SZGR: a comprehensive schizophrenia gene resource

A comprehensive framework for trans-ancestry pathway analysis using GWAS summary data from diverse populations

As more multi-ancestry GWAS summary data become available, we have developed a comprehensive trans-ancestry pathway analysis framework that effectively utilizes this diverse genetic information. Within this framework, we evaluated various strategies for integrating genetic data at different levels-SNP, gene, and pathway-from multiple ancestry groups. Through extensive simulation studies, we have identified robust strategies that demonstrate superior performance across diverse scenarios. Applying these methods, we analyzed 6,970 pathways for their association with schizophrenia, incorporating data from African, East Asian, and European populations. Our analysis identified over 200 pathways significantly associated with schizophrenia, even after excluding genes near genome-wide significant loci. This approach substantially enhances detection efficiency compared to traditional single-ancestry pathway analysis and the conventional approach that amalgamates single-ancestry pathway analysis results across different ancestry groups. Our framework provides a flexible and effective tool for leveraging the expanding pool of multi-ancestry GWAS summary data, thereby improving our ability to identify biologically relevant pathways that contribute to disease susceptibility.

The absurdity of the latent disease model in mental health: 10,130,814 ways to have a DSM-5-TR psychological disorder

BACKGROUND

Latent disease classification is currently the accepted approach to mental illness diagnosis. In the United States, this takes the form of the Diagnostic and Statistical Manual of Mental Disorders-5-Text Revision (DSM-5-TR). Latent disease classification has been criticized for reliability and validity problems, particularly regarding diagnostic heterogeneity. No authors have calculated the scope of the heterogeneity problem of the entire DSM-5-TR.

AIMS

We addressed this issue by calculating the unique diagnostic profiles that exist for every DSM-5-TR diagnosis.

METHODS

We did this by applying formulas previously used in smaller heterogeneity analyses to all diagnoses within the DSM-5-TR.

RESULTS

We found that there are 10,130,814 ways to be diagnosed with a mental illness using DSM-5-TR criteria. When specifiers are considered, this number balloons to over 161 septillion unique diagnostic presentations (driven mainly by bipolar II disorder). Additionally, there are 1,951,065 ways to present with psychiatric symptoms, yet not meet diagnostic criteria.

CONCLUSIONS

Latent disease classification leads to considerable heterogeneity in possible presentations. We provide examples of how latent disease classification harms research and treatment programs. We echo recommendations for the dismissal of latent disease classification as a mental illness diagnostic program.

Multiple myeloma, IL6, and risk of schizophrenia: A Mendelian randomization, transcriptome, and Bayesian colocalization study

Numerous clinical studies speculated the association between multiple myeloma (MM) and inflammatory diseases; however, there is limited validation of these claims via establishing a causal relationship and revealing the underlying mechanism. This exploratory study employed bidirectional Mendelian randomization (MR) analysis to investigate the causal relationships between MM and inflammatory diseases, including atherosclerosis, asthma, ankylosing spondylitis, Alzheimer's disease (AD), Parkinson's disease (PD), sarcoidosis, inflammatory bowel disease, nonalcoholic fatty liver disease, type II diabetes, and schizophrenia (SZ). Transcriptomic and genome-wide Bayesian colocalization analyses were further applied to reveal the underlying mechanism. A significant and previously unrecognized positive association was identified between genetic predisposition to MM and the risk of SZ. Two independent case reports showed that treatment-resistant psychosis is due to underlying MM and is resolved by treating MM. From our MR analyses, various statistical methods confirmed this association without detecting heterogeneity or pleiotropy effects. Transcriptomic analysis revealed shared inflammation-relevant pathways in MM and SZ patients, suggesting inflammation as a potential pathophysiological mediator of MM's causal effect on SZ. Bayesian colocalization analysis identified rs9273086, which maps to the protein-coding region of HLA-DRB1, as a common risk variant for both MM and SZ. Polymorphism of the HLA-DRB1 allele has been implicated in AD and PD, further highlighting the impact of our results. Additionally, we confirmed that interleukin-6 (IL-6) is a risk factor for SZ through secondary MR, reinforcing the role of neuroinflammation in SZ etiology. Overall, our findings showed that genetic predisposition to MM, HLA-DRB1 polymorphism, and enhanced IL-6 signaling are associated with the increased risk of SZ, providing evidence for a causal role for neuroinflammation in SZ etiology.

Network-based analysis on the genes and their interactions reveals link between schizophrenia and Alzheimer's disease

Schizophrenia (SCZ) is a heterogeneous psychiatric disorder marked by impaired thinking, emotions, and behaviors. Studies have suggested a strong connection between SCZ and Alzheimer's disease (AD), however, controversies exist and the underlying mechanisms linking these two disorders remain largely unknown. Therefore, systematic studies of SCZ- and AD-related genes will provide valuable insights into the molecular features of these two diseases and their comorbidities. In this study, we obtained 331 SCZ-related genes, 650 AD-related genes, 65 shared genes between SCZ and AD. Enrichment analysis shown that these 65 shared genes were mainly involved in cognition, neural development, synaptic transmission, drug reactions, metabolic processes and immune related processes, suggesting a complex mechanism for the co-existence of SCZ and AD. In addition, we performed pathway enrichment analysis and found a total of 57 common pathways between SCZ and AD, which could be largely grouped into three modules: immune module, neurodevelopment module and cancer module. We eventually identified the potential disease-related genes whose interactions provide clues to the overlapping symptoms between SCZ and AD.

A human forebrain organoid model of fragile X syndrome exhibits altered neurogenesis and highlights new treatment strategies

Fragile X syndrome (FXS) is caused by the loss of fragile X mental retardation protein (FMRP), an RNA-binding protein that can regulate the translation of specific mRNAs. In this study, we developed an FXS human forebrain organoid model and observed that the loss of FMRP led to dysregulated neurogenesis, neuronal maturation and neuronal excitability. Bulk and single-cell gene expression analyses of FXS forebrain organoids revealed that the loss of FMRP altered gene expression in a cell-type-specific manner. The developmental deficits in FXS forebrain organoids could be rescued by inhibiting the phosphoinositide 3-kinase pathway but not the metabotropic glutamate pathway disrupted in the FXS mouse model. We identified a large number of human-specific mRNAs bound by FMRP. One of these human-specific FMRP targets, CHD2, contributed to the altered gene expression in FXS organoids. Collectively, our study revealed molecular, cellular and electrophysiological abnormalities associated with the loss of FMRP during human brain development.

