Copy number variation and schizophrenia

CNVDeep: deep association of copy number variants with neurocognitive disorders

BACKGROUND

Copy number variants (CNVs) have become increasingly instrumental in understanding the etiology of all diseases and phenotypes, including Neurocognitive Disorders (NDs). Among the well-established regions associated with ND are small parts of chromosome 16 deletions (16p11.2) and chromosome 15 duplications (15q3). Various methods have been developed to identify associations between CNVs and diseases of interest. The majority of methods are based on statistical inference techniques. However, due to the multi-dimensional nature of the features of the CNVs, these methods are still immature. The other aspect is that regions discovered by different methods are large, while the causative regions may be much smaller.

RESULTS

In this study, we propose a regularized deep learning model to select causal regions for the target disease. With the help of the proximal [20] gradient descent algorithm, the model utilizes the group LASSO concept and embraces a deep learning model in a sparsity framework. We perform the CNV analysis for 74,811 individuals with three types of brain disorders, autism spectrum disorder (ASD), schizophrenia (SCZ), and developmental delay (DD), and also perform cumulative analysis to discover the regions that are common among the NDs. The brain expression of genes associated with diseases has increased by an average of 20 percent, and genes with homologs in mice that cause nervous system phenotypes have increased by 18 percent (on average). The DECIPHER data source also seeks other phenotypes connected to the detected regions alongside gene ontology analysis. The target diseases are correlated with some unexplored regions, such as deletions on 1q21.1 and 1q21.2 (for ASD), deletions on 20q12 (for SCZ), and duplications on 8p23.3 (for DD). Furthermore, our method is compared with other machine learning algorithms.

CONCLUSIONS

Our model effectively identifies regions associated with phenotypic traits using regularized deep learning. Rather than attempting to analyze the whole genome, CNVDeep allows us to focus only on the causative regions of disease.

A comprehensive update on genetic inheritance, epigenetic factors, associated pathology, and recent therapeutic intervention by gene therapy in schizophrenia

Schizophrenia is a severe psychological disorder in which reality is interpreted abnormally by the patient. The symptoms of the disease include delusions and hallucinations, associated with extremely disordered behavior and thinking, which may affect the daily lives of the patients. Advancements in technology have led to understanding the dynamics of the disease and the identification of the underlying causes. Multiple investigations prove that it is regulated genetically, and epigenetically, and is affected by environmental factors. The molecular and neural pathways linked to the regulation of schizophrenia have been extensively studied. Over 180 Schizophrenic risk loci have now been recognized due to several genome-wide association studies (GWAS). It has been observed that multiple transcription factors (TF) binding-disrupting single nucleotide polymorphisms (SNPs) have been related to gene expression responsible for the disease in cerebral complexes. Copy number variation, SNP defects, and epigenetic changes in chromosomes may cause overexpression or underexpression of certain genes responsible for the disease. Nowadays, gene therapy is being implemented for its treatment as several of these genetic defects have been identified. Scientists are trying to use viral vectors, miRNA, siRNA, and CRISPR technology. In addition, nanotechnology is also being applied to target such genes. The primary aim of such targeting was to either delete or silence such hyperactive genes or induce certain genes that inhibit the expression of these genes. There are challenges in delivering the gene/DNA to the site of action in the brain, and scientists are working to resolve the same. The present article describes the basics regarding the disease, its causes and factors responsible, and the gene therapy solutions available to treat this disease.

Genetic and psychosocial stressors have independent effects on the level of subclinical psychosis: findings from the multinational EU-GEI study

AIMS

Gene x environment (G×E) interactions, i.e. genetic modulation of the sensitivity to environmental factors and/or environmental control of the gene expression, have not been reliably established regarding aetiology of psychotic disorders. Moreover, recent studies have shown associations between the polygenic risk scores for schizophrenia (PRS-SZ) and some risk factors of psychotic disorders, challenging the traditional gene v. environment dichotomy. In the present article, we studied the role of GxE interaction between psychosocial stressors (childhood trauma, stressful life-events, self-reported discrimination experiences and low social capital) and the PRS-SZ on subclinical psychosis in a population-based sample.

METHODS

Data were drawn from the EUropean network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI) study, in which subjects without psychotic disorders were included in six countries. The sample was restricted to European descendant subjects (n = 706). Subclinical dimensions of psychosis (positive, negative, and depressive) were measured by the Community Assessment of Psychic Experiences (CAPE) scale. Associations between the PRS-SZ and the psychosocial stressors were tested. For each dimension, the interactions between genes and environment were assessed using linear models and comparing explained variances of 'Genetic' models (solely fitted with PRS-SZ), 'Environmental' models (solely fitted with each environmental stressor), 'Independent' models (with PRS-SZ and each environmental factor), and 'Interaction' models (Independent models plus an interaction term between the PRS-SZ and each environmental factor). Likelihood ration tests (LRT) compared the fit of the different models.

RESULTS

There were no genes-environment associations. PRS-SZ was associated with positive dimensions (β = 0.092, R² = 7.50%), and most psychosocial stressors were associated with all three subclinical psychotic dimensions (except social capital and positive dimension). Concerning the positive dimension, Independent models fitted better than Environmental and Genetic models. No significant GxE interaction was observed for any dimension.

CONCLUSIONS

This study in subjects without psychotic disorders suggests that (i) the aetiological continuum hypothesis could concern particularly the positive dimension of subclinical psychosis, (ii) genetic and environmental factors have independent effects on the level of this positive dimension, (iii) and that interactions between genetic and individual environmental factors could not be identified in this sample.

Highly Recurrent Copy Number Variations in GABRB2 Associated With Schizophrenia and Premenstrual Dysphoric Disorder

OBJECTIVE

Although single-nucleotide polymorphisms in GABRB2, the gene encoding for GABAA receptors β2 subunit, have been associated with schizophrenia (SCZ), it is unknown whether there is any association of copy number variations (CNVs) in this gene with either SCZ or premenstrual dysphoric disorder (PMDD).

METHODS

In this study, the occurrences of the recurrent CNVs esv2730987 in Intron 6 and nsv1177513 in Exon 11 of GABRB2 in Chinese and German SCZ, and Chinese PMDD patients were compared to controls of same ethnicity and gender by quantitative PCR (qPCR).

RESULTS

The results demonstrated that copy-number-gains were enriched in both SCZ and PMDD patients with significant odds ratios (OR). For combined-gender SCZ patients versus controls, about two-fold increases were observed in both ethnic groups at both esv2730987 (OR = 2.15, p = 5.32E-4 in Chinese group; OR = 2.79, p = 8.84E-3 in German group) and nsv1177513 (OR = 3.29, p = 1.28E-11 in Chinese group; OR = 2.44, p = 6.17E-5 in German group). The most significant copy-number-gains were observed in Chinese females at nsv1177513 (OR = 3.41), and German females at esv2730987 (OR=3.96). Copy-number-gains were also enriched in Chinese PMDD patients versus controls at esv2730987 (OR = 10.53, p = 4.34E-26) and nsv1177513 (OR = 2.39, p = 3.19E-5).

CONCLUSION

These findings established for the first time the association of highly recurrent CNVs with SCZ and PMDD, suggesting the presence of an overlapping genetic basis with shared biomarkers for these two common psychiatric disorders.

