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BACKGROUND: Schizophrenia (SCZ) is associated with genetic factors, and specific risk loci have been identified. Still, the biology and clinical translation of genetic risk remain largely unknown. Gene co-expression networks are relevant to functional and clinical translation of SCZ risk. We hypothesized that SCZ risk genes may converge into co-expression pathways which, in turn, may be associated with clinical phenotypes in SCZ patients. METHODS: We used Weighted Gene Co-expression Network Analysis to identify co-expression modules in two prefrontal cortex post-mortem RNA sequencing datasets, including 379 healthy controls (HC) and 309 SCZ. We used four replication datasets (HC=339). To identify modules enriched for SCZ risk genes, we computed hypergeometric tests and corrected for multiple comparisons. To translate post mortem information into clinical phenotypes, we identified polymorphisms predicting co-expression and combined them to obtain an index approximating module co-expression (Polygenic Co-expression Index: PCI). We used two independent replication datasets (HC=131). Finally, we tested the association between PCI and treatment response in two independent SCZ cohorts (SCZ=167). RESULTS: We identified and replicated (all p-values

Specific cell populations may have unique contributions to schizophrenia but may be missed in studies of homogenate tissue. Here laser capture microdissection followed by RNA sequencing (LCM-seq) was used to transcriptomically profile the granule cell layer of the dentate gyrus (DG-GCL) in human hippocampus and contrast these data to those obtained from bulk hippocampal homogenate. We identified widespread cell-type-enriched aging and genetic effects in the DG-GCL that were either absent or directionally discordant in bulk hippocampus data. Of the ~9 million expression quantitative trait loci identified in the DG-GCL, 15% were not detected in bulk hippocampus, including 15 schizophrenia risk variants. We created transcriptome-wide association study genetic weights from the DG-GCL, which identified many schizophrenia-associated genetic signals not found in transcriptome-wide association studies from bulk hippocampus, including GRM3 and CACNA1C. These results highlight the improved biological resolution provided by targeted sampling strategies like LCM and complement homogenate and single-nucleus approaches in human brain. Jaffe et al. profile the granule cell layer of the human hippocampus and find unique molecular associations for aging and genetic variation, as well as diagnosis with schizophrenia and its genetic risk, that were previously undiscovered in homogenate tissue.

Transcriptome compartmentalization by the nuclear membrane provides both stochastic and functional buffering of transcript activity in the cytoplasm and has recently been implicated in neurodegenerative disease processes. Although many mechanisms regulating transcript compartmentalization are also prevalent in brain development, the extent to which subcellular localization differs as the brain matures has yet to be addressed. To characterize the nuclear and cytoplasmic transcriptomes during brain development, we sequenced both RNA fractions from homogenate prenatal and adult human postmortem cortex using PolyA+ and RiboZero library preparation methods. We find that while many genes are differentially expressed by fraction and developmental expression changes are similarly detectable in nuclear and cytoplasmic RNA, the compartmented transcriptomes become more distinct as the brain matures, perhaps reflecting increased utilization of nuclear retention as a regulatory strategy in adult brain. We examined potential mechanisms of this developmental divergence including alternative splicing, RNA editing, nuclear pore composition, RNA binding protein motif enrichment, and RNA secondary structure. Intron retention is associated with greater nuclear abundance in a subset of transcripts, as is enrichment for several splicing factor binding motifs. Finally, we examined disease association with fraction-regulated gene sets and found nuclear-enriched genes were also preferentially enriched in gene sets associated with neurodevelopmental psychiatric diseases. These results suggest that although gene-level expression is globally comparable between fractions, nuclear retention of transcripts may play an underappreciated role in developmental regulation of gene expression in brain, particularly in genes whose dysregulation is related to neuropsychiatric disorders.

Posttraumatic stress disorder (PTSD) follows exposure to a traumatic event in susceptible individuals. Recently, genome-wide association studies have identified a number of genetic sequence variants that are associated with the risk of developing PTSD. To follow up on identifying the molecular mechanisms of these risk variants, we performed genotype to RNA sequencing-derived quantitative expression (whole gene, exon, and exon junction levels) analysis in the dorsolateral prefrontal cortex (DLPFC) of normal postmortem human brains. We further investigated genotype-gene expression associations within the amygdala in a smaller independent RNA sequencing (Genotype-Tissue Expression [GTEx]) dataset. Our DLPFC analyses identified significant expression quantitative trait loci (eQTL) associations for a "candidate" PTSD risk SNP rs363276 and the expression of two genes: SLC18A2 and PDZD8, where the PTSD risk/minor allele T was associated with significantly lower levels of gene expression for both genes, in the DLPFC. These eQTL associations were independently confirmed in the amygdala from the GTEx database. Rs363276 "T" carriers also showed significantly increased activity in the amygdala during an emotional face-matching task in healthy volunteers. Taken together, our preliminary findings in normal human brains represent a tractable approach to identify mechanisms by which genetic variants potentially increase an individual's risk for developing PTSD. © 2016 Wiley Periodicals, Inc.

Context: C1q/TNF-related protein-9 (CTRP9) is a novel adipokine that has beneficial metabolic and cardiovascular effects in various animal models. Alterations in circulating CTRP9 have also been observed in patients with cardiovascular disease and diabetes, but little is known about the impact of obesity and bariatric surgery on CTRP9 concentrations. Objective: The aim of this study was to compare CTRP9 levels in obese and lean subjects and to determine whether circulating CTRP9 levels in morbidly obese patients are altered by bariatric surgery. Design, Setting, and Participants: Fifty-nine obese bariatric surgical patients and 62 lean controls were recruited to participate in a cross-sectional study at an academic medical center. The obese patients were further invited to participate in a cohort study, and 21 returned for analysis at 3 and 6 months postsurgery. Intervention: Bariatric surgery (Roux-en-Y gastric bypass and vertical sleeve gastrectomy) was the intervention for this study. Main Outcome Measures: Fasting serum was obtained from all subjects on entry to the study and was analyzed in the core laboratory for hemoglobin A1c, glucose, aspartate aminotransferase, alanine aminotransferase, total cholesterol, high- and low-density lipoprotein cholesterol, and triglycerides; CTRP9, insulin, adiponectin, and leptin were measured by ELISA. Serum from the patients in the cohort study was also analyzed at 3 and 6 months. Results: Serum CTRP9 was significantly higher in the obese group compared to the lean group. CTRP9 was associated with obesity, even after controlling for age, gender, and ethnicity. Following bariatric surgery, there was a significant decrease in weight at 3 and 6 months postprocedure, accompanied by decreases in CTRP9, hemoglobin A1c and leptin, and an increase in serum adiponectin. Conclusions: CTRP9 levels are elevated in obesity and significantly decrease following weight loss surgery. Our data suggest that CTRP9 may play a compensatory role in obesity, similar to that of insulin, and is down-regulated following weight loss surgery.

