Genome-wide association studies in psychiatry: what have we learned?

Decoding frontotemporal and cell-type-specific vulnerabilities to neuropsychiatric disorders and psychoactive drugs

Frontotemporal lobe abnormalities are linked to neuropsychiatric disorders and cognition, but the role of cellular heterogeneity between temporal lobe (TL) and frontal lobe (FL) in the vulnerability to genetic risk factors remains to be elucidated. We integrated single-nucleus transcriptome analysis in 'fresh' human FL and TL with genetic susceptibility, gene dysregulation in neuropsychiatric disease and psychoactive drug response data. We show how intrinsic differences between TL and FL contribute to the vulnerability of specific cell types to both genetic risk factors and psychoactive drugs. Neuronal populations, specifically PVALB neurons, were most highly vulnerable to genetic risk factors for psychiatric disease. These psychiatric disease-associated genes were mostly upregulated in the TL, and dysregulated in the brain of patients with obsessive-compulsive disorder, bipolar disorder and schizophrenia. Among these genes, GRIN2A and SLC12A5, implicated in schizophrenia and bipolar disorder, were significantly upregulated in TL PVALB neurons and in psychiatric disease patients' brain. PVALB neurons from the TL were twofold more vulnerable to psychoactive drugs than to genetic risk factors, showing the influence and specificity of frontotemporal lobe differences on cell vulnerabilities. These studies provide a cell type resolved map of the impact of brain regional differences on cell type vulnerabilities in neuropsychiatric disorders.

Emotional dysregulation, alexithymia and neuroticism: a systematic review on the genetic basis of a subset of psychological traits

Neuroticism, alexithymia and emotion dysregulation are key traits and known risk factors for several psychiatric conditions. In this systematic review, the aim is to evaluate the genetic contribution to these psychological phenotypes. A systematic review of articles found in PubMed was conducted. Search terms included 'genetic', 'GWAS', 'neuroticism', 'alexithymia' and 'emotion dysregulation'. Risk of bias was assessed utilizing the STREGA checklist. Two hundred two papers were selected from existing literature based on the inclusion and exclusion criteria. Among these, 27 were genome-wide studies and 175 were genetic association studies. Single gene association studies focused on selected groups of genes, mostly involved in neurotransmission, with conflicting results. GWAS studies on neuroticism, on the other hand, found several relevant and replicated intergenic and intronic loci affecting the expression and regulation of crucial and well-known genes (such as DRD2 and CRHR1). Mutations in genes coding for trascriptional factors were also found to be associated with neuroticism (DCC, XKR6, TCF4, RBFOX1), as well as a noncoding regulatory RNA (LINC00461). On the other hand, little GWAS data are available on alexythima and emotional dysregulation.

Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences

MOTIVATION

Genome-wide association studies have systematically identified thousands of single nucleotide polymorphisms (SNPs) associated with complex genetic diseases. However, the majority of those SNPs were found in non-coding genomic regions, preventing the understanding of the underlying causal mechanism. Predicting molecular processes based on the DNA sequence represents a promising approach to understand the role of those non-coding SNPs. Over the past years, deep learning was successfully applied to regulatory sequence prediction using supervised learning. Supervised learning required DNA sequences associated with functional data for training, whose amount is strongly limited by the finite size of the human genome. Conversely, the amount of mammalian DNA sequences is exponentially increasing due to ongoing large sequencing projects, but without functional data in most cases.

RESULTS

To alleviate the limitations of supervised learning, we propose a paradigm shift with semi-supervised learning, which does not only exploit labeled sequences (e.g. human genome with ChIP-seq experiment), but also unlabeled sequences available in much larger amounts (e.g. from other species without ChIP-seq experiment, such as chimpanzee). Our approach is flexible and can be plugged into any neural architecture including shallow and deep networks, and shows strong predictive performance improvements compared to supervised learning in most cases (up to [Formula: see text]).

AVAILABILITY AND IMPLEMENTATION

https://forgemia.inra.fr/raphael.mourad/deepgnn .

Exploring the role of ketone bodies in the diagnosis and treatment of psychiatric disorders

In recent times, advances in the field of metabolomics have shed greater light on the role of metabolic disturbances in neuropsychiatric conditions. The following review explores the role of ketone bodies and ketosis in both the diagnosis and treatment of three major psychiatric disorders: major depressive disorder, anxiety disorders, and schizophrenia. Distinction is made between the potential therapeutic effects of the ketogenic diet and exogenous ketone preparations, as exogenous ketones in particular offer a standardized, reproducible manner for inducing ketosis. Compelling associations between symptoms of mental distress and dysregulation in central nervous system ketone metabolism have been demonstrated in preclinical studies with putative neuroprotective effects of ketone bodies being elucidated, including effects on inflammasomes and the promotion of neurogenesis in the central nervous system. Despite emerging pre-clinical data, clinical research on ketone body effectiveness as a treatment option for psychiatric disorders remains lacking. This gap in understanding warrants further investigating, especially considering that safe and acceptable ways of inducing ketosis are readily available.