The genomics of schizophrenia: Shortcomings and solutions

Due to recent advances in human genomic technologies, there have been explosive interests and extensive research on the genomics of schizophrenia, a severe psychiatric disorder characterized by social cognitive deficits, hallucinations, and delusions. These new technologies, including next-generation sequencing (NGS), genome-wide association studies (GWAS), and the Clustered Regularly Interspaced Short Palindromic Repeats-associated nuclease 9 (CRISPR/Cas9) genome editing platform are capable of interrogating and editing the genome directly. In the past few years, these efforts have led to the identification of important loci and genes susceptible to schizophrenia. The findings have increased our understanding of the underlying genetic causes of schizophrenia and aided in the development of new approaches for more effectively diagnosing and treating schizophrenia. Despite the substantial progress, there are several unanswered questions about the genomics of schizophrenia, and there are a number of potential shortcomings in the current literature considering the complexity of the disease and limits of the current technologies. In the present review, we assessed the existing literature on the genomics of schizophrenia, identifying the strengths and study design shortcomings from the following aspects: elucidation of the pathogenesis, early risk prediction and diagnosis, and the treatment of schizophrenia. Moreover, we have proposed solutions to overcome the shortcomings of past studies. Lastly, we have discussed the importance of developing multidisciplinary teams and global research groups in order to improve the lives of schizophrenic patients globally.

Autistic Symptoms in Schizophrenia Spectrum Disorders: A Systematic Review and Meta-Analysis

Background: Recent studies have examined the association between autism spectrum disorder and schizophrenia spectrum disorders, describing a number of cognitive features common to both conditions (e.g., weak central coherence, difficulties in set-shifting, impairment in theory of mind). Several studies have reported high levels of autistic symptoms in population with schizophrenia spectrum disorders. Our study systematically reviews and quantitatively synthetizes the current evidence on the presence of autistic symptoms in individuals with schizophrenia spectrum disorders. Methods: A comprehensive literature search of the PubMed/MEDLINE, Cochrane Library, CINHAL, and Embase databases was performed from the date of their inceptions until March 2018. The primary outcome measure was the Autism Spectrum Quotient (AQ). As secondary outcome measures, we analyzed the AQ subscales. Data were extracted and analyzed by using a conservative model and expressed by standardized mean difference (SMD). Results: Thirteen studies comprising a total of 1,958 individuals were included in the analysis. Results showed that individuals with schizophrenia spectrum disorders have higher levels of autistic symptoms compared to healthy controls [SMD: 1.39, 95% confidence interval (CI): 1.11 to 1.68] and lower levels of autistic symptoms compared to individuals with autism (SMD: -1.27, 95% CI: -1.77 to -0.76). Conclusions: Current findings support that individuals with schizophrenia spectrum disorders have higher autistic symptoms than healthy controls. Therefore, further studies are needed in order to shed light on the association between these two conditions.

A remark on copy number variation detection methods

Copy number variations (CNVs) are gain and loss of DNA sequence of a genome. High throughput platforms such as microarrays and next generation sequencing technologies (NGS) have been applied for genome wide copy number losses. Although progress has been made in both approaches, the accuracy and consistency of CNV calling from the two platforms remain in dispute. In this study, we perform a deep analysis on copy number losses on 254 human DNA samples, which have both SNP microarray data and NGS data publicly available from Hapmap Project and 1000 Genomes Project respectively. We show that the copy number losses reported from Hapmap Project and 1000 Genome Project only have < 30% overlap, while these reports are required to have cross-platform (e.g. PCR, microarray and high-throughput sequencing) experimental supporting by their corresponding projects, even though state-of-art calling methods were employed. On the other hand, copy number losses are found directly from HapMap microarray data by an accurate algorithm, i.e. CNVhac, almost all of which have lower read mapping depth in NGS data; furthermore, 88% of which can be supported by the sequences with breakpoint in NGS data. Our results suggest the ability of microarray calling CNVs and the possible introduction of false negatives from the unessential requirement of the additional cross-platform supporting. The inconsistency of CNV reports from Hapmap Project and 1000 Genomes Project might result from the inadequate information containing in microarray data, the inconsistent detection criteria, or the filtration effect of cross-platform supporting. The statistical test on CNVs called from CNVhac show that the microarray data can offer reliable CNV reports, and majority of CNV candidates can be confirmed by raw sequences. Therefore, the CNV candidates given by a good caller could be highly reliable without cross-platform supporting, so additional experimental information should be applied in need instead of necessarily.

Prospects for Modeling Abnormal Neuronal Function in Schizophrenia Using Human Induced Pluripotent Stem Cells

Excitatory dopaminergic neurons, inhibitory GABAergic neurons, microglia, and oligodendrocytes have all been implicated in schizophrenia (SZ) network pathology. Still, SZ has been a difficult disorder to study, not only because of the limitations of animal models in capturing the complexity of the human mind, but also because it is greatly polygenic, with high rates of variability across the population. The advent of patient-derived pluripotent stem cells and induced neural and glial cultures has brought hope for modeling the molecular dysfunction underlying SZ pathology in a patient-specific manner. Here I review the successes of the patient-specific induced cultures in generating different cell types for the study of SZ, with special emphasis on the utility of co-culture techniques, both two- and three-dimensional, for modeling network dysfunction in disease.

Potential Value of Genomic Copy Number Variations in Schizophrenia

Schizophrenia is a devastating neuropsychiatric disorder affecting approximately 1% of the global population, and the disease has imposed a considerable burden on families and society. Although, the exact cause of schizophrenia remains unknown, several lines of scientific evidence have revealed that genetic variants are strongly correlated with the development and early onset of the disease. In fact, the heritability among patients suffering from schizophrenia is as high as 80%. Genomic copy number variations (CNVs) are one of the main forms of genomic variations, ubiquitously occurring in the human genome. An increasing number of studies have shown that CNVs account for population diversity and genetically related diseases, including schizophrenia. The last decade has witnessed rapid advances in the development of novel genomic technologies, which have led to the identification of schizophrenia-associated CNVs, insight into the roles of the affected genes in their intervals in schizophrenia, and successful manipulation of the target CNVs. In this review, we focus on the recent discoveries of important CNVs that are associated with schizophrenia and outline the potential values that the study of CNVs will bring to the areas of schizophrenia research, diagnosis, and therapy. Furthermore, with the help of the novel genetic tool known as the Clustered Regularly Interspaced Short Palindromic Repeats-associated nuclease 9 (CRISPR/Cas9) system, the pathogenic CNVs as genomic defects could be corrected. In conclusion, the recent novel findings of schizophrenia-associated CNVs offer an exciting opportunity for schizophrenia research to decipher the pathological mechanisms underlying the onset and development of schizophrenia as well as to provide potential clinical applications in genetic counseling, diagnosis, and therapy for this complex mental disease.

A copy number variation in PKD1L2 is associated with colorectal cancer predisposition in korean population

Recently reported genome-wide association studies have identified more than 20 common low-penetrance colorectal cancer (CRC) susceptibility loci. Recent studies have reported that copy number variations (CNVs) are considered important human genomic variants related to cancer, while the contribution of CNVs remains unclear. We performed array comparative genomic hybridization (aCGH) in 36 CRC patients and 47 controls. Using breakpoint PCR, we confirmed the breakpoint of the PKD1L2 deletion region. High frequency of PKD1L2 CNV was observed in CRC cases. We validated the association between PKD1L2 variation and CRC risk in 1,874 cases and 2,088 controls (OR = 1.44, 95% CI = 1.04-1.98, p = 0.028). Additionally, PKD1L2 CNV is associated with increased CRC risk in patients younger than 50 years (OR = 2.14, 95% CI 1.39-3.30, p = 5.8 × 10^-4 ). In subgroup analysis according to body mass index (BMI), we found that the CN loss of PKD1L2 with BMI above or equal to 25 exhibited a significant increase in CRC risk (OR = 2.29, 95% CI 1.29-4.05, p = 0.005). PKD1L2 CNV with BMI above or equal to 25 and age below 50 is associated with a remarkably increased risk of colorectal cancer (OR = 5.24, 95% CI 2.36-11.64, p= 4.8 × 10^-5 ). Moreover, we found that PKD1L2 variation in obese patients (BMI ≥ 25) was associated with poor survival rate (p = 0.026). Our results suggest that the common PKD1L2 CNV is associated with CRC, and PKD1L2 CNV with high BMI and/or age below 50 exhibited a significant increased risk of CRC. In obese patients, PKD1L2 variation was associated with poor survival.

