key: cord-102219-d3gkfo7s
authors: Perzel Mandell, Kira A.; Price, Amanda J.; Wilton, Richard; Collado-Torres, Leonardo; Tao, Ran; Eagles, Nicholas J.; Szalay, Alexander S.; Hyde, Thomas M.; Weinberger, Daniel R.; Kleinman, Joel E.; Jaffe, Andrew E.
title: Characterizing the dynamic and functional DNA methylation landscape in the developing human cortex
date: 2019-10-30
journal: bioRxiv
DOI: 10.1101/823781
sha: 
doc_id: 102219
cord_uid: d3gkfo7s

DNA methylation (DNAm) is a key epigenetic regulator of gene expression across development. The developing prenatal brain is a highly dynamic tissue, but our understanding of key drivers of epigenetic variability across development is limited. We therefore assessed genomic methylation at over 39 million sites in the prenatal cortex using whole genome bisulfite sequencing and found loci and regions in which methylation levels are dynamic across development. We saw that DNAm at these loci was associated with nearby gene expression and enriched for enhancer chromatin states in prenatal brain tissue. Additionally, these loci were enriched for genes associated with psychiatric disorders and genes involved with neurogenesis. We also found autosomal differences in DNAm between the sexes during prenatal development, though these have less clear functional consequences. We lastly confirmed that the dynamic methylation at this critical period is specifically CpG methylation, with very low levels of CpH methylation. Our findings provide detailed insight into prenatal brain development as well as clues to the pathogenesis of psychiatric traits seen later in life.

sites change over time and these changes lead to adjustments in gene expression and splicing. These key regions have also been linked to neurodevelopmental disorders such as schizophrenia, in which early dysregulation plays a vital role 2, 3 . DNAm is an attractive epigenetic mechanism to study in post-mortem human brain tissue, because it represents an interaction between genetic and environmental effects. External factors such as changes in diet 4 , exposure to cigarette smoking 5 , and exposure to arsenic 6 have been associated with both global and site-specific changes to DNAm levels.

In order to better understand the causes and consequences of deviant DNAm patterns in psychiatric disease development, we must first understand the normal landscape. Illuminating typical methylation changes in prenatal development will both provide insight into gene expression and molecular pathways active in the postnatal developing brain, and provide a baseline for identification of aberrant DNAm in postnatal disease states, as the pathological changes that lead to symptoms of psychiatric disease may precede the onset of illness by several decades 7 .

The dorsolateral prefrontal cortex (DLPFC) is a dynamic region of the brain throughout development, essential for motor planning, conceptual organization, and working memory, and is often functionally dysregulated in patients with schizophrenia 8 . Previous studies using microarrays to quantify DNAm have revealed that there are many DNAm changes in the DLPFC around the time of birth 9 , but the dynamics of the prenatal brain are far less characterized. Previous work assessing fetal brain DNAm has largely used microarrays, and sampled whole brain rather than a specific region 10 . Most cells in the developing brain are neuronal 11 , and it has been shown that neuronal DNA methylation is especially dynamic in the earliest stages of postnatal life, making the DLPFC a potentially fruitful region for deeper interrogation in prenatal samples 1 . In the present report, we describe the use of whole genome bisulfite sequencing (WGBS) to capture an unbiased map of the DNAm landscape, and to characterize both CpG and CpH methylation during prenatal brain development. Dynamic regions of DNAm even in the early developmental context are likely to be associated with psychiatric risk-associated genes, and connection to gene expression data can validate the importance of these genomic regions. These important regions will help point to pathways and mechanisms of normal brain development as well as psychiatric and neurodevelopmental conditions.

