Recent advances in the genetic epidemiology and molecular genetics of substance use disorders

Abstract

This article reviews current advances in the genetics of substance use disorders (SUDs). Both genetic and environmental sources of risk are required to develop a complete picture of SUD etiology. Genetic sources of risk for SUDs are not highly substance specific in their effects. Genetic and environmental risks for SUDs typically do not only add together but also interact with each other over development. Risk gene identification for SUDs has been difficult, with one recent success in identifying nicotinic receptor variants that affect risk for nicotine dependence. The impact of genetic variants on SUD risk will individually be small. Although genetic epidemiologic methods are giving us an increasingly accurate map of broad causal pathways to SUDs, gene discovery will be needed to identify the specific biological systems. Identifying these risk genes and understanding their action will require large clinical samples, and interaction between these studies and work in model organisms.

Figure 1

Parameter estimates for the best-fitting confirmatory model for symptoms of cannabis, cocaine, alcohol, caffeine and nicotine dependence. The double-headed arrow connecting the illicit and licit substance genetic factors represents the genetic correlation between these factors. A stands for additive genetic and E for environmental effects unique to each twin. E1 reflects the common factor for the unique environmental effects that affects risk for dependence across all of the psychoactive substances examined. The top part of the figure depicts genetic factors that influence drug dependence and the bottom part reflects environmental factors. The numbers on the paths represent standardized loadings and thus need to be squared to reflect the proportion of variance in the observed SUD accounted for by the factor.

Figure 2

Parameter estimates for the contributions to variation in liability to psychoactive drug use of additive genetic effects (a², red), familial environmental factors (c², light blue) and the individual-specific environment (e², dark blue) by year for average daily number of cigarettes, ages 13–40. Data from ref. .

Figure 3

An example of gene-environment interaction: as parental monitoring increases, the importance of genetic factors significantly decreases, and the influence of common environmental factors becomes more important such that the most important etiological factors affecting adolescent smoking vary markedly as a function of parental monitoring. Data from ref. ; graph shows results of model-fitting as described in detail in ref. .

Figure 4

Hypothetical item response curves for SUD diagnostic criteria. The item depicted by curve 1 is a criterion likely to be met by all individuals, except those with very low risk of SUD. It discriminates well between those who meet it and those who do not because the curve is steep, and this is also true curve 2, which has the same steepness but distinguishes between individuals 1 s.d. higher on the liability distribution. Curve 3 is the flattest and is consequently the least discriminating because the probability of showing this symptom changes more slowly as the risk of SUD increases. Curve 4 discriminates perfectly, but it is only useful for measuring individuals close to the 1.5 s.d. point on the distribution.