Genetic identification of brain cell types underlying schizophrenia

Focus on Causality in ESC/iPSC-Based Modeling of Psychiatric Disorders

Genome-wide association studies (GWAS) have identified an increasing number of genetic variants that significantly associate with psychiatric disorders. Despite this wealth of information, our knowledge of which variants causally contribute to disease, how they interact, and even more so of the functions they regulate, is still poor. The availability of embryonic stem cells (ESCs) and the advent of patient-specific induced pluripotent stem cells (iPSCs) has opened new opportunities to investigate genetic risk variants in living disease-relevant cells. Here, we analyze how this progress has contributed to the analysis of causal relationships between genetic risk variants and neuronal phenotypes, especially in schizophrenia (SCZ) and bipolar disorder (BD). Studies on rare, highly penetrant risk variants have originally led the field, until more recently when the development of (epi-) genetic editing techniques spurred studies on cause-effect relationships between common low risk variants and their associated neuronal phenotypes. This reorientation not only offers new insights, but also raises issues on interpretability. Concluding, we consider potential caveats and upcoming developments in the field of ESC/iPSC-based modeling of causality in psychiatric disorders.

Schizophrenia-An Overview

IMPORTANCE

Schizophrenia is a common, severe mental illness that most clinicians will encounter regularly during their practice. This report provides an overview of the clinical characteristics, epidemiology, genetics, neuroscience, and psychopharmacology of schizophrenia to provide a basis to understand the disorder and its treatment. This educational review is integrated with a clinical case to highlight how recent research findings can inform clinical understanding.

OBSERVATIONS

The first theme considered is the role of early-life environmental and genetic risk factors in altering neurodevelopmental trajectories to predispose an individual to the disorder and leading to the development of prodromal symptoms. The second theme is the role of cortical excitatory-inhibitory imbalance in the development of the cognitive and negative symptoms of the disorder. The third theme considers the role of psychosocial stressors, psychological factors, and subcortical dopamine dysfunction in the onset of the positive symptoms of the disorder. The final theme considers the mechanisms underlying treatment for schizophrenia and common adverse effects of treatment.

CONCLUSIONS AND RELEVANCE

Schizophrenia has a complex presentation with a multifactorial cause. Nevertheless, advances in neuroscience have identified roles for key circuits, particularly involving frontal, temporal, and mesostriatal brain regions, in the development of positive, negative, and cognitive symptoms. Current pharmacological treatments operate using the same mechanism, blockade of dopamine D2 receptor, which contribute to their adverse effects. However, the circuit mechanisms discussed herein identify novel potential treatment targets that may be of particular benefit in symptom domains not well served by existing medications.

Genome-Wide Analysis of Differential Gene Expression and Splicing in Excitatory Neurons and Interneuron Subtypes

Cortical circuit activity is shaped by the parvalbumin (PV) and somatostatin (SST) interneurons that inhibit principal excitatory (EXC) neurons and the vasoactive intestinal peptide (VIP) interneurons that suppress activation of other interneurons. To understand the molecular-genetic basis of functional specialization and identify potential drug targets specific to each neuron subtype, we performed a genome wide assessment of both gene expression and splicing across EXC, PV, SST and VIP neurons from male and female mouse brains. These results reveal numerous examples where neuron subtype-specific gene expression, as well as splice-isoform usage, can explain functional differences between neuron subtypes, including in presynaptic plasticity, postsynaptic receptor function, and synaptic connectivity specification. We provide a searchable web resource for exploring differential mRNA expression and splice form usage between excitatory, PV, SST, and VIP neurons (http://research-pub.gene.com/NeuronSubtypeTranscriptomes). This resource, combining a unique new dataset and novel application of analysis methods to multiple relevant datasets, identifies numerous potential drug targets for manipulating circuit function, reveals neuron subtype-specific roles for disease-linked genes, and is useful for understanding gene expression changes observed in human patient brains.SIGNIFICANCE STATEMENT Understanding the basis of functional specialization of neuron subtypes and identifying drug targets for manipulating circuit function requires comprehensive information on cell-type-specific transcriptional profiles. We sorted excitatory neurons and key inhibitory neuron subtypes from mouse brains and assessed differential mRNA expression. We used a genome-wide analysis which not only examined differential gene expression levels but could also detect differences in splice isoform usage. This analysis reveals numerous examples of neuron subtype-specific isoform usage with functional importance, identifies potential drug targets, and provides insight into the neuron subtypes involved in psychiatric disease. We also apply our analysis to two other relevant datasets for comparison, and provide a searchable website for convenient access to the resource.

Diversity of Interneurons in the Dorsal Striatum Revealed by Single-Cell RNA Sequencing and PatchSeq

Striatal locally projecting neurons, or interneurons, act on nearby circuits and shape functional output to the rest of the basal ganglia. We performed single-cell RNA sequencing of striatal cells enriching for interneurons. We find seven discrete interneuron types, six of which are GABAergic. In addition to providing specific markers for the populations previously described, including those expressing Sst/Npy, Th, Npy without Sst, and Chat, we identify two small populations of cells expressing Cck with or without Vip. Surprisingly, the Pvalb-expressing cells do not constitute a discrete cluster but rather are part of a larger group of cells expressing Pthlh with a spatial gradient of Pvalb expression. Using PatchSeq, we show that Pthlh cells exhibit a continuum of electrophysiological properties correlated with expression of Pvalb. Furthermore, we find significant molecular differences that correlate with differences in electrophysiological properties between Pvalb-expressing cells of the striatum and those of the cortex.

Novel Approaches for Identifying the Molecular Background of Schizophrenia

Recent advances in psychiatric genetics have led to the discovery of dozens of genomic loci associated with schizophrenia. However, a gap exists between the detection of genetic associations and understanding the underlying molecular mechanisms. This review describes the basic approaches used in the so-called post-GWAS studies to generate biological interpretation of the existing population genetic data, including both molecular (creation and analysis of knockout animals, exploration of the transcriptional effects of common variants in human brain cells) and computational (fine-mapping of causal variability, gene set enrichment analysis, partitioned heritability analysis) methods. The results of the crucial studies, in which these approaches were used to uncover the molecular and neurobiological basis of the disease, are also reported.

