Genome-wide mapping of genetic determinants influencing DNA methylation and gene expression in human hippocampus

Genetic effects on the skin methylome in healthy older twins

Whole-skin DNA methylation variation has been implicated in several diseases, including melanoma, but its genetic basis has not yet been fully characterized. Using bulk skin tissue samples from 414 healthy female UK twins, we performed twin-based heritability and methylation quantitative trait loci (meQTL) analyses for >400,000 DNA methylation sites. We find that the human skin DNA methylome is on average less heritable than previously estimated in blood and other tissues (mean heritability: 10.02%). meQTL analysis identified local genetic effects influencing DNA methylation at 18.8% (76,442) of tested CpG sites, as well as 1,775 CpG sites associated with at least one distal genetic variant. As a functional follow-up, we performed skin expression QTL (eQTL) analyses in a partially overlapping sample of 604 female twins. Colocalization analysis identified over 3,500 shared genetic effects affecting thousands of CpG sites (10,067) and genes (4,475). Mediation analysis of putative colocalized gene-CpG pairs identified 114 genes with evidence for eQTL effects being mediated by DNA methylation in skin, including in genes implicating skin disease such as ALOX12 and CSPG4. We further explored the relevance of skin meQTLs to skin disease and found that skin meQTLs and CpGs under genetic influence were enriched for multiple skin-related genome-wide and epigenome-wide association signals, including for melanoma and psoriasis. Our findings give insights into the regulatory landscape of epigenomic variation in skin.

NERD-seq: a novel approach of Nanopore direct RNA sequencing that expands representation of non-coding RNAs

Non-coding RNAs (ncRNAs) are frequently documented RNA modification substrates. Nanopore Technologies enables the direct sequencing of RNAs and the detection of modified nucleobases. Ordinarily, direct RNA sequencing uses polyadenylation selection, studying primarily mRNA gene expression. Here, we present NERD-seq, which enables detection of multiple non-coding RNAs, excluded by the standard approach, alongside natively polyadenylated transcripts. Using neural tissues as a proof of principle, we show that NERD-seq expands representation of frequently modified non-coding RNAs, such as snoRNAs, snRNAs, scRNAs, srpRNAs, tRNAs, and rRFs. NERD-seq represents an RNA-seq approach to simultaneously study mRNA and ncRNA epitranscriptomes in brain tissues and beyond.

An atlas of genetic effects on the monocyte methylome across European and African populations

Elucidating the genetic architecture of DNA methylation (DNAm) is crucial for decoding the etiology of complex diseases. However, current epigenomic studies often suffer from incomplete coverage of methylation sites and the use of tissues containing heterogeneous cell populations. To address these challenges, we present a comprehensive human methylome atlas based on deep whole-genome bisulfite sequencing (WGBS) and whole-genome sequencing (WGS) of purified monocytes from 298 European Americans (EA) and 160 African Americans (AA) in the Louisiana Osteoporosis Study. Our atlas enables the analysis of over 25 million DNAm sites. We identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in cis -regions for EA and AA populations, respectively, with 880,108 sites shared between ancestries. While cis -meQTLs exhibited population-specific patterns, primarily due to differences in minor allele frequencies, shared cis -meQTLs showed high concordance across ancestries. Notably, cis -heritability estimates revealed significantly higher mean values in the AA population (0.09) compared to the EA population (0.04). Furthermore, we developed population-specific DNAm imputation models using Elastic Net, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. The performance of our MWAS models was validated through a systematic multi-ancestry analysis of 41 complex traits from the Million Veteran Program. Our findings bridge the gap between genomics and the monocyte methylome, uncovering novel methylation-phenotype associations and their transferability across diverse ancestries. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .

An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human traits or diseases in the past decade. Nevertheless, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits. In contrast to individual-level data, GWAS summary statistics are usually publicly available, and thus methods using only summary statistics have greater usage. Although many methods have been developed for joint analysis of multiple traits using summary statistics, there are many issues, including inconsistent performance, computational inefficiency, and numerical problems when considering lots of traits. To address these challenges, we propose a multi-trait adaptive Fisher method for summary statistics (MTAFS), a computationally efficient method with robust power performance. We applied MTAFS to two sets of brain imaging derived phenotypes (IDPs) from the UK Biobank, including a set of 58 Volumetric IDPs and a set of 212 Area IDPs. Through annotation analysis, the underlying genes of the SNPs identified by MTAFS were found to exhibit higher expression and are significantly enriched in brain-related tissues. Together with results from a simulation study, MTAFS shows its advantage over existing multi-trait methods, with robust performance across a range of underlying settings. It controls type 1 error well and can efficiently handle a large number of traits.

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.

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.

Multi-omics and pathway analyses of genome-wide associations implicate regulation and immunity in verbal declarative memory performance

BACKGROUND

Uncovering the functional relevance underlying verbal declarative memory (VDM) genome-wide association study (GWAS) results may facilitate the development of interventions to reduce age-related memory decline and dementia.

METHODS

We performed multi-omics and pathway enrichment analyses of paragraph (PAR-dr) and word list (WL-dr) delayed recall GWAS from 29,076 older non-demented individuals of European descent. We assessed the relationship between single-variant associations and expression quantitative trait loci (eQTLs) in 44 tissues and methylation quantitative trait loci (meQTLs) in the hippocampus. We determined the relationship between gene associations and transcript levels in 53 tissues, annotation as immune genes, and regulation by transcription factors (TFs) and microRNAs. To identify significant pathways, gene set enrichment was tested in each cohort and meta-analyzed across cohorts. Analyses of differential expression in brain tissues were conducted for pathway component genes.

RESULTS

The single-variant associations of VDM showed significant linkage disequilibrium (LD) with eQTLs across all tissues and meQTLs within the hippocampus. Stronger WL-dr gene associations correlated with reduced expression in four brain tissues, including the hippocampus. More robust PAR-dr and/or WL-dr gene associations were intricately linked with immunity and were influenced by 31 TFs and 2 microRNAs. Six pathways, including type I diabetes, exhibited significant associations with both PAR-dr and WL-dr. These pathways included fifteen MHC genes intricately linked to VDM performance, showing diverse expression patterns based on cognitive status in brain tissues.

CONCLUSIONS

VDM genetic associations influence expression regulation via eQTLs and meQTLs. The involvement of TFs, microRNAs, MHC genes, and immune-related pathways contributes to VDM performance in older individuals.

