Singleton deletions throughout the genome increase risk of bipolar disorder

Genetic overlap between autism, schizophrenia and bipolar disorder

There is strong evidence that genetic factors make substantial contributions to the etiology of autism, schizophrenia and bipolar disorders, with heritability estimates being at least 80% for each. These illnesses have complex inheritance, with multiple genetic and environmental factors influencing disease risk; however, in psychiatry, complex genetics is further compounded by phenotypic complexity. Autism, schizophrenia and bipolar disorder are effectively syndromic constellations of symptoms that define groups of patients with broadly similar outcomes and responses to treatment. As such the diagnostic categories are likely to be heterogeneous and the boundaries between them somewhat arbitrary. Recent applications of whole-genome technologies have discovered rare copy number variants and common single-nucleotide polymorphisms that are associated with risk of developing these disorders. Furthermore, these studies have shown an overlap between the genetic loci and even alleles that predispose to the different phenotypes. The findings have several implications. First, they show that copy number variations are likely to be important risk factors for autism and schizophrenia, whereas common single-nucleotide polymorphism alleles have a role in all disorders. Second, they imply that there are specific genetic loci and alleles that increase an individual's risk of developing any of these disorders. Finally, the findings suggest that some of the specific genetic loci implicated so far encode proteins, such as neurexins and neuroligins, that function in synaptic development and plasticity, and therefore may represent a common biological pathway for these disorders.

Sensitive and accurate detection of copy number variants using read depth of coverage

Methods for the direct detection of copy number variation (CNV) genome-wide have become effective instruments for identifying genetic risk factors for disease. The application of next-generation sequencing platforms to genetic studies promises to improve sensitivity to detect CNVs as well as inversions, indels, and SNPs. New computational approaches are needed to systematically detect these variants from genome sequence data. Existing sequence-based approaches for CNV detection are primarily based on paired-end read mapping (PEM) as reported previously by Tuzun et al. and Korbel et al. Due to limitations of the PEM approach, some classes of CNVs are difficult to ascertain, including large insertions and variants located within complex genomic regions. To overcome these limitations, we developed a method for CNV detection using read depth of coverage. Event-wise testing (EWT) is a method based on significance testing. In contrast to standard segmentation algorithms that typically operate by performing likelihood evaluation for every point in the genome, EWT works on intervals of data points, rapidly searching for specific classes of events. Overall false-positive rate is controlled by testing the significance of each possible event and adjusting for multiple testing. Deletions and duplications detected in an individual genome by EWT are examined across multiple genomes to identify polymorphism between individuals. We estimated error rates using simulations based on real data, and we applied EWT to the analysis of chromosome 1 from paired-end shotgun sequence data (30x) on five individuals. Our results suggest that analysis of read depth is an effective approach for the detection of CNVs, and it captures structural variants that are refractory to established PEM-based methods.

Genetics of psychosis; insights from views across the genome

The major psychotic illnesses, schizophrenia and bipolar disorder (BD), are among the most heritable common disorders, but finding specific susceptibility genes for them has not been straightforward. The reasons are widely assumed to include lack of valid phenotypic definition, absence of good theories of pathophysiology for candidate gene studies, and the involvement of many genes, each making small contributions to population risk. Within the last year or so, a number of genome wide association (GWAS) of schizophrenia and BD have been published. These have produced stronger evidence for association to specific risk loci than have earlier studies, specifically for the zinc finger binding protein 804A (ZNF804A) locus in schizophrenia and for the calcium channel, voltage-dependent, L type, alpha 1C subunit (CACNA1C) and ankyrin 3, node of Ranvier (ANK3) loci in bipolar disorder. The ZNF804A and CACNA1C loci appear to influence risk for both disorders, a finding that supports the hypothesis that schizophrenia and BD are not aetiologically distinct. In the case of schizophrenia, a number of rare copy number variants have also been detected that have fairly large effect sizes on disease risk, and that additionally influence risk of autism, mental retardation, and other neurodevelopmental disorders. The existing findings point to some likely pathophysiological mechanisms but also challenge current concepts of disease classification. They also provide grounds for optimism that larger studies will reveal more about the origins of these disorders, although currently, very little of the genetic risk of either disorder is explained.

Psychosis genetics: modeling the relationship between schizophrenia, bipolar disorder, and mixed (or "schizoaffective") psychoses

As a result of improving technologies and greatly increased sample sizes, the last 2 years has seen unprecedented advances in identification of specific genetic risk factors for psychiatric phenotypes. Strong genetic associations have been reported at common polymorphisms within ANK3 and CACNA1C in bipolar disorder and ZNF804A in schizophrenia and a relatively specific association between common variation in GABA(A) receptor genes and cases with features of both bipolar disorder and schizophrenia. Further, the occurrence of rare copy number variants (CNVs) has been shown to be increased in schizophrenia compared with controls. These emerging data provide a powerful resource for exploring the relationship between psychiatric phenotypes and can, and should, be used to inform conceptualization, classification, and diagnosis in psychiatry. It is already clear that, in general, genetic associations are not specific to one of the traditional diagnostic categories. For example, variation at ZNF804A is associated with risk of both bipolar disorder and schizophrenia, and some rare CNVs are associated with risk of autism and epilepsy as well as schizophrenia. These data are not consistent with a simple dichotomous model of functional psychosis and indicate the urgent need for moves toward approaches that (a) better represent the range of phenotypic variation seen in the clinical population and (b) reflect the underlying biological variation that gives rise to the phenotypes. We consider the implications for models of psychosis and the importance of recognizing and studying illness that has prominent affective and psychotic features. We conclude that if psychiatry is to translate the opportunities offered by new research methodologies, we must finally abandon a 19th-century dichotomy and move to a classificatory approach that is worthy of the 21st century.