MGP-HMM: Detecting genome-wide CNVs using an HMM for modeling mate pair insertion sizes and read counts

MOTIVATION

Association of Copy Number Variation (CNV) with schizophrenia, autism, developmental disabilities and fatal diseases such as cancer is verified. Recent developments in Next Generation Sequencing (NGS) have facilitated the CNV studies. However, many of the current CNV detection tools are not capable of discriminating tandem duplication from non-tandem duplications.

RESULTS

In this study, we propose MGP-HMM as a tool which besides detecting genome-wide deletions discriminates tandem duplications from non-tandem duplications. MGP-HMM takes mate pair abnormalities into account and predicts the digitized number of tandem or non-tandem copies. Abnormalities in the mate pair directions and insertion sizes, after being mapped to the reference genome, are elucidated using a Hidden Markov Model (HMM). For this purpose, a Mixture Gaussian density with time-dependent parameters is applied for emitting mate pair insertion sizes from HMM states. Indeed, depending on observed abnormalities in mate pair insertion size or its orientation, each component in the mixture density will have different parameters. MGP-HMM also applies a Poisson distribution for modeling read depth data. This parametric modeling of the mate pair reads enables us to estimate the length of CNVs precisely, which is an advantage over methods which rely only on read depth approach for the CNV detection. Hidden state of the proposed HMM is the digitized copy number of a genomic segment and states correspond to the multipliers of the mixture Gaussian components. The accuracy of our model is validated on a set of next generation sequencing real and simulated data and is compared to other tools.

Mouse Model of Chromosome 15q13.3 Microdeletion Syndrome Demonstrates Features Related to Autism Spectrum Disorder

The chromosome 15q13.3 microdeletion is a pathogenic copy number variation conferring epilepsy, intellectual disability, schizophrenia, and autism spectrum disorder (ASD). We generated mice carrying a deletion of 1.2 Mb homologous to the 15q13.3 microdeletion in human patients. Here, we report that mice with a heterozygous deletion on a C57BL/6 background (D/+ mice) demonstrated phenotypes including enlarged/heavier brains (macrocephaly) with enlarged lateral ventricles, decreased social interactions, increased repetitive grooming behavior, reduced ultrasonic vocalizations, decreased auditory-evoked gamma band EEG, and reduced event-related potentials. D/+ mice had normal body weight, activity levels, sensory gating, and cognitive abilities and no signs of epilepsy/seizures. Our results demonstrate that D/+ mice represent ASD-related phenotypes associated with 15q13.3 microdeletion syndrome. Further investigations using this chromosome-engineered mouse model may uncover the common mechanism(s) underlying ASD and other neurodevelopmental/psychiatric disorders representing the 15q13.3 microdeletion syndrome, including epilepsy, intellectual disability, and schizophrenia.

SIGNIFICANCE STATEMENT

Recently discovered pathologic copy number variations (CNVs) from patients with neurodevelopmental/psychiatric disorders show very strong penetrance and thus are excellent candidates for mouse models of disease that can mirror the human genetic conditions with high fidelity. A 15q13.3 microdeletion in humans results in a range of neurodevelopmental/psychiatric disorders, including epilepsy, intellectual disability, schizophrenia, and autism spectrum disorder (ASD). The disorders conferred by a 15q13.3 microdeletion also have overlapping genetic architectures and comorbidity in other patient populations such as those with epilepsy and schizophrenia/psychosis, as well as schizophrenia and ASD. We generated mice carrying a deletion of 1.2 Mb homologous to the 15q13.3 microdeletion in human patients, which allowed us to investigate the potential causes of neurodevelopmental/psychiatric disorders associated with the CNV.

DUF1220 copy number is associated with schizophrenia risk and severity: implications for understanding autism and schizophrenia as related diseases

The copy number of DUF1220, a protein domain implicated in human brain evolution, has been linearly associated with autism severity. Given the possibility that autism and schizophrenia are related disorders, the present study examined DUF1220 copy number variation in schizophrenia severity. There are notable similarities between autism symptoms and schizophrenia negative symptoms, and divergence between autism symptoms and schizophrenia positive symptoms. We therefore also examined DUF1220 copy number in schizophrenia subgroups defined by negative and positive symptom features, versus autistic individuals and controls. In the schizophrenic population (N=609), decreased DUF1220 copy number was linearly associated with increasing positive symptom severity (CON1 P=0.013, HLS1 P=0.0227), an association greatest in adult-onset schizophrenia (CON1 P=0.00155, HLS1 P=0.00361). In schizophrenic males, DUF1220 CON1 subtype copy number increase was associated with increased negative symptom severity (P=0.0327), a finding similar to that seen in autistic populations. Subgroup analyses demonstrated that schizophrenic individuals with predominantly positive symptoms exhibited reduced CON1 copy number compared with both controls (P=0.0237) and schizophrenic individuals with predominantly negative symptoms (P=0.0068). These findings support the view that (1) autism and schizophrenia exhibit both opposing and partially overlapping phenotypes and may represent a disease continuum, (2) variation in DUF1220 copy number contributes to schizophrenia disease risk and to the severity of both disorders, and (3) schizophrenia and autism may be, in part, a harmful by-product of the rapid and extreme evolutionary increase in DUF1220 copy number in the human species.

Transcriptomics analysis of iPSC-derived neurons and modeling of neuropsychiatric disorders

Induced pluripotent stem cell (iPSC)-derived neurons and neural progenitors are great resources for studying neural development and differentiation and their disruptions in disease conditions, and hold the promise of future cell therapy. In general, iPSC lines can be established either specifically from patients with neuropsychiatric disorders or from healthy subjects. The iPSCs can then be induced to differentiate into neural lineages and the iPSC-derived neurons are valuable for various types of cell-based assays that seek to understand disease mechanisms and identify and test novel therapies. In addition, it is an ideal system for gene expression profiling (i.e., transcriptomic analysis), an efficient and cost-effective way to explore the genetic programs regulating neurodevelopment. Moreover, transcriptomic comparison, which can be performed between patient-derived samples and controls, or in control lines in which the expression of specific genes has been disrupted, can uncover convergent gene targets and pathways that are downstream of the hundreds of candidate genes that have been associated with neuropsychiatric disorders. The results, especially after integration with spatiotemporal transcriptomic profiles of normal human brain development, have indeed helped to uncover gene networks, molecular pathways, and cellular signaling that likely play critical roles in disease development and progression. On the other hand, despite the great promise, many challenges remain in the usage of iPSC-derived neurons for modeling neuropsychiatric disorders, for example, how to generate relatively homogenous populations of specific neuronal subtypes that are affected in a particular disorder and how to better address the genetic heterogeneity that exists in the patient population.

DeAnnCNV: a tool for online detection and annotation of copy number variations from whole-exome sequencing data

With the decrease in costs, whole-exome sequencing (WES) has become a very popular and powerful tool for the identification of genetic variants underlying human diseases. However, integrated tools to precisely detect and systematically annotate copy number variations (CNVs) from WES data are still in great demand. Here, we present an online tool, DeAnnCNV (Detection and Annotation of Copy Number Variations from WES data), to meet the current demands of WES users. Upon submitting the file generated from WES data by an in-house tool that can be downloaded from our server, DeAnnCNV can detect CNVs in each sample and extract the shared CNVs among multiple samples. DeAnnCNV also provides additional useful supporting information for the detected CNVs and associated genes to help users to find the potential candidates for further experimental study. The web server is implemented in PHP + Perl + MATLAB and is online available to all users for free at http://mcg.ustc.edu.cn/db/cnv/.

