Common genetic variants and gene-expression changes associated with bipolar disorder are over-represented in brain signaling pathway genes

In silico analysis of metabolic effects of bipolar disorder on prefrontal cortex identified altered GABA, glutamate-glutamine cycle, energy metabolism and amino acid synthesis pathways

Bipolar disorder (BP) is a lifelong psychiatric condition, which often disrupts the daily life of the patients. It is characterized by unstable and periodic mood changes, which cause patients to display unusual shifts in mood, energy, activity levels, concentration and the ability to carry out day-to-day tasks. BP is a major psychiatric condition, and it is still undertreated. The causes and neural mechanisms of bipolar disorder are unclear, and diagnosis is still mostly based on psychiatric examination, furthermore the unstable character of the disorder makes diagnosis challenging. Identification of the molecular mechanisms underlying the disease may improve the diagnosis and treatment rates. Single nucleotide polymorphisms (SNP) and transcriptome profiles of patients were studied along with signalling pathways that are thought to be associated with bipolar disorder. Here, we present a computational approach that uses publicly available transcriptome data from bipolar disorder patients and healthy controls. Along with statistical analyses, data are integrated with a genome-scale metabolic model and protein-protein interaction network. Healthy individuals and bipolar disorder patients are compared based on their metabolic profiles. We hypothesize that energy metabolism alterations in bipolar disorder relate to perturbations in amino-acid metabolism and neuron-astrocyte exchange reactions. Due to changes in amino acid metabolism, neurotransmitters and their secretion from neurons and metabolic exchange pathways between neurons and astrocytes such as the glutamine-glutamate cycle are also altered. Changes in negatively charged (-1) KIV and KMV molecules are also observed, and it indicates that charge balance in the brain is highly altered in bipolar disorder. Due to this fact, we also hypothesize that positively charged lithium ions may stabilize the disturbed charge balance in neurons in addition to its effects on neurotransmission. To the best of our knowledge, our approach is unique as it is the first study using genome-scale metabolic models in neuropsychiatric research.

Genetic Pathways and Functional Subnetworks for the Complex Nature of Bipolar Disorder in Genome-Wide Association Study

Bipolar disorder is a complex psychiatric trait that is also recognized as a high substantial heritability from a worldwide distribution. The success in identifying susceptibility loci for bipolar disorder (BPD) has been limited due to its complex genetic architecture. Growing evidence from association studies including genome-wide association (GWA) studies points to the need of improved analytic strategies to pinpoint the missing heritability for BPD. More importantly, many studies indicate that BPD has a strong association with dementia. We conducted advanced pathway analytics strategies to investigate synergistic effects of multilocus within biologically functional pathways, and further demonstrated functional effects among proteins in subnetworks to examine mechanisms underlying the complex nature of bipolarity using a GWA dataset for BPD. We allowed bipolar susceptible loci to play a role that takes larger weights in pathway-based analytic approaches. Having significantly informative genes identified from enriched pathways, we further built function-specific subnetworks of protein interactions using MetaCore. The gene-wise scores (i.e., minimum p-value) were corrected for the gene-length, and the results were corrected for multiple tests using Benjamini and Hochberg’s method. We found 87 enriched pathways that are significant for BPD; of which 36 pathways were reported. Most of them are involved with several metabolic processes, neural systems, immune system, molecular transport, cellular communication, and signal transduction. Three significant and function-related subnetworks with multiple hotspots were reported to link with several Gene Ontology processes for BPD. Our comprehensive pathway-network frameworks demonstrated that the use of prior knowledge is promising to facilitate our understanding between complex psychiatric disorders (e.g., BPD) and dementia for the access to the connection and clinical implications, along with the development and progression of dementia.

Unveiling the Pathogenesis of Psychiatric Disorders Using Network Models

Psychiatric disorders are complex brain disorders with a high degree of genetic heterogeneity, affecting millions of people worldwide. Despite advances in psychiatric genetics, the underlying pathogenic mechanisms of psychiatric disorders are still largely elusive, which impedes the development of novel rational therapies. There has been accumulating evidence suggesting that the genetics of complex disorders can be viewed through an omnigenic lens, which involves contextualizing genes in highly interconnected networks. Thus, applying network-based multi-omics integration methods could cast new light on the pathophysiology of psychiatric disorders. In this review, we first provide an overview of the recent advances in psychiatric genetics and highlight gaps in translating molecular associations into mechanistic insights. We then present an overview of network methodologies and review previous applications of network methods in the study of psychiatric disorders. Lastly, we describe the potential of such methodologies within a multi-tissue, multi-omics approach, and summarize the future directions in adopting diverse network approaches.

Social boldness correlates with brain gene expression in male green anoles

Within populations, some individuals tend to exhibit a bold or shy social behavior phenotype relative to the mean. The neural underpinnings of these differing phenotypes - also described as syndromes, personalities, and coping styles - is an area of ongoing investigation. Although a social decision-making network has been described across vertebrate taxa, most studies examining activity within this network do so in relation to exhibited differences in behavioral expression. Our study instead focuses on constitutive gene expression in bold and shy individuals by isolating baseline gene expression profiles that influence social boldness predisposition, rather than those reflecting the results of social interaction and behavioral execution. We performed this study on male green anole lizards (Anolis carolinensis), an established model organism for behavioral research, which provides a crucial comparison group to investigations of birds and mammals. After identifying subjects as bold or shy through repeated reproductive and agonistic behavior testing, we used RNA sequencing to compare gene expression profiles between these groups within various forebrain, midbrain, and hindbrain regions. The ventromedial hypothalamus had the largest group differences in gene expression, with bold males having increased expression of neuroendocrine and neurotransmitter receptor and calcium channel genes compared to shy males. Conversely, shy males express more integrin alpha-10 in the majority of examined regions. There were no significant group differences in physiology or hormone levels. Our results highlight the ventromedial hypothalamus as an important center of behavioral differences across individuals and provide novel candidates for investigations into the regulation of individual variation in social behavior phenotype.

The "missing heritability"-Problem in psychiatry: Is the interaction of genetics, epigenetics and transposable elements a potential solution?

Psychiatric disorders exhibit an enormous burden on the health care systems worldwide accounting for around one-third of years lost due to disability among adults. Their etiology is largely unknown and diagnostic classification is based on symptomatology and course of illness and not on objective biomarkers. Most psychiatric disorders are moderately to highly heritable. However, it is still unknown what mechanisms may explain the discrepancy between heritability estimates and the present data from genetic analysis. In addition to genetic differences also epigenetic modifications are considered as potentially relevant in the transfer of susceptibility to psychiatric diseases. Though, whether or not epigenetic alterations can be inherited for many generations is highly controversial. In the present article, we will critically summarize both the genetic findings and the results from epigenetic analyses, including also those of noncoding RNAs. We will argue that one possible solution to the "missing heritability" problem in psychiatry is a potential role of retrotransposons, the exploration of which is presently only in its beginnings.

Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities

Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for in-depth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years, uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold: (1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data; (2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and (3) discuss remaining challenges and future directions for further method developments.

Weighted Gene Coexpression Network Analysis Reveals Essential Genes and Pathways in Bipolar Disorder

Bipolar disorder (BD) is a major and highly heritable mental illness with severe psychosocial impairment, but its etiology and pathogenesis remains unclear. This study aimed to identify the essential pathways and genes involved in BD using weighted gene coexpression network analysis (WGCNA), a bioinformatic method studying the relationships between genes and phenotypes. Using two available BD gene expression datasets (GSE5388, GSE5389), we constructed a gene coexpression network and identified modules related to BD. The analyses of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways were performed to explore functional enrichment of the candidate modules. A protein-protein interaction (PPI) network was further constructed to identify the potential hub genes. Ten coexpression modules were identified from the top 5,000 genes in 77 samples and three modules were significantly associated with BD, which were involved in several biological processes (e.g., the actin filament-based process) and pathways (e.g., MAPK signaling). Four genes (NOTCH1, POMC, NGF, and DRD2) were identified as candidate hub genes by PPI analysis and CytoHubba. Finally, we carried out validation analyses in a separate dataset, GSE12649, and verified NOTCH1 as a hub gene and the involvement of several biological processes such as actin filament-based process and axon development. Taken together, our findings revealed several candidate pathways and genes (NOTCH1) in the pathogenesis of BD and call for further investigation for their potential research values in BD diagnosis and treatment.

Emerging Methods and Resources for Biological Interrogation of Neuropsychiatric Polygenic Signal

Most neuropsychiatric disorders are highly polygenic, implicating hundreds to thousands of causal genetic variants that span much of the genome. This widespread polygenicity complicates biological understanding because no single variant can explain disease etiology. A strategy to advance biological insight is to seek convergent functions among the large set of variants and map them to a smaller set of disease-relevant genes and pathways. Accordingly, functional genomic resources that provide data on intermediate molecular phenotypes, such as gene-expression and methylation status, can be leveraged to functionally annotate variants and map them to genes. Such molecular quantitative trait locus mappings can be integrated with genome-wide association studies to make sense of the polygenic signal that underlies complex disease. Other resources that provide data on the 3-dimensional structure of chromatin and functional importance of specific genomic regions can be integrated similarly. In addition, mapped genes can then be tested for convergence in biological function, tissue, cell type, or developmental stage. In this review, we provide an overview of functional genomic resources and methods that can be used to interpret results from genome-wide association studies, and we discuss current challenges for biological understanding and future requirements to overcome them.

Acute alcohol withdrawal and recovery in men lead to profound changes in DNA methylation profiles: a longitudinal clinical study

BACKGROUND AND AIMS

Withdrawal is a serious and sometimes life-threatening event in alcohol-dependent individuals. It has been suggested that epigenetic processes may play a role in this context. This study aimed to identify genes and pathways involved in such processes which hint to relevant mechanisms underlying withdrawal.

DESIGN

Cross-sectional case-control study and longitudinal within-cases study during alcohol withdrawal and after 2 weeks of recovery SETTING: Addiction medicine departments in two university hospitals in southern Germany.

PARTICIPANTS/CASES

Ninety-nine alcohol-dependent male patients receiving in-patient treatment and suffering from severe withdrawal symptoms during detoxification and 95 age-matched male controls.

MEASUREMENTS

Epigenome-wide methylation patterns were analyzed in patients during acute alcohol withdrawal and after 2 weeks of recovery, as well as in age-matched controls using Illumina EPIC bead chips. Methylation levels of patients and controls were tested for association with withdrawal status. Tests were adjusted for technical and batch effects, age, smoking and cell type distribution. Single-site analysis, as well as an analysis of differentially methylated regions and gene ontology analysis, were performed.

FINDINGS

We found pronounced epigenome-wide significant [false discovery rate (FDR) < 0.05] differences between patients during withdrawal and after 2 weeks [2876 cytosine-phosphate-guanine (CpG) sites], as well as between patients and controls (9845 and 6094 CpG sites comparing patients at time-point 1 and patients at time-point 2 versus controls, respectively). Analysis of differentially methylated regions and involved pathways revealed an over-representation of gene ontology terms related to the immune system response. Differences between patients and controls diminished after recovery (> 800 CpG sites less), suggesting a partial reversibility of alcohol- and withdrawal-related methylation.

CONCLUSIONS

Acute alcohol withdrawal in severely dependent male patients appears to be associated with extensive changes in epigenome-wide methylation patterns. In particular, genes involved in immune system response seem to be affected by this condition.

Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges

Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.

SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse

Synapses are fundamental information-processing units of the brain, and synaptic dysregulation is central to many brain disorders ("synaptopathies"). However, systematic annotation of synaptic genes and ontology of synaptic processes are currently lacking. We established SynGO, an interactive knowledge base that accumulates available research about synapse biology using Gene Ontology (GO) annotations to novel ontology terms: 87 synaptic locations and 179 synaptic processes. SynGO annotations are exclusively based on published, expert-curated evidence. Using 2,922 annotations for 1,112 genes, we show that synaptic genes are exceptionally well conserved and less tolerant to mutations than other genes. Many SynGO terms are significantly overrepresented among gene variations associated with intelligence, educational attainment, ADHD, autism, and bipolar disorder and among de novo variants associated with neurodevelopmental disorders, including schizophrenia. SynGO is a public, universal reference for synapse research and an online analysis platform for interpretation of large-scale -omics data (https://syngoportal.org and http://geneontology.org).

Simultaneous Enrichment Analysis of all Possible Gene-sets: Unifying Self-Contained and Competitive Methods

Studying sets of genomic features is increasingly popular in genomics, proteomics and metabolomics since analyzing at set level not only creates a natural connection to biological knowledge but also offers more statistical power. Currently, there are two gene-set testing approaches, self-contained and competitive, both of which have their advantages and disadvantages, but neither offers the final solution. We introduce simultaneous enrichment analysis (SEA), a new approach for analysis of feature sets in genomics and other omics based on a new unified null hypothesis, which includes the self-contained and competitive null hypotheses as special cases. We employ closed testing using Simes tests to test this new hypothesis. For every feature set, the proportion of active features is estimated, and a confidence bound is provided. Also, for every unified null hypotheses, a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$P$\end{document}-value is calculated, which is adjusted for family-wise error rate. SEA does not need to assume that the features are independent. Moreover, users are allowed to choose the feature set(s) of interest after observing the data. We develop a novel pipeline and apply it on RNA-seq data of dystrophin-deficient mdx mice, showcasing the flexibility of the method. Finally, the power properties of the method are evaluated through simulation studies.