Combined glyoxalase 1 dysfunction and vitamin B6 deficiency in a schizophrenia model system causes mitochondrial dysfunction in the prefrontal cortex

Methylglyoxal (MG) is a reactive and cytotoxic α-dicarbonyl byproduct of glycolysis. Our bodies have several bio-defense systems to detoxify MG, including an enzymatic system by glyoxalase (GLO) 1 and GLO2. We identified a subtype of schizophrenia patients with novel mutations in the GLO1 gene that results in reductions of enzymatic activity. Moreover, we found that vitamin B6 (VB6) levels in peripheral blood of the schizophrenia patients with GLO1 dysfunction are significantly lower than that of healthy controls. However, the effects of GLO1 dysfunction and VB6 deficiency on the pathophysiology of schizophrenia remains poorly understood. Here, we generated a novel mouse model for this subgroup of schizophrenia patients by feeding Glo1 knockout mice VB6-deficent diets (KO/VB6(−)) and evaluated the combined effects of GLO1 dysfunction and VB6 deficiency on brain function. KO/VB6(−) mice accumulated homocysteine in plasma and MG in the prefrontal cortex (PFC), hippocampus, and striatum, and displayed behavioral deficits, such as impairments of social interaction and cognitive memory and a sensorimotor deficit in the prepulse inhibition test. Furthermore, we found aberrant gene expression related to mitochondria function in the PFC of the KO/VB6(−) mice by RNA-sequencing and weighted gene co-expression network analysis (WGCNA). Finally, we demonstrated abnormal mitochondrial respiratory function and subsequently enhanced oxidative stress in the PFC of KO/VB6(−) mice in the PFC. These findings suggest that the combination of GLO1 dysfunction and VB6 deficiency may cause the observed behavioral deficits via mitochondrial dysfunction and oxidative stress in the PFC.

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A combination of Glo1 KO and VB6 deficiency induces MG accumulation in the brain.

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Glo1 KO/VB6(−) mice exhibit schizophrenia-like behavioral deficits.

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Gene expression related to mitochondria is impaired in the PFC of the Glo1 KO/VB6(−).

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Mitochondria in the PFC of the Glo1 KO/VB6(−) mice show respiratory dysfunction.

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Oxidative stress is enhanced in the PFC of the Glo1 KO/VB6(−).

Neuroimaging and DNA Methylation: An Innovative Approach to Study the Effects of Early Life Stress on Developmental Plasticity

DNA methylation plays a key role in neural cell fate and provides a molecular link between early life stress and later-life behavioral phenotypes. Here, studies that combine neuroimaging methods and DNA methylation analysis in pediatric population with a history of adverse experiences were systematically reviewed focusing on: targeted genes and neural correlates; statistical models used to examine the link between DNA methylation and neuroimaging data also considering early life stress and behavioral outcomes. We identified 8 studies that report associations between DNA methylation and brain structure/functions in infants, school age children and adolescents faced with early life stress condition (e.g., preterm birth, childhood maltreatment, low socioeconomic status, and less-than optimal caregiving). Results showed that several genes were investigated (e.g., OXTR, SLC6A4, FKBP5, and BDNF) and different neuroimaging techniques were performed (MRI and f-NIRS). Statistical model used ranged from correlational to more complex moderated mediation models. Most of the studies (n = 5) considered DNA methylation and neural correlates as mediators in the relationship between early life stress and behavioral phenotypes. Understanding what role DNA methylation and neural correlates play in interaction with early life stress and behavioral outcomes is crucial to promote theory-driven studies as the future direction of this research fields.

nMAGMA: a network-enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia

MOTIVATION

Annotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multimarker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. In biology, SNPs may also affect genes that are far away in the genome, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue specificity of signals have not been taken into account.

RESULTS

We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals (i.e. interactions between distal DNA elements), and tissue-specific gene networks. When applied to schizophrenia (SCZ), nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are involved in SCZ compared with MAGMA and H-MAGMA, and more of nMAGMA results can be validated with known SCZ risk genes. Some disease-related functions (e.g. the ATPase pathway in Cortex) are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multitissue origins.

Translational bioinformatics in mental health: open access data sources and computational biomarker discovery

Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data.

Assessment of complementarity of WGCNA and NERI results for identification of modules associated to schizophrenia spectrum disorders

Psychiatric disorders involve both changes in multiple genes as well different types of variations. As such, gene co-expression networks allowed the comparison of different stages and parts of the brain contributing to an integrated view of genetic variation. Two methods based on co-expression networks presents appealing results: Weighted Gene Correlation Network Analysis (WGCNA) and Network-Medicine Relative Importance (NERI). By selecting two different gene expression databases related to schizophrenia, we evaluated the biological modules selected by both WGCNA and NERI along these databases as well combining both WGCNA and NERI results (WGCNA-NERI). Also we conducted a enrichment analysis for the identification of partial biological function of each result (as well a replication analysis). To appraise the accuracy of whether both algorithms (as well our approach, WGCNA-NERI) were pointing to genes related to schizophrenia and its complex genetic architecture we conducted the MSET analysis, based on a reference gene list of schizophrenia database (SZDB) related to DNA Methylation, Exome, GWAS as well as copy number variation mutation studies. The WGCNA results were more associated with inflammatory pathways and immune system response; NERI obtained genes related with cellular regulation, embryological pathways e cellular growth factors. Only NERI were able to provide a statistical meaningful results to the MSET analysis (for Methylation and de novo mutations data). However, combining WGCNA and NERI provided a much more larger overlap in these two categories and additionally on Transcriptome database. Our study suggests that using both methods in combination is better for establishing a group of modules and pathways related to a complex disease than using each method individually. NERI is available at: https://bitbucket.org/sergionery/neri.

Species-Specific Changes in a Primate Transcription Factor Network Provide Insights into the Molecular Evolution of the Primate Prefrontal Cortex

The human prefrontal cortex (PFC) differs from that of other primates with respect to size, histology, and functional abilities. Here, we analyzed genome-wide expression data of humans, chimpanzees, and rhesus macaques to discover evolutionary changes in transcription factor (TF) networks that may underlie these phenotypic differences. We determined the co-expression networks of all TFs with species-specific expression including their potential target genes and interaction partners in the PFC of all three species. Integrating these networks allowed us inferring an ancestral network for all three species. This ancestral network as well as the networks for each species is enriched for genes involved in forebrain development, axonogenesis, and synaptic transmission. Our analysis allows us to directly compare the networks of each species to determine which links have been gained or lost during evolution. Interestingly, we detected that most links were gained on the human lineage, indicating increase TF cooperativity in humans. By comparing network changes between different tissues, we discovered that in brain tissues, but not in the other tissues, the human networks always had the highest connectivity. To pinpoint molecular changes underlying species-specific phenotypes, we analyzed the sub-networks of TFs derived only from genes with species-specific expression changes in the PFC. These sub-networks differed significantly in structure and function between the human and chimpanzee. For example, the human-specific sub-network is enriched for TFs implicated in cognitive disorders and for genes involved in synaptic plasticity and cognitive functions. Our results suggest evolutionary changes in TF networks that might have shaped morphological and functional differences between primate brains, in particular in the human PFC.