Connecting Anxiety and Genomic Copy Number Variation: A Genome-Wide Analysis in CD-1 Mice

Genomic copy number variants (CNVs) have been implicated in multiple psychiatric disorders, but not much is known about their influence on anxiety disorders specifically. Using next-generation sequencing (NGS) and two additional array-based genotyping approaches, we detected CNVs in a mouse model consisting of two inbred mouse lines showing high (HAB) and low (LAB) anxiety-related behavior, respectively. An influence of CNVs on gene expression in the central (CeA) and basolateral (BLA) amygdala, paraventricular nucleus (PVN), and cingulate cortex (Cg) was shown by a two-proportion Z-test (p = 1.6 x 10^-31), with a positive correlation in the CeA (p = 0.0062), PVN (p = 0.0046) and Cg (p = 0.0114), indicating a contribution of CNVs to the genetic predisposition to trait anxiety in the specific context of HAB/LAB mice. In order to confirm anxiety-relevant CNVs and corresponding genes in a second mouse model, we further examined CD-1 outbred mice. We revealed the distribution of CNVs by genotyping 64 CD 1 individuals using a high-density genotyping array (Jackson Laboratory). 78 genes within those CNVs were identified to show nominally significant association (48 genes), or a statistical trend in their association (30 genes) with the time animals spent on the open arms of the elevated plus-maze (EPM). Fifteen of them were considered promising candidate genes of anxiety-related behavior as we could show a significant overlap (permutation test, p = 0.0051) with genes within HAB/LAB CNVs. Thus, here we provide what is to our knowledge the first extensive catalogue of CNVs in CD-1 mice and potential corresponding candidate genes linked to anxiety-related behavior in mice.

Common variants of IRF3 conferring risk of schizophrenia

Schizophrenia is a brain disorder with high heritability. Recent studies have implicated genes involved in the immune response pathway in the pathogenesis of schizophrenia. Interferon regulatory factor 3 (IRF3), a virus-immune-related gene, activates the transcription of several interferon-induced genes, and functionally interacts with several schizophrenia susceptibility genes. To test whether IRF3 is a schizophrenia susceptibility gene, we analyzed the associations of its SNPs with schizophrenia in independent population samples as well as reported data from expression quantitative trait loci (eQTL) in healthy individuals. We observed multiple independent SNPs in IRF3 showing nominally significant associations with schizophrenia (P < 0.05); more intriguingly, a SNP (rs11880923), which is significantly correlated with IRF3 expression in independent samples (P < 0.05), is also consistently associated with schizophrenia across different cohorts and in combined samples (odds ratio = 1.075, Pmeta = 2.08 × 10(-5)), especially in Caucasians (odds ratio = 1.078, Pmeta = 2.46 × 10(-5)). These results suggested that IRF3 is likely a risk gene for schizophrenia, at least in Caucasians. Although the clinical associations of IRF3 with diagnosis did not achieve genome-wide level of statistical significance, the observed odds ratio is comparable with other susceptibility loci identified through large-scale genetic association studies on schizophrenia, which could be regarded simply as small but detectable effects.

Global patterns of apparent copy number variation in birds revealed by cross-species comparative genomic hybridization

There is a growing interest in copy number variation (CNV) and the recognition of its importance in phenotype, disease, adaptation and speciation. CNV data is usually ascertained by array-CGH within-species, but similar inter-species comparisons have also been made in primates, mice and domestic mammals. Here, we conducted a broad appraisal of putative cross-species CNVs in birds, 16 species in all, using the standard array-CGH approach. Using a chicken oligonucleotide microarray, we detected 790 apparent CNVs within 135 unique regions and developed a bioinformatic tool 'CNV Analyser' for analysing and visualising cross-species data sets. We successfully addressed four hypotheses as follows: (a) Cross-species CNVs (compared to chicken) are, as suggested from preliminary evidence, smaller and fewer in number than in mammals; this 'dogma' was rejected in the light of the new evidence. (b) CNVs in birds are likely to have a functional effect through an association with genes; a large proportion of detected regions (70 %) were indeed associated with genes (suggesting functional significance), however, not necessarily more so than in mammals. (c) There are more CNVs in birds with more rearranged karyotypes; this hypothesis was rejected. Indeed, Falco species contained fewer than most with relatively standard (chicken-like) karyotypes. (d) There are more CNVs per megabase on micro-chromosomes than macrochromosomes; this hypothesis was accepted. Indeed, in species with rearranged karyotypes characterised by chromosomal fusions, the fused former microchromosomes still 'behaved' as though they were their microchromosomal ancestors. Gene ontology analysis of CNVRs revealed enrichment in immune response and antigen presentation genes and five CNVRs were perfectly correlated with the unique loss of sexual dichromatism in one Galliformes species.

Does rare matter? Copy number variants at 16p11.2 and the risk of psychosis: a systematic review of literature and meta-analysis

BACKGROUND

In the last 5 years an increasing number of studies have found that individuals who have micro-duplications at 16p11.2 may have an increased risk of mental disorders including psychotic syndromes.

OBJECTIVE

Our main aim was to review all the evidence in the literature for the association between copy number variants (CNVs) at 16p11.2 and psychosis.

METHODS

We have conducted a systematic review and a meta-analysis utilising the PRISMA statement criteria. We included all original studies (published in English) which presented data on CNVs at 16p11.2 in patients affected by schizophrenia, schizoaffective disorder or bipolar disorder.

RESULTS

We retrieved 15 articles which fulfilled our inclusion criteria. Eleven articles were subsequently selected for a meta-analysis that showed a 10 fold increased risk of psychosis in patients with proximal 16p11.2 duplications. We conducted a second meta-analysis of those studies with low risk of overlap in order to obtain the largest possible sample with the lowest risk of repeated results: 5 studies were selected and we found an odds ratio (OR) of 14.4 (CI=5.2-39.8; p<0.001) for psychosis with proximal 16p11.2 duplications. The results were not significant for micro-deletions in the same region. Finally extracting only those studies that included patients with schizophrenia we found an OR=16.0 (CI=5.4-47.3: p<0.001) CONCLUSIONS: There is a fourteen fold-increased risk of psychosis and a sixteen fold increased risk of schizophrenia in individuals with micro-duplication at proximal 16p11.2.

The phenotypic manifestations of rare CNVs in schizophrenia

There is compelling evidence for the role of copy number variants (CNVs) in schizophrenia susceptibility, and it has been estimated that up to 2-3% of schizophrenia cases may carry rare CNVs. Despite evidence that these events are associated with an increased risk across categorical neurodevelopmental disorders, there is limited understanding of the impact of CNVs on the core features of disorders like schizophrenia. Our objective was to evaluate associations between rare CNVs in differentially brain expressed (BE) genes and the core features and clinical correlates of schizophrenia. The sample included 386 cases of Irish ancestry with a diagnosis of schizophrenia, at least one rare CNV impacting any gene, and a core set of phenotypic measures. Statistically significant associations between deletions in differentially BE genes were found for family history of mental illness (decreased prevalence of all CNVs and deletions, unadjusted and adjusted) and for paternal age (increase in deletions only, unadjusted, among those with later ages at birth of patient). The strong effect of a lack of a family history on BE genes suggests that CNVs may comprise one pathway to schizophrenia, whereas a positive family history could index other genetic mechanisms that increase schizophrenia vulnerability. To our knowledge, this is the first investigation of the association between genome-wide CNVs and risk factors and sub-phenotypic features of schizophrenia beyond cognitive function.

Prevalence of Asperger syndrome among patients of an Early Intervention in Psychosis team

BACKGROUND

There is a lack of systematic studies into comorbidity of Asperger syndrome and psychosis.

AIM

To determine the prevalence of Asperger syndrome among patients of an early intervention in psychosis service.

METHODS

This study was a cross-sectional survey consisting of three phases: screening, case note review and diagnostic interviews. All patients on caseload (n = 197) were screened using the Autism Spectrum Disorder in Adults Screening Questionnaire. The case notes of patients screened positive were then reviewed for information relevant to Asperger syndrome. Those suspected of having Asperger syndrome were invited for a diagnostic interview.

RESULTS

Thirty patients were screened positive. Three of them already had a diagnosis of Asperger syndrome made by child and adolescent mental health services. After case note review, 13 patients were invited to interview. Four did not take part, so nine were interviewed. At interview, four were diagnosed with Asperger syndrome. In total, seven patients had Asperger syndrome. Thus, the prevalence rate in this population is at least 3.6%.

