-stratified EWAS meta-analyses improve the identification of schizophrenia-associated epigenetic changes and highlight an interaction between sex and schizophrenia status on DNA methylation. Sex-specific DNA methylation may have potential implications for precision psychiatry and the development of stratified treatments for schizophrenia.

Insights into ageing rates comparison across tissues from recalibrating cerebellum DNA methylation clock

DNA methylation (DNAm)-based age clocks have been studied extensively as a biomarker of human ageing and a risk factor for age-related diseases. Despite different tissues having vastly different rates of proliferation, it is still largely unknown whether they age at different rates. It was previously reported that the cerebellum ages slowly; however, this claim was drawn from a single clock using a relatively small sample size and so warrants further investigation. We collected the largest cerebellum DNAm dataset (N = 752) to date. We found the respective epigenetic ages are all severely underestimated by six representative DNAm age clocks, with the underestimation effects more pronounced in the four clocks whose training datasets do not include brain-related tissues. We identified 613 age-associated CpGs in the cerebellum, which accounts for only 14.5% of the number found in the middle temporal gyrus from the same population (N = 404). From the 613 cerebellum age-associated CpGs, we built a highly accurate age prediction model for the cerebellum named CerebellumClockspecific (Pearson correlation=0.941, MAD=3.18 years). Ageing rate comparisons based on the two tissue-specific clocks constructed on the 201 overlapping age-associated CpGs support the cerebellum has younger DNAm age. Nevertheless, we built BrainCortexClock to prove a single DNAm clock is able to unbiasedly estimate DNAm ages of both cerebellum and cerebral cortex, when they are adequately and equally represented in the training dataset. Comparing ageing rates across tissues using DNA methylation multi-tissue clocks is flawed. The large underestimation of age prediction for cerebellums by previous clocks mainly reflects the improper usage of these age clocks. There exist strong and consistent ageing effects on the cerebellar methylome, and we suggest the smaller number of age-associated CpG sites in cerebellum is largely attributed to its extremely low average cell replication rates.

Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles

BACKGROUND

Due to interindividual variation in the cellular composition of the human cortex, it is essential that covariates that capture these differences are included in epigenome-wide association studies using bulk tissue. As experimentally derived cell counts are often unavailable, computational solutions have been adopted to estimate the proportion of different cell types using DNA methylation data. Here, we validate and profile the use of an expanded reference DNA methylation dataset incorporating two neuronal and three glial cell subtypes for quantifying the cellular composition of the human cortex.

RESULTS

We tested eight reference panels containing different combinations of neuronal- and glial cell types and characterised their performance in deconvoluting cell proportions from computationally reconstructed or empirically derived human cortex DNA methylation data. Our analyses demonstrate that while these novel brain deconvolution models produce accurate estimates of cellular proportions from profiles generated on postnatal human cortex samples, they are not appropriate for the use in prenatal cortex or cerebellum tissue samples. Applying our models to an extensive collection of empirical datasets, we show that glial cells are twice as abundant as neuronal cells in the human cortex and identify significant associations between increased Alzheimer's disease neuropathology and the proportion of specific cell types including a decrease in NeuNNeg/SOX10Neg nuclei and an increase of NeuNNeg/SOX10Pos nuclei.

CONCLUSIONS

Our novel deconvolution models produce accurate estimates for cell proportions in the human cortex. These models are available as a resource to the community enabling the control of cellular heterogeneity in epigenetic studies of brain disorders performed on bulk cortex tissue.

A higher dysregulation burden of brain DNA methylation in female patients implicated in the sex bias of Schizophrenia

Sex differences are pervasive in schizophrenia (SCZ), but the extent and magnitude of DNA methylation (DNAm) changes underlying these differences remain uncharacterized. In this study, sex-stratified differential DNAm analysis was performed in postmortem brain samples from 117 SCZ and 137 controls, partitioned into discovery and replication datasets. Three differentially methylated positions (DMPs) were identified (adj.p < 0.05) in females and 29 DMPs in males without overlap between them. Over 81% of these sex-stratified DMPs were directionally consistent between sexes but with different effect sizes. Females experienced larger magnitude of DNAm changes and more DMPs (based on data of equal sample size) than males, contributing to a higher dysregulation burden of DNAm in females SCZ. Additionally, despite similar proportions of female-related DMPs (fDMPs, 8%) being under genetic control compared with males (10%), significant enrichment of DMP-related single nucleotide polymorphisms (SNPs) in signals of genome-wide association studies was identified only in fDMPs. One DMP in each sex connected the SNPs and gene expression of CALHM1 in females and CCDC149 in males. PPI subnetworks revealed that both female- and male-related differential DNAm interacted with synapse-related dysregulation. Immune-related pathways were unique for females and neuron-related pathways were associated with males. This study reveals remarkable quantitative differences in DNAm-related sexual dimorphism in SCZ and that females have a higher dysregulation burden of SCZ-associated DNAm than males.

Hierarchical deconvolution for extensive cell type resolution in the human brain using DNA methylation

Introduction

The human brain comprises heterogeneous cell types whose composition can be altered with physiological and pathological conditions. New approaches to discern the diversity and distribution of brain cells associated with neurological conditions would significantly advance the study of brain-related pathophysiology and neuroscience. Unlike single-nuclei approaches, DNA methylation-based deconvolution does not require special sample handling or processing, is cost-effective, and easily scales to large study designs. Existing DNA methylation-based methods for brain cell deconvolution are limited in the number of cell types deconvolved.

Methods

Using DNA methylation profiles of the top cell-type-specific differentially methylated CpGs, we employed a hierarchical modeling approach to deconvolve GABAergic neurons, glutamatergic neurons, astrocytes, microglial cells, oligodendrocytes, endothelial cells, and stromal cells.

Results

We demonstrate the utility of our method by applying it to data on normal tissues from various brain regions and in aging and diseased tissues, including Alzheimer's disease, autism, Huntington's disease, epilepsy, and schizophrenia.

Discussion

We expect that the ability to determine the cellular composition in the brain using only DNA from bulk samples will accelerate understanding brain cell type composition and cell-type-specific epigenetic states in normal and diseased brain tissues.

Exploring the mediation of DNA methylation across the epigenome between childhood adversity and First Episode of Psychosis-findings from the EU-GEI study

ABTRACT

Studies conducted in psychotic disorders have shown that DNA-methylation (DNAm) is sensitive to the impact of Childhood Adversity (CA). However, whether it mediates the association between CA and psychosis is yet to be explored. Epigenome wide association studies (EWAS) using the Illumina Infinium-Methylation EPIC array in peripheral blood tissue from 366 First-episode of psychosis and 517 healthy controls was performed. Adversity scores were created for abuse, neglect and composite adversity with the Childhood Trauma Questionnaire (CTQ). Regressions examining (I) CTQ scores with psychosis; (II) with DNAm EWAS level and (III) between DNAm and caseness, adjusted for a variety of confounders were conducted. Divide-Aggregate Composite-null Test for the composite null-hypothesis of no mediation effect was conducted. Enrichment analyses were conducted with missMethyl package and the KEGG database. Our results show that CA was associated with psychosis (Composite: OR = 1.68; p = <0.001; abuse: OR = 2.16; p < 0.001; neglect: OR = 2.27; p = <0.001). None of the CpG sites significantly mediated the adversity-psychosis association after Bonferroni correction (p < 8.1 × 10-8). However, 28, 34 and 29 differentially methylated probes associated with 21, 27, 20 genes passed a less stringent discovery threshold (p < 5 × 10-5) for composite, abuse and neglect respectively, with a lack of overlap between abuse and neglect. These included genes previously associated to psychosis in EWAS studies, such as PANK1, SPEG TBKBP1, TSNARE1 or H2R. Downstream gene ontology analyses did not reveal any biological pathways that survived false discovery rate correction. Although at a non-significant level, DNAm changes in genes previously associated with schizophrenia in EWAS studies may mediate the CA-psychosis association. These results and associated involved processes such as mitochondrial or histaminergic disfunction, immunity or neural signalling requires replication in well powered samples. The lack of overlap between mediating genes associated with abuse and neglect suggests differential biological trajectories linking CA subtypes and psychosis.