There is growing evidence to suggest that human gene annotation remains incomplete, with a disproportionate impact on the brain transcriptome. We used RNA-sequencing data from GTEx to detect novel transcription in an annotation-agnostic manner across 13 human brain regions and 28 human tissues. We found that genes highly expressed in brain are significantly more likely to be re-annotated, as are genes associated with Mendelian and complex neurodegenerative disorders. We improved the annotation of 63% of known OMIM-morbid genes and 65% of those with a neurological phenotype. We determined that novel transcribed regions, particularly those identified in brain, tend to be poorly conserved across mammals but are significantly depleted for genetic variation within humans. As exemplified by SNCA, we explored the implications of re-annotation for Mendelian and complex Parkinson’s disease. We validated in silico and experimentally a novel, brain-specific, potentially protein-coding exon of SNCA. We release our findings as tissue-specific transcriptomes in BED format and via vizER: http://rytenlab.com/browser/app/vizER. Together these resources will facilitate basic genomics research with the greatest impact on neurogenetics.

INTRODUCTION Strong genetic associations have been found for a number of psychiatric disorders. However, understanding the underlying molecular mechanisms remains challenging. RATIONALE To address this challenge, the PsychENCODE Consortium has developed a comprehensive online resource and integrative models for the functional genomics of the human brain. RESULTS The base of the pyramidal resource is the datasets generated by PsychENCODE, including bulk transcriptome, chromatin, genotype, and Hi-C datasets and single-cell transcriptomic data from ~32,000 cells for major brain regions. We have merged these with data from Genotype-Tissue Expression (GTEx), ENCODE, Roadmap Epigenomics, and single-cell analyses. Via uniform processing, we created a harmonized resource, allowing us to survey functional genomics data on the brain over a sample size of 1866 individuals. From this uniformly processed dataset, we created derived data products. These include lists of brain-expressed genes, coexpression modules, and single-cell expression profiles for many brain cell types; ~79,000 brain-active enhancers with associated Hi-C loops and topologically associating domains; and ~2.5 million expression quantitative-trait loci (QTLs) comprising ~238,000 linkage-disequilibrium–independent single-nucleotide polymorphisms and of other types of QTLs associated with splice isoforms, cell fractions, and chromatin activity. By using these, we found that >88% of the cross-population variation in brain gene expression can be accounted for by cell fraction changes. Furthermore, a number of disorders and aging are associated with changes in cell-type proportions. The derived data also enable comparison between the brain and other tissues. In particular, by using spectral analyses, we found that the brain has distinct expression and epigenetic patterns, including a greater extent of noncoding transcription than other tissues. The top level of the resource consists of integrative networks for regulation and machine-learning models for disease prediction. The networks include a full gene regulatory network (GRN) for the brain, linking transcription factors, enhancers, and target genes from merging of the QTLs, generalized element-activity correlations, and Hi-C data. By using this network, we link disease genes to genome-wide association study (GWAS) variants for psychiatric disorders. For schizophrenia, we linked 321 genes to the 142 reported GWAS loci. We then embedded the regulatory network into a deep-learning model to predict psychiatric phenotypes from genotype and expression. Our model gives a ~6-fold improvement in prediction over additive polygenic risk scores. Moreover, it achieves a ~3-fold improvement over additive models, even when the gene expression data are imputed, highlighting the value of having just a small amount of transcriptome data for disease prediction. Lastly, it highlights key genes and pathways associated with disorder prediction, including immunological, synaptic, and metabolic pathways, recapitulating de novo results from more targeted analyses. CONCLUSION Our resource and integrative analyses have uncovered genomic elements and networks in the brain, which in turn have provided insight into the molecular mechanisms underlying psychiatric disorders. Our deep-learning model improves disease risk prediction over traditional approaches and can be extended with additional data types (e.g., microRNA and neuroimaging). A comprehensive functional genomic resource for the adult human brain. The resource forms a three-layer pyramid. The bottom layer includes sequencing datasets for traits, such as schizophrenia. The middle layer represents derived datasets, including functional genomic elements and QTLs. The top layer contains integrated models, which link genotypes to phenotypes. DSPN, Deep Structured Phenotype Network; PC1 and PC2, principal components 1 and 2; ref, reference; alt, alternate; H3K27ac, histone H3 acetylation at lysine 27.

Increased expression of the 3.1 isoform of the KCNH2 potassium channel has been associated with cognitive dysfunction and with schizophrenia, yet little is known about the underlying pathophysiological mechanisms. Here, by using in vivo wireless local field potential recordings during working memory processing, in vitro brain slice whole-cell patching recordings and in vivo stereotaxic hippocampal injection of AAV-encoded expression, we identified specific and delayed disruption of hippocampal-mPFC synaptic transmission and functional connectivity associated with reductions of SERPING1, CFH, and CD74 in the KCNH2-3.1 overexpression transgenic mice. The differentially expressed genes in mice are enriched in neurons and microglia, and reduced expression of these genes dysregulates the complement cascade, which has been previously linked to synaptic plasticity. We find that knockdown of these genes in primary neuronal–microglial cocultures from KCNH2-3.1 mice impairs synapse formation, and replenishing reduced CFH gene expression rescues KCNH2-3.1-induced impaired synaptogenesis. Translating to humans, we find analogous dysfunctional interactions between hippocampus and prefrontal cortex in coupling of the fMRI blood oxygen level-dependent (BOLD) signal during working memory in healthy subjects carrying alleles associated with increased KCNH2-3.1 expression in brain. Our data uncover a previously unrecognized role of the truncated KCNH2-3.1 potassium channel in mediating complement activation, which may explain its association with altered hippocampal–prefrontal connectivity and synaptic function. These results provide a potential molecular link between increased KCNH2-3.1 expression, synapse alterations, and hippocampal–prefrontal circuit abnormalities implicated in schizophrenia.