Genome-wide DNA methylation sequencing identifies epigenetic perturbations in the upper airways under long-term exposure to moderate levels of ambient air pollution

While the link between exposure to high levels of ambient particulate matter (PM) and increased incidences of respiratory and cardiovascular diseases is widely recognized, recent epidemiological studies have shown that low PM concentrations are equally associated with adverse health effects. As DNA methylation is one of the main mechanisms by which cells regulate and stabilize gene expression, changes in the methylome could constitute early indicators of dysregulated signaling pathways. So far, little is known about PM-associated DNA methylation changes in the upper airways, the first point of contact between airborne pollutants and the human body. Here, we focused on cells of the upper respiratory tract and assessed their genome-wide DNA methylation pattern to explore exposure-associated early regulatory changes. Using a mobile epidemiological laboratory, nasal lavage samples were collected from a cohort of 60 adults that lived in districts with records of low (Simmerath) or moderate (Stuttgart) PM10 levels in Germany. PM10 concentrations were verified by particle measurements on the days of the sample collection and genome-wide DNA methylation was determined by enzymatic methyl sequencing at single-base resolution. We identified 231 differentially methylated regions (DMRs) between moderately and lowly PM10 exposed individuals. A high proportion of DMRs overlapped with regulatory elements, and DMR target genes were involved in pathways regulating cellular redox homeostasis and immune response. In addition, we found distinct changes in DNA methylation of the HOXA gene cluster whose methylation levels have previously been linked to air pollution exposure but also to carcinogenesis in several instances. The findings of this study suggest that regulatory changes in upper airway cells occur at PM10 levels below current European thresholds, some of which may be involved in the development of air pollution-related diseases.

Genetic impacts on DNA methylation help elucidate regulatory genomic processes

BACKGROUND

Pinpointing genetic impacts on DNA methylation can improve our understanding of pathways that underlie gene regulation and disease risk.

RESULTS

We report heritability and methylation quantitative trait locus (meQTL) analysis at 724,499 CpGs profiled with the Illumina Infinium MethylationEPIC array in 2358 blood samples from three UK cohorts. Methylation levels at 34.2% of CpGs are affected by SNPs, and 98% of effects are cis-acting or within 1 Mbp of the tested CpG. Our results are consistent with meQTL analyses based on the former Illumina Infinium HumanMethylation450 array. Both SNPs and CpGs with meQTLs are overrepresented in enhancers, which have improved coverage on this platform compared to previous approaches. Co-localisation analyses across genetic effects on DNA methylation and 56 human traits identify 1520 co-localisations across 1325 unique CpGs and 34 phenotypes, including in disease-relevant genes, such as USP1 and DOCK7 (total cholesterol levels), and ICOSLG (inflammatory bowel disease). Enrichment analysis of meQTLs and integration with expression QTLs give insights into mechanisms underlying cis-meQTLs (e.g. through disruption of transcription factor binding sites for CTCF and SMC3) and trans-meQTLs (e.g. through regulating the expression of ACD and SENP7 which can modulate DNA methylation at distal sites).

CONCLUSIONS

Our findings improve the characterisation of the mechanisms underlying DNA methylation variability and are informative for prioritisation of GWAS variants for functional follow-ups. The MeQTL EPIC Database and viewer are available online at https://epicmeqtl.kcl.ac.uk .

Relationships between neurotransmitter receptor densities and expression levels of their corresponding genes in the human hippocampus

Neurotransmitter receptors are key molecules in signal transmission, their alterations are associated with brain dysfunction. Relationships between receptors and their corresponding genes are poorly understood, especially in humans. We combined in vitro receptor autoradiography and RNA sequencing to quantify, in the same tissue samples (7 subjects), the densities of 14 receptors and expression levels of their corresponding 43 genes in the Cornu Ammonis (CA) and dentate gyrus (DG) of human hippocampus. Significant differences in receptor densities between both structures were found only for metabotropic receptors, whereas significant differences in RNA expression levels mostly pertained ionotropic receptors. Receptor fingerprints of CA and DG differ in shapes but have similar sizes; the opposite holds true for their "RNA fingerprints", which represent the expression levels of multiple genes in a single area. In addition, the correlation coefficients between receptor densities and corresponding gene expression levels vary widely and the mean correlation strength was weak-to-moderate. Our results suggest that receptor densities are not only controlled by corresponding RNA expression levels, but also by multiple regionally specific post-translational factors.

RegVar: Tissue-specific Prioritization of Non-coding Regulatory Variants

Non-coding genomic variants constitute the majority of trait-associated genome variations; however, the identification of functional non-coding variants is still a challenge in human genetics, and a method for systematically assessing the impact of regulatory variants on gene expression and linking these regulatory variants to potential target genes is still lacking. Here, we introduce a deep neural network (DNN)-based computational framework, RegVar, which can accurately predict the tissue-specific impact of non-coding regulatory variants on target genes. We show that by robustly learning the genomic characteristics of massive variant-gene expression associations in a variety of human tissues, RegVar vastly surpasses all current non-coding variant prioritization methods in predicting regulatory variants under different circumstances. The unique features of RegVar make it an excellent framework for assessing the regulatory impact of any variant on its putative target genes in a variety of tissues. RegVar is available as a web server at https://regvar.omic.tech/.

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

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

Multi-omic association study identifies DNA methylation-mediated genotype and smoking exposure effects on lung function in children living in urban settings

Impaired lung function in early life is associated with the subsequent development of chronic respiratory disease. Most genetic associations with lung function have been identified in adults of European descent and therefore may not represent those most relevant to pediatric populations and populations of different ancestries. In this study, we performed genome-wide association analyses of lung function in a multiethnic cohort of children (n = 1,035) living in low-income urban neighborhoods. We identified one novel locus at the TDRD9 gene in chromosome 14q32.33 associated with percent predicted forced expiratory volume in one second (FEV1) (p = 2.4x10-9; βz = -0.31, 95% CI = -0.41- -0.21). Mendelian randomization and mediation analyses revealed that this genetic effect on FEV1 was partially mediated by DNA methylation levels at this locus in airway epithelial cells, which were also associated with environmental tobacco smoke exposure (p = 0.015). Promoter-enhancer interactions in airway epithelial cells revealed chromatin interaction loops between FEV1-associated variants in TDRD9 and the promoter region of the PPP1R13B gene, a stimulator of p53-mediated apoptosis. Expression of PPP1R13B in airway epithelial cells was significantly associated the FEV1 risk alleles (p = 1.3x10-5; β = 0.12, 95% CI = 0.06-0.17). These combined results highlight a potential novel mechanism for reduced lung function in urban youth resulting from both genetics and smoking exposure.

mir152 hypomethylation as a mechanism for non-syndromic cleft lip and palate

Non-syndromic cleft lip with or without cleft palate (NSCLP), the most common human craniofacial malformation, is a complex disorder given its genetic heterogeneity and multifactorial component revealed by genetic, epidemiological, and epigenetic findings. Epigenetic variations associated with NSCLP have been identified; however, functional investigation has been limited. Here, we combined a reanalysis of NSCLP methylome data with genetic analysis and used both in vitro and in vivo approaches to dissect the functional effects of epigenetic changes. We found a region in mir152 that is frequently hypomethylated in NSCLP cohorts (21-26%), leading to mir152 overexpression. mir152 overexpression in human neural crest cells led to downregulation of spliceosomal, ribosomal, and adherens junction genes. In vivo analysis using zebrafish embryos revealed that mir152 upregulation leads to craniofacial cartilage impairment. Also, we suggest that zebrafish embryonic hypoxia leads to mir152 upregulation combined with mir152 hypomethylation and also analogous palatal alterations. We therefore propose that mir152 hypomethylation, potentially induced by hypoxia in early development, is a novel and frequent predisposing factor to NSCLP.

