Microduplications of 16p11.2 are associated with schizophrenia

Genome-wide approaches to schizophrenia

Schizophrenia (SZ) is a common and severe psychiatric disorder with both environmental and genetic risk factors, and a high heritability. After over 20 years of molecular genetics research, new molecular strategies, primarily genome-wide association studies (GWAS), have generated major tangible progress. This new data provides evidence for: (1) a number of chromosomal regions with common polymorphisms showing genome-wide association with SZ (the major histocompatibility complex, MHC, region at 6p22-p21; 18q21.2; and 2q32.1). The associated alleles present small odds ratios (the odds of a risk variant being present in cases vs. controls) and suggest causative involvement of gene regulatory mechanisms in SZ. (2) Polygenic inheritance. (3) Involvement of rare (<1%) and large (>100kb) copy number variants (CNVs). (4) A genetic overlap of SZ with autism and with bipolar disorder (BP) challenging the classical clinical classifications. Most new SZ findings (chromosomal regions and genes) have generated new biological leads. These new findings, however, still need to be translated into a better understanding of the underlying biology and into causal mechanisms. Furthermore, a considerable amount of heritability still remains unexplained (missing heritability). Deep resequencing for rare variants and system biology approaches (e.g., integrating DNA sequence and functional data) are expected to further improve our understanding of the genetic architecture of SZ and its underlying biology.

New findings in the genetics of major psychoses

Schizophrenia and bipolar disorder have a largely unknown pathophysiology and etiology, but they are highly heritable. Although linkage and association studies have identified a series of chromosomal regions likely to contain susceptibility genes, progress in identifying causative genes has been largely disappointing. However, rapid technological advances are beginning to lead to new insights. Systematic genome-wide association and follow-up studies have reported genome-wide significant association findings of common variants for schizophrenia and bipolar disorder. The risk conferred by individual variants is small, and some variants confer a risk for both disorders. In addition, recent studies have identified rare, large structural variants (copy number variants) that confer a greater risk for schizophrenia. This review summarizes recent developments in genetic research into schizophrenia and bipolar disorder, and discusses possible future directions in this field.

Whole genome association studies in complex diseases: where do we stand?

Hundreds of genome-wide association studies have been performed in recent years in order to try to identify common variants that associate with complex disease. These have met with varying success. Some of the strongest effects of common variants have been found in lateonset diseases and in drug response. The major histocompatibility complex has also shown very strong association with a variety of disorders. Although there have been some notable success stories in neuropsychiatric genetics, on the whole, common variation has explained little of the high heritability of these traits. In contrast, early studies of rare copy number variants have led rapidly to a number of genes and loci that strongly associate with neuropsychiatric disorders. It is likely that the use of whole-genome sequencing to extend the study of rare variation in neuropsychiatry will greatly advance our understanding of neuropsychiatric genetics.

Accurate distinction of pathogenic from benign CNVs in mental retardation

Copy number variants (CNVs) have recently been recognized as a common form of genomic variation in humans. Hundreds of CNVs can be detected in any individual genome using genomic microarrays or whole genome sequencing technology, but their phenotypic consequences are still poorly understood. Rare CNVs have been reported as a frequent cause of neurological disorders such as mental retardation (MR), schizophrenia and autism, prompting widespread implementation of CNV screening in diagnostics. In previous studies we have shown that, in contrast to benign CNVs, MR-associated CNVs are significantly enriched in genes whose mouse orthologues, when disrupted, result in a nervous system phenotype. In this study we developed and validated a novel computational method for differentiating between benign and MR-associated CNVs using structural and functional genomic features to annotate each CNV. In total 13 genomic features were included in the final version of a Naïve Bayesian Tree classifier, with LINE density and mouse knock-out phenotypes contributing most to the classifier's accuracy. After demonstrating that our method (called GECCO) perfectly classifies CNVs causing known MR-associated syndromes, we show that it achieves high accuracy (94%) and negative predictive value (99%) on a blinded test set of more than 1,200 CNVs from a large cohort of individuals with MR. These results indicate that this classification method will be of value for objectively prioritizing CNVs in clinical research and diagnostics.

