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Review Article

Schizophrenia in a genomic era

a review from the pathogenesis, genetic and environmental etiology to diagnosis and treatment insights

Zamanpoor, Mansoura,,b

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doi: 10.1097/YPG.0000000000000245

Abstract

Introduction

Schizophrenia is a relatively common and debilitating neurological disorder characterized by chronic psychotic symptoms and psychosocial impairment (Wong and Van Tol, 2003). Schizophrenia is a multigenic disorder with an estimated heritability of 81% and affects about 1% of the population worldwide (Sullivan et al., 2003; Ross et al., 2006). There are substantial co-morbidity of social isolation, depression, substance abuse, and suicide in patients with schizophrenia as a psychiatric illness (Lewis and Gonzalez-Burgos, 2006).

Schizophrenia is made up of three types of symptoms, negative, positive, and cognitive (Birnbaum and Weinberger, 2017). Negative symptoms have been conceptualized as a core aspect of schizophrenia and consist of five constructs including affective flattening or blunting (a decrease in the observed emotional expression and reactivity), alogia (lack of additional, unprompted content seen in normal speech), anhedonia (inability to experience pleasure), asociality (lack of motivation to engage in social interaction), and avolition (lack of desire or motivation) (Marder and Galderisi, 2017). Positive symptoms include paranoid delusions, hallucinations, bizarre behavior, and positive formal thought disorder (Birnbaum and Weinberger, 2017; Bliksted et al., 2017). Cognitive symptoms includes poor trouble focusing, deficits in executive functioning and impaired working memory (Birnbaum and Weinberger, 2017).

Pathophysiology of schizophrenia

One of the complexities of schizophrenia is that no central pathophysiology mechanism, diagnostic neuropathology, or biological markers, has been recognized in schizophrenia (Wong and Van Tol, 2003). To explain the neuropathology of schizophrenia, a number of different hypotheses including neurodevelopmental and neurochemical hypotheses have been proposed (Birnbaum and Weinberger, 2017). The absence of pathological evidence of neurodegenerative such as cytopathological inclusion bodies, dystrophic neuritis, reactive gliosis, dysmyelination, and overall neuronal loss in schizophrenia, support the role of the neurodevelopmental process in schizophrenia neuropathology (Wong and Van Tol, 2003). However, the proposed neurodevelopmental and neurodegenerative models of the pathology are not necessarily exclusive (Wong and Van Tol, 2003). The incorporation of the neurodevelopmental and neurodegenerative models has also been hypothesized in neuropathology of schizophrenia (Kochunov and Hong, 2014).

Postmortem studies investigating the macroscopic and histological pathology of schizophrenic brain tissue discovered decreased brain weight, increased ventricular volume, and abnormal neural distribution in the prefrontal cortex and hippocampus of schizophrenic brain tissue (Wong and Van Tol, 2003; Brennand et al., 2011). Neuropharmacological studies confirmed the involvement of dopaminergic, glutamatergic and GABAergic activity in schizophrenia (Javitt et al., 2008; Brennand et al., 2011).

Induced pluripotent stem cells (iPSCs) have been used as disease-relevant cell types from early brain development to investigate the molecular and cellular underpinnings of schizophrenia (Brennand et al., 2011; Hoffmann et al., 2018; Liu et al., 2019). The potential of the iPSCs-based modeling of schizophrenia has been supported by the high concordance between transcriptional signatures in iPSCs-derived cells from patients with childhood-onset schizophrenia and differential expression results from postmortem brain (reviewed by Hoffmann et al., 2018).

The two-hit hypothesis, the combination of genetic and environmental insult during early life, is consistent with the neurodevelopmental hypothesis (Feigenson et al., 2014). The neurodevelopmental model of schizophrenia suggest that abnormal neurodevelopmental processes start many years before the onset of the psychotic illness, as a result of an interaction between multiple susceptibility genes and environmental factors (Jarskog et al., 2007; Rapoport et al., 2012). This hypothesis has been widely accepted and supported in the childhood-onset neuropsychiatric disorders in which brain abnormalities may pre-date disease onset (Rapoport et al., 2012).

