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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Nat Neurosci. Author manuscript; available in PMC 2013 June 21.
Published in final edited form as:
PMCID: PMC3689007

Diverse types of genetic variation converge on functional gene networks involved in schizophrenia


Despite the successful identification of several relevant genomic loci, the underlying molecular mechanisms of schizophrenia remain largely unclear. We developed a computational approach (NETBAG+) that allows an integrated analysis of diverse disease-related genetic data using a unified statistical framework. The application of this approach to schizophrenia-associated genetic variations, obtained using unbiased whole-genome methods, allowed us to identify several cohesive gene networks related to axon guidance, neuronal cell mobility, synaptic function and chromosomal remodeling. The genes forming the networks are highly expressed in the brain, with higher brain expression during prenatal development. The identified networks are functionally related to genes previously implicated in schizophrenia, autism and intellectual disability. A comparative analysis of copy number variants associated with autism and schizophrenia suggests that although the molecular networks implicated in these distinct disorders may be related, the mutations associated with each disease are likely to lead, at least on average, to different functional consequences.

A pressing challenge of human genetics is to combine diverse diseaserelated genetic variations to illuminate pathways and networks affected in common disorders. Schizophrenia represents an important example of a common psychiatric disorder in which a statistically significant contribution to disease susceptibility has now been demonstrated for different types of genetic variations. Specifically, several genomic loci associated with common human polymorphisms have been implicated by genome-wide association studies (GWAS)14, a contribution from de novo and rare copy number variants (CNVs) has been established57, and a significant contribution from de novo single nucleotide variants (SNVs) was demonstrated in a recent study based on exome sequencing in two populations8.

Biological networks provide a natural framework for integration of diverse genetic variations associated with such a complex and multifactorial phenotype as schizophrenia9,10. To identify affected molecular networks, we have developed an algorithm (NETBAG+) that searches for cohesive clusters of genes perturbed by disease-associated genetic variations (Fig. 1a). The approach is based on the previously described phenotype network11, which assigns every pair of human genes a score proportional to the likelihood ratio that these genes are involved in the same genetic phenotype (Online Methods). The phenotype network was used previously to identify a functionally cohesive gene cluster perturbed by de novo CNVs in autism11. The new NETBAG+ approach is able to integrate data from multiple types of genetic variation: SNVs, CNVs and GWAS-implicated loci. The greedy search algorithm identifies highly connected gene clusters that are affected by genetic variations, and the significance of the identified clusters is then established using an appropriate randomization (Online Methods). Although we and others have previously developed several methods to identify and analyze disease-related gene networks1115, to our knowledge NETBAG+ is the first principled approach for integration of diverse sources of genome-wide genetic variation under a unified framework. The statistical power of this integrative approach stems from the convergence of different types of genetic variations on a set of interrelated molecular processes.

Figure 1
The NETBAG+ approach and the identified schizophrenia gene clusters. (a) The NETBAG+ algorithm: different types of genetic variations are mapped to a phenotype network (pale gray) in which every pair of genes is assigned a score proportional to the likelihood ...

Here we applied the NETBAG+ algorithm to integrate several unbiased whole-genome data sets associated with schizophrenia. We identified several cohesive gene networks related to the disorder and characterized their biological and cellular functions. We also investigated the expression of the network genes in the brain. Finally, we examined the relationship between the genes forming the identified schizophrenia networks and genes associated with other neurodevelopmental disorders, such as autism and intellectual disability.


Gene clusters affected by schizophrenia-associated variations

We considered non-synonymous de novo SNVs from recent studies8,16, de novo CNVs from published genome-wide scans7,1723 and genomic regions implicated by GWAS14,2428. In total, this set contained 1,044 genes (159 from non-synonymous de novo SNVs, 712 from de novo CNVs, 173 from GWAS) from 213 genomic locations. In searching for cohesive gene clusters, the algorithm was allowed to pick any gene affected by a de novo SNV, any gene in a de novo CNV (one gene per CNV) or any gene in a GWAS-implicated region (one gene per region).