Deep learning-based integration of genetics with registry data for stratification of schizophrenia and depression

Currently, psychiatric diagnoses are, in contrast to most other medical fields, based on subjective symptoms and observable signs and call for new and improved diagnostics to provide the most optimal care. On the basis of a deep learning approach, we performed unsupervised patient stratification of 19,636 patients with depression [major depressive disorder (MDD)] and/or schizophrenia (SCZ) and 22,467 population controls from the iPSYCH2012 case cohort. We integrated data of disorder severity, history of mental disorders and disease comorbidities, genetics, and medical birth data. From this, we stratified the individuals in six and seven unique clusters for MDD and SCZ, respectively. When censoring data until diagnosis, we could predict MDD clusters with areas under the curve (AUCs) of 0.54 to 0.80 and SCZ clusters with AUCs of 0.71 to 0.86. Overall cases and controls could be predicted with an AUC of 0.81, illustrating the utility of data-driven subgrouping in psychiatry.

Overview of CAPICE-Childhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe-an EU Marie Skłodowska-Curie International Training Network

The Roadmap for Mental Health and Wellbeing Research in Europe (ROAMER) identified child and adolescent mental illness as a priority area for research. CAPICE (Childhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe) is a European Union (EU) funded training network aimed at investigating the causes of individual differences in common childhood and adolescent psychopathology, especially depression, anxiety, and attention deficit hyperactivity disorder. CAPICE brings together eight birth and childhood cohorts as well as other cohorts from the EArly Genetics and Life course Epidemiology (EAGLE) consortium, including twin cohorts, with unique longitudinal data on environmental exposures and mental health problems, and genetic data on participants. Here we describe the objectives, summarize the methodological approaches and initial results, and present the dissemination strategy of the CAPICE network. Besides identifying genetic and epigenetic variants associated with these phenotypes, analyses have been performed to shed light on the role of genetic factors and the interplay with the environment in influencing the persistence of symptoms across the lifespan. Data harmonization and building an advanced data catalogue are also part of the work plan. Findings will be disseminated to non-academic parties, in close collaboration with the Global Alliance of Mental Illness Advocacy Networks-Europe (GAMIAN-Europe).

Cutting-edge genetics in obsessive-compulsive disorder

This article reviews recent advances in the genetics of obsessive-compulsive disorder (OCD). We cover work on the following: genome-wide association studies, whole-exome sequencing studies, copy number variation studies, gene expression, polygenic risk scores, gene–environment interaction, experimental animal systems, human cell models, imaging genetics, pharmacogenetics, and studies of endophenotypes. Findings from this work underscore the notion that the genetic architecture of OCD is highly complex and shared with other neuropsychiatric disorders. Also, the latest evidence points to the participation of gene networks involved in synaptic transmission, neurodevelopment, and the immune and inflammatory systems in this disorder. We conclude by highlighting that further study of the genetic architecture of OCD, a great part of which remains to be elucidated, could benefit the development of diagnostic and therapeutic approaches based on the biological basis of the disorder. Studies to date revealed that OCD is not a simple homogeneous entity, but rather that the underlying biological pathways are variable and heterogenous. We can expect that translation from bench to bedside, through continuous effort and collaborative work, will ultimately transform our understanding of what causes OCD and thus how best to treat it.

Gut Biofactory-Neurocompetent Metabolites within the Gastrointestinal Tract. A Scoping Review

The gut microbiota have gained much scientific attention recently. Apart from unravelling the taxonomic data, we should understand how the altered microbiota structure corresponds to functions of this complex ecosystem. The metabolites of intestinal microorganisms, especially bacteria, exert pleiotropic effects on the human organism and contribute to the host systemic balance. These molecules play key roles in regulating immune and metabolic processes. A subset of them affect the gut brain axis signaling and balance the mental wellbeing. Neurotransmitters, short chain fatty acids, tryptophan catabolites, bile acids and phosphatidylcholine, choline, serotonin, and L-carnitine metabolites possess high neuroactive potential. A scoping literature search in PubMed/Embase was conducted up until 20 June 2020, using three major search terms "microbiota metabolites" AND "gut brain axis" AND "mental health". This review aimed to enhance our knowledge regarding the gut microbiota functional capacity, and support current and future attempts to create new compounds for future clinical interventions.

