Decoding Advances in Psychiatric Genetics

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Decoding Advances in Psychiatric Genetics: A Focus on Neural Circuits in Rodent Models J.R. Heckenast*, x, {, L.S. Wilkinson*, x, {, 1 and M.W. Jonesjj, 1 *School of Psychology, Cardiff University, Cardiff, UK x School of Medicine, Cardiff University, Cardiff, UK { Behavioural Genetics Group, MRC Centre for Neuropsychiatric Genetics and Genomics, Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK jj School of Physiology and Pharmacology, University of Bristol, University Walk, Bristol, UK 1 Corresponding authors: E-mail: [email protected]; [email protected]

Contents 1. Introduction 2. Brief Overview of Genetics of SCZ 2.1 Common, Low Penetrance, Single-Nucleotide Polymorphism (SNP) Variants 2.2 Rare, Higher Penetrance Genetic Variants 2.3 Genetic Pathway Analysis 3. Putting Psychiatric Genetics to Work 3.1 Genome Editing 3.1.1 The Dawn of Genetic Models in Rats 3.1.2 Limitations 3.1.3 Reducing Off-target Effects

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4. Prioritizing Phenotyping Strategies for Animal Models of Psychiatric Disorders 4.1 Circuit Dysfunction in SCZ 4.2 Strategies for In vivo Circuit-Level Analysis in Genetic Models for SCZ 4.3 The Hippocampal-Prefrontal Circuit in SCZ 4.4 In vivo Circuit Dysfunction in Genetic Animal Models 4.4.1 Sleep-related Network Activity in SCZ 4.4.2 Perturbing Circuits in a Top-down Approach

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5. Conclusion Acknowledgments References

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Abstract Appropriately powered genome-wide association studies combined with deepsequencing technologies offer the prospect of real progress in revealing the complex biological underpinnings of schizophrenia and other psychiatric disorders. Meanwhile, recent developments in genome engineering, including CRISPR, constitute better tools Advances in Genetics, Volume 92 ISSN 0065-2660 http://dx.doi.org/10.1016/bs.adgen.2015.09.001

© 2015 Elsevier Inc. All rights reserved.

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to move forward with investigating these genetic leads. This review aims to assess how these advances can inform the development of animal models for psychiatric disease, with a focus on schizophrenia and in vivo electrophysiological circuit-level measures with high potential as disease biomarkers.

1. INTRODUCTION Schizophrenia (SCZ) carries a significant genetic risk, first identified through family and twin studies. The genetic architecture underlying this risk is highly polygenic and complex, but the past five years have seen great advances in cataloging associated loci, with suspected candidate genes confirmed, new ones discovered, and importantly, convergent evidence implicating key, possibly causal, molecular pathways (Hall, Trent, Thomas, O’Donovan, & Owen, 2015; Need & Goldstein, 2014; Rees et al., 2014; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). These findings have stimulated the creation of new genetic models, in vivo and in vitro, in order to delineate the biological mechanisms by which genetic variants can contribute to risk for disorder. Genome editing technologies have also made rapid progress in recent years, enabling epidemiological studies of psychiatric genetics to inform mechanistic studies in cellular and animal models (McCarthy, McCombie, & Corvin, 2014). In particular, the CRISPR (clustered regularly interspaced short palindromic repeats) system, exploiting a naturally occurring adaptive immune system found in bacteria, promises to revolutionize the way genetically altered models are created (Doudna & Charpentier, 2014) by both increasing the ease and efficiency of the method and by enabling genetic manipulation in a wider range of model species (Hamra, 2010). One major aim of animal models is to identify biomarkers of fundamental disease processes, supporting better patient stratification to allow more effective targeting of current and new drugs, and to provide more valid indicators of drug efficacy in drug development and clinical trials. Electrophysiological recordings from behaving animals provide one of the most directly translatable measures with which to investigate the activity of neural networks underlying cognitive processes, and may represent useful disease biomarkers. The “electrophysiological toolbox” allows for direct assessment of local circuit function as well as long-distance network interactions which are both known to be abnormal in conditions such as SCZ (Uhlhaas & Singer, 2010). This review draws together how genome editing can be

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used to harness the knowledge gained from genetic studies, and how electrophysiological methods can be an essential tool to probe the resulting animal models for disease mechanisms and biomarkers.

2. BRIEF OVERVIEW OF GENETICS OF SCZ The genetic architecture of SCZ is highly complex: risk alleles have varying frequencies in the population, different penetrance levels, and interacting properties both with other genes and the environment, all of which contribute to the intricate relationship between genotype and phenotype. In broad terms, the main sources of genetic risk can be divided into: (1) alleles that are common in the population but have small individual effect sizes; (2) rare alleles with a larger effect on disease risk. It has been proposed that this basic genetic architecture arises from the effects of natural selection, whereby common alleles persist because they are individually less damaging to fecundity, whereas more damaging variants, such as copy number variants (CNVs), are under negative selection because they adversely impact on fecundity. In support of this notion, de novo (i.e., newly arisen) rare variants have particularly high penetrance, potentially because they have not yet been subjected to the rigors of selection (Sullivan, Daly, & O’Donovan, 2012). Genome-wide association studies (GWAS) using array-based methods and more recently sequencing methods are being used to identify the variants associated with an increased risk for mental disorder (see Box 1 for a brief overview of genetic methods). This work is most advanced in the case of SCZ, the focus of the current review, where the genetic information is beginning to provide insights into possible disease mechanisms.