DNA methylation (DNAm) is heritable and plays a role in brain development and function through transcriptional regulation. Aberrant DNAm in human brain has been linked to psychiatric disorders, potentially as mediators of common genetic risk variants. In this study, we hypothesize that common risk variants for psychiatric disorders may act through affecting DNAm level in human brain. We first aimed to investigate the heritability pattern of DNAm levels in the human prefrontal cortex. Secondly, through imputation-driven methylome-wide association study (MWAS), we aimed to identify CpG sites whose methylation levels are genetically associated and that show methylation-trait associations in the prefrontal cortex of patients with schizophrenia or bipolar disorder. Our heritability analysis showed that, of ~370,000 CpG sites measured with the Illumina HumanMethylation450 microarray, 17% were heritable (p < 0.05), with a mean heritability of 0.22. Heritable CpG sites were enriched in intergenic regions, CpG shore, and regulatory regions in prefrontal cortex. Our MWAS approach identified known and potentially novel risk genes harboring CpG sites of methylation-trait associations for schizophrenia or bipolar disorder, which were not detectable using three alternative strategies (blood-based methylome reference, transcriptome-wide association study, and two gene-based association tests). Gene set enrichment analysis for genes with methylation-trait association evidence revealed pathways clearly related to neuronal functions, but also highlighted additional biological mechanisms that may underlie psychiatric disorders, such as microRNA-related regulation. In conclusion, our results showed the power of integrating brain methylation data with GWAS for psychiatric risk gene discovery, with potential applications in brain-related disorders or traits.

We have characterized the landscape of DNA methylation (DNAm) across the first two decades of human neocortical development in NeuN+ neurons using whole-genome bisulfite sequencing and compared them to non-neurons (primarily glia) and prenatal homogenate cortex. We show that DNAm changes more dramatically during the first five years of postnatal life than during the entire remaining period. We further refined global patterns of increasingly divergent neuronal CpG and CpH methylation (mCpG and mCpH) into six developmental trajectories and found that in contrast to genome-wide patterns, neighboring mCpG and mCpH levels within these regions were highly correlated. We then integrated paired RNA-seq data and identified direct regulation of hundreds of transcripts and their splicing events exclusively by mCpH levels, independently from mCpG levels, across this period. We finally explored the relationship between DNAm patterns and development of brain-related phenotypes and found enriched heritability for many phenotypes within DNAm features we identify.

BackgroundLate-onset Alzheimer’s disease (AD) is a complex age-related neurodegenerative disorder that likely involves epigenetic factors. To better understand the epigenetic state associated with AD represented as variation in DNA methylation (DNAm), we surveyed 420,852 DNAm sites from neurotypical controls (N=49) and late-onset AD patients (N=24) across four brain regions (hippocampus, entorhinal cortex, dorsolateral prefrontal cortex and cerebellum).ResultsWe identified 858 sites with robust differential methylation, collectively annotated to 772 possible genes (FDRANK1, DUSP22) and novel genes involved in Alzheimer’s disease, such as ANKRD30B.ConclusionsThese results highlight DNAm changes in Alzheimer’s disease that have gene expression correlates, implicating DNAm as an epigenetic mechanism underlying pathological molecular changes associated with AD. Furthermore, our framework illustrates the value of integrating epigenetic and transcriptomic data for understanding complex disease.

Brain-derived neurotrophic factor (Bdnf) transcription is controlled by several promoters, which drive expression of multiple transcripts encoding an identical protein. We previously reported that BDNF derived from promoters I and II is highly expressed in hypothalamus and is critical for regulating aggression in male mice. Here we report that BDNF loss from these promoters causes reduced sexual receptivity and impaired maternal care in female mice, which is concomitant with decreased oxytocin (Oxt) expression during development. We identify a novel link between BDNF signaling, oxytocin, and maternal behavior by demonstrating that ablation of TrkB selectively in OXT neurons partially recapitulates maternal care impairments observed in BDNF-deficient females. Using translating ribosome affinity purification and RNA-sequencing we define a molecular profile for OXT neurons and delineate how BDNF signaling impacts gene pathways critical for structural and functional plasticity. Our findings highlight BDNF as a modulator of sexually-dimorphic hypothalamic circuits that govern female-typical behaviors.

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

BackgroundAntibody class switch recombination (CSR) to IgG, IgA or IgE is a hallmark of adaptive immunity, allowing antibody function diversification beyond IgM. CSR involves a deletion of the IgM/IgD constant region genes placing a new acceptor Constant (CH) gene, downstream of the VDJH exon. CSR depends on non-coding (CSRnc) transcription of donor Iμ and acceptor IH exons, located 5’ upstream of each CH coding gene. Although our knowledge of the role of CSRnc transcription has advanced greatly, its extension and importance in healthy and diseased humans is scarce.MethodsWe analyzed CSRnc transcription in 70,603 publicly available RNA-seq samples, including GTEx, TCGA and the Sequence Read Archive (SRA) using recount2, an online resource consisting of normalized RNA-seq gene and exon counts, as well as coverage BigWig files that can be programmatically accessed through R. CSRnc transcription was validated with a qRT-PCR assay for Iμ, Iγ3 and Iγ1 in humans in response to vaccination.ResultsWe mapped IH transcription for the human IgH locus, including the less understood IGHD gene. CSRnc transcription was restricted to B cells and is widely distributed in normal adult tissues, but predominant in blood, spleen, MALT-containing tissues, visceral adipose tissue and some so-called “immune privileged” tissues. However, significant Iγ4 expression was found even in non-lymphoid fetal tissues. CSRnc expression in cancer tissues mimicked the expression of their normal counterparts, with notable pattern changes in some common cancer subsets. CSRnc transcription in tumors appears to result from tumor infiltration by B cells, since CSRnc transcription was not detected in corresponding tumor-derived immortal cell lines. Additionally, significantly increased I5 transcription in ileal mucosa in Crohn’s disease with ulceration was found.ConclusionsCSRnc transcription occurs in multiple anatomical locations beyond classical secondary lymphoid organs, representing a potentially useful marker of effector B cell responses in normal and pathological immune responses. The pattern of IH exon expression may reveal clues of the local immune response (i.e. cytokine milieu) in health and disease. This is a great example of how the public recount2 data can be used to further our understanding of transcription, including regions outside the known transcriptome.