Beyond genome-wide significance: integrative approaches to the interpretation and extension of GWAS findings for alcohol use disorder

Alcohol use disorder (AUD) is a heritable complex behavior. Due to the highly polygenic nature of AUD, identifying genetic variants that comprise this heritable variation has proved to be challenging. With the exception of functional variants in alcohol metabolizing genes (e.g. ADH1B and ALDH2), few other candidate loci have been confidently linked to AUD. Genome-wide association studies (GWAS) of AUD and other alcohol-related phenotypes have either produced few hits with genome-wide significance or have failed to replicate on further study. These issues reinforce the complex nature of the genetic underpinnings for AUD and suggest that both GWAS studies with larger samples and additional analysis approaches that better harness the nominally significant loci in existing GWAS are needed. Here, we review approaches of interest in the post-GWAS era, including in silico functional analyses; functional partitioning of single nucleotide polymorphism heritability; aggregation of signal into genes and gene networks; and validation of identified loci, genes and gene networks in postmortem brain tissue and across species. These integrative approaches hold promise to illuminate our understanding of the biological basis of AUD; however, we recognize that the main challenge continues to be the extremely polygenic nature of AUD, which necessitates large samples to identify multiple loci associated with AUD liability.

Regional enrichment analyses on genetic profiles for schizophrenia and bipolar disorder

Both schizophrenia (SZ) and bipolar disorder (BD) are highly heritable psychiatric disorders. The significant genomic risk loci are of great importance but with no guarantee of known functional impact and they cannot totally explain the genetic inheritance. In this study we present regional enrichment analyses across the genome, aiming to strike a balance between individual risk loci and integrated regional effects. Chromosomes were partitioned into 2 million base-pair regions (indicated by an underscore sign in the cytogenetic bands) on which enrichment tests are performed. In the discovery phase, we leverage the Psychiatric Genomics Consortium SZ and BD initial association test results for European Ancestry (EA) population and dbGAP SNP data for African Ancestry (AA) population. 78 and 48 regions show significantly enriched associations with SZ and BD respectively in the EA population, and nine are in common including MHC, 3p21.1, 7p22.3_2, 2q32.3_2, 8q24.3_4, and 19q13.33_1. The most unique SZ associated region is 1p21.3_3, while the most unique BD associated region is 6q25.2_1. For the AA population fewer regions are discovered with only 10% overlapping with that of EA population. A replication test using Wellcome Trust Case Control Consortium data for EA population verified 9% of the SZ enriched regions and 40% of the BD enriched regions. In summary, we showed that regional enrichment analyses produce reliable genetic association profiles using about one tenth of samples compared to single base-pair genome wide association approach. The identified association regions will be useful for further genetic functional studies.

Critical Issues in the Inclusion of Genetic and Epigenetic Information in Prevention and Intervention Trials

Human genetic research in the past decade has generated a wealth of data from the genome-wide association scan era, much of which is catalogued and freely available. These data will typically test the relationship between a single nucleotide variant or polymorphism (SNP) and some outcome, disease, or trait. Ongoing investigations will yield a similar wealth of data regarding epigenetic phenomena. These data will typically test the relationship between DNA methylation at a single genomic location/region and some outcome. Most of these findings will be the result of cross-sectional investigations typically using ascertained cases and controls. Consequently, most methodological consideration focuses on methods appropriate for simple case-control comparisons. It is expected that a growing number of investigators with longitudinal experimental prevention or intervention cohorts will also measure genetic and epigenetic indicators as part of their investigations, harvesting the wealth of information generated by the genome-wide association study (GWAS) era to allow for targeted hypothesis testing in the next generation of prevention and intervention trials. Herein, we discuss appropriate quality control and statistical modelling of genetic, polygenic, and epigenetic measures in longitudinal models. We specifically discuss quality control, population stratification, genotype imputation, pathway approaches, and proper modelling of an interaction between a specific genetic variant and an environment variable (GxE interaction).

Understanding the pathophysiology of postpartum psychosis: Challenges and new approaches

Postpartum psychosis is a severe psychiatric condition which affects 1-2 of every 1000 mothers shortly after childbirth. Whilst there is convincing evidence that the condition is precipitated by a complex combination of biological and environmental factors, as yet the pathophysiological mechanisms remain extremely poorly defined. Here, I critically review approaches that have been, or are being, employed to identify and characterise such mechanisms; I also review a recent animal model approach, and describe a novel biological risk model that it suggests. Clarification of biological risk mechanisms underlying disorder risk should permit the identification of relevant predictive biomarkers which will ensure that “at risk” subjects receive prompt clinical intervention if required.

The statistical properties of gene-set analysis

The rapid increase in loci discovered in genome-wide association studies has created a need to understand the biological implications of these results. Gene-set analysis provides a means of gaining such understanding, but the statistical properties of gene-set analysis are not well understood, which compromises our ability to interpret its results. In this Analysis article, we provide an extensive statistical evaluation of the core structure that is inherent to all gene- set analyses and we examine current implementations in available tools. We show which factors affect valid and successful detection of gene sets and which provide a solid foundation for performing and interpreting gene-set analysis.

Bipolar II disorder as the initial presentation of CADASIL: an underdiagnosed manifestation

Mood disturbances have been documented in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). The highly varied morbidity indicates that the affective symptoms in CADASIL have not been cataloged systematically, leading to ineffective treatment, affecting the patients' quality of life, and possibly resulting in suicide. We present a case of CADASIL with bipolar II disorder as the first manifestation. A middle-aged female reported recurrent depressive episodes and appeared treatment resistant to adequate dosages and durations of antidepressants. Following a structured psychiatric interview and neuropsychological assessment, a past episode of hypomania was identified. Added treatment with sodium valproate alleviated most symptoms. Considering late-onset bipolar disorder with unexplained decline in cognition, a medical history of migraine, and a suspected family history of stroke, further cranial magnetic resonance imaging scan was performed and revealed severe leukoencephalopathy, prompting further investigation. The diagnosis was revised to CADASIL after Arg587Cys NOTCH3 mutation was confirmed. This case highlights the evolving process of affective disorder diagnosis and underlying organic etiologies. Based on the overlap of white matter hyperintensities, NOTCH3 mutation, and valproate therapy in bipolar disorder and CADASIL, bipolar II depression may be a poorly recognized manifestation of CADASIL. Well-designed clinical trials are warranted to verify the current findings.