A causal association of ANKRD37 with human hippocampal volume

Human hippocampal volume has been separately associated with single nucleotide polymorphisms (SNPs), DNA methylation and gene expression, but their causal relationships remain largely unknown. Here, we aimed at identifying the causal relationships of SNPs, DNA methylation, and gene expression that are associated with hippocampal volume by integrating cross-omics analyses with genome editing, overexpression and causality inference. Based on structural neuroimaging data and blood-derived genome, transcriptome and methylome data, we prioritized a possibly causal association across multiple molecular phenotypes: rs1053218 mutation leads to cg26741686 hypermethylation, thus leads to overactivation of the associated ANKRD37 gene expression in blood, a gene involving hypoxia, which may result in the reduction of human hippocampal volume. The possibly causal relationships from rs1053218 to cg26741686 methylation to ANKRD37 expression obtained from peripheral blood were replicated in human hippocampal tissue. To confirm causality, we performed CRISPR-based genome and epigenome-editing of rs1053218 homologous alleles and cg26741686 methylation in mouse neural stem cell differentiation models, and overexpressed ANKRD37 in mouse hippocampus. These in-vitro and in-vivo experiments confirmed that rs1053218 mutation caused cg26741686 hypermethylation and ANKRD37 overexpression, and cg26741686 hypermethylation favored ANKRD37 overexpression, and ANKRD37 overexpression reduced hippocampal volume. The pairwise relationships of rs1053218 with hippocampal volume, rs1053218 with cg26741686 methylation, cg26741686 methylation with ANKRD37 expression, and ANKRD37 expression with hippocampal volume could be replicated in an independent healthy young (n = 443) dataset and observed in elderly people (n = 194), and were more significant in patients with late-onset Alzheimer's disease (n = 76). This study revealed a novel causal molecular association mechanism of ANKRD37 with human hippocampal volume, which may facilitate the design of prevention and treatment strategies for hippocampal impairment.

Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits.

The importance of enhancer methylation for epigenetic regulation of tumorigenesis in squamous lung cancer

Lung squamous cell carcinoma (LUSC) is a subtype of non-small cell lung cancer (NSCLC). LUSC occurs at the bronchi, shows a squamous appearance, and often occurs in smokers. To determine the epigenetic regulatory mechanisms of tumorigenesis, we performed a genome-wide analysis of DNA methylation in tumor and adjacent normal tissues from LUSC patients. With the Infinium Methylation EPIC Array, > 850,000 CpG sites, including ~350,000 CpG sites for enhancer regions, were profiled, and the differentially methylated regions (DMRs) overlapping promoters (pDMRs) and enhancers (eDMRs) between tumor and normal tissues were identified. Dimension reduction based on DMR profiles revealed that eDMRs alone and not pDMRs alone can differentiate tumors from normal tissues with the equivalent performance of total DMRs. We observed a stronger negative correlation of LUSC-specific gene expression with methylation for enhancers than promoters. Target genes of eDMRs rather than pDMRs were found to be enriched for tumor-associated genes and pathways. Furthermore, DMR methylation associated with immune infiltration was more frequently observed among enhancers than promoters. Our results suggest that methylation of enhancer regions rather than promoters play more important roles in epigenetic regulation of tumorigenesis and immune infiltration in LUSC.

Integrative Analysis of Genome-Wide Association Studies and DNA Methylation Profile Identified Genetic Control Genes of DNA Methylation for Kashin-Beck Disease

OBJECTIVE

Epigenetic modifications of DNA are regarded as a crucial factor for understanding the molecular basis of complex phenotypes. This study aims to uncover insight into the epigenetic modifications for Kashin-Beck disease (KBD) by integrating genome-wide association studies (GWAS), methylation quantitative trait loci (meQTLs), and DNA methylation profiles data.

DESIGN

The knee articular cartilages of 5 KBD patients and 5 healthy controls were collected for DNA methylation profiling, using Illumina Infinium HumanMethylation450 BeadChip. Mass spectrograph validation of identified differently methylated genes was conducted using independent samples of 4 KBD patients and 3 healthy controls, together with a previous sample of 2743 Han Chinese individuals of GWAS study for KBD and a study of 697 normal subjects for meQTLs annotation datasets. KBD GWAS single nucleotide polymorphisms (SNPs) and normal meQTLs SNPs were integrated with DNA methylation profiles of KBD articular cartilage to identify genetic control (GC) genes of DNA methylation for KBD. Quantitative polymerase chain reaction (qPCR) was performed to validate the mRNA expression of several identified candidate genes.

RESULTS

A total of 162 CpG sites, 253 SNPs, and 123 GC genes for KBD were identified. Enrichment analysis detected 642 marked GO terms and 19 KEGG pathways (P < 0.05). Six potential key GC genes were conducted for qPCR experiment (ERG, MN1, MITF, WISP1, TRIO, and NOSTRIN).

CONCLUSIONS

The results suggest that GC genes of DNA methylation may lead to the erosion of cartilage in KBD, which may help us in understanding the epigenetic alteration of KBD.

Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic

Breakthrough advances in informatics over the last decade have thoroughly influenced the field of immunology. The intermingling of machine learning with wet lab applications and clinical results has hatched the newly defined immunoinformatics society. Immunoinformatics of the central neural system, referred to as neuroimmunoinformatics (NII), investigates symmetrical and asymmetrical interactions of the brain-immune interface. This interdisciplinary overview on NII is addressed to bioscientists and computer scientists. We delineate the dominating trajectories and field-shaping achievements and elaborate on future directions using bridging language and terminology. Computation, varying from linear modeling to complex deep learning approaches, fuels neuroimmunology through three core directions. Firstly, by providing big-data analysis software for high-throughput methods such as next-generation sequencing and genome-wide association studies. Secondly, by designing models for the prediction of protein morphology, functions, and symmetrical and asymmetrical protein–protein interactions. Finally, NII boosts the output of quantitative pathology by enabling the automatization of tedious processes such as cell counting, tracing, and arbor analysis. The new classification of microglia, the brain’s innate immune cells, was an NII achievement. Deep sequencing classifies microglia in “sensotypes” to accurately describe the versatility of immune responses to physiological and pathological challenges, as well as to experimental conditions such as xenografting and organoids. NII approaches complex tasks in the brain-immune interface, recognizes patterns and allows for hypothesis-free predictions with ultimate targeted individualized treatment strategies, and personalizes disease prognosis and treatment response.

Genome-wide sequencing-based identification of methylation quantitative trait loci and their role in schizophrenia risk

DNA methylation (DNAm) is an epigenetic regulator of gene expression and a hallmark of gene-environment interaction. Using whole-genome bisulfite sequencing, we have surveyed DNAm in 344 samples of human postmortem brain tissue from neurotypical subjects and individuals with schizophrenia. We identify genetic influence on local methylation levels throughout the genome, both at CpG sites and CpH sites, with 86% of SNPs and 55% of CpGs being part of methylation quantitative trait loci (meQTLs). These associations can further be clustered into regions that are differentially methylated by a given SNP, highlighting the genes and regions with which these loci are epigenetically associated. These findings can be used to better characterize schizophrenia GWAS-identified variants as epigenetic risk variants. Regions differentially methylated by schizophrenia risk-SNPs explain much of the heritability associated with risk loci, despite covering only a fraction of the genomic space. We provide a comprehensive, single base resolution view of association between genetic variation and genomic methylation, and implicate schizophrenia GWAS-associated variants as influencing the epigenetic plasticity of the brain.