Cigarette smoking during pregnancy is a major public health concern. While there are well-described consequences in early child development, there is very little known about the effects of maternal smoking on human cortical biology during prenatal life. We therefore performed a genome-wide differential gene expression analysis using RNA sequencing (RNA-seq) on prenatal (N = 33; 16 smoking-exposed) as well as adult (N = 207; 57 active smokers) human postmortem prefrontal cortices. Smoking exposure during the prenatal period was directly associated with differential expression of 14 genes; in contrast, during adulthood, despite a much larger sample size, only two genes showed significant differential expression (FDR

SummaryHuman induced pluripotent stem cells (hiPSCs) are a powerful model of neural differentiation and maturation. We present a hiPSC transcriptomics resource on corticogenesis from 5 iPSC donor and 13 subclonal lines across nine time points over 5 broad conditions: self-renewal, early neuronal differentiation, neural precursor cells (NPCs), assembled rosettes, and differentiated neuronal cells that were validated using electrophysiology. We identified widespread changes in the expression of individual transcript features and their splice variants, gene networks, and global patterns of transcription. We next demonstrated that co-culturing human NPCs with rodent astrocytes resulted in mutually synergistic maturation, and that cell type-specific expression data can be extracted using only sequencing read alignments without potentially disruptive cell sorting. We lastly developed and validated a computational tool to estimate the relative neuronal maturity of iPSC-derived neuronal cultures and human brain tissue, which were maturationally heterogeneous but contained subsets of cells most akin to adult human neurons.

Genome-wide association studies (GWAS) have identified many genomic loci associated with risk for schizophrenia, but unambiguous identification of the relationship between disease-associated variants and specific genes, and in particular their effect on risk conferring transcripts, has proven difficult. To better understand the specific molecular mechanism(s) at the schizophrenia locus in 11q25, we undertook cis expression quantitative trait loci (cis-eQTL) mapping for this 2 megabase genomic region using postmortem human brain samples. To comprehensively assess the effects of genetic risk upon local expression, we evaluated multiple transcript features: genes, exons, and exon-exon junctions in multiple brain regions-dorsolateral prefrontal cortex (DLPFC), hippocampus, and caudate. Genetic risk variants strongly associated with expression of SNX19 transcript features that tag multiple rare classes of SNX19 transcripts, whereas they only weakly affected expression of an exon-exon junction that tags the majority of abundant transcripts. The most prominent class of SNX19 risk-associated transcripts is predicted to be overexpressed, defined by an exon-exon splice junction between exons 8 and 10 (junc8.10) and that is predicted to encode proteins that lack the characteristic nexin C terminal domain. Risk alleles were also associated with either increased or decreased expression of multiple additional classes of transcripts. With RACE, molecular cloning, and long read sequencing, we found a number of novel SNX19 transcripts that further define the set of potential etiological transcripts. We explored epigenetic regulation of SNX19 expression and found that DNA methylation at CpG sites near the primary transcription start site and within exon 2 partially mediate the effects of risk variants on risk-associated expression. ATAC sequencing revealed that some of the most strongly risk-associated SNPs are located within a region of open chromatin, suggesting a nearby regulatory element is involved. These findings indicate a potentially complex molecular etiology, in which risk alleles for schizophrenia generate epigenetic alterations and dysregulation of multiple classes of SNX19 transcripts.

Publicly available genomic data are a valuable resource for studying normal human variation and disease, but these data are often not well labeled or annotated. The lack of phenotype information for public genomic data severely limits their utility for addressing targeted biological questions. We develop an in silico phenotyping approach for predicting critical missing annotation directly from genomic measurements using well-annotated genomic and phenotypic data produced by consortia like TCGA and GTEx as training data. We apply in silico phenotyping to a set of 70 000 RNA-seq samples we recently processed on a common pipeline as part of the recount2 project. We use gene expression data to build and evaluate predictors for both biological phenotypes (sex, tissue, sample source) and experimental conditions (sequencing strategy). We demonstrate how these predictions can be used to study cross-sample properties of public genomic data, select genomic projects with specific characteristics, and perform downstream analyses using predicted phenotypes. The methods to perform phenotype prediction are available in the phenopredict R package and the predictions for recount2 are available from the recount R package. With data and phenotype information available for 70,000 human samples, expression data is available for use on a scale that was not previously feasible.

Schizophrenia polygenic risk is plausibly manifested by complex transcriptional dysregulation in the brain, involving networks of co-expressed and functionally related genes. The main purpose of this study was to identify and prioritize co-expressed gene sets in a hierarchical manner, based on the strength of the relationships with clinical diagnosis and with polygenic risk for schizophrenia. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to RNA-quality-adjusted DLPFC RNA-Seq data from the LIBD Postmortem Human Brain Repository (90 controls, 74 schizophrenia cases; all Caucasians) to construct co-expression networks and detect "modules" of co-expressed genes. After multiple internal and external validation procedures, modules of selected interest were tested for enrichment in biological ontologies, for association with schizophrenia polygenic risk scores (PRSs) and with diagnosis, and also for enrichment in genes within the significant GWAS loci reported by the Psychiatric Genomic Consortium (PGC2). The association between schizophrenia genetic signals and modules of co-expression converged on one module showing not only a significant association with both diagnosis and PRS but also significant overlap with 36 PGC2 loci genes, deemed the strongest candidates for drug targets. This module contained many genes involved in synaptic signaling and neuroplasticity. Fifty-three PGC2 genes were in modules associated only with diagnosis and 59 in modules unrelated to diagnosis or PRS. Our study highlights complex relationships between gene co-expression networks in the brain and clinical state and polygenic risk for SCZ and provides a strategy for using this information in selecting and prioritizing potentially targetable gene sets for therapeutic drug development.