The neurochemical hypothesis states the involvement of neurotransmitters such as dopamine, serotonin, and glutamate in the pathophysiology of schizophrenia (Howes and Kapur, 2009). Discovery of the dopamine receptor’s role in inducing the schizophrenia-like psychosis led to the ‘dopamine hypothesis’ of schizophrenia that implicates dopamine hyperactivity in the psychotic symptoms (Seeman, 1987; Wong and Van Tol, 2003; Howes and Kapur, 2009). However, there is inconclusive evidence for the implication of an overactive dopamine system in the etiology of schizophrenia (Wong and Van Tol, 2003). The glutamate neurotransmitter system and more arguably, glutamate receptor genes, have been implicated in schizophrenia pathophysiology (Harrison and Weinberger, 2005). Furthermore, abnormal glutamate signaling is a well-established characteristic of schizophrenia pathophysiology (Feigenson et al., 2014).

There is growing evidence for the involvement of disrupted synaptic connectivity in the pathophysiology of schizophrenia; this has been supported by several neuroimaging, neurocognitive, gene array, and post mortem neuropathological studies (Jarskog et al., 2007). Clinical findings consistently suggested the role of molecular alterations in three neurotransmitter systems including glutamate, dopamine, and gamma-aminobutyric acid neurotransmission in the pathophysiology of working memory impairments in schizophrenia (Lewis and Gonzalez-Burgos, 2006).

Genetic factors that contribute to schizophrenia

The heritability of schizophrenia was estimated to be 79%–81% by using meta-analysis of twin studies (Sullivan et al., 2003; Lichtenstein et al., 2009; Hilker et al., 2018). The high heritability also applies in a broader neuropsychiatric and schizophrenia spectrum disorders (Hilker et al., 2018). Meta-analyses of linkage studies suggested that many chromosomal regions may contain schizophrenia susceptibility loci (Ng et al., 2009; Henriksen et al., 2017). Many genes play a role each with small to moderate effect sizes in schizophrenia (Wong and Van Tol, 2003; Modinos et al., 2013).

Prior to genome-wide association studies (GWAS), only a few genes had been proposed as schizophrenia candidate genes based on their involvement in the nervous system or their position from findings in linkage analyses (Gejman et al., 2011). Evidence of association with neurological and neurodevelopmental pathways of schizophrenia has been shown in some genes including ERBB4 (Benzel et al., 2007), dystrobrevin-binding protein 1 (DTNBP1) (Weickert et al., 2004), neuroregulin 1 (NGR-1) (Stefansson et al., 2002; Munafò et al., 2006), disrupted in schizophrenia 1 (DISC1) (Kamiya et al., 2005), AKT1 (Tan et al., 2008), regulator of G-protein signaling 4 (RGS-4) (Mirnics et al., 2001), catechol-O-methyl-transferase (COMT) (Chen et al., 2004), vesicular monoamine transporter 2 (VMAT2) (Richards et al., 2006), and cardiomyopathy-associated 5 (CMYA5) (Chen et al., 2011). The overview of gene loci associated with schizophrenia, including their function and discovery method, are shown in Table 1.

Table 1
Table 1:
Overview of associated gene loci with schizophrenia including their method of discovery and function

Some of associated loci discovered by linkage and candidate gene studies have proved difficult to replicate in subsequent studies (Henriksen et al., 2017). The low replication can be due to false discovery, limited knowledge of the candidate genes (believed to be implicated in the pathophysiology of schizophrenia) that makes it difficult to select relevant genes for testing, or that the power of these studies was inadequate (Henriksen et al., 2017). This low replication provides more support for the hypotheses indicating that the etiology of schizophrenia involves multiple genes with a small contribution in risk, interacting together or with environmental risk factors (Purcell et al., 2009; Sun et al., 2010). Furthermore, this polygenic model of schizophrenia susceptibility has been supported by the International Schizophrenia Consortium (Gejman et al., 2011).

