Despite enormous efforts employing various approaches, the molecular pathology in the schizophrenia brain remains elusive. On the other hand, the knowledge of the association between the disease risk and changes in the DNA sequences, in other words, our understanding of the genetic pathology of schizophrenia, has dramatically improved over the past two decades. As the consequence, now we can explain more than 20% of the liability to schizophrenia by considering all analyzable common genetic variants including those with weak or no statistically significant association. Also, a large-scale exome sequencing study identified single genes whose rare mutations substantially increase the risk for schizophrenia, of which six genes (SETD1A, CUL1, XPO7, GRIA3, GRIN2A, and RB1CC1) showed odds ratios larger than ten. Based on these findings together with the preceding discovery of copy number variants (CNVs) with similarly large effect sizes, multiple disease models with high etiological validity have been generated and analyzed. Studies of the brains of these models, as well as transcriptomic and epigenomic analyses of patient postmortem tissues, have provided new insights into the molecular pathology of schizophrenia. In this review, we overview the current knowledge acquired from these studies, their limitations, and directions for future research that may redefine schizophrenia based on biological alterations in the responsible organ rather than operationalized criteria.
The term pathology is defined as the study of the essential nature of disease . In the pathology of physical diseases, abnormal changes in responsible organs or systems, such as invasion of malignant cells in cancers, and loss of nigrostriatal dopaminergic neurons in Parkinson’s disease, are examined typically by microscopes. On the other hand, a field of study of pathological cognition and behaviors often observed in patients with psychiatric disorders, psychopathology, does not analyze the brain, the organ presumably responsible for these disorders. This may not be unreasonable given that psychiatry is etymologically a study of the psyche, a Greek word ψυχή whose derived meaning includes invisible spirit or soul. Nevertheless, It has long been thought that mental illness is fundamentally a disease of the brain, as classically advocated by Wilhelm Griesinger, Emil Kraepelin, and others [2, 3]. Based on this concept, the brain pathology of schizophrenia, a common psychiatric disorder with a lifetime prevalence of ~1% , has been investigated in numerous studies. However, despite many efforts, no definite pathological changes, like senile plaques and neurofibrillary tangles in Alzheimer’s disease, were identified in their postmortem brains. Meanwhile, recent rapid advances in molecular biology and engineering have prompted the development of methods to analyze molecules such as nucleotides and proteins more comprehensively, more sensitively, with a higher cellular and spatial resolution, and quantitatively. Besides, multiple collaborative research frameworks have been established to ensure sample sizes sufficient to solve the critical problem of multiple testing and relevant statistical power. This has especially been true in comprehensive studies of variants in DNA, which encodes fundamental biological information for all organs, including the brain and can be analyzed by using easily accessible peripheral tissue samples, resulting in an accumulation of statistically robust findings. Given these, while we should refrain from being overly optimistic, perhaps now might be the time to bring together these technologies and resources to elucidate the molecular pathology of schizophrenia. In this review, we overview the findings from research aiming to decipher the molecular pathology of schizophrenia, emphasizing large-scale omics studies with substantial statistical power and analyses of etiologically valid disease models, and summarize the current achievements. Following that, the challenges and obstacles to be overcome and the future research directions will be discussed, along with an introduction of preliminary results from studies utilizing cutting-edge technologies that will surely facilitate the elucidation of the fundamental pathology of schizophrenia.
The genetic pathology of schizophrenia
Almost unquestionably, the brain is the organ primarily responsible for the pathogenesis of schizophrenia. However, the human brain is covered by thick cranial bones, and therefore it is almost impossible to access and analyze living human brain tissues at the molecular level in a non-invasive manner, nor is it easy to collect postmortem brains on a large scale. On the other hand, the sequences of DNA, the molecule encoding fundamental biological information of all tissues, can be analyzed without accessing the brain, since its sequences are in principle identical in every cell and invariant throughout life, with few exceptions. Based on this relative ease in obtaining samples as well as the high heritability of schizophrenia, reported to be 60–80% in epidemiological studies [5,6,7,8], large-scale genetic studies, analyzing samples from more than 100,000 individuals these days, have been conducted. Reflecting this large number, among various research on the molecular pathology of schizophrenia, statistically robust findings have been particularly accumulated from human genetics studies analyzing variations of the sequences of DNA molecules. For this reason, we begin with an overview of our knowledge of the genetic pathology of schizophrenia.
Robust findings from large-scale analyses of common and rare variants
Although schizophrenia is a highly heritable disorder, no single variant explaining a large portion of the overall heritability has been identified. Therefore, comprehensive studies of various allele frequencies and effect sizes of genetic variants contributing to its risk are warranted. In a simplified view, there are two major types of genetic studies on different frequencies and effect sizes of variants, that is, genome-wide association studies (GWAS) of common single nucleotide polymorphisms (SNPs) with small effect sizes (typically odds ratio [OR] < 1.2) and sequencing studies of rare variants potentially with large effect sizes (sometimes OR > 10). The scales of these two types of studies have consistently grown [9,10,11,12,13,14,15,16,17,18,19], and the results of the two largest studies to date, each looking into common SNPs  or rare variants , have recently been published after peer review.
In the newest GWAS of common SNPs by the Psychiatric Genomics Consortium (PGC) analyzing 76,755 schizophrenia cases and 243,649 control individuals , 287 distinct loci with genome-wide significant association (P < 5 × 10−8) were identified. The SNP with the largest effect size was the rs140365013 variant on chromosome 6 near the major histocompatibility complex region, with an OR of 1.23, confirming that individual SNPs do not greatly increase the disease risk. On the other hand, the proportion of the variance in schizophrenia liability explained by all measured SNPs, including numerous variants that did not show statistically significant association with schizophrenia, was reported to be 24%. This proportion is much larger than that calculated solely from loci associated with genome-wide significance. Therefore, a significant part of the heritability is attributable to common SNPs that individually show weak associations and effects, and it can be said that schizophrenia is a highly polygenic disorder. Since each of the 287 associated loci often contains multiple genes (specifically, of the 287 loci, 206 and 108 contain ≥2 and ≥5 genes, respectively), it is essential to identify functionally important causal genes and variants in order to understand the molecular pathology from the genetic pathology. To this end, gene prioritization was performed in this GWAS by PGC using various approaches such as fine-mapping of credible sets of causal SNPs by an adaptation of a Bayesian inference algorithm  and Mendelian randomization to identify SNPs whose causal effects are likely to be mediated through regulation of gene expression . As a result, a total of 120 prioritized genes were nominated, of which 70 and 55 received support from the fine-mapping and Mendelian randomization analyses, respectively. There were five genes supported by both lines of evidence (CUL9, FURIN, LINC00320, SNAP91, and ZNF823). In addition, two other prioritized genes (GRIN2A, and SP4) are supported by statistical evidence significant after conservative multiple testing correction in the rare variant study described below (Table 1a, b).
In a companion study of rare variants by the Schizophrenia Exome Meta-Analysis (SCHEMA) Consortium, rare single nucleotide variants (SNVs, any frequency of single nucleotide substitutions including both SNPs and rare variants) and short insertion/deletions (indels) in protein-coding regions were systematically analyzed by using exome (all protein-coding exonic regions) sequencing data of 24,248 schizophrenia cases, 97,322 controls, and 3402 trios consisting of schizophrenia probands and their unaffected parents. By evaluating the burden of rare loss-of-function (LOF: nonsense, splice site, and frameshift indel) variants (also known as protein-truncating variants: PTVs) and damaging missense variants (defined by the missense badness, PolyPhen-2, and constraint [MPC] score), including those arisen de novo in the probands, this study identified ten genes (SETD1A, CUL1, XPO7, TRIO, CACNA1G, SP4, GRIA3, GRIN2A, HERC1, and RB1CC1; Table 1b) surpassing the exome-wide significance threshold (defined as 2.14 × 10−6 based on the number of protein-coding genes analyzed) and 22 additional genes at a false discovery rate (FDR) < 0.05. Considering their effect sizes, six (SETD1A, CUL1, XPO7, GRIA3, GRIN2A, and RB1CC1) out of the ten exome-wide significant genes were enriched for rare PTV and damaging missense (MPC > 3) variants with OR > 10, indicating that rare deleterious variants of these genes confer the schizophrenia risk with large effects as well as robust statistical significance. Another notable thing is that five (SETD1A, TRIO, CACNA1G, GRIA3, and GRIN2A) of the ten genes are also implicated in other neurodevelopmental disorders, such as intellectual disability and epilepsy, as registered on the Online Mendelian Inheritance in Man (OMIM) database . Therefore, on the one hand, it is possible that these cases with deleterious variants in known neurodevelopmental disorder genes might be individuals who should have been molecularly diagnosed as patients of highly heritable neurodevelopmental diseases, while on the other hand, another possibility is that the carriers of these variants did not exhibit developmental and physical symptoms sufficient to be diagnosed with a neurodevelopmental disease due to some modifying factors and were operationally diagnosed with schizophrenia. If the former is true, this suggests that genetic testing may detect overlooked patients with highly heritable neurodevelopmental diseases and provide clues for better intervention. If the latter is the case, it implies that some modifying factors influence the severity of the symptoms. Indeed, there is accumulating evidence supporting the existence of modifying factors, such as common and rare variants other than the diagnostic mutation, in patients with neurodevelopmental diseases [25,26,27,28]. A more detailed analysis of such modifying factors may pave the way toward the development of new treatment and prevention strategies.