2.1 Common, Low Penetrance, Single-Nucleotide Polymorphism (SNP) Variants Successive GWAS in SCZ have seen the number of significantly associated SNPs increase in parallel with sample sizes. The largest GWAS to date, which included 36,989 patients diagnosed with SCZ and 113,075 controls (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014), identified 108 risk loci. The data were amassed through huge collaborative efforts among many research groups to combine phenotypee genotype data sets, and it is likely that as sample sizes increase further, more risk loci will be identified (Owen, 2014). The currently known 108 SNP risk loci are located both within genes (coding (exons) and noncoding (introns) regions) and between genes. As a proportion of the SNP variance is

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Box 1 Primer on genetic methods Common, low-penetrant genetic variants: DNA array methods, offering a high coverage of the genome, have been used in GWAS, which attempt to screen the whole genome in an unbiased “hypothesisfree” manner (in contrast to candidate-led approaches) for single-nucleotide polymorphism (SNP) variants that statistically appear to increase risk for disorder. This approach is best equipped to detect common alleles with small effect size and has revealed, in the case of schizophrenia, numerous (currently over a 100, see main text) common “risk SNPs” of individual low penetrance. SNPs can act as markers of variation in the surrounding region, enabling studies to compare several million SNPs across large cohorts of patients versus controls, to see which SNPs, and therefore which genomic loci, are associated with increased risk of disorder. A SNP that passes the stringent association significance threshold could in many cases be a marker for nearby SNPs that segregate together as a short chromosomal segment via linkage disequilibrium; this region is known as the genomic locus. Due to the huge amount of natural variation in the human genome, it is difficult to statistically determine which particular variant carries the increased risk. This leads to a formidable signal-to-noise issue in psychiatric genetics, the solution of which has required extremely large cohorts combined with advances in the ease and cost of genotyping thousands of SNPs in thousands of individuals. This method would have been impossible without advances in genotyping technology, which saw the development of DNA microarrays drive down the cost and improve the accuracy of studies of such vast scales (for a review on GWAS methods, see Corvin, Craddock, and Sullivan (2010)). It has been shown that the power of GWAS studies could be further increased by making better use of the phenotypic information of existing participants. The current method of splitting participants into cases versus controls based on a clinical cut-off point erases rich phenotypic datadfor example, controls are viewed as phenotypically uniform, yet they may have phenotypic traits associated with psychiatric disease and therefore risk SNPs (Van der Sluis, Posthuma, Nivard, Verhage, & Dolan, 2013). A further issue is the biological validity of current diagnostic categories: cross-disorder GWAS for autism spectrum disorder, attention deficit-hyperactivity disorder, bipolar disorder, major depressive disorder, and SCZ have found risk loci associated with multiple conditions (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013). Furthermore, a recent analysis found the joint analysis of disorders increased the accuracy of risk prediction for each disorder, compared to looking at conditions separately (Maier et al., 2015).

Rare, highly penetrant variants It has become apparent that genetic risk for psychiatric disorders, including schizophrenia, also involves a contribution from rare (in the population as a whole) but highly penetrant (for an individual) variants. Array methods have

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Box 1 Primer on genetic methods (cont'd) been used to identify a number of structural variants, such as CNVs. CNVs are variations in larger segments of DNA, as opposed to changes at the base pair level such as in SNPs, ranging from a kilobase to several megabases in size. They can be in the form of deletions, duplications, insertions, inversions, or more complex rearrangements, and cannot be detected by conventional karyotyping methods (i.e., they are submicroscopic). Since pathogenic CNVs are rare in the population, (see main text) risk CNVs for schizophrenia and other disorders have been identified following advances in DNA microarray methods and access to large cohorts. Duplications or deletions in a given region can be inferred by determining the dosage of SNPs, and then an increased prevalence in cases can be identified (for a review, see Malhotra and Sebat (2012)). The search for additional rare, highly pathogenic variants has been helped by the advent of deep sequencing methods, which offer the potential, eventually, for a nucleotide-to-nucleotide analysis of the entire genome. At the moment, cost implications limit the approach to exome sequencing (sequencing the coding part of a gene) and the molecular mining at this high level of resolution is throwing up a variety of rare variants, including SNVs (i.e., point mutations with high penetrance; not to be confused with the common risk SNPs of generally low penetrance) and tiny insertions and deletions (indels) within exons. These variants are so rare, associations for single variants cannot yet be made; rather, pathway analyses are used to assess the association of groups of variants. Currently, an exome can be sequenced for under $1000, but as the cost of sequencing falls, whole-genome sequencing will increasingly be used to explore the entire genome and no doubt further risk variants will be uncovered.

likely to reflect markers for risk genes in close genomic proximity, the 108 loci are estimated to implicate at least 350 genes (or more, depending on how liberally the associations are defined, see Need and Goldstein (2014)) across a number of putative pathogenic pathways. These implicated genes include some previously suspected biological systems based on non-genetic evidence, as well as highlighting new associations. In line with a major theory of SCZ, which suggests aberrant dopamine signaling, the gene coding for the dopamine D2 receptor (DRD2) was in a risk locus. The most (statistically) significant association related to the genes in the major histocompatibility complex (MHC), some of which are involved in acquired immunity, lending support to an increasingly recognized hypothesis regarding the immune system’s role in SCZ (reviewed in Patterson (2009), and Sperner-Unterweger and Fuchs (2015)). However, due to the

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sheer size of the MHC genomic region, it is impossible at this point to ascertain which specific genes are responsible for the association, and therefore precisely which systems may be involved (Corvin & Morris, 2014). Other risk SNP loci implicate genes and pathways in other biological systems, notably those involved in glutamatergic neurotransmission, calcium signaling, and synaptic function and plasticity. These findings demonstrate convergence with recent data from rare variants, as described below.

2.2 Rare, Higher Penetrance Genetic Variants A onefold to threefold genome-wide enrichment of relatively rare CNVs (submicroscopic deletions or duplications of short stretches of chromosome, sometimes a single gene but more often spanning multiple genes) in clinical cases indicates significant contributions of these structural abnormalities to SCZ risk (International Schizophrenia Consortium. 2008; Stefansson et al., 2008; Walsh et al., 2008). The deletion at 22q11.2 was the first identified SCZ-associated CNV, and larger sample sizes have allowed at least 15 more pathogenic loci to be identified. While only around 2.5% of patients with SCZ carry at least one pathogenic CNV, their odds ratios are between 2 and 60 (Rees, O’Donovan, & Owen, 2015), a substantially larger effect size than SNPs. Almost all CNVs are also associated with a range of other neurodevelopmental disorders, such as autism spectrum disorder and intellectual disability (Girirajan et al., 2014). While much of the overall population genetic risk for SCZ is accounted for by inherited alleles, the hardest hitting, most penetrant individual mutations found in a small fraction of patients are often de novo CNVs (Kirov, 2015). The method used to study de novo CNVs is a probandeparent trio design, where CNVs which occur in the child but neither of the parents are identified. This design has proven successful in identifying de novo mutations in autism spectrum disorder and intellectual disability (Rauch et al., 2012; Sanders et al., 2012). In a sample of 662 SCZ probandeparent trios and 2623 controls, Kirov et al. (2012) found rare de novo CNVs to be significantly more common in cases than in controls (5.1% vs 2.2%). Further de novo mutations identified were located at known SCZ risk loci, 3q29, 15q11.2, 15q13.3, and 16p11.2, while others included genes for the discs large (DLG) protein family, which are part of the postsynaptic density, and euchromatic histone-lysine N-methyltransferase 1 (EHMT1), a histone methyl transferase which interacts directly with DLG proteins. Rare and de novo mutations can also occur as single-nucleotide variants (SNVs) and tiny insertions and deletions (indels), which until recently were