Automated ISH for Validated Histological Mapping of Lowly Expressed Genes

An essential part of the drug discovery and development process in the pharmaceutical industry is to provide a full characterization of cells expressing a given drug target and potential downstream markers in human tissues and in relevant preclinical animal species. This task is best solved by a combination of methods, including histological assessment of target protein and mRNA using immunohistochemistry (IHC) and in situ hybridization (ISH), respectively, as well as non-histology-based methods, such as fluorescence-activated cell sorting (FACS), and single-cell (SCS) or single-nuclei (SNS) sequencing. In reality, this work is often complicated by a combination of low target expression levels and a less than optimal availability of specific reagents for detection. In particular, the ability to specifically detect low-abundance receptor targets using IHC is notoriously difficult, due to a daunting lack of commercially available specific antibodies validated for use in IHC. In the absence of fully validated antibodies and protocols for IHC, the specific detection of target mRNA using ISH is often the only available histological method. A highly sensitive, nonradioactive, automated, and robust ISH method for use on formalin-fixed, paraffin-embedded (FFPE) tissue sections is presented for assessing histological localization of mRNA transcripts of lowly expressed genes.

Genetics of Childhood-Onset Schizophrenia: 2019 Update

The genetic architecture of schizophrenia is complex and highly polygenic. This article discusses key findings from genetic studies of childhood-onset schizophrenia (COS) and the more common adult-onset schizophrenia (AOS), including studies of familial aggregation and common, rare, and copy number variants. Extant literature suggests that COS is a rare variant of AOS involving greater familial aggregation of schizophrenia spectrum disorders and a potentially higher occurrence of pathogenic copy number variants. The direct utility of genetics to clinical practice for COS is currently limited; however, identifying common pathways through which risk genes affect brain function offers promise for novel interventions.

Calcium Imaging in Drug Discovery for Psychiatric Disorders

The past 5 years have seen a sharp increase in the number of studies using calcium imaging in behaving rodents. These studies have helped identify important roles for individual cells, brain regions, and circuits in some of the core behavioral phenotypes of psychiatric disorders, such as schizophrenia and autism, and have characterized network dysfunction in well-established models of these disorders. Since rescuing clinically relevant behavioral deficits in disease model mice remains a foundation of preclinical CNS research, these studies have the potential to inform new therapeutic approaches targeting specific cell types or projections, or perhaps most importantly, the network-level context in which neurons function. In this mini-review, we will provide a brief overview of recent insights into psychiatric disease-associated mouse models and behavior paradigms, focusing on those achieved by cellular resolution imaging of calcium dynamics in neural populations. We will then discuss how these experiments can support efforts within the pharmaceutical industry, such as target identification, assay development, and candidate screening and validation. Calcium imaging is uniquely capable of bridging the gap between two of the key resources that currently enable CNS drug discovery: genomic and transcriptomic data from human patients, and translatable, population-resolution measures of brain activity (such as fMRI and EEG). Applying this knowledge could yield real value to patients in the near future.

Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution

The human interactome is instrumental in the systems-level study of the cell and the contextualization of disease-associated gene perturbations. However, reference organismal interactomes do not capture the cell-type-specific context in which proteins and modules preferentially act. Here, we introduce SCINET, a computational framework that reconstructs an ensemble of cell-type-specific interactomes by integrating a global, context-independent reference interactome with a single-cell gene-expression profile. SCINET addresses technical challenges of single-cell data by robustly imputing, transforming, and normalizing the initially noisy and sparse expression of data. Inferred cell-level gene interaction probabilities and group-level interaction strengths define cell-type-specific interactomes. We use SCINET to reconstruct and analyze interactomes of the major human brain and immune cell types, revealing specificity and modularity of perturbations associated with neurodegenerative, neuropsychiatric, and autoimmune disorders. We report cell-type interactomes for brain and immune cell types, together with the SCINET package.

Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income

Socioeconomic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. In a sample of 286,301 participants from UK Biobank, we identify 30 (29 previously unreported) independent-loci associated with income. Using a method to meta-analyze data from genetically-correlated traits, we identify an additional 120 income-associated loci. These loci show clear evidence of functionality, with transcriptional differences identified across multiple cortical tissues, and links to GABAergic and serotonergic neurotransmission. By combining our genome wide association study on income with data from eQTL studies and chromatin interactions, 24 genes are prioritized for follow up, 18 of which were previously associated with intelligence. We identify intelligence as one of the likely causal, partly-heritable phenotypes that might bridge the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. These results indicate that, in modern era Great Britain, genetic effects contribute towards some of the observed socioeconomic inequalities.

Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income

Socioeconomic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. In a sample of 286,301 participants from UK Biobank, we identify 30 (29 previously unreported) independent-loci associated with income. Using a method to meta-analyze data from genetically-correlated traits, we identify an additional 120 income-associated loci. These loci show clear evidence of functionality, with transcriptional differences identified across multiple cortical tissues, and links to GABAergic and serotonergic neurotransmission. By combining our genome wide association study on income with data from eQTL studies and chromatin interactions, 24 genes are prioritized for follow up, 18 of which were previously associated with intelligence. We identify intelligence as one of the likely causal, partly-heritable phenotypes that might bridge the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. These results indicate that, in modern era Great Britain, genetic effects contribute towards some of the observed socioeconomic inequalities.

BEST: a web server for brain expression Spatio-temporal pattern analysis

Background

Dysregulated gene expression patterns have been reported in several mental disorders. Limited by the difficulty of obtaining samples, psychiatric molecular mechanism research still relies heavily on clues from genetics studies. By using reference data from brain expression studies, multiple types of comprehensive gene expression pattern analysis have been performed on psychiatric genetic results. These systems-level spatial-temporal expression pattern analyses provided evidence on specific brain regions, developmental stages and molecular pathways that are possibly involved in psychiatric pathophysiology. At present, there is no online tool for such systematic analysis, which hinders the applications of analysis by non-informatics researchers such as experimental biologists and clinical molecular biologists.

Results

We developed the BEST web server to support Brain Expression Spatio-Temporal pattern analysis. There are three highlighted features of BEST: 1) visualization: it generates user-friendly visual results that are easy to interpret, including heatmaps, Venn diagrams, gene co-expression networks and cluster-based Manhattan gene plots; these results illustrate the complex spatio-temporal expression patterns, including expression quantification and correlation between genes; 2) integration: it provides comprehensive human brain spatio-temporal expression patterns by integrating data from currently available databases; 3) multi-dimensionality: it analyses input genes as both a whole set and several subsets (clusters) which are enriched according to co-expression patterns, and it also presents the correlation between genetic and expression data.

