The dice are rolling for schizophrenia genetics

Psychosis Incident Cohort Outcome Study (PICOS). A multisite study of clinical, social and biological characteristics, patterns of care and predictors of outcome in first-episode psychosis. Background, methodology and overview of the patient sample

AIMS

This paper aims at providing an overview of the background, design and initial findings of Psychosis Incident Cohort Outcome Study (PICOS).

METHODS

PICOS is a large multi-site population-based study on first-episode psychosis (FEP) patients attending public mental health services in the Veneto region (Italy) over a 3-year period. PICOS has a naturalistic longitudinal design and it includes three different modules addressing, respectively, clinical and social variables, genetics and brain imaging. Its primary aims are to characterize FEP patients in terms of clinical, psychological and social presentation, and to investigate the relative weight of clinical, environmental and biological factors (i.e. genetics and brain structure/functioning) in predicting the outcome of FEP.

RESULTS

An in-depth description of the research methodology is given first. Details on recruitment phase and baseline and follow-up evaluations are then provided. Initial findings relating to patients' baseline assessments are also presented. Future planned analyses are outlined.

CONCLUSIONS

Both strengths and limitations of PICOS are discussed in the light of issues not addressed in the current literature on FEP. This study aims at making a substantial contribution to research on FEP patients. It is hoped that the research strategies adopted in PICOS will enhance the convergence of methodologies in ongoing and future studies on FEP.

Modelling the contribution of family history and variation in single nucleotide polymorphisms to risk of schizophrenia: a Danish national birth cohort-based study

BACKGROUND

Epidemiological studies indicate that having any family member with schizophrenia increases the risk of schizophrenia in the probands. However, genome-wide association studies (GWAS) have accounted for little of this variation. The aim of this study was to use a population-based sample to explore the influence of single-nucleotide polymorphisms (SNPs) on the excess schizophrenia risk in offspring of parents with a psychotic, bipolar affective or other psychiatric disorder.

METHOD

A nested case-control study with 739 cases with schizophrenia and 800 controls. Their parents and siblings. Information from national health registers and GWAS data from the national neonatal biobank.

RESULTS

Offspring schizophrenia risk was elevated in those whose mother, father or siblings had been diagnosed with schizophrenia or related psychosis, bipolar affective disorder or any other psychiatric disorder. The rate ratio was 9.31 (3.85; 22.44) in offspring whose 1st degree relative was diagnosed with schizophrenia. This rate ranged between 8.31 and 11.34 when adjusted for each SNP individually and shrank to 8.23 (3.13; 21.64) when adjusted for 25% of the SNP-variation in candidate genes. The percentage of the excess risk associated with a family history of schizophrenia mediated through genome-wide SNP-variation ranged between -6.1%(-17.0%;2.6%) and 4.1%(-3.9%;15.2%). Analogous results were seen for each parent and for histories of bipolar affective and other psychiatric diagnoses.

CONCLUSIONS

The excess risk of schizophrenia in offspring of parents who have a psychotic, bipolar affective or other psychiatric disorder is not currently explained by the SNP variation included in this study in accordance with findings from published genetic studies.

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.

Human behavioral informatics in genetic studies of neuropsychiatric disease: multivariate profile-based analysis

While genome-wide association (GWA) studies have yielded notable findings with regard to the identification of risk variants in diseases such as obesity and diabetes, similar studies of schizophrenia - and neuropsychiatric diseases in general - have failed to produce strong findings. One, plausible explanation for this relates to phenotypic heterogeneity and what may be inherent imprecision associated with diagnostic categories in neuropsychiatric disorders. In this review we discuss a general approach to addressing the problem of heterogeneity that draws on concepts in behavioral informatics and the use of multivariable behavioral profiles in genetic studies of neuropsychiatric disease. The use of behavioral profiles as phenotypes eliminates the need for categorizing individuals with different 'subtypes' of a disease into one group and provides a way to investigate genetic susceptibility to different neuropsychiatric disorders that share similar clinical characteristics, such as schizophrenia and bipolar disorder. Further, behavioral profiles are a direct, quantitative representation of the emotional, personality, and neurocognitive functioning of the individuals being studied, and as such, the use of these profiles may provide increased statistical power to detect genetic associations and linkages. We describe and discuss four general data analysis approaches that can be used to analyze and integrate multivariate behavioral profile data and high-dimensional genomic data. Ultimately, we propose that behavioral profile-based phenotypes provide a meaningful alternative to the use of single measures, such as diagnostic category, in genetic association studies of neuropsychiatric disease.

Genome-wide association studies: a primer

There have been nearly 400 genome-wide association studies (GWAS) published since 2005. The GWAS approach has been exceptionally successful in identifying common genetic variants that predispose to a variety of complex human diseases and biochemical and anthropometric traits. Although this approach is relatively new, there are many excellent reviews of different aspects of the GWAS method. Here, we provide a primer, an annotated overview of the GWAS method with particular reference to psychiatric genetics. We dissect the GWAS methodology into its components and provide a brief description with citations and links to reviews that cover the topic in detail.