On the basis of the aforementioned input data, NETBAG+ identified a significant gene cluster (P < 0.001) containing in total 47 genes (22 from SNVs, 20 from CNVs, 6 from GWAS regions) (Fig. 1b). The identified cluster contained two weakly connected subclusters (subcluster Ia and subcluster Ib). In addition to combining all genetic data (SNVs, CNVs and GWAS regions), we also performed NETBAG+ searches using different combinations of genetic variations as the algorithm input (Supplementary Table 1). For example, we obtained a marginally significant (P = 0.056) cluster using only de novo SNVs (Fig. 1c); all genes in this cluster were also members of the cluster obtained using the combined data (cluster I). The highest significance was achieved when all types of genetic variations were considered together (Supplementary Table 1). Thus, different sources of genetic variations appear to reinforce each other, increasing the overall cluster significance. After masking the genes forming cluster I—that is, removing these genes from the input data—the NETBAG+ algorithm was able to identify another marginally significant cluster, cluster II (Fig. 1d, P = 0.071). Notably, cluster I and cluster II included three of the four genes (LAMA2, TRRAP, DPYD) with recurrent non-synonymous SNVs in the cohort analyzed in a recent study8 (Fisher’s exact test, one-tailed, P = 0.05), supporting the NETBAG+ clustering results and also providing more evidence that these genes are involved in schizophrenia pathophysiology.

In contrast to the results for non-synonymous SNVs and CNVs from schizophrenia patients, we detected no significant clusters in various control sets (Supplementary Table 1). For example, there were no significant clusters identified when searching genes affected by de novo non-synonymous SNVs observed in a control population8, synonymous de novo SNVs observed in schizophrenia patients8, or non-synonymous de novo SNVs observed in unaffected siblings of autism patients in two recently published studies29,30. Furthermore, we identified no significant clusters when the aforementioned sets were combined with de novo CNVs seen in unaffected siblings of autism patients in another recent study31 (Online Methods).

Biological processes associated with schizophrenia clusters

To determine functions of genes forming the identified schizophrenia clusters, we used two computational tools (FuncAssociate32 and DAVID33) that identify over-represented Gene Ontology (GO) terms in a given gene set. These analyses showed that the genes in cluster I participate in several important neurodevelopmental processes, such as axon guidance, neuron projection development, and cell migration and locomotion (Table 1 and Supplementary Tables 2 and 3). The GO analysis also implicated several cellular pathways, including signaling through essential second messengers: calcium, cyclic AMP and inositol trisphosphate. Separate analysis of genes forming subclusters Ia and Ib (Supplementary Tables 2 and 3) showed that the former was enriched for gene functions related to signaling and axon guidance, the latter for functions related to neuron mobility and locomotion.

Table 1
GO terms associated with cluster I

The genes forming cluster II (Supplementary Tables 2 and 3) were enriched for functions related to chromosomal organization and chromosomal remodeling. Notably, a similar GO enrichment analysis of all genes affected by non-synonymous de novo SNVs or de novo CNVs did not identify any significantly enriched functional terms. Thus, the developed computational approach reveals cohesive functional networks hidden within the genomic loci affected in schizophrenia.

Temporal expression of genes in schizophrenia clusters

Complementary to curated gene ontology terms, another important descriptor of biological function is temporal gene expression profile. To investigate brain-related gene expression, we took advantage of the Human Brain Transcriptome (HBT) database34 and calculated the median brain expression profiles for the genes forming the identified clusters across 15 developmental stages from embryonic to late adulthood (Fig. 2a; average expression profiles are shown in Supplementary Fig. 1). The level of brain expression for all genes forming the identified clusters was significantly higher than expression of all genes in the HBT database (Wilcoxon rank-sum test, P < 1 × 10−20) and all genes used as the input for NETBAG+ but not selected by the algorithm (P < 1 × 10−20). Moreover, the expression of the cluster genes was higher during prenatal than the postnatal developmental stages (P < 1 × 10−20). This result is in agreement with significant enrichment of nonsynonymous de novo mutations in genes with high prenatal expression observed in a recent study8, and it suggests that prenatal genetic insults are particularly important for the etiology of schizophrenia.