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undetectable due to their rarity and miniscule size. Exome sequencing now allows all gene exons to be scanned at a much higher resolution in order to detect these mutations. Fromer et al. (2014) sequenced the exome of SCZ trios, and also found enrichment of small de novo mutations in the N-methyl-D-aspartate receptor (NMDA) signaling complex, in interactors with activity-regulated cytoskeleton-associated protein (ARC) at the synapse, and in the targets of the fragile X mental retardation protein (FMRP) complex. Further evidence supporting these findings came from a large-scale, caseecontrol exome sequencing study, which found enrichment of small, rare mutations in PSD-95 (postsynaptic density protein 95), an NMDA receptor associated protein, and targets of the FMRP complex (Purcell et al., 2014). In addition they also found an enrichment in voltage-gated calcium channels, which have also been strongly implicated in previous GWAS analyses (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014) and play a key role in synaptic plasticity (Berger & Bartsch, 2014). In the near future, whole-genome sequencing will extend the analysis of rare variants to intronic regions of genes and also to genomic regions between genes, with such studies becoming increasingly common as the cost of sequencing drops markedly.

2.3 Genetic Pathway Analysis The worldwide collaborative effort in psychiatric discovery genetics, most advanced in SCZ, has given rise to a multitude of risk loci and implicated genes. In order to make biological sense of this huge data set, researchers have made use of pathway analysis methods in order to group genes into common pathways which may underlie disease etiology (Figure 1). While the basic GWAS approach finds the significance of association to phenotype for each individual variant, pathway analyses evaluate the significance of association for a set of functionally related genes, which are often identified via databases developed using experimental proteomics. It should be noted that, though the term “pathway analysis” is commonly used, it implies a directional relationship between the genes in the set, which is not always necessarily established. A recent study used data from the Psychiatric Genomics Consortium mega-analysis GWAS database to identify biological pathways in SCZ, bipolar disorder, and major depression (Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium, 2015). As pathway analysis software have differing assumptions and strengths, they combined five different pathway analysis algorithms (see K. Wang, Li, and Hakonarson

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Figure 1 Genetic pathways implicated by different types of genetic risk variants and the related variants/genes discussed in this review that have been investigated with circuit-level electrophysiology. Zdhhc8 is a gene within the 22q11.2 CNV. The links from DISC1 and 15q13.3 to certain genetic pathways may emerge in due course, following the increasingly finely resolved analysis of the genome in terms of risk for SCZ. Pathway data based on data from Kirov et al. (2012), Fromer et al. (2014), Purcell et al. (2014), Szatkiewicz et al. (2014), Pocklington et al. (2015), Network and Pathway Analysis Subgroup of the Psychiatric Genomics Consortium (2015).

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(2010) for a review of pathway analysis methods and the following for recently developed novel tools: Lips, Kooyman, Leeuw, and Posthuma (2015), De Leeuw, Mooij, Heskes, and Posthuma (2015)). The top pathways indicated by SCZ risk loci included postsynaptic density, dendritic spine, axon, and histone methylation, while an integrative pathway analysis for all three disorders also suggested the involvement of immune related processes. Pathways were highly correlated in particular between SCZ and bipolar disorder, where significant genetic overlap is beginning to emerge (Cardno & Owen, 2014). Pathway analysis from CNV data has shown convergent findings with the SNP-GWAS data. Most CNVs span several genes and regulatory regions, which can make it challenging to deduce the precise biological mechanisms which play a role in disease etiology. A handful of CNVs affecting single genes, including 2p16.3 deletion (NRXN1; neurexin 1) and 7q36.3 duplication (VIPR2; vasoactive intestinal peptide receptor 2) have been found, but only NRXN1, a presynaptic cell adhesion molecule, has been robustly associated with SCZ. For the remaining majority of pathogenic CNVs, pathway analysis has been used to try and point toward the underlying biologically relevant mechanisms. In a study of SCZ probandparent trios, cases with de novo CNVs were found to be enriched for the postsynaptic density proteome, the association largely brought about by genes coding for the NMDA receptor and ARC signaling complexes, systems known to be involved in synaptic plasticity (Kirov et al., 2012). The same associations were found for small de novo mutations, as well as associations with proteins regulating actin filament dynamics, and mRNA targets of FMRP (Fromer et al., 2014). An important consideration when interpreting pathway analysis or any other approach attempting to make biological sense of genetic data, is that the timing of expression of mutated genes during neurodevelopment has a critical effect on the impact of the variant/ mutation. Gulsuner et al. (2013) mapped SCZ risk genes, identified from de novo point mutations, onto developmental transcriptome profiles. They found these genes were consistent with being part of a network critical during fetal development in the prefrontal cortex, suggesting that disruption of prefrontal neurogenesis in the fetus is important in the pathogenesis of SCZ. Most recently, gene set pathways were analyzed in the largest CNV data set to date, with 11,255 cases and 16,416 controls (Pocklington et al., 2015). Their findings reinforced the importance of the synaptic plasticity complexes, the NMDA receptor and ARC systems, but found the second, most highly associated pathway to be the g-aminobutyric acid (GABA) A

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receptor complex. This finding is the first strong genetic evidence for the role of proteins in the inhibitory GABAergic system, which has been previously implicated through other approaches, such as imaging studies, animal models, and postmortem expression studies (Gonzalez-Burgos, Cho, & Lewis, 2015). These findings lend support to hypotheses regarding altered excitatory/inhibitory balance in SCZ (Kehrer, Maziashvili, Dugladze, & Gloveli, 2008; Yizhar et al., 2011). These imbalances are thought to impact on oscillatory activity in neuronal networks, which are central to the communication of different brain regions (Buzs
aki & Watson, 2012). Oscillations in the gamma frequency have been found to be impaired in SCZ, proposed to be due in part to the altered function of GABAergic interneurons (for a review see Uhlhaas and Singer (2010)). In the final section of this review, we focus on studies probing oscillatory abnormalities in genetic models for SCZ.