The authors provide a comprehensive, single base resolution view of association between genetic variation and DNA methylation in human brain. They also show that heritability attributed to schizophrenia GWAS-associated variants reflects the epigenetic plasticity of the brain.

Cell-type-specific meQTLs extend melanoma GWAS annotation beyond eQTLs and inform melanocyte gene-regulatory mechanisms

Although expression quantitative trait loci (eQTLs) have been powerful in identifying susceptibility genes from genome-wide association study (GWAS) findings, most trait-associated loci are not explained by eQTLs alone. Alternative QTLs, including DNA methylation QTLs (meQTLs), are emerging, but cell-type-specific meQTLs using cells of disease origin have been lacking. Here, we established an meQTL dataset by using primary melanocytes from 106 individuals and identified 1,497,502 significant cis-meQTLs. Multi-QTL colocalization with meQTLs, eQTLs, and mRNA splice-junction QTLs from the same individuals together with imputed methylome-wide and transcriptome-wide association studies identified candidate susceptibility genes at 63% of melanoma GWAS loci. Among the three molecular QTLs, meQTLs were the single largest contributor. To compare melanocyte meQTLs with those from malignant melanomas, we performed meQTL analysis on skin cutaneous melanomas from The Cancer Genome Atlas (n = 444). A substantial proportion of meQTL probes (45.9%) in primary melanocytes is preserved in melanomas, while a smaller fraction of eQTL genes is preserved (12.7%). Integration of melanocyte multi-QTLs and melanoma meQTLs identified candidate susceptibility genes at 72% of melanoma GWAS loci. Beyond GWAS annotation, meQTL-eQTL colocalization in melanocytes suggested that 841 unique genes potentially share a causal variant with a nearby methylation probe in melanocytes. Finally, melanocyte trans-meQTLs identified a hotspot for rs12203592, a cis-eQTL of a transcription factor, IRF4, with 131 candidate target CpGs. Motif enrichment and IRF4 ChIP-seq analysis demonstrated that these target CpGs are enriched in IRF4 binding sites, suggesting an IRF4-mediated regulatory network. Our study highlights the utility of cell-type-specific meQTLs.

Identifying causal models between genetically regulated methylation patterns and gene expression in healthy colon tissue

BACKGROUND

DNA methylation is involved in the regulation of gene expression and phenotypic variation, but the inter-relationship between genetic variation, DNA methylation and gene expression remains poorly understood. Here we combine the analysis of genetic variants related to methylation markers (methylation quantitative trait loci: mQTLs) and gene expression (expression quantitative trait loci: eQTLs) with methylation markers related to gene expression (expression quantitative trait methylation: eQTMs), to provide novel insights into the genetic/epigenetic architecture of colocalizing molecular markers.

RESULTS

Normal mucosa from 100 patients with colon cancer and 50 healthy donors included in the Colonomics project have been analyzed. Linear models have been used to find mQTLs and eQTMs within 1 Mb of the target gene. From 32,446 eQTLs previously detected, we found a total of 6850 SNPs, 114 CpGs and 52 genes interrelated, generating 13,987 significant combinations of co-occurring associations (meQTLs) after Bonferromi correction. Non-redundant meQTLs were 54, enriched in genes involved in metabolism of glucose and xenobiotics and immune system. SNPs in meQTLs were enriched in regulatory elements (enhancers and promoters) compared to random SNPs within 1 Mb of genes. Three colorectal cancer GWAS SNPs were related to methylation changes, and four SNPs were related to chemerin levels. Bayesian networks have been used to identify putative causal relationships among associated SNPs, CpG and gene expression triads. We identified that most of these combinations showed the canonical pathway of methylation markers causes gene expression variation (60.1%) or non-causal relationship between methylation and gene expression (33.9%); however, in up to 6% of these combinations, gene expression was causing variation in methylation markers.

CONCLUSIONS

In this study we provided a characterization of the regulation between genetic variants and inter-dependent methylation markers and gene expression in a set of 150 healthy colon tissue samples. This is an important finding for the understanding of molecular susceptibility on colon-related complex diseases.

Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney

The kidney is an organ of key relevance to blood pressure (BP) regulation, hypertension and antihypertensive treatment. However, genetically mediated renal mechanisms underlying susceptibility to hypertension remain poorly understood. We integrated genotype, gene expression, alternative splicing and DNA methylation profiles of up to 430 human kidneys to characterize the effects of BP index variants from genome-wide association studies (GWASs) on renal transcriptome and epigenome. We uncovered kidney targets for 479 (58.3%) BP-GWAS variants and paired 49 BP-GWAS kidney genes with 210 licensed drugs. Our colocalization and Mendelian randomization analyses identified 179 unique kidney genes with evidence of putatively causal effects on BP. Through Mendelian randomization, we also uncovered effects of BP on renal outcomes commonly affecting patients with hypertension. Collectively, our studies identified genetic variants, kidney genes, molecular mechanisms and biological pathways of key relevance to the genetic regulation of BP and inherited susceptibility to hypertension.

Genetic impacts on DNA methylation: research findings and future perspectives

Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.

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.

Heritability of DNA methylation in threespine stickleback (Gasterosteus aculeatus)

Epigenetic mechanisms underlying phenotypic change are hypothesized to contribute to population persistence and adaptation in the face of environmental change. To date, few studies have explored the heritability of intergenerationally stable methylation levels in natural populations, and little is known about the relative contribution of cis- and trans-regulatory changes to methylation variation. Here, we explore the heritability of DNA methylation, and conduct methylation quantitative trait loci (meQTLs) analysis to investigate the genetic architecture underlying methylation variation between marine and freshwater ecotypes of threespine stickleback (Gasterosteus aculeatus). We quantitatively measured genome-wide DNA methylation in fin tissue using reduced representation bisulfite sequencing of F1 and F2 crosses, and their marine and freshwater source populations. We identified cytosines (CpG sites) that exhibited stable methylation levels across generations. We found that additive genetic variance explained an average of 24-35% of the methylation variance, with a number of CpG sites possibly autonomous from genetic control. We also detected both cis- and trans-meQTLs, with only trans-meQTLs overlapping with previously identified genomic regions of high differentiation between marine and freshwater ecotypes. Finally, we identified the genetic architecture underlying two key CpG sites that were differentially methylated between ecotypes. These findings demonstrate a potential role for DNA methylation in facilitating adaptation to divergent environments and improve our understanding of the heritable basis of population epigenomic variation.