Gene-set Enrichment with Mathematical Biology (GEMB)

BACKGROUND

Gene-set analyses measure the association between a disease of interest and a "set" of genes related to a biological pathway. These analyses often incorporate gene network properties to account for differential contributions of each gene. We extend this concept further-defining gene contributions based on biophysical properties-by leveraging mathematical models of biology to predict the effects of genetic perturbations on a particular downstream function.

RESULTS

We present a method that combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. We demonstrate in simulation how such a method can improve statistical power. To this effect, we identify a gene set, weighted by model-predicted contributions to intracellular calcium ion concentration, that is significantly related to bipolar disorder in a small dataset (P = 0.04; n = 544). We reproduce this finding using publicly available summary data from the Psychiatric Genomics Consortium (P = 1.7 × 10-4; n = 41,653). By contrast, an approach using a general calcium signaling pathway did not detect a significant association with bipolar disorder (P = 0.08). The weighted gene-set approach based on intracellular calcium ion concentration did not detect a significant relationship with schizophrenia (P = 0.09; n = 65,967) or major depression disorder (P = 0.30; n = 500,199).

CONCLUSIONS

Together, these findings show how incorporating math biology into gene-set analyses might help to identify biological functions that underlie certain polygenic disorders.

High polygenic burden is associated with blood DNA methylation changes in individuals with suicidal behavior

Suicidal behavior is result of the interaction of several contributors, including genetic and environmental factors. The integration of approaches considering the polygenic component of suicidal behavior, such as polygenic risk scores (PRS) and DNA methylation is promising for improving our understanding of the complex interplay between genetic and environmental factors in this behavior. The aim of this study was the evaluation of DNA methylation differences between individuals with high and low genetic burden for suicidality. The present study was divided into two phases. In the first phase, genotyping with the Psycharray chip was performed in a discovery sample of 568 Mexican individuals, of which 149 had suicidal behavior (64 individuals with suicidal ideation, 50 with suicide attempt and 35 with completed suicide). Then, a PRS analysis based on summary statistics from the Psychiatric Genomic Consortium was performed in the discovery sample. In a second phase, we evaluated DNA methylation differences between individuals with high and low genetic burden for suicidality in a sub-sample of the discovery sample (target sample) of 94 subjects. We identified 153 differentially methylated sites between individuals with low and high-PRS. Among genes mapped to differentially methylated sites, we found genes involved in neurodevelopment (CHD7, RFX4, KCNA1, PLCB1, PITX1, NUMBL) and ATP binding (KIF7, NUBP2, KIF6, ATP8B1, ATP11A, CLCN7, MYLK, MAP2K5). Our results suggest that genetic variants might increase the predisposition to epigenetic variations in genes involved in neurodevelopment. This study highlights the possible implication of polygenic burden in the alteration of epigenetic changes in suicidal behavior.

Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction

Coordinated activity-induced transcriptional changes across multiple neuron subtypes of the prefrontal cortex (PFC) play a pivotal role in encoding and regulating major cognitive behaviors. Yet, the specific transcriptional programs in each neuron subtype remain unknown. Using single-cell RNA sequencing (scRNA-seq), here we comprehensively classify all unique cell subtypes in the PFC. We analyze transcriptional dynamics of each cell subtype under a naturally adaptive and an induced condition. Adaptive changes during adolescence (between P21 and P60), a highly dynamic phase of postnatal neuroplasticity, profoundly impacted transcription in each neuron subtype, including cell type-specific regulation of genes implicated in major neuropsychiatric disorders. On the other hand, an induced plasticity evoked by chronic cocaine addiction resulted in progressive transcriptional changes in multiple neuron subtypes and became most pronounced upon prolonged drug withdrawal. Our findings lay a foundation for understanding cell type-specific postnatal transcriptional dynamics under normal PFC function and in neuropsychiatric disease states.

Qualitative vs. Quantitative Methods in Psychiatric Research: Updated

Since the incipiency of psychiatry as a medical specialty, the "holy grail" has been neuroscience-based diagnostic system and treatment strategies, but this lofty, yet necessary, goal has eluded the greatest minds for centuries. Now, with advances in molecular genetics and resting-state neuroimaging, neuroscience-based diagnosis and treatment are now more possible than ever. However, clinical symptomatology, longitudinal course, and delimitation of illnesses (i.e., phenotypic classification) remain indispensable for responsible, reproducible, and meaningful use of these new methodologies.