Conclusions

To the best of our knowledge, BEST is the first data tool to support comprehensive human brain spatial-temporal expression pattern analysis. It helps to bridge disease-related genetic studies and mechanism studies, provides clues for key gene and molecular system identification, and supports the analysis of disease sensitive brain region and age stages. BEST is freely available at http://best.psych.ac.cn.

Informing disease modelling with brain-relevant functional genomic annotations

The past decade has seen a surge in the number of disease/trait-associated variants, largely because of the union of studies to share genetic data and the availability of electronic health records from large cohorts for research use. Variant discovery for neurological and neuropsychiatric genome-wide association studies, including schizophrenia, Parkinson's disease and Alzheimer's disease, has greatly benefitted; however, the translation of these genetic association results to interpretable biological mechanisms and models is lagging. Interpreting disease-associated variants requires knowledge of gene regulatory mechanisms and computational tools that permit integration of this knowledge with genome-wide association study results. Here, we summarize key conceptual advances in the generation of brain-relevant functional genomic annotations and amongst tools that allow integration of these annotations with association summary statistics, which together provide a new and exciting opportunity to identify disease-relevant genes, pathways and cell types in silico. We discuss the opportunities and challenges associated with these developments and conclude with our perspective on future advances in annotation generation, tool development and the union of the two.

A systems-level framework for anti-epilepsy drug discovery

Modern anti-seizure drug development yielded benefits in terms of improved pharmacokinetics, safety and tolerability profiles, but offered no advances in efficacy compared to previous older generations of anti-seizure drugs. Despite significant advances in our understanding of the genetic bases to epilepsy, and a welcome renewed interest on the severe monogenic epilepsies, modern genetics has yet to directly inform more effective or disease-modifying anti-seizure drugs. Here, we describe a new approach to the identification of novel disease modifying anti-epilepsy drugs. The systems genetics approach aims to first identify pathophysiological mechanisms by integrating polygenic risk with cellular gene expression profiles and then to relate these molecular mechanisms to druggable targets using a gene regulatory (regulome) framework. The approach offers an exciting and flexible framework for future drug discovery in epilepsy, and is applicable to any disease for which appropriate cell-type and disease-context specific data exist. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'.

Oligodendrocytes as A New Therapeutic Target in Schizophrenia: From Histopathological Findings to Neuron-Oligodendrocyte Interaction

Imaging and postmortem studies have revealed disturbed oligodendroglia-related processes in patients with schizophrenia and provided much evidence for disturbed myelination, irregular gene expression, and altered numbers of oligodendrocytes in the brains of schizophrenia patients. Oligodendrocyte deficits in schizophrenia might be a result of failed maturation and disturbed regeneration and may underlie the cognitive deficits of the disease, which are strongly associated with impaired long-term outcome. Cognition depends on the coordinated activity of neurons and interneurons and intact connectivity. Oligodendrocyte precursors form a synaptic network with parvalbuminergic interneurons, and disturbed crosstalk between these cells may be a cellular basis of pathology in schizophrenia. However, very little is known about the exact axon-glial cellular and molecular processes that may be disturbed in schizophrenia. Until now, investigations were restricted to peripheral tissues, such as blood, correlative imaging studies, genetics, and molecular and histological analyses of postmortem brain samples. The advent of human-induced pluripotent stem cells (hiPSCs) will enable functional analysis in patient-derived living cells and holds great potential for understanding the molecular mechanisms of disturbed oligodendroglial function in schizophrenia. Targeting such mechanisms may contribute to new treatment strategies for previously treatment-resistant cognitive symptoms.

Polygenic burden associated to oligodendrocyte precursor cells and radial glia influences the hippocampal volume changes induced by aerobic exercise in schizophrenia patients

Hippocampal volume decrease is a structural hallmark of schizophrenia (SCZ), and convergent evidence from postmortem and imaging studies suggests that it may be explained by changes in the cytoarchitecture of the cornu ammonis 4 (CA4) and dentate gyrus (DG) subfields. Increasing evidence indicates that aerobic exercise increases hippocampal volume in CA subfields and improves cognition in SCZ patients. Previous studies showed that the effects of exercise on the hippocampus might be connected to the polygenic burden of SCZ risk variants. However, little is known about cell type-specific genetic contributions to these structural changes. In this secondary analysis, we evaluated the modulatory role of cell type-specific SCZ polygenic risk scores (PRS) on volume changes in the CA1, CA2/3, and CA4/DG subfields over time. We studied 20 multi-episode SCZ patients and 23 healthy controls who performed aerobic exercise, and 21 multi-episode SCZ patients allocated to a control intervention (table soccer) for 3 months. Magnetic resonance imaging-based assessments were performed with FreeSurfer at baseline and after 3 months. The analyses showed that the polygenic burden associated with oligodendrocyte precursor cells (OPC) and radial glia (RG) significantly influenced the volume changes between baseline and 3 months in the CA4/DG subfield in SCZ patients performing aerobic exercise. A higher OPC- or RG-associated genetic risk burden was associated with a less pronounced volume increase or even a decrease in CA4/DG during the exercise intervention. We hypothesize that SCZ cell type-specific polygenic risk modulates the aerobic exercise-induced neuroplastic processes in the hippocampus.

From genetics to biology: advancing mental health research in the Genomics ERA

The Genomics Workgroup of the National Advisory Mental Health Council (NAMHC) recently issued a set of recommendations for advancing the NIMH psychiatric genetics research program and prioritizing subsequent follow-up studies. The report emphasized the primacy of rigorous statistical support from properly designed, well-powered studies for pursuing genetic variants robustly associated with disease. Here we discuss the major points NIMH program staff consider when assessing research applications based on common and rare variants, as well as genetic syndromes, associated with psychiatric disorders. These are broad guiding principles for investigators to consider prior to submission of their applications. NIMH staff weigh these points in the context of reviewer comments, the existing literature, and current investments in related projects. Following the recommendations of the NAMHC, statistical strength and robustness of the underlying genetic discovery weighs heavily in our funding considerations as does the suitability of the proposed experimental approach. We specifically address our evaluation of applications motivated in whole, or in part, by an association between human DNA sequence variation and a disease or trait relevant to the mission of the NIMH.

Migraine-associated gene expression in cell types of the central and peripheral nervous system

Background

Genome-wide association studies have implicated dozens of genes with migraine susceptibility, but it remains unclear in which nervous system cell types these genes are expressed.

Methods

Using single-cell RNA sequencing data from the central and peripheral nervous system, including the trigeminal ganglion, the expression of putative migraine-associated genes was compared across neuronal, glial and neurovascular cell types within these tissues.