Schizophrenia polygenic risk is plausibly manifested by complex transcriptional dysregulation in the brain, involving networks of co-expressed and functionally related genes. The main purpose of this study was to identify and prioritize co-expressed gene sets in a hierarchical manner, based on the strength of the relationships with clinical diagnosis and with polygenic risk for schizophrenia. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to RNA-quality-adjusted DLPFC RNA-Seq data from the LIBD Postmortem Human Brain Repository (90 controls, 74 schizophrenia cases; all Caucasians) to construct co-expression networks and detect “modules” of co-expressed genes. After multiple internal and external validation procedures, modules of selected interest were tested for enrichment in biological ontologies, for association with schizophrenia polygenic risk scores (PRSs) and with diagnosis, and also for enrichment in genes within the significant GWAS loci reported by the Psychiatric Genomic Consortium (PGC2). The association between schizophrenia genetic signals and modules of co-expression converged on one module showing not only a significant association with both diagnosis and PRS but also significant overlap with 36 PGC2 loci genes, deemed the strongest candidates for drug targets. This module contained many genes involved in synaptic signaling and neuroplasticity. Fifty-three PGC2 genes were in modules associated only with diagnosis and 59 in modules unrelated to diagnosis or PRS. Our study highlights complex relationships between gene co-expression networks in the brain and clinical state and polygenic risk for SCZ and provides a strategy for using this information in selecting and prioritizing potentially targetable gene sets for therapeutic drug development.

We outline an ambitious project to characterize the genetic and epigenetic regulation of multiple facets of transcription in distinct brain regions across the human lifespan in samples of major neuropsychiatric disorders and controls. Initially focused on schizophrenia and mood disorders, the goal of this consortium is to elucidate the underlying molecular mechanisms of genetic associations with the goal of identifying novel therapeutic targets. The consortium currently consists of seven pharmaceutical companies and a not-for-profit medical research institution working as a precompetitive team to generate and analyze publicly available archival brain genomic data related to neuropsychiatric illness.

DNA methylation (DNAm) is important in brain development and is potentially important in schizophrenia. We characterized DNAm in prefrontal cortex from 335 non-psychiatric controls across the lifespan and 191 patients with schizophrenia and identified widespread changes in the transition from prenatal to postnatal life. These DNAm changes manifest in the transcriptome, correlate strongly with a shifting cellular landscape and overlap regions of genetic risk for schizophrenia. A quarter of published genome-wide association studies (GWAS)-suggestive loci (4,208 of 15,930, P < 10(-100)) manifest as significant methylation quantitative trait loci (meQTLs), including 59.6% of GWAS-positive schizophrenia loci. We identified 2,104 CpGs that differ between schizophrenia patients and controls that were enriched for genes related to development and neurodifferentiation. The schizophrenia-associated CpGs strongly correlate with changes related to the prenatal-postnatal transition and show slight enrichment for GWAS risk loci while not corresponding to CpGs differentiating adolescence from later adult life. These data implicate an epigenetic component to the developmental origins of this disorder.

We have built a statistical package called Ballgown for estimating differential expression of genes, transcripts, or exons from RNA sequencing experiments. Ballgown is designed to work with the popular Cufflinks transcript assembly software and uses well-motivated statistical methods to provide estimates of changes in expression. It permits statistical analysis at the transcript level for a wide variety of experimental designs, allows adjustment for confounders, and handles studies with continuous covariates. Ballgown provides improved statistical significance estimates as compared to the Cuffdiff differential expression tool included with Cufflinks. We demonstrate the flexibility of the Ballgown package by re-analyzing 667 samples from the GEUVADIS study to identify transcript-level eQTLs and identify non-linear artifacts in transcript data. Our package is freely available from: https://github.com/alyssafrazee/ballgown

Cigarette smoking during pregnancy is a major public health concern. While there are well-described consequences in early child development, there is very little known about the effects of maternal smoking on human cortical biology during prenatal life. We therefore performed a genome-wide differential gene expression analysis using RNA sequencing (RNA-seq) on prenatal (N=33; 16 smoking-exposed) as well as adult (N=207; 57 active smokers) human post-mortem prefrontal cortices. Smoking exposure during the prenatal period was directly associated with differential expression of 14 genes; in contrast, during adulthood, despite a much larger sample size, only 2 genes showed significant differential expression (FDRin utero exposure to smoking and the heightened risks for the subsequent development of neuropsychiatric disorders.One Sentence SummaryMaternal smoking during pregnancy alters the expression of genes within the developing human cortex and these changes are enriched for genes implicated in neuropsychiatric disorders.

Summary: GWAS have identified 108 schizophrenia risk loci, but risk mechanisms for individual loci are largely unknown. Using developmental, genetic, and illness-based RNA sequencing expression analysis in human brain, we characterized the human brain transcriptome around these loci and found enrichment for developmentally regulated genes with novel examples of shifting isoform usage across pre- and post-natal life. Across the genome, we found widespread expression quantitative trait loci (eQTLs), including many with transcript specificity and previously unannotated sequence that were independently replicated. We leveraged this general eQTL database to show that 48.1% of risk variants for schizophrenia associated with nearby expression. We lastly found 237 genes significantly differentially expressed between patients and controls which replicated in an independent dataset, implicated synaptic processes and were strongly regulated in early development. These findings together offer genetic- and diagnosis-related targets for better modeling schizophrenia risk. This publicly-available resource is available at: http://eqtl.brainseq.org/phase1

BackgroundGene co-expression networks capture diverse biological relationships between genes, and are important tools in predicting gene function and understanding disease mechanisms. Functional interactions between genes have not been fully characterized for most organisms, and therefore reconstruction of gene co-expression networks has been of common interest in a variety of settings. However, methods routinely used for reconstruction of gene co-expression networks do not account for confounding artifacts known to affect high dimensional gene expression measurements.ResultsIn this study, we show that artifacts such as batch effects in gene expression data confound commonly used network reconstruction algorithms. Both theoretically and empirically, we demonstrate that removing the effects of top principal components from gene expression measurements prior to network inference can reduce false discoveries, especially when well annotated technical covariates are not available. Using expression data from the GTEx project in multiple tissues and hundreds of individuals, we show that this latent factor residualization approach often reduces false discoveries in the reconstructed networks.ConclusionNetwork reconstruction is susceptible to confounders that affect measurements of gene expression. Even controlling for major individual known technical covariates fails to fully eliminate confounding variation from the data. In studies where a wide range of annotated technical factors are measured and available, correcting gene expression data with multiple covariates can also improve network reconstruction, but such extensive annotations are not always available. Our study shows that principal component correction, which does not depend on study design or annotation of all relevant confounders, removes patterns of artifactual variation and improves network reconstruction in both simulated data, and gene expression data from GTEx project. We have implemented our PC correction approach in the Bioconductor package sva which can be used prior to network reconstruction with a range of methods.

