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Psychol Med. 2007 Jan;37(1):49-60. Epub 2006 Nov 1.

Contrasting models of genetic co-morbidity for cannabis and other illicit drugs in adult Australian twins.

Author information

1
Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA. arpana@wustl.edu

Abstract

BACKGROUND:

The use of cannabis and other illicit drugs (OIDs) and their co-morbid misuse are frequently reported in the literature. Correlated vulnerabilities and causal or gateway influences have been implicated in this association. We investigated the source of this co-morbidity between cannabis use (experimentation, early and repeated use, and problems) and OID experimentation and problems using genetic models proposed by Neale and Kendler (American Journal of Human Genetics 1995, 57, 935-953).

METHOD:

In a sample of 4152 same-sex male and female adult Australian twin individuals, we fit 13 genetically informative models of co-morbidity to data on experimentation, early use, repeated use of cannabis and co-morbid OID experimentation, and to abuse/dependence (A/D) problems with cannabis and OIDs.

RESULTS:

Model-fitting results suggest that common genetic, shared and unique environmental factors are responsible for the association between cannabis experimentation, early use, repeated use and A/D problems and OID experimentation or problems. The liability causation model, which is a reduced form of the correlated vulnerabilities model, also fit very well. In women, we found evidence for high-risk cannabis experimenters and repeated users to be at increased risk for OID experimentation, despite being below the risk threshold on the liability distribution for OID experimentation (extreme multiformity).

CONCLUSIONS:

Co-morbid cannabis and OID use and misuse are due partly to a common predisposition to substance use disorders. Putative causal effects could not be ruled out. These models warrant further research, so that features of the correlated vulnerabilities model and the gateway models can be studied jointly in a single series of adaptive nested models.

PMID:
17076916
DOI:
10.1017/S0033291706009287
[Indexed for MEDLINE]

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