A Higher Dysregulation Burden of Brain DNA Methylation in Female Patients Implicated in the Sex Bias of Schizophrenia

Sex differences are pervasive in schizophrenia (SCZ), but the extent and magnitude of DNA methylation (DNAm) changes underlying these differences remain uncharacterized. In this study, sex-stratified differential DNAm analysis was performed in postmortem brain samples from 117 SCZ and 137 controls, partitioned into discovery and replication datasets. Three differentially methylated positions (DMPs) were identified (adj. p  < 0.05) in females and 29 DMPs in males without overlap between them. Over 81% of these sex-stratified DMPs were directionally consistent between sexes but with different effect sizes. Down-sampling analysis revealed more DMPs in females than in males when the sample sizes matched. Females had higher DNAm levels in healthy individuals and larger magnitude of DNAm changes in patients than males. Despite similar proportions of female-related DMPs (fDMPs, 8%) being under genetic control compared with males (10%), significant enrichment of DMP-related SNPs in signals of genome-wide association studies was identified only in fDMPs. One DMP in each sex connected the SNPs and gene expression of CALHM1 in females and CCDC149 in males. PPI subnetworks revealed that both female- and male-related differential DNAm interacted with synapse-related dysregulation. Immune-related pathways were unique for females and neuron-related pathways were associated with males. This study reveals remarkable quantitative differences in DNAm-related sexual dimorphism in SCZ and that females have a higher dysregulation burden of SCZ-associated DNAm than males.

Methylome-wide Association Study of Patients with Recent-onset Psychosis

Objective

Dysregulation of gene expression through epigenetic mechanisms may have a vital role in the pathogenesis of schizophrenia (SZ). In this study, we investigated the association of altered methylation patterns with SZ symptoms and early trauma in patients and healthy controls.

Methods

The present study was conducted to identify methylation changes in CpG sites in peripheral blood associated with recent-onset (RO) psychosis using methylome-wide analysis. Lifestyle factors, such as smoking, alcohol, exercise, and diet, were controlled.

Results

We identified 2,912 differentially methylated CpG sites in patients with RO psychosis compared to controls. Most of the genes associated with the top 20 differentially methylated sites had not been reported in previous methylation studies and were involved in apoptosis, autophagy, axonal growth, neuroinflammation, protein folding, etc. The top 15 significantly enriched Kyoto Encyclopedia of Genes and Genomes pathways included the oxytocin signaling pathway, long-term depression pathway, axon guidance, endometrial cancer, long-term potentiation, mitogen-activated protein kinase signaling pathway, and glutamatergic pathway, among others. In the patient group, significant associations of novel methylated genes with early trauma and psychopathology were observed.

Conclusion

Our results suggest an association of differential DNA methylation with the pathophysiology of psychosis and early trauma. Blood DNA methylation signatures show promise as biomarkers of future psychosis.

Epigenetic regulation of fetal brain development in pig

How fetal brain development is regulated at the molecular level is not well understood. Due to ethical challenges associated with research on the human fetus, large animals particularly pigs are increasingly used to study development and disorders of fetal brain. The pig fetal brain grows rapidly during the last ∼ 50 days before birth which is around day 60 (d60) of pig gestation. But what regulates the onset of accelerated growth of the brain is unknown. The current study tests the hypothesis that epigenetic alteration around d60 is involved in the onset of rapid growth of fetal brain of pig. To test this hypothesis, DNA methylation changes of fetal brain was assessed in a genome-wide manner by Enzymatic Methyl-seq (EM-seq) during two gestational periods (GP): d45 vs. d60 (GP1) and d60 vs. d90 (GP2). The cytosine-guanine (CpG) methylation data was analyzed in an integrative manner with the RNA-seq data generated from the same brain samples from our earlier study. A neural network based modeling approach was implemented to learn changes in methylation patterns of the differentially expressed genes, and then predict methylations of the brain in a genome-wide manner during rapid growth. This approach identified specific methylations that changed in a mutually informative manner during rapid growth of the fetal brain. These methylations were significantly overrepresented in specific genic as well as intergenic features including CpG islands, introns, and untranslated regions. In addition, sex-bias methylations of known single nucleotide polymorphic sites were also identified in the fetal brain ide during rapid growth.

NRN1 Gene as a Potential Marker of Early-Onset Schizophrenia: Evidence from Genetic and Neuroimaging Approaches

Included in the neurotrophins family, the Neuritin 1 gene (NRN1) has emerged as an attractive candidate gene for schizophrenia (SZ) since it has been associated with the risk for the disorder and general cognitive performance. In this work, we aimed to further investigate the association of NRN1 with SZ by exploring its role on age at onset and its brain activity correlates. First, we developed two genetic association analyses using a family-based sample (80 early-onset (EO) trios (offspring onset ≤ 18 years) and 71 adult-onset (AO) trios) and an independent case–control sample (120 healthy subjects (HS), 87 EO and 138 AO patients). Second, we explored the effect of NRN1 on brain activity during a working memory task (N-back task; 39 HS, 39 EO and 39 AO; matched by age, sex and estimated IQ). Different haplotypes encompassing the same three Single Nucleotide Polymorphisms(SNPs, rs3763180–rs10484320–rs4960155) were associated with EO in the two samples (GCT, TCC and GTT). Besides, the GTT haplotype was associated with worse N-back task performance in EO and was linked to an inefficient dorsolateral prefrontal cortex activity in subjects with EO compared to HS. Our results show convergent evidence on the NRN1 association with EO both from genetic and neuroimaging approaches, highlighting the role of neurotrophins in the pathophysiology of SZ.

Can epigenetics shine a light on the biological pathways underlying major mental disorders?

A significant proportion of the global burden of disease can be attributed to mental illness. Despite important advances in identifying risk factors for mental health conditions, the biological processing underlying causal pathways to disease onset remain poorly understood. This represents a limitation to implement effective prevention and the development of novel pharmacological treatments. Epigenetic mechanisms have emerged as mediators of environmental and genetic risk factors which might play a role in disease onset, including childhood adversity (CA) and cannabis use (CU). Particularly, human research exploring DNA methylation has provided new and promising insights into the role of biological pathways implicated in the aetio-pathogenesis of psychiatric conditions, including: monoaminergic (Serotonin and Dopamine), GABAergic, glutamatergic, neurogenesis, inflammatory and immune response and oxidative stress. While these epigenetic changes have been often studied as disease-specific, similarly to the investigation of environmental risk factors, they are often transdiagnostic. Therefore, we aim to review the existing literature on DNA methylation from human studies of psychiatric diseases (i) to identify epigenetic modifications mapping onto biological pathways either transdiagnostically or specifically related to psychiatric diseases such as Eating Disorders, Post-traumatic Stress Disorder, Bipolar and Psychotic Disorder, Depression, Autism Spectrum Disorder and Anxiety Disorder, and (ii) to investigate a convergence between some of these epigenetic modifications and the exposure to known risk factors for psychiatric disorders such as CA and CU, as well as to other epigenetic confounders in psychiatry research.

Epigenetic Basis of Psychiatric Disorders: A Narrative Review

BACKGROUND

Psychiatric disorders are complex, multifactorial illnesses with a demonstrated biological component in their etiopathogenesis. Epigenetic modifications, through the modulation of DNA methylation, histone modifications and RNA interference, tune tissue-specific gene expression patterns and play a relevant role in the etiology of psychiatric illnesses.