The detection of chromosomal rearrangements in patients with schizophrenia provided evidence that structural variations or copy number variants (CNVs) are implicated in the genetic etiology of schizophrenia (Walsh et al., 2008; Marshall et al., 2017; Kushima et al., 2018; Sriretnakumar et al., 2019). Genome-wide enrichment of rare deletions and duplications, and a higher rate of de-novo CNVs have been reported in schizophrenia cases compared to controls (Marshall et al., 2017; Sriretnakumar et al., 2019). Most CNVs involved in schizophrenia are unique and rare in the population but confer significant risk for schizophrenia (Gulsuner and McClellan, 2015; Marshall et al., 2017; Kushima et al., 2018; Sriretnakumar et al., 2019). Rare structural variants play a role in schizophrenia by disrupting multiple genes such as NRXN1, APBA2, NRG1, and CNTNAP2 that are involved in neurodevelopmental pathways (e.g. neuregulin and neurexin) related to synaptic development and functions (Walsh et al., 2008; Tam et al., 2009). Several large-scale whole-genome schizophrenia association studies reported the detection of CNVs that uncovered some schizophrenia susceptibility loci and also highlighted some aspects of schizophrenia pathophysiology (Tam et al., 2009; Marshall et al., 2017).

GWAS interrogate the genome purely empirically and scan millions of single nucleotide polymorphisms (SNPs) for associations with schizophrenia using a case-control study design (Henriksen et al., 2017). The Molecular Genetics of Schizophrenia (MGS) research group, namely Genetic Association Information Network, used two sample sets, one of European ancestry (1440 cases and 1469 controls) and the other with African ancestry (1280 cases and 1000 controls) (Manolio et al., 2007). However few markers reached genome-wide significance for association with schizophrenia in the schizophrenia GWAS (Ripke et al., 2011). The combination of SNP data from several large studies confirmed associations of schizophrenia with several markers in the MHC region of chromosome 6, and in NRGN and TCF4 (Stefansson et al., 2009). MYO18B and ZNF804A were associated with schizophrenia by GWAS (Purcell et al., 2009). Research groups of the schizophrenia Psychiatric GWAS Consortium (PGC) collaborated to create a large schizophrenia sample set (Ripke et al., 2011). A two-stage analysis of 51 695 individuals of European ancestry identified five novel schizophrenia loci and two previously implicated loci (PCGEM1, TRIM26, CSMD1, MMP16, CNNM2-NT5C2, STT3A, and CCDC68-TCF4) at a genome-wide level of significance (Ripke et al., 2011). Interestingly four of these loci contain MIR137 (microRNA 137) predicted targets that suggest an implication of MIR137-mediated dysregulation in schizophrenia etiology (Ripke et al., 2011). MIR137 plays a role in neuronal development by regulating neuronal maturation and function (Ripke et al., 2011).

PGC conducted another GWAS with a substantially larger sample set, combining the Swedish national sample set of 5001 cases and 6243 controls with the PGC1 GWAS dataset (PGC1 + SWE) (Ripke et al., 2013). The number of significant schizophrenia loci at genome-wide significance was increased to 22 by this multi-stage meta-analyzed GWAS (Ripke et al., 2013). This GWAS meta-analysis highlighted the importance of larger studies for discovery of common genetic variants associated with schizophrenia (Ripke et al., 2013).

Another large multi-stage GWAS of schizophrenia was conducted by PGC. A total of 36 989 cases and 113 075 controls, including the primary PGC GWAS dataset and the MGS samples, were included in the analysis (PGC2) (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). 128 SNPs covering 108 defined loci including 83 novel loci were discovered at a genome-wide level of significance (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). Recently, Li et al. (2017) performed a GWAS including 7699 cases and 18 327 controls of Chinese ancestry that identified four novel schizophrenia risk loci (Li et al., 2017). A transancestral GWAS meta-analysis of Chinese individuals from the Li et al. (2017) study with PGC2 samples identified 30 new risk loci for schizophrenia (Supplementary Table, Supplemental digital content 1, http://links.lww.com/PG/A231). This large molecular genetic study of schizophrenia highlighted the power of GWAS with large sample sizes to find additional risk loci and indicated the existence of shared genetic risk across populations (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). Ikeda et al. (2019) reported the shared genetic risk of schizophrenia across East Asian and European populations and found 15 novel association loci with schizophrenia across Japanese, East Asian and European populations.