Transcriptome Changes in Relation to Manic Episode

Bipolar disorder (BD) is highly heritable and well known for its recurrent manic and depressive episodes. The present study focused on manic episode in BD patients and aimed to investigate state-specific transcriptome alterations between acute episode and remission, including messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and micro-RNAs (miRNAs), using microarray and RNA sequencing (RNA-Seq) platforms. BD patients were enrolled with clinical information, and peripheral blood samples collected at both acute and remission status spanning for at least 2 months were confirmed by follow-ups. Symptom severity was assessed by Young Mania Rating Scale. We enrolled six BD patients as the discovery samples and used the Affymetrix Human Transcriptome Array 2.0 to capture transcriptome data at the two time points. For replication, expression data from Gene Expression Omnibus that consisted of 11 BD patients were downloaded, and we performed a mega-analysis for microarray data of 17 patients. Moreover, we conducted RNA sequencing (RNA-Seq) in additional samples of 7 BD patients. To identify intraindividual differentially expressed genes (DEGs), we analyzed data using a linear model controlling for symptom severity. We found that noncoding genes were of majority among the top DEGs in microarray data. The expression fold change of coding genes among DEGs showed moderate to high correlations (∼0.5) across platforms. A number of lncRNAs and two miRNAs (MIR181B1 and MIR103A1) exhibited high levels of gene expression in the manic state. For coding genes, we reported that the taste function-related genes, including TAS2R5 and TAS2R3, may be mania state-specific markers. Additionally, four genes showed a nominal p-value of less than 0.05 in all our microarray data, mega-analysis, and RNA-Seq analysis. They were upregulated in the manic state and consisted of MS4A14, PYHIN1, UTRN, and DMXL2, and their gene expression patterns were further validated by quantitative real-time polymerase chain reaction (PCR) (qRT-PCR). We also performed weight gene coexpression network analysis to identify gene modules for manic episode. Genes in the mania-related modules were different from the susceptible loci of BD obtained from genome-wide association studies, and biological pathways in relation to these modules were mainly related to immune function, especially cytokine-cytokine receptor interaction. Results of the present study elucidated potential molecular targets and genomic networks that are involved in manic episode. Future studies are needed to further validate these biomarkers for their roles in the etiology of bipolar illness.

De novo Mutations From Whole Exome Sequencing in Neurodevelopmental and Psychiatric Disorders: From Discovery to Application

Neurodevelopmental and psychiatric disorders are a highly disabling and heterogeneous group of developmental and mental disorders, resulting from complex interactions of genetic and environmental risk factors. The nature of multifactorial traits and the presence of comorbidity and polygenicity in these disorders present challenges in both disease risk identification and clinical diagnoses. The genetic component has been firmly established, but the identification of all the causative variants remains elusive. The development of next-generation sequencing, especially whole exome sequencing (WES), has greatly enriched our knowledge of the precise genetic alterations of human diseases, including brain-related disorders. In particular, the extensive usage of WES in research studies has uncovered the important contribution of de novo mutations (DNMs) to these disorders. Trio and quad familial WES are a particularly useful approach to discover DNMs. Here, we review the major WES studies in neurodevelopmental and psychiatric disorders and summarize how genes hit by discovered DNMs are shared among different disorders. Next, we discuss different integrative approaches utilized to interrogate DNMs and to identify biological pathways that may disrupt brain development and shed light on our understanding of the genetic architecture underlying these disorders. Lastly, we discuss the current state of the transition from WES research to its routine clinical application. This review will assist researchers and clinicians in the interpretation of variants obtained from WES studies, and highlights the need to develop consensus analytical protocols and validated lists of genes appropriate for clinical laboratory analysis, in order to reach the growing demands.

Leveraging genome-wide association and clinical data in revealing schizophrenia subgroups

Schizophrenia (SCZ) has long been recognized as a highly heterogeneous disorder. Patients differed in their clinical manifestations, prognosis, and underlying pathophysiologies. Here we presented and applied a framework for finding subtypes of SCZ utilizing genome-wide association study (GWAS) and clinical data. We postulated that genetic information may help stratify patient into useful subgroups, and incorporation of other clinical information and cognitive profiles will further improve patient subtyping. We conducted cluster analysis in 387 Hong Kong Chinese with SCZ. First we performed 'single-view' clustering using genetic or clinical data alone, then proceeded to 'multi-view' clustering (MVC) accounting for both types of information. We validated clustering results by assessing subgroup differences in various outcomes. We found significant differences in outcomes including treatment response, disease course and symptom severity (Simes overall p-value using MVC = 1.64E-9). Overall speaking, we identified three subgroups with good, intermediate and poor prognosis respectively. MVC generally out-performed single-view methods. The analysis was repeated for different sets of input SNPs, and stratified analysis of male and female patients, and the results remained largely robust. We also found significant enrichment for SCZ loci among the SNPs selected by the cluster algorithm. Numerous selected genes (e.g. NRG1, ERBB4, NRXN1, ANK3) and pathways (e.g. neuregulin-ErbB4 and calcium signaling) were implicated in SCZ or related pathophysiological processes. This is first study to combine both genetic and clinical data for subtyping SCZ, and to employ genome-wide SNP data in cluster analysis of a complex disease. This work points to a new way of GWAS analysis of translational potential.