Besides common SNPs analyzed in GWAS and rare SNVs/indels analyzed in exome sequencing studies, another important class of genetic variation of which several are known to be robustly associated with schizophrenia is copy number variants (CNVs) [29,30,31,32]. Among important works on CNVs, a large genome-wide study by the CNV Working Groups of PGC analyzing 21,094 schizophrenia cases and 20,227 controls  identified copy number losses at six loci (1q21.1, 2p16.3 involving NRXN1, 3q29, 15q13.3, distal 16p11.2, and 22q11.2) and gains at two loci (7q11.23 and proximal 16p11.2) that are significantly (P < 1.33 × 10−4 for losses and 4.33 × 10−5 for gains) associated with schizophrenia after multiple testing corrections (Table 1c). All of these are rare in controls and contribute to the risk for schizophrenia with ORs ranging from 3.8 to infinity. Also, like the known neurodevelopmental disorder genes identified in the SCHEMA study described above, all, or nearly all of these schizophrenia-associated CNVs are phenotypically pleiotropic and often more strongly associated with disorders other than schizophrenia, such as ASD and intellectual disability. Therefore, complex phenotype-genotype relationships should be considered when predicting disease risks from the information on CNVs and generating and analyzing CNV-based animal and cellular models.
Aggregating the findings from studies of common SNPs and rare SNVs/indels and CNVs, we can now explain a substantial part of the schizophrenia heritability (mainly by common SNPs) and have produced a list consisting of six genes and six CNVs associated with schizophrenia with observed ORs larger than ten. Also, there is a convergence of the results of studies of common and rare variants. At the level of individual genes, GRIN2A and SP4 are included in the list of 120 genes prioritized in the PGC GWAS and showed exome-wide significant enrichment of rare deleterious variants in the SCHEMA study, as described above. At the level of overall enrichment patterns, the sets of genes implicated in the SCHEMA study and studies of rare coding variants in other neurodevelopmental disorders (e.g., ASD and intellectual disability) are shown to be significantly enriched for common variant associations in the PGC GWAS.
We provide a compiled list of genes and CNVs identified through large-scale studies, which underlie the genetic pathology of schizophrenia, in Table 1, together with the landscape of their population frequencies and effect sizes in Fig. 1. Meanwhile, as shown in Table 1 and Fig. 1, it should be noted that there are wide ranges of confidence intervals for ORs for rare risk variants. Also, there is a possibility of the so-called winner’s curse  in genetic studies. To more accurately estimate their effect sizes and the robustness of the association, further larger studies are always warranted.
Functional convergence of the findings in genetic studies
As described above, genetic studies have identified a number of statistically robust new genes and variants; however, these in themselves only show “association” with the phenotype. Thus, further analysis is needed to translate genetic findings into the knowledge of the molecular brain pathology of schizophrenia. This can be facilitated, for example, by testing if specific molecular and biological pathways are enriched among the associated genes.
More specifically, gene ontology (GO) enrichment analyses were performed in both the PGC GWAS and the SCHEMA study by utilizing MAGMA  and DNENRICH  that appropriately control for confounding factors such as gene sizes and linkage disequilibrium. We, therefore, examined how the results of these two studies converge at the levels of molecular function. In the PGC GWAS and the SCHEMA study, the results of GO enrichment analysis for 7315 and 1491 terms are available, respectively. Of these, 1431 GO terms were commonly analyzed and 111 of them showed significant enrichment at uncorrected P < 0.05 in both of these two studies (Fig. 2a). The statistical significance (−log10 P value) for each term in the PGC GWAS and the SCHEMA study showed a highly significant correlation (Fig. 2b, Pearson’s correlation coefficient = 0.39, P = 6.67 × 10−54). This result indicates that there is a convergence of molecular and biological pathways implicated by common SNPs and rare deleterious variants. Specifically, four GO terms, all related to voltage-gated channels and synaptic transmissions, were significant after Bonferroni correction in both the PGC GWAS and the SCHEMA study (Fig. 2b). When the 32 GO terms with Bonferroni-corrected P < 0.05 in either dataset (respectively 25 and 11 terms in the PGC GWAS and the SCHEMA study, of which four are common as above) were visualized as networks by connecting them based on the similarity of the contained genes (Fig. 2c), we observed the formation of three clusters, each related to channel or transporter activities; neuronal components (synapse, axon, and dendrite); chromatin or histone organization. Though the cluster of chromatin or histone organization was only supported by SCHEMA, the former two clusters (“channel or transporter activities” and “neuronal components”) showed convergent enrichment in both studies. Taken together, these can be considered molecular pathways whose involvement in schizophrenia pathogenesis is supported by both common and rare variant studies.
In addition to molecular pathways, both the PGC GWAS and the SCHEMA study performed cell-type enrichment analysis utilizing data from single-cell RNA sequencing, which is rapidly developing in recent years. For this analysis, both studies employed the statistical method described by Skene et al.  and used the data of 265 cell types defined in a single-cell RNA sequencing study of the mouse nervous system by Zeisel et al. . When we plotted the enrichment ranks for the 265 cell types in the PGC GWAS and the SCHEMA study, which are detailed in Supplementary Fig. 3 and Supplementary Table 10 of the corresponding papers, respectively, we again found that there is a highly significant correlation (Fig. 2d, Pearson’s correlation coefficient = 0.74, P = 4.50 × 10−47) (note that we used the enrichment ranks because exact statistics were not available in Supplementary Fig. 3 of the PGC GWAS). Overall, enrichment was particularly strong in excitatory neurons, followed by inhibitory neurons, and less pronounced in other cell types such as glial, vascular, and immune cells. Among the top ten highest-ranked cell types, eight were excitatory neurons, of which five, including the top two (“TEGLU4: Excitatory neurons, Cortex pyramidal layer 5, Cingulate/Retrosplenial area (superficial and deep)” [1st in PGC GWAS and 7th in SCHEMA) and “TEGLU20: Excitatory neurons, Cortex pyramidal layer 6” [7th in PGC GWAS and 4th in SCHEMA]), were annotated as deep layer excitatory neurons. These results represent another form of functional convergence of the findings from genetic studies of common and rare variants, and provide insight into the cell types likely playing a critical role in the molecular pathology of schizophrenia.
Other types of variants potentially explain still-missing heritability
Although large-scale genetic studies and refinements in statistical methods have elucidated a substantial part of the genetic architecture of schizophrenia, there remains a large gap between the overall heritability reported in epidemiological studies (60–80%) [5,6,7,8] and that explained by common SNPs (24% in ref. ) or rare gene-disruptive rare SNVs, indels, and CNVs (<10% according to refs. [37, 38]). Provisional evidence suggests other types of variants that are not captured by GWAS or exome sequencing are likely to be involved, including rare non-coding variants  and tandem repeat variants  identified through whole genome sequencing. Also, there is preliminary evidence suggesting the role of postzygotic somatic variants [31, 41,42,43,44], while these are not transmitted and do not contribute to heritability. More detailed information on results from these pioneering but preliminary studies can be found in Supplementary Note. To more accurately estimate the contributions from these under-studied types of variants, further large-scale studies are mandatory.
Transcriptomic and epigenomic pathology in schizophrenia
GO enrichment analysis of rare coding variants identified regulation of transcription and chromatin organization as one of the molecular pathways implicated in schizophrenia (Fig. 2c). Also, multiple single genes with large effect sizes, such as SP4 encoding the transcription factor Sp4 and SETD1A encoding a histone methyltransferase, are core components of transcriptional and epigenetic regulation. In line with these findings, studies of transcriptomic and epigenomic pathology of schizophrenia using patient-derived tissues have been conducted at scale.
One of the largest studies of transcriptomic brain pathology in schizophrenia was conducted by the PsychENCODE consortium . In this study, gene- and transcript isoform-level differential expression was comprehensively analyzed by performing RNA-sequencing (RNA-seq) of bulk postmortem cerebral cortex tissues from 559 schizophrenia cases and 936 control individuals, together with 51 ASD and 222 bipolar disorder brains. They identified that the expression of 4821 genes and 3803 isoforms significantly differed between schizophrenia and controls (FDR < 0.05). Genes related to “inflammatory response” and “receptor activity” were enriched in the significantly upregulated and downregulated genes/isoforms, respectively. The enrichment of the genes related to receptor activities is consistent with the results summarized in the previous section. Schizophrenia heritability was enriched among genes and transcripts dysregulated in schizophrenia brains, especially in down-regulated transcript isoforms.
Regarding epigenomic brain pathology, a recent large-scale study analyzed two major histone modifications, histone 3 lysine 27 acetylations (H3K27ac) and histone 3 lysine 4 trimethylations (H3K4me3) , in postmortem prefrontal cortical samples (sorted neurons or bulk tissues) from 303 schizophrenia cases and 388 controls along with 48 bipolar disorder brains by chromatin immunoprecipitation sequencing (ChIP-seq) . In the analysis of differential H3K4me3/H3K27ac peaks, 6219 differential H3K27ac peaks (FDR < 0.05) between schizophrenia and controls were identified though there were no differential H3K4me3 peaks. Of these, schizophrenia heritability based on GWAS  was enriched in the H3K27ac peaks hyper-acetylated in schizophrenia. Subsequently, this study mapped cis-regulatory domains (CRDs), which often overlap with topologically associating domains defined by the analysis of 3D chromosomal conformations but constitute smaller regulatory units of 104–106 bp [48, 49], in the brain using the information of inter-individual correlations between histone peaks. In an analysis integrating information on CRDs and differential H3K27ac peaks, it was shown that schizophrenia heritability is strongly enriched at differential H3K27ac peaks in CRDs hyper-acetylated in schizophrenia, suggesting that dysregulated H3K27ac peaks within dysregulated CRDs particularly are associated with the genetic schizophrenia risk.