OBJECTIVE

This review aims to discuss the epigenetic mechanisms involved in psychiatric disorders, their modulation by environmental factors and their interactions with genetic variants, in order to provide a comprehensive picture of their mutual crosstalk.

METHODS

In accordance with the PRISMA guidelines, systematic searches of Medline, EMBASE, PsycINFO, Web of Science, Scopus, and the Cochrane Library were conducted.

RESULTS

Exposure to environmental factors, such as poor socio-economic status, obstetric complications, migration, and early life stressors, may lead to stable changes in gene expression and neural circuit function, playing a role in the risk of psychiatric diseases. The most replicated genes involved by studies using different techniques are discussed. Increasing evidence indicates that these sustained abnormalities are maintained by epigenetic modifications in specific brain regions and they interact with genetic variants in determining the risk of psychiatric disorders.

CONCLUSION

An increasing amount of evidence suggests that epigenetics plays a pivotal role in the etiopathogenesis of psychiatric disorders. New therapeutic approaches may work by reversing detrimental epigenetic changes that occurred during the lifespan.

DNA methylation signature as a biomarker of major neuropsychiatric disorders

DNA methylation is a broadly-investigated epigenetic modification that has been considered as a heritable and reversible change. Previous findings have indicated that DNA methylation regulates gene expression in the central nervous system (CNS). Also, disturbance of DNA methylation patterns has been associated with destructive consequences that lead to human brain diseases such as neuropsychiatric disorders (NPDs). In this review, we comprehensively discuss the mechanism and function of DNA methylation and its most recent associations with the pathology of NPDs-including major depressive disorder (MDD), schizophrenia (SZ), autism spectrum disorder (ASD), bipolar disorder (BD), and attention/deficit hyperactivity disorder (ADHD). We also discuss how heterogeneous findings demand further investigations. Finally, based on the recent studies we conclude that DNA methylation status may have implications in clinical diagnostics and therapeutics as a potential epigenetic biomarker of NPDs.

DNA methylation differences in cortical grey and white matter in schizophrenia

Aim: Identify grey- and white-matter-specific DNA-methylation differences between schizophrenia (SCZ) patients and controls in postmortem brain cortical tissue. Materials & methods: Grey and white matter were separated from postmortem brain tissue of the superior temporal and medial frontal gyrus from SCZ (n = 10) and control (n = 11) cases. Genome-wide DNA-methylation analysis was performed using the Infinium EPIC Methylation Array (Illumina, CA, USA). Results: Four differentially methylated regions associated with SCZ status and tissue type (grey vs white matter) were identified within or near KLF9, SFXN1, SPRED2 and ALS2CL genes. Gene-expression analysis showed differential expression of KLF9 and SFXN1 in SCZ. Conclusion: Our data show distinct differences in DNA methylation between grey and white matter that are unique to SCZ, providing new leads to unravel the pathogenesis of SCZ.

Mapping chromatin accessibility and active regulatory elements reveals pathological mechanisms in human gliomas

Chromatin structure and accessibility, and combinatorial binding of transcription factors to regulatory elements in genomic DNA control transcription. Genetic variations in genes encoding histones, epigenetics-related enzymes or modifiers affect chromatin structure/dynamics and result in alterations in gene expression contributing to cancer development or progression. Gliomas are brain tumors frequently associated with epigenetics-related gene deregulation. We perform whole-genome mapping of chromatin accessibility, histone modifications, DNA methylation patterns and transcriptome analysis simultaneously in multiple tumor samples to unravel epigenetic dysfunctions driving gliomagenesis. Based on the results of the integrative analysis of the acquired profiles, we create an atlas of active enhancers and promoters in benign and malignant gliomas. We explore these elements and intersect with Hi-C data to uncover molecular mechanisms instructing gene expression in gliomas.

Cord blood DNA methylation modifications in infants are associated with white matter microstructure in the context of prenatal maternal depression and anxiety

Maternal and environmental factors influence brain networks and architecture via both physiological pathways and epigenetic modifications. In particular, prenatal maternal depression and anxiety symptoms appear to impact infant white matter (WM) microstructure, leading us to investigate whether epigenetic modifications (i.e., DNA methylation) contribute to these WM differences. To determine if infants of women with depression and anxiety symptoms exhibit epigenetic modifications linked to neurodevelopmental changes, 52 umbilical cord bloods (CBs) were profiled. We observed 219 differentially methylated genomic positions (DMPs; FDR p < 0.05) in CB that were associated with magnetic resonance imaging measures of WM microstructure at 1 month of age and in regions previously described to be related to maternal depression and anxiety symptoms. Genomic characterization of these associated DMPs revealed 143 unique genes with significant relationships to processes involved in neurodevelopment, GTPase activity, or the canonical Wnt signaling pathway. Separate regression models for female (n = 24) and male (n = 28) infants found 142 associated DMPs in females and 116 associated DMPs in males (nominal p value < 0.001, R > 0.5), which were annotated to 98 and 81 genes, respectively. Together, these findings suggest that umbilical CB DNA methylation levels at birth are associated with 1-month WM microstructure.

Human brain region-specific variably methylated regions are enriched for heritability of distinct neuropsychiatric traits

BACKGROUND

DNA methylation dynamics in the brain are associated with normal development and neuropsychiatric disease and differ across functionally distinct brain regions. Previous studies of genome-wide methylation differences among human brain regions focus on limited numbers of individuals and one to two brain regions.

RESULTS

Using GTEx samples, we generate a resource of DNA methylation in purified neuronal nuclei from 8 brain regions as well as lung and thyroid tissues from 12 to 23 donors. We identify differentially methylated regions between brain regions among neuronal nuclei in both CpG (181,146) and non-CpG (264,868) contexts, few of which were unique to a single pairwise comparison. This significantly expands the knowledge of differential methylation across the brain by 10-fold. In addition, we present the first differential methylation analysis among neuronal nuclei from basal ganglia tissues and identify unique CpG differentially methylated regions, many associated with ion transport. We also identify 81,130 regions of variably CpG methylated regions, i.e., variable methylation among individuals in the same brain region, which are enriched in regulatory regions and in CpG differentially methylated regions. Many variably methylated regions are unique to a specific brain region, with only 202 common across all brain regions, as well as lung and thyroid. Variably methylated regions identified in the amygdala, anterior cingulate cortex, and hippocampus are enriched for heritability of schizophrenia.

CONCLUSIONS

These data suggest that epigenetic variation in these particular human brain regions could be associated with the risk for this neuropsychiatric disorder.

A region-based method for causal mediation analysis of DNA methylation data

Exposure to environmental factors can affect DNA methylation at a 5'-cytosine-phosphate-guanine-3' (CpG) site or a genomic region, which can then affect an outcome. In other words, environmental effects on an outcome could be mediated by DNA methylation. To date, single CpG-site-based mediation analysis has been employed extensively. More recently, however, there has been considerable interest in studying differentially methylated regions (DMRs), both because DMRs are more likely to have functional effects than single CpG sites and because testing DMRs reduces multiple testing. In this report, we propose a novel causal mediation approach under the counterfactual framework to test the significance of total (TE), direct (DE), and indirect effects (IE) of predictors on response variable with a methylated region (MR) as the mediator (denoted as MR-Mediation). Functional linear transformation is used to reduce the possible high dimension of the CpG sites in a predefined MR and to account for their location information. In our simulation studies, MR-Mediation retained the desired Type I error rates for TE, DE, and IE tests. Furthermore, MR-Mediation had better power performance than testing mean methylation level as the mediator in most considered scenarios, especially for IE (i.e., mediated effect) test, which could be more interesting than the other two effect tests. We further illustrate our proposed method by analysing the methylation mediated effect of exposure to gun violence on total immunoglobulin E or atopic asthma among participants in the Epigenetic Variation and Childhood Asthma in Puerto Ricans study.