3. PUTTING PSYCHIATRIC GENETICS TO WORK An important next step in order to take advantage of the recent progress in identifying risk genes is the creation of animal and cellular models able to reveal the mechanisms by which genetic variants influence risk for disorder. While animal models in psychiatry in particular have encountered significant hurdles (Arguello & Gogos, 2006; Nestler & Hyman, 2010; Wong & Josselyn, 2015), the high construct-validity of genetic approaches still have the strong potential to shed light on the mechanisms underlying disease etiology. An initial challenge arises in prioritizing exactly which genetic animal models should be created. There has been some debate regarding the most advantageous genetic strategy for modeling purposes. By modeling common variants, with an individually small contribution to risk, it may prove difficult to see their effect on the phenotype, despite their significant overall contribution to risk in concert with other common and rare variants. However, it should not be forgotten that common variants have the potential to point to genes and pathways of large effect, which can then be investigated and other elements of the pathway targeted by drugs (McCarroll & Hyman, 2013). An alternative basic strategy, one now widely supported (Karayiorgou, FlintGogos, & Malenka, 2012; Need & Goldstein, 2014), is to focus on the rare, highly penetrant mutations. It is proposed that identifying the mechanism of these (predicted) stronger phenotypes will lead to finding convergent disturbances at the

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mechanistic level. In addition to the 22q11.2 mouse model, which has been intensively studied, a handful of mouse models have been created to model the newly identified, high-penetrant CNVs for neuropsychiatric disorders, such as 7q11.23 (Li et al., 2009), 15q11-13 (Nakatani et al., 2009), 16p11.2 (Horev et al., 2011), and 15q13.3 (Fejgin et al., 2014). If the decision is to pursue highly penetrant CNV variants, then the next challenge is to identify the causative and mechanistically interesting individual genes within the CNVs responsible for the observed risk phenotype. While it is important to investigate all genes affected by the CNV (insofar as most deletions and duplications contain more than a single gene, though there are examples of single gene CNVs, as mentioned above), it makes practical sense to prioritize ones where other lines of evidence suggest it may be mechanistically important. One such example in the case of SCZ risk is the investigation of the cytoplasmic FMR1-interacting protein 1 (Cyfip1) gene, one of the four genes in the rare 15q11.2 CNV, with odds ratio of 2.15 (Rees et al., 2014). Cyfip1 has been shown to have a role in two main functional complexes involved in the development and maintenance of neuronal structures at the synapse, processes implicated in pathway analyses. On the one hand, it interacts with the WAVE complex to mediate actin polymerization, and on the other, it exerts translational control in combination with FMRP, which is disrupted in a major cause of autism, fragile X syndrome (Napoli et al., 2008; Schenck et al., 2003; Schenck, Bardoni, Moro, Bagni, & Mandel, 2001). It is clear that the step change in our understanding of the biological processes that are likely to underlie complex brain disorders such as SCZ, as informed by genetic approaches, is going to stimulate the generation of many new model systems with potential enhanced biological validity, both in vivo and in vitro. It is fortunate that this demand has coincided with a similar revolutionary step change in the ability to edit the genome, with CRISPR methods in particular to the fore. The following section will focus on the application of CRISPR to creating animal models for psychiatric disease and the potential pitfalls.

3.1 Genome Editing The latest tool in genome editing, known as CRISPR, has combined a simply programmable targeting system with the efficiency of site-directed nuclease approaches to create a faster, more cost-effective, and easy-to-use technology to target the genome. The molecular components of CRISPR technology originate from bacteria and archaea, where they serve as an

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adaptive immune system protecting the organism against alien DNA (for an excellent review of CRISPR technology, see Doudna and Charpentier (2014)). As well as many other uses, including engineering isogenic induced pluripotent stem (iPS) cell disease models, to potential therapeutic applications, CRISPR can be used to create genetic animal models of disease much more efficiently than other methods. The technology is also revolutionizing the creation of genetic models in rats and other species, which until now, have been far outshone in the field by their mouse counterparts (Zheng, Geghman, Shenoy, & Li, 2012). 3.1.1 The Dawn of Genetic Models in Rats The dominance of the engineered mutant mouse has largely been due to the use of gene targeting via embryonic stem (ES) cells, allowing precise gene replacement (knock-in) or loss of function (knockout) mutations at a specific locus. The desired mutation is introduced into the ES cells by homologous recombination, and the cell is injected into an early embryo. In some of the early embryos, the mutant ES cells will develop into the germ cells, which will eventually become sperm and eggs. The offspring of this animal will then carry the mutation in every single one of its cells. However, to make animal models, this technique was limited to the mouse, as the precise culture conditions for the stem cells, perfected over the past 35 years, did not work routinely for other species, including the rat. The culture conditions are crucial to encourage the ES cell to go on to develop into a germ cell, to be germ line competent. Without the right conditions, ES cells will develop into other cell lineages, such as nerves or muscle, and never be passed on to the next generation. Germ linecompetent rat ES cells are particularly difficult to create, so very few injections of ES cells will end up creating a mutant rat line. While some groups have succeeded in creating rat lines using this technique, it has been technically challenging and inefficient (Hamra, 2010; Tong, Li, Wu, Yan, & Ying, 2010). The laboratory rat, however, remains a preferred model for many researchers in physiology, psychology, behavioral and translational studies. They are arguably more similar to humans in many physiological aspects, their sociability and ability to perform complex learning tasks may give them an advantage in many behavioral studies, and in particular their larger size makes them more amenable for various procedures including in vivo imaging, surgery, and electrophysiology (Parker et al., 2014; Zheng et al., 2012). The ability to create genome-edited rats will begin to reconcile