A new locus regulating MICALL2 expression was identified for association with executive inhibition in children with attention deficit hyperactivity disorder

Impaired executive inhibition is a core deficit of attention deficit hyperactivity disorder (ADHD), which is a common childhood-onset psychiatric disorder with high heritability. In this study, we performed a two-stage genome-wide association study of executive inhibition in ADHD in Han Chinese. We used the Stroop color-word interference test to evaluate executive inhibition. After quality control, 780 samples with phenotype and covariate data were included in the discovery stage, whereas 922 samples were included in the replication stage. We identified one new significant locus at 7p22.3 for the Stroop word interference time (rs11514810, P=3.42E-09 for discovery, P=0.01176 for replication and combined P=5.249E-09). Regulatory feature analysis and expression quantitative trait loci (eQTL) data showed that this locus contributes to MICALL2 expression in the human brain. Most genes in the network interacting with MICALL2 were associated with psychiatric disorders. Furthermore, hyperactive-impulsive-like behavior was induced by reducing the expression of the zebrafish gene that is homologous to MICALL2, which could be rescued by tomoxetine (atomoxetine), a clinical medication for ADHD. Our results suggested that MICALL2 is a new susceptibility gene for executive inhibition deficiency related to hyperactive-impulsive behavior in ADHD, further emphasizing the possible role of neurodevelopmental genes in the pathogenic mechanism of ADHD.

Analyzing the genes related to nicotine addiction or schizophrenia via a pathway and network based approach

The prevalence of tobacco use in people with schizophrenia is much higher than in general population, which indicates a close relationship between nicotine addiction and schizophrenia. However, the molecular mechanism underlying the high comorbidity of tobacco smoking and schizophrenia remains largely unclear. In this study, we conducted a pathway and network analysis on the genes potentially associated with nicotine addiction or schizophrenia to reveal the functional feature of these genes and their interactions. Of the 276 genes associated with nicotine addiction and 331 genes associated with schizophrenia, 52 genes were shared. From these genes, 12 significantly enriched pathways associated with both diseases were identified. These pathways included those related to synapse function and signaling transduction, and drug addiction. Further, we constructed a nicotine addiction-specific and schizophrenia-specific sub-network, identifying 11 novel candidate genes potentially associated with the two diseases. Finally, we built a schematic molecular network for nicotine addiction and schizophrenia based on the results of pathway and network analysis, providing a systematic view to understand the relationship between these two disorders. Our results illustrated that the biological processes underlying the comorbidity of nicotine addiction and schizophrenia was complex, and was likely induced by the dysfunction of multiple molecules and pathways.

ANCO-GeneDB: annotations and comprehensive analysis of candidate genes for alcohol, nicotine, cocaine and opioid dependence

Studies have shown that genetic factors play an important role in the risk to substance addiction and abuse. So far, various genetic and genomic studies have reported the related evidence. These rich, but highly heterogeneous, data provide us an unprecedented opportunity to systematically collect, curate and assess the genetic and genomic signals from published studies and to perform a comprehensive analysis of their features, functional roles and druggability. Such genetic data resources have been made available for other disease or phenotypes but not for major substance dependence yet. Here, we report comprehensive data collection and secondary analyses of four phenotypes of dependence: alcohol dependence, nicotine dependence, cocaine dependence and opioid dependence, collectively named as Alcohol, Nicotine, Cocaine and Opioid (ANCO) dependence. We built the ANCO-GeneDB, an ANCO-dependence-associated gene resource database. ANCO-GeneDB includes resources from genome-wide association studies and candidate gene-based studies, transcriptomic studies, methylation studies, literature mining and drug-target data, as well as the derived data such as spatial-temporal gene expression, promoters, enhancers and expression quantitative trait loci. All associated genes and genetic variants are well annotated by using the collected evidence. Based on the collected data, we performed integrative, secondary analyses to prioritize genes, pathways, eQTLs and tissues that are significantly enriched in ANCO-related phenotypes.

DNA N6-methyladenine is dynamically regulated in the mouse brain following environmental stress

Chemical modifications on DNA molecules, such as 5-methylcytosine and 5-hydroxymethylcytosine, play important roles in the mammalian brain. A novel DNA adenine modification, N(6)-methyladenine (6mA), has recently been found in mammalian cells. However, the presence and function(s) of 6mA in the mammalian brain remain unclear. Here we demonstrate 6mA dynamics in the mouse brain in response to environmental stress. We find that overall 6mA levels are significantly elevated upon stress. Genome-wide 6mA and transcriptome profiling reveal an inverse association between 6mA dynamic changes and a set of upregulated neuronal genes or downregulated LINE transposon expression. Genes bearing stress-induced 6mA changes significantly overlap with loci associated with neuropsychiatric disorders. These results suggest an epigenetic role for 6mA in the mammalian brain as well as its potential involvement in neuropsychiatric disorders.

N6-methyladenine is a covalent epigenetic modification of the genome. Here, Yao and colleagues show that N6-methyladenine level in the mouse brain is dynamic following environmental stress, and the subsequent differential gene expression is correlated with LINE transposon expression.

Whole-genome sequencing of monozygotic twins discordant for schizophrenia indicates multiple genetic risk factors for schizophrenia

Schizophrenia is a common disorder with a high heritability, but its genetic architecture is still elusive. We implemented whole-genome sequencing (WGS) analysis of 8 families with monozygotic (MZ) twin pairs discordant for schizophrenia to assess potential association of de novo mutations (DNMs) or inherited variants with susceptibility to schizophrenia. Eight non-synonymous DNMs (including one splicing site) were identified and shared by twins, which were either located in previously reported schizophrenia risk genes (p.V24689I mutation in TTN, p.S2506T mutation in GCN1L1, IVS3+1G > T in DOCK1) or had a benign to damaging effect according to in silico prediction analysis. By searching the inherited rare damaging or loss-of-function (LOF) variants and common susceptible alleles from three classes of schizophrenia candidate genes, we were able to distill genetic alterations in several schizophrenia risk genes, including GAD1, PLXNA2, RELN and FEZ1. Four inherited copy number variations (CNVs; including a large deletion at 16p13.11) implicated for schizophrenia were identified in four families, respectively. Most of families carried both missense DNMs and inherited risk variants, which might suggest that DNMs, inherited rare damaging variants and common risk alleles together conferred to schizophrenia susceptibility. Our results support that schizophrenia is caused by a combination of multiple genetic factors, with each DNM/variant showing a relatively small effect size.

Connectome Disconnectivity and Cortical Gene Expression in Patients With Schizophrenia

BACKGROUND

Genome-wide association studies have identified several common risk loci for schizophrenia (SCZ). In parallel, neuroimaging studies have shown consistent findings of widespread white matter disconnectivity in patients with SCZ.

METHODS

We examined the role of genes in brain connectivity in patients with SCZ by combining transcriptional profiles of 43 SCZ risk genes identified by the recent genome-wide association study of the Schizophrenia Working Group of the Psychiatric Genomics Consortium with data on macroscale connectivity reductions in patients with SCZ. Expression profiles of 43 Psychiatric Genomics Consortium SCZ risk genes were extracted from the Allen Human Brain Atlas, and their average profile across the cortex was correlated to the pattern of cortical disconnectivity as derived from diffusion-weighted magnetic resonance imaging data of patients with SCZ (n = 48) and matched healthy controls (n = 43).