Psychiatry and developmental psychopathology: Unifying themes and future directions

In the past 35 years, developmental psychopathology has grown into a flourishing discipline that shares a scientific agenda with contemporary psychiatry. In this editorial, which introduces the special issue, we describe the history of developmental psychopathology, including core principles that bridge allied disciplines. These include (1) emphasis on interdisciplinary research, (2) elucidation of multicausal pathways to seemingly single disorders (phenocopies), (3) description of divergent multifinal outcomes from common etiological start points (pathoplasticity), and (4) research conducted across multiple levels of analysis spanning genes to environments. Next, we discuss neurodevelopmental models of psychopathology, and provide selected examples. We emphasize differential neuromaturation of subcortical and cortical neural networks and connectivity, and how both acute and protracted environmental insults can compromise neural structure and function. To date, developmental psychopathology has placed greater emphasis than psychiatry on neuromaturational models of mental illness. However, this gap is closing rapidly as advances in technology render etiopathophysiologies of psychopathology more interrogable. We end with suggestions for future interdisciplinary research, including the need to evaluate measurement invariance across development, and to construct more valid assessment methods where indicated.

The 3D genome: From fundamental principles to disease and cancer

In higher eukaryotes, the three-dimensional (3D) organization of the genome is intimately related to numerous key biological functions including gene expression, DNA repair and DNA replication regulations. Alteration of this 3D organization is detrimental to the organism and can give rise to a broad range of diseases such as cancers. Here, we review recent advances in the field. We first describe how the genome is packed in 3D to form chromosome territories, compartments and domains. We also give an overview of the recent techniques that allow to map the genome in 3D up to the kilobase resolution. We then discuss potential mechanisms by which genome misfolding can affect proper gene expression by distal enhancers, and how the 3D genome influences the formation of genomic rearrangements.

Heterozygosity for the Mood Disorder-Associated Variant Gln460Arg Alters P2X7 Receptor Function and Sleep Quality

A single nucleotide polymorphism substitution from glutamine (Gln, Q) to arginine (Arg, R) at codon 460 of the purinergic P2X7 receptor (P2X7R) has repeatedly been associated with mood disorders. The P2X7R-Gln460Arg variant per se is not compromised in its function. However, heterologous expression of P2X7R-Gln460Arg together with wild-type P2X7R has recently been demonstrated to impair receptor function. Here we show that this also applies to humanized mice coexpressing both human P2X7R variants. Primary hippocampal cells derived from heterozygous mice showed an attenuated calcium uptake upon agonist stimulation. While humanized mice were unaffected in their behavioral repertoire under basal housing conditions, mice that harbor both P2X7R variants showed alterations in their sleep quality resembling signs of a prodromal disease stage. Also healthy heterozygous human subjects showed mild changes in sleep parameters. These results indicate that heterozygosity for the wild-type P2X7R and its mood disorder-associated variant P2X7R-Gln460Arg represents a genetic risk factor, which is potentially able to convey susceptibility to mood disorders.SIGNIFICANCE STATEMENT Depression and bipolar disorder are the most common mood disorders. The P2X7 receptor (P2X7R) regulates many cellular functions. Its polymorphic variant Gln460Arg has repeatedly been associated with mood disorders. Genetically engineered mice, with human P2X7R, revealed that heterozygous mice (i.e., they coexpress the disease-associated Gln460Arg variant together with its normal version) have impaired receptor function and showed sleep disturbances. Human participants with the heterozygote genotype also had subtle alterations in their sleep profile. Our findings suggest that altered P2X7R function in heterozygote individuals disturbs sleep and might increase the risk for developing mood disorders.

On the transdiagnostic nature of peripheral biomarkers in major psychiatric disorders: A systematic review

The search for biomarkers has been a leading endeavor in biological psychiatry. To analyze its evolution over the years, we performed a systematic review to evaluate (a) the most studied peripheral molecular markers in major psychiatric disorders, (b) the main features of studies proposing them as biomarkers and (c) whether their patterns of variation are similar across disorders. Of the six molecules most commonly studied as plasmatic markers of schizophrenia, major depressive disorder or bipolar disorder, five (BDNF, TNF-alpha, IL-6, C-reactive protein and cortisol) were the same across diagnoses. An analysis of this literature showed that, while 66% of studies compared patients and controls, only 34% were longitudinal, and only 10% presented a measure of diagnostic or prognostic efficacy. Meta-analyses showed variation in the levels of these molecules to be robust across studies, but similar among disorders, suggesting them to reflect transdiagnostic systemic consequences of psychiatric illness. Based on this, we discuss how current publication practices have led to research fragmentation across diagnoses, and suggest approaches to face this issue.