Figure 2
Temporal gene expression profiles in the brain across developmental stages for genes forming the identified clusters. Gene expression data were obtained from the Human Brain Transcriptome database ( Median expression levels for each ...

Of note, genes forming subcluster Ia, subcluster Ib and cluster II showed distinct expression profiles. Subcluster Ia contains many genes with broad brain-related functions that are essential across all developmental periods. The median gene expression in this subcluster was very uniform across the developmental stages considered, but with higher levels during prenatal periods (P = 1 × 10−6). Genes forming cluster II are primarily responsible for chromosomal organization and remodeling; their expression is likely to be particularly important during periods of neuronal development and differentiation. Naturally, the median expression profile for the cluster II genes was much higher in prenatal than postnatal developmental stages (P < 1 × 10−20). Although the genes forming subcluster Ib also displayed higher prenatal expression (P = 5 × 10−11), their median expression profile showed a prominent decrease between early fetal and late mid-fetal stages, approximately corresponding to the period between 10 and 20 weeks after conception. Several genes (DOCK1, ITGA6, COL3A1, LAMA2, THBS1) in this subcluster independently showed U-like expression profiles (Fig. 2b). This observation suggests that in the context of this subcluster, specific processes occurring early or late in corticogenesis may be predominantly affected in schizophrenia.

Processes perturbed in schizophrenia-derived neurons

To further validate biological processes implicated by considering diverse genetic variations associated with schizophrenia, we considered expression data from a recent study35. In that study, fibroblasts from schizophrenia patients were reprogrammed into pluripotent stem cells and subsequently differentiated into neurons. The analysis implicated a set of 596 genes with significantly altered expression levels in patient-derived neurons compared to neurons derived from matched controls.

The functional analysis of the differentially expressed genes with DAVID identified multiple significant GO terms (Table 2). Many of the identified terms matched the terms associated with the functional clusters implicated by our analysis of genetic variations (Table 1): neuronal differentiation, cell migration and motility, axonogenesis, neuron projection development and differentiation. This suggests that multiple lines of evidence converge on similar functions and processes.

Table 2
GO terms associated with expression changes in neurons derived from schizophrenia patients (DAVID)

Relation of schizophrenia clusters to related disorders

As we and others demonstrated previously, genes implicated in diverse psychiatric and neurological disorders are often closely related in terms of their biological and molecular function12,13,36. We explored the relationships between the cluster genes (Fig. 1) and genes previously implicated in schizophrenia, autism and intellectual disability using the strength of their connections (that is, likelihood ratio scores) in the NETBAG+ phenotype network (Online Methods). For this analysis, we took each gene in each curated set and calculated its connectivity strength to the schizophrenia cluster genes. We then compared the distribution of these connectivities to the connectivities between the schizophrenia cluster genes and all genes sequenced in a recent study8 (Fig. 3 and Table 3). This analysis demonstrated that genes in cluster I were strongly related to two curated sets of schizophrenia-implicated genes3739 (Wilcoxon rank-sum test, P = 3 × 10−4 and P = 9 × 10−12). We also observed a significant relationship (P = 1 × 10−6) to a curated set of genes associated with intellectual disability40. As expected, we found no significant relationship to either of two control sets8: synonymous schizophrenia de novo SNVs (P = 0.9) or de novo SNVs in unaffected controls (P = 0.3).

Figure 3
Distributions of connectivity strengths between schizophrenia clusters and genes previously implicated in schizophrenia and other related disorders. (a) Distributions of connectivity strengths between cluster I and disease sets. (b) Distributions of connectivity ...
Table 3
Connectivity strengths between schizophrenia clusters and other disease sets