RESULTS

The expression profile of SCZ risk genes across cortical regions was significantly correlated with the regional macroscale disconnectivity (r = .588; p = .017). In addition, effects were found to be potentially specific to SCZ, with transcriptional profiles not related to cortical disconnectivity in patients with bipolar I disorder (diffusion-weighted magnetic resonance imaging data; 216 patients, 144 controls). Further examination of correlations across all 20,737 genes present in the Allen Human Brain Atlas showed the set of top 100 strongest correlating genes to display significant enrichment for the disorder, potentially identifying new genes involved in the pathophysiology of SCZ.

CONCLUSIONS

Our results suggest that under disease conditions, cortical areas with pronounced expression of risk genes implicated in SCZ form central areas for white matter disconnectivity.

SZDB: A Database for Schizophrenia Genetic Research

Schizophrenia (SZ) is a debilitating brain disorder with a complex genetic architecture. Genetic studies, especially recent genome-wide association studies (GWAS), have identified multiple variants (loci) conferring risk to SZ. However, how to efficiently extract meaningful biological information from bulk genetic findings of SZ remains a major challenge. There is a pressing need to integrate multiple layers of data from various sources, eg, genetic findings from GWAS, copy number variations (CNVs), association and linkage studies, gene expression, protein-protein interaction (PPI), co-expression, expression quantitative trait loci (eQTL), and Encyclopedia of DNA Elements (ENCODE) data, to provide a comprehensive resource to facilitate the translation of genetic findings into SZ molecular diagnosis and mechanism study. Here we developed the SZDB database (http://www.szdb.org/), a comprehensive resource for SZ research. SZ genetic data, gene expression data, network-based data, brain eQTL data, and SNP function annotation information were systematically extracted, curated and deposited in SZDB. In-depth analyses and systematic integration were performed to identify top prioritized SZ genes and enriched pathways. Multiple types of data from various layers of SZ research were systematically integrated and deposited in SZDB. In-depth data analyses and integration identified top prioritized SZ genes and enriched pathways. We further showed that genes implicated in SZ are highly co-expressed in human brain and proteins encoded by the prioritized SZ risk genes are significantly interacted. The user-friendly SZDB provides high-confidence candidate variants and genes for further functional characterization. More important, SZDB provides convenient online tools for data search and browse, data integration, and customized data analyses.

DNA methylation in schizophrenia in different patient-derived cell types

DNA methylation of gene promoter regions represses transcription and is a mechanism via which environmental risk factors could affect cells during development in individuals at risk for schizophrenia. We investigated DNA methylation in patient-derived cells that might shed light on early development in schizophrenia. Induced pluripotent stem cells may reflect a “ground state” upon which developmental and environmental influences would be minimal. Olfactory neurosphere-derived cells are an adult-derived neuro-ectodermal stem cell modified by developmental and environmental influences. Fibroblasts provide a non-neural control for life-long developmental and environmental influences. Genome-wide profiling of DNA methylation and gene expression was done in these three cell types from the same individuals. All cell types had distinct, statistically significant schizophrenia-associated differences in DNA methylation and linked gene expression, with Gene Ontology analysis showing that the differentially affected genes clustered in networks associated with cell growth, proliferation, and movement, functions known to be affected in schizophrenia patient-derived cells. Only five gene loci were differentially methylated in all three cell types. Understanding the role of epigenetics in cell function in the brain in schizophrenia is likely to be complicated by similar cell type differences in intrinsic and environmentally induced epigenetic regulation.

Schizophrenia-associated differences in the DNA methylation status of patient-derived cells suggest it could affect early brain development. Mechanisms that control gene expression without altering the genetic code, such as DNA methylation, could explain how environmental risk factors contribute to schizophrenia in genetically susceptible individuals. Alan Mackay-Sim and colleagues from Griffith University, Australia, carried out genome-wide comparisons of DNA methylation in induced pluripotent stem (iPS) cells, olfactory neurosphere-derived cells and fibroblasts from patients and controls. Differences in the DNA methylation pattern between patient and control iPS cells, which could reflect what happens in the embryo, suggest a disease-associated effect very early on in development. Only five genes were differentially methylated in all three patient-derived cell types compared to controls. None of these genes has previously been associated with schizophrenia and may represent new targets for future research.

ENFORCING CO-EXPRESSION IN MULTIMODAL REGRESSION FRAMEWORK

We consider the problem of multimodal data integration for the study of complex neurological diseases (e.g. schizophrenia). Among the challenges arising in such situation, estimating the link between genetic and neurological variability within a population sample has been a promising direction. A wide variety of statistical models arose from such applications. For example, Lasso regression and its multitask extension are often used to fit a multivariate linear relationship between given phenotype(s) and associated observations. Other approaches, such as canonical correlation analysis (CCA), are widely used to extract relationships between sets of variables from different modalities. In this paper, we propose an exploratory multivariate method combining these two methods. More Specifically, we rely on a 'CCA-type' formulation in order to regularize the classical multimodal Lasso regression problem. The underlying motivation is to extract discriminative variables that display are also co-expressed across modalities. We first evaluate the method on a simulated dataset, and further validate it using Single Nucleotide Polymorphisms (SNP) and functional Magnetic Resonance Imaging (fMRI) data for the study of schizophrenia.

Integrated Post-GWAS Analysis Sheds New Light on the Disease Mechanisms of Schizophrenia

Schizophrenia is a severe mental disorder with a large genetic component. Recent genome-wide association studies (GWAS) have identified many schizophrenia-associated common variants. For most of the reported associations, however, the underlying biological mechanisms are not clear. The critical first step for their elucidation is to identify the most likely disease genes as the source of the association signals. Here, we describe a general computational framework of post-GWAS analysis for complex disease gene prioritization. We identify 132 putative schizophrenia risk genes in 76 risk regions spanning 120 schizophrenia-associated common variants, 78 of which have not been recognized as schizophrenia disease genes by previous GWAS. Even more significantly, 29 of them are outside the risk regions, likely under regulation of transcriptional regulatory elements contained therein. These putative schizophrenia risk genes are transcriptionally active in both brain and the immune system, and highly enriched among cellular pathways, consistent with leading pathophysiological hypotheses about the pathogenesis of schizophrenia. With their involvement in distinct biological processes, these putative schizophrenia risk genes, with different association strengths, show distinctive temporal expression patterns, and play specific biological roles during brain development.

