Identification Of Risk Genes Research Articles

Chapter 21 - Genetics of autism spectrum disorder

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder characterized by impaired social interaction and stereotyped behaviors. ASD has a strong and complex genetic component, with multiple familial inheritance patterns and an estimate of up to 1000 genes potentially implicated. Over the past decade, genomic technologies have enabled rapid progress in the identification of risk genes for ASD. In this chapter, we review the delineation of ASD disease genes starting from traditional genetic studies such as linkage and association, and then focusing on more recent studies utilizing genomic technologies, such as high-throughput genotyping and exome sequencing.

Identification of risk genes associated with myocardial infarction based on the recursive feature elimination algorithm and support vector machine classifier.

The aim of the present study was to identify risk genes in myocardial infarction. Microarray data GSE34198, containing data from the peripheral blood of 49 myocardial infarction samples and 48 corresponding control samples, were downloaded from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). The DEGs were used to construct a protein-protein interaction (PPI) network of patient samples, from which the feature genes were identified using the neighboring score method. The recursive feature elimination (RFE) algorithm was employed to select the risk genes among feature genes, which were subsequently applied to perform a support vector machine (SVM) classifier to identify the specific signature in myocardial infarction samples. Another dataset, GSE61144, was also downloaded to verify the efficacy of the classifier. A total of 724 downregulated and 483 upregulated DEGs were screened in patient samples compared with control samples in the GSE34198 dataset. The PPI network of myocardial infarction was comprised of 1,083 nodes (genes) and 46,363 lines (connections). Using the neighborhood scoring method, the top 100 feature genes in myocardial infarction samples were identified as the disease feature genes, which distinguish the myocardial infarction samples from the control samples. The RFE algorithm screened 15 risk genes, which were employed to construct a SVM classifier with an average precision of 88% to the patient sample following visualization by a confusion matrix. The predictive precision of the classifier on another microarray dataset, GSE61144, was 0.92, with an average true positive of 0.9278 and an average false positive of 0.2361. A-kinase-anchoring protein 12 (AKAP12) and glycine receptor α2 (GLRA2) were two risk genes in the SVM classifier. Therefore, AKAP12 and GLRA2 exert potential roles in the development of myocardial infarction, potentially by influencing cardiac contractility and protecting against ischemia-reperfusion injury, which may provide clues in developing potential diagnostic biomarkers or therapeutic targets for myocardial infarction.

Analysis of shared homozygosity regions in Saudi siblings with attention deficit hyperactivity disorder

AimGenetic and clinical complexities are common features of most psychiatric illnesses that pose a major obstacle in risk-gene identification. Attention deficit hyperactivity disorder (ADHD) is the most prevalent child-onset psychiatric illness, with high heritability. Over the past decade, numerous genetic studies utilizing various approaches, such as genome-wide association, candidate-gene association, and linkage analysis, have identified a multitude of candidate loci/genes. However, such studies have yielded diverse findings that are rarely reproduced, indicating that other genetic determinants have not been discovered yet. In this study, we carried out sib-pair analysis on seven multiplex families with ADHD from Saudi Arabia. We aimed to identify the candidate chromosomal regions and genes linked to the disease.Patients and methodsA total of 41 individuals from multiplex families were analyzed for shared regions of homozygosity. Genes within these regions were prioritized according to their potential relevance to ADHD.ResultsWe identified multiple genomic regions spanning different chromosomes to be shared among affected members of each family; these included chromosomes 3, 5, 6, 7, 8, 9, 10, 13, 17, and 18. We also found specific regions on chromosomes 8 and 17 to be shared between affected individuals from more than one family. Among the genes present in the regions reported here were involved in neurotransmission (GRM3, SIGMAR1, CHAT, and SLC18A3) and members of the HLA gene family (HLA-A, HLA-DPA1, and MICC).ConclusionThe candidate regions identified in this study highlight the genetic diversity of ADHD. Upon further investigation, these loci may reveal candidate genes that enclose variants associated with ADHD. Although most ADHD studies were conducted in other populations, our study provides insight from an understudied, ethnically interesting population.

Support vector machine model of developmental brain gene expression data for prioritization of Autism risk gene candidates.