Genome-wide association study of facial emotion recognition in children and association with polygenic risk for mental health disorders

Emotion recognition is disrupted in many mental health disorders, which may reflect shared genetic aetiology between this trait and these disorders. We explored genetic influences on emotion recognition and the relationship between these influences and mental health phenotypes. Eight-year-old participants (n = 4,097) from the Avon Longitudinal Study of Parents and Children (ALSPAC) completed the Diagnostic Analysis of Non-Verbal Accuracy (DANVA) faces test. Genome-wide genotype data was available from the Illumina HumanHap550 Quad microarray. Genome-wide association studies were performed to assess associations with recognition of individual emotions and emotion in general. Exploratory polygenic risk scoring was performed using published genomic data for schizophrenia, bipolar disorder, depression, autism spectrum disorder, anorexia, and anxiety disorders. No individual genetic variants were identified at conventional levels of significance in any analysis although several loci were associated at a level suggestive of significance. SNP-chip heritability analyses did not identify a heritable component of variance for any phenotype. Polygenic scores were not associated with any phenotype. The effect sizes of variants influencing emotion recognition are likely to be small. Previous studies of emotion identification have yielded non-zero estimates of SNP-heritability. This discrepancy is likely due to differences in the measurement and analysis of the phenotype.

Meaning and barriers to quality care service provision in Child and Adolescent Mental Health Services: Qualitative study of stakeholder perspectives

BACKGROUND

Defining quality in health presents many challenges. The Institute of Medicine (IOM) defined quality clinical care as care that is equitable, timely, safe, efficient, effective and patient centred. However, it is not clear how different stakeholders within a child and adolescent mental health service (CAMHS) understand and/or apply this framework. This project aims to identify key stakeholders" understanding of the meaning of quality in the context of CAMHS.

METHOD

The study sample comprised of three groups: (i) patients and carers, (ii) CAMHS clinical staff, and (iii) commissioners (Total N = 24). Semi-structured interviews were used to collect data and thematic analysis was applied to explore participant's views on the meaning and measurement of quality and how these might reflect the IOM indicators and their relevance in CAMHS.

RESULTS

An initial barrier to implementing quality care in CAMHS was the difficulty and limited agreement in defining the meaning of quality care, its measurement and implementation for all participants. Clinical staff defined quality as personal values, a set of practical rules, or clinical discharge rates; while patients suggested being more involved in the decision-making process. Commissioners, while supportive of adequate safeguarding and patient satisfaction procedures, did not explicitly link their view on quality to commissioning guidelines. Identifying practical barriers to implementing quality care was easier for all interviewees and common themes included: lack of meaningful measures, recourses, accountability, and training. All interviewees considered the IOM six markers as comprehensive and relevant to CAMHS.

CONCLUSIONS

No respondent individually or within one stakeholder group identified more than a few of the indicators or barriers of a quality CAMHS service. However, the composite responses of the respondents enable us to develop a more complete picture of how to improve quality care in practice and guide future research in the area.

A current snapshot of common genomic variants contribution in psychiatric disorders

In the past decade, numerous advances were achieved in psychiatric genetics. Particularly, the genome wide association studies (GWAS) have contributed to uncovering new genes and pathways associated to psychiatric disorders (PDs). At the same time, with increasing sample sizes in the GWAS, the polygenic risk score (PRS) promoted an additional tool for identification and evaluation the genetic risk quantitatively in PDs. This concept review presents the state of the art GWAS analysis and PRS focusing on the genetic underpinnings of PDs. © 2016 Wiley Periodicals, Inc.

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

Personalized medicine in psychiatry is in need of biomarkers that resemble central nervous system function at the level of neuronal activity. Electroencephalography (EEG) during sleep or resting-state conditions and event-related potentials (ERPs) have not only been used to discriminate patients from healthy subjects, but also for the prediction of treatment outcome in various psychiatric diseases, yielding information about tailored therapy approaches for an individual. This review focuses on baseline EEG markers for two psychiatric conditions, namely major depressive disorder and attention deficit hyperactivity disorder. It covers potential biomarkers from EEG sleep research and vigilance regulation, paroxysmal EEG patterns and epileptiform discharges, quantitative EEG features within the EEG main frequency bands, connectivity markers and ERP components that might help to identify favourable treatment outcome. Further, the various markers are discussed in the context of their potential clinical value and as research domain criteria, before giving an outline for future studies that are needed to pave the way to an electrophysiological biomarker-based personalized medicine.

Research and partnerships with schools

PURPOSE

Despite the quantity of research on child and adolescent mental health being done in schools, little output has focused on the practical aspects of recruiting schools and students into a study. Furthermore, there is limited knowledge on how to develop and sustain productive and mutually beneficial partnerships with schools after the project finishes.