SZGR 2.0: a one-stop shop of schizophrenia candidate genes

SZGR 2.0 is a comprehensive resource of candidate variants and genes for schizophrenia, covering genetic, epigenetic, transcriptomic, translational and many other types of evidence. By systematic review and curation of multiple lines of evidence, we included almost all variants and genes that have ever been reported to be associated with schizophrenia. In particular, we collected ∼4200 common variants reported in genome-wide association studies, ∼1000 de novo mutations discovered by large-scale sequencing of family samples, 215 genes spanning rare and replication copy number variations, 99 genes overlapping with linkage regions, 240 differentially expressed genes, 4651 differentially methylated genes and 49 genes as antipsychotic drug targets. To facilitate interpretation, we included various functional annotation data, especially brain eQTL, methylation QTL, brain expression featured in deep categorization of brain areas and developmental stages and brain-specific promoter and enhancer annotations. Furthermore, we conducted cross-study, cross-data type and integrative analyses of the multidimensional data deposited in SZGR 2.0, and made the data and results available through a user-friendly interface. In summary, SZGR 2.0 provides a one-stop shop of schizophrenia variants and genes and their function and regulation, providing an important resource in the schizophrenia and other mental disease community. SZGR 2.0 is available at https://bioinfo.uth.edu/SZGR/.

Cognitive endophenotypes inform genome-wide expression profiling in schizophrenia

OBJECTIVE

We performed a whole-genome expression study to clarify the nature of the biological processes mediating between inherited genetic variations and cognitive dysfunction in schizophrenia.

METHOD

Gene expression was assayed from peripheral blood mononuclear cells using Illumina Human WG6 v3.0 chips in twins discordant for schizophrenia or bipolar disorder and control twins. After quality control, expression levels of 18,559 genes were screened for association with the California Verbal Learning Test (CVLT) performance, and any memory-related probes were then evaluated for variation by diagnostic status in the discovery sample (N = 190), and in an independent replication sample (N = 73). Heritability of gene expression using the twin design was also assessed.

RESULTS

After Bonferroni correction (p < 2.69 × 10-6), CVLT performance was significantly related to expression levels for 76 genes, 43 of which were differentially expressed in schizophrenia patients, with comparable effect sizes in the same direction in the replication sample. For 41 of these 43 transcripts, expression levels were heritable. Nearly all identified genes contain common or de novo mutations associated with schizophrenia in prior studies.

CONCLUSION

Genes increasing risk for schizophrenia appear to do so in part via effects on signaling cascades influencing memory. The genes implicated in these processes are enriched for those related to RNA processing and DNA replication and include genes influencing G-protein coupled signal transduction, cytokine signaling, and oligodendrocyte function.

The Unique Evolutionary Signature of Genes Associated with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a common heritable neurodevelopmental disorder, which is characterized by communication and social deficits that reduce the reproductive fitness of individuals with the disorder. Here, we studied the genomic characteristics of 651 ASD genes in a whole-exome sequencing dataset, to search for traces of the evolutionary forces that helped maintain ASD in the human population. We show that ASD genes are ~65 longer and ~20 % less variable than non-ASD genes. The mutational shortage in ASD genes was particularly eminent when considering only deleterious genetic variations, which is a hallmark of negative selection. We further show that these genomic characteristics are unique to ASD genes, as compared with brain-specific genes or with genes of other diseases. Our findings suggest that ASD genes have evolved under complex evolutionary forces, which have left a unique signature that can be used to identify new candidate ASD genes.

Integrative genomic testing of cancer survival using semiparametric linear transformation models

The wide availability of multi-dimensional genomic data has spurred increasing interests in integrating multi-platform genomic data. Integrative analysis of cancer genome landscape can potentially lead to deeper understanding of the biological process of cancer. We integrate epigenetics (DNA methylation and microRNA expression) and gene expression data in tumor genome to delineate the association between different aspects of the biological processes and brain tumor survival. To model the association, we employ a flexible semiparametric linear transformation model that incorporates both the main effects of these genomic measures as well as the possible interactions among them. We develop variance component tests to examine different coordinated effects by testing various subsets of model coefficients for the genomic markers. A Monte Carlo perturbation procedure is constructed to approximate the null distribution of the proposed test statistics. We further propose omnibus testing procedures to synthesize information from fitting various parsimonious sub-models to improve power. Simulation results suggest that our proposed testing procedures maintain proper size under the null and outperform standard score tests. We further illustrate the utility of our procedure in two genomic analyses for survival of glioblastoma multiforme patients. Copyright © 2016 John Wiley & Sons, Ltd.

Association study of MiRSNPs with schizophrenia, tardive dyskinesia and cognition

MicroRNAs (miRNAs) bind to 3'UTRs of genes and negatively regulate their expression. With ~50% of miRNAs expressing in the brain, they play an important role in neuronal development, plasticity, cognition and neurological disorders. Conserved miRNA targets are present in >60% genes in humans and are under evolutionary pressure to maintain pairing with miRNA. However, such binding may be affected by genetic variant(s) in the target sites (MiRSNPs), thereby altering gene expression. Differential expression of a large number of genes in postmortem brains of schizophrenia (SZ) patients compared to controls has been documented. Thus studying the role of MiRSNPs which are underinvestigated in SZ becomes attractive. We systematically selected 35 MiRSNPs with predicted functional relevance in 3'UTRs of genes shown previously to be associated with SZ, genotyped and tested their association with disease, using independent discovery and replication samples (total n=1017 cases; n=1073 controls). We also explored genetic associations with two sets of quantitative traits, namely tardive dyskinesia (TD) and cognitive functions disrupted in SZ in subsets of the study cohort. In the primary analysis, a significant association of MiRSNP rs7430 at PPP3CC was observed with SZ in the discovery and the replication samples [discovery: P=0.01; OR (95% CI) 1.24 (1.04-1.48); replication: P=0.03; OR (95% CI) 1.20 (1.02-1.43)]. In the exploratory analyses, five SNPs were nominally associated with TD (P values 0.04-0.004). Separately, 12 SNPs were associated with one or more of the eight cognitive domains (P values 0.05-0.003). These associations, particularly the SNP at PPP3CC merit further investigations.

Opposing actions of the synapse-associated protein of 97-kDa molecular weight (SAP97) and Disrupted in Schizophrenia 1 (DISC1) on Wnt/β-catenin signaling

It has been suggested that synapse-associated protein of 97-kDa molecular weight (SAP97) is a susceptibility factor for childhood and adult neuropsychiatric disorders. SAP97 is a scaffolding protein that shares direct and indirect binding partners with the Disrupted in Schizophrenia 1 (DISC1) gene product, a gene with strong association with neuropsychiatric disorders. Here we investigated the possibility that these two proteins converge upon a common molecular pathway. Since DISC1 modifies Wnt/β-catenin signaling via changes in glycogen synthase kinase 3 beta (GSK3β) phosphorylation, we asked if SAP97 impacts Wnt/β-catenin signaling and GSK3β phosphorylation. We find that SAP97 acts as inhibitor of Wnt signaling activity and can suppress the stimulatory effects of DISC1 on β-catenin transcriptional activity. Reductions in SAP97 abundance also decrease GSK3β phosphorylation. In addition, we find that over expression of DISC1 leads to an increase in the abundance of SAP97, by inhibiting its proteasomal degradation. Our findings suggest that SAP97 and DISC1 contribute to maintaining Wnt/β-catenin signaling activity within a homeostatic range by regulating GSK3β phosphorylation.

A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe

Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies.