Study samples: Brain tissue from these second-trimester prenatal samples was obtained via a Material Transfer Agreement with the National Institute of Child Health and Human Development Brain and Tissue Bank. All specimens were flash-frozen, then brain pH was measured and postmortem interval (PMI, in hours) was calculated for every sample. Postmortem tissue homogenates of the dorsolateral prefrontal cortex (DLPFC) were obtained from all subjects. Samples were obtained from the developing prefrontal cortex from the dorsolateral convexity of the frontal lobe half-way between the frontal pole and temporal pole, 4 mm lateral to the central sulcus. Specimens extended from the surface of the brain to the ventricular zone. There were 10 of each male and female subjects, and 17 subjects were African American and 3 were European ancestries (Table S1 ).

Data generation: Genomic DNA was extracted from 100 mg of pulverized DLPFC tissue with the phenol-chloroform method. DNA was subjected to bisulfite conversion followed by sequencing library preparation using the TruSeq DNA methylation kit from Illumina. Lambda DNA was spiked in prior to bisulfite conversion to assess its rate, and we used 20% PhiX to better calibrate Illumina base calling on these lower complexity libraries. Resulting libraries were pooled and sequenced on an Illumina HiSeq X Ten sequencer with paired end 150bp reads (2x150bp), targeting 90Gb per sample. This corresponds to 30x coverage of the human genome as extra reads were generated to account for the addition of PhiX.

Data Processing: The raw WGBS data was processed using FastQC to control for quality of reads 12 , Trim Galore to trim reads and remove adapter content 13 , Arioc for alignment to the GRCh38.p12 genome (obtained from ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.27_GRCh38.p12/GC A_000001405.27_GRCh38.p12_assembly_structure/Primary_Assembly/assembled_chromoso mes/) 14 , duplicate alignments were removed with SAMBLASTER 15 , and the Bismark methylation extractor to extract methylation data from the sequencing data 16 . We then used the bsseq R/Bioconductor package (v1.18) to process and combine the DNA methylation proportions across the samples for all further manipulation and analysis 17 . After initial data metrics were calculated, the methylation data was smoothed using BSmooth for modelling. CpGs were filtered to those that had ≥ 3 coverage in all samples, and CpHs were filtered to those that had ≥ 3 coverage and non-zero methylation in at least half (≥10) of the samples.

Comparison to 450k: In order to compare WGBS methylation levels to 450k methylation levels, we used data from the same samples using the two methods, and compared their methylation levels at the same sites both graphically ( Figure 1D ) and assessing the mean differences and root-mean-square deviation ( Figure 1C ). We then validated our model's findings by applying the same model to the microarray data to overlapping sites and considered loci significant at FDR < 0.05 and validated if it's association had p < 0.05 in the validating set ( Figure S3 ). Gene set enrichment: We annotated our data using Gencode v. 29 on hg38. We performed gene ontology and gene set enrichment using clusterProfiler (v3.12) 18 with a p-value cutoff of 0.01 and q-value cutoff of 0.04. We used SFARI 2.0 19 for an autism spectrum disorder gene set, and a set of clinical gene sets defined by Birnbaum et al for other neuropsychiatric and neurodevelopmental disorders 20 . Enrichment was calculated on a background of genes expressed in our samples, to avoid brain bias. We performed LD score regression as described by and with data from Finucane et al 21 .

Data Analysis: Cell composition of samples was deconvoluted using flow-sorted DLPFC microarray data from fluorescence-activated nuclear sorted neurons and non-neurons 22 and R package minfi 23 . Linear regression modelling was performed accounting for sex, age, and embryonic stem cell content. Sex-related CpGs and their surrounding sequence were re-aligned to the genome with Bowtie2 to check for homology. Association was tested using limma (v3.30) 24,25 to create a linear regression model accounting for sex, age, and embryonic stem cell content.

The homogenate RNA-seq samples were also part of a larger study of RNA-seq data from homogenate DLPFC tissue (BrainSeq Consortium Phase I) 26 . We compared a site's methylation or mean methylation (for DMRs) to the expression level from RNA-seq using a linear model adjusting for the strongest covariates, including sex, age, and estimated embryonic stem cell (ESC) content.