Defining the environmental context in which genes enhance disease susceptibility can provide insight into the pathogenesis of complex disorders. We report that the intra-uterine environment modulates the association of schizophrenia with genomic risk (in this study, genome-wide association study–derived polygenic risk scores (PRSs)). In independent samples from the United States, Italy, and Germany, the liability of schizophrenia explained by PRS is more than five times greater in the presence of early-life complications (ELCs) compared with their absence. Patients with ELC histories have significantly higher PRS than patients without ELC histories, which is confirmed in additional samples from Germany and Japan. The gene set composed of schizophrenia loci that interact with ELCs is highly expressed in placenta, is differentially expressed in placentae from complicated in comparison with normal pregnancies, and is differentially upregulated in placentae from male compared with female offspring. Pathway analyses reveal that genes driving the PRS-ELC interaction are involved in cellular stress response; genes that do not drive such interaction implicate orthogonal biological processes (for example, synaptic function). We conclude that a subset of the most significant genetic variants associated with schizophrenia converge on a developmental trajectory sensitive to events that affect the placental response to stress, which may offer insights into sex biases and primary prevention.

Defining the environmental context in which genes enhance susceptibility can provide insight into the pathogenesis of complex disorders, like schizophrenia. Here we show that the intrauterine and perinatal environment modulates the association of schizophrenia with genomic risk, as measured with polygenic risk scores (PRS) based primarily on GWAS significant variants. Genomic risk interacts with intrauterine and perinatal complications (Early Life Complications, ELCs) in each of three independent samples from USA, Italy and Germany (overall n= 1693, p= 6e-05). In each sample, the liability of schizophrenia explained by PRS is nominally more than five times greater in the presence of a history of ELCs compared with its absence. In each sample, patients with positive ELC histories have higher PRS than patients without ELCs, which is further confirmed in two additional patient samples from Germany and Japan (overall n=2038, p= 1e-04). The gene set based on the schizophrenia loci interacting with ELCs is highly expressed in multiple placental compartments and dynamically regulated in placenta from complicated in comparison with normal pregnancies. The same genes are differentially up-regulated in placentae from male compared with female offspring. The interaction between genomic risk and ELCs is mainly driven by GWAS significant loci enriched for genes highly expressed in the various placenta samples. Molecular pathway analyses based on the genes not driving this interaction reflect previous analyses about schizophrenia risk-genes, while genes highly and differentially expressed in placentae implicate an orthogonal biology involving cellular stress. These results suggest that the most significant genetic variants detected by current schizophrenia GWAS contribute to risk in part by converging on a developmental trajectory sensitive to events affecting placentation, which may underlie the male preponderance of schizophrenia and offer new insights into primary prevention.

Autism spectrum disorder (ASD) is genetically heterogeneous with convergent symptomatology, suggesting common dysregulated pathways. We analyzed brain transcriptional changes in five mouse models of Pitt-Hopkins Syndrome (PTHS), a syndromic form of ASD caused by mutations in TCF4 (transcription factor 4, not TCF7L2 / T-Cell Factor 4). Analyses of differentially expressed genes (DEGs) highlighted oligodendrocyte (OL) dysregulation, which we confirmed in two additional mouse models of syndromic ASD (Ptenm3m4/m3m4 and Mecp2tm1.1Bird). The PTHS mouse models showed cell-autonomous reductions in OL numbers and myelination, functionally confirming OL transcriptional signatures. Next, we integrated PTHS mouse model DEGs with human idiopathic ASD postmortem brain RNA-seq data, and found significant enrichment of overlapping DEGs and common myelination-associated pathways. Importantly, DEGs from syndromic ASD mouse models, and reduced deconvoluted OL numbers, distinguished human idiopathic ASD cases from controls across three postmortem brain datasets. These results implicate disruptions in OL biology as a cellular mechanism in ASD pathology.

Autism spectrum disorder (ASD) affects approximately 1:68 individuals and has incalculable burdens on affected individuals, their families, and health care systems. While the genetic contributions to idiopathic ASD are heterogeneous and largely unknown, the causal mutations for syndromic forms of ASD - including truncations and copy number variants - provide a genetic toehold with which to gain mechanistic insights1-3. Models of these syndromic disorders have been used to better characterize the molecular and physiological processes disrupted by these mutations4. Two fundamental questions remain - how biologically similar are the mouse models of syndromic forms of ASD, and how relevant are these mouse models to their human analogs? To address these questions, we performed integrative transcriptomic analyses of seven independent mouse models of three syndromic forms of ASD generated across five laboratories, and assessed dysregulated genes and their pathways in human postmortem brain from patients with ASD and unaffected controls. These cross-species analyses converged on shared disruptions in myelination and axon development across both syndromic and idiopathic ASD, highlighting both the face validity of mouse models for these disorders and identifying novel convergent molecular phenotypes amendable to rescue with therapeutics.

SummaryGWAS have identified 108 loci that confer risk for schizophrenia, but risk mechanisms for individual loci are largely unknown. Using developmental, genetic, and illness-based RNA sequencing expression analysis, we characterized the human brain transcriptome around these loci and found enrichment for developmentally regulated genes with novel examples of shifting isoform usage across pre- and post-natal life. We found widespread expression quantitative trait loci (eQTLs), including many with transcript specificity and previously unannotated sequence that were independently replicated. We leveraged this eQTL database to show that 48.1% of risk variants for schizophrenia associated with nearby expression. Within patients and controls, we implemented a novel algorithm for RNA quality adjustment, and identified 237 genes significantly associated with diagnosis that replicated in an independent case-control dataset. These genes implicated synaptic processes and were strongly regulated in early development (p < 10-20). These data offer new targets for modeling schizophrenia risk in cellular systems.