Rare copy number variants (CNVs) are a frequent cause of neurological disorders such as mental retardation (MR). However CNVs are also commonly identified in healthy individuals. It is therefore crucial for both diagnostic and research applications to be able to distinguish between disease-causing CNVs and “benign” CNVs occurring as normal genomic variation. Separating these two types can take advantage of significant differences in their genomic contents. For example, benign CNVs are enriched in repetitive sequences. By contrast, CNVs associated with MR tend to have high densities of functional elements, including genes whose mouse orthologues, when knocked-out, lead to specific nervous system abnormalities. We have developed a novel objective approach that is effective in distinguishing MR-associated CNVs from benign CNVs based on the presence of 13 genomic attributes. This method is able to achieve high accuracies in a cohort of CNVs known to cause MR and in a cohort of individuals with unexplained MR. The development of this technique promises to substantially improve the methodology for determining the pathogenicity of CNVs.

Nature and nurture in neuropsychiatric genetics: where do we stand?

Both genetic and nongenetic risk factors, as well as interactions and correlations between them, are thought to contribute to the etiology of psychiatric and behavioral phenotypes. Genetic epidemiology consistently supports the involvement of genes in liability. Molecular genetic studies have been less successful in identifying liability genes, but recent progress suggests that a number of specific genes contributing to risk have been identified. Collectively, the results are complex and inconsistent, with a single common DNA variant in any gene influencing risk across human populations. Few specific genetic variants influencing risk have been unambiguously identified. Contemporary approaches, however, hold great promise to further elucidate liability genes and variants, as well as their potential inter-relationships with each other and with the environment. We will review the fields of genetic epidemiology and molecular genetics, providing examples from the literature to illustrate the key concepts emerging from this work.

Approaches for evaluating rare polymorphisms in genetic association studies

Most current genetic association studies, including genome-wide association studies, look for the single nucleotide polymorphisms (SNPs) with a relatively large minor allele frequency (MAF) (e.g. >5%) in the search for genetic loci underlying the susceptibility for complex diseases. The strategy of focusing on common SNPs in genetic association studies is very effective under the common-disease-common-variant (CDCV) hypothesis, which claims that common diseases are caused by common variants that have relatively small to moderate effects. Although the CDCV hypothesis has become the dogma guiding the conduct of association studies over the past decade, growing evidence from recent empirical data and simulations suggests that the causal genetic polymorphisms, including SNPs and copy number variants (CNVs), for common diseases have a wide spectrum of MAFs, ranging from rare to common. Unlike the analysis for common genetic variants, statistical approaches for the analysis of rare variants receive very little attention. Methods developed for common variants usually rely on their asymptotic properties, which can be inaccurate for the study of the rare variants with limited sample size. Although Fisher's exact test can be used for such a scenario, it is usually conservative and thus its usefulness is diminished to some extent. Here we propose two novel approaches for the analysis of rare genetic variants. Simulation studies and two real examples demonstrate the advantages of the proposed methods over the existing methods.

Development of animal models for schizophrenia

Schizophrenia is a devastating psychiatric disorder that affects around 1% of the population worldwide. The disease is characterized by 'positive symptoms', 'negative symptoms' and cognitive deficits. Over the last 60 years, a large number of family, twin and adoption studies have clearly demonstrated a strong genetic component for schizophrenia, but the mode of inheritance of the disease is complex and, in all likelihood, involves contribution from multiple genes in conjunction with environmental and stochastic factors. Recently, several genome-wide scans have demonstrated that rare alleles contribute significantly to schizophrenia risk. Assessments of rare variants have identified specific and probably causative, disease-associated structural mutations or copy number variants (CNVs, which result from genomic gains or losses). The fact that the effects of such lesions are transparent allows the generation of etiologically valid animal models and the opportunity to explore the molecular, cellular and circuit-level abnormalities underlying the expression of psychopathology. To date, the most common genomic structural rearrangements that are unequivocally associated with the development of schizophrenia, are de novo microdeletions of the 22q11.2 locus. Fortunately, the human 22q11.2 locus is conserved within the syntenic region of mouse chromosome 16, which harbors nearly all orthologues of the human genes. This has made it possible to engineer genetically faithful, and thus etiologically valid, animal models of this schizophrenia susceptibility locus.