A pharmacological mouse model suggests a novel risk pathway for postpartum psychosis

Postpartum psychosis (PP) is a severe psychiatric disorder affecting a small proportion of new mothers shortly after childbirth. The molecular pathophysiology underlying the disorder is currently poorly understood, and there are no amenable animal models for the condition; maternal deficiency for the enzyme steroid sulfatase has been proposed as a potential risk mechanism. Here we show that inhibition of steroid sulfatase with 667-COUMATE (10mg/kg p.o.) in new mouse mothers results in behavioural abnormalities that can be partially alleviated by the administration of the clinically-efficacious antipsychotic ziprasidone (0.3-1.0mg/kg i.p.). The pattern of behavioural abnormalities in 667-COUMATE-treated mice implicated a genetic substrate at 21-23cM on chromosome 15; of the 17 genes within this chromosomal interval, only one (Nov/Ccn3) was significantly differentially expressed in the brains of vehicle and 667-COUMATE-treated mice. Two additional members of the Ccn family (Ccn2/Ctgf and Ccn4/Wisp1) were also significantly differentially expressed between the two groups, as were three further genes co-expressed with Nov/Ccn3 in brain (Arhgdig) or previously implicated in disorder risk by clinical studies (Adcy8 and Ccl2). The expression of Nov/Ccn3, but not of the other differentially-expressed genes, could be normalised by ziprasidone administration (1.0mg/kg). NOV/CCN3 lies directly under a linkage peak for PP risk at 8q24, and the associated protein possesses numerous characteristics that make it an excellent candidate mediator of PP risk. Our data suggest the 667-COUMATE-treated mouse as a model for PP with some degree of face, construct, and predictive validity, and implicate a novel, and biologically-plausible, molecular risk pathway for PP.

Gene-set analysis based on the pharmacological profiles of drugs to identify repurposing opportunities in schizophrenia

Genome-wide association studies (GWAS) have identified thousands of novel genetic associations for complex genetic disorders, leading to the identification of potential pharmacological targets for novel drug development. In schizophrenia, 108 conservatively defined loci that meet genome-wide significance have been identified and hundreds of additional sub-threshold associations harbour information on the genetic aetiology of the disorder. In the present study, we used gene-set analysis based on the known binding targets of chemical compounds to identify the 'drug pathways' most strongly associated with schizophrenia-associated genes, with the aim of identifying potential drug repositioning opportunities and clues for novel treatment paradigms, especially in multi-target drug development. We compiled 9389 gene sets (2496 with unique gene content) and interrogated gene-based p-values from the PGC2-SCZ analysis. Although no single drug exceeded experiment wide significance (corrected p<0.05), highly ranked gene-sets reaching suggestive significance including the dopamine receptor antagonists metoclopramide and trifluoperazine and the tyrosine kinase inhibitor neratinib. This is a proof of principle analysis showing the potential utility of GWAS data of schizophrenia for the direct identification of candidate drugs and molecules that show polypharmacy.

The Genetics of Stress-Related Disorders: PTSD, Depression, and Anxiety Disorders

Research into the causes of psychopathology has largely focused on two broad etiologic factors: genetic vulnerability and environmental stressors. An important role for familial/heritable factors in the etiology of a broad range of psychiatric disorders was established well before the modern era of genomic research. This review focuses on the genetic basis of three disorder categories-posttraumatic stress disorder (PTSD), major depressive disorder (MDD), and the anxiety disorders-for which environmental stressors and stress responses are understood to be central to pathogenesis. Each of these disorders aggregates in families and is moderately heritable. More recently, molecular genetic approaches, including genome-wide studies of genetic variation, have been applied to identify specific risk variants. In this review, I summarize evidence for genetic contributions to PTSD, MDD, and the anxiety disorders including genetic epidemiology, the role of common genetic variation, the role of rare and structural variation, and the role of gene-environment interaction. Available data suggest that stress-related disorders are highly complex and polygenic and, despite substantial progress in other areas of psychiatric genetics, few risk loci have been identified for these disorders. Progress in this area will likely require analysis of much larger sample sizes than have been reported to date. The phenotypic complexity and genetic overlap among these disorders present further challenges. The review concludes with a discussion of prospects for clinical translation of genetic findings and future directions for research.

Network-assisted analysis of primary Sjögren's syndrome GWAS data in Han Chinese

Primary Sjögren's syndrome (pSS) is a complex autoimmune disorder. So far, genetic research in pSS has lagged far behind and the underlying biological mechanism is unclear. Further exploring existing genome-wide association study (GWAS) data is urgently expected to uncover disease-related gene combination patterns. Herein, we conducted a network-based analysis by integrating pSS GWAS in Han Chinese with a protein-protein interactions network to identify pSS candidate genes. After module detection and evaluation, 8 dense modules covering 40 genes were obtained for further functional annotation. Additional 31 MHC genes with significant gene-level P-values (sigMHC-gene) were also remained. The combined module genes and sigMHC-genes, a total of 71 genes, were denoted as pSS candidate genes. Of these pSS candidates, 14 genes had been reported to be associated with any of pSS, RA, and SLE, including STAT4, GTF2I, HLA-DPB1, HLA-DRB1, PTTG1, HLA-DQB1, MBL2, TAP2, CFLAR, NFKBIE, HLA-DRA, APOM, HLA-DQA2 and NOTCH4. This is the first report of the network-assisted analysis for pSS GWAS data to explore combined gene patterns associated with pSS. Our study suggests that network-assisted analysis is a useful approach to gaining further insights into the biology of associated genes and providing important clues for future research into pSS etiology.

Identification of shared and unique susceptibility pathways among cancers of the lung, breast, and prostate from genome-wide association studies and tissue-specific protein interactions

Results from genome-wide association studies (GWAS) have indicated that strong single-gene effects are the exception, not the rule, for most diseases. We assessed the joint effects of germline genetic variations through a pathway-based approach that considers the tissue-specific contexts of GWAS findings. From GWAS meta-analyses of lung cancer (12 160 cases/16 838 controls), breast cancer (15 748 cases/18 084 controls) and prostate cancer (14 160 cases/12 724 controls) in individuals of European ancestry, we determined the tissue-specific interaction networks of proteins expressed from genes that are likely to be affected by disease-associated variants. Reactome pathways exhibiting enrichment of proteins from each network were compared across the cancers. Our results show that pathways associated with all three cancers tend to be broad cellular processes required for growth and survival. Significant examples include the nerve growth factor (P = 7.86 × 10(-33)), epidermal growth factor (P = 1.18 × 10(-31)) and fibroblast growth factor (P = 2.47 × 10(-31)) signaling pathways. However, within these shared pathways, the genes that influence risk largely differ by cancer. Pathways found to be unique for a single cancer focus on more specific cellular functions, such as interleukin signaling in lung cancer (P = 1.69 × 10(-15)), apoptosis initiation by Bad in breast cancer (P = 3.14 × 10(-9)) and cellular responses to hypoxia in prostate cancer (P = 2.14 × 10(-9)). We present the largest comparative cross-cancer pathway analysis of GWAS to date. Our approach can also be applied to the study of inherited mechanisms underlying risk across multiple diseases in general.

