J D Grant1, M T Lynskey2, P A F Madden1, E C Nelson1, L R Few1, K K Bucholz1, D J Statham3, N G Martin4, A C Heath1, A Agrawal1. 1. Department of Psychiatry,Washington University School of Medicine,St Louis,MO,USA. 2. Institute of Psychiatry, Psychology & Neuroscience, Addictions Department,King's College London,London,UK. 3. University of the Sunshine Coast,Queensland,Australia. 4. QIMR Berghofer Medical Research Institute,Queensland,Australia.
Abstract
BACKGROUND: Genetic influences contribute significantly to co-morbidity between conduct disorder and substance use disorders. Estimating the extent of overlap can assist in the development of phenotypes for genomic analyses. METHOD: Multivariate quantitative genetic analyses were conducted using data from 9577 individuals, including 3982 complete twin pairs and 1613 individuals whose co-twin was not interviewed (aged 24-37 years) from two Australian twin samples. Analyses examined the genetic correlation between alcohol dependence, nicotine dependence and cannabis abuse/dependence and the extent to which the correlations were attributable to genetic influences shared with conduct disorder. RESULTS: Additive genetic (a(2) = 0.48-0.65) and non-shared environmental factors explained variance in substance use disorders. Familial effects on conduct disorder were due to additive genetic (a(2) = 0.39) and shared environmental (c(2) = 0.15) factors. All substance use disorders were influenced by shared genetic factors (rg = 0.38-0.56), with all genetic overlap between substances attributable to genetic influences shared with conduct disorder. Genes influencing individual substance use disorders were also significant, explaining 40-73% of the genetic variance per substance. CONCLUSIONS: Among substance users in this sample, the well-documented clinical co-morbidity between conduct disorder and substance use disorders is primarily attributable to shared genetic liability. Interventions targeted at generally reducing deviant behaviors may address the risk posed by this shared genetic liability. However, there is also evidence for genetic and environmental influences specific to each substance. The identification of these substance-specific risk factors (as well as potential protective factors) is critical to the future development of targeted treatment protocols.
BACKGROUND: Genetic influences contribute significantly to co-morbidity between conduct disorder and substance use disorders. Estimating the extent of overlap can assist in the development of phenotypes for genomic analyses. METHOD: Multivariate quantitative genetic analyses were conducted using data from 9577 individuals, including 3982 complete twin pairs and 1613 individuals whose co-twin was not interviewed (aged 24-37 years) from two Australian twin samples. Analyses examined the genetic correlation between alcohol dependence, nicotine dependence and cannabis abuse/dependence and the extent to which the correlations were attributable to genetic influences shared with conduct disorder. RESULTS: Additive genetic (a(2) = 0.48-0.65) and non-shared environmental factors explained variance in substance use disorders. Familial effects on conduct disorder were due to additive genetic (a(2) = 0.39) and shared environmental (c(2) = 0.15) factors. All substance use disorders were influenced by shared genetic factors (rg = 0.38-0.56), with all genetic overlap between substances attributable to genetic influences shared with conduct disorder. Genes influencing individual substance use disorders were also significant, explaining 40-73% of the genetic variance per substance. CONCLUSIONS: Among substance users in this sample, the well-documented clinical co-morbidity between conduct disorder and substance use disorders is primarily attributable to shared genetic liability. Interventions targeted at generally reducing deviant behaviors may address the risk posed by this shared genetic liability. However, there is also evidence for genetic and environmental influences specific to each substance. The identification of these substance-specific risk factors (as well as potential protective factors) is critical to the future development of targeted treatment protocols.
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