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the field of genetics with a species that has had a long and successful history as an experimental model species. In the past decade, several alternative approaches have been developed, circumventing the need for rat ES cells, including zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and most recently, CRISPR-based methods (Gaj, Gersbach, & Barbas, 2013; Hu et al., 2013). The core technology shared by these methods involves a specially engineered nuclease composed of a target-DNA binding domain joined to a DNA-cleaving domain. These components are injected into donor zygotes to create the mutation and are implanted back into a female. While ZFN and TALEN require more substantial protein engineering to produce specific nucleases for each target site, the CRISPR system needs only a simple change in the guide RNA. In addition, genome editing allows genetic manipulations on an isogenic background, obviating the need to backcross new strains, which is an often necessary, time-consuming step in homologous recombination methods in mice. This simpler and more efficient genome editing tool looks like it will finally bring the genetic possibilities in the rat up to speed. Alongside creating conventional genetic models of single risk loci/risk genes, CRISPR enables researchers to use a strategy of “multiplexing”: creating animals with multiple genetic mutations in one single manipulation (Cong et al., 2013; Wang et al., 2013; Ma et al., 2014). It may be interesting to make use of this to systematically interrogate entire signaling complexes and protein networks on a larger scale, or to produce animals with multiple knockouts of redundant genes. Consider an animal model carrying mutations in several functionally related risk genes, as identified by genetic studies. This model could be probed with a view to understanding more about how the risk genes interact to create a phenotype with a polygenic basis. Perhaps it would be most sensible to start by simultaneously editing two functionally related genes affected by the same CNV, an approach already being applied in iPS cell research. 3.1.2 Limitations There is no question that genome editing technology far surpasses homologous recombination in efficiency and can provide genomic manipulations on an immediate isogenic background. However, the target specificity, the ability to edit the right part of the genome, can be a significant confounding factor when using this technique. Several studies using human cells have shown that mismatches between the guide RNA and target DNA can

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be tolerated, thus producing some unwanted off-target cleavage sites (Carroll, 2013; Fu et al., 2013; Hsu et al., 2013; Mali et al., 2013; Pattanayak et al., 2013). Hsu and colleagues designed a set of single guide RNAs (sgRNA) that contained all possible single-nucleotide substitutions, systematically changing each nucleotide downstream (towards the 50 end) of the protospacer adjacent motif (PAM, a key targeting component of the CRISPR system), and then assessed how these mismatches were tolerated by looking at cleavage activity at the target site (Hsu et al., 2013). Subsequently, they increased the number of mismatches. The extent to which mismatches influenced targeting depended on the number, distribution, and position relative to the PAM site. The more mismatches there were, and the closer they were to the PAM site, the less tolerated they were, with adjacent mismatches having a greater effect. A similar study, however, using a different target site found that single mismatches were well tolerated at any point in the sgRNA, even close to the PAM (Fu et al., 2013). The specificity, therefore, appears to be complex and dependent on the particular target site used. Both groups then looked at the genomic regions outside of the initial target site, to look for off-target cleavage sites. Candidate offtarget sites were predicted based on having few mismatches to the sgRNA. While many candidate sites were untouched, off-target events were found to occur at sites with mismatches near the PAM site, as well as sites with as many as five mismatches, with appreciably high mutation rates (ranging from 5.6% to 125% (mean: 40%) of the rate seen at the intended target site) (Fu et al., 2013). 3.1.3 Reducing Off-target Effects Off-target effects would confound the validity of animal models and could severely restrict the use of CRISPR in gene therapy due to the potential for harmful unintended chromosomal deletions (Liang et al., 2015; Zhang, Wen, & Guo, 2014). Therefore, researchers have focused on minimizing off-target effects, as well as methods to detect genome-wide off-target sites (Koo, Lee, & Kim, 2015). As suggested from the studies described above, the first strategy to increase specificity is to design the best sgRNA. At least eight online tools have been developed using algorithms of various degrees of complexity to predict the sgRNA that will have fewest off-target sites (Koo, Lee, & Kim, 2015). A second type of approach is to modify the sgRNA. Cho et al. (2014) found that adding two extra guanine nucleotides at the 50 end of the sgRNA reduced off-target mutations in human cells, although it is not clear as to why this works. Another group found that a

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truncated 17-nucleotide sgRNA, rather than the usual 20, increases the specificity, although at the same time, a shorter guide sequence means more similar regions elsewhere in the genome (Fu et al., 2013). These truncated sgRNAs seem to be more sensitive to mismatches due to the reduced binding energy between itself and the DNA. A third strategy, perhaps the one with the most potential, is to modify the use of the Cas9 protein itself (the nuclease in the CRISPR system) to make the targeting mechanism of the RNA more stringent. Making use of a system used by ZFNs and TALEN, Mali and colleagues designed a “nickase” variant of the Cas9 protein which makes only a single strand break instead of the usual double strand break (Mali et al., 2013). By using a pair of sgRNAs (spaced within about 100 bp of each other, one complementary to the sense strand, the other to the antisense strand), the nickase creates two “nicks” on opposite strands, close enough to create an effective double strand break. Several groups have shown that this significantly reduces the chance of off-target sites, as the likelihood of the two sgRNA’s off-target binding being close to each other is small (Cho et al., 2014; Ran et al., 2013). However, this method requires designing two sgRNAs, with target sequences containing two PAMs the right distance apart, which will limit the choice of targetable sites. A further method delivers Cas9 via a cellpenetrating peptide, rather than a Cas9-encoding plasmid, which is degraded quickly after inducing mutations at target sites, leading to fewer off-target effects than using plasmids (Ramakrishna et al., 2014). The greatest implication of unwanted off-target effects is for applications of CRISPR in gene therapy (Moore, 2015). As an example, a phase I/II trial has been run using a ZFN to target a gene involved with the human immunodeficiency virus (HIV). Off-target effects of this ZFN have been shown to cause chromosomal changes in a related gene, and it is unknown whether this might cause side effects or oncological consequences in patients (Koo, Lee, & Kim, 2015). However, when generating animal models, it is essential that there are no off-target effects, in order to know that the phenotype that is seen is due to the intended genetic manipulation. Most studies publishing data about CRISPR-generated animal models check for off-target effects, however, it is unclear whether the number of sites checked and whether the method used is sensitive enough to pick up all unintentional mutations. This issue will likely be resolved by the community in the near future; the ultimate solution might be to use whole-genome sequencing to check for off-target effects, when costs allow for this to be done routinely.

