The DOI matches its Crossref record; 288 of the 288 references it cites were found in a registry or an index.
Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction
Open accessAbstract
We have used a translational convergent functional genomics (CFG) approach to identify and prioritize genes involved in schizophrenia, by gene-level integration of genome-wide association study data with other genetic and gene expression studies in humans and animal models. Using this polyevidence scoring and pathway analyses, we identify top genes (DISC1, TCF4, MBP, MOBP, NCAM1, NRCAM, NDUFV2, RAB18, as well as ADCYAP1, BDNF, CNR1, COMT, DRD2, DTNBP1, GAD1, GRIA1, GRIN2B, HTR2A, NRG1, RELN, SNAP-25, TNIK), brain development, myelination, cell adhesion, glutamate receptor signaling, G-protein-coupled receptor signaling and cAMP-mediated signaling as key to pathophysiology and as targets for therapeutic intervention. Overall, the data are consistent with a model of disrupted connectivity in schizophrenia, resulting from the effects of neurodevelopmental environmental stress on a background of genetic vulnerability. In addition, we show how the top candidate genes identified by CFG can be used to generate a genetic risk prediction score (GRPS) to aid schizophrenia diagnostics, with predictive ability in independent cohorts. The GRPS also differentiates classic age of onset schizophrenia from early onset and late-onset disease. We also show, in three independent cohorts, two European American and one African American, increasing overlap, reproducibility and consistency of findings from single-nucleotide polymorphisms to genes, then genes prioritized by CFG, and ultimately at the level of biological pathways and mechanisms. Finally, we compared our top candidate genes for schizophrenia from this analysis with top candidate genes for bipolar disorder and anxiety disorders from previous CFG analyses conducted by us, as well as findings from the fields of autism and Alzheimer. Overall, our work maps the genomic and biological landscape for schizophrenia, providing leads towards a better understanding of illness, diagnostics and therapeutics. It also reveals the significant genetic overlap with other major psychiatric disorder domains, suggesting the need for improved nosology.
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Cites
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Common polygenic variation contributes to risk of schizophrenia and bipolar disorder
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Modelling schizophrenia using human induced pluripotent stem cells
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Common variants on chromosome 6p22.1 are associated with schizophrenia
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Genome Scan Meta-Analysis of Schizophrenia and Bipolar Disorder, Part II: Schizophrenia
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Self-citationPsychiatric Comorbidities and Schizophrenia
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Sugathan, A., Biagioli, M., Golzio, C., Erdin, S., Blumenthal, I., Manavalan, P., Ragavendran, A., Brand, H., Lucente, D., Miles, J., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405266111 · Cited by 342
Treating Working Memory Deficits in Schizophrenia: A Review of the Neurobiology
Lett, T. A., Voineskos, A. N., Kennedy, J. L., Levine, B., & Daskalakis, Z. J. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.07.026 · Cited by 257
Discovery and validation of blood biomarkers for suicidality
Le-Niculescu, H., Levey, D. F., Ayalew, M., Palmer, L., Gavrin, L. M., Jain, N., Winiger, E., Bhosrekar, S., Shankar, G., Radel, M., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2013.95 · Cited by 193
Self-citationKendler, K. S. (2013) · Molecular Psychiatry · doi:10.1038/mp.2013.50 · Cited by 178
Metadata
- Authors
- Ayalew, M., Le-Niculescu, H., Levey, D. F., Jain, N., Changala, B., Patel, S. D., Winiger, E., Breier, A., Shekhar, A., Amdur, R., Koller, D., Nurnberger, J. I., Corvin, A., Geyer, M., Tsuang, M. T., Salomon, D., Schork, N. J., Fanous, A. H., O'Donovan, M. C., & Niculescu, A. B.
- Venue
- Molecular Psychiatry
- Published
- 2012-05-15
- Volume
- 17
- Issue
- 9
- Pages
- 887-905
- ISSN
- 1359-4184 (ISSN-L)
- 1476-5578
- OpenAlex
- W2016854825
- Cited by
- 397
- References
- 288
- FWCI
- 35.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Ayalew, M., Le-Niculescu, H., Levey, D. F., Jain, N., Changala, B., Patel, S. D., Winiger, E., Breier, A., Shekhar, A., Amdur, R., Koller, D., Nurnberger, J. I., Corvin, A., Geyer, M., Tsuang, M. T., Salomon, D., Schork, N. J., Fanous, A. H., O'Donovan, M. C., & Niculescu, A. B. (2012). Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction. Molecular Psychiatry. https://doi.org/10.1038/mp.2012.37
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
