Substance use disorders (SUDs) are one of the world’s leading public health problems with the World Health Organization (WHO; 2004
) estimating tens of millions of alcohol and drug abusers and more than a billion smokers worldwide (see related WHO reports available online at http://www.who.int/topics/substance_abuse/en/
). In economic terms, hundreds of millions of dollars are lost due to health care costs and lost productivity each year (Harwood, Fountain, & Livermore, 1998
; WHO, 2004
). Substance abuse also shortens lives, increases risk for chronic illness, and contributes to broken families, ruined careers, and violent victimization underscoring the need to mitigate the consequences and prevent the development of SUDs (WHO, 2004
). Notably, SUDs remain a major problem despite evidence that public policies can reduce the prevalence of SUDs (WHO, 2008
) and effective treatments have been devised for addictions (Nathan & Gorman, 1998
). Given that SUDs also exhibit substantial heritable influences (Goldman, Oroszi, & Ducci, 2005
), many researchers have focused their efforts on understanding the etiological contribution of genetic factors in the development of SUDs.
Among the many challenges associated with identifying genes that increase risk for SUDs is that these conditions exhibit high levels of comorbidity, that is, co-occurrence at greater than chance levels (Compton, Thomas, Stinson, & Grant, 2007
; Hasin, Stinson, Ogburn, & Grant, 2007
). For example, a person who exhibits alcohol use-related problems also tends to have similar problems with their use of nicotine and illicit drugs. This creates the potential for interpretative confounds as any association between a risk allele and a SUD could be accounted for by a comorbid SUD. Comorbidity also results in complications when designing the appropriate recruitment strategy such as inclusion/exclusion criteria for case-control studies (e.g., should a gene association study of alcohol dependence exclude participants who also meet criteria for nicotine or illicit drug dependence?). Another issue in genetic studies of substance abuse is how best to define the phenotype for gene association studies, for example, whether to use diagnostic categories, dimensional measures of use, or composite measures of use and abuse (Agrawal et al., 2009
). Further, it is important to determine the comparability of diagnostic measures of SUDs and non-diagnostic measures of substance use, as the latter are available in many genetic studies not specifically designed to investigate SUDs. If substance use measures can serve as adequate proxies for the more labor-intensive diagnostic measures, this would provide additional opportunities for replication and better meta-analytic studies (Grant et al., 2009
One solution to dealing with these challenges is to employ a dimensional approach that recognizes the importance of SUD comorbidity and leverages it to enhance genetic identification efforts. This approach posits that a general liability to all SUDs and related conditions such as antisocial behavior and disinhibited personality traits underlies their comorbidity and should be the target phenotype for gene association studies (Hicks et al., 2004
; Krueger et al., 2002
). Rather than noise, comorbidity becomes the signal as people with multiple SUDs are the most likely to carry the greatest genetic risk and so become the most informative members of the sample. The different SUDs are then conceptualized as alternative manifestations of this general inherited liability with the final phenotypic expression determined by disorder-specific genetic (e.g., pharmacological sensitivities to specific substances) and environmental risk factors (e.g., availability of specific substances).
There is substantial evidence that supports conceptualizing the comorbidity among SUDs and related conditions as being indicative of a highly heritable, general liability that underlies these commonly co-occurring disorders. Factor analyses of childhood disruptive behaviors (aggression, rule breaking; Achenbach & Edelbrock, 1984
), adolescent problem behaviors (delinquency, substance use; Jessor & Jessor, 1977
), and diagnoses of adult psychiatric disorders (SUDs, antisocial personality disorder; Krueger, 1999; Markon, 2010
) all indicate a single, common factor best accounts for their covariance. Further, structural models of temperament and personality typically include a dimension characterized by traits related to behavioral undercontrol (impulsivity, sensation seeking, aggression) (Markon, Krueger, & Watson, 2005
). These strong phenotypic associations have prompted several researchers to posit a general liability that is commonly referred to as externalizing.
Twin studies have demonstrated that the general externalizing factor (i.e., the common variance across measures of SUDs, antisocial behavior, and disinhibited personality traits) is highly heritable (h2
= .80 to .85; higher than any specific SUD), and accounts for much of the genetic risk for SUDs and related phenotypes (Kendler et al. 2003
; Krueger et al., 2002
; Young et al., 2000
). However, each SUD is distinguished by disorder-specific genetic and environmental risk factors. For example, genes involved in the metabolism of alcohol such as ADH
are associated with risk for alcohol dependence (Higuchi et al., 2004
), but do not seem to increase risk for other SUDs and antisocial behavior (Irons et al., 2006). Further, twin-family studies have shown that the similarity between parents and offspring on SUDs, antisocial behavior, and childhood disruptive disorders is best accounted for by the transmission of a highly heritable, general externalizing liability rather than disorder-specific liabilities (Bornovalova et al., 2010
; Hicks et al., 2004
). Further, research on endophenotypes of SUDs, specifically measures of brain electrophysiology such as reduced P3 amplitude (P3-AR), supports a common genetic liability across externalizing phenotypes. For example, P3-AR is heritable (h2
= .60 to .65; Carlson & Iacono, 2006
; Hicks et al., 2007
), associated with a diverse set of externalizing phenotypes (Iacono et al., 2002
; Iacono & McGue, 2007; Yoon et al., 2006
), and predicts the onset of new SUDs (Carlson et al., 2007
; Iacono et al., 2002
). Additionally, the common variance across externalizing phenotypes accounts for the link with P3-AR (Patrick et al., 2006
), and twin studies have shown common genetic effects underlie the P3AR-externalizing association (Hicks et al., 2007
). Finally, candidate gene studies have linked specific genes (GABRA2
) to multiple externalizing phenotypes (Dick, 2007
), with effects strongest for comorbid phenotypes (Dick et al., 2007
) or composite measures that more directly index the general externalizing liability (Dick et al., 2008
; Stallings et al., 2005
). These last findings provide proof of concept for the utility of employing a dimensional approach in gene association studies for SUDs.
The first step to implementing this approach is to delineate the psychometric structure among measures of substance use and abuse including measures of different substances (alcohol, nicotine, illicit drugs) and related conditions such as antisocial behavior and disinhibited personality traits. The goal of such an analysis is to determine which measures are well suited to index the general propensity to use and abuse substances (e.g., number of different drug classes ever tried), and which measures best index risk for a specific substance (e.g., substance specific withdrawal symptoms). Twin and family data can augment these analyses by the parsing of genetic and environmental variance of these measures. Such analyses can more precisely determine the extent to which different measures (e.g., frequency of drinking and symptoms of alcohol dependence) index the genetic risk of a particular SUD construct (e.g., alcohol use-related problems) versus the general externalizing liability.
We report results from such analyses utilizing data from large twin, family, and adoption studies conducted at the Minnesota Center for Twin and Family Research (MCTFR; Iacono, McGue, & Krueger, 2006
). The sample of each study is composed of nuclear families that include the parents and two adolescent offspring who are twins, non-twin biological siblings, or adoptive (i.e., unrelated) siblings. Using our extensive assessment battery of SUDs and related externalizing phenotypes, we first examine the psychometric structure of these measures. We assume a hierarchical structure such that by analyzing the patterns of covariance among many relatively narrow or facet-level measures, we will identify a smaller number of broader SUD constructs. Next, we will select the best measures of these SUD constructs for confirmatory and biometric analyses taking advantage of the genetically informative nature of the data. Specifically, we will examine the heritability of the facet measures and SUD constructs, how well each measure taps the genetic risk for the general externalizing liability, and the amount of measure-specific genetic and environmental variance.