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders with clinical heterogeneity and a substantial polygenic component. High-throughput methods for ASD risk gene identification produce numerous candidate genes that are time-consuming and expensive to validate. Prioritization methods can identify high-confidence candidates. Previous ASD gene prioritization methods have focused on a priori knowledge, which excludes genes with little functional annotation or no protein product such as long non-coding RNAs (lncRNAs). We have developed a support vector machine (SVM) model, trained using brain developmental gene expression data, for the classification and prioritization of ASD risk genes. The selected feature model had a mean accuracy of 76.7%, mean specificity of 77.2% and mean sensitivity of 74.4%. Gene lists comprised of an ASD risk gene and adjacent genes were ranked using the model's decision function output. The known ASD risk genes were ranked on average in the 77.4th, 78.4th and 80.7th percentile for sets of 101, 201 and 401 genes respectively. Of 10,840 lncRNA genes, 63 were classified as ASD-associated candidates with a confidence greater than 0.95. Genes previously associated with brain development and neurodevelopmental disorders were prioritized highly within the lncRNA gene list. liangjw@clemson.eduSupplementary information: Supplementary data are available at Bioinformatics online.

EPS: an empirical Bayes approach to integrating pleiotropy and tissue-specific information for prioritizing risk genes.

Researchers worldwide have generated a huge volume of genomic data, including thousands of genome-wide association studies (GWAS) and massive amounts of gene expression data from different tissues. How to perform a joint analysis of these data to gain new biological insights has become a critical step in understanding the etiology of complex diseases. Due to the polygenic architecture of complex diseases, the identification of risk genes remains challenging. Motivated by the shared risk genes found in complex diseases and tissue-specific gene expression patterns, we propose as an Empirical Bayes approach to integrating Pleiotropy and Tissue-Specific information (EPS) for prioritizing risk genes. As demonstrated by extensive simulation studies, EPS greatly improves the power of identification for disease-risk genes. EPS enables rigorous hypothesis testing of pleiotropy and tissue-specific risk gene expression patterns. All of the model parameters can be adaptively estimated from the developed expectation-maximization (EM) algorithm. We applied EPS to the bipolar disorder and schizophrenia GWAS from the Psychiatric Genomics Consortium, along with the gene expression data for multiple tissues from the Genotype-Tissue Expression project. The results of the real data analysis demonstrate many advantages of EPS. The EPS software is available on https://sites.google.com/site/liujin810822 CONTACT: eeyang@hkbu.edu.hk Supplementary data are available at Bioinformatics online.

Towards "biophysical psychiatry": a modeling approach for studying effects of schizophrenia-linked genes on single-neuron excitability

Genome-wide association studies (GWAS) employing large sample sizes and sophisticated statistical methods have recently yielded detailed information on the set of genes affected in various psychiatric disorders. This is especially important for polygenic, highly heritable disorders such as schizophrenia (SCZ) [1]. The success lately witnessed in gene discovery brings up the next big challenge for psychiatric genetics - translation of the genetic associations into biological insights [2]. In this study, we propose a computational approach for investigating the effects of a collection of excitability-related genes on various neuronal characteristics. As a proof of principle, we apply our approach for studying effects of SCZ-linked genes on firing behavior of a layer V pyramidal cell (L5PC). A total of 108 genetic loci were recently confirmed to be associated with the risk of SCZ [3]. These loci span a wide set of protein-coding genes. The disorder is associated with genes affecting transmembrane currents of all major cationic species, Na+, K+, and Ca2+. In addition, some of the SCZ-linked genes are involved in regulation of the Ca2+ concentration in the intracellular medium, which is another great contributor to neuron excitability. All above aspects of electrophysiology are included in a recent multi-compartmental model of L5PC [4], which accurately describes the perisomatic firing behavior and its interplay with the generation of an apical Ca2+ spike. In this work, we rely on data from functional genomics studies that describe the effects of variants of certain ion channel or calcium transporter-encoding genes on the channel activation/inactivation properties or intracellular Ca2+ dynamics. We carry out our study by linking these effects to a change in the corresponding neuron model parameters, and observing the implication that these variants have on the information integration in an L5PC. It should be noted that information does not generally exist for the effect of single nucleotide polymorphism (SNP) variants identified through GWASs on the parameters required for the computational models. We instead use information obtained from in vitro studies of more extreme genetic variations, including loss of function mutations. A central assumption of this approach is that the effects of SNP variants can be represented as scaled-down versions of those of the more extreme variants, and that the emergence of the full psychiatric disease phenotype results from the combined effect of a large number of subtle SNP effects. Our results show a multitude of alterations of the firing behavior and integration of apical stimuli in neurons equipped with the considered gene variants. An especially interesting observation is that the variants affect the suppression of a second apical stimulus, which might have a connection with the deficit in prepulse inhibition, a condition often observed in SCZ patients. Although the analyses presented here are specific to L5PCs and SCZ-related genes, our biophysical psychiatry framework may be directly applicable to other cell types and other polygenic diseases, such as bipolar disorder and autism, given an identification of risk genes related to neuronal excitability. Furthermore, our approach could be directly applied to biophysically detailed models of neuronal networks and extended to consider synaptic ion channel-encoding genes that are relevant in SCZ [5].