Lynx: a knowledge base and an analytical workbench for integrative medicine

Lynx (http://lynx.ci.uchicago.edu) is a web-based database and a knowledge extraction engine. It supports annotation and analysis of high-throughput experimental data and generation of weighted hypotheses regarding genes and molecular mechanisms contributing to human phenotypes or conditions of interest. Since the last release, the Lynx knowledge base (LynxKB) has been periodically updated with the latest versions of the existing databases and supplemented with additional information from public databases. These additions have enriched the data annotations provided by Lynx and improved the performance of Lynx analytical tools. Moreover, the Lynx analytical workbench has been supplemented with new tools for reconstruction of co-expression networks and feature-and-network-based prioritization of genetic factors and molecular mechanisms. These developments facilitate the extraction of meaningful knowledge from experimental data and LynxKB. The Service Oriented Architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.

NERI: network-medicine based integrative approach for disease gene prioritization by relative importance

Background

Complex diseases are characterized as being polygenic and multifactorial, so this poses a challenge regarding the search for genes related to them. With the advent of high-throughput technologies for genome sequencing, gene expression measurements (transcriptome), and protein-protein interactions, complex diseases have been sistematically investigated. Particularly, Protein-Protein Interaction (PPI) networks have been used to prioritize genes related to complex diseases according to its topological features. However, PPI networks are affected by ascertainment bias, in which more studied proteins tend to have more connections, degrading the results quality. Additionally, methods using only PPI networks can provide only static and non-specific results, since the topologies of these networks are not specific of a given disease.

Results

The goal of this work is to develop a methodology that integrates PPI networks with disease specific data sources, such as GWAS and gene expression, to find genes more specific of a given complex disease. After the integration of PPI networks and gene expression data, the resulting network is used to connect genes related to the disease through the shortest paths that have the greatest concordance between their gene expressions. Both case and control expression data are used separately and, at the end, the most altered genes between the two conditions are selected. To evaluate the method, schizophrenia was adopted as case study.

Conclusion

Results show that the proposed method successfully retrieves differentially coexpressed genes in two conditions, while avoiding the bias from literature. Moreover we were able to achieve a greater concordance in the selection of important genes from different microarray studies of the same disease and to produce a more specific gene set related to the studied disease.

Mediation analysis for survival data using semiparametric probit models

Causal mediation modeling has become a popular approach for studying the effect of an exposure on an outcome through mediators. Currently, the literature on mediation analyses with survival outcomes largely focused on settings with a single mediator and quantified the mediation effects on the hazard, log hazard and log survival time (Lange and Hansen 2011; VanderWeele 2011). In this article, we propose a multi-mediator model for survival data by employing a flexible semiparametric probit model. We characterize path-specific effects (PSEs) of the exposure on the outcome mediated through specific mediators. We derive closed form expressions for PSEs on a transformed survival time and the survival probabilities. Statistical inference on the PSEs is developed using a nonparametric maximum likelihood estimator under the semiparametric probit model and the functional Delta method. Results from simulation studies suggest that our proposed methods perform well in finite sample. We illustrate the utility of our method in a genomic study of glioblastoma multiforme survival.

Common variants in the MKL1 gene confer risk of schizophrenia

Genome-wide association studies (GWAS) of schizophrenia have identified multiple risk variants with robust association signals for schizophrenia. However, these variants could explain only a small proportion of schizophrenia heritability. Furthermore, the effect size of these risk variants is relatively small (eg, most of them had an OR less than 1.2), suggesting that additional risk variants may be detected when increasing sample size in analysis. Here, we report the identification of a genome-wide significant schizophrenia risk locus at 22q13.1 by combining 2 large-scale schizophrenia cohort studies. Our meta-analysis revealed that 7 single nucleotide polymorphism (SNPs) on chromosome 22q13.1 reached the genome-wide significance level (P < 5.0×10(-8)) in the combined samples (a total of 38441 individuals). Among them, SNP rs6001946 had the most significant association with schizophrenia (P = 2.04×10(-8)). Interestingly, all 7 SNPs are in high linkage disequilibrium and located in the MKL1 gene. Expression analysis showed that MKL1 is highly expressed in human and mouse brains. We further investigated functional links between MKL1 and proteins encoded by other schizophrenia susceptibility genes in the whole human protein interaction network. We found that MKL1 physically interacts with GSK3B, a protein encoded by a well-characterized schizophrenia susceptibility gene. Collectively, our results revealed that genetic variants in MKL1 might confer risk to schizophrenia. Further investigation of the roles of MKL1 in the pathogenesis of schizophrenia is warranted.

LPA signaling initiates schizophrenia-like brain and behavioral changes in a mouse model of prenatal brain hemorrhage

Genetic, environmental and neurodevelopmental factors are thought to underlie the onset of neuropsychiatric disorders such as schizophrenia. How these risk factors collectively contribute to pathology is unclear. Here, we present a mouse model of prenatal intracerebral hemorrhage--an identified risk factor for schizophrenia--using a serum-exposure paradigm. This model exhibits behavioral, neurochemical and schizophrenia-related gene expression alterations in adult females. Behavioral alterations in amphetamine-induced locomotion, prepulse inhibition, thigmotaxis and social interaction--in addition to increases in tyrosine hydroxylase-positive dopaminergic cells in the substantia nigra and ventral tegmental area and decreases in parvalbumin-positive cells in the prefrontal cortex--were induced upon prenatal serum exposure. Lysophosphatidic acid (LPA), a lipid component of serum, was identified as a key molecular initiator of schizophrenia-like sequelae induced by serum. Prenatal exposure to LPA alone phenocopied many of the schizophrenia-like alterations seen in the serum model, whereas pretreatment with an antagonist against the LPA receptor subtype LPA1 prevented many of the behavioral and neurochemical alterations. In addition, both prenatal serum and LPA exposure altered the expression of many genes and pathways related to schizophrenia, including the expression of Grin2b, Slc17a7 and Grid1. These findings demonstrate that aberrant LPA receptor signaling associated with fetal brain hemorrhage may contribute to the development of some neuropsychiatric disorders.

MIR137 variants identified in psychiatric patients affect synaptogenesis and neuronal transmission gene sets

Sequence analysis of 13 microRNA (miRNA) genes expressed in the human brain and located in genomic regions associated with schizophrenia and/or bipolar disorder, in a northern Swedish patient/control population, resulted in the discovery of two functional variants in the MIR137 gene. On the basis of their location and the allele frequency differences between patients and controls, we explored the hypothesis that the discovered variants impact the expression of the mature miRNA and consequently influence global mRNA expression affecting normal brain functioning. Using neuronal-like SH-SY5Y cells, we demonstrated significantly reduced mature miR-137 levels in the cells expressing the variant miRNA gene. Subsequent transcriptome analysis showed that the reduction in miR-137 expression led to the deregulation of gene sets involved in synaptogenesis and neuronal transmission, all implicated in psychiatric disorders. Our functional findings add to the growing data, which implicate that miR-137 has an important role in the etiology of psychiatric disorders and emphasizes its involvement in nervous system development and proper synaptic function.