Recently, a study by Rahmani et al [1] claimed that a reference-free cell-type deconvolution method, called ReFACTor, leads to improved power and improved estimates of cell-type composition compared to competing reference-free and reference-based methods in the context of Epigenome-Wide Association Studies (EWAS). However, we identified many critical flaws (both conceptual and statistical in nature), which seriously question the validity of their claims. We outlined constructive criticism in a recent correspondence letter, Zheng et al [2]. The purpose of this letter is two-fold. First, to present additional analyses, which demonstrate that our original criticism is statistically sound. Second, to highlight additional serious concerns, which Rahmani et al have not yet addressed. In summary, we find that ReFACTor has not been demonstrated to outperform state-of-the-art reference-free methods such as SVA or RefFreeEWAS, nor state-of-the-art reference-based methods. Thus, the claim by Rahmani et al (a claim reiterated in their recent response letter [3]) that ReFACT or represents an advance over the state-of-the-art is not supported by an objective and rigorous statistical analysis of the data.

Correcting for cell-type heterogeneity in epigenome-wide association studies: revisiting previous analyses

Background Schizophrenia polygenic risk is plausibly manifested by complex transcriptional dysregulation in the brain, involving networks of co-expressed and functionally related genes. Methods 1. Samples: DLPFC gene expression (RNA-Seq) abundance was quantified (RPKM) in postmortem human brains from the LIBD Postmortem Human Brain Repository (90 controls- CTRL, 76 schizophrenia- SCZ, Caucasians, age: 16–80, RIN≥7). Common Mind Consortium (CMC) RNA-Seq postmortem DLPFC (125 CTRL, 109 SCZ, Caucasians, age, RIN as above) was used for replication of LIBD co-expression networks. 2. Expression measures/ quality control: only genes with median RPKM≥0.1 (N=23132 genes; LIBD samples) were used. Expression data was normalized by log2 transformation and adjusted for RNA quality measures (RIN, PMI, total gene assignment, proportion of mitochondrial RNA) by empirical Bayesian models. 3. Data analysis: WGCNA was applied to the adjusted expression data to construct co-expression networks, separately for LIBD- CTRL and LIBD- SCZ. Modules of co-expressed genes were detected by dynamic tree cutting method and summarized as “module eigengenes” for further analyses. 4. Modules of LIBD (CTRL; SCZ) were tested for enrichment in PGC2 genes within the 108 loci associated with the schizophrenia risk in the latest GWAS. 5. Composite module preservation statistics- median rank and Zsummary was used for assessing the preservation of LIBD SCZ modules in LIBD CTRL network and for replication of LIBD co-expression modules in the CMC independent data sets. Gene enrichment analyses for testing modules’ enrichment in Gene Ontology (GO) biological processes (BP) was performed with AmiGO/ PANTHER. Results 17 co-expression modules were identified in each LIBD group. Module preservation analysis showed a good preservation of co-expression modules between LIBD CTRL and SCZ networks, with all but one SCZ module showing evidence for strong preservation (Zsummary≥10). PGC2 genes were overrepresented in two CTRL modules (p=0.001973, p=0.028297) enriched for GO-BP such as translation, ATP synthesis, oxidative phosphorylation, homophilic cell adhesion via plasma membrane adhesion molecules, chemical synaptic transmission, axonogenesis, and in one SCZ module (p=0.000519) enriched for RNA processing, nervous system development and modulation of synaptic transmission. 58 PGC2 genes overlapped with the SCZ module (e.g., DRD2, FURIN, TSNARE1), whereas 48 + 17 PGC2 genes were overrepresented in the two CTRL modules (e.g., CACNA1C, CHRM4, CACNA1I, TCF20). Of note, only 9 overrepresented PGC2 genes were shared by CTRL and SCZ: AMBRA1, ANKRD63, HSPA9, LRP1, PRR12, PTPRF, RERE, TOM1L2, ZDHHC5. Replication of co-expression LIBD networks in CMC data set showed evidence of weak to strong preservation (Zsummary>2, respectively ≥10) for 16/ 17 modules for each LIBD group. Discussion Our results illustrate the complexity of SCZ risk distribution within the DLPFC co-expression networks in postmortem brains of affected and unaffected individuals. Importantly, not the same PGC2 genes are members of co-expression modules in CTRL and SCZ, nor do they aggregate in similar modules. These observations may be explained by differential transcriptional regulation due to pleiotropy, epistasis, epigenetic dysregulation, or even hidden RNA quality artifacts. Future studies are necessary to elucidate the participation of SCZ risk genes in brain co-expression networks.

Overall Abstract Epigenetic mechanisms play a role in the dynamic regulation of various genomic functions through covalent modifications to DNA and histones. These mechanisms establish and maintain cell-specific gene expression programs to orchestrate brain development, adult neurogenesis and synaptic plasticity. Given their role in the development and function of the brain, there is increasing awareness about the involvement of altered gene regulatory processes in the etiology of many neuropsychiatric phenotypes. This symposium will highlight how studies from different areas of epigenetics research can provide important insights into the molecular etiology of schizophrenia and cognitive function, and enable the identification of potential therapeutic targets. The first presentation will discuss a study of the NuRD nucleosome remodeling complex that mediates the projection of neurons across the cerebral hemispheres during development. SATB2, BCL11B and GATAD2A encode proteins within this complex and contain genome-wide significant risk SNPs for schizophrenia. Beyond these individually associated genes, this study will describe a gene set analysis of 127 genes involved in this process during cortical development that indicates strong enrichment of association signals in both schizophrenia and educational attainment GWAS data. These data point to a role for this chromatin remodeling process in the biology of schizophrenia and cognition. The second presentation will describe an analysis of dynamic DNA modifications (5mC and 5hmC) across human brain development, highlighting how the prenatal period is a time of considerable epigenomic plasticity in the brain, and the importance of neurodevelopmentally-dynamic loci in psychiatric phenotypes. It will highlight the impact of genetic variation on the epigenome during brain development showing that although most DNA methylation quantitative trait loci (mQTLs) are developmentally stable, a subset are characterized by fetal-specific effects and enriched amongst risk loci identified in recent schizophrenia GWAS. We will highlight the utility of integrating genetic and epigenetic data to fine-map GWAS regions using co-localization approaches. The third presentation will highlight results from a study of cell type-specific genome-wide maps of chromatin accessibility, which are critical resources to complement genomic sequence data and to correlate functional and genetic brain architecture. This study will describe the generation and analysis of an open chromatin atlas of different cell types in the adult human brain generated across multiple brain regions. Transcription factor-gene regulome networks were generated for each cell type and brain region and subnetworks affected by schizophrenia risk loci were identified. The final presentation will describe a study of non-coding RNAs in neuronal function. This study will highlight how epigenetic processes are potential therapeutic targets in neuropsychiatric disorders and will then detail research work showing that non-coding RNAs regulate epigenetic dynamics in neuronal systems to support cognitive function.

