Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex

Neonatal handling enhances behavioural and attentional domains, and frontocortical synaptic maturation in rat models of schizophrenia-like behaviour and anxiety-related responses

The Roman inbred rat strains are a neurodevelopmental model, with the Roman High Avoidance (RHA) presenting specific behaviours and frontal cortex (FC) gene expression changes relevant to schizophrenia symptoms. We wanted to assess the potentially positive modulatory and enduring effects of neonatal handling (NH) on the innate traits associated with both the RHA and their counterpart Roman Low Avoidance (RLA). Male rats received NH or were left untreated (controls). Two different age groups were considered: adolescent and adults. The assessment encompassed exploratory behaviour, social behaviour, anxiety-related behaviour (self-grooming), sensorimotor gating (prepulse inhibition; PPI), and the analysis of gene expression associated with synaptic processes, cortical maturation, and neuroplasticity in the FC. In adolescent rats, NH increased novelty exploration and activity, and reduced novelty-induced self-grooming in RLAs, whereas it improved PPI in RHAs. In adult rats, NH increased novelty-induced activity in both strains, reduced self-grooming in RLA rats, and enhanced social interaction and PPI in RHAs. NH produced significant effects on gene expression in adolescent RHA rats. These effects were observed at the presynaptic level by a reduction of Snap25 and increases of Cables1 and Cdk5, and at the postsynaptic level by increases of Grin2b, Homer1 and Nrg1, as well as by a NH-induced enhancement of Bdnf. NH also increased Nrg1 and Bdnf expression in adult RLA rats. These findings show for the first time that NH is able to modulate several genetically linked synaptic/neuroplasticity alterations in RHA vs. RLA rats, which are paralleled by NH-induced improvements in novelty exploration, social behaviour and sensorimotor gating (PPI).

A Genome-Wide Association Study of First-Episode Psychosis: A Genetic Exploration in an Italian Cohort

BACKGROUND

Psychosis, particularly schizophrenia (SZ), is influenced by genetic and environmental factors. The neurodevelopmental hypothesis suggests that genetic factors affect neuronal circuit connectivity during perinatal periods, hence causing the onset of the diseases. In this study, we performed a genome-wide association study (GWAS) in a sample of the first episode of psychosis (FEP).

METHODS

A sample of 147 individuals diagnosed with non-affective psychosis and 102 controls were recruited and assessed. After venous blood and DNA extraction, the samples were genotyped. Genetic data underwent quality controls, genotype imputation, and a case-control genome-wide association study (GWAS). After the GWAS, results were investigated using an in silico functional mapping and annotation approach.

RESULTS

Our GWAS showed the association of 27 variants across 13 chromosomes at genome-wide significance (p < 1 × 10-7) and a total of 1976 candidate variants across 188 genes at suggestive significance (p < 1 × 10-5), mostly mapping in non-coding or intergenic regions. Gene-based tests reported the association of the SUFU (p = 4.8 × 10-7) and NCAN (p = 1.6 × 10-5) genes. Gene-sets enrichment analyses showed associations in the early stages of life, spanning from 12 to 24 post-conception weeks (p < 1.4 × 10-3) and in the late prenatal period (p = 1.4 × 10-3), in favor of the neurodevelopmental hypothesis. Moreover, several matches with the GWAS Catalog reported associations with strictly related traits, such as SZ, as well as with autism spectrum disorder, which shares some genetic overlap, and risk factors, such as neuroticism and alcohol dependence.

CONCLUSIONS

The resulting genetic associations and the consequent functional analysis displayed common genetic liability between the non-affective psychosis, related traits, and risk factors. In sum, our investigation provided novel hints supporting the neurodevelopmental hypothesis in SZ and-in general-in non-affective psychoses.

Schizophrenia is associated with altered DNA methylation variance

Varying combinations of genetic and environmental risk factors are thought to underpin phenotypic heterogeneity between individuals in psychiatric conditions such as schizophrenia. While epigenome-wide association studies in schizophrenia have identified extensive alteration of mean DNA methylation levels, less is known about the location and impact of DNA methylation variance, which could contribute to phenotypic and treatment response heterogeneity. To explore this question, we conducted the largest meta-analysis of blood DNA methylation variance in schizophrenia to date, leveraging three cohorts comprising 1036 individuals with schizophrenia and 954 non-psychiatric controls. Surprisingly, only a small proportion (0.1%) of the 213 variably methylated positions (VMPs) associated with schizophrenia (Benjamini-Hochberg FDR < 0.05) were shared with differentially methylated positions (DMPs; sites with mean changes between cases and controls). These blood-derived VMPs were found to be overrepresented in genes previously associated with schizophrenia and amongst brain-enriched genes, with evidence of concordant changes at VMPs in the cerebellum, hippocampus, prefrontal cortex, or striatum. Epigenetic covariance was also observed with respect to clinically significant metrics including age of onset, cognitive deficits, and symptom severity. We also uncovered a significant VMP in individuals with first-episode psychosis (n = 644) from additional cohorts and a non-psychiatric comparison group (n = 633). Collectively, these findings suggest schizophrenia is associated with significant changes in DNA methylation variance, which may contribute to individual-to-individual heterogeneity.

Perturbed cell fate decision by schizophrenia-associated AS3MTd2d3 isoform during corticogenesis

The neurodevelopmental theory of schizophrenia emphasizes early brain development in its etiology. Genome-wide association studies have linked schizophrenia to genetic variations of AS3MT (arsenite methyltransferase) gene, particularly the increased expression of AS3MTd2d3 isoform. To investigate the biological basis of this association with schizophrenia pathophysiology, we established a transgenic mouse model (AS3MTd2d3-Tg) ectopically expressing AS3MTd2d3 at the cortical neural stem cells. AS3MTd2d3-Tg mice exhibited enlarged ventricles and deficits in sensorimotor gating and sociability. Single-cell and single-nucleus RNA sequencing analyses of AS3MTd2d3-Tg brains revealed cell fate imbalances and altered excitatory neuron composition. AS3MTd2d3 localized to centrosome, disrupting mitotic spindle orientation and differentiation in developing neocortex and organoids, in part through NPM1 (Nucleophosmin 1). The structural analysis identified that hydrophobic residues exposed in AS3MTd2d3 are critical for its pathogenic function. Therefore, our findings may help to explain the early pathological features of schizophrenia.

Mendelian randomization reveals causalities between DNA methylation and schizophrenia

BACKGROUND

Epigenetic factors (such as DNA methylation) have been widely reported to be associated with schizophrenia (SCZ). However, the causal relationships between epigenetic factors and SCZ remain largely unknown.

METHODS

Here we conducted a Mendelian randomization study to investigate the causal relationships between DNA methylation and SCZ. Brain methylation quantitative trait loci (mQTL) (N = 1,160) and blood mQTL data (N = 27,750) were used as exposures, and genome-wide association data of SCZ (53,386 cases and 77,258 controls) were used as outcome.

RESULTS

We identified 172 (mapped to 160 genes) and 157 (mapped to 155 genes) methylation sites whose methylation levels in the brain and blood are causally associated with SCZ, respectively. Among the mapped genes, 36 overlapping genes were identified. Interestingly, three methylation sites (near BRD2, CNNM2, and RERE) showed significant associations in both brain and blood, with the same direction of effect. We further performed MR analysis using brain expression quantitative trait (eQTL) as exposures and identified 123 genes whose expression levels were causally associated with SCZ. Comparing the significant genes from eQTL and brain mQTL prioritized 15 overlapping genes, suggesting that both epigenetic modification and expression of these genes confer risk of SCZ. Finally, we validated our findings with genome editing and animal model experiments.

CONCLUSIONS

Our study identified methylation sites whose methylation levels are causally associated with SCZ and demonstrated the important roles of epigenetic factors in SCZ. Besides, our findings also reveal pivotal risk genes whose expression and epigenetic regulation are causally associated with SCZ.