Methylome-wide association study of first-episode schizophrenia reveals a hypermethylated CpG site in the promoter region of the TNIK susceptibility gene

Accumulating evidence suggests that epigenetics plays an important role in the etiology of schizophrenia. Here, we performed a methylome-wide association study (MWAS) of first-onset schizophrenia patients and controls from the Han Chinese population using microarray technology. The DNA methylation profiles revealed 4494 differentially methylated CpG sites. Gene ontology (GO) analysis showed that the functions of differentially methylated genes were primarily involved in enzymatic activity, cytoskeleton organization and cell adhesion, and the TNIK (encoding TRAF2- and NCK-interacting kinase) gene was enriched in most of these terms. By combining the MWAS results with those of previous genome-wide association studies (GWASs), we identified 72 candidate genes located in 49 human genome loci. Among the overlapping genes, the most significantly methylated CpG sites were in the transcriptional start site (TSS) 200 region (cg21413905, P_unadjusted = 3.20 × 10^-5) of TNIK. TNIK was listed in the top 50 differentially methylated loci. The results of pyrosequencing and TNIK mRNA expression were consistent with those of the microarray study. Bioinformatics analyses, dual-luciferase reporter assays and chromatin immunoprecipitation (ChIP) studies showed that TNIK interacted with genes associated with schizophrenia and NRF1 was identified as a novel transcription factor (TF) that binds to TNIK in its TSS200 region. Thus, the regulatory function of NRF1 may be influenced by the status of the methylated CpG site in this region. In summary, our study provides new insights into the epigenetic mechanisms that regulate schizophrenia. Studies of the functions of TNIK methylation should be performed in vitro and in vivo to provide a better understanding of the pathophysiology of schizophrenia.

Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex

Human DNA methylation data have been used to develop biomarkers of ageing, referred to as ‘epigenetic clocks’, which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into ‘training’ and ‘testing’ samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

Machine learning for deciphering cell heterogeneity and gene regulation

Epigenetics studies inheritable and reversible modifications of DNA that allow cells to control gene expression throughout their development and in response to environmental conditions. In computational epigenomics, machine learning is applied to study various epigenetic mechanisms genome wide. Its aim is to expand our understanding of cell differentiation, that is their specialization, in health and disease. Thus far, most efforts focus on understanding the functional encoding of the genome and on unraveling cell-type heterogeneity. Here, we provide an overview of state-of-the-art computational methods and their underlying statistical concepts, which range from matrix factorization and regularized linear regression to deep learning methods. We further show how the rise of single-cell technology leads to new computational challenges and creates opportunities to further our understanding of epigenetic regulation.

DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia

We performed a systematic analysis of blood DNA methylation profiles from 4483 participants from seven independent cohorts identifying differentially methylated positions (DMPs) associated with psychosis, schizophrenia, and treatment-resistant schizophrenia. Psychosis cases were characterized by significant differences in measures of blood cell proportions and elevated smoking exposure derived from the DNA methylation data, with the largest differences seen in treatment-resistant schizophrenia patients. We implemented a stringent pipeline to meta-analyze epigenome-wide association study (EWAS) results across datasets, identifying 95 DMPs associated with psychosis and 1048 DMPs associated with schizophrenia, with evidence of colocalization to regions nominated by genetic association studies of disease. Many schizophrenia-associated DNA methylation differences were only present in patients with treatment-resistant schizophrenia, potentially reflecting exposure to the atypical antipsychotic clozapine. Our results highlight how DNA methylation data can be leveraged to identify physiological (e.g., differential cell counts) and environmental (e.g., smoking) factors associated with psychosis and molecular biomarkers of treatment-resistant schizophrenia.

Epigenetic Modifications in Schizophrenia and Related Disorders: Molecular Scars of Environmental Exposures and Source of Phenotypic Variability

Epigenetic modifications are increasingly recognized to play a role in the etiology and pathophysiology of schizophrenia and other psychiatric disorders with developmental origins. Here, we summarize clinical and preclinical findings of epigenetic alterations in schizophrenia and relevant disease models and discuss their putative origin. Recent findings suggest that certain schizophrenia risk loci can influence stochastic variation in gene expression through epigenetic processes, highlighting the intricate interaction between genetic and epigenetic control of neurodevelopmental trajectories. In addition, a substantial portion of epigenetic alterations in schizophrenia and related disorders may be acquired through environmental factors and may be manifested as molecular "scars." Some of these scars can influence brain functions throughout the entire lifespan and may even be transmitted across generations via epigenetic germline inheritance. Epigenetic modifications, whether caused by genetic or environmental factors, are plausible molecular sources of phenotypic heterogeneity and offer a target for therapeutic interventions. The further elucidation of epigenetic modifications thus may increase our knowledge regarding schizophrenia's heterogeneous etiology and pathophysiology and, in the long term, may advance personalized treatments through the use of biomarker-guided epigenetic interventions.

Epigenetic mechanisms in schizophrenia and other psychotic disorders: a systematic review of empirical human findings

Schizophrenia and other psychotic disorders are highly debilitating psychiatric conditions that lack a clear etiology and exhibit polygenic inheritance underlain by pleiotropic genes. The prevailing explanation points to the interplay between predisposing genes and environmental exposure. Accumulated evidence suggests that epigenetic regulation of the genome may mediate dynamic gene-environment interactions at the molecular level by modulating the expression of psychiatric phenotypes through transcription factors. This systematic review summarizes the current knowledge linking schizophrenia and other psychotic disorders to epigenetics, based on PubMed and Web of Science database searches conducted according to the PRISMA guidelines. Three groups of mechanisms in case-control studies  of human tissue (i.e., postmortem brain and bio-fluids) were considered: DNA methylation, histone modifications, and non-coding miRNAs. From the initial pool of 3,204 records, 152 studies met our inclusion criteria (11,815/11,528, 233/219, and 2,091/1,827 cases/controls for each group, respectively). Many of the findings revealed associations with epigenetic modulations of genes regulating neurotransmission, neurodevelopment, and immune function, as well as differential miRNA expression (e.g., upregulated miR-34a, miR-7, and miR-181b). Overall, actual evidence moderately supports an association between epigenetics and schizophrenia and other psychotic disorders. However, heterogeneous results and cross-tissue extrapolations call for future work. Integrating epigenetics into systems biology may critically enhance research on psychosis and thus our understanding of the disorder. This may have implications for psychiatry in risk stratification, early recognition, diagnostics, precision medicine, and other interventional approaches targeting epigenetic fingerprints.

Association of a Reproducible Epigenetic Risk Profile for Schizophrenia With Brain Methylation and Function

IMPORTANCE

Schizophrenia is a severe mental disorder in which epigenetic mechanisms may contribute to illness risk. Epigenetic profiles can be derived from blood cells, but to our knowledge, it is unknown whether these predict established brain alterations associated with schizophrenia.

OBJECTIVE

To identify an epigenetic signature (quantified as polymethylation score [PMS]) of schizophrenia using machine learning applied to genome-wide blood DNA-methylation data; evaluate whether differences in blood-derived PMS are mirrored in data from postmortem brain samples; test whether the PMS is associated with alterations of dorsolateral prefrontal cortex hippocampal (DLPFC-HC) connectivity during working memory in healthy controls (HC); explore the association between interactions between polygenic and epigenetic risk with DLPFC-HC connectivity; and test the specificity of the signature compared with other serious psychiatric disorders.