Results

Fifty-four putative migraine-associated genes were expressed in the central nervous system, peripheral nervous system or neurovascular cell types analyzed. Six genes (11.1%) were selectively enriched in central nervous system cell types, three (5.5%) in neurovascular cell types, and two (3.7%) in peripheral nervous system cell types. The remaining genes were expressed in multiple cell types.

Conclusions

Single-cell RNA sequencing of the brain and peripheral nervous system localizes each migraine-associated gene to its respective nervous system tissue and the cell types in which it is expressed. While the majority of migraine-associated genes are broadly expressed, we identified several cell-type-specific migraine-associated genes in the central nervous system, peripheral nervous system, and neurovasculature. Trial registration: not applicable.

Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration

Genome-wide association studies (GWAS) have identified genetic variants associated with age-related macular degeneration (AMD), one of the leading causes of blindness in the elderly. However, it has been challenging to identify the cell types associated with AMD given the genetic complexity of the disease. Here we perform massively parallel single-cell RNA sequencing (scRNA-seq) of human retinas using two independent platforms, and report the first single-cell transcriptomic atlas of the human retina. Using a multi-resolution network-based analysis, we identify all major retinal cell types, and their corresponding gene expression signatures. Heterogeneity is observed within macroglia, suggesting that human retinal glia are more diverse than previously thought. Finally, GWAS-based enrichment analysis identifies glia, vascular cells, and cone photoreceptors to be associated with the risk of AMD. These data provide a detailed analysis of the human retina, and show how scRNA-seq can provide insight into cell types involved in complex, inflammatory genetic diseases.

Progress in iPSC-Based Modeling of Psychiatric Disorders

Progress in iPSC-based cellular systems provides new insights into human brain development and early neurodevelopmental deviations in psychiatric disorders. Among these, studies on schizophrenia (SCZ) take a prominent role owing to its high heritability and multifarious evidence that it evolves from a genetically induced vulnerability in brain development. Recent iPSC studies on patients with SCZ indicate that functional impairments of neural progenitor cells (NPCs) in monolayer culture extend to brain organoids by disrupting neocorticogenesis in an in vitro model. In addition, the formation of hippocampal circuit-like structures in vitro is impaired in patients with SCZ as is the case for glia development. Intriguingly, chimeric-mice experiments show altered oligodendrocyte and astrocyte development in vivo that highlights the importance of cell-cell interactions in the pathogenesis of early-onset SCZ. Likewise, cortical imbalances in excitatory-inhibitory signaling may result from a cell-autonomous defect in cortical interneuron (cIN) development. Overall, these findings indicate that genetic risk in SCZ impacts neocorticogenesis, hippocampal circuit formation, and the development of distinct glial and neuronal subtypes. In light of this remarkable progress, we discuss current limitations and further steps necessary to harvest the full potential of iPSC-based investigations on psychiatric disorders.

Synergistic effects of common schizophrenia risk variants

The mechanisms by which common risk variants of small effect interact to contribute to complex genetic disorders are unclear. Here, we apply a genetic approach, using isogenic human induced pluripotent stem cells, to evaluate the effects of schizophrenia (SZ)-associated common variants predicted to function as SZ expression quantitative trait loci (eQTLs). By integrating CRISPR-mediated gene editing, activation and repression technologies to study one putative SZ eQTL (FURIN rs4702) and four top-ranked SZ eQTL genes (FURIN, SNAP91, TSNARE1 and CLCN3), our platform resolves pre- and postsynaptic neuronal deficits, recapitulates genotype-dependent gene expression differences and identifies convergence downstream of SZ eQTL gene perturbations. Our observations highlight the cell-type-specific effects of common variants and demonstrate a synergistic effect between SZ eQTL genes that converges on synaptic function. We propose that the links between rare and common variants implicated in psychiatric disease risk constitute a potentially generalizable phenomenon occurring more widely in complex genetic disorders.

A framework for the investigation of rare genetic disorders in neuropsychiatry

De novo and inherited rare genetic disorders (RGDs) are a major cause of human morbidity, frequently involving neuropsychiatric symptoms. Recent advances in genomic technologies and data sharing have revolutionized the identification and diagnosis of RGDs, presenting an opportunity to elucidate the mechanisms underlying neuropsychiatric disorders by investigating the pathophysiology of high-penetrance genetic risk factors. Here we seek out the best path forward for achieving these goals. We think future research will require consistent approaches across multiple RGDs and developmental stages, involving both the characterization of shared neuropsychiatric dimensions in humans and the identification of neurobiological commonalities in model systems. A coordinated and concerted effort across patients, families, researchers, clinicians and institutions, including rapid and broad sharing of data, is now needed to translate these discoveries into urgently needed therapies.

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

BACKGROUND

DNA methylation (DNAm) is a critical regulator of both development and cellular identity and shows unique patterns in neurons. To better characterize maturational changes in DNAm patterns in these cells, we profile the DNAm landscape at single-base resolution across the first two decades of human neocortical development in NeuN+ neurons using whole-genome bisulfite sequencing and compare them to non-neurons (primarily glia) and prenatal homogenate cortex.

RESULTS

We show that DNAm changes more dramatically during the first 5 years of postnatal life than during the entire remaining period. We further refine global patterns of increasingly divergent neuronal CpG and CpH methylation (mCpG and mCpH) into six developmental trajectories and find that in contrast to genome-wide patterns, neighboring mCpG and mCpH levels within these regions are highly correlated. We integrate paired RNA-seq data and identify putative regulation of hundreds of transcripts and their splicing events exclusively by mCpH levels, independently from mCpG levels, across this period. We finally explore the relationship between DNAm patterns and development of brain-related phenotypes and find enriched heritability for many phenotypes within identified DNAm features.

CONCLUSIONS

By profiling DNAm changes in NeuN-sorted neurons over the span of human cortical development, we identify novel, dynamic regions of DNAm that would be masked in homogenate DNAm data; expand on the relationship between CpG methylation, CpH methylation, and gene expression; and find enrichment particularly for neuropsychiatric diseases in genomic regions with cell type-specific, developmentally dynamic DNAm patterns.