Section 1: Whole genome bisulfite sequencing in the prenatal human cortex

We performed whole genome bisulfite sequencing (WGBS) to better characterize the shifting DNAm landscape in the developing human dorsolateral prefrontal cortex (DLPFC) in 20 prenatal samples during the second trimester in utero (Table S1 ). After data processing and quality control (see Methods), we analyzed 28,612,402 CpG and 13,166,520 CpH (H=A, T, or C) sites across the epigenome. We first focused on CpG DNAm and performed a series of quality control analyses. We quantified read coverage at every CpG, which after processing and alignment resulted in an average of 17.7 reads per CpG ( Figure 1A ). Most of these sites are methylated (>80% DNAm) and a minority are unmethylated (<20% DNAm) (Table S2) . We then performed principal component analysis (PCA) on the DNAm levels across these high-coverage CpGs and found that the top principal components were most associated with sex and estimated fractions of embryonic stem cells (ESCs) via deconvolution ( Figure 1B ; Figure S1 ; see Methods), in line with our previous work 9 . We lastly compared the DNAm levels from WGBS to levels from Illumina 450k microarray measured on 15 of the 20 samples for the subset of CpGs in common to both technologies (N=456305), and found that overall, CpG DNAm methylation levels were highly concordant regardless of CpG read coverage ( Figure 1C -D). These analyses together suggested that our DNAm data was of high quality and available for subsequent differential methylation analysis.

To understand the changes in the prenatal epigenetic landscape, we performed linear modeling across all CpG sites. We found that DNAm changes were abundant even during this relatively restricted period in prenatal development, with 36,546 CpG sites differentially methylated across the ages of 14-20 post-conception weeks (PCW, at FDR < 0.05, Table S3 ). On average, each week of development was associated with a 1.8% change in DNAm (IQR: 1.18% -2.33%) at a given CpG, with some sites changing as much as 8.7% per week. These differentially methylated CpGs were not evenly distributed across the genome, with none on the Y chromosome, and a small number on the X chromosome (Table S4 ). There was also some unevenness on the autosomes, with some chromosomes having up to 2.5x higher frequency of age-associated CpGs than others (Table S4 ). 2,743 of these CpGs lie in previously annotated CpG islands, with 5,933 in shores, defined as 2 kb from island ends. The vast majority (73%) of these CpGs lie within genes, and 2,932 lie within 1 kb of a transcription start site and thus potentially in promoters. This leaves 6.5% in intergenic space. Sites were fairly evenly split in the direction of methylation change by age, with 46% increasing in methylation as the cortex develops. Additionally, less than half of these CpG sites are significantly associated with the estimated ESC proportion in the sample, suggesting that many of these CpG sites have true prenatal age changes in methylation rather than reflecting effects of maturing cell type composition.

We further explored whether these sites could be organized into differentially methylated regions (DMRs), which have been shown to be more functionally relevant than individual CpGs. We therefore used a "bumphunting" technique adapted to WGBS data 17 to identify regions of methylation with 1.5% DNAm changes per week (corresponding to 10.5% changes across our developmental window) change in DNAm levels across adjacent CpGs, and calculated statistical significance using 1000 bootstrap-based permutations 27 . Using a conservative cutoff (FWER < 0.2) there were 34 DMRs across prenatal development, though a less conservative cutoff (p < 0.05) identified 3,446 DMRs (Table S5, Figure S2A for DMR plot). The DMRs are similarly unevenly distributed throughout the genome as the CpGs, being far less frequent on chrX and variable among the autosomes. The DMRs on average had a width of 1696 bp (IQR: 921-2181), 75% overlap with annotated genes, and 12% overlap with annotated CpG islands. Like the CpGs, the DMRs were split between hypo-and hypermethylation, where in 49% of the DMRs, methylation increased with age.