DESIGN, SETTING, AND PARTICIPANTS

In this case-control study conducted from 2008 to 2018 in sites in Germany, the United Kingdom, the United States, and Australia, blood DNA-methylation data from 2230 whole-blood samples from 6 independent cohorts comprising HC (1238 [55.5%]) and participants with schizophrenia (803 [36.0%]), bipolar disorder (39 [1.7%]), major depressive disorder 35 [1.6%]), and autism (27 [1.2%]), and first-degree relatives of all patient groups (88 [3.9%]) were analyzed. DNA-methylation data were further explored from 244 postmortem DLPFC samples from 136 HC and 108 patients with schizophrenia. Neuroimaging and genome-wide association data were available for 393 HC. The latter data was used to calculate a polygenic risk score (PRS) for schizophrenia. The data were analyzed in 2019.

MAIN OUTCOMES AND MEASURES

The accuracy of schizophrenia control classification based on machine learning using epigenetic data; association of schizophrenia PMS scores with DLPFC-HC connectivity; and association of the interaction between PRS and PMS with DLPFC-HC connectivity.

RESULTS

This study included 7488 participants (4395 men [58.7%]), of whom 3158 (2230 men [70.6%]) received a diagnosis of schizophrenia. The PMS signature was associated with schizophrenia across 3 independent data sets (area under the curve [AUC] from 0.69 to 0.78; P value from 0.049 to 1.24 × 10-7) and data from postmortem DLPFC samples (AUC = 0.63; P = 1.42 × 10-4), but not with major depressive disorder (AUC = 0.51; P = .16), autism (AUC = 0.53; P = .66), or bipolar disorder (AUC = 0.58; P = .21). Pathways contributing most to the classification included synaptic processes. Healthy controls with schizophrenia-like PMS showed significantly altered DLPFC-HC connectivity (validation methylation/magnetic resonance imaging, t < -3.81; P for familywise error, <.04; validation magnetic resonance imaging, t < -3.54; P for familywise error, <.02), mirroring the lack of functional decoupling in schizophrenia. There was no significant association of the interaction between PMS and PRS with DLPFC-HC connectivity (P > .19).

CONCLUSIONS AND RELEVANCE

We identified a reproducible blood DNA-methylation signature specific for schizophrenia that was correlated with altered functional DLPFC-HC coupling during working memory and mapped to methylation differences found in DLPFC postmortem samples. This indicates a possible epigenetic contribution to a schizophrenia intermediate phenotype and suggests that PMS could be of interest to be studied in the context of multimodal biomarkers for disease stratification and treatment personalization.

Profiling haplotype specific CpG and CpH methylation within a schizophrenia GWAS locus on chromosome 14 in schizophrenia and healthy subjects

Interrogating DNA methylation within schizophrenia risk loci holds promise to identify mechanisms by which genes influence the disease. Based on the hypothesis that allele specific methylation (ASM) of a single CpG, or perhaps CpH, might mediate or mark the effects of genetic variants on disease risk and phenotypes, we explored haplotype specific methylation levels of individual cytosines within a genomic region harbouring the BAG5, APOPT1 and KLC1 genes in peripheral blood of schizophrenia patients and healthy controls. Three DNA fragments located in promoter, intronic and intergenic areas were studied by single-molecule real-time bisulfite sequencing enabling the analysis of long reads of DNA with base-pair resolution and the determination of haplotypes directly from sequencing data. Among 1,012 cytosines studied, we did not find any site where methylation correlated with the disease or cognitive deficits after correction for multiple testing. At the same time, we determined the methylation profile associated with the schizophrenia risk haplotype within the KLC1 fourth intron and confirmed ASM for cytosines located in the vicinity of rs67899457. These genetically associated DNA methylation variations may be related to the pathophysiological mechanism differentiating the risk and non-risk haplotypes and merit further investigation.

Genome-wide DNA methylation meta-analysis in the brains of suicide completers

Suicide is the second leading cause of death globally among young people representing a significant global health burden. Although the molecular correlates of suicide remains poorly understood, it has been hypothesised that epigenomic processes may play a role. The objective of this study was to identify suicide-associated DNA methylation changes in the human brain by utilising previously published and unpublished methylomic datasets. We analysed prefrontal cortex (PFC, n = 211) and cerebellum (CER, n = 114) DNA methylation profiles from suicide completers and non-psychiatric, sudden-death controls, meta-analysing data from independent cohorts for each brain region separately. We report evidence for altered DNA methylation at several genetic loci in suicide cases compared to controls in both brain regions with suicide-associated differentially methylated positions enriched among functional pathways relevant to psychiatric phenotypes and suicidality, including nervous system development (PFC) and regulation of long-term synaptic depression (CER). In addition, we examined the functional consequences of variable DNA methylation within a PFC suicide-associated differentially methylated region (PSORS1C3 DMR) using a dual luciferase assay and examined expression of nearby genes. DNA methylation within this region was associated with decreased expression of firefly luciferase but was not associated with expression of nearby genes, PSORS1C3 and POU5F1. Our data suggest that suicide is associated with DNA methylation, offering novel insights into the molecular pathology associated with suicidality.

coMethDMR: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes

Recent technology has made it possible to measure DNA methylation profiles in a cost-effective and comprehensive genome-wide manner using array-based technology for epigenome-wide association studies. However, identifying differentially methylated regions (DMRs) remains a challenging task because of the complexities in DNA methylation data. Supervised methods typically focus on the regions that contain consecutive highly significantly differentially methylated CpGs in the genome, but may lack power for detecting small but consistent changes when few CpGs pass stringent significance threshold after multiple comparison. Unsupervised methods group CpGs based on genomic annotations first and then test them against phenotype, but may lack specificity because the regional boundaries of methylation are often not well defined. We present coMethDMR, a flexible, powerful, and accurate tool for identifying DMRs. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first. Next, coMethDMR tests association between methylation levels within the sub-region and phenotype via a random coefficient mixed effects model that models both variations between CpG sites within the region and differential methylation simultaneously. coMethDMR offers well-controlled Type I error rate, improved specificity, focused testing of targeted genomic regions, and is available as an open-source R package.

Systematic underestimation of the epigenetic clock and age acceleration in older subjects

Background

The Horvath epigenetic clock is widely used. It predicts age quite well from 353 CpG sites in the DNA methylation profile in unknown samples and has been used to calculate “age acceleration” in various tissues and environments.

Results

The model systematically underestimates age in tissues from older people. This is seen in all examined tissues but most strongly in the cerebellum and is consistently observed in multiple datasets. Age acceleration is thus age-dependent, and this can lead to spurious associations. The current literature includes examples of association tests with age acceleration calculated in a wide variety of ways.

Conclusions

The concept of an epigenetic clock is compelling, but caution should be taken in interpreting associations with age acceleration. Association tests of age acceleration should include age as a covariate.

DNA Methylation and Susceptibility to Autism Spectrum Disorder

The prevalence of autism spectrum disorder (ASD) has been increasing steadily over the last 20 years; however, the molecular basis for the majority of ASD cases remains unknown. Recent advances in next-generation sequencing and detection of DNA modifications have made methylation-dependent regulation of transcription an attractive hypothesis for being a causative factor in ASD etiology. Evidence for abnormal DNA methylation in ASD can be seen on multiple levels, from genetic mutations in epigenetic machinery to loci-specific and genome-wide changes in DNA methylation. Epimutations in DNA methylation can be acquired throughout life, as global DNA methylation reprogramming is dynamic during embryonic development and the early postnatal period that corresponds to the peak time of synaptogenesis. However, technical advances and causative evidence still need to be established before abnormal DNA methylation and ASD can be confidently associated.