This observation raises a question: how can mutations in related and overlapping genes lead to different clinical phenotypes? Although a detailed understanding of this question will certainly require extensive clinical and biological research, we decided to gain an initial insight by focusing on a distinct phenotype previously considered by us and others: growth of dendrites and dendritic spines. Most excitatory glutamatergic synapses in the human brain are formed on dendritic spines, and their structural aberrations have been implicated in several psychiatric and neurological disorders41,42. Likely impact on the growth of dendrites or dendritic spines by a gene in a CNV can be investigated on the basis of the corresponding dosage change—a deletion or a duplication. Using this approach, we previously noted that CNVs associated with autism should primarily lead to an increase in spine or dendritic growth11. Notably, a similar analysis in schizophrenia based on known mutant phenotypes for CNV-associated cluster genes (Supplementary Table 4) revealed the opposite effect (Fig. 4): a majority of schizophrenia-associated CNVs should lead to a decrease in growth of dendrites or spines. A spine density increase in autism43 and decrease in schizophrenia44 was observed in postmortem brain analyses. We note that many mutations leading to a decrease in spine density were also observed in autism45, and an increase in spine density can actually lead to weaker synaptic connections, for example due to immature spine morphology46. Clearly, changes in spine and dendritic growth are not the only factors contributing to distinct clinical phenotypes. Nevertheless, our analysis does suggest that mutations associated with different neurodevelopmental disorders may lead, at least on average, to different functional consequences.

Figure 4
Likely impact of genes from de novo CNVs in autism and schizophrenia on growth of dendrites or dendritic spines. Using the dosage changes (deletion or duplication) for CNV-associated genes in the schizophrenia and autism11 clusters, we explored available ...


It is worthwhile to consider the genes forming the identified clusters not only as a network of binary interactions (Fig. 1) but also in the context of relevant signaling pathways (Fig. 5). Individual components of the presented network are active in diverse developmental and functional contexts, such as cell motility, axonal guidance and synaptogenesis. Several conceptual signaling levels can be delineated in the network. The first layer is formed primarily by a diverse array of receptors and channels, ranging from receptors involved in axonal guidance (such as ephrins and DCC) to ionotropic and metabotropic neurotransmitter receptors (such as CHRNA7 and HTR7). The second signaling layer is formed by cellular kinases, phosphatases and GTPases that are, either directly or indirectly, regulated by the first signaling layer. The third layer consists of regulatory (such as CREB) or structural (such as Cofilin) proteins involved in neurite outgrowth, synaptogenesis and synaptic plasticity. In addition to the aforementioned horizontal layers, several well-defined top-down pathways that were previously discussed in connection with schizophrenia and other brain-related diseases can be recognized47,48. These include the reelin, WNT and insulin signaling pathways; pathways involving Akt and phosphatidylinositol 3-OH kinase, MAP kinase, and mTOR signaling; and the protein kinase C and protein kinase A pathways. Considering the remarkable diversity of the implicated molecular circuits, it is likely that many hundreds of genes (>800, according to a recent estimate8) may ultimately contribute to the etiology of schizophrenia.

Figure 5
Genes forming cluster I in the context of cellular signaling pathways. Proteins encoded by cluster genes are shown in yellow, and those corresponding to other relevant genes that were present in the input data but not selected by the NETBAG+ algorithm ...

Although genetic variations considered here differ in their type and origin, in combination they perturb a complex but interrelated set of molecular processes. This functional convergence allows the presented integrative approach to identify the cohesive functional networks. A similar convergence, resulting from common biological mechanisms underlying disease phenotypes, should also occur in many other human disorders. If this is indeed the case, it is likely that genetic data collected using unbiased whole-genome approaches and analyzed by proper computational methods will soon reveal the underlying molecular networks for a significant fraction of common human maladies, thus realizing an important goal of the human genome project.


Schizophrenia-associated genetic variation

We used three types of genetic variation: 159 non-synonymous de novo SNVs from two recent studies8,16, de novo CNVs from several previous analyses7,1723 and 14 genomic regions that were implicated by SNPs (P < 5 × 10−8) in recent genome-wide association studies14,2428 (GWAS). We considered all genes affected by non-synonymous de novo SNVs, all genes that overlap the de novo CNVs events according to the human genome NCBI build 36 and—following previous studies—all genes overlapping a region 250 kb in either direction from SNPs implicated by GWAS; similar results were obtained using calculations with distances of 100 kb and 450 kb from GWAS-implicates SNPs (Supplementary Table 1). In total, our set contained 1,044 genes from 213 genomic regions: 159 from SNVs, 712 from CNVs, and 173 from loci implicated by GWAS.