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

Increasing evidence supports the role of the placenta in neurodevelopment and in the onset of neuropsychiatric disorders. Recently, mQTL and iQTL maps have proven useful in understanding relationships between SNPs and GWAS that are not captured by eQTL. In this context, we propose that part of the genetic predisposition to complex neuropsychiatric disorders acts through placental DNA methylation. We construct a public placental cis-mQTL database including 214,830 CpG sites calculated in 368 fetal placenta DNA samples from the INMA project, and run cell type-, gestational age- and sex-imQTL models. We combine these data with summary statistics of GWAS on ten neuropsychiatric disorders using summary-based Mendelian randomization and colocalization. We also evaluate the influence of identified DNA methylation sites on placental gene expression in the RICHS cohort. We find that placental cis-mQTLs are enriched in placenta-specific active chromatin regions, and establish that part of the genetic burden for schizophrenia, bipolar disorder, and major depressive disorder confers risk through placental DNA methylation. The potential causality of several of the observed associations is reinforced by secondary association signals identified in conditional analyses, the involvement of cell type-imQTLs, and the correlation of identified DNA methylation sites with the expression levels of relevant genes in the placenta.

Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia

DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell technologies enable profiling DNAm at cytosines in individual cells, but they often suffer from low coverage for CpG sites. We introduce scMeFormer, a transformer-based deep learning model for imputing DNAm states at each CpG site in single cells. Comprehensive evaluations across five single-nucleus DNAm datasets from human and mouse demonstrate scMeFormer's superior performance over alternative models, achieving high-fidelity imputation even with coverage reduced to 10% of original CpG sites. Applying scMeFormer to a single-nucleus DNAm dataset from the prefrontal cortex of patients with schizophrenia and controls identified thousands of schizophrenia-associated differentially methylated regions that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia. We anticipate that scMeFormer will be a valuable tool for advancing single-cell DNAm studies.

Methylation of the telomerase gene promoter region in umbilical cord blood of patients with gestational diabetes mellitus is associated with decreased telomerase expression levels and shortened telomere length

Objective

This study speculates that gestational diabetes mellitus (GDM) may reduce fetal telomere length (TL),which may be related to modification of methylation in the promoter region of the telomerase (TE) gene promoter region.

Methods

In this study, umbilical cord blood samples from patients with and without GDM (N = 100 each) were analyzed by prospective case-control. The TL, TE expression levels, and methylation levels of TERT and TERC gene promoter regions in two groups were measured. The significance of the methylation level of each CpG locus employed logistic regression analysis of R software, and the analysis of covariance (ANCOVA) was used to control the influence of confounding factors. Correlation analysis was performed by the Spearman.

Results

The TL and TE expression levels of the offspring of GDM patients were decreased despite adjusting for PBMI, PWG, and TG. A total of two CpG islands were screened in the promoter region of the TERT gene and three fragments (TERT_2, TERT_3, and TERT_4) containing a total of 70 CpG sites were designed. Additionally, four CpG sites of the TERT gene in the GDM group (TERT_2_40, TERT_2_47, TERT_3_46, and TERT_3_212) showed increased methylation levels compared with the control group (all P < 0.05). In the promoter region of the TERC gene, one CpG island containing 19 CpG loci was screened and designed, and the methylation levels of the two CpG sites were significantly different in TERC_1_67 (0.65 ± 0.21 versus 0.57 ± 0.30; P = 0.040) and TERC_1_120 (0.68 ± 0.23 versus 0.59 ± 0.27; P = 0.014). The methylation levels of TERC gene fragments of GDM patients were significantly higher than those of the control group (0.69 ± 0.06 versus 0.65 ± 0.08, P = 0.001).

Conclusion

This study revealed that GDM may induce decreased TE expression by increasing the methylation levels of TE genes promoter region, thereby reducing the TL.

Molecular insights into trauma: A framework of epigenetic pathways to resilience through intervention

Experiences of complex trauma and adversity, especially for children, are ongoing global crises necessitating adaptation. Bioadaptability to adversity and its health consequences emphasizes the dynamism of adaptation to trauma and the potential for research to inform intervention strategies. Epigenetic variability, particularly DNA methylation, associates with chronic adversity while allowing for resilience and adaptability. Epigenetics, including age- and site-specific changes in DNA methylation, gene-environment interactions, pharmacological responses, and biomarker characterization and evaluation, may aid in understanding trauma responses and promoting well-being by facilitating psychological and biological adaptation. Understanding these molecular processes provides a foundation for a biologically adaptive framework to shift public health strategies from restorative to long-term adaptation and resilience. Psychological, cultural, and biological trauma must be addressed in innovative interventions for vulnerable populations, particularly children and adolescents. Understanding molecular changes may provide a biopsychosocial perspective for culturally sensitive, evidence-based interventions that promote resilience and thriving in new settings.

A brain DNA co-methylation network analysis of psychosis in Alzheimer's disease

INTRODUCTION

The presence of psychosis in Alzheimer's disease (AD) is suggested to be associated with distinct molecular and neuropathological profiles in the brain.

METHODS

We assessed brain DNA methylation in AD donors with psychosis (AD+P) and without psychosis (AD-P) using the EPIC array. Weighted gene correlation network analysis identified modules of co-methylated genes in a discovery cohort (PITT-ADRC: N = 113 AD+P, N = 40 AD-P), with validation in an independent cohort (BDR: N = 79 AD+P, N = 117 AD-P), with Gene Ontology and cell-type enrichment analysis. Genetic data were integrated to identify methylation quantitative trait loci (mQTLs), which were co-localized with GWAS for related traits.

RESULTS

We replicated one AD+P associated module, which was enriched for synaptic pathways and in excitatory and inhibitory neurons. mQTLs in this module co-localized with variants associated with schizophrenia and educational attainment.

DISCUSSION

This represents the largest epigenetic study of AD+P to date, identifying pleiotropic relationships between AD+P and related traits.

HIGHLIGHTS

DNA methylation was assessed in the prefrontal cortex in subjects with AD+P and AD-P. WGCNA identified six modules of co-methylated loci associated with AD+P in a discovery cohort. One of the modules was replicated in an independent cohort. This module was enriched for synaptic genes and in excitatory and inhibitory neurons. mQTLs mapping to genes in the module co-localized with GWAS loci for schizophrenia and educational attainment.

Acid sphingomyelinase activity suggests a new antipsychotic pharmaco-treatment strategy for schizophrenia

Schizophrenia is a chronic and severe mental disorder. It is currently treated with antipsychotic drugs (APD). However, APD's work only in a limited number of patients and may have cognition impairing side effects. A growing body of evidence points out the potential involvement of abnormal sphingolipid metabolism in the pathophysiology of schizophrenia. Here, an analysis of human gene polymorphisms and brain gene expression in schizophrenia patients identified an association of SMPD1 and SMPD3 genes coding for acid- (ASM) and neutral sphingomyelinase-2 (NSM). In a rat model of psychosis using amphetamine hypersensitization, we found a locally restricted increase of ASM activity in the prefrontal cortex (PFC). Short-term haloperidol (HAL) treatment reversed behavioral symptoms and the ASM activity. A sphingolipidomic analysis confirmed an altered ceramide metabolism in the PFC during psychosis. Targeting enhanced ASM activity in a psychotic-like state with the ASM inhibitor KARI201 reversed psychotic like behavior and associated changes in the sphingolipidome. While effective HAL treatment led to locomotor decline and cognitive impairments, KARI201 did not. An RNA sequencing analysis of the PFC suggested a dysregulation of numerous schizophrenia related genes including Olig1, Fgfr1, Gpr17, Gna12, Abca2, Sox1, Dpm2, and Rab2a in the rat model of psychosis. HAL and KARI201 antipsychotic effects were associated with targeting expression of other schizophrenia associated genes like Col6a3, Slc22a8, and Bmal1, or Nr2f6a, respectively, but none affecting expression of sphingolipid regulating genes. Our data provide new insight into a potentially pathogenic mechanism of schizophrenia and suggest a new pharmaco-treatment strategy with reduced side effects.

Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders

DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory variants affecting DNAm levels in specific brain cell types, leveraging existing single-nucleus DNAm data from the human brain. We show that INTERACT accurately predicts cell type-specific DNAm profiles, achieving an average area under the receiver operating characteristic curve of 0.99 across cell types. Furthermore, INTERACT predicts cell type-specific DNAm regulatory variants, which reflect cellular context and enrich the heritability of brain-related traits in relevant cell types. We demonstrate that incorporating predicted variant effects and DNAm levels of CpG sites enhances the fine mapping for three brain disorders-schizophrenia, depression, and Alzheimer's disease-and facilitates mapping causal genes to particular cell types. Our study highlights the power of deep learning in identifying cell type-specific regulatory variants, which will enhance our understanding of the genetics of complex traits.

Circadian Rhythms Correlated in DNA Methylation and Gene Expression Identified in Human Blood and Implicated in Psychiatric Disorders

Circadian rhythms modulate the biology of many human tissues and are driven by a nearly 24-h transcriptional feedback loop. Dynamic DNA methylation may play a role in driving 24-h rhythms of gene expression in the human brain. However, little is known about the degree of circadian regulation between the DNA methylation and the gene expression in the peripheral tissues, including human blood. We hypothesized that 24-h rhythms of DNA methylation play a role in driving 24-h RNA expression in human blood. To test this hypothesis, we analyzed DNA methylation levels and RNA expression in blood samples collected from eight healthy males at six-time points over 24 h. We assessed 442,703 genome-wide CpG sites in methylation and 12,364 genes in expression for 24-h rhythmicity using the cosine model. Our analysis revealed significant rhythmic patterns in 6345 CpG sites and 21 genes. Next, we investigated the relationship between methylation and expression using powerful circadian signals. We found a modest negative correlation (ρ = -0.83, p = 0.06) between the expression of gene TXNDC5 and the methylation at the nearby CpG site (cg19116172). We also observed that circadian CpGs significantly overlapped with genetic risk loci of schizophrenia and autism spectrum disorders. Notably, one gene, TXNDC5, showed a significant correlation between circadian methylation and expression and has been reported to be association with neuropsychiatric diseases.

Epigenetic Control in Schizophrenia

Schizophrenia is a severe and complex psychiatric condition ranking among the top 15 leading causes of disability worldwide. Despite the well-established heritability component, a complex interplay between genetic and environmental risk factors plays a key role in the development of schizophrenia and psychotic disorders in general. This chapter covers all the clinical evidence showing how the analysis of the epigenetic modulation in schizophrenia might be relevant to understand the pathogenesis of schizophrenia as well as potentially useful to develop new pharmacotherapies.

Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution

The ability to accurately quantify biological age could help monitor and control healthy aging. Epigenetic clocks have emerged as promising tools for estimating biological age, yet they have been developed from heterogeneous bulk tissues, and are thus composites of two aging processes, one reflecting the change of cell-type composition with age and another reflecting the aging of individual cell-types. There is thus a need to dissect and quantify these two components of epigenetic clocks, and to develop epigenetic clocks that can yield biological age estimates at cell-type resolution. Here we demonstrate that in blood and brain, approximately 39% and 12% of an epigenetic clock's accuracy is driven by underlying shifts in lymphocyte and neuronal subsets, respectively. Using brain and liver tissue as prototypes, we build and validate neuron and hepatocyte specific DNA methylation clocks, and demonstrate that these cell-type specific clocks yield improved estimates of chronological age in the corresponding cell and tissue-types. We find that neuron and glia specific clocks display biological age acceleration in Alzheimer's Disease with the effect being strongest for glia in the temporal lobe. Moreover, CpGs from these clocks display a small but significant overlap with the causal DamAge-clock, mapping to key genes implicated in neurodegeneration. The hepatocyte clock is found accelerated in liver under various pathological conditions. In contrast, non-cell-type specific clocks do not display biological age-acceleration, or only do so marginally. In summary, this work highlights the importance of dissecting epigenetic clocks and quantifying biological age at cell-type resolution.

Modified Electroconvulsive Therapy Normalizes Plasma GNA13 Following Schizophrenic Relapse

OBJECTIVE

GNA13 is an important member of the G protein family, and its coding gene GNA13 has been identified as one of the risk genes for schizophrenia (SCZ). This study aimed to investigate the relationship between GNA13 levels and the clinical symptoms of SCZ following treatment with modified electroconvulsive therapy (MECT).

METHODS

This study recruited 82 SCZ patients and 86 healthy controls (HCs). Each SCZ patient received 6 sessions of MECT. The Positive and Negative Syndrome Scale (PANSS) was used to assess SCZ symptom severity. Plasma levels of GNA13 were measured by enzyme-linked immunosorbent assay.

RESULTS

Pretreatment, SCZ patients had a higher GNA13 level than HC ( t = 8.199, P < 0.001). MECT reduced the GNA13 level significantly ( t = 11.13, P < 0.001) and normalized the difference between SCZ and HC ( t = 0.219, P = 0.827). After treatment, the downregulation of GNA13 (ΔGNA13) was negatively correlated with the positive symptoms score reduction rate (ΔP) ( r = -0.379, P = 0.027) and positively correlated with the negative score reduction rate (ΔN) ( r = 0.480, P = 0.004) in females. In both males and females, the receiver operating characteristic curve revealed that the pretreatment GNA13 level could help differentiate SCZ from HC (male: area under the curve = 0.792, P < 0.001; female: area under the curve = 0.814, P < 0.001).

CONCLUSION

The reduced expression of GNA13 after MECT may be related to the exhibition of both negative and positive symptoms of SCZ in female patients.

Traversing the epigenetic landscape: DNA methylation from retina to brain in development and disease

DNA methylation plays a crucial role in development, aging, degeneration of various tissues and dedifferentiated cells. This review explores the multifaceted impact of DNA methylation on the retina and brain during development and pathological processes. First, we investigate the role of DNA methylation in retinal development, and then focus on retinal diseases, detailing the changes in DNA methylation patterns in diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. Since the retina is considered an extension of the brain, its unique structure allows it to exhibit similar immune response mechanisms to the brain. We further extend our exploration from the retina to the brain, examining the role of DNA methylation in brain development and its associated diseases, such as Alzheimer's disease (AD) and Huntington's disease (HD) to better understand the mechanistic links between retinal and brain diseases, and explore the possibility of communication between the visual system and the central nervous system (CNS) from an epigenetic perspective. Additionally, we discuss neurodevelopmental brain diseases, including schizophrenia (SZ), autism spectrum disorder (ASD), and intellectual disability (ID), focus on how DNA methylation affects neuronal development, synaptic plasticity, and cognitive function, providing insights into the molecular mechanisms underlying neurodevelopmental disorders.

Integrative analysis of genetics, epigenetics and RNA expression data reveal three susceptibility loci for smoking behavior in Chinese Han population

Despite numerous studies demonstrate that genetics and epigenetics factors play important roles on smoking behavior, our understanding of their functional relevance and coordinated regulation remains largely unknown. Here we present a multiomics study on smoking behavior for Chinese smoker population with the goal of not only identifying smoking-associated functional variants but also deciphering the pathogenesis and mechanism underlying smoking behavior in this under-studied ethnic population. After whole-genome sequencing analysis of 1329 Chinese Han male samples in discovery phase and OpenArray analysis of 3744 samples in replication phase, we discovered that three novel variants located near FOXP1 (rs7635815), and between DGCR6 and PRODH (rs796774020), and in ARVCF (rs148582811) were significantly associated with smoking behavior. Subsequently cis-mQTL and cis-eQTL analysis indicated that these variants correlated significantly with the differential methylation regions (DMRs) or differential expressed genes (DEGs) located in the regions where these variants present. Finally, our in silico multiomics analysis revealed several hub genes, like DRD2, PTPRD, FOXP1, COMT, CTNNAP2, to be synergistic regulated each other in the etiology of smoking.