REVIEW: Genome-wide findings in schizophrenia and the role of gene-environment interplay

The recent advent of genome-wide mass-marker technology has resulted in renewed optimism to unravel the genetic architecture of psychotic disorders. Genome-wide association studies have identified a number of common polymorphisms robustly associated with schizophrenia, in ZNF804A, transcription factor 4, major histocompatibility complex, and neurogranin. In addition, copy number variants (CNVs) in 1q21.1, 2p16.3, 15q11.2, 15q13.3, 16p11.2, and 22q11.2 were convincingly implicated in schizophrenia risk. Furthermore, these studies have suggested considerable genetic overlap with bipolar disorder (particularly for common polymorphisms) and neurodevelopmental disorders such as autism (particularly for CNVs). The influence of these risk variants on relevant intermediate phenotypes needs further study. In addition, there is a need for etiological models of psychosis integrating genetic risk with environmental factors associated with the disorder, focusing specifically on environmental impact on gene expression (epigenetics) and convergence of genes and environment on common biological pathways bringing about larger effects than those of genes or environment in isolation (gene-environment interaction). Collaborative efforts that bring together expertise in statistics, genetics, epidemiology, experimental psychiatry, brain imaging, and clinical psychiatry will be required to succeed in this challenging task.

Animal models of schizophrenia

Schizophrenia may well represent one of the most heterogenous mental disorders in human history. This heterogeneity encompasses (1) etiology; where numerous putative genetic and environmental factors may contribute to disease manifestation, (2) symptomatology; with symptoms characterized by group; positive--behaviors not normally present in healthy subjects (e.g. hallucinations), negative--reduced expression of normal behaviors (e.g. reduced joy), and cognitive--reduced cognitive capabilities separable from negative symptoms (e.g. impaired attention), and (3) individual response variation to treatment. The complexity of this uniquely human disorder has complicated the development of suitable animal models with which to assay putative therapeutics. Moreover, the development of animal models is further limited by a lack of positive controls because currently approved therapeutics only addresses psychotic symptoms, with minor negative symptom treatment. Despite these complexities however, many animal models of schizophrenia have been developed mainly focusing on modeling individual symptoms. Validation criteria have been established to assay the utility of these models, determining the (1) face, (2) predictive, (3) construct, and (4) etiological validities, as well as (5) reproducibility of each model. Many of these models have been created following the development of major hypotheses of schizophrenia, including the dopaminergic, glutamatergic, and neurodevelopmental hypotheses. The former two models have largely consisted of manipulating these neurotransmitter systems to produce behavioral abnormalities with some relevance to symptoms or putative etiology of schizophrenia. Given the serotonergic link to hallucinations and cholinergic link to attention, other models have manipulated these systems also. Finally, there has also been a drive toward creating mouse models of schizophrenia utilizing transgenic technology. Thus, there are opportunities to combine both environmental and genetic factors to create more suitable models of schizophrenia. More sophisticated animal tasks are also being created with which to ascertain whether these models produce behavioral abnormalities consistent with patients with schizophrenia. While animal models of schizophrenia continue to be developed, we must be cognizant that (1) validating these models are limited to the degree by which Clinicians can provide relevant information on the behavior of these patients, and (2) any putative treatments that are developed are also likely to be given with concurrent antipsychotic treatment. While our knowledge of this devastating disorder increases and our animal models and tasks with which to measure their behaviors become more sophisticated, caution must still be taken when validating these models to limit complications when introducing putative therapeutics to human trials.

Why schizophrenia epidemiology needs neurobiology--and vice versa

Schizophrenia epidemiology can provide us with valuable information to guide research directions. However, while epidemiology is useful for generating candidate risk factors, it can not always deliver studies that prove causality. We argue that the field needs more translational research that links schizophrenia epidemiology with molecular, cellular, and behavioral neuroscience. Cross-disciplinary projects related to candidate genetic or nongenetic risk factors not only can address the biological plausibility of these factors, but they can serve as catalysts for discovery in neuroscience. This type of cross disciplinary research is likely to be more efficient compared to clinically dislocated basic neuroscience. Examples of this type of translational research are provided based on (a) the impact of prenatal nutrition and prenatal infection on brain development and (b) understanding the causes and consequences of agenesis of the corpus callosum. We need to build shared discovery platforms that encourage greater cross-fertilization between schizophrenia epidemiology and basic neuroscience research.

Gene-environment interactions in schizophrenia: review of epidemiological findings and future directions

Concern is building about high rates of schizophrenia in large cities, and among immigrants, cannabis users, and traumatized individuals, some of which likely reflects the causal influence of environmental exposures. This, in combination with very slow progress in the area of molecular genetics, has generated interest in more complicated models of schizophrenia etiology that explicitly posit gene-environment interactions (EU-GEI. European Network of Schizophrenia Networks for the Study of Gene Environment Interactions. Schizophrenia aetiology: do gene-environment interactions hold the key? [published online ahead of print April 25, 2008] Schizophr Res; S0920-9964(08) 00170-9). Although findings of epidemiological gene-environment interaction (G x E) studies are suggestive of widespread gene-environment interactions in the etiology of schizophrenia, numerous challenges remain. For example, attempts to identify gene-environment interactions cannot be equated with molecular genetic studies with a few putative environmental variables "thrown in": G x E is a multidisciplinary exercise involving epidemiology, psychology, psychiatry, neuroscience, neuroimaging, pharmacology, biostatistics, and genetics. Epidemiological G x E studies using indirect measures of genetic risk in genetically sensitive designs have the advantage that they are able to model the net, albeit nonspecific, genetic load. In studies using direct molecular measures of genetic variation, a hypothesis-driven approach postulating synergistic effects between genes and environment impacting on a final common pathway, such as "sensitization" of mesolimbic dopamine neurotransmission, while simplistic, may provide initial focus and protection against the numerous false-positive and false-negative results that these investigations engender. Experimental ecogenetic approaches with randomized assignment may help to overcome some of the limitations of observational studies and allow for the additional elucidation of underlying mechanisms using a combination of functional enviromics and functional genomics.