The clinical context of copy number variation in the human genome

During the past five years, copy number variation (CNV) has emerged as a highly prevalent form of genomic variation, bridging the interval between long-recognised microscopic chromosomal alterations and single-nucleotide changes. These genomic segmental differences among humans reflect the dynamic nature of genomes, and account for both normal variations among us and variations that predispose to conditions of medical consequence. Here, we place CNVs into their historical and medical contexts, focusing on how these variations can be recognised, documented, characterised and interpreted in clinical diagnostics. We also discuss how they can cause disease or influence adaptation to an environment. Various clinical exemplars are drawn out to illustrate salient characteristics and residual enigmas of CNVs, particularly the complexity of the data and information associated with CNVs relative to that of single-nucleotide variation. The potential is immense for CNVs to explain and predict disorders and traits that have long resisted understanding. However, creative solutions are needed to manage the sudden and overwhelming burden of expectation for laboratories and clinicians to assay and interpret these complex genomic variations as awareness permeates medical practice. Challenges remain for understanding the relationship between genomic changes and the phenotypes that might be predicted and prevented by such knowledge.

Cognition in mouse models of schizophrenia susceptibility genes

Cognitive deficits are core features of psychiatric disorders and contribute substantially to functional outcome. It is still unclear, however, how cognitive deficits are related to underlying genetic liability and overt clinical symptoms. Fortunately, animal models of susceptibility genes can illuminate how the products of disease-associated genetic variants affect brain function and ultimately alter behavior. Using as a reference findings from the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia program and the SchizophreniaGene database, we review cognitive data from mutant models of rare and common genetic variants associated with schizophrenia.

The role of genetics in the etiology of schizophrenia

Schizophrenia is a complex genetic disorder manifesting combined environmental and genetic causation. Recently, genome-wide association experiments yielded remarkable new experimental evidence that is leading to a better understanding of the genetic models and the biological risk factors involved in schizophrenia. These studies have discovered uncommon copy number variations (mainly deletions) and common single nucleotide polymorphisms with alleles associated with schizophrenia. The aggregate data provide support for polygenic inheritance and for genetic overlap of schizophrenia with autism and with bipolar disorder. It is anticipated that the application of a myriad of tools from systems biology, in combination with biological functional experiments, will lead to a delineation of biological pathways involved in the pathophysiology of schizophrenia, and eventually to new therapies.

The role of copy number variation in schizophrenia

Recent developments in microarray technology have revealed the presence of many submicroscopic deletions and duplications in the human genome. Some of these have been found to increase the risk for neuropsychiatric disorders. Over the last 2 years, several large studies on schizophrenia have implicated large deletions and duplications that increase the risk of developing this disorder. It is now clear that rare deletions at 1q21.1, 15q13.3, 15q11.2 and 22q11.2, as well as duplications at 16p11.2 and 16p13.1, increase the risk of developing schizophrenia. They are found collectively in up to 3% of patients; therefore, they account for only a small proportion of the genetic causes of schizophrenia. In this paper I will review the evidence for these findings.

Distinct disorders affecting the brain share common genetic origins

Over the last few years, large cohorts of patients with distinct brain disorders of neuropsychiatric and neurological origin have been analyzed for copy number variation. Surprisingly, the same genetic abnormalities were found in cohorts of patients affected with mental retardation, autism, or schizophrenia.

A new highly penetrant form of obesity due to deletions on chromosome 16p11.2

Obesity has become a major worldwide challenge to public health, owing to an interaction between the Western 'obesogenic' environment and a strong genetic contribution. Recent extensive genome-wide association studies (GWASs) have identified numerous single nucleotide polymorphisms associated with obesity, but these loci together account for only a small fraction of the known heritable component. Thus, the 'common disease, common variant' hypothesis is increasingly coming under challenge. Here we report a highly penetrant form of obesity, initially observed in 31 subjects who were heterozygous for deletions of at least 593 kilobases at 16p11.2 and whose ascertainment included cognitive deficits. Nineteen similar deletions were identified from GWAS data in 16,053 individuals from eight European cohorts. These deletions were absent from healthy non-obese controls and accounted for 0.7% of our morbid obesity cases (body mass index (BMI) >or= 40 kg m(-2) or BMI standard deviation score >or= 4; P = 6.4 x 10(-8), odds ratio 43.0), demonstrating the potential importance in common disease of rare variants with strong effects. This highlights a promising strategy for identifying missing heritability in obesity and other complex traits: cohorts with extreme phenotypes are likely to be enriched for rare variants, thereby improving power for their discovery. Subsequent analysis of the loci so identified may well reveal additional rare variants that further contribute to the missing heritability, as recently reported for SIM1 (ref. 3). The most productive approach may therefore be to combine the 'power of the extreme' in small, well-phenotyped cohorts, with targeted follow-up in case-control and population cohorts.

