neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders
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2014
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Abstract
Major neuropsychiatric disorders are highly heritable, with mounting evidence suggesting that these disorders share overlapping sets of molecular and cellular underpinnings. In the current article we systematically test the degree of genetic commonality across six major neuropsychiatric disorders—attention deficit hyperactivity disorder, anxiety disorders, autistic spectrum disorders, bipolar disorder, major depressive disorder and schizophrenia. We curated a well-vetted list of genes based on large-scale human genetic studies and verified their appearance on the NHGRI catalog of published genome-wide association studies. A total of 180 genes were accepted into the analysis on the basis of low but liberal GWAS p-values (<10-5). 22% of genes overlapped two or more disorders. The most widely shared subset of genes—common to five of six disorders–included ANK3, AS3MT, CACNA1C, CACNB2, CNNM2, CSMD1, DPCR1, ITIH3, NT5C2, PPP1R11, SYNE1, TCF4, TENM4, TRIM26, and ZNRD1. Using a suite of neuroinformatic resources, we showed that many of the shared genes are implicated in the postsynaptic density, expressed in immune tissues and co-expressed in developing human brain.. Using a translational cross-species approach, we detected two distinct genetic components that were both shared by each of the six disorders; the 1st component is involved in CNS development, neural projections and synaptic transmission, while the 2nd is implicated in various cytoplasmic organelles and cellular processes. Combined, these genetic components account for 20–30% of the genetic load. The remaining risk is conferred by distinct, disorder-specific variants. Nevertheless, the convergence of different analytical approaches on similar targets may bear important implications. Thus, although adding mostly confirmatory findings, higher resolution of shared and unique genetic factors provided in this manuscript could ultimately translate into improved diagnosis and treatment of these debilitating disorders.
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| Reference Key |
elotan2014frontiersneuroinformatic
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| Authors | ;Amit eLotan;Michaela eFenckova;Michaela eFenckova;Michaela eFenckova;Janita eBralten;Janita eBralten;Janita eBralten;Aet eAlttoa;Luanna eDixson;Robert W Williams;Monique evan der Voet;Monique evan der Voet;Monique evan der Voet |
| Journal | Journal of enzyme inhibition and medicinal chemistry |
| Year | 2014 |
| DOI |
10.3389/fnins.2014.00331
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| URL | |
| Keywords |
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