Genome-wide analysis of pseudogenes reveals HBBP1's human-specific essentiality in erythropoiesis and implication in β-thalassemia

The human genome harbors 14,000 duplicated or retroposed pseudogenes. Given their functionality as regulatory RNAs and low conservation, we hypothesized that pseudogenes could shape human-specific phenotypes. To test this, we performed co-expression analyses and found that pseudogene exhibited tissue-specific expression, especially in the bone marrow. By incorporating genetic data, we identified a bone-marrow-specific duplicated pseudogene, HBBP1 (η-globin), which has been implicated in β-thalassemia. Extensive functional assays demonstrated that HBBP1 is essential for erythropoiesis by binding the RNA-binding protein (RBP), HNRNPA1, to upregulate TAL1, a key regulator of erythropoiesis. The HBBP1/TAL1 interaction contributes to a milder symptom in β-thalassemia patients. Comparative studies further indicated that the HBBP1/TAL1 interaction is human-specific. Genome-wide analyses showed that duplicated pseudogenes are often bound by RBPs and less commonly bound by microRNAs compared with retropseudogenes. Taken together, we not only demonstrate that pseudogenes can drive human evolution but also provide insights on their functional landscapes.

CancerImmunityQTL: a database to systematically evaluate the impact of genetic variants on immune infiltration in human cancer

Tumor-infiltrating immune cells as integral component of the tumor microenvironment are associated with tumor progress, prognosis and responses to immunotherapy. Genetic variants have been demonstrated to impact tumor-infiltrating, underscoring the heritable character of immune landscape. Therefore, identification of immunity quantitative trait loci (immunQTLs), which evaluate the effect of genetic variants on immune cells infiltration, might present a critical step toward fully understanding the contribution of genetic variants in tumor development. Although emerging studies have demonstrated the determinants of germline variants on immune infiltration, no database has yet been developed to systematically analyze immunQTLs across multiple cancer types. Using genotype data from TCGA database and immune cell fractions estimated by CIBERSORT, we developed a computational pipeline to identify immunQTLs in 33 cancer types. A total of 913 immunQTLs across different cancer types were identified. Among them, 5 immunQTLs are associated with patient overall survival. Furthermore, by integrating immunQTLs with GWAS data, we identified 527 immunQTLs overlapping with known GWAS linkage disequilibrium regions. Finally, we constructed a user-friendly database, CancerImmunityQTL (http://www.cancerimmunityqtl-hust.com/) for users to browse, search and download data of interest. This database provides an informative resource to understand the germline determinants of immune infiltration in human cancer and benefit from personalized cancer immunotherapy.

Incorporation of DNA methylation into eQTL mapping in African Americans

Epigenetics is a reversible molecular mechanism that plays a critical role in many developmental, adaptive, and disease processes. DNA methylation has been shown to regulate gene expression and the advent of high throughput technologies has made genome-wide DNA methylation analysis possible. We investigated the effect of DNA methylation on eQTL mapping (methylation-adjusted eQTLs), by incorporating DNA methylation as a SNP-based covariate in eQTL mapping in African American derived hepatocytes. We found that the addition of DNA methylation uncovered new eQTLs and eGenes. Previously discovered eQTLs were significantly altered by the addition of DNA methylation data suggesting that methylation may modulate the association of SNPs to gene expression. We found that methylation-adjusted eQTLs that were less significant compared to PC-adjusted eQTLs were enriched in lipoprotein measurements (FDR=0.0040), immune system disorders (FDR = 0.0042), and liver enzyme measurements (FDR=0.047), suggesting that DNA methylation modulates the genetic regulation of these phenotypes. Our methylation-adjusted eQTL analysis also uncovered novel SNP-gene pairs. For example, we found that the SNP, rs1332018, was associated to GSTM3. GSTM3 expression has been linked to Hepatitis B which African Americans suffer from disproportionately. Our methylation-adjusted method adds new understanding to the genetic basis of complex diseases that disproportionally affect African Americans.

Gene expression analysis in epileptic hippocampi reveals a promoter haplotype conferring reduced aldehyde dehydrogenase 5a1 expression and responsiveness

Increasing evidence indicates the pathogenetic relevance of regulatory genomic motifs for variability in the manifestation of brain disorders. In this context, cis-regulatory effects of single nucleotide polymorphisms (SNPs) on gene expression can contribute to changing transcript levels of excitability-relevant molecules and episodic seizure manifestation in epilepsy. Biopsy specimens of patients undergoing epilepsy surgery for seizure relief provide unique insights into the impact of promoter SNPs on corresponding mRNA expression. Here, we have scrutinized whether two linked regulatory SNPs (rs2744575; 4779C > G and rs4646830; 4854C > G) located in the aldehyde dehydrogenase 5a1 (succinic semialdehyde dehydrogenase; ALDH5A1) gene promoter are associated with expression of corresponding mRNAs in epileptic hippocampi (n = 43). The minor ALDH5A1-GG haplotype associates with significantly lower ALDH5A1 transcript abundance. Complementary in vitro analyses in neural cell cultures confirm this difference and further reveal a significantly constricted range for the minor ALDH5A1 haplotype of promoter activity regulation through the key epileptogenesis transcription factor Egr1 (early growth response 1). The present data suggest systematic analyses in human hippocampal tissue as a useful approach to unravel the impact of epilepsy candidate SNPs on associated gene expression. Aberrant ALDH5A1 promoter regulation in functional terms can contribute to impaired γ-aminobutyric acid homeostasis and thereby network excitability and seizure propensity.

Genetic and Epigenetic Analysis Revealing Variants in the NCAM1-TTC12-ANKK1-DRD2 Cluster Associated Significantly With Nicotine Dependence in Chinese Han Smokers

BACKGROUNDS

Although studies have demonstrated that the NCAM1-TTC12-ANKK1-DRD2 gene cluster plays essential roles in addictions in subjects of European and African origin, study of Chinese Han subjects is limited. Further, the underlying biological mechanisms of detected associations are largely unknown.

METHODS

Sixty-four single-nucleotide polymorphisms (SNPs) in this cluster were analyzed for association with Fagerstrőm Test for Nicotine Dependence score (FTND) and cigarettes per day (CPD) in male Chinese Han smokers (N = 2616). Next-generation bisulfite sequencing was used to discover smoking-associated differentially methylated regions (DMRs). Both cis-eQTL and cis-mQTL analyses were applied to assess the cis-regulatory effects of these risk SNPs.

RESULTS

Association analysis revealed that rs4648317 was significantly associated with FTND and CPD (p = .00018; p = .00072). Moreover, 14 additional SNPs were marginally significantly associated with FTND or CPD (p = .05-.01). Haplotype-based association analysis showed that one haplotype in DRD2, C-T-A-G, formed by rs4245148, rs4581480, rs4648317, and rs11214613, was significantly associated with CPD (p = .0005) and marginally associated with FTND (p = .003). Further, we identified four significant smoking-associated DMRs, three of which are located in the DRD2/ANKK1 region (p = .0012-.00005). Finally, we found five significant CpG-SNP pairs (p = 7.9 × 10-9-6.6 × 10-6) formed by risk SNPs rs4648317, rs11604671, and rs2734849 and three methylation loci.

CONCLUSIONS

We found two missense variants (rs11604671; rs2734849) and an intronic variant (rs4648317) with significant effects on ND and further explored their mechanisms of action through expression and methylation analysis. We found the majority of smoking-related DMRs are located in the ANKK1/DRD2 region, indicating a likely causative relation between non-synonymous SNPs and DMRs.