EW_dmGWAS: edge-weighted dense module search for genome-wide association studies and gene expression profiles

We previously developed dmGWAS to search for dense modules in a human protein-protein interaction (PPI) network; it has since become a popular tool for network-assisted analysis of genome-wide association studies (GWAS). dmGWAS weights nodes by using GWAS signals. Here, we introduce an upgraded algorithm, EW_dmGWAS, to boost GWAS signals in a node- and edge-weighted PPI network. In EW_dmGWAS, we utilize condition-specific gene expression profiles for edge weights. Specifically, differential gene co-expression is used to infer the edge weights. We applied EW_dmGWAS to two diseases and compared it with other relevant methods. The results suggest that EW_dmGWAS is more powerful in detecting disease-associated signals.

Reelin influences the expression and function of dopamine D2 and serotonin 5-HT2A receptors: a comparative study

Reelin is an extracellular matrix protein that plays a critical role in neuronal guidance during brain neurodevelopment and in synaptic plasticity in adults and has been associated with schizophrenia. Reelin mRNA and protein levels are reduced in various structures of post-mortem schizophrenic brains, in a similar way to those found in heterozygous reeler mice (HRM). Reelin is involved in protein expression in dendritic spines that are the major location where synaptic connections are established. Thus, we hypothesized that a genetic deficit in reelin would affect the expression and function of dopamine D2 and serotonin 5-HT2A receptors that are associated with the action of current antipsychotic drugs. In this study, D2 and 5-HT2A receptor expression and function were quantitated by using radioligand binding studies in the frontal cortex and striatum of HRM and wild-type mice (WTM). We observed increased expression (p<0.05) in striatum membranes and decreased expression (p<0.05) in frontal cortex membranes for both dopamine D2 and serotonin 5-HT2A receptors from HRM compared to WTM. Our results show parallel alterations of D2 and 5-HT2A receptors that are compatible with a possible hetero-oligomeric nature of these receptors. These changes are similar to changes described in schizophrenic patients and provide further support for the suitability of using HRM as a model for studying this disease and the effects of antipsychotic drugs.

Laminar and dorsoventral molecular organization of the medial entorhinal cortex revealed by large-scale anatomical analysis of gene expression

Neural circuits in the medial entorhinal cortex (MEC) encode an animal's position and orientation in space. Within the MEC spatial representations, including grid and directional firing fields, have a laminar and dorsoventral organization that corresponds to a similar topography of neuronal connectivity and cellular properties. Yet, in part due to the challenges of integrating anatomical data at the resolution of cortical layers and borders, we know little about the molecular components underlying this organization. To address this we develop a new computational pipeline for high-throughput analysis and comparison of in situ hybridization (ISH) images at laminar resolution. We apply this pipeline to ISH data for over 16,000 genes in the Allen Brain Atlas and validate our analysis with RNA sequencing of MEC tissue from adult mice. We find that differential gene expression delineates the borders of the MEC with neighboring brain structures and reveals its laminar and dorsoventral organization. We propose a new molecular basis for distinguishing the deep layers of the MEC and show that their similarity to corresponding layers of neocortex is greater than that of superficial layers. Our analysis identifies ion channel-, cell adhesion- and synapse-related genes as candidates for functional differentiation of MEC layers and for encoding of spatial information at different scales along the dorsoventral axis of the MEC. We also reveal laminar organization of genes related to disease pathology and suggest that a high metabolic demand predisposes layer II to neurodegenerative pathology. In principle, our computational pipeline can be applied to high-throughput analysis of many forms of neuroanatomical data. Our results support the hypothesis that differences in gene expression contribute to functional specialization of superficial layers of the MEC and dorsoventral organization of the scale of spatial representations.

Schizophrenia and Depression Co-Morbidity: What We have Learned from Animal Models

Patients with schizophrenia are at an increased risk for the development of depression. Overlap in the symptoms and genetic risk factors between the two disorders suggests a common etiological mechanism may underlie the presentation of comorbid depression in schizophrenia. Understanding these shared mechanisms will be important in informing the development of new treatments. Rodent models are powerful tools for understanding gene function as it relates to behavior. Examining rodent models relevant to both schizophrenia and depression reveals a number of common mechanisms. Current models which demonstrate endophenotypes of both schizophrenia and depression are reviewed here, including models of CUB and SUSHI multiple domains 1, PDZ and LIM domain 5, glutamate Delta 1 receptor, diabetic db/db mice, neuropeptide Y, disrupted in schizophrenia 1, and its interacting partners, reelin, maternal immune activation, and social isolation. Neurotransmission, brain connectivity, the immune system, the environment, and metabolism emerge as potential common mechanisms linking these models and potentially explaining comorbid depression in schizophrenia.

Integrated systems analysis reveals a molecular network underlying autism spectrum disorders

Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology.

Systematic prioritization and integrative analysis of copy number variations in schizophrenia reveal key schizophrenia susceptibility genes

Schizophrenia is a common mental disorder with high heritability and strong genetic heterogeneity. Common disease-common variants hypothesis predicts that schizophrenia is attributable in part to common genetic variants. However, recent studies have clearly demonstrated that copy number variations (CNVs) also play pivotal roles in schizophrenia susceptibility and explain a proportion of missing heritability. Though numerous CNVs have been identified, many of the regions affected by CNVs show poor overlapping among different studies, and it is not known whether the genes disrupted by CNVs contribute to the risk of schizophrenia. By using cumulative scoring, we systematically prioritized the genes affected by CNVs in schizophrenia. We identified 8 top genes that are frequently disrupted by CNVs, including NRXN1, CHRNA7, BCL9, CYFIP1, GJA8, NDE1, SNAP29, and GJA5. Integration of genes affected by CNVs with known schizophrenia susceptibility genes (from previous genetic linkage and association studies) reveals that many genes disrupted by CNVs are also associated with schizophrenia. Further protein-protein interaction (PPI) analysis indicates that protein products of genes affected by CNVs frequently interact with known schizophrenia-associated proteins. Finally, systematic integration of CNVs prioritization data with genetic association and PPI data identifies key schizophrenia candidate genes. Our results provide a global overview of genes impacted by CNVs in schizophrenia and reveal a densely interconnected molecular network of de novo CNVs in schizophrenia. Though the prioritized top genes represent promising schizophrenia risk genes, further work with different prioritization methods and independent samples is needed to confirm these findings. Nevertheless, the identified key candidate genes may have important roles in the pathogenesis of schizophrenia, and further functional characterization of these genes may provide pivotal targets for future therapeutics and diagnostics.

