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

Abstract

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.

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Figure 1: The NETBAG+ approach and the identified schizophrenia gene clusters.
Figure 2: Temporal gene expression profiles in the brain across developmental stages for genes forming the identified clusters.
Figure 3: Distributions of connectivity strengths between schizophrenia clusters and genes previously implicated in schizophrenia and other related disorders.
Figure 4: Likely impact of genes from de novo CNVs in autism and schizophrenia on growth of dendrites or dendritic spines.
Figure 5: Genes forming cluster I in the context of cellular signaling pathways.

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Acknowledgements

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.

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Authors and Affiliations

Authors

Contributions

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.

Corresponding author

Correspondence to Dennis Vitkup.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 and Supplementary Table 1 (PDF 549 kb)

Supplementary Table 2

Gene Ontology (GO) terms associated with cluster genes using FuncAssociate. (XLSX 15 kb)

Supplementary Table 3

Gene Ontology (GO) terms associated with cluster genes using DAVID. (XLSX 25 kb)

Supplementary Tables

Supplementary Tables 4–6 (XLSX 28 kb)

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Gilman, S., Chang, J., Xu, B. et al. Diverse types of genetic variation converge on functional gene networks involved in schizophrenia. Nat Neurosci 15, 1723–1728 (2012). https://doi.org/10.1038/nn.3261

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