Associated loci were enriched with genes expressed in tissues with central immune functions in addition to the brain such as dopamine receptor D2 (DRD2) and several genes involved in glutamatergic neurotransmission and synaptic plasticity (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). Furthermore, association between variants within the MHC locus and schizophrenia has been replicated in several studies implicating the MHC locus as the strongest schizophrenia locus (Ripke et al., 2013; Shi et al., 2009; Stefansson et al., 2009; Schizophrenia Psychiatric Genome-Wide Association Study, 2011; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014; Sekar et al., 2016). The fact that schizophrenia associations were also strongly enriched among immune system-related genes such as CD19 and CD20 provide genetic support for the hypothetical role of immune dysregulation in schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). Recently, Inoubli et al. (2018) replicated an association of variants within TNFA and LTA genes with paranoid schizophrenia in the Tunisian population.

Higher polygenic risk score (PRS) for schizophrenia has been significantly associated with lower cognitive function with a potential to act as a predictor of decline in brain structure in apparently healthy populations (Alloza et al., 2018). The recent longitudinal study conducted by Alloza et al. (2018) has confirmed the link between the higher genetic liability for schizophrenia and the accelerated brain aging among healthy older adults. The cross-sectional data have showed the age-related declines in structural brain connectivity (Alloza et al., 2018). Santoro et al. (2018) reported that schizophrenia PRS is also associated with different clinical symptoms in different stages of first-episode psychosis treatment (Santoro et al., 2018). Zhang et al. (2019) reported the less favorable improvement with antipsychotic drug treatment in patients with higher PRS for schizophrenia and therefore suggested using the PRS as a predictor of antipsychotic efficacy in first-episode psychosis.

Cai et al. (2018) integrated the expression quantitative trait locus (eQTL) mapping analysis and GWAS data from ten brain tissues of patients with schizophrenia to investigate the genes related to schizophrenia. Cerebellum tissue has been reported to be the most closely linked tissue in brain and sought to play a role in the pathogenesis of schizophrenia. Bettella et al. (2018) reported significant enrichment of schizophrenia associations among eQTLs in four non-CNS tissues including adipose tissue, epidermal tissue, lymphoblastoid cells, and blood.

Pathway enrichment analysis using the hypergeometric test identified three major pathways to confer risk of schizophrenia including EIF2, IGF-1, and 14-3-3-mediated signaling pathway (Cai et al., 2018). Wang et al. (2018) conducted the KEGG pathway enrichment analysis on schizophrenia GWAS data and found axon guidance pathway-related genes are significantly associated with schizophrenia risk. Chang et al. (2018) reported ERBB, MAPK, synaptic plasticity, T cell receptor, and N-methyl-D-aspartate receptor (NMDAR) signaling pathways among the most enrichment pathways relevant to schizophrenia (Chang et al., 2018). Genes interacting with the NMDAR pathway such as ERBB4, GABRB2, and GRIN2B are highly targeted by common SNPs and harboring the de-novo mutations and NMDAR pathway has been suggested to plays a leading role in the pathology of schizophrenia (Chang et al., 2018).

Transcriptome analysis of ~2000 postmortem brains revealed some new insights into biological networks involved in schizophrenia and related neuropsychiatric disorders (Gandal et al., 2018). Integrating of RNA-sequencing data and genomic data revealed the differential splicing or expression in over 25% of transcriptome in neuropsychiatric disorders including schizophrenia (Gandal et al., 2018). Next-generation sequencing and in particular whole-exome sequencing revealed the implications of de-novo mutations in the disease etiology. Functional analyses of the disrupted downstream biological process by these de-novo events warrant a further investigation for potential therapeutics adventure (Wang et al., 2019).