CONCLUSIONS

The results suggest that the prevalence of Asperger syndrome in first-episode psychosis is considerably higher than that in the general population. Clinicians working in early intervention teams need to be alert to the possibility of Asperger syndrome when assessing patients.

Protein-protein interaction and pathway analyses of top schizophrenia genes reveal schizophrenia susceptibility genes converge on common molecular networks and enrichment of nucleosome (chromatin) assembly genes in schizophrenia susceptibility loci

Recent genome-wide association studies have identified many promising schizophrenia candidate genes and demonstrated that common polygenic variation contributes to schizophrenia risk. However, whether these genes represent perturbations to a common but limited set of underlying molecular processes (pathways) that modulate risk to schizophrenia remains elusive, and it is not known whether these genes converge on common biological pathways (networks) or represent different pathways. In addition, the theoretical and genetic mechanisms underlying the strong genetic heterogeneity of schizophrenia remain largely unknown. Using 4 well-defined data sets that contain top schizophrenia susceptibility genes and applying protein-protein interaction (PPI) network analysis, we investigated the interactions among proteins encoded by top schizophrenia susceptibility genes. We found proteins encoded by top schizophrenia susceptibility genes formed a highly significant interconnected network, and, compared with random networks, these PPI networks are statistically highly significant for both direct connectivity and indirect connectivity. We further validated these results using empirical functional data (transcriptome data from a clinical sample). These highly significant findings indicate that top schizophrenia susceptibility genes encode proteins that significantly directly interacted and formed a densely interconnected network, suggesting perturbations of common underlying molecular processes or pathways that modulate risk to schizophrenia. Our findings that schizophrenia susceptibility genes encode a highly interconnected protein network may also provide a novel explanation for the observed genetic heterogeneity of schizophrenia, ie, mutation in any member of this molecular network will lead to same functional consequences that eventually contribute to risk of schizophrenia.

Duplication of the 15q11-q13 region: clinical and genetic study of 30 new cases

BACKGROUND

15q11-q13 region is an area of well-known susceptibility to genomic rearrangements, in which several breakpoints have been identified (BP1-BP5). Duplication of this region is observed in two instances: presence of a supernumerary marker chromosome (SMC) derived of chromosome 15, or interstitial tandem duplication. Duplications are clinically characterized by a variable phenotype that includes central hypotonia, developmental delay, speech delay, seizure, minor dysmorphic features and autism.

METHODS

Retrospective clinical and molecular study of 30 unrelated patients who were identified among the patients seen at the genetic clinics of Robert DEBRE hospital with microduplication of the 15q11-q13 region.

RESULTS

Fifteen patients presented with a supernumerary marker derived from chromosome 15. In fourteen cases the SMC was of large size, encompassing the Prader-Willi/Angelman critical region. All but one was maternal in origin. One patient had a PWS-like phenotype in absence of maternal UPD. In one case, the marker had a smaller size and contained only the BP1-BP2 region. Fifteen patients presented with interstitial duplication. Four cases were inherited from phenotypically normal parents (3 maternal and 1 paternal). Phenotypic features were somewhat variable and 57% presented with autism. Twelve patients showed cerebral anomalies and 18 patients had an abnormal EEG with a typical, recognizable pattern of excessive diffuse rapid spikes in the waking record, similar to the pattern observed after benzodiazepine exposure. Duplication of paternally expressed genes MKRN3, MAGEL2 and NDN in two autistic patients without extra material of a neighboring region enhances their likelihood to be genes related to autism.

Revisiting the relationship between autism and schizophrenia: toward an integrated neurobiology

Schizophrenia and autism have been linked since their earliest descriptions. Both are disorders of cerebral specialization originating in the embryonic period. Genetic, molecular, and cytologic research highlights a variety of shared contributory mechanisms that may lead to patterns of abnormal connectivity arising from altered development and topology. Overt behavioral pathology likely emerges during or after neurosensitive periods in which resource demands overwhelm system resources and the individual's ability to compensate using interregional activation fails. We are at the threshold of being able to chart autism and schizophrenia from the inside out. In so doing, the door is opened to the consideration of new therapeutics that are developed based upon molecular, synaptic, and systems targets common to both disorders.

Rare CNVs and tag SNPs at 15q11.2 are associated with schizophrenia in the Han Chinese population

BACKGROUND

Rare copy number variations (CNVs) were involved in the etiology of neuropsychiatric disorders, and some of them appeared to be shared risk factors for several different diseases. One of those promising loci is the CNV at 15q11.2, including 4 genes, TUBGCP5, CYFIP1, NIPA2, and NIPA1. Several studies showed that microdeletions at this locus were significant associated with schizophrenia. In the current study, we investigated the role of both rare CNVs and common single nucleotide polymorphisms (SNPs) at 15q11.2 in schizophrenia in the Chinese Han population.

METHODS

We screened deletions at 15q11.2 in 2058 schizophrenia patients and 3275 normal controls in Chinese Han population by Affymetrix 500K/6.0 SNP arrays and SYBR green real-time polymerase chain reaction and then validated deletions by multiplex ligation-dependent probe amplification and Taqman real-time assays. We successfully genotyped 27 tag SNPs in total and tested associations in 1144 schizophrenia cases and 1144 normal controls.

RESULTS

We found a triple increase of deletions in cases over controls, with OR=4.45 (95% CI=1.36-14.60) and P=.014. In the analysis of common SNPs, we found that the most significant SNP in schizophrenia was rs4778334 (OR=.72, 95% CI=0.60-0.87, allelic P=.0056 after permutation, genotypic P=.015 after permutation). We also found SNP rs1009153 in CYFIP1 was associated with schizophrenia (OR=0.82, 95% CI=0.73-0.93, allelic P=.044 after permutation).

CONCLUSION

We found that both rare deletions and common variants at 15q11.2 were associated with schizophrenia in the Chinese Han population.

Risk architecture of schizophrenia: the role of epigenetics

PURPOSE OF REVIEW

To systematize existing data and review new findings on the cause of schizophrenia and outline an improved mixed model of schizophrenia risk.

RECENT FINDINGS

Multiple and variable genetic and environmental factors interact to influence the risk of schizophrenia. Both rare variants with large effect and common variants with small effect contribute to genetic risk of schizophrenia, with no indication for differential impact on its clinical features. Accumulating evidence supports a genetic architecture of schizophrenia with multiple scenarios, including additive polygenic, heterogeneity, and mixed polygenic-heterogeneity. The epigenetic mechanisms that mediate gene-environment (GxE) interactions provide a framework to incorporate environmental factors into models of schizophrenia risk. Environmental pathogens with small effect on risk have robust effects in the context of family history of schizophrenia. Hence, genetic risk for schizophrenia may be expressed in part as sensitivity to environmental factors.

SUMMARY

We propose an improved mixed model of schizophrenia risk in which abnormal epigenetic states with large effects are superimposed on a polygenic liability to schizophrenia. This scenario can account for GxE interactions and shared family environment, which in many cases are not explained by a single structural variant of large effect superimposed on polygenes (the traditional mixed model).

What have we learnt about the causes of ADHD?

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) and its possible causes still attract controversy. Genes, pre and perinatal risks, psychosocial factors and environmental toxins have all been considered as potential risk factors.

METHOD

This review (focussing on literature published since 1997, selected from a search of PubMed) critically considers putative risk factors with a focus on genetics and selected environmental risks, examines their relationships with ADHD and discusses the likelihood that these risks are causal as well as some of the main implications.