Psychiatric genetics, neurogenetics, and neurodegeneration.

Psychiatric genetics, neurogenetics, and neurodegeneration.

Towards a �biophysical psychiatry�: A modeling approach for studying effects of schizophrenia-linked genes on single-neuron excitability

Towards a �biophysical psychiatry�: A modeling approach for studying effects of schizophrenia-linked genes on single-neuron excitability

Leveraging Genetics and Genomics to Define the Causes of Mental Illness

Psychiatric genetics is in the midst of a profound transformation. For decades, the primary challenge for the field was simply the reliable identification of risk genes. Now, for multiple disorders, the path to systematic discovery is clear. Recent findings in studies of schizophrenia, autism, bipolar disorder, and substance abuse have demonstrated that a comprehensive elucidation of genetic risk factors is now within reach—dependent only on sufficient sample sizes and the resources to pursue these studies.

Identification of risk genes for autism spectrum disorder through copy number variation analysis in Austrian families

Autism or autism spectrum disorder (ASD) is a range of neurodevelopmental disorders starting in early childhood and is characterized by impairments in communication and reciprocal social interaction and presence of restricted and repetitive patterns of behavior. The contribution of genetic factors to autism is clear in twin and family studies. It is apparent that, overall, ASD is a complex non-Mendelian disorder. Recent studies suggest that copy number variations (CNVs) play a significant role in the etiology of ASD. For the current work, we recruited 245 family members from 73 ASD families from Styria, Austria. The DNA from probands was genotyped with Affymetrix single nucleotide polymorphism (SNP) 6.0 microarrays to screen for CNVs in their genomes. Analysis of the microarray data was performed using three different algorithms, and a list of stringent calls was compared to existing CNV data from over 2,357 controls of European ancestry. For stringent calls not present in controls, quantitative real-time PCR (qRT-PCR) was used to validate the CNVs in the probands and in their family members. Twenty-two CNVs were validated from this set (five of which are apparently de novo), many of which appear likely to disrupt genes that may be considered as good candidates for neuropsychiatric disorders, including DLG2, S100B, ARX, DIP2A, HPCAL1, and GPHN. Several others disrupt genes that have previously been implicated in autism, such as BDNF, AUTS2, DPP6, and C18orf22, and our data add to the growing evidence of their involvement in ASD.

Use of a Genealogical Database Demonstrates Heritability of Pulmonary Fibrosis

Pulmonary fibrosis (PF) is a progressive fatal disease of unknown etiology. Identification of risk genes and pathways will enhance our understanding of this disease. Analysis of Utah genealogical resources has shown previously strong evidence for a genetic contribution to other disease, such as cancer. This approach has led to gene discovery in diseases, such as breast cancer and colon cancer and is used here for PF to quantify the heritability. We hypothesize that there is a heritable contribution to death from PF and use existing genealogic and death certificate data to examine patterns of relatedness amongst individuals who have died of PF. We analyzed familial clustering of individuals who died from PF using the Utah Population Database, a unique population-based genealogical resource that has been linked to death certificates dating from 1904. We identified 1,000 individuals with at least three generations of genealogy data and a cause of death documented as PF (cases). We estimated the relative risk (RR) of death from PF among the first-, second-, and third-degree relatives of cases. We also tested the hypothesis of excess relatedness among the cases by comparing the average pairwise relatedness of all cases to the average pair-wise relatedness of 1,000 sets of matched controls. We observed significantly increased risk for death from PF among the first- (RR=4.69), second- (RR=1.92), and third-degree relatives (RR=1.14) of cases. The average relatedness of the 1,000 cases was significantly higher than the expected average relatedness of matched control sets (p<0.001). When close (first- and second-degree) relationships were ignored, significantly increased relatedness remained (p=0.002). Our results demonstrate significant clustering among both close and distant relatives, providing strong support for genetic contributions to death from PF. High-risk pedigrees derived from this unique resource may help identify new risk genes and gene pathways.