Role of immune system in schizophrenia

The etiologies of schizophrenia are best explained by a multi-factorial polygenic threshold model that invokes multiple genetic risk factors modified by the environment (Malavia et al., 2017). Immunological alterations observed in schizophrenia indicate the involvement of a immune-related pathway in schizophrenia (de la Fontaine et al., 2006). Several studies have reported the contribution of an active immunological system in the etiology of schizophrenia (de la Fontaine et al., 2006). The role of inflammatory mechanisms in schizophrenia is supported by the role of immune dysregulation and alterations in neuroinflammatory pathways in schizophrenia (Altamura et al., 2014; Malavia et al., 2017). This might be explained by the genetically modulated inflammatory reactions such as dopamine-induced activation of autoimmune T cells in the brain tissue and/or immune system (de la Fontaine et al., 2006). Several studies suggest the role of cytokines as a mediator of metabolic/brain changes associated with clinical symptoms of schizophrenia (Kronfol and Remick, 2000; Monji et al., 2009; Altamura et al., 2014; Gallego et al., 2018; Misiak et al., 2018). Elevated levels of proinflammatory markers and cytokines have been found in the peripheral blood, cerebrospinal fluid and prefrontal cortex neurons of schizophrenic patients (Altamura et al., 2014; Malavia et al., 2017). Cytokines play a critical role in infectious and inflammatory processes by mediating between immune abnormalities and neurodevelopment (Altamura et al., 2014). Cytokines interact with monoaminergic system such as dopamine, serotonin, and glutamate, and the autonomic nervous system (Kim et al., 2007; Altamura et al., 2014). Schizophrenia is associated with an imbalance in inflammatory cytokines suggesting a possible target for pharmacological treatments (Altamura et al., 2014). The positive effect of non-steroidal anti-inflammatory drugs to reduce psychotic symptom severity supports the possibility of inflammatory mechanisms underlying schizophrenia pathogenesis (Malavia et al., 2017). This immune-based anti-inflammatory therapeutic approach opens interesting perspectives for immune therapy in schizophrenia (Müller et al., 2016).

Increased serum level of cytokines such as IL-6 in schizophrenia suggests the implication of the adaptive immune response and supports brain immune activation in schizophrenic patients (Altamura et al., 2014; Schwieler et al., 2015). IL-6 plays a critical role by stimulation of B lymphocyte proliferation through hyperactivation of humoral immunity that stimulates the conversion of the amino acid tryptophan into kynurenic acid that acts as an antagonist of glutamatergic NMDARs (Altamura et al., 2014; Hu et al., 2015). Abnormal kynurenic acid levels are involved in the pathophysiology of schizophrenia (Schwieler et al., 2015; Plitman et al., 2017). Neurotransmitter dysfunctions as a result of cytokine-induced neuroinflammation through microglial activation, leads to the inflammatory process and neurodegeneration in schizophrenia (Aricioglu et al., 2016).

Analysis of the network of protein-protein interactions or interactome analyses demonstrated several immune-related pathways schizophrenia interactome such as interleukins and natural killer cell signaling, NF-kB signaling, and B cell receptor signaling, that are associated with immune function and inflammation (Malavia et al., 2017). The NF-kB signaling pathway has been implicated in schizophrenia as NF-kB pathway plays a role in immune response regulation and also in synaptic plasticity and memory (Roman-Blas and Jimenez, 2006; Snow et al., 2014; Malavia et al., 2017).

The HLA system is the most studied locus candidate genes for association with schizophrenia (Gorwood et al., 2004). The MHC region is the most associated region in GWAS of schizophrenia. This considerably strong genetic evidence supports the immune hypothesis that variation within immune genes contributes to schizophrenia (Pouget et al., 2016). HLA class II, containing the HLA-DR4 (DRB1*04) allele of the HLA-DRB1 gene, is the most frequently reported genetic allele in association with schizophrenia (Wright et al., 2001). Associated HLA class II antigens or alleles include HLA-DRB1*0101 and HLA-DRB1*04 (*0401, *0403, *0405, and *0406) that control antibody-mediated immune responses (Wright et al., 2001). HLA-DRB1, including the HLA-DR4 serotype, is associated inversely with schizophrenia (Watanabe et al., 2009).

