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

Nature Neuroscience volume 15, pages 17231728 (2012) | Download Citation

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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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.

Author information

Affiliations

  1. Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, USA.

    • Sarah R Gilman
    • , Jonathan Chang
    • , Tejdeep S Bawa
    •  & Dennis Vitkup
  2. Department of Biomedical Informatics, Columbia University, New York, New York, USA.

    • Sarah R Gilman
    • , Jonathan Chang
    • , Tejdeep S Bawa
    •  & Dennis Vitkup
  3. Department of Psychiatry, Columbia University, New York, New York, USA.

    • Bin Xu
    •  & Maria Karayiorgou
  4. Department of Physiology & Cellular Biophysics, Columbia University, New York, New York, USA.

    • Joseph A Gogos
  5. Department of Neuroscience, Columbia University, New York, New York, USA.

    • Joseph A Gogos

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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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Dennis Vitkup.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–4 and Supplementary Table 1

Excel files

  1. 1.

    Supplementary Table 2

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

  2. 2.

    Supplementary Table 3

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

  3. 3.

    Supplementary Tables

    Supplementary Tables 4–6

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DOI

https://doi.org/10.1038/nn.3261