Genome-wide association study of post-traumatic stress disorder reexperiencing symptoms in >165,000 US veterans

Post-traumatic stress disorder (PTSD) is a major problem among military veterans and civilians alike, yet its pathophysiology remains poorly understood. We performed a genome-wide association study and bioinformatic analyses, which included 146,660 European Americans and 19,983 African Americans in the US Million Veteran Program, to identify genetic risk factors relevant to intrusive reexperiencing of trauma, which is the most characteristic symptom cluster of PTSD. In European Americans, eight distinct significant regions were identified. Three regions had values of P < 5 × 10-10: CAMKV; chromosome 17 closest to KANSL1, but within a large high linkage disequilibrium region that also includes CRHR1; and TCF4. Associations were enriched with respect to the transcriptomic profiles of striatal medium spiny neurons. No significant associations were observed in the African American cohort of the sample. Results in European Americans were replicated in the UK Biobank data. These results provide new insights into the biology of PTSD in a well-powered genome-wide association study.

Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes

Autism spectrum disorder (ASD) is a genetically heterogeneous condition, caused by a combination of rare de novo and inherited variants as well as common variants in at least several hundred genes. However, significantly larger sample sizes are needed to identify the complete set of genetic risk factors. We conducted a pilot study for SPARK (SPARKForAutism.org) of 457 families with ASD, all consented online. Whole exome sequencing (WES) and genotyping data were generated for each family using DNA from saliva. We identified variants in genes and loci that are clinically recognized causes or significant contributors to ASD in 10.4% of families without previous genetic findings. In addition, we identified variants that are possibly associated with ASD in an additional 3.4% of families. A meta-analysis using the TADA framework at a false discovery rate (FDR) of 0.1 provides statistical support for 26 ASD risk genes. While most of these genes are already known ASD risk genes, BRSK2 has the strongest statistical support and reaches genome-wide significance as a risk gene for ASD (p-value = 2.3e-06). Future studies leveraging the thousands of individuals with ASD who have enrolled in SPARK are likely to further clarify the genetic risk factors associated with ASD as well as allow accelerate ASD research that incorporates genetic etiology.

CTIP2-Regulated Reduction in PKA-Dependent DARPP32 Phosphorylation in Human Medium Spiny Neurons: Implications for Huntington Disease

The mechanisms underlying the selective degeneration of medium spiny neurons (MSNs) in Huntington disease (HD) remain largely unknown. CTIP2, a transcription factor expressed by all MSNs, is implicated in HD pathogenesis because of its interactions with mutant huntingtin. Here, we report a key role for CTIP2 in protein phosphorylation via governing protein kinase A (PKA) signaling in human striatal neurons. Transcriptomic analysis of CTIP2-deficient MSNs implicates CTIP2 target genes at the heart of cAMP-Ca²⁺ signal integration in the PKA pathway. These findings are further supported by experimental evidence of a substantial reduction in phosphorylation of DARPP32 and GLUR1, two PKA targets in CTIP2-deficient MSNs. Moreover, we show that CTIP2-dependent dysregulation of protein phosphorylation is shared by HD hPSC-derived MSNs and striatal tissues of two HD mouse models. This study therefore establishes an essential role for CTIP2 in human MSN homeostasis and provides mechanistic and potential therapeutic insight into striatal neurodegeneration.

Recent advances in our understanding of central and peripheral nervous system progenitors

Several decades of intense research provided us with a grand framework describing the emergence of neurons in central (CNS) and peripheral (PNS) nervous systems. However, the specifics of molecular events and lineage control leading to a plethora of neuronal subtypes stayed largely unclear. Today, the advances in single cell omics, sample clearing and 3D-microscopy techniques, brain organoids, and synaptic connectivity tracing enabled systematic and unbiased understanding of neuronal diversity, development, circuitry and cell identity control. Novel technological advancements stimulated the transition from conceptual scheme of neuronal differentiation into precise maps of molecular events leading to the diversity of specific neuronal subtypes in relation to their locations and microenvironment. These high-resolution data opened a set of new questions including how transcriptional and epigenetics states control the proportionality of neuronal subpopulations or what are the evolutionary mechanisms of origin of different neuronal subtypes. In this review, we outline the most recent advancements in our understanding of how the neuronal diversity is generated in CNS and PNS and briefly address the challenges and questions arising in the field of neurogenesis.

Stem cell models of schizophrenia, what have we learned and what is the potential?

Schizophrenia is a complex disorder with clinical manifestations in early adulthood. However, it may start with disruption of brain development caused by genetic or environmental factors, or both. Early deteriorating effects of genetic/environmental factors on neural development might be key to described disease causing mechanisms. Establishing cellular models with cells from affected individual using the induced pluripotent stem cells (iPSC) technology could be used to mimic early neurodevelopment alterations caused by risk genes or environmental stressors. Indeed, cellular models have allowed identification and further study of risk factors and the biological pathways in which they are involved. New advancements in differentiation methods such as defined and robust monolayer protocols and cerebral 3D organoids have made it possible to faithfully mimic neural development and neuronal functionality while CRISPR-editing tools assist to engineer isogenic cell lines to precisely explore genetic variation in polygenic diseases such as schizophrenia. Here we review the current field of iPSC models of schizophrenia and how risk factors can be modelled as well as discussing the common biological pathways involved.

Deciphering Brain Complexity Using Single-cell Sequencing

The human brain contains billions of highly differentiated and interconnected cells that form intricate neural networks and collectively control the physical activities and high-level cognitive functions, such as memory, decision-making, and social behavior. Big data is required to decipher the complexity of cell types, as well as connectivity and functions of the brain. The newly developed single-cell sequencing technology, which provides a comprehensive landscape of brain cell type diversity by profiling the transcriptome, genome, and/or epigenome of individual cells, has contributed substantially to revealing the complexity and dynamics of the brain and providing new insights into brain development and brain-related disorders. In this review, we first introduce the progresses in both experimental and computational methods of single-cell sequencing technology. Applications of single-cell sequencing-based technologies in brain research, including cell type classification, brain development, and brain disease mechanisms, are then elucidated by representative studies. Lastly, we provided our perspectives into the challenges and future developments in the field of single-cell sequencing. In summary, this mini review aims to provide an overview of how big data generated from single-cell sequencing have empowered the advancements in neuroscience and shed light on the complex problems in understanding brain functions and diseases.