Identification of risk genes linking inflammation and fibrosis in passively acquired autoimmune disease. (P4170)

Abstract Fetuses exposed to maternal Ro60 autoantibodies can develop life threatening cardiac conduction disease and cardiomyopathy, manifestations of neonatal lupus (NL). Recent data support an injury model where immune complexes (IC) comprised of Ro60 autoantigen, hY3 single stranded RNA, and anti-Ro60 engage Toll-like receptor (TLR) 7 and promote secretion of inflammatory and fibrotic factors by macrophages. This study sought to identify candidate risk genes based on:1) expression arrays from IC-stimulated human macrophages with and without TLR7 inhibitor; and 2) single nucleotide polymorphism (SNP) genotyping of 116 children with cardiac NL. In IC stimulated macrophages, 891 upregulated genes were reduced by at least 50% with the TLR7 inhibitor. Among these, there was an enrichment of genes related to inflammation and fibrosis. Ingenuity Pathway Analysis was used to generate a list of 327 genes with fibrotic functions that showed a significant enrichment of associations with cardiac NL (P=2.27x10-9). Joint analysis of these fibrotic genes identified 20 candidates which were also overexpressed by macrophages in TLR7-dependent arrays. Among the dataset was Relaxin receptor 1 (RXFP1), a G-protein coupled receptor that binds relaxin and plays a role in collagen and connective tissue remodeling. Key inflammatory mediators included IL6 and TNF. These data support the applicability of a novel model which can help uncover biological pathways underlying autoantibody-mediated tissue injury.

Genetics of the epilepsies

We aim to review the most recent advances in the field of epilepsy genetics with particular focus on the progress in gene discovery in monogenic epilepsies, identification of risk genes in complex genetic epilepsies and recent findings in the field of epilepsy pharmacogenomics. During the last 12 months, the use of massive parallel sequencing technologies has allowed for the discovery of several genes for monogenic epilepsies. Most importantly, PRRT2 was identified as the long-sought gene for benign familial infantile seizures. Mutations in KCNT1 were found in two seemingly unrelated monogenic epilepsies including malignant migrating partial seizures of infancy and severe autosomal dominant nocturnal frontal lobe epilepsy. A genome-wide association study in idiopathic generalized epilepsy revealed the first common risk variants for human seizure disorders including variants in VRK2, PNPO and SCN1A. Furthermore, a landmark study provided evidence that screening for the HLA-B1502 variant may prevent carbamazepine CBZ-induced side effects in the Taiwanese population. Also, HLA-A3101 variants were identified as a risk factor for carbamazepine side effects in Europeans. Novel technologies and an unprecedented level of international collaboration have resulted in identification of novel genes for monogenic and complex genetic epilepsies as well as risk factors for side effects of antiepileptic drugs. This review provides an overview of the most relevant studies in the last year and highlights the future direction of the field.

Insights into type 1 diabetes provided by genetic analyses

Recent identification of over 60 loci contributing to the susceptibility of developing type 1 diabetes (T1D) provides a timely opportunity to assess what is currently known of the genetics of T1D, and what these discoveries may tell us about the disease itself. The major findings will be discussed under five main themes: T1D risk gene identification, molecular mechanisms of susceptibility, shared genetic cause with other diseases, development of novel analytical methods, and understanding disease heterogeneity. The plethora of T1D risk genes that have been identified risk overwhelming clinicians with lists of gene names and symbols that have little bearing on management, and provide a challenge for researchers to place the genetics of T1D in a more amenable clinical context.