Neurodevelopmental origins of bipolar disorder: iPSC models

Bipolar disorder (BP) is a chronic neuropsychiatric condition characterized by pathological fluctuations in mood from mania to depression. Adoption, twin and family studies have consistently identified a significant hereditary component to BP, yet there is no clear genetic event or consistent neuropathology. BP has been suggested to have a developmental origin, although this hypothesis has been difficult to test since there are no viable neurons or glial cells to analyze, and research has relied largely on postmortem brain, behavioral and imaging studies, or has examined proxy tissues including saliva, olfactory epithelium and blood cells. Neurodevelopmental factors, particularly pathways related to nervous system development, cell migration, extracellular matrix, H3K4 methylation, and calcium signaling have been identified in large gene expression and GWAS studies as altered in BP. Recent advances in stem cell biology, particularly the ability to reprogram adult somatic tissues to a pluripotent state, now make it possible to interrogate these pathways in viable cell models. A number of induced pluripotent stem cell (iPSC) lines from BP patient and healthy control (C) individuals have been derived in several laboratories, and their ability to form cortical neurons examined. Early studies suggest differences in activity, calcium signaling, blocks to neuronal differentiation, and changes in neuronal, and possibly glial, lineage specification. Initial observations suggest that differentiation of BP patient-derived neurons to dorsal telencephalic derivatives may be impaired, possibly due to alterations in WNT, Hedgehog or Nodal pathway signaling. These investigations strongly support a developmental contribution to BP and identify novel pathways, mechanisms and opportunities for improved treatments.

Gene set analysis: A step-by-step guide

To maximize the potential of genome-wide association studies, many researchers are performing secondary analyses to identify sets of genes jointly associated with the trait of interest. Although methods for gene-set analyses (GSA), also called pathway analyses, have been around for more than a decade, the field is still evolving. There are numerous algorithms available for testing the cumulative effect of multiple SNPs, yet no real consensus in the field about the best way to perform a GSA. This paper provides an overview of the factors that can affect the results of a GSA, the lessons learned from past studies, and suggestions for how to make analysis choices that are most appropriate for different types of data. © 2015 Wiley Periodicals, Inc.

Network-Based Analysis of Schizophrenia Genome-Wide Association Data to Detect the Joint Functional Association Signals

Schizophrenia is a common psychiatric disorder with high heritability and complex genetic architecture. Genome-wide association studies (GWAS) have identified several significant loci associated with schizophrenia. However, the explained heritability is still low. Growing evidence has shown schizophrenia is attributable to multiple genes with moderate effects. In-depth mining and integration of GWAS data is urgently expected to uncover disease-related gene combination patterns. Network-based analysis is a promising strategy to better interpret GWAS to identify disease-related network modules. We performed a network-based analysis on three independent schizophrenia GWASs by using a refined analysis framework, which included a more accurate gene P-value calculation, dynamic network module searching algorithm and detailed functional analysis for the obtained modules genes. The result generated 79 modules including 238 genes, which form a highly connected subnetwork with more statistical significance than expected by chance. The result validated several reported disease genes, such as MAD1L1, MCC, SDCCAG8, VAT1L, MAPK14, MYH9 and FXYD6, and also obtained several novel candidate genes and gene-gene interactions. Pathway enrichment analysis of the module genes suggested they were enriched in several neural and immune system related pathways/GO terms, such as neurotrophin signaling pathway, synaptosome, regulation of protein ubiquitination, and antigen processing and presentation. Further crosstalk analysis revealed these pathways/GO terms were cooperated with each other, and identified several important genes, which might play vital roles to connect these functions. Our network-based analysis of schizophrenia GWASs will facilitate the understanding of genetic mechanisms of schizophrenia.

Abnormal development of monoaminergic neurons is implicated in mood fluctuations and bipolar disorder

Subtle mood fluctuations are normal emotional experiences, whereas drastic mood swings can be a manifestation of bipolar disorder (BPD). Despite their importance for normal and pathological behavior, the mechanisms underlying endogenous mood instability are largely unknown. During embryogenesis, the transcription factor Otx2 orchestrates the genetic networks directing the specification of dopaminergic (DA) and serotonergic (5-HT) neurons. Here we behaviorally phenotyped mouse mutants overexpressing Otx2 in the hindbrain, resulting in an increased number of DA neurons and a decreased number of 5-HT neurons in both developing and mature animals. Over the course of 1 month, control animals exhibited stable locomotor activity in their home cages, whereas mutants showed extended periods of elevated or decreased activity relative to their individual average. Additional behavioral paradigms, testing for manic- and depressive-like behavior, demonstrated that mutants showed an increase in intra-individual fluctuations in locomotor activity, habituation, risk-taking behavioral parameters, social interaction, and hedonic-like behavior. Olanzapine, lithium, and carbamazepine ameliorated the behavioral alterations of the mutants, as did the mixed serotonin receptor agonist quipazine and the specific 5-HT2C receptor agonist CP-809101. Testing the relevance of the genetic networks specifying monoaminergic neurons for BPD in humans, we applied an interval-based enrichment analysis tool for genome-wide association studies. We observed that the genes specifying DA and 5-HT neurons exhibit a significant level of aggregated association with BPD but not with schizophrenia or major depressive disorder. The results of our translational study suggest that aberrant development of monoaminergic neurons leads to mood fluctuations and may be associated with BPD.

Genome-wide association study in obsessive-compulsive disorder: results from the OCGAS

Obsessive-compulsive disorder (OCD) is a psychiatric condition characterized by intrusive thoughts and urges and repetitive, intentional behaviors that cause significant distress and impair functioning. The OCD Collaborative Genetics Association Study (OCGAS) is comprised of comprehensively assessed OCD patients with an early age of OCD onset. After application of a stringent quality control protocol, a total of 1065 families (containing 1406 patients with OCD), combined with population-based samples (resulting in a total sample of 5061 individuals), were studied. An integrative analyses pipeline was utilized, involving association testing at single-nucleotide polymorphism (SNP) and gene levels (via a hybrid approach that allowed for combined analyses of the family- and population-based data). The smallest P-value was observed for a marker on chromosome 9 (near PTPRD, P=4.13 × 10(-)(7)). Pre-synaptic PTPRD promotes the differentiation of glutamatergic synapses and interacts with SLITRK3. Together, both proteins selectively regulate the development of inhibitory GABAergic synapses. Although no SNPs were identified as associated with OCD at genome-wide significance level, follow-up analyses of genome-wide association study (GWAS) signals from a previously published OCD study identified significant enrichment (P=0.0176). Secondary analyses of high-confidence interaction partners of DLGAP1 and GRIK2 (both showing evidence for association in our follow-up and the original GWAS study) revealed a trend of association (P=0.075) for a set of genes such as NEUROD6, SV2A, GRIA4, SLC1A2 and PTPRD. Analyses at the gene level revealed association of IQCK and C16orf88 (both P<1 × 10(-)(6), experiment-wide significant), as well as OFCC1 (P=6.29 × 10(-)(5)). The suggestive findings in this study await replication in larger samples.