Besides histone modifications, DNA methylation is another major epigenetic modifier with regulatory functions. Several studies have explored genome-wide DNA methylation status in postmortem schizophrenia brains. Among them, a study of microarray-based analysis of DNA methylation at ~450,000 loci in postmortem dorsolateral PFC (DLPFC) tissues from 526 individuals was reported . In this study, a total of 2104 sites differentially methylated between quality-controlled 108 schizophrenia cases and 136 controls (Bonferroni-corrected P < 0.05), of which 97.1% were hypomethylated in schizophrenia, were identified. A GO enrichment analysis of genes in or near the differentially methylated sites showed an overrepresentation of genes related to embryo development, cell fate commitment, and nervous system differentiation. Also, modest enrichment of schizophrenia-associated loci among the differentially methylated sites (1.9% among differentially methylated sites vs. 1.3% among others, P = 0.004, Chi-square test) was observed. On the other hand, in a study of postmortem brain samples overlapping with the above-described microarray-based study (70 and 95 schizophrenia DLPFC and hippocampus, and 77 and 102 control DLPFC and hippocampus) using whole-genome bisulfite sequencing, a technique that can detect DNA methylation at the single base resolution, much smaller numbers of differentially methylated sites, none in DLPFC and 70 in the hippocampus, were identified despite less stringent multiple testing corrections (FDR < 0.05) . This discrepancy could be explained by the difference in sample sizes as well as the sensitivity to detect differentially methylated sites and the number of hypotheses tested, as a larger number of sites are covered by whole-genome bisulfite sequencing.
In addition to the studies using postmortem brain tissues, there are large-scale studies of peripheral samples aiming to identify disease biomarkers. In a study by Aberg et al. , analyzing genome-wide DNA methylation profiles in blood samples from 759 schizophrenia cases and 738 controls by methyl-CpG–binding domain protein-enriched genome sequencing, 25 and 139 sites associated with the diagnosis at Bonferroni-corrected P < 0.05 and FDR < 0.01, respectively, were identified. The most significant association was observed in the region involving FAM63B, a part of networks regulated by microRNAs associated with neuronal differentiation and dopaminergic gene expression. This association was replicated in an independent cohort of >1000 individuals. The observed effect sizes for three associated methylation sites at FAM63B were moderate (Cohen’s d = 0.42–0.45). In a recent meta-analysis of blood DNA methylation profiles from 4483 participants from seven cohorts, including 1681 schizophrenia cases and 1583 controls, by Hannon et al. , 1013 differentially methylated loci with methylome-wide significance (P < 9 × 10−8), which were annotated to 692 genes, were identified. Among 158 schizophrenia-associated loci identified by GWAS , overall differential DNA methylation was observed at 21 loci after correcting for multiple testing, supporting co-localization of signals from GWAS and epigenome-wide association study. On the other hand, an integrative analysis of DNA methylation and genetic variants exploring the causal relationships was not performed in their study. Further studies of the interaction between genetic and epigenetic factors that are expected to provide additional insights into the molecular mechanisms underlying co-localization are warranted.
Overall, while the significant overlap between differentially expressed or modified genes and the genetic risk of schizophrenia has been reported in some studies, transcriptomic or epigenomic alterations of single genes that can biologically define the general population of schizophrenia or serve as a high-sensitivity and specificity biomarker have not been discovered, or perhaps there is no such universal molecular marker. Therefore, further studies are necessary to identify conclusive transcriptomic and epigenomic pathology in schizophrenia.
Studies of etiologically valid mouse and cellular models of schizophrenia
As summarized in Table 1 and Fig. 1, recent large-scale genetic studies have identified specific genes, SNVs/indels, and CNVs that confer a substantial risk of schizophrenia. Based on this, rodents or cells carrying the alleles equivalent to the above-described risk variants identified in humans have been generated and analyzed. In this section, we overview studies of such etiologically valid, i.e., having the same causal conditions as in human patients, models of schizophrenia. (Tables 2 and 3), which have provided various insights into the connection between genetic pathology and pathological changes at the levels of molecules (e.g., transcripts and proteins), cells, neural circuits, whole tissues, or individuals’ behaviors.
We systematically surveyed studies of mice with mutant alleles orthologous to the variants listed in Table 1 with an observed OR greater than ten. We found that there are studies on the following variants: 22q11.2 deletion, 16p11.2 deletion/duplication, 3q29 deletion, 15q11.2–13.1 duplication, 2p16.3 (NRXN1) deletion, GRIN2A LOF/PTV, GRIA3 LOF/PTV, and SETD1A LOF/PTV (Table 2) [54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92]. While there are studies of mice with mutations in other genes, such as RB1CC1 (also known as FIP200), the introduced alleles were not equivalent to ones in human patients, and/or the mice were not analyzed in the context of neuroscience, and thereby not highlighted in this review.
Regarding the molecular phenotypes mainly analyzed by transcriptomic profiling, commonly dysregulated pathways include neural transmission and regulation of transcription [54, 63, 70, 88, 91, 92], in agreement with the findings from human genetics and genomics studies. Also, analyses utilizing results of large-scale human genetics studies have reported enrichment of genetic risk for schizophrenia in genes differentially expressed in the models or molecular targets of the genetically modified genes [88, 91]. Meanwhile, mutant mice originally created not as a schizophrenia model but to elucidate gene function in the central nervous system, such as knockout mice for GRIN2A or GRIA3 encoding a glutamate receptor subunit, have not been subjected to omics analysis. Molecular profiling of these etiologically valid models may provide further convergent insights into the brain pathology of schizophrenia.
Morphological analysis of neuronal cells in these models has reported reduced axonal and dendritic complexity and abnormal spine morphology in Setd1a heterozygous knockout mice and several mouse models with CNVs [54, 55, 57, 58, 88, 91]. Common electrophysiological phenotypes include altered synaptic transmissions, such as diminished excitability indicated by reduced excitatory postsynaptic currents [55, 58, 60, 75, 79, 82, 85, 88, 91] or deficits in long-term potentiation [68, 79], though some mice showed increased excitability or altered activities of other neuronal subtypes such as GABAergic neurons [70, 84].
Behavioral alterations common in these mice include deficits in sociality, cognitive performance, and prepulse inhibition [54, 61, 66,67,68, 70,71,72,73, 75,76,77, 79, 88, 90,91,92]. These phenotypes are generally consistent with those in human schizophrenia patients , though there would be biases that these phenotypes are more likely to be investigated and reported. Also, in some cases, there were inconsistent results even among models with mutations in the same gene. This could be explained by differences in the method of introduction of the mutations (e.g., CRISPR/Cas9 or gene targeting), genetic backgrounds (e.g., C57BL/6J and C57BL/6N), and other factors. In addition, the acquisition of more definitive results will be facilitated by strict standardization of analysis protocols and ensuring a sufficient sample size, as have been done in human genetics studies.
Recent technological advances have enabled the reproduction of pathological conditions in vitro by creating patient-derived or mutation-carrying induced pluripotent stem (iPS) cells and then differentiating them into central nervous system cells or miniature brains. Studies of etiologically valid cellular models of schizophrenia produced with this technology, including those with 22q11.2 deletion, 16p11.2 deletion/duplication, SETD1A LOF/PTV, and NRXN LOF/PTV, have been conducted and reported [94,95,96,97,98,99,100,101,102,103,104,105,106] (Table 3).
In line with the findings in etiologically valid mouse models, molecular profiling of these cellular models generally supports dysregulation of genes related to neural transmission, especially synaptic genes [95,96,97, 100, 101, 106], transcriptional regulators including microRNAs [94, 104], and schizophrenia-associated genes discovered by human genetics studies [96, 97, 104]. Also, morphological alteration of soma and dendrite were common except for iPS cell-derived neurons with NRXN1 deletion [94, 98, 101, 102, 106]. Abnormal neural activities are identified in multiple patient-derived or genetically engineered iPS cell-derived neurons, however, the directions of the abnormalities are sometimes inconsistent across models manipulating different genes [97, 101,102,103,104,105,106]. Though it may be due to artifacts depending on the differences in the experimental designs, another possibility is that imbalanced excitatory/inhibitory activities themselves , regardless of the direction of abnormality, are important in schizophrenia pathology. It is also worth noting that multiple studies have investigated interventions to improve abnormal phenotypes observed in cellular models [96, 100]. Overall, the iPS cell technology is a powerful tool to analyze the molecular pathology of schizophrenia using human samples, and further studies are warranted.
Considerations on the model validity
In the previous sections, we have defined etiologically valid models using the criteria that the modified gene showed a significant association with schizophrenia after stringent multiple testing correction and that the ORs observed in studies that found the association was greater than ten. However, we would like to explicitly state that there is still uncertainty regarding the validity of these models.