RESULTS

No single risk factor explains ADHD. Both inherited and noninherited factors contribute and their effects are interdependent. ADHD is familial and heritable. Research into the inherited and molecular genetic contributions to ADHD suggest an important overlap with other neurodevelopmental problems, notably, autism spectrum disorders. Having a biological relative with ADHD, large, rare copy number variants, some small effect size candidate gene variants, extreme early adversity, pre and postnatal exposure to lead and low birth weight/prematurity have been most consistently found as risk factors, but none are yet known to be definitely causal. There is a large literature documenting associations between ADHD and a wide variety of putative environmental risks that can, at present, only be regarded as correlates. Findings from research designs that go beyond simply testing for association are beginning to contest the robustness of some environmental exposures previously thought to be ADHD risk factors.

CONCLUSIONS

The genetic risks implicated in ADHD generally tend to have small effect sizes or be rare and often increase risk of many other types of psychopathology. Thus, they cannot be used for prediction, genetic testing or diagnostic purposes beyond what is predicted by a family history. There is a need to consider the possibility of parents and siblings being similarly affected and how this might impact on engagement with families, influence interventions and require integration with adult services. Genetic contributions to disorder do not necessarily mean that medications are the treatment of choice. We also consider how findings might influence the conceptualisation of ADHD, public health policy implications and why it is unhelpful and incorrect to dichotomise genetic/biological and environmental explanations. It is essential that practitioners can interpret genetic and aetiological research findings and impart informed explanations to families.

Structural and copy number variants in the human genome: implications for psychiatry

Copy number variants are small chromosomal deletions and duplications. When they alter the dose of genes critical for normal brain development and adult brain functioning they may cause severe disorders such as autism and schizophrenia. Numerous such loci have recently been identified. They are offering amazing leads for neuropsychiatric research.

The hunt for gene effects pertinent to behavioral traits and psychiatric disorders: from mouse to human

The field of behavioral genetics was reviewed in the classic 1960 text by Fuller and Thompson. Since then, there has been remarkable progress in the genetic analysis of animal behavior. Many molecular genetic methods in common use today were not even anticipated in 1960. Animal models for many human psychiatric disorders have been discovered or created. In human behavior genetics, however, powerful new methods have failed to reveal even one bona fide, replicable gene effect pertinent to the normal range of variation in intelligence and personality. There is no explanatory or predictive value in that genetic information. For several psychiatric disorders, including autism and schizophrenia, many large genetic effects arise from de novo mutations. Genetically, the disorders are heterogeneous; different cases with the same diagnosis have different causes. The promises of the molecular genetic revolution have not been fulfilled in behavioral domains of most interest to human psychology.

Using latent class growth analysis to form trajectories of premorbid adjustment in schizophrenia

Premorbid adjustment varies widely among individuals with schizophrenia and has been shown to bear significantly on prodrome and onset characteristics, and on cognition, symptoms, and functioning after onset. The current analysis focused on the Premorbid Adjustment Scale, a retrospective measure assessing social and academic function at several time points from early childhood to illness onset. In an effort to explore discrete developmental subtypes, we applied latent class growth analysis to data from the Premorbid Adjustment Scale in our sample of individuals with schizophrenia (N = 208), finding three latent trajectory classes. The first of these classes showed consistently adequate-to-good social and academic functioning before onset; the second showed initially good function and deterioration with time until onset; the third showed poor functioning in childhood that deteriorated further during the years up to diagnosis. The classes differed significantly in terms of age of onset, processing speed, and functioning after onset. There were no significant differences in symptomatology. Our findings illustrate a potentially powerful methodological approach to the problem of heterogeneity in schizophrenia research, and add weight to the notion that aspects of premorbid history may be useful for subtyping schizophrenia patients. The potential implications of this subtyping strategy, including those pertaining to potential genetics studies, are discussed.

Modeling read counts for CNV detection in exome sequencing data

Varying depth of high-throughput sequencing reads along a chromosome makes it possible to observe copy number variants (CNVs) in a sample relative to a reference. In exome and other targeted sequencing projects, technical factors increase variation in read depth while reducing the number of observed locations, adding difficulty to the problem of identifying CNVs. We present a hidden Markov model for detecting CNVs from raw read count data, using background read depth from a control set as well as other positional covariates such as GC-content. The model, exomeCopy, is applied to a large chromosome X exome sequencing project identifying a list of large unique CNVs. CNVs predicted by the model and experimentally validated are then recovered using a cross-platform control set from publicly available exome sequencing data. Simulations show high sensitivity for detecting heterozygous and homozygous CNVs, outperforming normalization and state-of-the-art segmentation methods.

Genome-wide algorithm for detecting CNV associations with diseases

Background

SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP data using the marker intensities. However, these algorithms lack specificity to detect small CNVs owing to the high false positive rate when calling CNVs based on the intensity values. Therefore, the resulting association tests lack power even if the CNVs affecting disease risk are common. An alternative procedure called PennCNV uses information from both the marker intensities as well as the genotypes and therefore has increased sensitivity.

Results

By using the hidden Markov model (HMM) implemented in PennCNV to derive the probabilities of different copy number states which we subsequently used in a logistic regression model, we developed a new genome-wide algorithm to detect CNV associations with diseases. We compared this new method with association test applied to the most probable copy number state for each individual that is provided by PennCNV after it performs an initial HMM analysis followed by application of the Viterbi algorithm, which removes information about copy number probabilities. In one of our simulation studies, we showed that for large CNVs (number of SNPs ≥ 10), the association tests based on PennCNV calls gave more significant results, but the new algorithm retained high power. For small CNVs (number of SNPs <10), the logistic algorithm provided smaller average p-values (e.g., p = 7.54e - 17 when relative risk RR = 3.0) in all the scenarios and could capture signals that PennCNV did not (e.g., p = 0.020 when RR = 3.0). From a second set of simulations, we showed that the new algorithm is more powerful in detecting disease associations with small CNVs (number of SNPs ranging from 3 to 5) under different penetrance models (e.g., when RR = 3.0, for relatively weak signals, power = 0.8030 comparing to 0.2879 obtained from the association tests based on PennCNV calls). The new method was implemented in software GWCNV. It is freely available at http://gwcnv.sourceforge.net, distributed under a GPL license.

Conclusions

We conclude that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than the existing HMM algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.

Advances in multidisciplinary and cross-species approaches to examine the neurobiology of psychiatric disorders

Current approaches to dissect the molecular neurobiology of complex neuropsychiatric disorders such as schizophrenia and major depression have been rightly criticized for failing to provide benefits to patients. Improving the translational potential of our efforts will require the development and refinement of better disease models that consider a wide variety of contributing factors, such as genetic variation, gene-by-environment interactions, endophenotype or intermediate phenotype assessment, cross species analysis, sex differences, and developmental stages. During a targeted expert meeting of the European College of Neuropsychopharmacology (ECNP) in Istanbul, we addressed the opportunities and pitfalls of current translational animal models of psychiatric disorders and agreed on a series of core guidelines and recommendations that we believe will help guiding further research in this area.

Genome-wide copy number variation analysis in attention-deficit/hyperactivity disorder: association with neuropeptide Y gene dosage in an extended pedigree

Attention-deficit/hyperactivity disorder (ADHD) is a common, highly heritable neurodevelopmental syndrome characterized by hyperactivity, inattention and increased impulsivity. To detect micro-deletions and micro-duplications that may have a role in the pathogenesis of ADHD, we carried out a genome-wide screen for copy number variations (CNVs) in a cohort of 99 children and adolescents with severe ADHD. Using high-resolution array comparative genomic hybridization (aCGH), a total of 17 potentially syndrome-associated CNVs were identified. The aberrations comprise 4 deletions and 13 duplications with approximate sizes ranging from 110 kb to 3 Mb. Two CNVs occurred de novo and nine were inherited from a parent with ADHD, whereas five are transmitted by an unaffected parent. Candidates include genes expressing acetylcholine-metabolizing butyrylcholinesterase (BCHE), contained in a de novo chromosome 3q26.1 deletion, and a brain-specific pleckstrin homology domain-containing protein (PLEKHB1), with an established function in primary sensory neurons, in two siblings carrying a 11q13.4 duplication inherited from their affected mother. Other genes potentially influencing ADHD-related psychopathology and involved in aberrations inherited from affected parents are the genes for the mitochondrial NADH dehydrogenase 1 α subcomplex assembly factor 2 (NDUFAF2), the brain-specific phosphodiesterase 4D isoform 6 (PDE4D6) and the neuronal glucose transporter 3 (SLC2A3). The gene encoding neuropeptide Y (NPY) was included in a ∼3 Mb duplication on chromosome 7p15.2-15.3, and investigation of additional family members showed a nominally significant association of this 7p15 duplication with increased NPY plasma concentrations (empirical family-based association test, P=0.023). Lower activation of the left ventral striatum and left posterior insula during anticipation of large rewards or losses elicited by functional magnetic resonance imaging links gene dose-dependent increases in NPY to reward and emotion processing in duplication carriers. These findings implicate CNVs of behaviour-related genes in the pathogenesis of ADHD and are consistent with the notion that both frequent and rare variants influence the development of this common multifactorial syndrome.