IMPLICATIONS

This study shows that there exist significant association of variants and haplotypes in ANKK1/DRD2 region with ND in Chinese male smokers. Further, this study also shows that DNA methylation plays an important role in mediating such associations.

Genetic association study of a novel indel polymorphism in HSPA1B with the risk of sudden cardiac death in the Chinese populations

Sudden cardiac death (SCD) has become a global problem due to its high mortality in the general population. Identification of genetic factors predisposed to SCD is significant since it enables genetic testing that would contribute to molecular diagnosis and risk stratification of SCD. It has been reported that HSPA1B gene mutations might be related with SCD. In this study, based on candidate-gene-based approach and systematic screening strategy, a 5-base pair insertion/deletion (Indel) polymorphism (rs3036297) in the 3'UTR of HSPA1B gene was selected to perform a case-control study aiming to investigate its association with SCD susceptibility in Chinese populations. Logistic regression analysis showed that the insertion allele of rs3036297 was correlated with a comparatively lower risk for SCD [OR=0.58, 95%CI=0.43-0.77, P=1.28×10^-4] compared with the deletion allele. Luciferase activity assay indicated that HSPA1B expression could be regulated by rs3036297 through interfering binding with miR-134-5p. Furthermore, analysis of database from Haploreg and GTEx revealed that the rs3036297 variant was involved in potential cis-regulatory element with the promoter of HLA-DRB5 through a long-range interaction and the deletion allele of rs3036297 increased HLA-DRB5 expression. In conclusion, the rs3036297 variant may regulate HSPA1B expression via a mechanism of miRNA binding and HLA-DRB5 expression via a long-range promoter interaction through which contributed to SCD susceptibility. Therefore, rs3036297 would be a potential marker for molecular diagnosis and genetic counseling of SCD.

Derivation of poly-methylomic profile scores for schizophrenia

Schizophrenia and bipolar disorder share biological features and environmental risk factors that may be associated with altered DNA methylation. In this study we sought to: 1) construct a novel 'Poly-Methylomic Profile Score (PMPS)' by transforming schizophrenia-associated epigenome-wide methylation from a previously published epigenome-wide association study (EWAS) into a single quantitative metric; and 2) examine associations between the PMPS and clinical status in an independent sample of 57 schizophrenia (SZ) cases, 59 bipolar disorder (BD) cases and 55 healthy controls (HC) for whom blood-derived DNA methylation was quantified using the Illumina 450 K methylation beadchip. We constructed five PMPSs at different p-value thresholds by summing methylation beta-values weighted by individual-CpG effect sizes from the meta-analysis of a previously published schizophrenia EWAS (comprising three separate cohorts with 675 [353 SZ and 322 HC] discovery cohort participants, 847 [414 SZ and 433 HC] replication cohort participants, and 96 monozygotic twin-pairs discordant for SZ). All SZ PMPSs were elevated in SZ participants relative to HCs, with the score calculated at a p-value threshold of 1 × 10^-5 accounting for the greatest amount of variance. All PMPSs were elevated in SZ relative to BD and none of the PMPSs were increased in BD, or in a combined cohort of BD and SZ cases, relative to HCs. PMPSs were also not associated with positive or negative symptom severity. That this SZ-derived PMPSs was elevated in SZ, but not BD, suggests that epigenome-wide methylation patterns may represent distinct pathophysiology that is yet to be elucidated.

Investigation of Schizophrenia with Human Induced Pluripotent Stem Cells

Schizophrenia is a chronic and severe neuropsychiatric condition manifested by cognitive, emotional, affective, perceptual, and behavioral abnormalities. Despite decades of research, the biological substrates driving the signs and symptoms of the disorder remain elusive, thus hampering progress in the development of treatments aimed at disease etiologies. The recent emergence of human induced pluripotent stem cell (hiPSC)-based models has provided the field with a highly innovative approach to generate, study, and manipulate living neural tissue derived from patients, making possible the exploration of fundamental roles of genes and early-life stressors in disease-relevant cell types. Here, we begin with a brief overview of the clinical, epidemiological, and genetic aspects of the condition, with a focus on schizophrenia as a neurodevelopmental disorder. We then highlight relevant technical advancements in hiPSC models and assess novel findings attained using hiPSC-based approaches and their implications for disease biology and treatment innovation. We close with a critical appraisal of the developments necessary for both further expanding knowledge of schizophrenia and the translation of new insights into therapeutic innovations.

Profiling the DNA methylation patterns of imprinted genes in abnormal semen samples by next-generation bisulfite sequencing

PURPOSE

Changes in DNA methylation modifications have been associated with male infertility. With the development of assisted reproductive technologies (ARTs), abnormal DNA methylation in sperm, especially in imprinted genes, may impact the health of offspring and requires an in-depth study.

METHODS

In this study, we collected abnormal human semen samples, including asthenospermic, oligospermic, oligoasthenospermic and deformed sperm, and investigated the methylation of imprinted genes by reduced representation bisulfite sequencing (RRBS) and bisulfite amplicon sequencing on the Illumina platform.

RESULTS

The differentially methylated regions (DMRs) of imprinted genes, including H19, GNAS, MEG8 and SNRPN, were different in the abnormal semen groups. MEG8 DMR methylation in the asthenospermic group was significantly increased. Furthermore, higher methylation levels of MEG8, GNAS and SNRPN DMR in the oligospermic and oligoasthenospermic groups and a decrease in the H19 DMR methylation level in the oligospermic group were observed. However, the methylation levels of these regions varied greatly among the different semen samples and among individual sperm within the same semen sample. The SNP rs2525883 genotype in the H19 DMR affected DNA methylation. Moreover, DNA methylation levels differed in the abnormal semen groups in the non-imprinted genomic regions, including repetitive sequence DNA transposons and long/short interspersed nuclear elements (LINEs and SINEs).

CONCLUSION

Our study established that imprinted gene DMRs, such as H19, GNAS, SNRPN and MEG8, were differentially methylated in the abnormal semen groups with obvious inter- and intra-sample heterogeneities. These results suggest that special attention needs to be paid to possible epigenetic risks during reproduction.

SNP2APA: a database for evaluating effects of genetic variants on alternative polyadenylation in human cancers

Alternative polyadenylation (APA) is an important post-transcriptional regulation that recognizes different polyadenylation signals (PASs), resulting in transcripts with different 3' untranslated regions, thereby influencing a series of biological processes and functions. Recent studies have revealed that some single nucleotide polymorphisms (SNPs) could contribute to tumorigenesis and development through dysregulating APA. However, the associations between SNPs and APA in human cancers remain largely unknown. Here, using genotype and APA data of 9082 samples from The Cancer Genome Atlas (TCGA) and The Cancer 3'UTR Altas (TC3A), we systematically identified SNPs affecting APA events across 32 cancer types and defined them as APA quantitative trait loci (apaQTLs). As a result, a total of 467 942 cis-apaQTLs and 30 721 trans-apaQTLs were identified. By integrating apaQTLs with survival and genome-wide association studies (GWAS) data, we further identified 2154 apaQTLs associated with patient survival time and 151 342 apaQTLs located in GWAS loci. In addition, we designed an online tool to predict the effects of SNPs on PASs by utilizing PAS motif prediction tool. Finally, we developed SNP2APA, a user-friendly and intuitive database (http://gong_lab.hzau.edu.cn/SNP2APA/) for data browsing, searching, and downloading. SNP2APA will significantly improve our understanding of genetic variants and APA in human cancers.