Alcohol Use Disorder-Associated DNA Methylation in the Nucleus Accumbens and Dorsolateral Prefrontal Cortex

Background

Alcohol use disorder (AUD) has a profound public health impact. However, understanding of the molecular mechanisms that underlie the development and progression of AUD remains limited. Here, we investigated AUD-associated DNA methylation changes within and across 2 addiction-relevant brain regions, the nucleus accumbens and dorsolateral prefrontal cortex.

Methods

Illumina HumanMethylation EPIC array data from 119 decedents (61 cases, 58 controls) were analyzed using robust linear regression with adjustment for technical and biological variables. Associations were characterized using integrative analyses of public annotation data and published genetic and epigenetic studies. We also tested for brain region-shared and brain region-specific associations using mixed-effects modeling and assessed implications of these results using public gene expression data from human brain.

Results

At a false discovery rate of ≤.05, we identified 105 unique AUD-associated CpGs (annotated to 120 genes) within and across brain regions. AUD-associated CpGs were enriched in histone marks that tag active promoters, and our strongest signals were specific to a single brain region. Some concordance was found between our results and those of earlier published alcohol use or dependence methylation studies. Of the 120 genes, 23 overlapped with previous genetic associations for substance use behaviors, some of which also overlapped with previous addiction-related methylation studies.

Conclusions

Our findings identify AUD-associated methylation signals and provide evidence of overlap with previous genetic and methylation studies. These signals may constitute predisposing genetic differences or robust methylation changes associated with AUD, although more work is needed to further disentangle the mechanisms that underlie these associations and their implications for AUD.

Privacy-preserving biological age prediction over federated human methylation data using fully homomorphic encryption

DNA methylation data play a crucial role in estimating chronological age in mammals, offering real-time insights into an individual's aging process. The epigenetic pacemaker (EPM) model allows inference of the biological age as deviations from the population trend. Given the sensitivity of this data, it is essential to safeguard both inputs and outputs of the EPM model. A privacy-preserving approach for EPM computation utilizing fully homomorphic encryption was recently introduced. However, this method has limitations, including having high communication complexity and being impractical for large data sets. The current work presents a new privacy-preserving protocol for EPM computation, analytically improving both privacy and complexity. Notably, we employ a single server for the secure computation phase while ensuring privacy even in the event of server corruption (compared to requiring two noncolluding servers in prior work). Using techniques from symbolic algebra and number theory, the new protocol eliminates the need for communication during secure computation, significantly improves asymptotic runtime, and offers better compatibility to parallel computing for further time complexity reduction. We implemented our protocol, demonstrating its ability to produce results similar to the standard (insecure) EPM model with substantial performance improvement compared to prior work. These findings hold promise for enhancing data security in medical applications where personal privacy is paramount. The generality of both the new approach and the EPM suggests that this protocol may be useful in other applications employing similar expectation-maximization techniques.

Smoking-informed methylation and expression QTLs in human brain and colocalization with smoking-associated genetic loci

Smoking is a leading cause of preventable morbidity and mortality. Smoking is heritable, and genome-wide association studies (GWASs) of smoking behaviors have identified hundreds of significant loci. Most GWAS-identified variants are noncoding with unknown neurobiological effects. We used genome-wide genotype, DNA methylation, and RNA sequencing data in postmortem human nucleus accumbens (NAc) to identify cis-methylation/expression quantitative trait loci (meQTLs/eQTLs), investigate variant-by-cigarette smoking interactions across the genome, and overlay QTL evidence at smoking GWAS-identified loci to evaluate their regulatory potential. Active smokers (N = 52) and nonsmokers (N = 171) were defined based on cotinine biomarker levels and next-of-kin reporting. We simultaneously tested variant and variant-by-smoking interaction effects on methylation and expression, separately, adjusting for biological and technical covariates and correcting for multiple testing using a two-stage procedure. We found >2 million significant meQTL variants (padj < 0.05) corresponding to 41,695 unique CpGs. Results were largely driven by main effects, and five meQTLs, mapping to NUDT12, FAM53B, RNF39, and ADRA1B, showed a significant interaction with smoking. We found 57,683 significant eQTL variants for 958 unique eGenes (padj < 0.05) and no smoking interactions. Colocalization analyses identified loci with smoking-associated GWAS variants that overlapped meQTLs/eQTLs, suggesting that these heritable factors may influence smoking behaviors through functional effects on methylation/expression. One locus containing MUSTN1 and ITIH4 colocalized across all data types (GWAS, meQTL, and eQTL). In this first genome-wide meQTL map in the human NAc, the enriched overlap with smoking GWAS-identified genetic loci provides evidence that gene regulation in the brain helps explain the neurobiology of smoking behaviors.

A perspective on epigenomic aging processes in the human brain and their plasticity in patients with mental disorders - a systematic review

The debate surrounding nature versus nurture remains a central question in neuroscience, psychology, and in psychiatry, holding implications for both aging processes and the etiology of mental illness. Epigenetics can serve as a bridge between genetic predisposition and environmental influences, thus offering a potential avenue for addressing these questions. Epigenetic clocks, in particular, offer a theoretical framework for measuring biological age based on DNA methylation signatures, enabling the identification of disparities between biological and chronological age. This structured review seeks to consolidate current knowledge regarding the relationship between mental disorders and epigenetic age within the brain. Through a comprehensive literature search encompassing databases such as EBSCO, PubMed, and ClinicalTrials.gov, relevant studies were identified and analyzed. Studies that met inclusion criteria were scrutinized, focusing on those with large sample sizes, analyses of both brain tissue and blood samples, investigation of frontal cortex markers, and a specific emphasis on schizophrenia and depressive disorders. Our review revealed a paucity of significant findings, yet notable insights emerged from studies meeting specific criteria. Studies characterized by extensive sample sizes, analysis of brain tissue and blood samples, assessment of frontal cortex markers, and a focus on schizophrenia and depressive disorders yielded particularly noteworthy results. Despite the limited number of significant findings, these studies shed light on the complex interplay between epigenetic aging and mental illness. While the current body of literature on epigenetic aging in mental disorders presents limited significant findings, it underscores the importance of further research in this area. Future studies should prioritize large sample sizes, comprehensive analyses of brain tissue and blood samples, exploration of specific brain regions such as the frontal cortex, and a focus on key mental disorders. Such endeavors will contribute to a deeper understanding of the relationship between epigenetic aging and mental illness, potentially informing novel diagnostic and therapeutic approaches.

Map of epigenetic age acceleration: A worldwide analysis

We present a systematic analysis of epigenetic age acceleration based on by far the largest collection of publicly available DNA methylation data for healthy samples (93 datasets, 23 K samples), focusing on the geographic (25 countries) and ethnic (31 ethnicities) aspects around the world. We employed the most popular epigenetic tools for assessing age acceleration and examined their quality metrics and ability to extrapolate to epigenetic data from different tissue types and age ranges different from the training data of these models. In most cases, the models proved to be inconsistent with each other and showed different signs of age acceleration, with the PhenoAge model tending to systematically underestimate and different versions of the GrimAge model tending to systematically overestimate the age prediction of healthy subjects. Referring to data availability and consistency, most countries and populations are still not represented in GEO, moreover, different datasets use different criteria for determining healthy controls. Because of this, it is difficult to fully isolate the contribution of "geography/environment", "ethnicity" and "healthiness" to epigenetic age acceleration. Among the explored metrics, only the DunedinPACE, which measures aging rate, appears to adequately reflect the standard of living and socioeconomic indicators in countries, although it has a limited application to blood methylation data only. Invariably, by epigenetic age acceleration, males age faster than females in most of the studied countries and populations.