First, it should be noted that in general, there are wide ranges of confidence intervals for ORs for rare risk variants (Fig. 1 and Table 1). Indeed, another large-scale study (20,403 cases and 26,628 controls) analyzing the association of schizophrenia and CNVs implicated in neurodevelopmental disorders reported no statistically significant associations of 16p11.2 deletion and 15q11.2–13.1 duplication with schizophrenia and association of 2p16.3 (NRXN1) deletion with OR smaller than ten [30, 32]. Therefore, there is the possibility that ORs are overestimated in the existing data. Second, as mentioned in the section on genetic pathology, many of the genes and variants highlighted here, especially almost all CNVs, are associated not only with schizophrenia but also with other neurodevelopmental disorders. Given this, the mice and cells harboring such mutations should not be considered as specifically modeling schizophrenia. Third, particularly in the case of CNV-based animal models, evaluation of the model validity and the interpretation of the phenotypes require caution because CNVs usually contain multiple genes and non-coding regions whose structure is sometimes not well conserved between rodents and humans. Lastly, we would like to emphasize that a number of valuable findings interpreted as being relevant to the molecular pathology of schizophrenia have also been obtained from analyses of models in which genes not strongly supported by currently available evidence from human genetics studies were manipulated. For example, given the mechanisms of action of known antipsychotics, it is quite obvious that dysregulation of the monoaminergic system is involved in the pathophysiology of schizophrenia [108, 109], and therefore various genes in this pathway have been intensively investigated. Although these include genes that do not have strong genetic support, unlike DRD2 identified in GWAS [9, 20] and others, they form a foundation not only for the study of schizophrenia patients but also for the study of animal and cellular models with genetic etiological validity [60, 110,111,112,113,114]. Besides, as evidenced by the fact that heritability is better explained by considering numerous SNPs, not only those in genome-wide significant loci, it is clear that there are true schizophrenia risk genes among those that did not reach the stringent significance threshold with the current sample sizes. This indicates that further identification of robust risk genes in larger studies will certainly increase the value of existing research using animal and cellular models with modifications of such genes. Meanwhile, it is also true that there are mice that have been interpreted as models of schizophrenia based on their face validity, despite the lack of etiological validity in light of currently available knowledge. Moreover, sometimes models are interpreted as meeting face validity based on phenotypes in homozygous mutants, even though heterozygous variants are associated with schizophrenia in humans. Therefore, caution should be exercised in discussing the validity of a model solely on the strength of its face validity, regardless of the robustness of the association between the manipulated gene and schizophrenia risk.
Current achievements, limitations, and new directions for future research
Based on the above-described existing knowledge of the genetic and molecular pathology of schizophrenia as well as the emerging insights into their links to other scales of pathologies from studies of etiologically valid models, we summarize the current achievements, limitations, and new directions for future research as follows.
Regarding the genetic pathology, significant advances, such as the elucidation of the highly polygenic nature of schizophrenia, the explanation of more than 20% of disease liability by measurable genetic variants, and the identification of specific genes and CNVs associated with schizophrenia with large effect sizes, have been achieved. While a substantial part of the heritability is still unexplained and the number of disease-responsible genes identified so far is not as large as in ASD, where similarly large studies have been conducted, it is certainly expected that by simply increasing the sample size and investigating under-studied types of variants, such as rare non-coding mutations, we can better explain the genetic liability to schizophrenia and identify additional responsible genes. Indeed, a more recent target sequencing study of candidate schizophrenia-associated genes in 11,580 cases and 10,555 controls, followed by a meta-analysis with the SCHEMA dataset identified two novel exome-wide significant genes, AKAP11 and SRRM2 , confirming the importance of increasing sample sizes. In addition to the promotion of basic genetic research, the application of genetic information to clinical psychiatry is a subject of active discussion. As an example, there are attempts to utilize polygenic risk scores (PRS) based on the profiles of common SNPs to predict clinical courses [116, 117], though further studies are needed. Also, the identification of patients with Mendelian genetic disease among patients clinically diagnosed with schizophrenia based on operationalized criteria and the optimization of their treatment based on genetic diagnosis is expected to be implemented in the near future.
Compared to the knowledge of genetic pathology, our understanding of the molecular pathology of schizophrenia, such as transcriptomic and epigenomic alterations, is insufficient and no convincing single molecular markers that can biologically define the schizophrenic brain have been identified. Nevertheless, collectively interpreting the results of large-scale omics analyses of human postmortem brains and studies of models with high etiological validity, one might be able to argue that small transcriptional and/or epigenetic alterations of many schizophrenia-associated genes and genes involved in neuronal processes such as the formation and regulation of synapses would be the underlying molecular brain pathology of schizophrenia. To obtain a clearer picture, the following directions would be considered.
First, as summarized in Tables 2 and 3, multiple etiologically valid models of schizophrenia have been generated and analyzed. One of the next important steps will be to elucidate the alterations that are commonly observed across them, and studies seeking this goal should be facilitated by investigating two or more models in the controlled same experimental settings. This is because the results of studies of disease models are often confounded by subtle differences in experimental design and conditions, such as apparatus, experimenter, mouse strain and genetic background, and others. However, to our knowledge, there have been no publications reporting the results of the analysis of multiple schizophrenia models with high etiological validity listed in Tables 2 and 3 under the same conditions. By conducting such studies of multiple models, which have already been done for ASD [118,119,120,121], it is expected that we can obtain convergent insight into the pathological changes in schizophrenia.
Second, while it must be recognized that collecting human postmortem brains on a large scale requires a great endeavor, there is an open question of whether the sample sizes in studies to date are sufficient. Evaluating the inter-study reproducibility, which may help answer this question, it is true that the result of the analysis of genes differentially expressed between schizophrenia cases and controls in the aforementioned PsychEncode study  is well correlated with that of a preceding study by the CommonMind Consortium (CMC)  (correlation coefficient between the two studies for 687 genes with FDR < 0.05 in the CMC study = 0.799). On the other hand, of the 23 genes that showed statistical significance after Bonferroni correction in the CMC study (uncorrected P < 3.04 × 10−6, 0.05 divided by the number of genes analyzed, 16,423), only nine genes surpassed the same significance threshold in the PsychENCODE study. This number is much more than random expectation; however, this contrasts with the observation in GWAS that 116 out of the 128 loci genome-wide significantly (P < 5 × 10−8) associated with schizophrenia in the previous PGC GWAS in 2014 were replicated with the genome-wide significant association in the same local regions in 2022 GWAS (and supporting evidence was obtained for 11 of the remaining 12 loci in an extended analysis). Given these, it is warranted to further increase the sample sizes in postmortem brain studies of schizophrenia in order to obtain robust and reproducible results, as has been done in GWAS throughout its history. The same would be true for human brain imaging studies that explore structural and functional alterations associated with phenotypes in a brain-wide manner .
Third, as is true in any field of science, often important unresolved problems, such as the mystery of the molecular brain pathology of schizophrenia, are addressed by the utilization of new technologies. Considering the major limitations of current research on molecular pathology using patient or model brains, while the etiological validity of the model is undoubtedly important, in this context, there is an inherent concern that the molecular pathology in human schizophrenia patients may not be adequately reproduced in rodent or cellular models, even when the exact same variants that are pathogenic in humans are introduced. Perhaps this problem would be addressed by studying species closer to humans, specifically non-human primates. Owing to recent innovations in genome editing technology, genetically engineered non-human primates carrying mutations that are pathogenic in humans, such as cynomolgus monkeys with mutations in MECP2 , the gene responsible for Rett syndrome, or SHANK3 , an ASD gene in the Phelan–McDermid syndrome critical region, and common marmosets with a mutation in PSEN1  causal for familial Alzheimer’s disease, have been created and analyzed. While research using primates is much more time- and cost-consuming than studies of mice, non-human primate models of schizophrenia will be an excellent resource to fill the gap between humans and rodents. Another major limitation is that while we can comprehensively analyze transcriptomic and epigenomic profiles in postmortem brain tissues, such analysis in the brain of living patients can not be performed. On the other hand, recent technological advances have allowed us to quantify some key molecules involved in the regulation of synapses and histone modifications, such as the synaptic vesicle glycoprotein 2A , AMPA-type glutamate receptors , and histone deacetylases (HDACs)  in the living human brain. By expanding the repertoire of measurable molecules and scaling up studies, we will better understand molecular changes in the brain of living schizophrenic patients.
Besides them, one of the most prominent new technologies that have become prevalent over the last decade is single-cell sequencing techniques, whose usefulness was mentioned in the above section describing the convergent results of cell type enrichment analysis in the PGC GWAS and SCHEMA study. Single-cell analyses are particularly powerful in studies of organs where different cell types are intermingled, such as the brain. By performing cell type-resolved analysis using single-cell technology, it may be possible to more clearly capture molecular pathology that was obscured in bulk tissues. At the end of this section, we highlight pioneering single-cell (nucleus) RNA sequencing studies of postmortem schizophrenia brains, while some of them have been posted to preprint servers and have not been published after peer review.
Single-nucleus RNA sequencing studies of postmortem schizophrenia brains
Technically, analysis of single “cells”, including cytoplasm, cannot be currently performed in studies of frozen postmortem brain tissues; therefore, single-“nucleus” RNA sequencing (snRNA-seq) studies of human schizophrenia brains have been conducted.