We also found differentially methylated sites and regions between the sexes in these prenatal samples. There were 618,978 significantly differential CpGs by sex and as expected, the vast majority (95%) of these were on the sex chromosomes. There were still 30,823 significant autosomal CpGs (Table S6) , and while 993 of them were in regions homologous to the sex chromosomes, the majority (97%) had no homology to chrX or chrY. Among these, we found conservatively 28 DMRs with a 10% methylation change between sexes (Table S7, Figure S2B for DMR plot). Again we saw that these were not global, but regional changes in methylation, with equal numbers of DMRs being hypo-and hypermethylated in males and females. These data show that there are many differences in the DNAm landscape throughout second trimester development, and between sexes prenatally.

Previously, many studies of brain DNA methylation have used the Illumina Infinium® HumanMethylation450 BeadChip ("450k") and more recent Infinium MethylationEPIC ("850k") microarray technologies. While these platforms can sensitively measure DNAm levels without the high coverage/sequencing requirements of WGBS, they assay a limited number of sites. To better identify the tradeoff between breadth (assaying more sites) and depth (assaying a given site more accurately), we compared our WGBS findings to analogous statistics calculated using these arrays on the same DNA extractions. First, using the probe coordinates alone, the 450k array does not measure DNAm levels at 35,401 (97%) of the significant age-correlated CpGs or 610,578 (98.7%) of the significant sex-correlated CpGs we identified. The 450k DNAm data validated 566 (49%) out of the 1,145 age-correlated sites that were covered. Performing the same age model on the 450k data, our WGBS data validated 279/423 (66%) of the significant results. The effects of age on methylation were generally directionally concordant between the two datasets ( Figure S3 ). Though DMRs have much wider spans, the 450k does not cover 10 (30%) and 12 (43%) of DMRs for age and sex, respectively. The newer 850k microarray had almost twice the number of probes, yet we found that it does not measure 95% of our significant sites for age and 98% of sites for sex. Additionally, it still failed to capture 24% of age DMRs and 36% of sex DMRs. Microarrays are potentially missing a great deal of significantly differential sites, suggesting WGBS is best for thorough analysis.

Section 3: Functional characterization of differential sites While differential methylation analysis provides detailed information about changes in the methylation landscape across brain prenatal development and between the sexes, we wanted to better understand the molecular consequences of these changes. We performed gene set enrichment and gene ontology to better understand in which processes the genes containing significant CpGs are involved. The top biological processes associated with genes containing differentially methylated CpGs across age were related to axon development and guidance, and regulation of neuron projection ( Figure S4 , Table S8 ). These sites were also shown to be enriched for enhancers in fetal brain tissue as well as enriched for transcription start site regions in brain tissue from the Epigenome Roadmap project 28 , implying that these areas are likely functional in prenatal brain 28 ( Figure 3A ). To better assess the putative functionality of the methylation at these CpGs and DMRs, we correlated these DNAm associations with nearby expression levels using RNA-seq data from the same cortical dissection. Among the 36,546 age-related CpGs, 59% were within an RNA-seq-measured gene (corresponding to 1,630 unique genes). 7,998 (37%) of these CpGs had methylation levels significantly correlated (p < 0.05) to expression of the gene they lie directly within (661 unique genes, 41%, see Methods, Figure 3B -C). These expression-correlated CpGs did not have any differential gene ontology from the overall group of genes. These sites were mostly in weak transcription, enhancer, and quiescent chromatin states in fetal brain ( Figure S5) 28 . With an expanded window of 5 kb around genes, 24,679 (66%) CpGs were accounted for, and 8,759 (35%) correlated to the nearest gene's expression. It is important to note that this only tested sites within actual genes, and only the effect on the same gene, so enhancer, upstream, and trans-acting effects were not accounted for.

The correlation to expression was much lower for sex-related CpGs, at 5% and 9% significant correlation depending on inclusion or exclusion of sex chromosomes. The lack of correlation to expression here is unsurprising because there are almost no transcriptomic differences between the sexes in these samples. Gene set enrichment on autosomal sex-related CpGs found genes involved with synaptic transmission and signaling, regulation of GTPase activity, and the glutamate receptor signaling pathway (Table S9) .