Genetic mechanisms for impaired synaptic plasticity in schizophrenia revealed by computational modeling

Schizophrenia phenotypes are suggestive of impaired cortical plasticity in the disease, but the mechanisms of these deficits are unknown. Genomic association studies have implicated a large number of genes that regulate neuromodulation and plasticity, indicating that the plasticity deficits have a genetic origin. Here, we used biochemically detailed computational modeling of postsynaptic plasticity to investigate how schizophrenia-associated genes regulate long-term potentiation (LTP) and depression (LTD). We combined our model with data from postmortem RNA expression studies (CommonMind gene-expression datasets) to assess the consequences of altered expression of plasticity-regulating genes for the amplitude of LTP and LTD. Our results show that the expression alterations observed post mortem, especially those in the anterior cingulate cortex, lead to impaired protein kinase A (PKA)-pathway-mediated LTP in synapses containing GluR1 receptors. We validated these findings using a genotyped electroencephalogram (EEG) dataset where polygenic risk scores for synaptic and ion channel-encoding genes as well as modulation of visual evoked potentials were determined for 286 healthy controls. Our results provide a possible genetic mechanism for plasticity impairments in schizophrenia, which can lead to improved understanding and, ultimately, treatment of the disorder.

'Almost nothing is firmly established': A History of Heredity and Genetics in Mental Health Science

Background

For more than a century, scientists have tried to find the key to causation of mental ill health in heredity and genetics. The difficulty of finding clear and actionable answers in our genes has not stopped them looking. This history offers important context to understanding mental health science today.

Methods

This article explores the main themes in research on genetics and inheritance in psychiatry from the second half of the nineteenth century to the present day, to address the question: what is the history of genetics as a causative explanation in mental health science? We take a critical historical approach to the literature, interrogating primary and secondary material for the light it brings to the research question, while considering the social and historical context.

Results

We begin with the statistics gathered in asylums and used to 'prove' the importance of heredity in mental ill health. We then move through early twentieth century Mendelian models of mental inheritance, the eugenics movement, the influence of social psychiatry, new classifications and techniques of the postwar era, the Human Genome Project and Genome Wide Association Studies (GWAS) and epigenetics. Setting these themes in historical context shows that this research was often popular because of wider social, political and cultural issues, which impacted the views of scientists just as they did those of policymakers, journalists and the general public.

Conclusions

We argue that attempting to unpick this complex history is essential to the modern ethics of mental health and genetics, as well as helping to focus our efforts to better understand causation in mental ill-health.For a succinct timeline of the history of psychiatric genetics, alongside the history of other proposed causes for mental ill-health, visit: https://historyofcauses.co.uk/.

Cell-type-specific effects of age and sex on human cortical neurons

Altered transcriptional and epigenetic regulation of brain cell types may contribute to cognitive changes with advanced age. Using single-nucleus multi-omic DNA methylation and transcriptome sequencing (snmCT-seq) in frontal cortex from young adult and aged donors, we found widespread age- and sex-related variation in specific neuron types. The proportion of inhibitory SST- and VIP-expressing neurons was reduced in aged donors. Excitatory neurons had more profound age-related changes in their gene expression and DNA methylation than inhibitory cells. Hundreds of genes involved in synaptic activity, including EGR1, were less expressed in aged adults. Genes located in subtelomeric regions increased their expression with age and correlated with reduced telomere length. We further mapped cell-type-specific sex differences in gene expression and X-inactivation escape genes. Multi-omic single-nucleus epigenomes and transcriptomes provide new insight into the effects of age and sex on human neurons.

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.

Using Genetics to Investigate Relationships between Phenotypes: Application to Endometrial Cancer

Genome-wide association studies (GWAS) have accelerated the exploration of genotype-phenotype associations, facilitating the discovery of replicable genetic markers associated with specific traits or complex diseases. This narrative review explores the statistical methodologies developed using GWAS data to investigate relationships between various phenotypes, focusing on endometrial cancer, the most prevalent gynecological malignancy in developed nations. Advancements in analytical techniques such as genetic correlation, colocalization, cross-trait locus identification, and causal inference analyses have enabled deeper exploration of associations between different phenotypes, enhancing statistical power to uncover novel genetic risk regions. These analyses have unveiled shared genetic associations between endometrial cancer and many phenotypes, enabling identification of novel endometrial cancer risk loci and furthering our understanding of risk factors and biological processes underlying this disease. The current status of research in endometrial cancer is robust; however, this review demonstrates that further opportunities exist in statistical genetics that hold promise for advancing the understanding of endometrial cancer and other complex diseases.

Single-cell and genome-wide Mendelian randomization identifies causative genes for gout

BACKGROUND

Gout is a prevalent manifestation of metabolic osteoarthritis induced by elevated blood uric acid levels. The purpose of this study was to investigate the mechanisms of gene expression regulation in gout disease and elucidate its pathogenesis.

METHODS

The study integrated gout genome-wide association study (GWAS) data, single-cell transcriptomics (scRNA-seq), expression quantitative trait loci (eQTL), and methylation quantitative trait loci (mQTL) data for analysis, and utilized two-sample Mendelian randomization study to comprehend the causal relationship between proteins and gout.

RESULTS

We identified 17 association signals for gout at unique genetic loci, including four genes related by protein-protein interaction network (PPI) analysis: TRIM46, THBS3, MTX1, and KRTCAP2. Additionally, we discerned 22 methylation sites in relation to gout. The study also found that genes such as TRIM46, MAP3K11, KRTCAP2, and TM7SF2 could potentially elevate the risk of gout. Through a Mendelian randomization (MR) analysis, we identified three proteins causally associated with gout: ADH1B, BMP1, and HIST1H3A.

CONCLUSION

According to our findings, gout is linked with the expression and function of particular genes and proteins. These genes and proteins have the potential to function as novel diagnostic and therapeutic targets for gout. These discoveries shed new light on the pathological mechanisms of gout and clear the way for future research on this condition.

The Genesis of Schizophrenia: An Origin Story

Schizophrenia is routinely referred to as a neurodevelopmental disorder, but the role of brain development in a disorder typically diagnosed during early adult life is enigmatic. The authors revisit the neurodevelopmental model of schizophrenia with genomic insights from the most recent schizophrenia clinical genetic association studies, transcriptomic and epigenomic analyses from human postmortem brain studies, and analyses from cellular models that recapitulate neurodevelopment. Emerging insights into schizophrenia genetic risk continue to converge on brain development, particularly stages of early brain development, that may be perturbed to deviate from a typical, normative course, resulting in schizophrenia clinical symptomatology. As the authors explicate, schizophrenia genetic risk is likely dynamic and context dependent, with effects of genetic risk varying spatiotemporally, across the neurodevelopmental continuum. Optimizing therapeutic strategies for the heterogeneous collective of individuals with schizophrenia may likely be guided by leveraging markers of genetic risk and derivative functional insights, well before the emergence of psychosis. Ultimately, rather than a focus on therapeutic intervention during adolescence or adulthood, principles of prediction and prophylaxis in the pre- and perinatal and neonatal stages may best comport with the biology of schizophrenia to address the early-stage perturbations that alter the normative neurodevelopmental trajectory.