To our knowledge, there are four publications on snRNA-seq of postmortem schizophrenia brains, including two preprints that have not yet been peer-reviewed. Among them, the largest study by Ruzicka et al. analyzed 266,431 nuclei from 24 schizophrenia patients and 293,589 nuclei from 24 controls using frontopolar cortex (Brodmann area 10) samples . In this study, 20 cell types/states were annotated based on their transcriptional profiles, and it was shown that the majority of genes differentially expressed in schizophrenia occurred in the neuronal population. The cell types with the strongest enrichment of schizophrenia-associated genes identified by GWAS among differentially expressed genes include cortico-cortical projection neurons in the deep layers V/VI, parvalbumin-positive basket interneurons, and excitatory neurons of a novel cell state enriched in the supragranular layers II/III. This novel type of supragranular excitatory neurons was more abundant in schizophrenia than in controls, but was preferentially found in schizophrenia individuals with less “transcriptional pathology score”, defined by overall schizophrenia-associated transcriptional dysregulation in each individual. Based on this observation, the authors speculated that this population of excitatory neurons, named Ex-SZTR, might be associated with “schizophrenia transcriptional resilience”. While further scrutinization through peer reviews is needed, this finding may contribute to conceptual advances in the understanding of the molecular/cellular pathology of schizophrenia.
The observation that the majority of differentially expressed genes are found in neuronal populations was also reported in another study by Reiner et al., where 127,930 and 145,120 nuclei from DLPFC of 12 schizophrenia and 14 control individuals were analyzed by snRNA-seq, respectively . In their study, ~96% of differentially expressed genes were observed in neuronal cell types, including excitatory neurons across layers II-V and parvalbumin-positive interneurons.
In a study by Batiuk et al., not only snRNA-seq of sorted neurons from DLPFC of 9 schizophrenia patients and 14 controls (81,817 and 127,236 nuclei, respectively) but also follow-up immunohistochemistry, single-molecule fluorescence in situ hybridization, and spatial transcriptomics analyses in an extended cohort were performed . Results of these analyses convergently suggested that transcriptional dysregulation and altered cellular composition within the upper cortical layer, involving both GABAergic interneurons and principal projection (excitatory in the cortex) neurons, might be a core substrate associated with the brain pathology of schizophrenia.
These results would collectively support that schizophrenia is primarily a disease of neuronal cells. On the other hand, a unique study focusing on cells constituting the blood-brain barrier (BBB) based on the neurovascular hypothesis of schizophrenia was conducted by Puvogel et al. . In their study, a total of 178,009 nuclei (NEUN and OLIG2 negative nuclei enriched for BBB cells and NEUN positive and OLIG2 negative nuclei enriched for neuronal cells) from postmortem midbrain tissues of 15 schizophrenia patients and 14 controls were analyzed by snRNA-seq. The results showed that there was no significant difference in the relative proportions of the major BBB cell types between schizophrenia and controls. A limited number of genes were differentially expressed in schizophrenia (14 genes with log2 fold change > 0.3 and FDR < 0.05). These differentially expressed genes were restricted to ependymal cells and pericytes, suggesting that BBB cells are not broadly affected in schizophrenia.
Overall, many of the findings in these studies are detectable only when cell type-resolved analysis is performed, demonstrating the value of snRNA-seq. Nevertheless, some of the above-described results should be considered preliminary because half of the four studies highlighted here have not been peer-reviewed yet. Also, the numbers of individuals analyzed in these studies are not large, while the numbers of nuclei examined were huge. Therefore, it is necessary to consider whether the sample size is sufficient.
Perspectives: a decade after the best of times, the worst of times for psychiatric disease
In 2012, Karayiorgou et al. on behalf of the Genetic and Neural Complexity in Psychiatry 2011 Working Group described the situation at that time as “the best of times, the worst of times for psychiatric disease” . This was because, on the one hand, the development and deployment of long-awaited new DNA sequencing technology (i.e., next-generation sequencing) made it possible to conduct genome-wide exploration of highly penetrant rare variants on a population scale (the best of times), while on the other hand, many pharmaceutical companies withdrew from the research and development of novel therapeutics due to their low success rates (the worst of the times). A decade later, as predicted, several robust risk genes with large effect sizes have been identified for schizophrenia, and the first results of pioneering studies using animal and cellular models created on the basis of the discovery of these genes are beginning to be harvested [88,89,90,91,92, 106]. Overall, it can be said that we have achieved the expected outcomes over the past ten years. Also, a number of powerful new technologies have been developed and implemented during this period. The important thing is to continue this progress, and such effort will reverse the retreat from research and development by pharmaceutical companies and other investors, which was recognized a decade ago and persists today.
In this context, it would be meaningful to provide a clearer picture of how the field of schizophrenia genetics and biology will further develop. In our view, the overarching challenge for the next decade will be how we translate the findings in basic genetic and biological research into clinical psychiatry. The first part of the path to resolving this problem has been clarified by the results of studies to date. Aggregating the existing knowledge, we are able to identify diagnostic genomic variants (e.g., Pathogenic or Likely Pathogenic variants in the American College of Medical Genetics and Genomics [ACMG] guidelines ) in 1–6% of schizophrenia patients by comprehensively analyzing rare variants [136,137,138,139], and to extract a small proportion of the population with high genetic risk (e.g., OR > 5) utilizing the overall profiles of common variants (i.e., PRS [140, 141]). On the other hand, to our knowledge, there are no genetic tests for schizophrenia approved by the government and covered by health insurance. Among several reasons for this situation, the primary one is that the clinical benefits gained from genetic testing are far less than the cost and potential side effects. More specifically, there are two major factors limiting the benefits: the performance of risk prediction from genetic information is insufficient, and the results of genetic testing rarely lead to changes in clinical actions. To improve the performance of genetic risk prediction, as described above, it is indispensable to expand the sample size and investigate various types of variants, which include not only common SNPs, rare coding SNVs, and CNVs but also non-coding rare variants, repeat element variants, complex structural variants, somatic variants, and others, with sufficient statistical power. In particular, the variants that are not common but not extremely rare, which can fill the blank region in Fig. 1, will be a major target in future research. Also, it is crucial to conduct sufficiently large studies in diverse ethnic populations. The importance of such studies is evident from the observation that the performance of PRS is greatly reduced when the ethnicity of the individuals being scored is different from that of the data used to construct the prediction model [142, 143]. Regarding the improvement of clinical actionability, it is expected that the generation and investigation of multiple etiologically valid schizophrenia models, as featured in this review, will play an important role. The realization of precision medicine, such as gene therapy, for specific genetic diseases frequently comorbid with schizophrenia (e.g., 22q11.2 deletion or SETD1A haploinsufficiency syndrome) leveraging the observations in studies of these models might be the achievable goal within the next decade. And beyond that, by integrating the results of human genetics and model studies as well as other areas of research, such as human functional imaging and brain circuity, it should be aimed to define biologically homogeneous schizophrenia subgroups and identify the optimal treatment and prevention for them.
The World Health Organization estimates that by 2030 mental disorders will be the leading cause of disease burden globally , of which a significant part should be accounted for by schizophrenia due to its chronic and often treatment-resistant nature. Studies toward the elucidation of the molecular pathology of schizophrenia, which forms the foundation for essential therapeutics, are of great social value. Therefore, continuous investments from academia, government, industry, and citizens, along with appropriate ethical, legal, and social considerations, are warranted.
Jones HB. Pathology. In: Wexler P. (eds). Information resources in toxicology (Fourth Edition). Academic Press: San Diego, 2009, pp 357–63.
Griesinger W. Die Pathologie und therapie der psychischen krankheiten. (Krabbe:Stuttgart, 1861).
Kraepelin E. Psychiatrie: ein Lehrbuch für Studierende und Aerzte. (Verlag von Johann Ambrosius Barth:Leipzig, 1899).
Jauhar S, Johnstone M, McKenna PJ. Schizophrenia. Lancet. 2022;399:473–86.
Owen MJ, Sawa A, Mortensen PB. Schizophrenia. Lancet. 2016;388:86–97.
Lichtenstein P, Yip BH, Bjork C, Pawitan Y, Cannon TD, Sullivan PF, et al. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet. 2009;373:234–9.
Sullivan PF, Kendler KS, Neale MC. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry. 2003;60:1187–92.
Wray NR. Gottesman II. Using summary data from the danish national registers to estimate heritabilities for schizophrenia, bipolar disorder, and major depressive disorder. Front Genet. 2012;3:118.
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 2014;511:421–7.
Pardiñas AF, Holmans P, Pocklington AJ, Escott-Price V, Ripke S, Carrera N, et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet. 2018;50:381–9.
Stefansson H, Ophoff RA, Steinberg S, Andreassen OA, Cichon S, Rujescu D, et al. Common variants conferring risk of schizophrenia. Nature 2009;460:744–7.
International Schizophrenia Consortium, Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460:748–52.
Xu B, Roos JL, Dexheimer P, Boone B, Plummer B, Levy S, et al. Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat Genet. 2011;43:864–8.
Xu B, Ionita-Laza I, Roos JL, Boone B, Woodrick S, Sun Y, et al. De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat Genet. 2012;44:1365–9.
Purcell SM, Moran JL, Fromer M, Ruderfer D, Solovieff N, Roussos P, et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature. 2014;506:185–90.
Fromer M, Pocklington AJ, Kavanagh DH, Williams HJ, Dwyer S, Gormley P, et al. De novo mutations in schizophrenia implicate synaptic networks. Nature. 2014;506:179–84.
Genovese G, Fromer M, Stahl EA, Ruderfer DM, Chambert K, Landén M, et al. Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat Neurosci. 2016;19:1433–41.
Singh T, Kurki MI, Curtis D, Purcell SM, Crooks L, McRae J, et al. Rare loss-of-function variants in SETD1A are associated with schizophrenia and developmental disorders. Nat Neurosci. 2016;19:571–7.