Pancan-meQTL: a database to systematically evaluate the effects of genetic variants on methylation in human cancer

DNA methylation is an important epigenetic mechanism for regulating gene expression. Aberrant DNA methylation has been observed in various human diseases, including cancer. Single-nucleotide polymorphisms can contribute to tumor initiation, progression and prognosis by influencing DNA methylation, and DNA methylation quantitative trait loci (meQTL) have been identified in physiological and pathological contexts. However, no database has been developed to systematically analyze meQTLs across multiple cancer types. Here, we present Pancan-meQTL, a database to comprehensively provide meQTLs across 23 cancer types from The Cancer Genome Atlas by integrating genome-wide genotype and DNA methylation data. In total, we identified 8 028 964 cis-meQTLs and 965 050 trans-meQTLs. Among these, 23 432 meQTLs are associated with patient overall survival times. Furthermore, we identified 2 214 458 meQTLs that overlap with known loci identified through genome-wide association studies. Pancan-meQTL provides a user-friendly web interface (http://bioinfo.life.hust.edu.cn/Pancan-meQTL/) that is convenient for browsing, searching and downloading data of interest. This database is a valuable resource for investigating the roles of genetics and epigenetics in cancer.

CancerSplicingQTL: a database for genome-wide identification of splicing QTLs in human cancer

Alternative splicing (AS) is a widespread process that increases structural transcript variation and proteome diversity. Aberrant splicing patterns are frequently observed in cancer initiation, progress, prognosis and therapy. Increasing evidence has demonstrated that AS events could undergo modulation by genetic variants. The identification of splicing quantitative trait loci (sQTLs), genetic variants that affect AS events, might represent an important step toward fully understanding the contribution of genetic variants in disease development. However, no database has yet been developed to systematically analyze sQTLs across multiple cancer types. Using genotype data from The Cancer Genome Atlas and corresponding AS values calculated by TCGASpliceSeq, we developed a computational pipeline to identify sQTLs from 9 026 tumor samples in 33 cancer types. We totally identified 4 599 598 sQTLs across all cancer types. We further performed survival analyses and identified 17 072 sQTLs associated with patient overall survival times. Furthermore, using genome-wide association study (GWAS) catalog data, we identified 1 180 132 sQTLs overlapping with known GWAS linkage disequilibrium regions. Finally, we constructed a user-friendly database, CancerSplicingQTL (http://www.cancersplicingqtl-hust.com/) for users to conveniently browse, search and download data of interest. This database provides an informative sQTL resource for further characterizing the potential functional roles of SNPs that control transcript isoforms in human cancer.

DNA Methylation-Mediated Modulation of Endocytosis as Potential Mechanism for Synaptic Function Regulation in Murine Inhibitory Cortical Interneurons

The balance of excitation and inhibition is essential for cortical information processing, relying on the tight orchestration of the underlying subcellular processes. Dynamic transcriptional control by DNA methylation, catalyzed by DNA methyltransferases (DNMTs), and DNA demethylation, achieved by ten–eleven translocation (TET)-dependent mechanisms, is proposed to regulate synaptic function in the adult brain with implications for learning and memory. However, focus so far is laid on excitatory neurons. Given the crucial role of inhibitory cortical interneurons in cortical information processing and in disease, deciphering the cellular and molecular mechanisms of GABAergic transmission is fundamental. The emerging relevance of DNMT and TET-mediated functions for synaptic regulation irrevocably raises the question for the targeted subcellular processes and mechanisms. In this study, we analyzed the role dynamic DNA methylation has in regulating cortical interneuron function. We found that DNMT1 and TET1/TET3 contrarily modulate clathrin-mediated endocytosis. Moreover, we provide evidence that DNMT1 influences synaptic vesicle replenishment and GABAergic transmission, presumably through the DNA methylation-dependent transcriptional control over endocytosis-related genes. The relevance of our findings is supported by human brain sample analysis, pointing to a potential implication of DNA methylation-dependent endocytosis regulation in the pathophysiology of temporal lobe epilepsy, a disease characterized by disturbed synaptic transmission.

Identification of 34 genes conferring genetic and pharmacological risk for the comorbidity of schizophrenia and smoking behaviors

The prevalence of smoking is significantly higher in persons with schizophrenia (SCZ) than in the general population. However, the biological mechanisms of the comorbidity of smoking and SCZ are largely unknown. This study aimed to reveal shared biological pathways for the two diseases by analyzing data from two genome-wide association studies with a total sample size of 153,898. With pathway-based analysis, we first discovered 18 significantly enriched pathways shared by SCZ and smoking, which were classified into five groups: postsynaptic density, cadherin binding, dendritic spine, long-term depression, and axon guidance. Then, by using an integrative analysis of genetic, epigenetic, and expression data, we found not only 34 critical genes (e.g., PRKCZ, ARHGEF3, and CDKN1A) but also various risk-associated SNPs in these genes, which convey susceptibility to the comorbidity of the two disorders. Finally, using both in vivo and in vitro data, we demonstrated that the expression profiles of the 34 genes were significantly altered by multiple psychotropic drugs. Together, this multi-omics study not only reveals target genes for new drugs to treat SCZ but also reveals new insights into the shared genetic vulnerabilities of SCZ and smoking behaviors.

ME-Class2 reveals context dependent regulatory roles for 5-hydroxymethylcytosine

Since the discovery of 5-hydroxymethylcytosine (5hmC) as a prominent DNA modification found in mammalian genomes, an emergent question has been what role this mark plays in gene regulation. 5hmC is hypothesized to function as an intermediate in the demethylation of 5-methylcytosine (5mC) and in the reactivation of silenced promoters and enhancers. Further, weak positive correlations are observed between gene body 5hmC and gene expression. We previously demonstrated that ME-Class is an effective tool to understand relationships between whole-genome bisulfite sequencing data and expression. In this work, we present ME-Class2, a machine-learning based tool to perform integrative 5mCG, 5hmCG and expression analysis. Using ME-Class2 we analyze whole-genome single-base resolution 5mCG and 5hmCG datasets from 20 primary tissue and cell samples to reveal relationships between 5hmCG and expression. Our analysis indicates that conversion of 5mCG to 5hmCG within 2 kb of the transcription start site associates with distinct functions depending on the summed level of 5mCG + 5hmCG. Unchanged levels of 5mCG + 5hmCG (conversion from 5mCG to stable 5hmCG) associate with repression. Meanwhile, decreases in 5mCG + 5hmCG (5hmCG-mediated demethylation) associate with gene activation. Our results demonstrate that ME-Class2 will prove invaluable to interpret genome-wide 5mC and 5hmC datasets and guide mechanistic studies into the function of 5hmCG.