Environmental factors that contribute to schizophrenia

Environmental risk factors play an important role in the development of schizophrenia (Caspi and Moffitt, 2006). Schizophrenia has been associated with several infectious agents and this has been supported by different immunologic, epidemiologic, microbiologic, and neuropsychiatric studies (Yolken et al., 2000; Fatemi, 2005). Many infections have been associated with schizophrenia, including influenza, rubella, herpes simplex virus (HSV), cytomegalovirus, poliovirus, and toxoplasma gondii (Brown and Susser, 2002; Ross et al., 2006; Brown and Derkits, 2010). Infections have been implicated as disrupters of fetal neurodevelopment leading to brain and behavioral abnormalities (Brown and Derkits, 2010). Maternal HSV-2 IgG antibody levels are associated with a significantly increased risk of schizophrenia in offspring (Buka et al., 2001; Brown and Derkits, 2010). Another study showed significant elevation in risk of schizophrenic among offspring of mothers who were seropositive for HSV-2 antibody (Buka et al., 2001; Brown and Derkits, 2010).

Schizophrenia development has been associated with the birth season, and schizophrenia patients are more likely to be born during the winter months (Torrey et al., 1997; Davies et al., 2003; Messias et al., 2007). It has been hypothesized that this winter birth effect might be due to the increased chance of the prenatal viral infections during winter months (Fatemi et al., 2012). Childhood viral infections have also been suggested to be associated with schizophrenia by some population-based studies (Khandaker et al., 2012; Nielsen et al., 2014; Birnbaum and Weinberger, 2017). There is evidence for the possible effect of prenatal exposure to infections on fetal brain development (Ross et al., 2006; Clarke et al., 2009). This can influence brain development through several physiological and immunological processes including triggering proinflammatory cytokine responses or by releasing stress hormones, producing hypoxia, hyperthermia, or malnutrition (Gilmore and Jarskog, 1997; Verdoux, 2004; Ross et al., 2006).

Many obstetric complications such as premature birth, low birth weight, preeclampsia, rhesus incompatibility, and prenatal nutritional deficiency have been implicated as early environmental risk factors for neurodevelopmental conditions such as schizophrenia (Cannon et al., 2002; St Clair et al., 2005; Kyle and Pichard, 2006; Ross et al., 2006). There are inconsistent findings from several studies exploring the relationship between schizophrenia and obstetric complications including hypertensive disorders of pregnancy and low birth-weight (Clarke and Kelleher, 2017; Dachew et al., 2018). Meta-analysis of population-based association studies between low birth-weight (<2500 g) and schizophrenia showed no significant increase in the risk of developing schizophrenia among those with a low birth-weight compared to those within the healthy birth-weight range (Clarke and Kelleher, 2017). However, a recent meta-analysis showed that there was 37% higher risk of developing schizophrenia in offspring exposed to maternal preeclampsia (relative risk = 1.37; 95% confidence interval: 1.08–1.72) (Dachew et al., 2018).

Advancing paternal age has been found as a risk factor for schizophrenia (Messias et al., 2007). It has been suggested that epigenetic processes such as age-related imprinting errors and DNA methylation changes in several brain-expressed imprinted genes contribute to the effect of advanced paternal age on schizophrenia (Perrin et al., 2007; Petersen et al., 2011; Smith et al., 2013). Accumulated de-novo mutations in paternal sperm have been also suggested to contribute to neurodevelopmental disorders in offspring (Janecka et al., 2017). The link between advanced paternal age and schizophrenia remains significant after controlling for possible confounders, including socioeconomic status, paternal psychiatric morbidity, and maternal age (Janecka et al., 2017).