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The ease and speed of CRISPR genome editing has enabled researchers in any laboratory, big or small, to create genetic animal models, a key approach to harnessing the deluge of genetic information we have gained from GWAS and sequencing research. In particular, the ability to create rat genetic models will be advantageous for in vivo circuit analysis methods, as their larger size allows more recording electrodes to be implanted and better resolution with imaging. Thanks to genome editing technology, it will be possible to combine genetic manipulations with the extensive pedigree of electrophysiological research in the rat.

4. PRIORITIZING PHENOTYPING STRATEGIES FOR ANIMAL MODELS OF PSYCHIATRIC DISORDERS Novel genetic models of psychiatric risk require extensive phenotyping to understand the impact of the engineered mutation on brain function. One general advantage of animal models is that the effect of the mutation can be studied throughout development, including during pre- and post-natal stages, which are thought to be critical for psychiatric disease (see for example, Ishii and Hashimoto-Torii (2015)). It is also important to prioritize approaches which may provide the most translationally relevant observations, which as mentioned previously, lead to biomarkers that could help stratify patients and give better measures of drug efficacy. As a disease of neural development, it is highly likely that aberrant neural network development in SCZ results in abnormal network activity that in turn, at least in part, underlies the pattern of symptoms seen in the disorder. Studies in patients have given some insights into how neural circuits may be malfunctioning, but animal models allow deeper examination of circuit function through a variety of techniques, including in vivo and ex vivo methods. While ex vivo studies, such as brain slices, are vital to understand synaptic mechanisms on a local scale, in vivo studies allow the action of a genetic risk variant to be observed in the context of an intact network in a behaving animal (for a comprehensive review on both approaches in animal models, see Sigurdsson (2015)). In the final section of this review, we will focus on in vivo circuit-level analysis as a first line phenotyping strategy, demonstrating how the technique has been prominent in shaping our understanding of SCZ.

4.1 Circuit Dysfunction in SCZ Cognitive processing is supported by neural oscillations, population activity patterns emerging from the coordinated, periodic electrical activity of large

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groups of neurons (Buzs
aki & Draguhn, 2004). Oscillations originate in distributed cortical areas and present a mechanism for short- and long-range circuit interactions spanning different brain areas via neural synchrony (Buzs
aki & Watson, 2012). Abnormalities in these oscillations and their synchronization have been observed in SCZ patients and are increasingly thought to play a role in the causes of cognitive deficits seen in patients (Haenschel & Linden, 2011), emphasizing the idea of SCZ as a “functional disconnection syndrome” (Stephan, Baldeweg, & Friston, 2006). Findings have seen deficits in local synchrony, such as changes in the properties of oscillations in a particular brain region, or in long-range connectivity between distant brain regions (reviewed in Spellman and Gordon (2015)). Network oscillations have been shown to have a strong genetic determination, as observed by highly correlated gamma oscillation characteristics in identical twins (van Pelt, Boomsma, & Fries, 2012). Indeed, many of the genetic pathways being implicated have significant roles in the generation of oscillations, such as synaptic proteins and GABA receptor complex proteins (Pocklington et al., 2015). Furthermore, numerous genetic animal models have found abnormal changes in neural network activity, sometimes as a direct result of the mutation (for example, the ERBB4 deletion, discussed below). Crucially, neural network activity as measured by electro/magneto encephalography (EEG/MEG) and functional magnetic resonance imaging (fMRI) have the potential to be highly informative biomarkers for diagnosis and efficacy of treatment, paving the way to patient stratification and the discovery of new therapies (Haenschel & Linden, 2011; Jones, Menniti, & Sivarao, 2015). A significant problem in drug trials for SCZ is the lack of reliable and objective biomarkers to measure improvement in disease state (Hyman, 2014).

4.2 Strategies for In vivo Circuit-Level Analysis in Genetic Models for SCZ Studies examining circuit-level abnormalities and functional connectivity in SCZ patients have used imaging techniques, such as fMRI and MEG, or electrophysiology, such as EEG (B€ahner et al., 2015; Spencer et al., 2004). While fMRI lacks the temporal resolution necessary to quantify rapid neural dynamics, it has nevertheless been used in genetic animal models as a highly translational tool to map neural activity patterns through the entire brain (Smucny, Wylie, & Tregellas, 2014; Song et al., 2015). The strategy for circuit-level analysis that we will focus on here is in vivo electrophysiology in behaving animals. Animal genetic models have used EEG

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recordings, which allow direct translation to human studies, as well as implanted electrodes to record directly from specific brain regions. Implanted electrodes can record local field potentials (local population activity) and individual spiking of neurons in behaving animals. Importantly, the characteristics of oscillations and mechanisms of generation have been conserved across species (Buzs
aki, Logothetis, & Singer, 2013), supporting the translatability of these measures. In addition, the high spatial and temporal resolution of this approach means that while it cannot give information regarding the entire brain, it is well suited to investigate one or multiple brain areas. Various paradigms exist to investigate neural circuit function in clinical and animal research, including event related potentials (ERP), oscillations during behavior, such as working memory tasks, or spontaneous activity during rest and sleep (Phillips & Uhlhaas, 2015). The ERP consists of a set of stereotypical and well-conserved patterns observed in the EEG occurring at specific time points after a discrete (typically sensory) stimulus (e.g., P50, N100, P200, P300, defined by the direction of voltage change, positive or negative, and latency following the stimulus) (reviewed in Featherstone et al. (2015)). Mismatch negativity (MMN), another ERP measure, describes the increase in negative EEG amplitude in response to an oddball stimulus in a sequence of similar stimuli. SCZ patients show deficits in these measures, which are associated with sensory processing and cognition. MMN in particular may be a biomarker for the aberrant neural circuit processes that lead to impaired cognition in SCZ (Light & Swerdlow, 2015). Neural circuit oscillations are also investigated during cognitive tasks and at rest. One circuit that has received particular attention is the hippocampaleprefrontal cortex (HPCePFC) circuit, disruptions of which are associated with cognitive impairments in several psychiatric diseases, including SCZ, major depression, and posttraumatic stress disorder (Godsil, Kiss, Spedding, & Jay, 2013).