Age-related DMRs were most enriched for genes related to stem cell proliferation and various cell fate specifications (Table S10 ). Among the DMRs across age, 21% were significantly correlated with gene expression. The genes represented by this are OTX1 , AC246817.1 , CYP2E1 , PLEKHH3 , and DUX4L32 . There was no enrichment for any gene category among the sex-related DMRs, but it is worth noting that many of these genes coded for lncRNAs. 25% of sex-related DMRs were correlated to gene expression within their own gene and these genes were LINC01606 , LINC01166 , SPATC1L , and RFPL2 . Correlation to gene expression overall was not different between differentially methylated regions and non-differentially methylated regions; the DMRs we found were not enriched for highly correlated regions. These results provide potential starting points for further understanding the DNA methylation landscape of the developing brain, allowing us to understand which processes are active during normal and disordered development.

While DNA methylation occurs exclusively in the CpG context in most somatic cell types, neurons in the human brain uniquely have high levels of DNA methylation in other cytosine contexts (predominantly CpA) 29 . We therefore investigated the potential role of CpH methylation across brain development. Only 2.4% (IQR: 2.33-2.49) of sites in a CHG context and 1.71% (IQR: 1.61-1.79) of sites in a CHH context were methylated. For comparison, around 10% of CpH sites are methylated in postnatal neurons, so it is likely as previously suggested 1 that CpH methylation accumulates beginning around the time of birth. This contrasted with CpG sites, which were predominantly methylated across the genome (Table S2) . Because there were so many more CpH than CpG sites in the genome, this means that there are actually similar numbers of methylated CpG and CpH sites, despite the different proportions. Using the same linear model as was used for CpGs on 13,166,520 CpH sites, no CpHs were significantly associated with age within this time period when accounting for the large multiple testing burden. However, 51 autosomal CpHs were significantly associated with sex (at FDR < 0.05, Table S11 ). Additionally, there were 40 associated CpHs on the sex chromosomes, though most of these were on chrY, overall very proportionally different from CpG methylation. There was no enrichment for any more specific trinucleotide context in these significant sites over the whole genome. These CpHs were less likely to be in or near genes than the CpGs, but of the 17 autosomal CpHs that were in or near genes, only 1 was significantly correlated (p < 0.05) with expression of the nearest gene within 10 kb. It is notable that the majority of these CpHs are relatively far from a gene. Additionally, the effect size of the CpH methylation at these significant sites is independent of the effect of CpGm in the surrounding area in most cases. Five of the CpHs' effect is reduced when accounting for the methylation of the nearest CpG ( Figure S6) , showing that these few are dependent on the local CpG landscape, but most function independently. These loci are found both in and outside of genes and their surrounding regions. Overall, CpH methylation does not seem to be very dynamic or functional in prenatal second trimester development, though later in early postnatal life it is 30 .

Section 5: Links to neuropsychiatric conditions and their (genetic) risk.

To understand the clinical implications of our findings, we tested the genes represented by prenatal age-associated CpGs and found that they were enriched for bipolar disorder-(p = 0.035), neurodevelopmental disorder-(p = 0.0011), and schizophrenia-(p = 0.00022) associated genes 20 . Age-differential CpGs with correlation to gene expression were not further enriched. The set of age-differential CpG genes was particularly enriched for autism-associated genes (p = 9x10 -20 , OR = 5.6), with 163 of our 1,630 genes being linked to autism spectrum disorder (ASD) 19 . Sex-differential CpGs were also enriched for ASD-associated genes (p = 6x10 -29 , OR = 2.9), but not for genes associated with other psychiatric disorders.