Diagnosis of schizophrenia

Schizophrenia is defined as a heterogeneous clinical syndrome and shares a common presentation with several other psychosocial disorders making diagnosis of schizophrenia difficult (Wong and Van Tol, 2003). To address its heterogeneity, schizophrenia is currently diagnosed as a disorder with subtypes. However, subtypes are based on several common clinical features that make the diagnosis imprecise (Keller et al., 2011).

Diagnosis of schizophrenia is based on the Diagnostic and Statistical Manual of Mental Disorders. The patients must have two or more of the defined symptoms for 1 month (American-Psychiatric-Association, 2013). These symptoms include delusions, hallucinations, disorganized speech, grossly disorganized behavior or catatonic behavior, or negative symptoms (such as affective flattening, alogia, avolition) (Keller et al., 2011; American-Psychiatric-Association, 2013). Onsets of schizophrenia have been reported to be different between individuals from acute onset to an extended prodrome (Messias et al., 2007). The negative symptoms might be observed about 5 years before the manifestation of the initial psychotic episode (Häfner et al., 1999). Recent advances in discovery of schizophrenia-associated loci together with the definition of distinct sets of schizophrenic phenotypes are promising that the associated SNPs can be used as potential biomarkers to assist the diagnosis of schizophrenia in the genomic era.

Treatment of schizophrenia

There is not much known about the pathophysiology of schizophrenia and therefore successful treatments are limited (Lewis and Levitt, 2002). There is no cure for schizophrenia and the available symptomatic treatment is only partially successful (Ross et al., 2006). Dopamine dysfunction is the core psychopathology of schizophrenia and the development of novel treatment targets requires consideration of the complex interactions between dopamine and other neurotransmitter systems (Yang and Tsai, 2017). The majority of schizophrenia treatment strategies are aimed at blockade of the dopamine receptors in the dopamine reward pathway in the central nervous system (Yang and Tsai, 2017).

The positive symptoms can be managed by using the typical antipsychotic drugs (first generation) such as chlorpromazine, haloperidol, and perphenazine (Jarskog et al., 2007). The typical antipsychotic drugs have shown little impact on negative symptoms or cognitive impairment in schizophrenia patients (Jarskog et al., 2007). Atypical antipsychotic drugs (second-generation drugs) such as clozapine or olanzapine have serotonin-dopamine antagonism that improve the psychotic symptoms of schizophrenia by partially blocking dopamine receptors (particularly D2 receptor) to prevent over-activity of dopamine in the striatum (Blasi et al., 2011). Clozapine is primarily used in schizophrenia that is unresponsive to at least two different antipsychotic drugs (Jarskog et al., 2007). Cognitive behavioral treatment is a recommended therapy to help schizophrenia patients to lower the stress of psychotic symptoms by linking their distressed feelings and patterns of thinking (Jones et al., 2004). Advances in pharmacogenomics and genetic findings in schizophrenia have generated optimism about developing more effective and specific treatments for schizophrenia (Yang and Tsai, 2017).

Conclusion and perspectives

Schizophrenia is a heterogeneous clinical syndrome with a high heritability. Genetic studies including gene mapping, linkage analysis, and GWAS have confirmed many chromosomal regions may contain schizophrenia susceptibility loci. Many genes play a role each with small to moderate effect sizes in schizophrenia. Findings of the recent GWAS studies confirmed the association of variants within the MHC region with schizophrenia as the most significantly associated locus with schizophrenia. This considerably strong genetic evidence for the implicating the MHC locus in schizophrenia pathophysiology and supports the hypothesis that variation within immune genes contributes to schizophrenia. Despite the increase in discovering more risk loci in recent years, the molecular and pathogenic mechanisms are still need to be fully understood. GWAS confirmed known genetic risk loci and provided further biological insights into the etiology and pathogenesis of this complex disorder. Although the role of genetic-environment interaction in schizophrenia pathogenesis and development are remained as challenge that requires further investigation.

Acknowledgements

I would like to thank Professor Tony Merriman and Professor Ian Morison for their helpful discussion and advice.

Conflicts of interest

There are no conflicts of interest.

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      Keywords:

      schizophrenia; neurogenetics; neuropathology; neuroimmune; genomics; GWAS

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