The fetal origins of mental illness

The impact of infections and inflammation during pregnancy on the developing fetal brain remains incompletely defined, with important clinical and research gaps. Although the classic infectious TORCH pathogens (ie, Toxoplasma gondii, rubella virus, cytomegalovirus [CMV], herpes simplex virus) are known to be directly teratogenic, emerging evidence suggests that these infections represent the most extreme end of a much larger spectrum of injury. We present the accumulating evidence that prenatal exposure to a wide variety of viral and bacterial infections-or simply inflammation-may subtly alter fetal brain development, leading to neuropsychiatric consequences for the child later in life. The link between influenza infections in pregnant women and an increased risk for development of schizophrenia in their children was first described more than 30 years ago. Since then, evidence suggests that a range of infections during pregnancy may also increase risk for autism spectrum disorder and depression in the child. Subsequent studies in animal models demonstrated that both pregnancy infections and inflammation can result in direct injury to neurons and neural progenitor cells or indirect injury through activation of microglia and astrocytes, which can trigger cytokine production and oxidative stress. Infectious exposures can also alter placental serotonin production, which can perturb neurotransmitter signaling in the developing brain. Clinically, detection of these subtle injuries to the fetal brain is difficult. As the neuropsychiatric impact of perinatal infections or inflammation may not be known for decades after birth, our construct for defining teratogenic infections in pregnancy (eg, TORCH) based on congenital anomalies is insufficient to capture the full adverse impact on the child. We discuss the clinical implications of this body of evidence and how we might place greater emphasis on prevention of prenatal infections. For example, increasing uptake of the seasonal influenza vaccine is a key strategy to reduce perinatal infections and the risk for fetal brain injury. An important research gap exists in understanding how antibiotic therapy during pregnancy affects the fetal inflammatory load and how to avoid inflammation-mediated injury to the fetal brain. In summary, we discuss the current evidence and mechanisms linking infections and inflammation with the increased lifelong risk of neuropsychiatric disorders in the child, and how we might improve prenatal care to protect the fetal brain.

Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods

Schizophrenia is a common mental disorder with high heritability. It is genetically complex and to date more than a hundred risk loci have been identified. Association of environmental factors and schizophrenia has also been reported, while epigenetic analyses have yielded ambiguous and sometimes conflicting results. Here, we analyzed fresh frozen post-mortem brain tissue from a cohort of 73 subjects diagnosed with schizophrenia and 52 control samples, using the Illumina Infinium HumanMethylation450 Bead Chip, to investigate genome-wide DNA methylation patterns in the two groups. Analysis of differential methylation was performed with the Bioconductor Minfi package and modern machine-learning and visualization techniques, which were shown previously to be successful in detecting and highlighting differentially methylated patterns in case-control studies. In this dataset, however, these methods did not uncover any significant signals discerning the patient group and healthy controls, suggesting that if there are methylation changes associated with schizophrenia, they are heterogeneous and complex with small effect.

Translational bioinformatics in mental health: open access data sources and computational biomarker discovery

Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data.

Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array

Background

There has been a steady increase in the number of studies aiming to identify DNA methylation differences associated with complex phenotypes. Many of the challenges of epigenetic epidemiology regarding study design and interpretation have been discussed in detail, however there are analytical concerns that are outstanding and require further exploration. In this study we seek to address three analytical issues. First, we quantify the multiple testing burden and propose a standard statistical significance threshold for identifying DNA methylation sites that are associated with an outcome. Second, we establish whether linear regression, the chosen statistical tool for the majority of studies, is appropriate and whether it is biased by the underlying distribution of DNA methylation data. Finally, we assess the sample size required for adequately powered DNA methylation association studies.

Results

We quantified DNA methylation in the Understanding Society cohort (n = 1175), a large population based study, using the Illumina EPIC array to assess the statistical properties of DNA methylation association analyses. By simulating null DNA methylation studies, we generated the distribution of p-values expected by chance and calculated the 5% family-wise error for EPIC array studies to be 9 × 10^− 8. Next, we tested whether the assumptions of linear regression are violated by DNA methylation data and found that the majority of sites do not satisfy the assumption of normal residuals. Nevertheless, we found no evidence that this bias influences analyses by increasing the likelihood of affected sites to be false positives. Finally, we performed power calculations for EPIC based DNA methylation studies, demonstrating that existing studies with data on ~ 1000 samples are adequately powered to detect small differences at the majority of sites.

Conclusion

We propose that a significance threshold of P < 9 × 10^− 8 adequately controls the false positive rate for EPIC array DNA methylation studies. Moreover, our results indicate that linear regression is a valid statistical methodology for DNA methylation studies, despite the fact that the data do not always satisfy the assumptions of this test. These findings have implications for epidemiological-based studies of DNA methylation and provide a framework for the interpretation of findings from current and future studies.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5761-7) contains supplementary material, which is available to authorized users.

Molecular windows into the human brain for psychiatric disorders

Delineating the pathophysiology of psychiatric disorders has been extremely challenging but technological advances in recent decades have facilitated a deeper interrogation of molecular processes in the human brain. Initial candidate gene expression studies of the postmortem brain have evolved into genome wide profiling of the transcriptome and the epigenome, a critical regulator of gene expression. Here, we review the potential and challenges of direct molecular characterization of the postmortem human brain, and provide a brief overview of recent transcriptional and epigenetic studies with respect to neuropsychiatric disorders. Such information can now be leveraged and integrated with the growing number of genome-wide association databases to provide a functional context of trait-associated genetic variants linked to psychiatric illnesses and related phenotypes. While it is clear that the field is still developing and challenges remain to be surmounted, these recent advances nevertheless hold tremendous promise for delineating the neurobiological underpinnings of mental diseases and accelerating the development of novel medication strategies.

DNA methylation and inflammation marker profiles associated with a history of depression

Depression is a common and disabling disorder, representing a major social and economic health issue. Moreover, depression is associated with the progression of diseases with an inflammatory etiology including many inflammatory-related disorders. At the molecular level, the mechanisms by which depression might promote the onset of these diseases and associated immune-dysfunction are not well understood. In this study we assessed genome-wide patterns of DNA methylation in whole blood-derived DNA obtained from individuals with a self-reported history of depression (n = 100) and individuals without a history of depression (n = 100) using the Illumina 450K microarray. Our analysis identified six significant (Šidák corrected P < 0.05) depression-associated differentially methylated regions (DMRs); the top-ranked DMR was located in exon 1 of the LTB4R2 gene (Šidák corrected P = 1.27 × 10-14). Polygenic risk scores (PRS) for depression were generated and known biological markers of inflammation, telomere length (TL) and IL-6, were measured in DNA and serum samples, respectively. Next, we employed a systems-level approach to identify networks of co-methylated loci associated with a history of depression, in addition to depression PRS, TL and IL-6 levels. Our analysis identified one depression-associated co-methylation module (P = 0.04). Interestingly, the depression-associated module was highly enriched for pathways related to immune function and was also associated with TL and IL-6 cytokine levels. In summary, our genome-wide DNA methylation analysis of individuals with and without a self-reported history of depression identified several candidate DMRs of potential relevance to the pathogenesis of depression and its associated immune-dysfunction phenotype.

Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of complex traits and disease

Integrative approaches that harness large-scale molecular datasets can help develop mechanistic insight into findings from genome-wide association studies (GWAS). We have performed extensive analyses to uncover transcriptional and epigenetic processes which may play a role in complex trait variation. This was undertaken by applying Bayesian multiple-trait colocalization systematically across the genome to identify genetic variants responsible for influencing intermediate molecular phenotypes as well as complex traits. In this analysis, we leveraged high-dimensional quantitative trait loci data derived from the prefrontal cortex tissue (concerning gene expression, DNA methylation and histone acetylation) and GWAS findings for five complex traits (Neuroticism, Schizophrenia, Educational Attainment, Insomnia and Alzheimer's disease). There was evidence of colocalization for 118 associations, suggesting that the same underlying genetic variant influenced both nearby gene expression as well as complex trait variation. Of these, 73 associations provided evidence that the genetic variant also influenced proximal DNA methylation and/or histone acetylation. These findings support previous evidence at loci where epigenetic mechanisms may putatively mediate effects of genetic variants on traits, such as KLC1 and schizophrenia. We also uncovered evidence implicating novel loci in disease susceptibility, including genes expressed predominantly in the brain tissue, such as MDGA1, KIRREL3 and SLC12A5. An inverse relationship between DNA methylation and gene expression was observed more than can be accounted for by chance, supporting previous findings implicating DNA methylation as a transcriptional repressor. Our study should prove valuable in helping future studies prioritize candidate genes and epigenetic mechanisms for in-depth functional follow-up analyses.