Analysis of t(9;17)(q33.2;q25.3) chromosomal breakpoint regions and genetic association reveals novel candidate genes for bipolar disorder

OBJECTIVES

Breakpoints of chromosomal abnormalities facilitate identification of novel candidate genes for psychiatric disorders. Genome-wide significant evidence supports the linkage between chromosome 17q25.3 and bipolar disorder (BD). Co-segregation of translocation t(9;17)(q33.2;q25.3) with psychiatric disorders has been reported. We aimed to narrow down these chromosomal breakpoint regions and to investigate the associations between single nucleotide polymorphisms within these regions and BD as well as schizophrenia (SZ) in large genome-wide association study samples.

METHODS

We cross-linked Danish psychiatric and cytogenetic case registers to identify an individual with both t(9;17)(q33.2;q25.3) and BD. Fluorescent in situ hybridization was employed to map the chromosomal breakpoint regions of this proband. We accessed the Psychiatric Genomics Consortium BD (n = 16,731) and SZ (n = 21,856) data. Genetic associations between these disorders and single nucleotide polymorphisms within these breakpoint regions were analysed by BioQ, FORGE, and RegulomeDB programmes.

RESULTS

Four protein-coding genes [coding for (endonuclease V (ENDOV), neuronal pentraxin I (NPTX1), ring finger protein 213 (RNF213), and regulatory-associated protein of mammalian target of rapamycin (mTOR) (RPTOR)] were found to be located within the 17q25.3 breakpoint region. NPTX1 was significantly associated with BD (p = 0.004), while ENDOV was significantly associated with SZ (p = 0.0075) after Bonferroni correction.

CONCLUSIONS

Prior linkage evidence and our findings suggest NPTX1 as a novel candidate gene for BD.

Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways

Genome-wide association studies (GWAS) of psychiatric disorders have identified multiple genetic associations with such disorders, but better methods are needed to derive the underlying biological mechanisms that these signals indicate. We sought to identify biological pathways in GWAS data from over 60,000 participants from the Psychiatric Genomics Consortium. We developed an analysis framework to rank pathways that requires only summary statistics. We combined this score across disorders to find common pathways across three adult psychiatric disorders: schizophrenia, major depression and bipolar disorder. Histone methylation processes showed the strongest association, and we also found statistically significant evidence for associations with multiple immune and neuronal signaling pathways and with the postsynaptic density. Our study indicates that risk variants for psychiatric disorders aggregate in particular biological pathways and that these pathways are frequently shared between disorders. Our results confirm known mechanisms and suggest several novel insights into the etiology of psychiatric disorders.

Early signs of anomalous neural functional connectivity in healthy offspring of parents with bipolar disorder

OBJECTIVES

Bipolar disorder (BD) has been associated with dysfunctional brain connectivity and with family chaos. It is not known whether aberrant connectivity occurs before illness onset, representing vulnerability for developing BD amidst family chaos. We used resting-state functional magnetic resonance imaging (fMRI) to examine neural network dysfunction in healthy offspring living with parents with BD and healthy comparison youth.

METHODS

Using two complementary methodologies [data-driven independent component analysis (ICA) and hypothesis-driven region-of-interest (ROI)-based intrinsic connectivity], we examined resting-state fMRI data in 8-17-year-old healthy offspring of a parent with BD (n = 24; high risk) and age-matched healthy youth without any personal or family psychopathology (n = 25; low risk).

RESULTS

ICA revealed that, relative to low-risk youth, high-risk youth showed increased connectivity in the ventrolateral prefrontal cortex (VLPFC) subregion of the left executive control network (ECN), which includes frontoparietal regions important for emotion regulation. ROI-based analyses revealed that high-risk versus low-risk youth had decreased connectivities between the left amygdala and pregenual cingulate, between the subgenual cingulate and supplementary motor cortex, and between the left VLPFC and left caudate. High-risk youth showed stronger connections in the VLPFC with age and higher functioning, which may be neuroprotective, and weaker connections between the left VLPFC and caudate with more family chaos, suggesting an environmental influence on frontostriatal connectivity.

CONCLUSIONS

Healthy offspring of parents with BD show atypical patterns of prefrontal and subcortical intrinsic connectivity that may be early markers of resilience to or vulnerability for developing BD. Longitudinal studies are needed to determine whether these patterns predict outcomes.

Investigation of the involvement of MIR185 and its target genes in the development of schizophrenia

BACKGROUND

Schizophrenia is a complex neuropsychiatric disorder of unclear etiology. The strongest known genetic risk factor is the 22q11.2 microdeletion. Research has yet to confirm which genes within the deletion region are implicated in schizophrenia. The minimal 1.5 megabase deletion contains MIR185, which encodes microRNA 185.

METHODS

We determined miR-185 expression in embryonic and adult mouse brains. Common and rare variants at this locus were then investigated using a human genetics approach. First, we performed gene-based analyses for MIR185 common variants and target genes using Psychiatric Genomics Consortium genome-wide association data. Second, MIR185 was resequenced in German patients (n = 1000) and controls (n = 500). We followed up promising variants by genotyping an additional European sample (patients, n = 3598; controls, n = 4082).

RESULTS

In situ hybridization in mice revealed miR-185 expression in brain regions implicated in schizophrenia. Gene-based tests revealed association between common variants in 3 MIR185 target genes (ATAT1, SH3PXD2A, NTRK3) and schizophrenia. Further analyses in mice revealed overlapping expression patterns for these target genes and miR-185. Resequencing identified 2 rare patient-specific novel variants flanking MIR185. However, follow-up genotyping provided no further evidence of their involvement in schizophrenia.

LIMITATIONS

Power to detect rare variant associations was limited.

CONCLUSION

Human genetic analyses generated no evidence of the involvement of MIR185 in schizophrenia. However, the expression patterns of miR-185 and its target genes in mice, and the genetic association results for the 3 target genes, suggest that further research into the involvement of miR-185 and its downstream pathways in schizophrenia is warranted.