Psychopathology: genetics and the stress-vulnerability hypothesis

Since the findings of dopamine blockade by antipsychotics, a dysfunction of the dopamine system has been proposed to underly psychopathological symptoms in schizophrenia. In SPECT and PET imaging studies, radioligand binding to the dopamine receptor or transporter reflects the occupancy by dopamine itself or antipsychotics. Using dual-isotope imaging for the evaluation of pre- and postsynaptic site, Schmitt et al. [1] examined striatal dopamine transporter and dopamine D2 receptor availability in first-episode schizophrenia patients and patients with haloperidol treatment compared to healthy controls. In drug-naive patients with positive symptoms, dopamine transporter availability correlated negatively with psychopathology as did D2 receptor binding with disorganization and hallucinatory behavior, thus strengthening the dopamine hypothesis for positive symptoms. D2 receptor and dopamine transporter availability was lower in the haloperidol group which confirms previous dopamine blockade studies. However, response to antipsychotics is individual, and genetic factors may contribute to a different outcome. Mossner et al. [2] reported that patients homozygous for the risk allele ZNF804A show poorer improvement of positive symptoms compared to patients with a protective allele. Thus, identification of risk genes may help identifying patients with a different course of the disorder. A pharmacogenetic study of Crisafulli et al. [3] investigated 10 SNPs of genes related to pathophysiological pathways in Korean patients. They found a SNP within 5HTR1A associated with clinical improvement in both positive and negative symptoms. In schizophrenia patients, improvement of quality of life as treatment outcome among others is dependent on support in case of problems and assistance in coping with daily life demands. Landolt et al. [4] revealed that unmet needs changing to fulfilled ones enhanced quality of life. In this context, psychosocial functioning and psychotic symptoms may be influenced by alterations of theory of mind and empathy as underlying social-cognitive deficits and possibly are heritable. In first-degree relatives of schizophrenia patients, Montag et al. [5] showed impairment in cognitive skills, suggesting that social-cognitive abilities may serve as an intermediate phenotype for schizophrenia. In a re-analysis of AMDP data by nonmetric multidimensional scaling, Lage et al. [6] replicated clusters of psychopathological symptoms in two independent samples dating from 1980 and 2003. In individual’s health, the psychological well-being dimension and depressive symptoms are important variables and Morelatto de Souza and Paz Loayza Hidalgo [7] validated the WHO well-being index in a Brazilian population and showed this scale to be a useful tool for screening of depressive symptoms. Hagihara and Abe [8] highlight the influence of media reports on suicide and counteracting effects of stopping the sale of suicide-related products in Japan. Beside genetics, stressful experience and trauma are risk factors for the development of psychiatric diseases. Linden et al. [9] analyzed 100 case files of German servicemen during World War I and classified them in agreement with modern diagnoses. Most ancient diagnoses were “psychopathic constitution” or “hysteria.” Using contemporary diagnostics, some of these soldiers had symptoms of post-traumatic stress disorder. There was a high incidence of pseudoseizures that highlights different human responses to traumatic experience and stress in a historical context. Stressful experiences are known to increase vulnerability to schizophrenia. Rossi et al. [10] evaluated the Community Assessment of Psychic Experiences (CAPE) in students who survived an earthquake 2009 in Italy. Unexpectedly, earth quake survivors showed lower CAPE scores than nonexposed subjects. This is in line with the hypothesis that stressful events preceding psychosis are not derived from external traumatic experiences. In a previous study, Puustinen et al. [11] reported C-reactive protein, marker of cardiovascular disease, to be associated with psychological distress. However, in a cross-sectional and 3-year follow-up study in Japanese male workers, Kawada [12] failed to show this relationship, which is probably due to ethnicity. Life experience, stressful events and other environmental factors are known to influence the risk of developing mental diseases. The missing link between environment and neurobiology may be explained by epigenetic mechanisms, such as DNA methylation and acetylation in disease-related brain regions. Wiers [13] refers to an fMRI study of the COMT gene with the Val allele being associated with DNA methylation [14]. She proposes the use of genetic imaging in combination with investigations of epigenetic markers in blood cells to unravel gene-environmental interactions in severe psychiatric disorders.

Recent advances in the genetic epidemiology and molecular genetics of substance use disorders

This article reviews current advances in the genetics of substance use disorders (SUDs). Both genetic and environmental sources of risk are required to develop a complete picture of SUD etiology. Genetic sources of risk for SUDs are not highly substance specific in their effects. Genetic and environmental risks for SUDs typically do not only add together but also interact with each other over development. Risk gene identification for SUDs has been difficult, with one recent success in identifying nicotinic receptor variants that affect risk for nicotine dependence. The impact of genetic variants on SUD risk will individually be small. Although genetic epidemiologic methods are giving us an increasingly accurate map of broad causal pathways to SUDs, gene discovery will be needed to identify the specific biological systems. Identifying these risk genes and understanding their action will require large clinical samples, and interaction between these studies and work in model organisms.

Meta-analysis of heterogeneous data sources for genome-scale identification of risk genes in complex phenotypes

Meta-analyses of large-scale association studies typically proceed solely within one data type and do not exploit the potential complementarities in other sources of molecular evidence. Here, we present an approach to combine heterogeneous data from genome-wide association (GWA) studies, protein-protein interaction screens, disease similarity, linkage studies, and gene expression experiments into a multi-layered evidence network which is used to prioritize the entire protein-coding part of the genome identifying a shortlist of candidate genes. We report specifically results on bipolar disorder, a genetically complex disease where GWA studies have only been moderately successful. We validate one such candidate experimentally, YWHAH, by genotyping five variations in 640 patients and 1,377 controls. We found a significant allelic association for the rs1049583 polymorphism in YWHAH (adjusted P = 5.6e-3) with an odds ratio of 1.28 [1.12-1.48], which replicates a previous case-control study. In addition, we demonstrate our approach's general applicability by use of type 2 diabetes data sets. The method presented augments moderately powered GWA data, and represents a validated, flexible, and publicly available framework for identifying risk genes in highly polygenic diseases. The method is made available as a web service at www.cbs.dtu.dk/services/metaranker.