4.3 The Hippocampal-Prefrontal Circuit in SCZ Abnormalities in the HPCePFC circuit in SCZ patients have been observed using structural and functional imaging and EEG recordings. Both regions have been extensively studied with regards to their roles in learning, memory, and decision-making, when interactions between the two regions play important roles in cognitive processing, via identified monosynaptic projections from HPC to PFC and reciprocal indirect connections (reviewed in Godsil et al. (2013)). Structural changes in anterior HPC and PFC are

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correlated with symptom severity (Qiu et al., 2010), while patients also showed decreased white matter in the HPC, PFC, and anterior cingulate cortex (Hao et al., 2009). Functional coupling between HPC and medial PFC is abnormal both at rest and during a working memory task (Wolf et al., 2009; Zhou et al., 2008). There is some evidence supporting the suggestion that these circuit abnormalities may represent a biomarker for SCZ. Esslinger et al. (2009) found that healthy carriers of a risk SNP in the gene ZNF804A had abnormally increased functional connectivity between HPC and dorsolateral PFC. These results were further supported by a recent study which found the risk allele to be associated with increased HPCePFC connectivity and decreased intrahippocampal theta, through simultaneous fMRI and MEG measures (Cousijn et al., 2015). Callicott et al. (2013) looked at healthy subjects carrying two risk alleles within SLC12A2 and DISC1, both involved in neural development, which together interact to increase SCZ risk. During a recognition memory task, these carriers showed decreased HPCePFC connectivity.

4.4 In vivo Circuit Dysfunction in Genetic Animal Models To date, relatively few genetic models of SCZ risk loci have investigated electrophysiological biomarkers in vivo. In particular, studies using animal models of CNVs have been sparse, but as the strategy to investigate CNVs moves forward, we can expect to see findings on CNV models published in the near future. Sigurdsson, Stark, Karayiorgou, Gogos, and Gordon (2010) examined the functional connectivity in the Df16(A)þ/ mouse, a model of the highly penetrant 22q11.2 CNV. They assessed synchronization of network activity between HPC and PFC during a working memory task, one of the cognitive functions impaired in the disease. It had been shown previously that HPCePFC synchrony increases in the time window preceding the decision point in a working memory task (Jones & Wilson, 2005). Synchrony was measured by phase-locking of prefrontal neurons to the hippocampal theta rhythm and by a coherence measure between the local field potentials in HPC and PFC. Not only were Df16(A)þ/ mice impaired in the acquisition of the task, but the extent to which synchrony was impaired correlated with the extent of the behavioral deficit. A recent study from the same group provided an interesting possible cause for the deficits seen in HPCePFC synchrony, by studying a mouse model deficient for Zdhhc8, one of the genes within the 22q11.2 CNV. The mouse deficient for this gene involved in axon growth showed reduced branching of neurons, paralleled with impaired HPCePFC

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synchrony, which also correlated with impaired acquisition of the working memory task. This is an excellent example of probing a CNV to find the individual genes responsible for aspects of the phenotype, getting closer to a potential therapeutic target. Another CNV animal model that has been shown to have electrophysiological signatures relevant to SCZ is the mouse model of the 15q13.3 deletion, another CNV with high risk for SCZ and also epilepsy (Stefansson et al., 2008). While the model appears fairly normal in typical behavioral tests associated with SCZ (such as prepulse inhibition, ketamine-induced hyperactivity, and working memory), Fejgin et al. (2014) found a range of SCZ-related electrophysiological deficits using EEG electrodes measuring local field potential. Auditory stimulation at gamma frequency can evoke gamma oscillations in the brain, which is reduced in SCZ patients. The mice showed increased spectral power in the gamma frequency during active movement, but decreased evoked gamma compared to controls in parietal cortex, frontal cortex, and hippocampus. Additionally, peak theta frequency was reduced in hippocampus and prelimbic cortex. Others have focused on risk genes identified via linkage studies or implicated by CNVs, such as neuregulin-1 (NRG1), a neurotrophic factor, and ERBB4, the synaptic protein it binds to (Stefansson et al., 2003; Walsh et al., 2008). ERBB4 is expressed predominantly on parvalbumin-positive (PVþ) interneurons (Fazzari et al., 2010), which mediate gamma oscillations (Sohal, Zhang, Yizhar, & Deisseroth, 2009). Gamma oscillations play an important role in cognition, hence the widely recognized hypothesis that the synaptic dysfunction of PVþ neurons may contribute to cognitive deficits in SCZ (Lisman et al., 2008). Following injection of NRG1 to mouse lateral ventricles, kainate-induced gamma oscillations showed aberrant increased power, an effect which disappeared when the receptor ERBB4 was knocked out from PVþ interneurons, suggesting a role for NRG1/ ERBB4 signaling in gamma regulation (Hou, Ni, Yang, & Li, 2014). Another study using PVþ knockout ERBB4 mice showed impaired HPCePFC synchrony under anesthesia, while also finding increased baseline gamma oscillations in freely moving mice and deficits in social and cognitive functions reminiscent of SCZ (Del Pino et al., 2013). Oscillations in the prefrontal cortex were studied in a mouse model of DISC1 mutation, a risk gene for SCZ and depression, with an important synaptic role (Brandon & Sawa, 2011). Sauer, Str€ uber, and Bartos (2015) saw an impairment of theta and low-gamma synchrony and power, suggesting it may be due to PVþ interneuron abnormalities. Aberrant gamma

