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    Cannabis and Depression: A Twin Model Approach to Co-morbidity

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    Author
    Smolkina, M; Morley, KI; Rijsdijk, F; Agrawal, A; Bergin, JE; Nelson, EC; Statham, D; Martin, NG; Lynskey, MT
    Date
    2017-07-01
    Source Title
    Behavior Genetics: an international journal devoted to research in the inheritance of behavior in animals and man
    Publisher
    SPRINGER
    University of Melbourne Author/s
    Morley, Katherine
    Affiliation
    Melbourne School of Population and Global Health
    Metadata
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    Document Type
    Journal Article
    Citations
    Smolkina, M., Morley, K. I., Rijsdijk, F., Agrawal, A., Bergin, J. E., Nelson, E. C., Statham, D., Martin, N. G. & Lynskey, M. T. (2017). Cannabis and Depression: A Twin Model Approach to Co-morbidity. BEHAVIOR GENETICS, 47 (4), pp.394-404. https://doi.org/10.1007/s10519-017-9848-0.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/259224
    DOI
    10.1007/s10519-017-9848-0
    NHMRC Grant code
    NHMRC/628911
    Abstract
    Cannabis use disorder (CUD) co-occurs with major depressive disorder (MDD) more frequently than would be expected by chance. However, studies to date have not produced a clear understanding of the mechanisms underlying this co-morbidity. Genetically informative studies can add valuable insight to this problem, as they allow the evaluation of competing models of co-morbidity. This study uses data from the Australian Twin Registry to compare 13 co-morbidity twin models initially proposed by Neale and Kendler (Am J Hum Genet 57:935-953, 1995). The analysis sample comprised 2410 male and female monozygotic and dizygotic twins (average age 32) who were assessed on CUD and MDD using the SSAGA-OZ interview. Data were analyzed in OpenMx. Of the 13 different co-morbidity models, two fit equally well: CUD causes MDD and Random Multiformity of CUD. Both fit substantially better than the Correlated Liabilities model. Although the current study cannot differentiate between them statistically, these models, in combination, suggest that CUD risk factors may causally influence the risk to develop MDD, but only when risk for CUD is high.

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