Motor control in children with ADHD and non‐affected siblings: deficits most pronounced using the left hand

Attention-deficit/hyperactivity disorder (ADHD) is strongly influenced by heritability. Identifying heritable vulnerability traits (endophenotypes) that mark a relatively high risk of developing the disorder can contribute to the identification of risk genes. A fruitful area for the search for such endophenotypes may be motor control in children with ADHD, since the disorder is frequently accompanied by motor problems. The current study used a large sample of 350 children with ADHD, 195 non-affected siblings and 271 normal controls aged 5-19 years. Children were administered two computerised motor control tasks in which they had to trace a path between two circles (Tracking task) and follow a randomly moving target (Pursuit task). Both tasks were performed with both the right and the left hand. Children with ADHD were less precise and stable than controls. Non-affected siblings also deviated from controls, but only on the Tracking task. Group differences were modulated by the use of the right versus the left hand: no group differences emerged when the right hand was used, yet group differences did emerge when the left hand was used. Performance on both tasks was significantly familial. Imprecision and instability of movements in children with ADHD and in their non-affected siblings as measured by the Tracking task might be suitable endophenotypic candidates: these deficits are familially present in children having ADHD as well as in their non-affected siblings. Motor performance might be best assessed in children using their left hand, because motor control deficits are most pronounced using the left hand. This might relate to right hemispheric brain pathology in children with ADHD (and possibly in their non-affected siblings) that is related to the control of the left hand and/or relate to differential effects of daily life practice on both hands, which may be smaller on the left hand.

Characteristics of the broader phenotype in autism: A study of siblings using the children's communication checklist‐2

Non-autistic relatives of people with autistic disorder have an increased risk of social and communicative difficulties: this is known as the "broad phenotype." Better methods for characterizing the broad phenotype are needed to facilitate identification of risk genes for autism. 29 siblings of 20 children with autistic disorder, 13 siblings of 9 children with PDDNOS, and 46 typically developing control children from 26 families were assessed by parental report using the Children's Communication Checklist-2 (CCC-2). Groups were matched on age and IQ and siblings with autism were excluded. Group mean scores on the CCC-2 differed on only one subscale, syntax. However, siblings of children with autism or PDDNOS were over-represented in the tails of the distributions of several scales, and 10 (24%) scored more than 2 SD below the control mean on a total score based on all 10 subscales. Only two of these 10 children scored above threshold on one or more scales of the Autism Diagnostic Interview-Revised (ADI-R). Children with abnormal scores on the CCC-2 total were characterized by low-verbal IQ and their fathers tended to score high on the social and communication scales of the Autism Quotient, a measure of the broad phenotype in adults. The CCC-2 shows promise as a quick screening device for the broad phenotype in non-autistic siblings of children with autism.

P.1.e.003 DTNBP1 (dysbindin) gene variants modulate prefrontal brain function in healthy individuals and schizophrenic patients

Genetic epidemiological studies suggest that individual variation in susceptibility to schizophrenia is largely genetic, but the mode of transmission is unknown. It is likely on theoretical and empirical grounds that risk in the population reflects both relatively common alleles with small effects and rare alleles with larger effects. Molecular genetic studies have identified several potential regions of linkage and two associated chromosomal abnormalities, and evidence is accumulating in favour of several positional candidate genes. Currently, the positional candidate genes for which we consider the evidence to be strong are those encoding dysbindin 1 (DTNBP1), neuregulin 1 (NRG1), and disrupted in schizophrenia 1 (DISC1). Recent advances in technology have made genome-wide association studies feasible. These have been used successfully to identify risk genes of small effect in many common diseases, and identification of risk genes for schizophrenia can be expected in the coming months. In addition, recent systematic searches for copy number variants (CNVs) in schizophrenia have provided evidence that sub-microscopic CNVs are over-represented in cases of schizophrenia. The identification of risk genes has already opened up new avenues for research aimed at understanding the pathogenesis of schizophrenia, and will continue to do so. It will also catalyse a re-appraisal of the classification of psychiatric disorders.