DMRs represented a greater portion of the genome than CpGs, and thus may have different functional effects. To further understand what phenotypes may be linked to our DMRs, we performed stratified LD score regression 21 . Our DMR sets represented a fairly small portion of the genome, but at 5.8 Mb, the less stringent age-based DMRs were wide enough to detect enrichment of BMI-and Subjective Well-Being-(SWB) associated markers (Table S12 ). These DMRs were also shown to be enriched for brain-linked traits overall, compared to non-brain-linked traits. Additionally, the DMRs overlap with 15 of 108 schizophrenia loci from Psychiatric Genetics Consortium 1 31 . These results suggest that dynamic methylation states in prenatal development may play a role in many disease and non-disease phenotypes that manifest later in life.

Here we demonstrate that CpG methylation changes are associated with prenatal cortical development, are likely functionally relevant, and may play a role in developmental psychiatric disorders such as autism spectrum disorder (ASD).

The shifting DNAm landscape during this restricted prenatal time period -the second trimesterlikely plays a role in neurogenesis of the developing human brain. Following current dogma, changes in DNAm at these critical sites presumably lead to altered gene expression which promotes the formation of new neural connections in the cortex. At this point in time, we see that it is CpGs that are the important sites in the DNAm landscape, as opposed to CpHs. This is in line with previous research which finds that most CpHm is established around birth and postnatally 1, 32 , later in neuron differentiation and development 33 . We also see that much of this regulation is occuring in cis -within the site's own gene. We do not currently understand the trans effects of our developmentally dynamic sites, but they may explain the effects of the methylation that does not correlate to its nearest gene's expression. The fact that the age-associated CpG sites we identified are often in enhancer regions in prenatal brain is further evidence that these sites may be regulating from a distance.

Our findings are in line with previous findings based on a microarray platform 10 , but using WGBS allows us to assess methylation at far more loci than the more commonly used Illumina microarrays. We've shown that these microarrays do not even measure the majority of sites that are dynamic in neural development. Despite potentially less precision, we also show that WGBS has enough coverage to assess DNAm dynamics and that its findings were consistent with deeper-coverage assays. Additionally, we present novel loci due to our narrower developmental age range, which means that we can detect more subtle changes in the second trimester but also means that we do not replicate all the findings of studies done across wider timespans. Confining our samples to just cortex tissue rather than whole brain tissue also allows us to detect more regionally specific changes. Most variation in our data likely comes from different cell types in the cortex, which we partially accounted for in our model by adjusting for ESC estimated proportions. We believe that most of the cell types in this area at this time are neurons, but there is likely a range of maturities leading to variation in methylation 11 . By accounting for ESC fraction in our model, we believe that the age affects we find are truly a result of prenatal age.

We also find that there are autosomal differences in DNAm between the sexes, though by our assessment they seem less functionally significant. They may be acting more in trans which makes it more difficult to assess significance, but there are also just generally far fewer transcriptomic differences between sexes than between ages. Sex-differential DNAm later in life has been linked to psychiatric genes, which has been proposed as a mechanism for sex differences in psychiatric disorders 34 , so perhaps the early changing DNAm is laying the groundwork for future effects. Like Xia et al 34 , we found that ASD-associated genes are differentially methylated between the sexes, but this does not readily translate into transcriptomic differences.

Given the enrichment in our developmentally-associated sites for psychiatric-linked genes, these data support the notion of early neurodevelopmental components for disorders such as schizophrenia 7, 35, 36 . Genes implicated in bipolar disorder, schizophrenia, and ASD are clearly dynamic at this time point in life, so dysregulation at this stage could lead to vulnerabilities to these disorders later in life. Our data also implies that BMI and subjective well beingnon-disease traits -could be linked to neural development at this stage of life.

As more data is generated, particularly through genome-scale methods like WGBS, we will be able to establish normal ranges of DNAm at all ages, which will undoubtedly provide insight into molecular dynamics in this hard-to-study period and organ, as well as give clues to where deviation from the norm is important. Unfortunately, WGBS still does not measure hydroxymethylation, another chemical modification thought to be epigenetically important. Our findings are only the beginning of what may be found given the limited sample size of our study, but even here we reveal important processes. There is room for much more characterization of these epigenetic marks, but it is clear that they are worth understanding. DNA methylation serves as an exciting potential avenue to understand neural development and psychiatric disorders. There are clear and functional changes in the neuronal DNAm landscape over this important window of brain development and between sexes, and further investigation will help elucidate unknown mechanisms in the brain.

