G72/G30 in schizophrenia and bipolar disorder: Review and meta-analysis

National Institute of Mental Health Intramural Research Program, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland 20892-3719, USA.
Biological Psychiatry (Impact Factor: 9.47). 08/2006; 60(2):106-14. DOI: 10.1016/j.biopsych.2006.01.019
Source: PubMed

ABSTRACT Association of the G72/G30 locus with schizophrenia and bipolar disorder has now been reported in several studies. The G72/G30 locus may be one of several that account for the evidence of linkage that spans a broad region of chromosome 13q. However, the story of G72/G30 is complex. Our meta-analysis of published association studies shows highly significant evidence of association between nucleotide variations in the G72/G30 region and schizophrenia, along with compelling evidence of association with bipolar disorder. But the associated alleles and haplotypes are not identical across studies, and some strongly associated variants are located approximately 50 kb telomeric of G72. Interestingly, G72 and G30 are transcribed in opposite directions; hence, their transcripts could cross-regulate translation. A functional native protein and functional motifs for G72 or G30 remain to be demonstrated. The interaction of G72 with d-amino acid oxidase, itself of interest as a modulator of N-methyl-d-aspartate receptors through regulation of d-serine levels, has been reported in one study and could be a key functional link that deserves further investigation. The association findings in the G72/G30 region, among the most compelling in psychiatry, may expose an important molecular pathway involved in susceptibility to schizophrenia and bipolar disorder.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Extracting Knowledge from genetics pertinent database has still remained one of the most exciting challenges in the data mining area. To note, most of the association studies have exclusively been devoted to highlight the basic determinant lying behind to a certain specific complex genetic disease. In this respect, the present study's major objective consists in devising a novel knowledge-discovery approach, whereby a genetics data base has been developed. Hence, this work has been primarily designed to propose some improvements to the predominantly applied algorithms, widely-applied in this field. In the second places, we intend to demonstrate that our newly-devised algorithm, dubbed NCA, has by far achieved highly accurate and effective results in respect of the prevalent algorithms. As a matter of fact, we have, willingly, applied and compared our approach, along with the existing approaches, to some biological ideas relevant to some acute hereditary complex illnesses, in which the concerned biological literature has identified the pertinent responsible variables. As for the last-section part, it depicts our concluding and proposed suggestions for further research.
    22nd International Workshop on Database and Expert Systems Applications (DEXA\BIOKDD), Toulouse, France; 01/2011
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The objective of our work is to develop a new approach for discovering knowledge from a large mass of data, the result of applying this approach will be an expert system that will serve as diagnostic tools of a phenomenon related to a huge information system. We first recall the general problem of learning Bayesian network structure from data and suggest a solution for optimizing the complexity by using organizational and optimization methods of data. Afterward we proposed a new heuristic of learning a Multi-Entities Bayesian Networks structures. We have applied our approach to biological facts concerning hereditary complex illnesses where the literatures in biology identify the responsible variables for those diseases. Finally we conclude on the limits arched by this work.
    International Conference on Pattern Recognition and Computer Vision (ICPRCV),World Academy of Science, Engineering and Technology, Amesterdam; 01/2011
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The objective of our study lies in developing a new data-reducing approach whose useful application is crucial as a prerequisite to learning Bayesians Networks (BNs) structures. The application of our approach may, in some cases, turn out to be significantly effective in reducing the computational complexity of the BNs structures learning. Firstly, it is essential to define BNs and recall its widely-common relevant problem of learning structure from massive data. Secondly, we suggest a solution for optimizing the computational complexity by means of data organizational and optimization methods. As a matter of fact we have applied our approach to biological facts concerning hereditary complex illness where the literatures in biology identify the responsible variables for those diseases. Finally, we conclude by highlighting the limits arched by this work and proposing suggestions for further research.
    7th International Conference on Data Mining (DMIN 2011), Lasvegas, USA; 01/2011

Full-text (2 Sources)

Available from
May 22, 2014