Phenotype network

The NETBAG+ algorithm is based on our previously described phenotype network11 in which all pairs of human genes are connected by weighted edges proportional to the likelihood that the genes share a genetic phenotype. These likelihood scores are based on a naive Bayesian integration of various protein-function descriptors. The functional descriptors used to build the phenotype network are: shared annotations in Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein domains from the InterPro database, tissue expression from the TiGER database; direct protein-protein interactions, or shared interaction partners in a number of databases (BIND, BioGRID, DIP, HPRD, InNetDB, IntAct, BiGG, MINT and MIPS); phylogenetic profiles and chromosomal co-clustering across sequenced genome49.

NETBAG+ algorithm

Genes affected by the considered genetic variations were mapped to the phenotype network. Clusters were assigned a score based on a weighted sum of their edges11, representing the likelihood that all cluster genes participate in the same genetic phenotype. Starting from each input gene, a greedy search algorithm was used to find high-scoring clusters of every size. A cluster significance was determined based on a distribution of cluster scores obtained by applying the same greedy search algorithm to randomized data. To generate random data sets, we selected genes with average connection strengths in the phenotype network similar to the corresponding disease-associated input genes. This ensures that overall connectivity of disease genes does not drive cluster significance. The average connection strength was calculated by averaging the 20 strongest edges from a particular gene to all other network genes. For a cluster of a given size, we assigned a size-specific P-value based on randomized clusters of the same size. To correct for multiple hypothesis testing (due to considering clusters at multiple sizes), we considered the best P-value from each random trial regardless of cluster size and used this distribution to assign a corrected (global) P-value to the size-specific P-value. Throughout the paper, we used this corrected P-value to characterize cluster significances. We ignored clusters with five genes or less to ensure that our analysis was not influenced by very small gene clusters with strong connections.

Cluster functional analysis

To establish specific biological functions associated with the schizophrenia clusters, we used two computational tools, FuncAssociate and DAVID, to find over-represented GO terms. For clarity, we only show GO terms associated with fewer than 350 human genes (Supplementary Table 2 for FuncAssociate and Supplementary Table 3 for DAVID). In the tables, we report P-values corrected for multiple hypothesis testing.

Expression changes in schizophrenia-derived neurons

We considered expression data from a recent study35. In that study fibroblasts from schizophrenia patients and controls were reprogrammed into pluripotent stem cells and subsequently differentiated into neurons. This analysis implicated a set of 596 genes with significantly altered expression levels in patient-derived neurons.

Likely impact of CN V events on dendrites and dendritic spines

To assess the impact of cluster genes associated with de novo CNVs on the growth of dendrites and dendritic spines, we performed a literature analysis. CNV polarity (deletion or duplication) allowed us to determine a likely change in the corresponding gene dosage. CNV-associated genes were taken from either the schizophrenia clusters identified in the present study or the autism cluster identified in our previous work11. For the two genes with both duplication and deletion events (CRKL and PIAS3), we used the reported CNV frequency5 in both disorders to determine the predominant polarity associated with each disease. The information about CNV-associated genes, polarities and phenotypes reported in the literature is provided in Supplementary Table 4.

Validation and analysis of the identified clusters

In order to validate the NETBAG+ phenotype network, the identified clusters and the associated biological functions, we performed several additional analyses.

First, we demonstrated that the phenotype network and scoring method can be used to rank genes responsible for a diverse set of genetic phenotypes. For this task, we considered known disease genes from the OMIM database, excluding diseases that were used in training of the phenotype network, diseases with less than three associated genes and diseases with somatic mutations such as cancer. In total, we considered 74 genetic phenotypes with 338 associated genes (Supplementary Table 5). For each gene in the test set, we randomly selected 99 decoy human genes with comparable network connectivity. We then ranked these 100 genes on the basis of the strength of connections in the phenotype network to the remaining OMIM genes responsible for the same phenotype. The results of this prioritization test showed that the phenotype network and the scoring method perform well in ranking disease genes. The correct gene was ranked as the top gene (out of 100 genes) in 39% of the cases, in the top three in 53% of the cases and in the top ten in 66% of the cases (Supplementary Fig. 2). This demonstrates that the network and the scoring method are not specific to schizophrenia or brain disorders and perform well across diverse phenotypes.