Identification of Key lncRNAs Associated With Atherosclerosis Progression Based on Public Datasets

Atherosclerosis is one of the most common type of cardiovascular disease and the prime cause of mortality in the aging population worldwide. However, the detail mechanisms and special biomarkers of atherosclerosis remain to be further investigated. Lately, long non-coding RNAs (lncRNAs) has attracted much more attention than other types of ncRNAs. In our work, we found and confirmed differently expressed lncRNAs and mRNAs in atherosclerosis by analyzing GSE28829. We performed the weighted gene co-expression network analysis (WGCNA) by analyzing GSE40231 to confirm highly correlated genes. Gene Ontology (GO) analysis were utilized to assess the potential functions of differential expressed lncRNAs in atherosclerosis. Co-expression networks were also constructed to confirm hub lncRNAs in atherosclerosis. A total of 5784 mRNAs and 654 lncRNAs were found to be dysregulated in the progression of atherosclerosis. A total of 15 lncRNA-mRNA co-expression modules were identified in this study based on WGCNA analysis. Moreover, a few lncRNAs, such as ZFAS1, LOC100506730, LOC100506691, DOCK9-AS2, RP11-6I2.3, LOC100130219, were confirmed as important lncRNAs in atherosclerosis. Taken together, bioinformatics analysis revealed these lncRNAs were involved in regulating the leukotriene biosynthetic process, gene expression, actin filament organization, t-circle formation, antigen processing, and presentation, interferon-gamma-mediated signaling pathway, and activation of GTPase activity. We believed that this study would provide potential novel therapeutic and prognostic targets for atherosclerosis.

Longitudinal investigation of DNA methylation changes preceding adolescent psychotic experiences

Childhood psychotic experiences (PEs), such as seeing or hearing things that others do not, or extreme paranoia, are relatively common with around 1 in 20 children reporting them at age 12. Childhood PEs are often distressing and can be predictive of schizophrenia, other psychiatric disorders, and suicide attempts in adulthood, particularly if they persist during adolescence. Previous research has demonstrated that methylomic signatures in blood could be potential biomarkers of psychotic phenomena. This study explores the association between DNA methylation (DNAm) and the emergence, persistence, and remission of PEs in childhood and adolescence. DNAm profiles were obtained from the ALSPAC cohort at birth, age 7, and age 15/17 (n = 901). PEs were assessed through interviews with participants at ages 12 and 18. We identified PE-associated probes (p < 5 × 10^-5) and regions (corrected p < 0.05) at ages 12 and 18. Several of the differentially methylated probes were also associated with the continuity of PEs across adolescence. One probe (cg16459265), detected consistently at multiple timepoints in the study sample, was replicated in an independent sample of twins (n = 1658). Six regions, including those spanning the HLA-DBP2 and GDF7 genes, were consistently differentially methylated at ages 7 and 15-17. Findings from this large, population-based study suggest that DNAm at multiple stages of development may be associated with PEs in late childhood and adolescence, though further replication is required. Research uncovering biomarkers associated with pre-clinical PEs is important as it has the potential to facilitate early identification of individuals at increased risk who could benefit from preventive interventions.

Meta-analysis of expression and methylation signatures indicates a stress-related epigenetic mechanism in multiple neuropsychiatric disorders

Similar environmental risk factors have been implicated in different neuropsychiatric disorders (including major psychiatric and neurodegenerative diseases), indicating the existence of common epigenetic mechanisms underlying the pathogenesis shared by different illnesses. To investigate such commonality, we applied an unsupervised computational approach identifying several consensus co-expression and co-methylation signatures from a data cohort of postmortem prefrontal cortex (PFC) samples from individuals with six different neuropsychiatric disorders-schizophrenia, bipolar disorder, major depression, alcoholism, Alzheimer's and Parkinson's-as well as healthy controls. Among our results, we identified a pair of strongly interrelated co-expression and co-methylation (E-M) signatures showing consistent and significant disease association in multiple types of disorders. This E-M signature was enriched for interneuron markers, and we further demonstrated that it is unlikely for this enrichment to be due to varying subpopulation abundance of normal interneurons across samples. Moreover, gene set enrichment analysis revealed overrepresentation of stress-related biological processes in this E-M signature. Our integrative analysis of expression and methylation profiles, therefore, suggests a stress-related epigenetic mechanism in the brain, which could be associated with the pathogenesis of multiple neuropsychiatric diseases.

A survey of inter-individual variation in DNA methylation identifies environmentally responsive co-regulated networks of epigenetic variation in the human genome

While population studies have resulted in detailed maps of genetic variation in humans, to date there are few robust maps of epigenetic variation. We identified sites containing clusters of CpGs with high inter-individual epigenetic variation, termed Variably Methylated Regions (VMRs) in five purified cell types. We observed that VMRs occur preferentially at enhancers and 3' UTRs. While the majority of VMRs have high heritability, a subset of VMRs within the genome show highly correlated variation in trans, forming co-regulated networks that have low heritability, differ between cell types and are enriched for specific transcription factor binding sites and biological pathways of functional relevance to each tissue. For example, in T cells we defined a network of 95 co-regulated VMRs enriched for genes with roles in T-cell activation; in fibroblasts a network of 34 co-regulated VMRs comprising all four HOX gene clusters enriched for control of tissue growth; and in neurons a network of 18 VMRs enriched for roles in synaptic signaling. By culturing genetically-identical fibroblasts under varying environmental conditions, we experimentally demonstrated that some VMR networks are responsive to the environment, with methylation levels at these loci changing in a coordinated fashion in trans dependent on cellular growth. Intriguingly these environmentally-responsive VMRs showed a strong enrichment for imprinted loci (p<10-80), suggesting that these are particularly sensitive to environmental conditions. Our study provides a detailed map of common epigenetic variation in the human genome, showing that both genetic and environmental causes underlie this variation.

Epigenome-wide association study of suicide attempt in schizophrenia

Schizophrenia is a major clinical problem and represents a major risk factor for suicide. The molecular mechanisms of suicidal behavior in psychosis remain poorly investigated, although it has been hypothesized that epigenetic processes are involved in the etiology of both psychosis and suicidality. In this study, epigenome-wide patterns of methylation were measured in schizophrenia suicide attempters (n = 54) and schizophrenia non-suicide attempters (n = 69) using DNA extracted from white blood cells (WBC). Analyses focused on identifying differentially methylated CpG sites and gene regions between the attempters and non-attempters. We identified the CpG site cg19647197 within the CCDC53 gene, which is characterized by hypomethylation of WBC in the attempters compared to the non-attempters. Our results suggest that there is variation in DNA methylation associated with suicide attempt that may offer novel highlights into the molecular mechanisms linked to suicide attempt associated with schizophrenia.