Recapitulation of Perturbed Striatal Gene Expression Dynamics of Donors' Brains With Ventral Forebrain Organoids Derived From the Same Individuals With Schizophrenia

OBJECTIVE

Schizophrenia is a brain disorder that originates during neurodevelopment and has complex genetic and environmental etiologies. Despite decades of clinical evidence of altered striatal function in affected patients, studies examining its cellular and molecular mechanisms in humans are limited. To explore neurodevelopmental alterations in the striatum associated with schizophrenia, the authors established a method for the differentiation of induced pluripotent stem cells (iPSCs) into ventral forebrain organoids (VFOs).

METHODS

VFOs were generated from postmortem dural fibroblast-derived iPSCs of four individuals with schizophrenia and four neurotypical control individuals for whom postmortem caudate genotypes and transcriptomic data were profiled in the BrainSeq neurogenomics consortium. Individuals were selected such that the two groups had nonoverlapping schizophrenia polygenic risk scores (PRSs).

RESULTS

Single-cell RNA sequencing analyses of VFOs revealed differences in developmental trajectory between schizophrenia and control individuals in which inhibitory neuronal cells from the patients exhibited accelerated maturation. Furthermore, upregulated genes in inhibitory neurons in schizophrenia VFOs showed a significant overlap with upregulated genes in postmortem caudate tissue of individuals with schizophrenia compared with control individuals, including the donors of the iPSC cohort.

CONCLUSIONS

The findings suggest that striatal neurons derived from high-PRS individuals with schizophrenia carry abnormalities that originated during early brain development and that the VFO model can recapitulate disease-relevant cell type-specific neurodevelopmental phenotypes in a dish.

The interaction between early life complications and a polygenic risk score for schizophrenia is associated with brain activity during emotion processing in healthy participants

BACKGROUND

Previous evidence suggests that early life complications (ELCs) interact with polygenic risk for schizophrenia (SCZ) in increasing risk for the disease. However, no studies have investigated this interaction on neurobiological phenotypes. Among those, anomalous emotion-related brain activity has been reported in SCZ, even if evidence of its link with SCZ-related genetic risk is not solid. Indeed, it is possible this relationship is influenced by non-genetic risk factors. Thus, this study investigated the interaction between SCZ-related polygenic risk and ELCs on emotion-related brain activity.

METHODS

169 healthy participants (HP) in a discovery and 113 HP in a replication sample underwent functional magnetic resonance imaging (fMRI) during emotion processing, were categorized for history of ELCs and genome-wide genotyped. Polygenic risk scores (PRSs) were computed using SCZ-associated variants considering the most recent genome-wide association study. Furthermore, 75 patients with SCZ also underwent fMRI during emotion processing to verify consistency of their brain activity patterns with those associated with risk factors for SCZ in HP.

RESULTS

Results in the discovery and replication samples indicated no effect of PRSs, but an interaction between PRS and ELCs in left ventrolateral prefrontal cortex (VLPFC), where the greater the activity, the greater PRS only in presence of ELCs. Moreover, SCZ had greater VLPFC response than HP.

CONCLUSIONS

These results suggest that emotion-related VLPFC response lies in the path from genetic and non-genetic risk factors to the clinical presentation of SCZ, and may implicate an updated concept of intermediate phenotype considering early non-genetic factors of risk for SCZ.

Environmental pollution and extreme weather conditions: insights into the effect on mental health

Environmental pollution exposures, including air, soil, water, light, and noise pollution, are critical issues that may implicate adverse mental health outcomes. Extreme weather conditions, such as hurricanes, floods, wildfires, and droughts, may also cause long-term severe concerns. However, the knowledge about possible psychiatric disorders associated with these exposures is currently not well disseminated. In this review, we aim to summarize the current knowledge on the impact of environmental pollution and extreme weather conditions on mental health, focusing on anxiety spectrum disorders, autism spectrum disorders, schizophrenia, and depression. In air pollution studies, increased concentrations of PM2.5, NO2, and SO2 were the most strongly associated with the exacerbation of anxiety, schizophrenia, and depression symptoms. We provide an overview of the suggested underlying pathomechanisms involved. We highlight that the pathogenesis of environmental pollution-related diseases is multifactorial, including increased oxidative stress, systematic inflammation, disruption of the blood-brain barrier, and epigenetic dysregulation. Light pollution and noise pollution were correlated with an increased risk of neurodegenerative disorders, particularly Alzheimer's disease. Moreover, the impact of soil and water pollution is discussed. Such compounds as crude oil, heavy metals, natural gas, agro-chemicals (pesticides, herbicides, and fertilizers), polycyclic or polynuclear aromatic hydrocarbons (PAH), solvents, lead (Pb), and asbestos were associated with detrimental impact on mental health. Extreme weather conditions were linked to depression and anxiety spectrum disorders, namely PTSD. Several policy recommendations and awareness campaigns should be implemented, advocating for the advancement of high-quality urbanization, the mitigation of environmental pollution, and, consequently, the enhancement of residents' mental health.

Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics

Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit (P2RX5 and TRPV3) and excitatory neurons (DPY30 and MEMO1). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.

Precision medicine for psychotic disorders: objective assessment, risk prediction, and pharmacogenomics

Psychosis occurs inside the brain, but may have external manifestations (peripheral molecular biomarkers, behaviors) that can be objectively and quantitatively measured. Blood biomarkers that track core psychotic manifestations such as hallucinations and delusions could provide a window into the biology of psychosis, as well as help with diagnosis and treatment. We endeavored to identify objective blood gene expression biomarkers for hallucinations and delusions, using a stepwise discovery, prioritization, validation, and testing in independent cohorts design. We were successful in identifying biomarkers that were predictive of high hallucinations and of high delusions states, and of future psychiatric hospitalizations related to them, more so when personalized by gender and diagnosis. Top biomarkers for hallucinations that survived discovery, prioritization, validation and testing include PPP3CB, DLG1, ENPP2, ZEB2, and RTN4. Top biomarkers for delusions include AUTS2, MACROD2, NR4A2, PDE4D, PDP1, and RORA. The top biological pathways uncovered by our work are glutamatergic synapse for hallucinations, as well as Rap1 signaling for delusions. Some of the biomarkers are targets of existing drugs, of potential utility in pharmacogenomics approaches (matching patients to medications, monitoring response to treatment). The top biomarkers gene expression signatures through bioinformatic analyses suggested a prioritization of existing medications such as clozapine and risperidone, as well as of lithium, fluoxetine, valproate, and the nutraceuticals omega-3 fatty acids and magnesium. Finally, we provide an example of how a personalized laboratory report for doctors would look. Overall, our work provides advances for the improved diagnosis and treatment for schizophrenia and other psychotic disorders.

Dopamine signaling enriched striatal gene set predicts striatal dopamine synthesis and physiological activity in vivo

The polygenic architecture of schizophrenia implicates several molecular pathways involved in synaptic function. However, it is unclear how polygenic risk funnels through these pathways to translate into syndromic illness. Using tensor decomposition, we analyze gene co-expression in the caudate nucleus, hippocampus, and dorsolateral prefrontal cortex of post-mortem brain samples from 358 individuals. We identify a set of genes predominantly expressed in the caudate nucleus and associated with both clinical state and genetic risk for schizophrenia that shows dopaminergic selectivity. A higher polygenic risk score for schizophrenia parsed by this set of genes predicts greater dopamine synthesis in the striatum and greater striatal activation during reward anticipation. These results translate dopamine-linked genetic risk variation into in vivo neurochemical and hemodynamic phenotypes in the striatum that have long been implicated in the pathophysiology of schizophrenia.