METHODS

A large study examining prevalence of mental health problems in young people involving nine schools is used as an example for the procedure of recruitment and carrying out a research project, while developing and sustaining partnerships with schools.

RESULTS

While recruiting the schools, a three-stage model was developed that corresponded closely to the school's needs and existing demands. The suggested procedure for the study, thus, closely reflected the varying existing cultures of participating schools. Partnerships, developed as a result of the project, were used in developing further projects and interventions for promoting good mental health in schools.

CONCLUSIONS

Rather than a blanket research recruitment and procedural approach with an end to school involvement at the end of the project, the paper advocates for a deeper understanding of the schools' internal culture for improved recruitment and study outcomes. Developed partnerships, when sustained past the completion of research, prove to be a useful tool in applying the findings in promoting good mental health in schools and continuing research further.

Thinking About Data, Research Methods, and Statistical Analyses: Commentary on Sijtsma's (2014) "Playing with Data"

We comment on Sijtsma's (2014) thought-provoking essay on how to minimize questionable research practices (QRPs) in psychology. We agree with Sijtsma that proactive measures to decrease the risk of QRPs will ultimately be more productive than efforts to target individual researchers and their work. In particular, we concur that encouraging researchers to make their data and research materials public is the best institutional antidote against QRPs, although we are concerned that Sijtsma's proposal to delegate more responsibility to statistical and methodological consultants could inadvertently reinforce the dichotomy between the substantive and statistical aspects of research. We also discuss sources of false-positive findings and replication failures in psychological research, and outline potential remedies for these problems. We conclude that replicability is the best metric of the minimization of QRPs and their adverse effects on psychological research.

Genome-wide linkage on chromosome 10q26 for a dimensional scale of major depression

Major depressive disorder (MDD) is a common and potentially life-threatening mood disorder. Identifying genetic markers for depression might provide reliable indicators of depression risk, which would, in turn, substantially improve detection, enabling earlier and more effective treatment. The aim of this study was to identify rare variants for depression, modeled as a continuous trait, using linkage and post-hoc association analysis. The sample comprised 1221 Mexican-American individuals from extended pedigrees. A single dimensional scale of MDD was derived using confirmatory factor analysis applied to all items from the Past Major Depressive Episode section of the Mini-International Neuropsychiatric Interview. Scores on this scale of depression were subjected to linkage analysis followed by QTL region-specific association analysis. Linkage analysis revealed a single genome-wide significant QTL (LOD=3.43) on 10q26.13, QTL-specific association analysis conducted in the entire sample revealed a suggestive variant within an intron of the gene LHPP (rs11245316, p=7.8×10(-04); LD-adjusted Bonferroni-corrected p=8.6×10(-05)). This region of the genome has previously been implicated in the etiology of MDD; the present study extends our understanding of the involvement of this region by highlighting a putative gene of interest (LHPP).

Imaging genetics studies on monoaminergic genes in major depressive disorder

Although depression is the leading cause of disability worldwide, current understanding of the neurobiology of depression has failed to be translated into clinical practice. Major depressive disorder (MDD) pathogenesis is considered to be significantly influenced by multiple risk genes, however genetic effects are not simply expressed at a behavioral level. Therefore the concept of endophenotype has been applied in psychiatric genetics. Imaging genetics applies anatomical or functional imaging technologies as phenotypic assays to evaluate genetic variation and their impact on behavior. This paper attempts to provide a comprehensive review of available imaging genetics studies, including reports on genetic variants that have most frequently been linked to MDD, such as the monoaminergic genes (serotonin transporter gene, monoamine oxidase A gene, tryptophan hydroxylase-2 gene, serotonin receptor 1A gene and catechol-O-methyl transferase gene), with regard to key structures involved in emotion processing, such as the hippocampus, amygdala, anterior cingulate cortex and orbitofrontal cortex.

Genetics of Autism Spectrum Disorder: Current Status and Possible Clinical Applications

Autism spectrum disorder (ASD) is one of the most complex behavioral disorders with a strong genetic influence. The objectives of this article are to review the current status of genetic research in ASD, and to provide information regarding the potential candidate genes, mutations, and genetic loci possibly related to pathogenesis in ASD. Investigations on monogenic causes of ASD, candidate genes among common variants, rare de novo mutations, and copy number variations are reviewed. The current possible clinical applications of the genetic knowledge and their future possibilities are highlighted.