Second, we examined direct protein-protein interactions between genes in the identified clusters annotated in BioGRID, HPRD and DIP (Supplementary Fig. 3). We performed a commonly used permutation test to understand whether clusters identified in our analysis were more densely connected than in structurally equivalent random networks. To generate structurally equivalent random networks, the real protein-protein network was permuted by swapping known interaction pairs, while conserving the number of connections (degree) of each gene. Thirteen known interactions exist between the 47 genes in cluster I, and five interactions exist between the 42 genes in cluster II. After permutation, there were fewer interactions on average, 8.74 (P = 0.11, Z-score = 1.36) for cluster I and 2.8 (P = 0.17, Z-score = 1.21) for cluster II. Consequently, there is only a marginal significance for the inter-connectivity of the genes forming the clusters in the real network compared to random networks. This result illustrates that integrative methods (such as NETBAG+) are more powerful in establishing the significance of functional connectivities in disease clusters compared to protein-protein interactions alone.

Third, we applied our algorithm to an independent set of schizophrenia-related CNVs. This set contained rare inherited CNVs, which are more likely to contain a smaller fraction of causative events, and de novo CNVs associated with childhood-onset schizophrenia (COS)6. Overall, the independent set included 48 CNV events (35 inherited and 13 de novo COS events) containing in total 244 genes. Using this set, NETBAG+ detected a small, but marginally significant (P = 0.05), cluster of ten genes (Supplementary Fig. 4). We used DAVID to identify GO terms associated with the alternative cluster (Supplementary Table 3). This analysis showed that the alternative cluster is associated with many biological and cellular functions that are also associated with the clusters identified in our main analysis: insulin receptor signaling, axonogenesis, regulation of cell mobility and locomotion, neuron morphogenesis and differentiation, and neuron projection development. Consequently, the alternative set of CNVs provides an independent confirmation that multiple functions identified in the paper are indeed likely to be affected in schizophrenia.

Finally, we performed a manual literature review of all 159 genes with de novo SNVs from recent studies8,16. Brief functional descriptions (obtained primarily from GenBank and NCBI) for these genes are shown in Supplementary Table 6. Using the literature information, we observed that our clusters are enriched in genes with known brain and neuronal functions. Specifically, the identified clusters contained 26 genes (out of 56 in total) with brain or neural functions (Fisher’s exact test P = 10−4, Barnard’s exact test P = 2 × 10−5).

Supplementary Material



We are grateful to all participating families and to clinical collaborators J.L. Roos and H. Pretorius, as well as to nursing sisters R. van Wyk, C. Botha and H. van den Berg for subject recruitment and evaluation. We would also like to sincerely thank M. Wigler, D. Geschwind, G. Fischbach and all members of the Vitkup laboratory for discussions. This work was supported in part by a grant from the Simons Foundation (SFARI award number SF51), US National Centers for Biomedical Computing (MAGNet) grant U54CA121852 to Columbia University, US National Institute of Mental Health grants MH061399 (to M.K.) and MH077235 (to J.A.G.) and the Lieber Center for Schizophrenia Research at Columbia University. S.R.G. was supported in part by US National Institute of General Medical Sciences training grant T32 GM082797. B.X. was partially supported by a US National Alliance for Research in Schizophrenia and Depression (NARSAD) Young Investigator Award.


Note: Supplementary information is available in the online version of the paper.


S.R.G. and J.C. performed computational analysis, interpreted the results and wrote the manuscript. T.S.B. contributed to the computational analysis. B.X., J.A.G. and M.K. designed the study, contributed data, interpreted the results, and contributed to functional analysis and manuscript writing. D.V. designed the study, supervised the project, interpreted the results and wrote the manuscript.


The authors declare no competing financial interests.

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