McCarthy SE, Gillis J, Kramer M, Lihm J, Yoon S, Berstein Y, et al. De novo mutations in schizophrenia implicate chromatin remodeling and support a genetic overlap with autism and intellectual disability. Mol Psychiatry. 2014;19:652–8.
Trubetskoy V, Pardiñas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604:502–8.
Singh T, Poterba T, Curtis D, Akil H, Al Eissa M, Barchas JD, et al. Rare coding variants in ten genes confer substantial risk for schizophrenia. Nature. 2022;604:509–16.
Benner C, Spencer CC, Havulinna AS, Salomaa V, Ripatti S, Pirinen M. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics. 2016;32:1493–501.
Meng XH, Chen XD, Greenbaum J, Zeng Q, You SL, Xiao HM, et al. Integration of summary data from GWAS and eQTL studies identified novel causal BMD genes with functional predictions. Bone 2018;113:41–8.
Amberger JS, Bocchini CA, Scott AF, Hamosh A. OMIM.org: leveraging knowledge across phenotype-gene relationships. Nucleic Acids Res. 2019;47:D1038–43.
Tansey KE, Rees E, Linden DE, Ripke S, Chambert KD, Moran JL, et al. Common alleles contribute to schizophrenia in CNV carriers. Mol Psychiatry. 2016;21:1085–9.
Niemi MEK, Martin HC, Rice DL, Gallone G, Gordon S, Kelemen M, et al. Common genetic variants contribute to risk of rare severe neurodevelopmental disorders. Nature. 2018;562:268–71.
Takata A, Nakashima M, Saitsu H, Mizuguchi T, Mitsuhashi S, Takahashi Y, et al. Comprehensive analysis of coding variants highlights genetic complexity in developmental and epileptic encephalopathy. Nat Commun. 2019;10:2506.
Davies RW, Fiksinski AM, Breetvelt EJ, Williams NM, Hooper SR, Monfeuga T, et al. Using common genetic variation to examine phenotypic expression and risk prediction in 22q11.2 deletion syndrome. Nat Med. 2020;26:1912–8.
Kirov G, Pocklington AJ, Holmans P, Ivanov D, Ikeda M, Ruderfer D, et al. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol Psychiatry. 2012;17:142–53.
Rees E, Kendall K, Pardiñas AF, Legge SE, Pocklington A, Escott-Price V, et al. Analysis of Intellectual Disability Copy Number Variants for Association With Schizophrenia. JAMA Psychiatry. 2016;73:963–9.
Marshall CR, Howrigan DP, Merico D, Thiruvahindrapuram B, Wu W, Greer DS, et al. Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nat Genet. 2017;49:27–35.
Rees E, Kirov G. Copy number variation and neuropsychiatric illness. Curr Opin Genet Dev. 2021;68:57–63.
Kraft P. Curses-winner’s and otherwise-in genetic epidemiology. Epidemiology. 2008;19:649–51.
de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11:e1004219.
Skene NG, Bryois J, Bakken TE, Breen G, Crowley JJ, Gaspar HA, et al. Genetic identification of brain cell types underlying schizophrenia. Nat Genet. 2018;50:825–33.
Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G, Juréus A, et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science. 2015;347:1138–42.
Visscher PM, Goddard ME, Derks EM, Wray NR. Evidence-based psychiatric genetics, AKA the false dichotomy between common and rare variant hypotheses. Mol Psychiatry. 2012;17:474–85.
Gaugler T, Klei L, Sanders SJ, Bodea CA, Goldberg AP, Lee AB, et al. Most genetic risk for autism resides with common variation. Nat Genet. 2014;46:881–5.
Halvorsen M, Huh R, Oskolkov N, Wen J, Netotea S, Giusti-Rodriguez P, et al. Increased burden of ultra-rare structural variants localizing to boundaries of topologically associated domains in schizophrenia. Nat Commun. 2020;11:1842.
Mojarad BA, Engchuan W, Trost B, Backstrom I, Yin Y, Thiruvahindrapuram B, et al. Genome-wide tandem repeat expansions contribute to schizophrenia risk. Mol Psychiatry. 2022;27:3692–8.
Maury EA, Sherman MA, Genovese G, Gilgenast TG, Rajarajan P, Flaherty E, et al. Schizophrenia-associated somatic copy number variants from 12,834 cases reveal contribution to risk and recurrent, isoform-specific NRXN1 disruptions. medRxiv. 2022; https://doi.org/10.1101/2021.12.24.21268385.
Kim MH, Kim IB, Lee J, Cha DH, Park SM, Kim JH, et al. Low-level brain somatic mutations are implicated in schizophrenia. Biol Psychiatry. 2021;90:35–46.
Fullard JF, Charney AW, Voloudakis G, Uzilov AV, Haroutunian V, Roussos P. Assessment of somatic single-nucleotide variation in brain tissue of cases with schizophrenia. Transl Psychiatry. 2019;9:21.
Bae T, Fasching L, Wang Y, Shin JH, Suvakov M, Jang Y, et al. Analysis of somatic mutations in 131 human brains reveals aging-associated hypermutability. Science. 2022;377:511–7.
Gandal MJ, Zhang P, Hadjimichael E, Walker RL, Chen C, Liu S, et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science. 2018;362:6420.
Gates LA, Foulds CE, O’Malley BW. Histone marks in the ‘driver’s seat’: functional roles in steering the transcription cycle. Trends Biochem Sci. 2017;42:977–89.
Girdhar K, Hoffman GE, Bendl J, Rahman S, Dong P, Liao W, et al. Chromatin domain alterations linked to 3D genome organization in a large cohort of schizophrenia and bipolar disorder brains. Nat Neurosci. 2022;25:474–83.
Waszak SM, Delaneau O, Gschwind AR, Kilpinen H, Raghav SK, Witwicki RM, et al. Population variation and genetic control of modular chromatin architecture in humans. Cell 2015;162:1039–50.
Delaneau O, Zazhytska M, Borel C, Giannuzzi G, Rey G, Howald C, et al. Chromatin three-dimensional interactions mediate genetic effects on gene expression. Science. 2019;364:6439.
Jaffe AE, Gao Y, Deep-Soboslay A, Tao R, Hyde TM, Weinberger DR, et al. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat Neurosci. 2016;19:40–7.
Perzel Mandell KA, Eagles NJ, Wilton R, Price AJ, Semick SA, Collado-Torres L, et al. Genome-wide sequencing-based identification of methylation quantitative trait loci and their role in schizophrenia risk. Nat Commun. 2021;12:5251.
Aberg KA, McClay JL, Nerella S, Clark S, Kumar G, Chen W, et al. Methylome-Wide Association Study of Schizophrenia: Identifying Blood Biomarker Signatures of Environmental Insults. JAMA Psychiatry. 2014;71:255–64.
Hannon E, Dempster EL, Mansell G, Burrage J, Bass N, Bohlken MM, et al. DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia. eLife. 2021;10:e58430.
Stark KL, Xu B, Bagchi A, Lai WS, Liu H, Hsu R, et al. Altered brain microRNA biogenesis contributes to phenotypic deficits in a 22q11-deletion mouse model. Nat Genet. 2008;40:751–60.
Mukai J, Dhilla A, Drew LJ, Stark KL, Cao L, MacDermott AB, et al. Palmitoylation-dependent neurodevelopmental deficits in a mouse model of 22q11 microdeletion. Nat Neurosci. 2008;11:1302–10.
Sigurdsson T, Stark KL, Karayiorgou M, Gogos JA, Gordon JA. Impaired hippocampal-prefrontal synchrony in a genetic mouse model of schizophrenia. Nature. 2010;464:763–7.
Xu B, Hsu PK, Stark KL, Karayiorgou M, Gogos JA. Derepression of a neuronal inhibitor due to miRNA dysregulation in a schizophrenia-related microdeletion. Cell. 2013;152:262–75.
Mukai J, Tamura M, Fénelon K, Rosen AM, Spellman TJ, Kang R, et al. Molecular substrates of altered axonal growth and brain connectivity in a mouse model of schizophrenia. Neuron. 2015;86:680–95.
Hamm JP, Peterka DS, Gogos JA, Yuste R. Altered cortical ensembles in mouse models of schizophrenia. Neuron. 2017;94:153–67.e8.
Chun S, Westmoreland JJ, Bayazitov IT, Eddins D, Pani AK, Smeyne RJ, et al. Specific disruption of thalamic inputs to the auditory cortex in schizophrenia models. Science. 2014;344:1178–82.
Saito R, Koebis M, Nagai T, Shimizu K, Liao J, Wulaer B, et al. Comprehensive analysis of a novel mouse model of the 22q11.2 deletion syndrome: a model with the most common 3.0-Mb deletion at the human 22q11.2 locus. Transl Psychiatry. 2020;10:35.
Horev G, Ellegood J, Lerch JP, Son YE, Muthuswamy L, Vogel H, et al. Dosage-dependent phenotypes in models of 16p11.2 lesions found in autism. Proc Natl Acad Sci USA. 2011;108:17076–81.
Blumenthal I, Ragavendran A, Erdin S, Klei L, Sugathan A, Guide Jolene R, et al. Transcriptional consequences of 16p11.2 deletion and duplication in mouse cortex and multiplex autism families. Am J Hum Genet. 2014;94:870–83.
Brunner D, Kabitzke P, He D, Cox K, Thiede L, Hanania T, et al. Comprehensive analysis of the 16p11.2 deletion and null Cntnap2 mouse models of autism spectrum disorder. PLoS One. 2015;10:e0134572.