Detection of recurrent rearrangement breakpoints from copy number data

BACKGROUND

Copy number variants (CNVs), including deletions, amplifications, and other rearrangements, are common in human and cancer genomes. Copy number data from array comparative genome hybridization (aCGH) and next-generation DNA sequencing is widely used to measure copy number variants. Comparison of copy number data from multiple individuals reveals recurrent variants. Typically, the interior of a recurrent CNV is examined for genes or other loci associated with a phenotype. However, in some cases, such as gene truncations and fusion genes, the target of variant lies at the boundary of the variant.

RESULTS

We introduce Neighborhood Breakpoint Conservation (NBC), an algorithm for identifying rearrangement breakpoints that are highly conserved at the same locus in multiple individuals. NBC detects recurrent breakpoints at varying levels of resolution, including breakpoints whose location is exactly conserved and breakpoints whose location varies within a gene. NBC also identifies pairs of recurrent breakpoints such as those that result from fusion genes. We apply NBC to aCGH data from 36 primary prostate tumors and identify 12 novel rearrangements, one of which is the well-known TMPRSS2-ERG fusion gene. We also apply NBC to 227 glioblastoma tumors and predict 93 novel rearrangements which we further classify as gene truncations, germline structural variants, and fusion genes. A number of these variants involve the protein phosphatase PTPN12 suggesting that deregulation of PTPN12, via a variety of rearrangements, is common in glioblastoma.

CONCLUSIONS

We demonstrate that NBC is useful for detection of recurrent breakpoints resulting from copy number variants or other structural variants, and in particular identifies recurrent breakpoints that result in gene truncations or fusion genes. Software is available at http://http.//cs.brown.edu/people/braphael/software.html.

Eating disorders and obesity: two sides of the same coin?

The eating disorders anorexia and bulimia nervosa have traditionally been regarded as entirely separate from obesity. Eating disorders have been regarded as Western culture-bound syndromes, arising in societies with excessive emphasis on weight, shape and appearance, and best treated by psychological therapies, in particular cognitive behavioural therapy or familybased interventions. In contrast, obesity has been considered a medical illness with metabolic and genetic origins, and thought to be best treated by mainstream medicine, involving dietary, drug or surgical treatment. We believe that this polarisation is fundamentally flawed, and research and treatment of both types of disorder would be better served by greater appreciation of the psychosocial components of obesity and the biological and genetic components of eating disorders. There are similarities in phenotype (such as excessive attempts at weight control, binge eating behaviours) and in risk factors (such as low self-esteem, external locus of control, childhood abuse and neglect, dieting, media exposure, body image dissatisfaction, weight-related teasing and shared susceptibility genes). One example of shared genetic risk is the brain-derived neurotrophic factor (BNDF) gene, in which the valine allele of the Va166Met amino acid polymorphism predisposes to obesity, whereas the methionine allele predisposes to eating disorders. Thus the evidence suggests that these disorders will have both shared and distinct susceptibility factors; some will predispose to both types of disorder, some will push in opposite directions, and some will separate them.

The contribution of epidemiology to defining the most appropriate approach to genetic research on schizophrenia

Psychosis is thought to have a strong genetic component, but many efforts to discover the underlying putative schizophrenia genes have yielded disappointing results. In fact, no strong associations emerged in the first genome-wide association studies in psychiatry and weakly observed associations were not related to the candidate genes identified in previous studies. These partially successful findings may be explained by the fact that genetic research in psychiatry suffers from confounding issues related to phenotype definition, the considerable degree of phenotypic variability and diagnostic uncertainty, absence of specific neuropathological features and environmental influences. To make progress it is first necessary to deconstruct psychosis based on symptomatology, and then to correlate particular phenotypes with genetic variants. Moreover, it is time to conduct studies that define persistent aspects of the schizophrenic profile that are more likely to represent an underlying biological pathogenesis, as opposed to fluctuating symptoms that are possibly environmentally mediated. In fact, progress in understanding the etiology of schizophrenia will depend upon the availability of good measures of genetic liability as well as relevant environmental exposures during critical periods of an individual's life. If environmental and/or genetic factors are not precisely measured, it is impossible to study their independent effects or interactions.

Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases

Recently, an increasing number of susceptibility variants have been identified for complex diseases. At the same time, the concern of "missing heritability" has also emerged. There is however no unified way to assess the heritability explained by individual genetic variants for binary outcomes. A systemic and quantitative assessment of the degree of "missing heritability" for complex diseases is lacking. In this study, we measure the variance in liability explained by individual variants, which can be directly interpreted as the locus-specific heritability. The method is extended to deal with haplotypes, multi-allelic markers, multi-locus genotypes, and markers in linkage disequilibrium. Methods to estimate the standard error and confidence interval are proposed. To assess our current level of understanding of the genetic basis of complex diseases, we conducted a survey of 10 diseases, evaluating the total variance explained by the known variants. The diseases under evaluation included Alzheimer's disease, bipolar disorder, breast cancer, coronary artery disease, Crohn's disease, prostate cancer, schizophrenia, systemic lupus erythematosus (SLE), type 1 diabetes and type 2 diabetes. The median total variance explained across the 10 diseases was 9.81%, while the median variance explained per associated SNP was around 0.25%. Our results suggest that a substantial proportion of heritability remains unexplained for the diseases under study. Programs to implement the methodologies described in this paper are available at http://sites.google.com/site/honcheongso/software/varexp.

Neurobiology and phenotypic expression in early onset schizophrenia

AIM

Early-onset schizophrenia (onset before adulthood) is a rare and severe form of the disorder that shows phenotypic and neurobiological continuity with adult-onset schizophrenia. Here, we provide a synthesis of keynote findings in this enriched population to understand better the neurobiology and pathophysiology of early-onset schizophrenia.

METHODS

A synthetic and integrative approach is applied to review studies stemming from epidemiology, phenomenology, cognition, genetics and neuroimaging data. We provide conclusions and future directions of research on early-onset schizophrenia.

RESULTS

Childhood and adolescent-onset schizophrenia is associated with severe clinical course, greater rates of premorbid abnormalities, poor psychosocial functioning and increased severity of brain abnormalities. Early-onset cases show similar neurobiological correlates and phenotypic deficits to adult-onset schizophrenia, but show worse long-term psychopathological outcome. Emerging technological advances have provided important insights into the genomic architecture of early-onset schizophrenia, suggesting that some genetic variations may occur more frequently and at a higher rate in young-onset than adult-onset cases.

CONCLUSIONS

Clinical, cognitive, genetic and imaging data suggest increased severity in early-onset schizophrenia. Studying younger-onset cases can provide useful insights into the neurobiological mechanisms of schizophrenia and the complexity of gene-environment interactions leading to the emergence of this debilitating disorder.