The Kraepelinian dichotomy - going, going... but still not gone

Recent genetic studies reinforce the view that current approaches to the diagnosis and classification of major psychiatric illness are inadequate. These findings challenge the distinction between schizophrenia and bipolar disorder, and suggest that more attention should be given to the relationship between the functional psychoses and neurodevelopmental disorders such as autism. We are entering a transitional period of several years during which psychiatry will need to move from using traditional descriptive diagnoses to clinical entities (categories and/or dimensions) that relate more closely to the underlying workings of the brain.

Evolution in health and medicine Sackler colloquium: Comparative genomics of autism and schizophrenia

We used data from studies of copy-number variants (CNVs), single-gene associations, growth-signaling pathways, and intermediate phenotypes associated with brain growth to evaluate four alternative hypotheses for the genomic and developmental relationships between autism and schizophrenia: (i) autism subsumed in schizophrenia, (ii) independence, (iii) diametric, and (iv) partial overlap. Data from CNVs provides statistical support for the hypothesis that autism and schizophrenia are associated with reciprocal variants, such that at four loci, deletions predispose to one disorder, whereas duplications predispose to the other. Data from single-gene studies are inconsistent with a hypothesis based on independence, in that autism and schizophrenia share associated genes more often than expected by chance. However, differentiation between the partial overlap and diametric hypotheses using these data is precluded by limited overlap in the specific genetic markers analyzed in both autism and schizophrenia. Evidence from the effects of risk variants on growth-signaling pathways shows that autism-spectrum conditions tend to be associated with up-regulation of pathways due to loss of function mutations in negative regulators, whereas schizophrenia is associated with reduced pathway activation. Finally, data from studies of head and brain size phenotypes indicate that autism is commonly associated with developmentally-enhanced brain growth, whereas schizophrenia is characterized, on average, by reduced brain growth. These convergent lines of evidence appear most compatible with the hypothesis that autism and schizophrenia represent diametric conditions with regard to their genomic underpinnings, neurodevelopmental bases, and phenotypic manifestations as reflecting under-development versus dysregulated over-development of the human social brain.

Experimental approaches for identifying schizophrenia risk genes

Schizophrenia is a severe, debilitating and common psychiatric disorder, which directly affects approximately 1% of the population worldwide. Although previous studies have unequivocally shown that schizophrenia has a strong genetic component, our understanding of its pathophysiology remains limited. The precise genetic architecture of schizophrenia remains elusive and is likely to be complex. It is believed that multiple genetic variants, with each contributing a modest effect on disease risk, interact with environmental factors resulting in the phenotype. In this chapter, we summarise the main molecular genetic approaches that have been utilised in identifying susceptibility genes for schizophrenia and discuss the advantages and disadvantages of each approach. First, we detail the findings of linkage mapping in pedigrees (affected families), which analyse the co-segregation of polymorphic genetic markers with disease phenotype. Second, the contribution of targeted and genome-wide association studies, which compare differential allelic frequencies in schizophrenia cases and matched controls, is presented. Third, we discuss about the identification of susceptibility genes through analysis of chromosomal structural variation (gains and losses of genetic material). Lastly, we introduce the concept of re-sequencing, where the entire genome/exome is sequenced both in affected and unaffected individuals. This approach has the potential to provide a clarified picture of the majority of the genetic variation underlying disease pathogenesis.

Nature and nurture in neuropsychiatric genetics: where do we stand?

Both genetic and nongenetic risk factors, as well as interactions and correlations between them, are thought to contribute to the etiology of psychiatric and behavioral phenotypes. Genetic epidemiology consistently supports the involvement of genes in liability. Molecular genetic studies have been less successful in identifying liability genes, but recent progress suggests that a number of specific genes contributing to risk have been identified. Collectively, the results are complex and inconsistent, with a single common DNA variant in any gene influencing risk across human populations. Few specific genetic variants influencing risk have been unambiguously identified, Contemporary approaches, however hold great promise to further elucidate liability genes and variants, as well as their potential inter-relationships with each other and with the environment. We will review the fields of genetic epidemiology and molecular genetics, providing examples from the literature to illustrate the key concepts emerging from this work.