Yin X, Jones N, Yang J, Asraoui N, Mathieu ME, Cai L, et al. Delayed motor learning in a 16p11.2 deletion mouse model of autism is rescued by locus coeruleus activation. Nat Neurosci. 2021;24:646–57.
Portmann T, Yang M, Mao R, Panagiotakos G, Ellegood J, Dolen G, et al. Behavioral abnormalities and circuit defects in the basal ganglia of a mouse model of 16p11.2 deletion syndrome. Cell Rep. 2014;7:1077–92.
Yang M, Mahrt EJ, Lewis F, Foley G, Portmann T, Dolmetsch RE, et al. 16p11.2 deletion syndrome mice display sensory and ultrasonic vocalization deficits during social interactions. Autism Res. 2015;8:507–21.
Arbogast T, Ouagazzal AM, Chevalier C, Kopanitsa M, Afinowi N, Migliavacca E, et al. Reciprocal effects on neurocognitive and metabolic phenotypes in mouse models of 16p11.2 deletion and duplication syndromes. PLoS Genet. 2016;12:e1005709.
Blizinsky KD, Diaz-Castro B, Forrest MP, Schürmann B, Bach AP, Martin-de-Saavedra MD, et al. Reversal of dendritic phenotypes in 16p11.2 microduplication mouse model neurons by pharmacological targeting of a network hub. Proc Natl Acad Sci USA. 2016;113:8520–5.
Rein B, Tan T, Yang F, Wang W, Williams J, Zhang F, et al. Reversal of synaptic and behavioral deficits in a 16p11.2 duplication mouse model via restoration of the GABA synapse regulator Npas4. Mol Psychiatry. 2021;26:1967–79.
Baba M, Yokoyama K, Seiriki K, Naka Y, Matsumura K, Kondo M, et al. Psychiatric-disorder-related behavioral phenotypes and cortical hyperactivity in a mouse model of 3q29 deletion syndrome. Neuropsychopharmacology. 2019;44:2125–35.
Rutkowski TP, Purcell RH, Pollak RM, Grewenow SM, Gafford GM, Malone T, et al. Behavioral changes and growth deficits in a CRISPR engineered mouse model of the schizophrenia-associated 3q29 deletion. Mol Psychiatry. 2021;26:772–83.
Nakatani J, Tamada K, Hatanaka F, Ise S, Ohta H, Inoue K, et al. Abnormal behavior in a chromosome-engineered mouse model for human 15q11-13 duplication seen in autism. Cell. 2009;137:1235–46.
Geppert M, Khvotchev M, Krasnoperov V, Goda Y, Missler M, Hammer RE, et al. Neurexin I alpha is a major alpha-latrotoxin receptor that cooperates in alpha-latrotoxin action. J Biol Chem. 1998;273:1705–10.
Etherton MR, Blaiss CA, Powell CM, Südhof TC. Mouse neurexin-1alpha deletion causes correlated electrophysiological and behavioral changes consistent with cognitive impairments. Proc Natl Acad Sci USA. 2009;106:17998–8003.
Grayton HM, Missler M, Collier DA, Fernandes C. Altered social behaviours in neurexin 1α knockout mice resemble core symptoms in neurodevelopmental disorders. PLoS One. 2013;8:e67114.
Dachtler J, Ivorra JL, Rowland TE, Lever C, Rodgers RJ, Clapcote SJ. Heterozygous deletion of α-neurexin I or α-neurexin II results in behaviors relevant to autism and schizophrenia. Behav Neurosci. 2015;129:765–76.
Davatolhagh MF, Fuccillo MV. Neurexin1α differentially regulates synaptic efficacy within striatal circuits. Cell Rep. 2021;34:108773.
Sakimura K, Kutsuwada T, Ito I, Manabe T, Takayama C, Kushiya E, et al. Reduced hippocampal LTP and spatial learning in mice lacking NMDA receptor ε1 subunit. Nature. 1995;373:151–5.
Takeuchi T, Kiyama Y, Nakamura K, Tsujita M, Matsuda I, Mori H, et al. Roles of the glutamate receptor ε2 and δ2 subunits in the potentiation and prepulse inhibition of the acoustic startle reflex. Eur J Neurosci. 2001;14:153–60.
Marquardt K, Saha M, Mishina M, Young JW, Brigman JL. Loss of GluN2A-containing NMDA receptors impairs extra-dimensional set-shifting. Genes Brain Behav. 2014;13:611–7.
Kadotani H, Hirano T, Masugi M, Nakamura K, Nakao K, Katsuki M, et al. Motor discoordination results from combined gene disruption of the NMDA receptor NR2A and NR2C subunits, but not from single disruption of the NR2A or NR2C subunit. J Neurosci. 1996;16:7859–67.
Spooren W, Mombereau C, Maco M, Gill R, Kemp JA, Ozmen L, et al. Pharmacological and genetic evidence indicates that combined inhibition of NR2A and NR2B subunit-containing NMDA receptors is required to disrupt prepulse inhibition. Psychopharmacology. 2004;175:99–105.
Meng Y, Zhang Y, Jia Z. Synaptic transmission and plasticity in the absence of AMPA glutamate receptor GluR2 and GluR3. Neuron. 2003;39:163–76.
Peng SX, Pei J, Rinaldi B, Chen J, Ge YH, Jia M, et al. Dysfunction of AMPA receptor GluA3 is associated with aggressive behavior in human. Mol Psychiatry. 2022;27:4092–102.
Adamczyk A, Mejias R, Takamiya K, Yocum J, Krasnova IN, Calderon J, et al. GluA3-deficiency in mice is associated with increased social and aggressive behavior and elevated dopamine in striatum. Behav Brain Res. 2012;229:265–72.
Renner MC, Albers EH, Gutierrez-Castellanos N, Reinders NR, van Huijstee AN, Xiong H, et al. Synaptic plasticity through activation of GluA3-containing AMPA-receptors. Elife. 2017;6:e25462.
Mukai J, Cannavò E, Crabtree GW, Sun Z, Diamantopoulou A, Thakur P, et al. Recapitulation and reversal of schizophrenia-related phenotypes in setd1a-deficient mice. Neuron 2019;104:471–87.e12.
Hamm JP, Shymkiv Y, Mukai J, Gogos JA, Yuste R. Aberrant cortical ensembles and schizophrenia-like sensory phenotypes in Setd1a(+/−) mice. Biol Psychiatry. 2020;88:215–23.
Bosworth ML, Isles AR, Wilkinson LS, Humby T. Behavioural consequences of Setd1a haploinsufficiency in mice: evidence for heightened emotional reactivity and impaired sensorimotor gating. bioRxiv. 2021; https://doi.org/10.1101/2021.12.10.471949.
Nagahama K, Sakoori K, Watanabe T, Kishi Y, Kawaji K, Koebis M, et al. Setd1a insufficiency in mice attenuates excitatory synaptic function and recapitulates schizophrenia-related behavioral abnormalities. Cell Rep. 2020;32:108126.
Chen R, Liu Y, Djekidel MN, Chen W, Bhattacherjee A, Chen Z, et al. Cell type-specific mechanism of Setd1a heterozygosity in schizophrenia pathogenesis. Sci Adv. 2022;8:eabm1077.
Mena A, Ruiz-Salas JC, Puentes A, Dorado I, Ruiz-Veguilla M, De la Casa LG. Reduced prepulse inhibition as a biomarker of schizophrenia. Front Behav Neurosci. 2016;10:202.
Toyoshima M, Akamatsu W, Okada Y, Ohnishi T, Balan S, Hisano Y, et al. Analysis of induced pluripotent stem cells carrying 22q11.2 deletion. Transl Psychiatry. 2016;6:e934.
Lin M, Pedrosa E, Hrabovsky A, Chen J, Puliafito BR, Gilbert SR, et al. Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion. BMC Syst Biol. 2016;10:105.
Khan TA, Revah O, Gordon A, Yoon SJ, Krawisz AK, Goold C, et al. Neuronal defects in a human cellular model of 22q11.2 deletion syndrome. Nat Med. 2020;26:1888–98.
Nehme R, Pietiläinen O, Artomov M, Tegtmeyer M, Valakh V, Lehtonen L, et al. The 22q11.2 region regulates presynaptic gene-products linked to schizophrenia. Nat Commun. 2022;13:3690.
Deshpande A, Yadav S, Dao DQ, Wu ZY, Hokanson KC, Cahill MK, et al. Cellular phenotypes in human iPSC-derived neurons from a genetic model of autism spectrum disorder. Cell Rep. 2017;21:2678–87.
Kostic M, Raymond JJ, Henry B, Tumkaya T, Khlghatyan J, Dvornik J, et al. Patient brain organoids identify a link between the 16p11.2 copy number variant and the RBFOX1 gene. bioRxiv. 2021; https://doi.org/10.1101/2021.11.21.469432.
Urresti J, Zhang P, Moran-Losada P, Yu NK, Negraes PD, Trujillo CA, et al. Cortical organoids model early brain development disrupted by 16p11.2 copy number variants in autism. Mol Psychiatry. 2021;26:7560–80.
Sundberg M, Pinson H, Smith RS, Winden KD, Venugopal P, Tai DJC, et al. 16p11.2 deletion is associated with hyperactivation of human iPSC-derived dopaminergic neuron networks and is rescued by RHOA inhibition in vitro. Nat Commun. 2021;12:2897.