Copy number variations of chromosome 16p13.1 region associated with schizophrenia

Deletions and reciprocal duplications of the chromosome 16p13.1 region have recently been reported in several cases of autism and mental retardation (MR). As genomic copy number variants found in these two disorders may also associate with schizophrenia, we examined 4345 schizophrenia patients and 35,079 controls from 8 European populations for duplications and deletions at the 16p13.1 locus, using microarray data. We found a threefold excess of duplications and deletions in schizophrenia cases compared with controls, with duplications present in 0.30% of cases versus 0.09% of controls (P=0.007) and deletions in 0.12 % of cases and 0.04% of controls (P>0.05). The region can be divided into three intervals defined by flanking low copy repeats. Duplications spanning intervals I and II showed the most significant (P = 0.00010) association with schizophrenia. The age of onset in duplication and deletion carriers among cases ranged from 12 to 35 years, and the majority were males with a family history of psychiatric disorders. In a single Icelandic family, a duplication spanning intervals I and II was present in two cases of schizophrenia, and individual cases of alcoholism, attention deficit hyperactivity disorder and dyslexia. Candidate genes in the region include NTAN1 and NDE1. We conclude that duplications and perhaps also deletions of chromosome 16p13.1, previously reported to be associated with autism and MR, also confer risk of schizophrenia.

A pilot multivariate parallel ICA study to investigate differential linkage between neural networks and genetic profiles in schizophrenia

Understanding genetic influences on both healthy and disordered brain function is a major focus in psychiatric neuroimaging. We utilized task-related imaging findings from an fMRI auditory oddball task known to be robustly associated with abnormal activation in schizophrenia, to investigate genomic factors derived from multiple single nucleotide polymorphisms (SNPs) from genes previously shown to be associated with schizophrenia. Our major aim was to investigate the relationship of these genomic factors to normal/abnormal brain functionality between controls and schizophrenia patients. We studied a Caucasian-only sample of 35 healthy controls and 31 schizophrenia patients. All subjects performed an auditory oddball task, which consists of detecting an infrequent sound within a series of frequent sounds. Each subject was characterized on 24 different SNP markers spanning multiple risk genes previously associated with schizophrenia. We used a recently developed technique named parallel independent component analysis (para-ICA) to analyze this multimodal data set (Liu et al., 2008). The method aims to identify simultaneously independent components of each modality (functional imaging, genetics) and the relationships between them. We detected three fMRI components significantly correlated with two distinct gene components. The fMRI components, along with their significant genetic profile (dominant SNP) correlations were as follows: (1) Inferior frontal-anterior/posterior cingulate-thalamus-caudate with SNPs from Brain derived neurotropic factor (BDNF) and dopamine transporter (DAT) [r=-0.51; p<0.0001], (2) superior/middle temporal gyrus-cingulate-premotor with SLC6A4_PR and SLC6A4_PR_AG (serotonin transporter promoter; 5HTTLPR) [r=0.27; p=0.03], and (3) default mode-fronto-temporal gyrus with Brain derived neurotropic factor and dopamine transporter (BDNF, DAT) [r=-0.25; p=0.04]. Functional components comprised task-relevant regions (including PFC, ACC, STG and MTG) frequently identified as abnormal in schizophrenia. Further, gene-fMRI combinations 1 (Z=1.75; p=0.03), 2 (Z=1.84; p=0.03) and 3 (Z=1.67; p=0.04) listed above showed significant differences between controls and patients, based on their correlated loading coefficients. We demonstrate a framework to identify interactions between "clusters" of brain function and of genetic information. Our results reveal the effect/influence of specific interactions, (perhaps epistastatic in nature), between schizophrenia risk genes on imaging endophenotypes representing attention/working memory and goal directed related brain function, thus establishing a useful methodology to probe multivariate genotype-phenotype relationships.

Genetic associations of brain structural networks in schizophrenia: a preliminary study

BACKGROUND

Schizophrenia is a complex genetic disorder, with multiple putative risk genes and many reports of reduced cortical gray matter. Identifying the genetic loci contributing to these structural alterations in schizophrenia (and likely also to normal structural gray matter patterns) could aid understanding of schizophrenia's pathophysiology. We used structural parameters as potential intermediate illness markers to investigate genomic factors derived from single nucleotide polymorphism (SNP) arrays.

METHOD

We used research quality structural magnetic resonance imaging (sMRI) scans from European American subjects including 33 healthy control subjects and 18 schizophrenia patients. All subjects were genotyped for 367 SNPs. Linked sMRI and genetic (SNP) components were extracted to reveal relationships between brain structure and SNPs, using parallel independent component analysis, a novel multivariate approach that operates effectively in small sample sizes.

RESULTS

We identified an sMRI component that significantly correlated with a genetic component (r = -.536, p < .00005); components also distinguished groups. In the sMRI component, schizophrenia gray matter deficits were in brain regions consistently implicated in previous reports, including frontal and temporal lobes and thalamus (p < .01). These deficits were related to SNPs from 16 genes, several previously associated with schizophrenia risk and/or involved in normal central nervous system development, including AKT, PI3K, SLC6A4, DRD2, CHRM2, and ADORA2A.

CONCLUSIONS

Despite the small sample size, this novel analysis method identified an sMRI component including brain areas previously reported to be abnormal in schizophrenia and an associated genetic component containing several putative schizophrenia risk genes. Thus, we identified multiple genes potentially underlying specific structural brain abnormalities in schizophrenia.

Elusive copy number variation in the mouse genome

BACKGROUND

Array comparative genomic hybridization (aCGH) to detect copy number variants (CNVs) in mammalian genomes has led to a growing awareness of the potential importance of this category of sequence variation as a cause of phenotypic variation. Yet there are large discrepancies between studies, so that the extent of the genome affected by CNVs is unknown. We combined molecular and aCGH analyses of CNVs in inbred mouse strains to investigate this question.

PRINCIPAL FINDINGS

Using a 2.1 million probe array we identified 1,477 deletions and 499 gains in 7 inbred mouse strains. Molecular characterization indicated that approximately one third of the CNVs detected by the array were false positives and we estimate the false negative rate to be more than 50%. We show that low concordance between studies is largely due to the molecular nature of CNVs, many of which consist of a series of smaller deletions and gains interspersed by regions where the DNA copy number is normal.

CONCLUSIONS

Our results indicate that CNVs detected by arrays may be the coincidental co-localization of smaller CNVs, whose presence is more likely to perturb an aCGH hybridization profile than the effect of an isolated, small, copy number alteration. Our findings help explain the hitherto unexplored discrepancies between array-based studies of copy number variation in the mouse genome.

Genome-wide association analysis of copy number variations in subarachnoid aneurysmal hemorrhage

Subarachnoid aneurysmal hemorrhage (SAH) due to cerebral aneurysm rupture is a very serious disease resulting in high mortality rate. It has been known that genetic factors are involved in the risk of SAH. A recent breakthrough in genomic variation called copy number variation (CNV) has been revealed to be involved in risks of human diseases. In this study, we hypothesized that CNVs can predict the risk of SAH. We used the Illumina HumanHap300 BeadChip (317 503 markers) to genotype 497 individuals in a Japanese population. Furthermore, individual CNVs were identified using signal and allelic intensities. The genetic effect of CNV on the risk of SAH was evaluated using multivariate logistic regression controlling for age and gender in 187 common CNV regions (frequency >1%). From a total of 4574 individual CNVs identified in this study (9.7 CNVs per individual), we were able to discover 1644 unique CNV regions containing 1232 genes. The identified variations were validated using visual examination of the genoplot image, overlapping analysis with the Database of Genomic Variants (73.2%), CNVpartition (72.4%) and quantitative PCR. Interestingly, two CNV regions, chr4:153210505-153212191 (deletion, 4q31.3, P=0.0005, P(corr) (corrected P-value)=0.04) and chr10:6265006-6267388 (duplication, 10p15.1, P=0.0006, P(corr)=0.05), were significantly associated with the risk of SAH after multiple testing corrections. Our results suggest that the newly identified CNV regions may contribute to SAH disease susceptibility.