Background Injection Drug Use (IDU) is associated with biological modification of the genome, i.e., epigenetics marks such as DNA methylation and histone modification. However, the biological mechanisms of how substance use and cessation affects epigenetic outcomes remain largely unknown. Methods In the AIDS Linked to the Intravenous Experience (ALIVE) study, blood was obtained from 288 current IDUs, resampled after cessation and then again after relapse (total samples=774). Blood DNA methylation marks were measured using the Illumina Infinium MethylationEPIC BeadChip. Standard procedures in the minfi R package were used to preprocess raw Illumina image files into noob processed methylation beta values. Differences in DNA methylation at individual probes by current injection status were tested using generalized linear regression including age, gender, race and cell heterogeneity as covariates. DNA methylation age was estimated for study subjects using the epigenetic clock method developed by Horvath et al. Results DNA methylation at individual loci (cg10801015, p=2.47⁎10^(-11); cg11415166, p=5.15⁎10^(-10); cg03426703, p=2.62⁎10^(-9); cg14977491, p=9.94⁎10^(-8) is significantly associated with current injection drug use status after correction for multiple testing. Those CpG sites were near the PDP1, NARFL, DVL2 and PFN2 genes correspondingly. Single-site epigenetic analysis of HIV status was also performed. HIV positive individual's average biological age is about 3 years older than their chronological age, but the biological age among HIV negative individuals, injection drug users and non-users is no different from their chronological age. Discussion In a preliminary study, we performed a genome-wide scan of methylation changes in a longitudinal study of injection drug use and identified genomic locations exhibiting significant changes in peripheral DNA methylation associated with injection drug use status. Individuals with HIV infection's biological age is older than their chronological age, which is consistent with the literature.

Background Early life events influence later susceptibility to many adult diseases and may contribute to define the environmental context in which genes enhance risk for complex disorder like schizophrenia. Here we analyze the role of intrauterine and perinatal environment in modulating the association of schizophrenia with genomic risk. Methods We evaluated whether genomic risk for schizophrenia interacts with intrauterine and perinatal complications (Early Life Complications, ELCs) on case-control status, in three independent samples of healthy subjects and patients with schizophrenia from USA (n=501), Italy (n=273) and Germany (n=919). We further analyzed the relationship between genomic risk and ELCs in two samples of only patients with schizophrenia from Germany (n=1019) and Japan (n=172). Genomic risk was measured with polygenic risk profile scores based on GWAS-significant alleles (PRS), while ELCs history was assessed with the McNeil-Sjöström Scale. We tested whether genes overlapping the schizophrenia loci interacting with ELCs are enriched in placenta and differentially expressed in placental samples from complicated pregnancies, in 8 independent placental datasets. Finally, we evaluated whether GWAS SNPs marking loci containing genes highly expressed and dynamically modulated in placenta (PlacPRS genes) drive the interaction between PRS and ELCs, and performed pathway analyses on PlacPRS genes. Results PRS interacts with ELCs on case-control status, in the three independent samples from USA (p= p=0.004), Italy (p=0.018) and Germany (p=0.018); in each sample the variance of schizophrenia explained by PRS is multiplicatively higher in the presence of a history of ELCs compared with the absence of such events. The relationship between genomic risk and ELCs is further replicated in the two independent samples of only cases from Germany (p=0.047) and Japan (p=0.044). The gene-set based on PRS loci interacting with ELCs is highly expressed in multiple placental tissues (p

Human olfactory cells obtained by rapid nasal biopsy have been suggested to be a good surrogate system to address brain disease-associated molecular changes. Nonetheless, whether use of this experimental strategy is justified remains unclear. Here we compared expression profiles of olfactory cells systematically with those from the brain tissues and other cells. Principal component analysis indicated that the expression profiles of olfactory cells are very different from those of blood cells, but are closer to those of stem cells, in particular mesenchymal stem cells, that can be differentiated into the cells of the central nervous system.

Gene annotations, such as those in GENCODE, are derived primarily from alignments of spliced cDNA sequences and protein sequences. The impact of RNA-seq data on annotation has been confined to major projects like ENCODE and Illumina Body Map 2.0. We aligned 21,504 Illumina-sequenced human RNA-seq samples from the Sequence Read Archive (SRA) to the human genome and compared detected exon-exon junctions with junctions in several recent gene annotations. We found 56,861 junctions (18.6%) in at least 1000 samples that were not annotated, and their expression associated with tissue type. Junctions well expressed in individual samples tended to be annotated. Newer samples contributed few novel well-supported junctions, with the vast majority of detected junctions present in samples before 2013. We compiled junction data into a resource called intropolis available at http://intropolis.rail.bio . We used this resource to search for a recently validated isoform of the ALK gene and characterized the potential functional implications of unannotated junctions with publicly available TRAP-seq data. Considering only the variation contained in annotation may suffice if an investigator is interested only in well-expressed transcript isoforms. However, genes that are not generally well expressed and nonetheless present in a small but significant number of samples in the SRA are likelier to be incompletely annotated. The rate at which evidence for novel junctions has been added to the SRA has tapered dramatically, even to the point of an asymptote. Now is perhaps an appropriate time to update incomplete annotations to include splicing present in the now-stable snapshot provided by the SRA.