EpilepsyGene: a genetic resource for genes and mutations related to epilepsy

Epilepsy is one of the most prevalent chronic neurological disorders, afflicting about 3.5–6.5 per 1000 children and 10.8 per 1000 elderly people. With intensive effort made during the last two decades, numerous genes and mutations have been published to be associated with the disease. An organized resource integrating and annotating the ever-increasing genetic data will be imperative to acquire a global view of the cutting-edge in epilepsy research. Herein, we developed EpilepsyGene (http://61.152.91.49/EpilepsyGene). It contains cumulative to date 499 genes and 3931 variants associated with 331 clinical phenotypes collected from 818 publications. Furthermore, in-depth data mining was performed to gain insights into the understanding of the data, including functional annotation, gene prioritization, functional analysis of prioritized genes and overlap analysis focusing on the comorbidity. An intuitive web interface to search and browse the diversified genetic data was also developed to facilitate access to the data of interest. In general, EpilepsyGene is designed to be a central genetic database to provide the research community substantial convenience to uncover the genetic basis of epilepsy.

Integrative modeling of multi-platform genomic data under the framework of mediation analysis

Given the availability of genomic data, there have been emerging interests in integrating multi-platform data. Here, we propose to model genetics (single nucleotide polymorphism (SNP)), epigenetics (DNA methylation), and gene expression data as a biological process to delineate phenotypic traits under the framework of causal mediation modeling. We propose a regression model for the joint effect of SNPs, methylation, gene expression, and their nonlinear interactions on the outcome and develop a variance component score test for any arbitrary set of regression coefficients. The test statistic under the null follows a mixture of chi-square distributions, which can be approximated using a characteristic function inversion method or a perturbation procedure. We construct tests for candidate models determined by different combinations of SNPs, DNA methylation, gene expression, and interactions and further propose an omnibus test to accommodate different models. We then study three path-specific effects: the direct effect of SNPs on the outcome, the effect mediated through expression, and the effect through methylation. We characterize correspondences between the three path-specific effects and coefficients in the regression model, which are influenced by causal relations among SNPs, DNA methylation, and gene expression. We illustrate the utility of our method in two genomic studies and numerical simulation studies.

Identifying novel interventional strategies for psychiatric disorders: integrating genomics, 'enviromics' and gene-environment interactions in valid preclinical models

Psychiatric disorders affect a substantial proportion of the population worldwide. This high prevalence, combined with the chronicity of the disorders and the major social and economic impacts, creates a significant burden. As a result, an important priority is the development of novel and effective interventional strategies for reducing incidence rates and improving outcomes. This review explores the progress that has been made to date in establishing valid animal models of psychiatric disorders, while beginning to unravel the complex factors that may be contributing to the limitations of current methodological approaches. We propose some approaches for optimizing the validity of animal models and developing effective interventions. We use schizophrenia and autism spectrum disorders as examples of disorders for which development of valid preclinical models, and fully effective therapeutics, have proven particularly challenging. However, the conclusions have relevance to various other psychiatric conditions, including depression, anxiety and bipolar disorders. We address the key aspects of construct, face and predictive validity in animal models, incorporating genetic and environmental factors. Our understanding of psychiatric disorders is accelerating exponentially, revealing extraordinary levels of genetic complexity, heterogeneity and pleiotropy. The environmental factors contributing to individual, and multiple, disorders also exhibit breathtaking complexity, requiring systematic analysis to experimentally explore the environmental mediators and modulators which constitute the 'envirome' of each psychiatric disorder. Ultimately, genetic and environmental factors need to be integrated via animal models incorporating the spatiotemporal complexity of gene-environment interactions and experience-dependent plasticity, thus better recapitulating the dynamic nature of brain development, function and dysfunction.

Lynx web services for annotations and systems analysis of multi-gene disorders

Lynx is a web-based integrated systems biology platform that supports annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Lynx has integrated multiple classes of biomedical data (genomic, proteomic, pathways, phenotypic, toxicogenomic, contextual and others) from various public databases as well as manually curated data from our group and collaborators (LynxKB). Lynx provides tools for gene list enrichment analysis using multiple functional annotations and network-based gene prioritization. Lynx provides access to the integrated database and the analytical tools via REST based Web Services (http://lynx.ci.uchicago.edu/webservices.html). This comprises data retrieval services for specific functional annotations, services to search across the complete LynxKB (powered by Lucene), and services to access the analytical tools built within the Lynx platform.

Evolutionary conservation in genes underlying human psychiatric disorders

Many psychiatric diseases observed in humans have tenuous or absent analogs in other species. Most notable among these are schizophrenia and autism. One hypothesis has posited that these diseases have arisen as a consequence of human brain evolution, for example, that the same processes that led to advances in cognition, language, and executive function also resulted in novel diseases in humans when dysfunctional. Here, the molecular evolution of the protein-coding regions of genes associated with these and other psychiatric disorders are compared among species. Genes associated with psychiatric disorders are drawn from the literature and orthologous sequences are collected from eleven primate species (human, chimpanzee, bonobo, gorilla, orangutan, gibbon, macaque, baboon, marmoset, squirrel monkey, and galago) and 34 non-primate mammalian species. Evolutionary parameters, including d_N/d_S, are calculated for each gene and compared between disease classes and among species, focusing on humans and primates compared to other mammals, and on large-brained taxa (cetaceans, rhinoceros, walrus, bear, and elephant) compared to their small-brained sister species. Evidence of differential selection in humans to the exclusion of non-human primates was absent, however elevated d_N/d_S was detected in catarrhines as a whole, as well as in cetaceans, possibly as part of a more general trend. Although this may suggest that protein changes associated with schizophrenia and autism are not a cost of the higher brain function found in humans, it may also point to insufficiencies in the study of these diseases including incomplete or inaccurate gene association lists and/or a greater role of regulatory changes or copy number variation. Through this work a better understanding of the molecular evolution of the human brain, the pathophysiology of disease, and the genetic basis of human psychiatric disease is gained.

Neurodevelopmental and neuropsychiatric disorders represent an interconnected molecular system

Many putative genetic factors that confer risk to neurodevelopmental disorders such as autism spectrum disorders (ASDs) and X-linked intellectual disability (XLID), and to neuropsychiatric disorders including attention deficit hyperactivity disorder (ADHD) and schizophrenia (SZ) have been identified in individuals from diverse human populations. Although there is significant aetiological heterogeneity within and between these conditions, recent data show that genetic factors contribute to their comorbidity. Many studies have identified candidate gene associations for these mental health disorders, albeit this is often done in a piecemeal fashion with little regard to the inherent molecular complexity. Here, we sought to abstract relationships from our knowledge of systems level biology to help understand the unique and common genetic drivers of these conditions. We undertook a global and systematic approach to build and integrate available data in gene networks associated with ASDs, XLID, ADHD and SZ. Complex network concepts and computational methods were used to investigate whether candidate genes associated with these conditions were related through mechanisms of gene regulation, functional protein-protein interactions, transcription factor (TF) and microRNA (miRNA) binding sites. Although our analyses show that genetic variations associated with the four disorders can occur in the same molecular pathways and functional domains, including synaptic transmission, there are patterns of variation that define significant differences between disorders. Of particular interest is DNA variations located in intergenic regions that comprise regulatory sites for TFs or miRNA. Our approach provides a hypothetical framework, which will help discovery and analysis of candidate genes associated with neurodevelopmental and neuropsychiatric disorders.