The Psychiatric Genomics Consortium Posttraumatic Stress Disorder Workgroup: Posttraumatic Stress Disorder Enters the Age of Large-Scale Genomic Collaboration

The development of posttraumatic stress disorder (PTSD) is influenced by genetic factors. Although there have been some replicated candidates, the identification of risk variants for PTSD has lagged behind genetic research of other psychiatric disorders such as schizophrenia, autism, and bipolar disorder. Psychiatric genetics has moved beyond examination of specific candidate genes in favor of the genome-wide association study (GWAS) strategy of very large numbers of samples, which allows for the discovery of previously unsuspected genes and molecular pathways. The successes of genetic studies of schizophrenia and bipolar disorder have been aided by the formation of a large-scale GWAS consortium: the Psychiatric Genomics Consortium (PGC). In contrast, only a handful of GWAS of PTSD have appeared in the literature to date. Here we describe the formation of a group dedicated to large-scale study of PTSD genetics: the PGC-PTSD. The PGC-PTSD faces challenges related to the contingency on trauma exposure and the large degree of ancestral genetic diversity within and across participating studies. Using the PGC analysis pipeline supplemented by analyses tailored to address these challenges, we anticipate that our first large-scale GWAS of PTSD will comprise over 10 000 cases and 30 000 trauma-exposed controls. Following in the footsteps of our PGC forerunners, this collaboration-of a scope that is unprecedented in the field of traumatic stress-will lead the search for replicable genetic associations and new insights into the biological underpinnings of PTSD.

Failure of deactivation in the default mode network: a trait marker for schizophrenia?

BACKGROUND

Functional imaging studies in relatives of schizophrenic patients have had inconsistent findings, particularly with respect to altered dorsolateral prefrontal cortex activation. Some recent studies have also suggested that failure of deactivation may be seen.

METHOD

A total of 28 patients with schizophrenia, 28 of their siblings and 56 healthy controls underwent functional magnetic resonance imaging during performance of the n-back working memory task. An analysis of variance was fitted to individual whole-brain maps from each set of patient-relative-matched pair of controls. Clusters of significant difference among the groups were then used as regions of interest to compare mean activations and deactivations among the groups.

RESULTS

In all, five clusters of significant differences were found. The schizophrenic patients, but not the relatives, showed reduced activation compared with the controls in the lateral frontal cortex bilaterally, the left basal ganglia and the cerebellum. In contrast, both the patients and the relatives showed significant failure of deactivation compared with the healthy controls in the medial frontal cortex, with the relatives also showing less failure than the patients. Failure of deactivation was not associated with schizotypy scores or presence of psychotic-like experiences in the relatives.

CONCLUSIONS

Both schizophrenic patients and their relatives show altered task-related deactivation in the medial frontal cortex. This in turn suggests that default mode network dysfunction may function as a trait marker for schizophrenia.

A polygenic risk score associated with measures of depressive symptoms among older adults

It has been suggested that depression is a polygenic trait, arising from the influences of multiple loci with small individual effects. The aim of this study is to generate a polygenic risk score (PRS) to examine the association between genetic variation and depressive symptoms. Our analytic sample included N = 10,091 participants aged 50 and older from the Health and Retirement Study (HRS). Depressive symptoms were measured by Center for Epidemiological Studies-Depression scale (CESD) scores assessed on up to nine occasions across 18 years. We conducted a genome-wide association analysis for a discovery set (n = 7,000) and used the top 11 single-nucleotide polymorphisms, all with p < 10(-5) to generate a weighted PRS for our replication sample (n = 3,091). Results showed that the PRS was significantly associated with mean CESD score in the replication sample (β = .08, p = .002). The R(2) change for the inclusion of the PRS was .003. Using a multinomial logistic regression model, we also examined the association between genetic risk and chronicity of high (4+) CESD scores. We found that a one-standard-deviation increase in PRS was associated with a 36 percent increase in the odds of having chronically high CESD scores relative to never having had high CESD scores. Our findings are consistent with depression being a polygenic trait and suggest that the cumulative influence of multiple variants increases an individual's susceptibility for chronically experiencing high levels of depressive symptoms.

Bioinformatics approach to understanding interacting pathways in neuropsychiatric disorders

Bioinformatics-based applications have been incorporated into several medical disciplines, including cancer, neuroscience, and recently psychiatry. Both the increasing interest in the molecular aspect of neuropsychiatry and the availability of high-throughput discovery and analysis tools have encouraged the incorporation of bioinformatics and neurosystems biology techniques into psychiatry and neuroscience research. As applied to neuropsychiatry, systems biology involves the acquisition and processing of high-throughput datasets to infer new information. A major component in bioinformatics output is pathway analysis that provides an insight into and prediction of possible underlying pathogenic processes which may help understand disease pathogenesis. In addition, this analysis serves as a tool to identify potential biomarkers implicated in these disorders. In this chapter, we summarize the different tools and algorithms used in pathway analysis along with their applications to the different layers of molecular investigations, from genomics to proteomics.