Comparative characterization of human induced pluripotent stem cells (hiPSC) derived from patients with schizophrenia and autism

Human induced pluripotent stem cells (hiPSC) provide an attractive tool to study disease mechanisms of neurodevelopmental disorders such as schizophrenia. A pertinent problem is the development of hiPSC-based assays to discriminate schizophrenia (SZ) from autism spectrum disorder (ASD) models. Healthy control individuals as well as patients with SZ and ASD were examined by a panel of diagnostic tests. Subsequently, skin biopsies were taken for the generation, differentiation, and testing of hiPSC-derived neurons from all individuals. SZ and ASD neurons share a reduced capacity for cortical differentiation as shown by quantitative analysis of the synaptic marker PSD95 and neurite outgrowth. By contrast, pattern analysis of calcium signals turned out to discriminate among healthy control, schizophrenia, and autism samples. Schizophrenia neurons displayed decreased peak frequency accompanied by increased peak areas, while autism neurons showed a slight decrease in peak amplitudes. For further analysis of the schizophrenia phenotype, transcriptome analyses revealed a clear discrimination among schizophrenia, autism, and healthy controls based on differentially expressed genes. However, considerable differences were still evident among schizophrenia patients under inspection. For one individual with schizophrenia, expression analysis revealed deregulation of genes associated with the major histocompatibility complex class II (MHC class II) presentation pathway. Interestingly, antipsychotic treatment of healthy control neurons also increased MHC class II expression. In conclusion, transcriptome analysis combined with pattern analysis of calcium signals appeared as a tool to discriminate between SZ and ASD phenotypes in vitro.

Genetic mapping of cell type specificity for complex traits

Single-cell RNA sequencing (scRNA-seq) data allows to create cell type specific transcriptome profiles. Such profiles can be aligned with genome-wide association studies (GWASs) to implicate cell type specificity of the traits. Current methods typically rely only on a small subset of available scRNA-seq datasets, and integrating multiple datasets is hampered by complex batch effects. Here we collated 43 publicly available scRNA-seq datasets. We propose a 3-step workflow with conditional analyses within and between datasets, circumventing batch effects, to uncover associations of traits with cell types. Applying this method to 26 traits, we identify independent associations of multiple cell types. These results lead to starting points for follow-up functional studies aimed at gaining a mechanistic understanding of these traits. The proposed framework as well as the curated scRNA-seq datasets are made available via an online platform, FUMA, to facilitate rapid evaluation of cell type specificity by other researchers.

Tissue- and cell type-specific information helps to interpret findings from genome-wide association studies. Here, the authors leverage multiple single cell expression datasets to infer cell type specificity of traits.

A statistical simulator scDesign for rational scRNA-seq experimental design

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within individual cells. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information.

RESULTS

Here we present a flexible and robust simulator, scDesign, the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings. In an evaluation based on 17 cell types and 6 different protocols, scDesign outperformed four state-of-the-art scRNA-seq simulation methods and led to rational experimental design. In addition, scDesign demonstrates reproducibility across biological replicates and independent studies. We also discuss the performance of multiple differential expression and dimension reduction methods based on the protocol-dependent scRNA-seq data generated by scDesign. scDesign is expected to be an effective bioinformatic tool that assists rational scRNA-seq experimental design and comparison of scRNA-seq computational methods based on specific research goals.

AVAILABILITY AND IMPLEMENTATION

We have implemented our method in the R package scDesign, which is freely available at https://github.com/Vivianstats/scDesign.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Psychiatric Risk Gene NT5C2 Regulates Adenosine Monophosphate-Activated Protein Kinase Signaling and Protein Translation in Human Neural Progenitor Cells

BACKGROUND

The 5'-nucleotidase, cytosolic II gene (NT5C2, cN-II) is associated with disorders characterized by psychiatric and psychomotor disturbances. Common psychiatric risk alleles at the NT5C2 locus reduce expression of this gene in the fetal and adult brain, but downstream biological risk mechanisms remain elusive.

METHODS

Distribution of the NT5C2 protein in the human dorsolateral prefrontal cortex and cortical human neural progenitor cells (hNPCs) was determined using immunostaining, publicly available expression data, and reverse transcriptase quantitative polymerase chain reaction. Phosphorylation quantification of adenosine monophosphate-activated protein kinase (AMPK) alpha (Thr172) and ribosomal protein S6 (Ser235/Ser236) was performed using Western blotting to infer the degree of activation of AMPK signaling and the rate of protein translation. Knockdowns were induced in hNPCs and Drosophila melanogaster using RNA interference. Transcriptomic profiling of hNPCs was performed using microarrays, and motility behavior was assessed in flies using the climbing assay.

RESULTS

Expression of NT5C2 was higher during neurodevelopment and was neuronally enriched in the adult human cortex. Knockdown in hNPCs affected AMPK signaling, a major nutrient-sensing mechanism involved in energy homeostasis, and protein translation. Transcriptional changes implicated in protein translation were observed in knockdown hNPCs, and expression changes to genes related to AMPK signaling and protein translation were confirmed using reverse transcriptase quantitative polymerase chain reaction. The knockdown in Drosophila was associated with drastic climbing impairment.

CONCLUSIONS

We provide an extensive neurobiological characterization of the psychiatric risk gene NT5C2, describing its previously unknown role in the regulation of AMPK signaling and protein translation in neural stem cells and its association with Drosophila melanogaster motility behavior.

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

We performed RNA sequencing on 40,000 cells to create a high-resolution single-cell gene expression atlas of developing human cortex, providing the first single-cell characterization of previously uncharacterized cell types, including human subplate neurons, comparisons with bulk tissue, and systematic analyses of technical factors. These data permit deconvolution of regulatory networks connecting regulatory elements and transcriptional drivers to single-cell gene expression programs, significantly extending our understanding of human neurogenesis, cortical evolution, and the cellular basis of neuropsychiatric disease. We tie cell-cycle progression with early cell fate decisions during neurogenesis, demonstrating that differentiation occurs on a transcriptomic continuum; rather than only expressing a few transcription factors that drive cell fates, differentiating cells express broad, mixed cell-type transcriptomes before telophase. By mapping neuropsychiatric disease genes to cell types, we implicate dysregulation of specific cell types in ASD, ID, and epilepsy. We developed CoDEx, an online portal to facilitate data access and browsing.

Prefrontal Coexpression of Schizophrenia Risk Genes Is Associated With Treatment Response in Patients

BACKGROUND

Gene coexpression networks are relevant to functional and clinical translation of schizophrenia risk genes. We hypothesized that schizophrenia risk genes converge into coexpression pathways that may be associated with gene regulation mechanisms and with response to treatment in patients with schizophrenia.

METHODS

We identified gene coexpression networks in two prefrontal cortex postmortem RNA sequencing datasets (n = 688) and replicated them in four more datasets (n = 1295). We identified and replicated (p values < .001) a single module enriched for schizophrenia risk loci (13 risk genes in 10 loci). In silico screening of potential regulators of the schizophrenia risk module via bioinformatic analyses identified two transcription factors and three microRNAs associated with the risk module. To translate postmortem information into clinical phenotypes, we identified polymorphisms predicting coexpression and combined them to obtain an index approximating module coexpression (Polygenic Coexpression Index [PCI]).