Antipsychotic-induced epigenomic reorganization in frontal cortex of individuals with schizophrenia

Genome-wide association studies have revealed >270 loci associated with schizophrenia risk, yet these genetic factors do not seem to be sufficient to fully explain the molecular determinants behind this psychiatric condition. Epigenetic marks such as post-translational histone modifications remain largely plastic during development and adulthood, allowing a dynamic impact of environmental factors, including antipsychotic medications, on access to genes and regulatory elements. However, few studies so far have profiled cell-specific genome-wide histone modifications in postmortem brain samples from schizophrenia subjects, or the effect of antipsychotic treatment on such epigenetic marks. Here, we conducted ChIP-seq analyses focusing on histone marks indicative of active enhancers (H3K27ac) and active promoters (H3K4me3), alongside RNA-seq, using frontal cortex samples from antipsychotic-free (AF) and antipsychotic-treated (AT) individuals with schizophrenia, as well as individually matched controls (n=58). Schizophrenia subjects exhibited thousands of neuronal and non-neuronal epigenetic differences at regions that included several susceptibility genetic loci, such as NRG1, DISC1, and DRD3. By analyzing the AF and AT cohorts separately, we identified schizophrenia-associated alterations in specific transcription factors, their regulatees, and epigenomic and transcriptomic features that were reversed by antipsychotic treatment; as well as those that represented a consequence of antipsychotic medication rather than a hallmark of schizophrenia in postmortem human brain samples. Notably, we also found that the effect of age on epigenomic landscapes was more pronounced in frontal cortex of AT-schizophrenics, as compared to AF-schizophrenics and controls. Together, these data provide important evidence of epigenetic alterations in the frontal cortex of individuals with schizophrenia, and remark for the first time on the impact of age and antipsychotic treatment on chromatin organization.

Unravelling the genetic basis of Schizophrenia

Neuronal development is a highly regulated mechanism that is central to organismal function in animals. In humans, disruptions to this process can lead to a range of neurodevelopmental phenotypes, including Schizophrenia (SCZ). SCZ has a significant genetic component, whereby an individual with an SCZ affected family member is eight times more likely to develop the disease than someone with no family history of SCZ. By examining a combination of genomic, transcriptomic and epigenomic datasets, large-scale 'omics' studies aim to delineate the relationship between genetic variation and abnormal cellular activity in the SCZ brain. Herein, we provide a brief overview of some of the key omics methods currently being used in SCZ research, including RNA-seq, the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and high-throughput chromosome conformation capture (3C) approaches (e.g., Hi-C), as well as single-cell/nuclei iterations of these methods. We also discuss how these techniques are being employed to further our understanding of the genetic basis of SCZ, and to identify associated molecular pathways, biomarkers, and candidate drug targets.

Methylome-wide and meQTL analysis helps to distinguish treatment response from non-response and pathogenesis markers in schizophrenia

Schizophrenia is a complex condition with entwined genetic and epigenetic risk factors, posing a challenge to disentangle the intermixed pathological and therapeutic epigenetic signatures. To resolve this, we performed 850K methylome-wide and 700K genome-wide studies on the same set of schizophrenia patients by stratifying them into responders, non-responders, and drug-naïve patients. The key genes that signified the response were followed up using real-time gene expression studies to understand the effect of antipsychotics at the gene transcription level. The study primarily implicates hypermethylation in therapeutic response and hypomethylation in the drug-non-responsive state. Several differentially methylated sites and regions colocalized with the schizophrenia genome-wide association study (GWAS) risk genes and variants, supporting the convoluted gene-environment association. Gene ontology and protein-protein interaction (PPI) network analyses revealed distinct patterns that differentiated the treatment response from drug resistance. The study highlights the strong involvement of several processes related to nervous system development, cell adhesion, and signaling in the antipsychotic response. The ability of antipsychotic medications to alter the pathology by modulating gene expression or methylation patterns is evident from the general increase in the gene expression of response markers and histone modifiers and the decrease in class II human leukocyte antigen (HLA) genes following treatment with varying concentrations of medications like clozapine, olanzapine, risperidone, and haloperidol. The study indicates a directional overlap of methylation markers between pathogenesis and therapeutic response, thereby suggesting a careful distinction of methylation markers of pathogenesis from treatment response. In addition, there is a need to understand the trade-off between genetic and epigenetic observations. It is suggested that methylomic changes brought about by drugs need careful evaluation for their positive effects on pathogenesis, course of disease progression, symptom severity, side effects, and refractoriness.

QTL mapping of human retina DNA methylation identifies 87 gene-epigenome interactions in age-related macular degeneration

DNA methylation provides a crucial epigenetic mark linking genetic variations to environmental influence. We have analyzed array-based DNA methylation profiles of 160 human retinas with co-measured RNA-seq and >8 million genetic variants, uncovering sites of genetic regulation in cis (37,453 methylation quantitative trait loci and 12,505 expression quantitative trait loci) and 13,747 DNA methylation loci affecting gene expression, with over one-third specific to the retina. Methylation and expression quantitative trait loci show non-random distribution and enrichment of biological processes related to synapse, mitochondria, and catabolism. Summary data-based Mendelian randomization and colocalization analyses identify 87 target genes where methylation and gene-expression changes likely mediate the genotype effect on age-related macular degeneration. Integrated pathway analysis reveals epigenetic regulation of immune response and metabolism including the glutathione pathway and glycolysis. Our study thus defines key roles of genetic variations driving methylation changes, prioritizes epigenetic control of gene expression, and suggests frameworks for regulation of macular degeneration pathology by genotype-environment interaction in retina.

Exponential dynamics of DNA methylation with age

The association of DNA methylation with age has been extensively studied. Previous work has investigated the trajectories of methylation with age, and developed predictive biomarkers of age. However, we still have a limited understanding of the functional form of methylation-age dynamics. To address this we present a theoretical framework to model the dynamics of DNA methylation at single sites. We show that this model leads to convergence to a steady-state methylation level at an exponential rate. By fitting the model to a dataset that measures changes in DNA methylation in the brain from birth to old age, we show that the timescales of this exponential convergence are heterogeneous across sites. To model this heterogeneity we generated a simulation of CpG Methylation changes with time and investigated the functional form of the dynamics of methylation with age under the empirical distribution of timescales estimated from the dataset. The resulting dynamics of the average methylation of the system were characterized and were found to closely follow an exponential trajectory. We conclude that DNA methylation can be modeled as a system that starts out of equilibrium at birth and approaches equilibrium with age in an exponential fashion. These insights illustrate the importance of accounting for nonlinear dynamics when utilizing age associated DNA methylation changes for constructing biomarkers of aging. Thus DNA methylation, along with the exponentially increasing risk of mortality with age, further establishes the exponential nature of aging.

Temporal changes of gene expression in health, schizophrenia, bipolar disorder, and major depressive disorder

The molecular events underlying the development, manifestation, and course of schizophrenia, bipolar disorder, and major depressive disorder span from embryonic life to advanced age. However, little is known about the early dynamics of gene expression in these disorders due to their relatively late manifestation. To address this, we conducted a secondary analysis of post-mortem prefrontal cortex datasets using bioinformatics and machine learning techniques to identify differentially expressed gene modules associated with aging and the diseases, determine their time-perturbation points, and assess enrichment with expression quantitative trait loci (eQTL) genes. Our findings revealed early, mid, and late deregulation of expression of functional gene modules involved in neurodevelopment, plasticity, homeostasis, and immune response. This supports the hypothesis that multiple hits throughout life contribute to disease manifestation rather than a single early-life event. Moreover, the time-perturbed functional gene modules were associated with genetic loci affecting gene expression, highlighting the role of genetic factors in gene expression dynamics and the development of disease phenotypes. Our findings emphasize the importance of investigating time-dependent perturbations in gene expression before the age of onset in elucidating the molecular mechanisms of psychiatric disorders.

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.