Tai DJC, Razaz P, Erdin S, Gao D, Wang J, Nuttle X, et al. Tissue- and cell-type-specific molecular and functional signatures of 16p11.2 reciprocal genomic disorder across mouse brain and human neuronal models. Am J Hum Genet. 2022;109:1789-813.
Pak C, Danko T, Zhang Y, Aoto J, Anderson G, Maxeiner S, et al. Human neuropsychiatric disease modeling using conditional deletion reveals synaptic transmission defects caused by heterozygous mutations in NRXN1. Cell Stem Cell. 2015;17:316–28.
Flaherty E, Zhu S, Barretto N, Cheng E, Deans PJM, Fernando MB, et al. Neuronal impact of patient-specific aberrant NRXN1α splicing. Nat Genet. 2019;51:1679–90.
Pak C, Danko T, Mirabella VR, Wang J, Liu Y, Vangipuram M, et al. Cross-platform validation of neurotransmitter release impairments in schizophrenia patient-derived NRXN1-mutant neurons. Proc Natl Acad Sci USA. 2021;118:e2025598118.
Wang S, Rhijn JV, Akkouh I, Kogo N, Maas N, Bleeck A, et al. Loss-of-function variants in the schizophrenia risk gene SETD1A alter neuronal network activity in human neurons through the cAMP/PKA pathway. Cell Rep. 2022;39:110790.
Liu Y, Ouyang P, Zheng Y, Mi L, Zhao J, Ning Y, et al. A selective review of the excitatory-inhibitory imbalance in schizophrenia: underlying biology, genetics, microcircuits, and symptoms. Front Cell Dev Biol. 2021;9:664535.
Meltzer HY, Stahl SM. The dopamine hypothesis of schizophrenia: a review. Schizophr Bull. 1976;2:19–76.
Barnes NM, Sharp T. A review of central 5-HT receptors and their function. Neuropharmacology. 1999;38:1083–152.
Drago J, Gerfen CR, Lachowicz JE, Steiner H, Hollon TR, Love PE, et al. Altered striatal function in a mutant mouse lacking D1A dopamine receptors. Proc Natl Acad Sci USA. 1994;91:12564–8.
Baik JH, Picetti R, Saiardi A, Thiriet G, Dierich A, Depaulis A, et al. Parkinsonian-like locomotor impairment in mice lacking dopamine D2 receptors. Nature. 1995;377:424–8.
Giros B, Jaber M, Jones SR, Wightman RM, Caron MG. Hyperlocomotion and indifference to cocaine and amphetamine in mice lacking the dopamine transporter. Nature. 1996;379:606–12.
Ramboz S, Oosting R, Amara DA, Kung HF, Blier P, Mendelsohn M, et al. Serotonin receptor 1A knockout: an animal model of anxiety-related disorder. Proc Natl Acad Sci USA. 1998;95:14476–81.
Bengel D, Murphy DL, Andrews AM, Wichems CH, Feltner D, Heils A, et al. Altered brain serotonin homeostasis and locomotor insensitivity to 3, 4-methylenedioxymethamphetamine (“Ecstasy”) in serotonin transporter-deficient mice. Mol Pharm. 1998;53:649–55.
Liu D, Meyer D, Fennessy B, Feng C, Cheng E, Johnson JS, et al. Rare schizophrenia risk variant burden is conserved in diverse human populations. medRxiv. 2022;https://doi.org/10.1101/2022.01.03.22268662.
Zhang JP, Robinson D, Yu J, Gallego J, Fleischhacker WW, Kahn RS, et al. Schizophrenia polygenic risk score as a predictor of antipsychotic efficacy in first-episode psychosis. Am J Psychiatry. 2019;176:21–8.
Landi I, Kaji DA, Cotter L, Van Vleck T, Belbin G, Preuss M, et al. Prognostic value of polygenic risk scores for adults with psychosis. Nat Med. 2021;27:1576–81.
Phan BN, Bohlen JF, Davis BA, Ye Z, Chen HY, Mayfield B, et al. A myelin-related transcriptomic profile is shared by Pitt-Hopkins syndrome models and human autism spectrum disorder. Nat Neurosci. 2020;23:375–85.
Zerbi V, Pagani M, Markicevic M, Matteoli M, Pozzi D, Fagiolini M, et al. Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes. Mol Psychiatry. 2021;26:7610–20.
Kelly E, Meng F, Fujita H, Morgado F, Kazemi Y, Rice LC, et al. Regulation of autism-relevant behaviors by cerebellar-prefrontal cortical circuits. Nat Neurosci. 2020;23:1102–10.
Paulsen B, Velasco S, Kedaigle AJ, Pigoni M, Quadrato G, Deo AJ, et al. Autism genes converge on asynchronous development of shared neuron classes. Nature. 2022;602:268–73.
Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci. 2016;19:1442–53.
Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP, Hatoum AS, et al. Reproducible brain-wide association studies require thousands of individuals. Nature. 2022;603:654–60.
Chen Y, Yu J, Niu Y, Qin D, Liu H, Li G, et al. Modeling Rett syndrome using TALEN-edited MECP2 mutant cynomolgus monkeys. Cell 2017;169:945–55.e10.
Zhou Y, Sharma J, Ke Q, Landman R, Yuan J, Chen H, et al. Atypical behaviour and connectivity in SHANK3-mutant macaques. Nature 2019;570:326–31.
Sato K, Sasaguri H, Kumita W, Inoue T, Kurotaki Y, Nagata K, et al. A non-human primate model of familial Alzheimer’s disease. bioRxiv. 2020; https://doi.org/10.1101/2020.08.24.264259.
Finnema SJ, Nabulsi NB, Eid T, Detyniecki K, Lin SF, Chen MK, et al. Imaging synaptic density in the living human brain. Sci Transl Med. 2016;8:348ra96.
Miyazaki T, Nakajima W, Hatano M, Shibata Y, Kuroki Y, Arisawa T, et al. Visualization of AMPA receptors in living human brain with positron emission tomography. Nat Med. 2020;26:281–8.
Wey HY, Gilbert TM, Zürcher NR, She A, Bhanot A, Taillon BD, et al. Insights into neuroepigenetics through human histone deacetylase PET imaging. Sci Transl Med. 2016;8:351ra106.
Ruzicka WB, Mohammadi S, Davila-Velderrain J, Subburaju S, Tso DR, Hourihan M, et al. Single-cell dissection of schizophrenia reveals neurodevelopmental-synaptic axis and transcriptional resilience. medRxiv. 2020; https://doi.org/10.1101/2020.11.06.20225342.
Reiner BC, Crist RC, Stein LM, Weller AE, Doyle GA, Arauco-Shapiro G, et al. Single-nuclei transcriptomics of schizophrenia prefrontal cortex primarily implicates neuronal subtypes. bioRxiv. 2021; https://doi.org/10.1101/2020.07.29.227355.
Batiuk MY, Tyler T, Dragicevic K, Mei S, Rydbirk R, Petukhov V, et al. Upper cortical layer-driven network impairment in schizophrenia. Sci Adv. 2022;8:eabn8367.
Puvogel S, Alsema A, Kracht L, Webster MJ, Weickert CS, Sommer IEC, et al. Single-nucleus RNA sequencing of midbrain blood-brain barrier cells in schizophrenia reveals subtle transcriptional changes with overall preservation of cellular proportions and phenotypes. Mol Psychiatry. 2022;27:4731–40.
Karayiorgou M, Flint J, Gogos JA, Malenka RC. Genetic and neural complexity in psychiatry 2011 Working Group. The best of times, the worst of times for psychiatric disease. Nat Neurosci. 2012;15:811–2.
Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17:405–24.
Alkelai A, Greenbaum L, Docherty AR, Shabalin AA, Povysil G, Malakar A, et al. The benefit of diagnostic whole genome sequencing in schizophrenia and other psychotic disorders. Mol Psychiatry. 2022;27:1435–47.
Zoghbi AW, Dhindsa RS, Goldberg TE, Mehralizade A, Motelow JE, Wang X, et al. High-impact rare genetic variants in severe schizophrenia. Proc Natl Acad Sci USA. 2021;118:e2112560118.
Mojarad BA, Yin Y, Manshaei R, Backstrom I, Costain G, Heung T, et al. Genome sequencing broadens the range of contributing variants with clinical implications in schizophrenia. Transl Psychiatry. 2021;11:84.
Balakrishna T, Curtis D. Assessment of potential clinical role for exome sequencing in schizophrenia. Schizophr Bull. 2020;46:328–35.
Murray GK, Lin T, Austin J, McGrath JJ, Hickie IB, Wray NR. Could polygenic risk scores be useful in psychiatry? A review. JAMA Psychiatry. 2021;78:210–9.
Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:1219–24.
Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51:584–91.
Curtis D. Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia. Psychiatr Genet. 2018;28:85–9.
World Health Organization. Global burden of mental disorders and the need for a comprehensive, coordinated response from health and social workers at the country level: report by the Secretariat. 2012.
Parts of Fig. 1 were drawn by using pictures from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/). This work was supported by grants from the Japan Agency for Medical Research and Development (AMED) under Grant Number JP21km0405214 (to AT) and JSPS KAKENHI under Grant Numbers JP20H05777 (to AT), 21H02855 (to AT), 22H02991 (to AT), and 21K15752 (to TN).
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Nakamura, T., Takata, A. The molecular pathology of schizophrenia: an overview of existing knowledge and new directions for future research. Mol Psychiatry (2023). https://doi.org/10.1038/s41380-023-02005-2