Cytokines in bipolar disorder: paving the way for neuroprogression

Bipolar disorder (BD) is a severe, chronic, and recurrent psychiatric illness. It has been associated with high prevalence of medical comorbidities and cognitive impairment. Its neurobiology is not completely understood, but recent evidence has shown a wide range of immune changes. Cytokines are proteins involved in the regulation and the orchestration of the immune response. We performed a review on the involvement of cytokines in BD. We also discuss the cytokines involvement in the neuroprogression of BD. It has been demonstrated that increased expression of cytokines in the central nervous system in postmortem studies is in line with the elevated circulating levels of proinflammatory cytokines in BD patients. The proinflammatory profile and the immune imbalance in BD might be regarded as potential targets to the development of new therapeutic strategies.

Beyond GWAS in COPD: probing the landscape between gene-set associations, genome-wide associations and protein-protein interaction networks

OBJECTIVES

To use a systems biology approach to integrate genotype and protein-protein interaction (PPI) data to identify disease network modules associated with chronic obstructive pulmonary disease (COPD) and to perform traditional pathway analysis.

METHODS

We utilized a standard gene-set association approach (FORGE) using gene-based association analysis and gene-set definitions from the molecular signatures database (MSigDB). As a discovery step, we analyzed GWAS results from 2 well-characterized COPD cohorts: COPDGene and GenKOLS. We used a third well-characterized COPD case-control cohort for replication: ECLIPSE. Next, we used dmGWAS, a method that integrates GWAS results with PPI, to identify COPD disease modules.

RESULTS

No gene-sets reached experiment-wide significance in either discovery population. We identified a consensus network of 10 genes identified in modules by integrating GWAS results with PPI that replicated in COPDGene, GenKOLS, and ECLIPSE. Members of 4 gene-sets were enriched among these 10 genes: (i) lung adenocarcinoma tumor-sequencing genes, (ii) IL-7 pathway genes, (iii) kidney cell response to arsenic, and (iv) CD4 T-cell responses. Further, several genes have also been associated with pathophysiology relevant to COPD including KCNK3, NEDD4L, and RIN3. In particular, KCNK3 has been associated with pulmonary arterial hypertension, a common complication in advanced COPD.

CONCLUSION

We report a set of new genes that may influence the etiology of COPD that would not have been identified using traditional GWAS and pathway analyses alone.

Investigation of manic and euthymic episodes identifies state- and trait-specific gene expression and STAB1 as a new candidate gene for bipolar disorder

Bipolar disorder (BD) is a highly heritable psychiatric disease characterized by recurrent episodes of mania and depression. To identify new BD genes and pathways, the present study employed a three-step approach. First, gene-expression profiles of BD patients were assessed during both a manic and an euthymic phase. These profiles were compared intra-individually and with the gene-expression profiles of controls. Second, those differentially expressed genes that were considered potential trait markers of BD were validated using data from the Psychiatric Genomics Consortiums' genome-wide association study (GWAS) of BD. Third, the implicated molecular mechanisms were investigated using pathway analytical methods. In the present patients, this novel approach identified: (i) sets of differentially expressed genes specific to mania and euthymia; and (ii) a set of differentially expressed genes that were common to both mood states. In the GWAS data integration analysis, one gene (STAB1) remained significant (P=1.9 × 10(-4)) after adjustment for multiple testing. STAB1 is located in close proximity to PBMR1 and the NEK4-ITIH1-ITIH3-ITIH4 region, which are the top findings from GWAS meta-analyses of mood disorder, and a combined BD and schizophrenia data set. Pathway analyses in the mania versus control comparison revealed three distinct clusters of pathways tagging molecular mechanisms implicated in BD, for example, energy metabolism, inflammation and the ubiquitin proteasome system. The present findings suggest that STAB1 is a new and highly promising candidate gene in this region. The combining of gene expression and GWAS data may provide valuable insights into the biological mechanisms of BD.

Identification of pathways for bipolar disorder: a meta-analysis

IMPORTANCE

Genome-wide investigations provide systematic information regarding the neurobiology of psychiatric disorders.

OBJECTIVE

To identify biological pathways that contribute to risk for bipolar disorder (BP) using genes with consistent evidence for association in multiple genome-wide association studies (GWAS).

DATA SOURCES

Four independent data sets with individual genome-wide data available in July 2011 along with all data sets contributed to the Psychiatric Genomics Consortium Bipolar Group by May 2012. A prior meta-analysis was used as a source for brain gene expression data.

STUDY SELECTION

The 4 published GWAS were included in the initial sample. All independent BP data sets providing genome-wide data in the Psychiatric Genomics Consortium were included as a replication sample.

DATA EXTRACTION AND SYNTHESIS

We identified 966 genes that contained 2 or more variants associated with BP at P < .05 in 3 of 4 GWAS data sets (n = 12,127 [5253 cases, 6874 controls]). Simulations using 10,000 replicates of these data sets corrected for gene size and allowed the calculation of an empirical P value for each gene; empirically significant genes were entered into a pathway analysis. Each of these pathways was then tested in the replication sample (n = 8396 [3507 cases, 4889 controls]) using gene set enrichment analysis for single-nucleotide polymorphisms. The 226 genes were also compared with results from a meta-analysis of gene expression in the dorsolateral prefrontal cortex.

MAIN OUTCOMES AND MEASURES

Empirically significant genes and biological pathways. RESULTS Among 966 genes, 226 were empirically significant (P < .05). Seventeen pathways were overrepresented in analyses of the initial data set. Six of the 17 pathways were associated with BP in both the initial and replication samples: corticotropin-releasing hormone signaling, cardiac β-adrenergic signaling, phospholipase C signaling, glutamate receptor signaling, endothelin 1 signaling, and cardiac hypertrophy signaling. Among the 226 genes, 9 differed in expression in the dorsolateral prefrontal cortex in patients with BP: CACNA1C, DTNA, FOXP1, GNG2, ITPR2, LSAMP, NPAS3, NCOA2, and NTRK3.

CONCLUSIONS AND RELEVANCE

Pathways involved in the genetic predisposition to BP include hormonal regulation, calcium channels, second messenger systems, and glutamate signaling. Gene expression studies implicate neuronal development pathways as well. These results tend to reinforce specific hypotheses regarding BP neurobiology and may provide clues for new approaches to treatment and prevention.