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frequency oscillations and reduced auditory-evoked potentials were also found in knockout mice lacking the SCZ risk-associated protein dysbindin (Allen et al., 2008), a protein thought to have a role in excitatory neurotransmission due to its presence in glutamatergic vesicles and postsynaptic densities (Carlson et al., 2011). 4.4.1 Sleep-related Network Activity in SCZ Sleep neurophysiology has been proposed to be a highly translatable measure of functional connectivity in the brain and may be a strong candidate for a biomarker of psychiatric disease such as SCZ for three independent reasons. Firstly, these characteristic coordinated network activity patterns spanning the neocortex, thalamus, and hippocampus have been found to be disrupted in psychiatric diseases, including SCZ. Patients also experience disrupted circadian rhythms as well sleep disturbances, (Wulff, Dijk, Middleton, Foster, & Joyce, 2012) which is echoed in a genetic model of the disease (the blind-drunk (Bdr) model, Oliver et al., 2012). Secondly, like other oscillatory mechanisms, sleep neurophysiology is evolutionarily highly conserved (Borbély & Achermann, 1999). Thirdly, these spontaneous circuit dynamics during sleep are unbiased by waking behavior, attention, and demands of any specific task (for a review see Gardner, Kersanté, Jones, and Bartsch (2014)). A recent study measured sleep EEG activity in a genetic risk variant model, the CACNA1C heterozygous (HET) knockout mouse (Kumar et al., 2015). CACNA1C encodes a subunit of the widely expressed Cav1.2 voltage dependent L-type calcium channel, and emerged as a candidate risk gene from the recent Psychiatric Genomics Consortium GWAS study. HET mice were found to have significantly lower EEG spectral power in beta to gamma frequencies both during wake and rapid eye movement (REM) sleep. In addition, they noted a trend of higher slow wave activity during non-REM sleep. Following a stress procedure (acute sleep deprivation or restraint stress), the recovery sleep in HET mice showed reduced REM sleep. Suh, Foster, Davoudi, Wilson, and Tonegawa (2013) recorded single unit activity to characterize hippocampal activity in a forebrain specific calcineurin knockout mouse. Calcineurin, a phosphatase involved in synaptic plasticity, has been associated with SCZ in linkage analyses (Gerber et al. 2003). They measured hippocampal ripples, transient, fast (140e200 Hz) oscillation events seen during non-REM sleep or quiet wakefulness, which play an important role in memory consolidation through an event known as “replay” (Girardeau & Zugaro, 2011). The authors reported specifically on

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awake ripples and found that knockout mice showed increased ripple power and density, together with abnormal overreactivity of place cells during ripple events, showing less replay of the recent spatial experience. A recent review suggests that findings from electrophysiological studies from different categories of rodent models, including genetic, pharmacological, and neurodevelopmental manipulations, show a broadly convergent set of electrophysiological biomarkers, including changes in excitatory/inhibitory balance, disrupted synaptic function and plasticity, and abnormal oscillations and evoked potentials (Rosen, Spellman, & Gordon, 2015). This convergence suggests that the various etiologies may lead to increased risk of SCZ by disruption of common neurobiological systems, and the authors argue that finding medication to target such biomarkers may be fruitful. 4.4.2 Perturbing Circuits in a Top-down Approach The investigation of circuit-level dysfunction in SCZ can be tackled with two main methods: a bottom-up approach, whereby aberrant circuit function is studied in genetic models, such as those discussed thus far, or a topdown approach, whereby circuits are perturbed in a healthy rat. The latter approach has seen optogenetic techniques used to dissect circuits in models of psychiatric disease including anxiety, addiction, social dysfunction (Steinberg, Christoffel, Deisseroth, & Malenka, 2015), as well as circuits relevant to SCZ (Cho & Sohal, 2014), such as distinguishing the roles of subtypes of ventral tegmental area neurons in the signaling of reward and punishment (Cohen, Haesler, Vong, Lowell, & Uchida, 2012). In addition, optogenetics was used to establish that PVþ interneurons drive gamma oscillations (Sohal et al., 2009). Prefrontal circuits in the mouse were perturbed using optogenetics to investigate the hypothesis that excitation/ inhibition (E/I) balance can lead to behavioral and cognitive deficits (Yizhar et al., 2011). When the E/I balance was artificially increased, mice showed specific social and cognitive deficits and an increase in gamma power, which were both reversed when inhibition was in turn elevated. Most recently, Duan et al. (2015) optogenetically stimulated the thalamic nucleus reuniens at delta frequency in rats during a T-maze working memory task. Delta oscillations are elevated in SCZ (Boutros et al., 2008; Siekmeier & Stufflebeam, 2010), and unlike gamma abnormalities, are not present in first-degree relatives or even twins (Venables, Bernat, & Sponheim, 2009; Weisbrod et al., 2004), suggesting they are associated with the disease itself rather than susceptibility. Under optogenetic stimulation, the rats showed a strong working memory deficit, which later returned to normal without

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stimulation. The authors speculated that the aberrant delta oscillation may disrupt the transmission of information through the thalamus to the hippocampus, thus introducing working memory deficits. However, a common issue with optogenetic stimulation is it is not known how representative the stimulation is of the nucleus delta activity in SCZ. An important next step will be to combine strategies: use optogenetic approaches in an animal model of an SCZ risk variant, to expose how particular circuits, driven by a specific class of neurons targeted by the optic fiber, respond in the context of the genetic manipulation. Some studies in other psychiatric conditions have explored this approach: for example, in a genetic model of obsessive compulsive disorder, optogenetic stimulation of a specific circuit rescued the behavioral phenotype in the genetic model (Burguiere, Monteiro, Feng, & Graybiel, 2013). By identifying how different circuits are perturbed by a mutation, optogenetics has the potential to fill the gap between the genetic mutation and the mechanism of its contribution to pathophysiology of SCZ (Marton & Sohal, 2015).

5. CONCLUSION Recent advances in our understanding of the genetic basis of SCZ and other psychiatric disorders, combined with the relative ease of creating new genetic models using CRISPR genome editing, are well placed to reinvigorate efforts to identify disease pathways and biomarkers to aid therapeutic research. Using circuit-level analysis is a potentially powerful strategy to finding highly informative biomarkers for clinical response, as well as a means to stratify patients by their genotype and circuit activity. To date, early-stage psychiatric drug research has suffered repeated setbacks, particularly in the context of cognition: what works in the rat model fails to translate to patients (Hyman, 2014; Papassotiropoulos & de Quervain, 2015). However, by focusing on the common ground offered by conserved neural circuits at a mesoscopic level of analysis, it is increasingly realistic to investigate directly analogous genetic effects in animals and patients/carriers, mapping between genetic and neurophysiological fingerprints in both preclinical and clinical settings.

ACKNOWLEDGMENTS Thanks to the Schools of Psychology and Medicine at Cardiff University (Ph.D. studentship to JRH), the Medical Research Council (Fellowship G1002064 to MWJ), the MRC Centre for Neuropsychiatric Genetics and Genomics (MR/L010305/1 to LW), and The Wellcome Trust (DEFINE Strategic Award, 100202/Z/12/Z, to LW) for support.

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