The schizophrenia syndrome, circa 2024: What we know and how that informs its nature

With new data about different aspects of schizophrenia being continually generated, it becomes necessary to periodically revisit exactly what we know. Along with a need to review what we currently know about schizophrenia, there is an equal imperative to evaluate the construct itself. With these objectives, we undertook an iterative, multi-phase process involving fifty international experts in the field, with each step building on learnings from the prior one. This review assembles currently established findings about schizophrenia (construct, etiology, pathophysiology, clinical expression, treatment) and posits what they reveal about its nature. Schizophrenia is a heritable, complex, multi-dimensional syndrome with varying degrees of psychotic, negative, cognitive, mood, and motor manifestations. The illness exhibits a remitting and relapsing course, with varying degrees of recovery among affected individuals with most experiencing significant social and functional impairment. Genetic risk factors likely include thousands of common genetic variants that each have a small impact on an individual's risk and a plethora of rare gene variants that have a larger individual impact on risk. Their biological effects are concentrated in the brain and many of the same variants also increase the risk of other psychiatric disorders such as bipolar disorder, autism, and other neurodevelopmental conditions. Environmental risk factors include but are not limited to urban residence in childhood, migration, older paternal age at birth, cannabis use, childhood trauma, antenatal maternal infection, and perinatal hypoxia. Structural, functional, and neurochemical brain alterations implicate multiple regions and functional circuits. Dopamine D-2 receptor antagonists and partial agonists improve psychotic symptoms and reduce risk of relapse. Certain psychological and psychosocial interventions are beneficial. Early intervention can reduce treatment delay and improve outcomes. Schizophrenia is increasingly considered to be a heterogeneous syndrome and not a singular disease entity. There is no necessary or sufficient etiology, pathology, set of clinical features, or treatment that fully circumscribes this syndrome. A single, common pathophysiological pathway appears unlikely. The boundaries of schizophrenia remain fuzzy, suggesting the absence of a categorical fit and need to reconceptualize it as a broader, multi-dimensional and/or spectrum construct.

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.

scMeFormer: a transformer-based deep learning model for imputing DNA methylation states in single cells enhances the detection of epigenetic alterations in schizophrenia

DNA methylation (DNAm), a crucial epigenetic mark, plays a key role in gene regulation, mammalian development, and various human diseases. Single-cell technologies enable the profiling of DNAm states at cytosines within the DNA sequence of individual cells, but they often suffer from limited coverage of CpG sites. In this study, we introduce scMeFormer, a transformer-based deep learning model designed to impute DNAm states for each CpG site in single cells. Through comprehensive evaluations, we demonstrate the superior performance of scMeFormer compared to alternative models across four single-nucleus DNAm datasets generated by distinct technologies. Remarkably, scMeFormer exhibits high-fidelity imputation, even when dealing with significantly reduced coverage, as low as 10% of the original CpG sites. Furthermore, we applied scMeFormer to a single-nucleus DNAm dataset generated from the prefrontal cortex of four schizophrenia patients and four neurotypical controls. This enabled the identification of thousands of differentially methylated regions associated with schizophrenia that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia within specific cell types. Our study highlights the power of deep learning in imputing DNAm states in single cells, and we expect scMeFormer to be a valuable tool for single-cell DNAm studies.

Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders

DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory variants impacting DNAm levels in specific brain cell types, leveraging existing single-nucleus DNAm data from the human brain. We show that INTERACT accurately predicts cell type-specific DNAm profiles, achieving an average area under the Receiver Operating Characteristic curve of 0.98 across cell types. Furthermore, INTERACT predicts cell type-specific DNAm regulatory variants, which reflect cellular context and enrich the heritability of brain-related traits in relevant cell types. Importantly, we demonstrate that incorporating predicted variant effects and DNAm levels of CpG sites enhances the fine mapping for three brain disorders-schizophrenia, depression, and Alzheimer's disease-and facilitates mapping causal genes to particular cell types. Our study highlights the power of deep learning in identifying cell type-specific regulatory variants, which will enhance our understanding of the genetics of complex traits.

Alcohol Use Disorder-Associated DNA Methylation in the Nucleus Accumbens and Dorsolateral Prefrontal Cortex

Background

Alcohol use disorder (AUD) has a profound public health impact. However, understanding of the molecular mechanisms underlying the development and progression of AUD remain limited. Here, we interrogate AUD-associated DNA methylation (DNAm) changes within and across addiction-relevant brain regions: the nucleus accumbens (NAc) and dorsolateral prefrontal cortex (DLPFC).

Methods

Illumina HumanMethylation EPIC array data from 119 decedents of European ancestry (61 cases, 58 controls) were analyzed using robust linear regression, with adjustment for technical and biological variables. Associations were characterized using integrative analyses of public gene regulatory data and published genetic and epigenetic studies. We additionally tested for brain region-shared and -specific associations using mixed effects modeling and assessed implications of these results using public gene expression data.

Results

At a false discovery rate ≤ 0.05, we identified 53 CpGs significantly associated with AUD status for NAc and 31 CpGs for DLPFC. In a meta-analysis across the regions, we identified an additional 21 CpGs associated with AUD, for a total of 105 unique AUD-associated CpGs (120 genes). AUD-associated CpGs were enriched in histone marks that tag active promoters and our strongest signals were specific to a single brain region. Of the 120 genes, 23 overlapped with previous genetic associations for substance use behaviors; all others represent novel associations.

Conclusions

Our findings identify AUD-associated methylation signals, the majority of which are specific within NAc or DLPFC. Some signals may constitute predisposing genetic and epigenetic variation, though more work is needed to further disentangle the neurobiological gene regulatory differences associated with AUD.

Distinctive Patterns of 5-Methylcytosine and 5-Hydroxymethylcytosine in Schizophrenia

Schizophrenia is a highly heritable neuropsychiatric disorder characterized by cognitive and social dysfunction. Genetic, epigenetic, and environmental factors are together implicated in the pathogenesis and development of schizophrenia. DNA methylation, 5-methycytosine (5mC) and 5-hydroxylcytosine (5hmC) have been recognized as key epigenetic elements in neurodevelopment, ageing, and neurodegenerative diseases. Recently, distinctive 5mC and 5hmC pattern and expression changes of related genes have been discovered in schizophrenia. Antipsychotic drugs that affect 5mC status can alleviate symptoms in patients with schizophrenia, suggesting a critical role for DNA methylation in the pathogenesis of schizophrenia. Further exploring the signatures of 5mC and 5hmC in schizophrenia and developing precision-targeted epigenetic drugs based on this will provide new insights into the diagnosis and treatment of schizophrenia.

Genome-wide QTL mapping across three tissues highlights several Alzheimer's and Parkinson's disease loci potentially acting via DNA methylation

DNA methylation (DNAm) is an epigenetic mark with essential roles in disease development and predisposition. Here, we created genome-wide maps of methylation quantitative trait loci (meQTL) in three peripheral tissues and used Mendelian randomization (MR) analyses to assess the potential causal relationships between DNAm and risk for two common neurodegenerative disorders, i.e. Alzheimer's disease (AD) and Parkinson's disease (PD). Genome-wide single nucleotide polymorphism (SNP; ~5.5M sites) and DNAm (~850K CpG sites) data were generated from whole blood (n=1,058), buccal (n=1,527) and saliva (n=837) specimens. We identified between 11 and 15 million genome-wide significant (p<10-14) SNP-CpG associations in each tissue. Combining these meQTL GWAS results with recent AD/PD GWAS summary statistics by MR strongly suggests that the previously described associations between PSMC3, PICALM, and TSPAN14 and AD may be founded on differential DNAm in or near these genes. In addition, there is strong, albeit less unequivocal, support for causal links between DNAm at PRDM7 in AD as well as at KANSL1/MAPT in AD and PD. Our study adds valuable insights on AD/PD pathogenesis by combining two high-resolution "omics" domains, and the meQTL data shared along with this publication will allow like-minded analyses in other diseases.