Genetic vulnerability to DUSP22 promoter hypermethylation is involved in the relation between in utero famine exposure and schizophrenia

Epigenetic changes may account for the doubled risk to develop schizophrenia in individuals exposed to famine in utero. We therefore investigated DNA methylation in a unique sample of patients and healthy individuals conceived during the great famine in China. Subsequently, we examined two case-control samples without famine exposure in whole blood and brain tissue. To shed light on the causality of the relation between famine exposure and DNA methylation, we exposed human fibroblasts to nutritional deprivation. In the famine-exposed schizophrenia patients, we found significant hypermethylation of the dual specificity phosphatase 22 (DUSP22) gene promoter (Chr6:291687-293285) (N = 153, p = 0.01). In this sample, DUSP22 methylation was also significantly higher in patients independent of famine exposure (p = 0.025), suggesting that hypermethylation of DUSP22 is also more generally involved in schizophrenia risk. Similarly, DUSP22 methylation was also higher in two separate case-control samples not exposed to famine using DNA from whole blood (N = 64, p = 0.03) and postmortem brains (N = 214, p = 0.007). DUSP22 methylation showed strong genetic regulation across chromosomes by a region on chromosome 16 which was consistent with new 3D genome interaction data. The presence of a direct link between famine and DUSP22 transcription was supported by data from cultured human fibroblasts that showed increased methylation (p = 0.048) and expression (p = 0.019) in response to nutritional deprivation (N = 10). These results highlight an epigenetic locus that is genetically regulated across chromosomes and that is involved in the response to early-life exposure to famine and that is relevant for a major psychiatric disorder.

Prenatal exposure to environmental insults and enhanced risk of developing Schizophrenia and Autism Spectrum Disorder: focus on biological pathways and epigenetic mechanisms

When considering neurodevelopmental disorders (NDDs), Schizophrenia (SZ) and Autism Spectrum Disorder (ASD) are considered to be among the most severe in term of prevalence, morbidity and impact on the society. Similar features and overlapping symptoms have been observed at multiple levels, suggesting common pathophysiological bases. Indeed, recent genome-wide association studies (GWAS) and epidemiological data report shared vulnerability genes and environmental triggers across the two disorders. In this review, we will discuss the possible biological mechanisms, including glutamatergic and GABAergic neurotransmissions, inflammatory signals and oxidative stress related systems, which are targeted by adverse environmental exposures and that have been associated with the development of SZ and ASD. We will also discuss the emerging role of the gut microbiome as possible interplay between environment, immune system and brain development. Finally, we will describe the involvement of epigenetic mechanisms in the maintenance of long-lasting effects of adverse environments early in life. This will allow us to better understand the pathophysiology of these NDDs, and also to identify novel targets for future treatment strategies.

Neuroepigenetics of Schizophrenia

Schizophrenia is a complex disorder of the brain, where genetic variants explain only a portion of risk. Neuroepigenetic mechanisms may explain the remaining share of risk, as well as the transition from susceptibility to the actual disease. Here, we discuss the most recent findings in the field of brain epigenetics applied to the study of schizophrenia. Methylome studies have found several candidates exhibiting methylation modifications in association with the disorder, but genes affected do not always overlap. Notably, these studies converge in that genes within the schizophrenia risk loci or genes differentially methylated in patients affected with the disorder are dynamically regulated during early life. They also imply that schizophrenia-associated genetic variation may affect DNA methylation in fetal and adult brains. Histone modifications may help mediating the effect of genetic risk variants associated with schizophrenia, and regulating chromatin higher-order structure. The 3D-organization of chromatin in the brain creates physical interactions within chromosomes, so that schizophrenia-associated genetic variants can be linked with genes distant from their loci; this suggests that chromatin conformation matters in the mechanism of risk for the disorder. Non-coding RNAs provide a novel and complex mechanism of gene regulation potentially significant for schizophrenia, as proposed by research on specific microRNAs and long non-coding RNAs (lncRNAs). Finally, a recent study in epitranscriptomics identifies RNA methylation as a further epigenetic mechanism active in human brain and specifically in a portion of the transcriptome associated with schizophrenia susceptibility. These findings indicate that, as expected from the complexity of the brain and its development, several epigenetic mechanisms may intervene in the etiopathogenesis of schizophrenia. An understanding of their roles calls for research approaches integrating the investigation of different epigenetic mechanisms and of environmental and genetic risk, in the context of development.

DNA Methylation and Psychiatric Disorders

DNA methylation has been an important area of research in the study of molecular mechanism to psychiatric disorders. Recent evidence has suggested that abnormalities in global methylation, methylation of genes, and pathways could play a role in the etiology of many forms of mental illness. In this article, we review the mechanisms of DNA methylation, including the genetic and environmental factors affecting methylation changes. We report and discuss major findings regarding DNA methylation in psychiatric patients, both within the context of global methylation studies and gene-specific methylation studies. Finally, we discuss issues surrounding data quality improvement, the limitations of current methylation analysis methods, and the possibility of using DNA methylation-based treatment for psychiatric disorders in the future.

Characterization of cross-tissue genetic-epigenetic effects and their patterns in schizophrenia

Background

One of the major challenges in current psychiatric epigenetic studies is the tissue specificity of epigenetic changes since access to brain samples is limited. Peripheral tissues have been studied as surrogates but the knowledge of cross-tissue genetic-epigenetic characteristics remains largely unknown. In this work, we conducted a comprehensive investigation of genetic influence on DNA methylation across brain and peripheral tissues with the aim to characterize cross-tissue genetic-epigenetic effects and their roles in the pathophysiology of psychiatric disorders.

Methods

Genome-wide methylation quantitative trait loci (meQTLs) from brain prefrontal cortex, whole blood, and saliva were identified separately and compared. Focusing on cis-acting effects, we tested the enrichment of cross-tissue meQTLs among cross-tissue expression QTLs and genetic risk loci of various diseases, including major psychiatric disorders. CpGs targeted by cross-tissue meQTLs were also tested for genomic distribution and functional enrichment as well as their contribution to methylation correlation across tissues. Finally, a consensus co-methylation network analysis on the cross-tissue meQTL targeted CpGs was performed on data of the three tissues collected from schizophrenia patients and controls.

Results

We found a significant overlap of cis meQTLs (45–73 %) and targeted CpG sites (31–68 %) among tissues. The majority of cross-tissue meQTLs showed consistent signs of cis-acting effects across tissues. They were significantly enriched in genetic risk loci of various diseases, especially schizophrenia, and also enriched in cross-tissue expression QTLs. Compared to CpG sites not targeted by any meQTLs, cross-tissue targeted CpGs were more distributed in CpG island shores and enhancer regions, and more likely had strong correlation with methylation levels across tissues. The targeted CpGs were also annotated to genes enriched in multiple psychiatric disorders and neurodevelopment-related pathways. Finally, we identified one co-methylation network shared between brain and blood showing significant schizophrenia association (p = 5.5 × 10⁻⁶).

Conclusions

Our results demonstrate prevalent cross-tissue meQTL effects and their contribution to the correlation of CpG methylation across tissues, while at the same time a large portion of meQTLs show tissue-specific characteristics, especially in brain. Significant enrichment of cross-tissue meQTLs in expression QTLs and genetic risk loci of schizophrenia suggests the potential of these cross-tissue meQTLs for studying the genetic effect on schizophrenia. The study provides compelling motivation for a well-designed experiment to further validate the use of surrogate tissues in the study of psychiatric disorders.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0519-4) contains supplementary material, which is available to authorized users.

[A search of target regions for association studies between DNA methylation and cognitive impairment in schizophrenia]

AIM

To develop a strategy for the search for candidate genes and targets for epigenetic studies of cognitive impairments in patients with schizophrenia.

MATERIAL AND METHODS

A search for literature on epigenetics of schizophrenia and cognitive functions was performed. Single nucleotide polymorphisms (SNPs) that can create or abolish a site for DNA methylation or transcription factor sites were determined using a custom script.

RESULTS AND CONCLUSION

Eight candidate genes, including BDNF, COMT, RELN, SNRPN, PSMA4, FAM63B, IL-1RAP, MAD1L1, as well as 750 targets in CpG islands in the linkage regions identified in GWAS of schizophrenia and 406 targets in SNV located within transcription factor binding sites were selected.