After GWAS: Searching for Genetic Risk for Schizophrenia and Bipolar Disorder

Department of Psychiatry and Behavioral Neuroscience, University of Chicago, USA.
American Journal of Psychiatry (Impact Factor: 12.3). 02/2011; 168(3):253-6. DOI: 10.1176/appi.ajp.2010.10091340
Source: PubMed


Ten years ago it was widely expected that the genetic basis of common disease would be resolved by genome-wide association studies (GWAS), large-scale studies in which the entire genome is covered by genetic markers. However, the bulk of heritable variance remains unexplained. The authors consider several alternative research strategies. For instance, whereas it has been hypothesized that a common disease is associated primarily with common genetic variants, it is now plausible that multiple rare variants each have a potent effect on disease risk and that they could accumulate to become a substantial component of common disease risk. This idea has become more appealing since the discovery that copy number variants (CNVs) are a substantial source of human mutations and are associated with multiple common diseases. CNVs are structural genomic variants consisting of microinsertions, microdeletions, and transpositions in the human genome. It has been argued that numerous rare CNVs are plausible causes of a substantial proportion of common disease, and rare CNVs have been found to be potent risk factors in schizophrenia and autism. Another approach is to "parse the genome," i.e., reanalyze subsets of current GWAS data, since the noise inherent in genome-wide approaches may be hiding valid associations. Lastly, technological advances and declining costs may allow large-scale genome-wide sequencing that would comprehensively identify all genetic variants. Study groups even larger than the 10,000 subjects in current meta-analyses would be required, but the outcomes may lead to resolution of our current dilemma in common diseases: Where is the missing heritability?

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    • "Schizophrenia patients differ as for the genetic patterns that predispose them to the illness (Gershon et al., 2011). A factor complicating the search for genes that underlie the disorder is that the course of the disease is usually characterized by different states of illness that fluctuate over time (i.e., a patient returns to a non-psychotic state in between psychotic episodes). "
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    ABSTRACT: Substantial evidence exists about emotion processing (EP) impairments in schizophrenia patients. However, whether these deficits are present primarily during psychosis (i.e., state dependent) or an integral part of the disorder (i.e., trait dependent) remains unclear. EP was assessed with the degraded facial affect recognition task in schizophrenia patients (N=521) and healthy controls (N=312) at baseline (T1) and after a three year follow-up (T2). In schizophrenia patients symptomatic remission was assessed with the Positive and Negative Syndrome Scale (PANSS) remission tool. Patients were divided into four groups: remission T1 and remission T2 (RR); remission T1 and non-remission T2 (RN); non-remission T1 and non-remission T2 (NN) and non-remission T1 and remission T2 (NR). Factorial repeated measures ANCOVA was used to compare EP performance over time between groups. Age, gender and general cognition were included as covariates. Schizophrenia patients performed worse than healthy controls on EP at T1 (p=0.001). The patients that were in symptomatic remission at both time points (the RR group) performed worse than the healthy controls at T2 (p<0.001). Significant group×time interactions were found between RR and RN (p=0.001), and between NR and RN (p=0.04), indicating a differential EP performance over time. No group×time interaction was found between NN and NR. The results show relatively poor EP performance in schizophrenia patients compared to healthy controls. EP performance in schizophrenia patients was associated with symptomatic remission. The results provide support for the hypothesis that EP deficits in schizophrenia are both state and trait dependent. Copyright © 2014 Elsevier B.V. All rights reserved.
    Schizophrenia Research 12/2014; 161(2-3). DOI:10.1016/j.schres.2014.11.027 · 3.92 Impact Factor
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    • "However, of relevance to the hypotheses detailed above, there have been reports implicating abnormalities in GABAergic and glutamatergic systems,97 as well as candidate genes involved in neurodevelopment (ie, EFHD1, RELN, ANK3, NRG1, etc,98–100). It should be noted, however, that individual results from GWAS have not identified overlapping polymorphisms nor do candidate genes consistently reach the level of genome-wide statistical significance,96,97,101 leaving many unanswered questions about the genetics of schizophrenia. This is not surprising, as it is commonly accepted that schizophrenia is associated with many genes that contribute modestly to the risk of developing the disease. "
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    ABSTRACT: Schizophrenia is a disease affecting up to 1% of the population. Current therapies are based on the efficacy of chlorpromazine, discovered over 50 years ago. These drugs block dopamine D2-like receptors and are effective at primarily treating positive symptoms in a subset of patients. Unfortunately, current therapies are far from adequate, and novel treatments require a better understanding of disease pathophysiology. Here we review the dopamine, gamma-aminobutyric acid (GABA), and glutamate hypotheses of schizophrenia and describe a pathway whereby a loss of inhibitory signaling in ventral regions of the hippocampus actually drives a dopamine hyperfunction. Moreover, we discuss novel therapeutic approaches aimed at attenuating ventral hippocampal activity in a preclinical model of schizophrenia, namely the MAM GD17 rat. Specifically, pharmacological (allosteric modulators of the α5 GABAA receptor), neurosurgical (deep brain stimulation), and cell-based (GABAergic precursor transplants) therapies are discussed. By better understanding the underlying circuit level dysfunctions in schizophrenia, novel treatments can be advanced that may provide better efficacy and a superior side effect profile to conventional antipsychotic medications.
    Drug Design, Development and Therapy 07/2014; 8:887-896. DOI:10.2147/DDDT.S42708 · 3.03 Impact Factor
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    • "Although it does not invalidate the main arguments of this review, it is important to note that at present, despite the high levels of heritability for psychiatric illness, only a proportion of genetic risk has actually been accounted for. In genetic parlance this is known as the 'missing heritability' conundrum, and is the source of much discussion within genetics and the broader scientific community (Manolio et al., 2009; Danchin et al., 2011; Gershon et al., 2011). For example , although the estimates of heritability for SCZ are ~ 65%, known genetic candidates explain currently <2% of this variation. "
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    ABSTRACT: Rodent models are a key factor in the process of translating psychiatric genetics and genomics findings, allowing us to shed light on how risk-genes confer changes in neurobiology by merging different types of data across fields, from behavioural neuroscience to the burgeoning omics (e.g. genomics, epigenomics, proteomics, etc.). Moreover, they also provide an indispensable first step for drug discovery. However, recent evidence from both clinical and genetic studies highlights possible limitations in the current methods for classifying psychiatric illness, as both symptomology and underlying genetic risk are found to increasingly overlap across disorder diagnoses. Meanwhile, integration of data from animal models across disorders is currently limited. Here, we argue that behavioural neuroscience is in danger of missing informative data because of the practice of trying to ‘diagnose’ an animal model with a psychiatric illness. What is needed is a shift in emphasis, from seeking to ally an animal model to a specific disorder, to one focused on a more systematic assessment of the neurobiological and behavioural outcomes of any given genetic or environmental manipulation.
    European Journal of Neuroscience 05/2014; 39(11). DOI:10.1111/ejn.12607 · 3.18 Impact Factor
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