Functional genetic variants can mediate their regulatory effects through alteration of transcription factor binding

Functional variants in the genome are usually identified by their association with local gene expression, DNA methylation or chromatin states. DNA sequence motif analysis and chromatin immunoprecipitation studies have provided indirect support for the hypothesis that functional variants alter transcription factor binding to exert their effects. In this study, we provide direct evidence that functional variants can alter transcription factor binding. We identify a multifunctional variant within the TBC1D4 gene encoding a canonical NFκB binding site, and edited it using CRISPR-Cas9 to remove this site. We show that this editing reduces TBC1D4 expression, local chromatin accessibility and binding of the p65 component of NFκB. We then used CRISPR without genomic editing to guide p65 back to the edited locus, demonstrating that this re-targeting, occurring ~182 kb from the gene promoter, is enough to restore the function of the locus, supporting the central role of transcription factors mediating the effects of functional variants.

[Breakthrough in understanding the molecular causes of psychiatric disorders]

A long-established hypothesis is that genetic factors contribute to the development of psychiatric diseases, including common illnesses such as schizophrenia and the affective disorders; however, reliable molecular identification of these factors represents a far more recent innovation. This has been rendered possible by technological advances in the individual characterization of the human genome and the combining of large genetic datasets at the international level. For the first time, the results of genome-wide analyses provide researchers with systematic insights into disease-relevant biological mechanisms. Here, the integrated analysis of different omics level data generates important insights into the functional interpretation of the genetic findings. The results of genetic studies also demonstrated the degree of etiological overlap between differing psychiatric disorders, with the greatest commonality having been observed to date between schizophrenia and bipolar affective disorder. Although the translation of genetic findings into routine clinical practice is being pursued at various levels, elaborate follow-up studies are typically necessary. The diagnostic investigation of rare genomic deletions/duplications (so-called copy number variants) in patients with schizophrenia is likely to represent one of the first examples of routine clinical application. The necessary prerequisites for this are currently being defined.

No evidence for a BRD2 promoter hypermethylation in blood leukocytes of Europeans with juvenile myoclonic epilepsy

Juvenile myoclonic epilepsy (JME) is a common syndrome of genetic generalized epilepsies (GGEs). Linkage and association studies suggest that the gene encoding the bromodomain-containing protein 2 (BRD2) may increase risk of JME. The present methylation and association study followed up a recent report highlighting that the BRD2 promoter CpG island (CpG76) is differentially hypermethylated in lymphoblastoid cells from Caucasian patients with JME compared to patients with other GGE subtypes and unaffected relatives. In contrast, we found a uniform low average percentage of methylation (<4.5%) for 13 CpG76-CpGs in whole blood cells from 782 unrelated European Caucasians, including 116 JME patients, 196 patients with genetic absence epilepsies, and 470 control subjects. We also failed to confirm an allelic association of the BRD2 promoter single nucleotide polymorphism (SNP) rs3918149 with JME (Armitage trend test, P = 0.98), and we did not detect a substantial impact of SNP rs3918149 on CpG76 methylation in either 116 JME patients (methylation quantitative trait loci [meQTL], P = 0.29) or 470 German control subjects (meQTL, P = 0.55). Our results do not support the previous observation that a high DNA methylation level of the BRD2 promoter CpG76 island is a prevalent epigenetic motif associated with JME in Caucasians.

Treating the "E" in "G × E": Trauma-Informed Approaches and Psychological Therapy Interventions in Psychosis

Despite advances in genetic research, causal variants affecting risk for schizophrenia remain poorly characterized, and the top 108 loci identified through genome-wide association studies (GWAS) explain only 3.4% of variance in risk profiles. Such work is defining the highly complex nature of this condition, with omnigenic models of schizophrenia suggesting that gene regulatory networks are sufficiently interconnected such that altered expression of any "peripheral" gene in a relevant cell type has the capacity to indirectly modulate the expression of "core" schizophrenia-associated genes. This wealth of associated genes with small effect sizes makes identifying new druggable targets difficult, and current pharmacological treatments for schizophrenia can involve serious side effects. However, the fact that the majority of schizophrenia genome-wide associated variants fall within non-coding DNA is suggestive of their potential to modulate gene regulation. This would be consistent with risks that can be mediated in a "gene × environment" (G × E) manner. Stress and trauma can alter the regulation of key brain-related pathways over the lifetime of an individual, including modulation of brain development, and neurochemistry in the adult. Recent studies demonstrate a significant overlap between psychotic symptoms and trauma, ranging from prior trauma contributing to psychosis, as well as trauma in response to the experience of psychosis itself or in response to treatment. Given the known effects of trauma on both CNS gene expression and severity of psychosis symptoms, it may be that pharmacological treatment alone risks leaving individuals with a highly stressful and unresolved environmental component that continues to act in a "G × E" manner, with the likelihood that this would negatively impact recovery and relapse risk. This review aims to cover the recent advances elucidating the complex genetic architecture of schizophrenia, as well as the long-term effects of early life trauma on brain function and future mental health risk. Further, the evidence demonstrating the role of ongoing responses to trauma or heightened stress sensitivity, and their impact on the course of illness and recovery, is presented. Finally, the need for trauma-informed approaches and psychological therapy-based interventions is discussed, and a brief overview of the evidence to determine their utility is presented.

Dichotomy in redundant enhancers points to presence of initiators of gene regulation

Background

The regulatory landscape of a gene locus often consists of several functionally redundant enhancers establishing phenotypic robustness and evolutionary stability of its regulatory program. However, it is unclear what mechanisms are employed by redundant enhancers to cooperatively orchestrate gene expression.

Results

By comparing redundant enhancers to single enhancers (enhancers present in a single copy in a gene locus), we observed that the DNA sequence encryption differs between these two classes of enhancers, suggesting a difference in their regulatory mechanisms. Initiator enhancers, which are a subset of redundant enhancers and show similar sequence encryption to single enhancers, differ from the rest of redundant enhancers in their sequence encryption, evolutionary conservation and proximity to target genes. Genes hosting initiator enhancers in their loci feature elevated levels of expression. Initiator enhancers show a high level of 3D chromatin contacts with both transcription start sites and regular enhancers, suggesting their roles as primary activators and intermediate catalysts of gene expression, through which the regulatory signals of redundant enhancers are propagated to the target genes. In addition, GWAS and eQTLs variants are significantly enriched in initiator enhancers compared to redundant enhancers, suggesting a key functional role these sequences play in gene regulation.

Conclusions

The specific characteristics and widespread abundance of initiator enhancers advocate for a possible universal hierarchical mechanism of tissue-specific gene regulation involving multiple redundant enhancers acting through initiator enhancers.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5335-0) contains supplementary material, which is available to authorized users.

Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies

The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology.

Epilepsies are common brain disorders and are classified based on clinical phenotyping, imaging and genetics. Here, the authors perform genome-wide association studies for 3 broad and 7 subtypes of epilepsy and identify 16 loci - 11 novel - that are further annotated by eQTL and partitioned heritability analyses.