RESULTS

The PCI-coexpression association was successfully replicated in two independent brain transcriptome datasets (n = 131; p values < .05). Finally, we tested the association between the PCI and short-term treatment response in two independent samples of patients with schizophrenia treated with olanzapine (n = 167). The PCI was associated with treatment response in the positive symptom domain in both clinical cohorts (p values < .05).

CONCLUSIONS

In summary, our findings in 1983 samples of human postmortem prefrontal cortex show that coexpression of a set of genes enriched for schizophrenia risk genes is relevant to treatment response. This coexpression pathway may be coregulated by transcription factors and microRNA associated with it.

Gene regulation and the architecture of complex human traits in the genomics era

Virtually all human psychological and behavioral traits are at least partially heritable. For nearly a century, classical genetic studies have sought to understand how genetic variation contributes to human variation in these traits. More recently, genome-wide association studies have identified large numbers of specific genetic variants linked with complex traits. Many of these variants fall outside of protein-coding genes, in putative gene regulatory elements. This suggests that some fraction of causal human genetic variation acts through gene regulation. New developments in the field of regulatory genomics offer resources and methods to understand how genetic variants that alter gene expression contribute to human psychology and risk for psychiatric disease.

Identification of the primate-specific gene BTN3A2 as an additional schizophrenia risk gene in the MHC loci

BACKGROUND

Schizophrenia is a complex mental disorder resulting in poor life quality and high social and economic burden. Despite the fact that genome-wide association studies (GWASs) have successfully identified a number of risk loci for schizophrenia, identifying the causal genes at the risk loci and elucidating their roles in disease pathogenesis remain major challenges.

METHODS

The summary data-based Mendelian randomization analysis (SMR) was used to integrate a large-scale GWAS of schizophrenia with brain expression quantitative trait loci (eQTL) data and brain methylation expression quantitative trait loci (meQTL) data, to identify novel risk gene(s) for schizophrenia. We then analyzed the mRNA expression and methylation statuses of the gene hit BTN3A2 during the early brain development. Electrophysiological analyses of CA1 pyramidal neurons were performed to evaluate the excitatory and inhibitory synaptic activity after overexpression of BTN3A2 in rat hippocampal slices. Cell surface binding assay was used to test the interaction of BTN3A2 and neurexins.

FINDINGS

We identified BTN3A2 as a potential risk gene for schizophrenia. The mRNA expression and methylation data showed that BTN3A2 expression in human brain is highest post-natally. Further electrophysiological analyses of rat hippocampal slices showed that BTN3A2 overexpression specifically suppressed the excitatory synaptic activity onto CA1 pyramidal neurons, most likely through its interaction with the presynaptic adhesion molecule neurexins.

INTERPRETATION

Increased expression of BTN3A2 might confer risk for schizophrenia by altering excitatory synaptic function. Our result constitutes a paradigm for distilling risk gene using an integrative analysis and functional characterization in the post-GWAS era. FUND: This study was supported by the Strategic Priority Research Program (B) of the Chinese Academy of Sciences (XDB02020003 to Y-GY), the National Natural Science Foundation of China (31730037 to Y-GY), and the Bureau of Frontier Sciences and Education, Chinese Academy of Sciences (QYZDJ-SSW-SMC005 to Y-GY).

Rational Design of Multitarget-Directed Ligands: Strategies and Emerging Paradigms

Due to the complexity of multifactorial diseases, single-target drugs do not always exhibit satisfactory efficacy. Recently, increasing evidence indicates that simultaneous modulation of multiple targets may improve both therapeutic safety and efficacy, compared with single-target drugs. However, few multitarget drugs are on market or in clinical trials, despite the best efforts of medicinal chemists. This article discusses the systematic establishment of target combination, lead generation, and optimization of multitarget-directed ligands (MTDLs). Moreover, we analyze some MTDLs research cases for several complex diseases in recent years and the physicochemical properties of 117 clinical multitarget drugs, with the aim to reveal the trends and insights of the potential use of MTDLs.

Moving beyond neurons: the role of cell type-specific gene regulation in Parkinson's disease heritability

Parkinson’s disease (PD), with its characteristic loss of nigrostriatal dopaminergic neurons and deposition of α-synuclein in neurons, is often considered a neuronal disorder. However, in recent years substantial evidence has emerged to implicate glial cell types, such as astrocytes and microglia. In this study, we used stratified LD score regression and expression-weighted cell-type enrichment together with several brain-related and cell-type-specific genomic annotations to connect human genomic PD findings to specific brain cell types. We found that PD heritability attributable to common variation does not enrich in global and regional brain annotations or brain-related cell-type-specific annotations. Likewise, we found no enrichment of PD susceptibility genes in brain-related cell types. In contrast, we demonstrated a significant enrichment of PD heritability in a curated lysosomal gene set highly expressed in astrocytic, microglial, and oligodendrocyte subtypes, and in LoF-intolerant genes, which were found highly expressed in almost all tested cellular subtypes. Our results suggest that PD risk loci do not lie in specific cell types or individual brain regions, but rather in global cellular processes detectable across several cell types.

Uncovering the Genetic Architecture of Major Depression

There have been several recent studies addressing the genetic architecture of depression. This review serves to take stock of what is known now about the genetics of depression, how it has increased our knowledge and understanding of its mechanisms, and how the information and knowledge can be leveraged to improve the care of people affected. We identify four priorities for how the field of MD genetics research may move forward in future years, namely by increasing the sample sizes available for genome-wide association studies (GWASs), greater inclusion of diverse ancestries and low-income countries, the closer integration of psychiatric genetics with electronic medical records, and the development of the neuroscience toolkit for polygenic disorders.

A Longitudinal Model of Human Neuronal Differentiation for Functional Investigation of Schizophrenia Polygenic Risk

BACKGROUND

Common psychiatric disorders are characterized by complex disease architectures with many small genetic effects that contribute and complicate biological understanding of their etiology. There is therefore a pressing need for in vitro experimental systems that allow for interrogation of polygenic psychiatric disease risk to study the underlying biological mechanisms.

METHODS

We have developed an analytical framework that integrates genome-wide disease risk from genome-wide association studies with longitudinal in vitro gene expression profiles of human neuronal differentiation.