The emerging role of dopamine-glutamate interaction and of the postsynaptic density in bipolar disorder pathophysiology: Implications for treatment

Aberrant synaptic plasticity, originating from abnormalities in dopamine and/or glutamate transduction pathways, may contribute to the complex clinical manifestations of bipolar disorder (BD). Dopamine and glutamate systems cross-talk at multiple levels, such as at the postsynaptic density (PSD). The PSD is a structural and functional protein mesh implicated in dopamine and glutamate-mediated synaptic plasticity. Proteins at PSD have been demonstrated to be involved in mood disorders pathophysiology and to be modulated by antipsychotics and mood stabilizers. On the other side, post-receptor effectors such as protein kinase B (Akt), glycogen synthase kinase-3 (GSK-3) and the extracellular signal-regulated kinase (Erk), which are implicated in both molecular abnormalities and treatment of BD, may interact with PSD proteins, and participate in the interplay of the dopamine-glutamate signalling pathway. In this review, we describe emerging evidence on the molecular cross-talk between dopamine and glutamate signalling in BD pathophysiology and pharmacological treatment, mainly focusing on dysfunctions in PSD molecules. We also aim to discuss future therapeutic strategies that could selectively target the PSD-mediated signalling cascade at the crossroads of dopamine-glutamate neurotransmission.

Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives

Genome-wide association studies (GWAS) have rapidly become a powerful tool in genetic studies of complex diseases and traits. Traditionally, single marker-based tests have been used prevalently in GWAS and have uncovered tens of thousands of disease-associated SNPs. Network-assisted analysis (NAA) of GWAS data is an emerging area in which network-related approaches are developed and utilized to perform advanced analyses of GWAS data in order to study various human diseases or traits. Progress has been made in both methodology development and applications of NAA in GWAS data, and it has already been demonstrated that NAA results may enhance our interpretation and prioritization of candidate genes and markers. Inspired by the strong interest in and high demand for advanced GWAS data analysis, in this review article, we discuss the methodologies and strategies that have been reported for the NAA of GWAS data. Many NAA approaches search for subnetworks and assess the combined effects of multiple genes participating in the resultant subnetworks through a gene set analysis. With no restriction to pre-defined canonical pathways, NAA has the advantage of defining subnetworks with the guidance of the GWAS data under investigation. In addition, some NAA methods prioritize genes from GWAS data based on their interconnections in the reference network. Here, we summarize NAA applications to various diseases and discuss the available options and potential caveats related to their practical usage. Additionally, we provide perspectives regarding this rapidly growing research area.

BDgene: a genetic database for bipolar disorder and its overlap with schizophrenia and major depressive disorder

BACKGROUND

Bipolar disorder (BD) is a common psychiatric disorder with complex genetic architecture. It shares overlapping genetic influences with schizophrenia (SZ) and major depressive disorder (MDD). Large numbers of genetic studies of BD and cross-disorder studies between BD and SZ/MDD have accumulated numerous genetic data. There is a growing need to integrate the data to provide a comprehensive data set to facilitate the genetic study of BD and its highly relevant diseases.

METHODS

BDgene database was developed to integrate BD-related genetic factors and shared ones with SZ/MDD from profound literature reading. On the basis of data from the literature, in-depth analyses were performed for further understanding of the data, including gene prioritization, pathway-based analysis, intersection analysis of multidisease candidate genes, and pathway enrichment analysis.

RESULTS

BDgene includes multiple types of literature-reported genetic factors of BD with both positive and negative results, including 797 genes, 3119 single nucleotide polymorphisms, and 789 regions. Shared genetic factors such as single nucleotide polymorphisms, genes, and regions from published cross-disorder studies among BD and SZ/MDD were also presented. In-depth data analyses identified 43 BD core genes; 70 BD candidate pathways; and 127, 79, and 107 new potential cross-disorder genes for BD-SZ, BD-MDD, and BD-SZ-MDD, respectively.

CONCLUSIONS

As a central genetic database for BD and the first cross-disorder database for BD and SZ/MDD, BDgene provides not only a comprehensive review of current genetic research but also high-confidence candidate genes and pathways for understanding of BD mechanism and shared etiology among its relevant diseases. BDgene is freely available at http://bdgene.psych.ac.cn.

Breaking free from the chains of pathway annotation: de novo pathway discovery for the analysis of disease processes

Interpreting the biological implications of high-throughput experiments such as gene-expression studies, genome-wide association studies and large-scale sequencing studies is not trivial. Gene-set and pathway analyses are useful tools to support the interpretation of such experiments, but rely on curated pathways or gene sets. The recent development of de novo pathway discovery methods aims to overcome this limitation. This article provides an overview of the methods currently available and reviews the advantages and challenges of this approach. In detail, it highlights the particular issues of de novo pathway discovery based on genome-wide association studies data, for which multiple different strategies have been proposed.

Incorporating Information of microRNAs into Pathway Analysis in a Genome-Wide Association Study of Bipolar Disorder

MicroRNAs (miRNAs) are known to be important post-transcriptional regulators that are involved in the etiology of complex psychiatric traits. The present study aimed to incorporate miRNAs information into pathway analysis using a genome-wide association dataset to identify relevant biological pathways for bipolar disorder (BPD). We selected psychiatric- and neurological-associated miRNAs (N = 157) from PhenomiR database. The miRNA target genes (miTG) predictions were obtained from microRNA.org. Canonical pathways (N = 4,051) were downloaded from the Molecule Signature Database. We employed a novel weighting scheme for miTGs in pathway analysis using methods of gene set enrichment analysis and sum-statistic. Under four statistical scenarios, 38 significantly enriched pathways (P-value < 0.01 after multiple testing correction) were identified for the risk of developing BPD, including pathways of ion channels associated (e.g., gated channel activity, ion transmembrane transporter activity, and ion channel activity) and nervous related biological processes (e.g., nervous system development, cytoskeleton, and neuroactive ligand receptor interaction). Among them, 19 were identified only when the weighting scheme was applied. Many miRNA-targeted genes were functionally related to ion channels, collagen, and axonal growth and guidance that have been suggested to be associated with BPD previously. Some of these genes are linked to the regulation of miRNA machinery in the literature. Our findings provide support for the potential involvement of miRNAs in the psychopathology of BPD. Further investigations to elucidate the functions and mechanisms of identified candidate pathways are needed.

The genetic basis of mood and anxiety disorders - changing paradigms

Family, twin and epidemiologic studies all point to an important genetic contribution to the risk to develop mood and anxiety disorders. While some progress has been made in identifying relevant pathomechanisms for these disorders, candidate based strategies have often yielded controversial findings. Hopes were thus high when genome-wide genetic association studies became available and affordable and allowed a hypothesis-free approach to study genetic risk factors for these disorders. In an unprecendented scientific collaborative effort, large international consortia formed to allow the analysis of these genome-wide association datasets across thousands of cases and controls ([1] and see also http://www.broadinstitute.org/mpg/ricopili/). Now that large meta-analyses of genome-wide association studies (GWAS) have been published for bipolar disorder and major depression it has become clear that main effects of common variants are difficult to identify in these disorders, suggesting that additional approaches maybe needed to understand the genetic basis of these disorders [2,3].