Supplemental Data include 6 figures and 12 tables.

Divergent neuronal DNA methylation patterns across human cortical development reveal critical periods and a unique role of CpH methylation

Spatio-temporal transcriptome of the human brain

Epigenetic mechanisms in neurological disease

Persistent epigenetic differences associated with prenatal exposure to famine in humans

Tobacco-smoking-related differential DNA methylation: 27K discovery and replication

Long term low-dose arsenic exposure induces loss of DNA methylation

Genetic insights into the neurodevelopmental origins of schizophrenia

Complexity of prefrontal cortical dysfunction in schizophrenia: more than up or down

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

Methylomic trajectories across human fetal brain development

A Single-Cell Transcriptomic Atlas of Human Neocortical Development during Mid-gestation

A wrapper around Cutadapt and FastQC to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data

Arioc: GPU-accelerated alignment of short bisulfite-treated reads

SAMBLASTER: fast duplicate marking and structural variant read extraction

Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications

BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions

clusterProfiler: an R package for comparing biological themes among gene clusters

SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs)

Prenatal expression patterns of genes associated with neuropsychiatric disorders

Partitioning heritability by functional annotation using genome-wide association summary statistics

FlowSorted.DLPFC.450k: Illumina HumanMethylation data on sorted frontal cortex cell populations (R: Bioconductor)

Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays

limma powers differential expression analyses for RNA-sequencing and microarray studies

Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression

Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis

Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies

Integrative analysis of 111 reference human epigenomes

Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain

Divergent neuronal DNA methylation patterns across human cortical development reveal critical periods and a unique role of CpH methylation

Biological insights from 108 schizophrenia-associated genetic loci

Global epigenomic reconfiguration during mammalian brain development

Principles Governing DNA Methylation during Neuronal Lineage and Subtype Specification

Sex-differential DNA methylation and associated regulation networks in human brain implicated in the sex-biased risks of psychiatric disorders

The neurodevelopmental hypothesis of schizophrenia, revisited

Implications of normal brain development for the pathogenesis of schizophrenia

The authors thank the UMB Brain Bank at the Department of Pediatrics in the University of Maryland School of Medicine for the tissue provided. This project was supported by The Lieber Institute for Brain Development and by NIH grants R21MH102791 and R01MH112751. Finally, we are indebted to the generosity of the families of the decedents, who donated the brain tissue used in these studies.

The authors declare no competing interests.

Raw and processed nucleic acid sequencing data generated to support the findings of this study are part of the PsychENCODE Consortium and the Brainseq Consortium data releases. Specifically, WGBS data have been deposited at www.Synapse.org along with the other PsychENCODE data, under the accession code syn5842535. The homogenate RNA-seq samples were also part of a larger study of RNA-seq data from homogenate DLPFC tissue (BrainSeq Consortium Phase I) 26 , which was also deposited at www.synapse.org and summarized in http://eqtl.brainseq.org/phase1 . The processed, homogenate RNA-seq data for this study have additionally been deposited via Globus under the jhpce#brainepi-cellsorted collection at the following location: http://research.libd.org/globus/jhpce_brainepi-cellsorted/ . NeuN-sorted RNA-seq data were originally published as part of phase II of the Brainseq Consortium ( http://eqtl.brainseq.org/phase2/ ) and have also been deposited via Globus under the jhpce#brainepi-polyA collection at the following location: http://research.libd.org/globus/jhpce_brainepi-polyA/ . Publicly available data reprocessed in support of the conclusions in this work were downloaded from the Gene Expression Omnibus under GEO accession GSE47966.