RESULTS

We demonstrate that the cumulative impact of risk loci of specific psychiatric disorders is significantly associated with genes that are differentially expressed and upregulated during differentiation. We find the strongest evidence for schizophrenia, a finding that we replicate in an independent dataset. A longitudinal gene cluster involved in synaptic function primarily drives the association with schizophrenia risk.

CONCLUSIONS

These findings reveal that in vitro human neuronal differentiation can be used to translate the polygenic architecture of schizophrenia to biologically relevant pathways that can be modeled in an experimental system. Overall, this work emphasizes the use of longitudinal in vitro transcriptomic signatures as a cellular readout and the application to the genetics of complex traits.

Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders

Studies of the genetics of psychiatric disorders have become one of the most exciting and fast-moving areas in human genetics. A decade ago, there were few reproducible findings, and now there are hundreds. In this review, we focus on the findings that have illuminated the genetic architecture of psychiatric disorders and the challenges of using these findings to inform our understanding of pathophysiology. The evidence is now overwhelming that psychiatric disorders are "polygenic"-that many genetic loci contribute to risk. With the exception of a subset of those with ASD, few individuals with a psychiatric disorder have a single, deterministic genetic cause; rather, developing a psychiatric disorder is influenced by hundreds of different genetic variants, consistent with a polygenic model. As progressively larger studies have uncovered more about their genetic architecture, the need to elucidate additional architectures has become clear. Even if we were to have complete knowledge of the genetic architecture of a psychiatric disorder, full understanding requires deep knowledge of the functional genomic architecture-the implicated loci impact regulatory processes that influence gene expression and the functional coordination of genes that control biological processes. Following from this is cellular architecture: of all brain regions, cell types, and developmental stages, where and when are the functional architectures operative? Given that the genetic architectures of different psychiatric disorders often strongly overlap, we are challenged to re-evaluate and refine the diagnostic architectures of psychiatric disorders using fundamental genetic and neurobiological data.

Building a schizophrenia genetic network: transcription factor 4 regulates genes involved in neuronal development and schizophrenia risk

The transcription factor 4 (TCF4) locus is a robust association finding with schizophrenia (SCZ), but little is known about the genes regulated by the encoded transcription factor. Therefore, we conducted chromatin immunoprecipitation sequencing (ChIP-seq) of TCF4 in neural-derived (SH-SY5Y) cells to identify genome-wide TCF4 binding sites, followed by data integration with SCZ association findings. We identified 11 322 TCF4 binding sites overlapping in two ChIP-seq experiments. These sites are significantly enriched for the TCF4 Ebox binding motif (>85% having ≥1 Ebox) and implicate a gene set enriched for genes downregulated in TCF4 small-interfering RNA (siRNA) knockdown experiments, indicating the validity of our findings. The TCF4 gene set was also enriched among (1) gene ontology categories such as axon/neuronal development, (2) genes preferentially expressed in brain, in particular pyramidal neurons of the somatosensory cortex and (3) genes downregulated in postmortem brain tissue from SCZ patients (odds ratio, OR = 2.8, permutation P < 4x10-5). Considering genomic alignments, TCF4 binding sites significantly overlapped those for neural DNA-binding proteins such as FOXP2 and the SCZ-associated EP300. TCF4 binding sites were modestly enriched among SCZ risk loci from the Psychiatric Genomic Consortium (OR = 1.56, P = 0.03). In total, 130 TCF4 binding sites occurred in 39 of the 108 regions published in 2014. Thirteen genes within the 108 loci had both a TCF4 binding site ±10kb and were differentially expressed in siRNA knockdown experiments of TCF4, suggesting direct TCF4 regulation. These findings confirm TCF4 as an important regulator of neural genes and point toward functional interactions with potential relevance for SCZ.

Regulatory genes and pathways disrupted in autism spectrum disorders

Autism spectrum disorder (ASD) is a highly prevalent and complex genetic disorder. The complex genetic make-up of ASD has been extensively studied and both common and rare genetic variants in up to 1000 genes have been linked to increased ASD risk. While these studies highlight the genetic complexity and begin to provide a window for delineating pathways at risk in ASD, the pathogenicity and specific contribution of many mutations to the disorder are poorly understood. Defining the convergent pathways disrupted by this large number of ASD-associated genetic variants will help to understand disease pathogenesis and direct future therapeutic efforts for the groups of patients with distinct etiologies. Here, we review some of the common regulatory pathways including chromatin remodeling, transcription, and alternative splicing that have emerged as common features from genetic and transcriptomic profiling of ASD. For each category, we focus on one gene (CHD8, FOXP1, and RBFOX1) that is significantly linked to ASD and functionally characterized in recent years. Finally, we discuss genetic and transcriptomic overlap between ASD and other neurodevelopmental disorders.

Novel Complex Interactions between Mitochondrial and Nuclear DNA in Schizophrenia and Bipolar Disorder

Mitochondrial dysfunction has been associated with schizophrenia (SZ) and bipolar disorder (BD). This review examines recent publications and novel associations between mitochondrial genes and SZ and BD. Associations of nuclear-encoded mitochondrial variants with SZ were found using gene- and pathway-based approaches. Two control region mitochondrial DNA (mtDNA) SNPs, T16519C and T195C, both showed an association with SZ and BD. A review of 4 studies of A15218G located in the cytochrome B oxidase gene (CYTB, SZ = 11,311, control = 35,735) shows a moderate association with SZ (p = 2.15E-03). Another mtDNA allele A12308G was nominally associated with psychosis in BD type I subjects and SZ. The first published study testing the epistatic interaction between nuclear-encoded and mitochondria-encoded genes demonstrated evidence for potential interactions between mtDNA and the nuclear genome for BD. A similar analysis for the risk of SZ revealed significant joint effects (34 nuclear-mitochondria SNP pairs with joint effect p ≤ 5E-07) and significant enrichment of projection neurons. The mitochondria-encoded gene CYTB was found in both the epistatic interactions for SZ and BD and the single SNP association of SZ. Future efforts considering population stratification and polygenic risk scores will test the role of mitochondrial variants in psychiatric disorders.

Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

Major depression is a debilitating psychiatric illness that is typically associated with low mood and anhedonia. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximize sample size, we meta-analyzed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 genesets associated with depression, including both genes and gene pathways associated with synaptic structure and neurotransmission. An enrichment analysis provided further evidence of the importance of prefrontal brain regions. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant after multiple testing correction. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding etiology and developing new treatment approaches.