Personality, Executive Functions, and Behavioral Disinhibition in Adolescence

Transcription

1 University of Colorado, Boulder CU Scholar Psychology and Neuroscience Graduate Theses & Dissertations Psychology and Neuroscience Spring Personality, Executive Functions, and Behavioral Disinhibition in Adolescence Joanna M. Vandever University of Colorado Boulder, Follow this and additional works at: Part of the Biological Psychology Commons, and the Genetics Commons Recommended Citation Vandever, Joanna M., "Personality, Executive Functions, and Behavioral Disinhibition in Adolescence" (2014). Psychology and Neuroscience Graduate Theses & Dissertations This Dissertation is brought to you for free and open access by Psychology and Neuroscience at CU Scholar. It has been accepted for inclusion in Psychology and Neuroscience Graduate Theses & Dissertations by an authorized administrator of CU Scholar. For more information, please contact

2 Personality, Executive Functions, and Behavioral Disinhibition in Adolescence by Joanna M. Vandever B.S., Black Hills State University, 2008 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirement for the degree of Doctor of Philosophy Department of Psychology and Neuroscience 2014

3 This thesis entitled: Personality, Executive Functions, and Behavioral Disinhibition in Adolescence written by Joanna M. Vandever has been approved for the Department of Psychology and Neuroscience Michael C. Stallings Naomi P. Friedman John K. Hewitt Matthew B. McQueen Yuko Munakata Soo Hyun Rhee Date 04/11/14 The final copy of this thesis has been examined by the signatories, and we Find that both the content and the form meet acceptable presentation standards Of scholarly work in the above mentioned discipline. IRB protocol # , , , ,

4 iii Vandever, Joanna M. (Ph.D., Psychology) Personality, Executive Functions, and Behavioral Disinhibition in Adolescence Thesis directed by Associate Professor Michael C. Stallings Prior studies suggest there are common genetic vulnerabilities underlying antisocial behavior and substance use disorders, which are often comorbid. It has been proposed that cognitive and personality factors related to behavioral disinhibition may explain some of the association between these behaviors. This dissertation uses adolescent twins from the Colorado Center for Antisocial Drug Dependence (CADD) to investigate (a) whether behavioral disinhibition and factors common and specific to executive functions share genetic influences, and (b) how genetic relations change with specific stages of substance use. Then, a subset of items from the Tridimensional Personality Questionnaire (TPQ) is examined for its usefulness in predicting antisocial behavior and substance use problems. In the first two studies, latent constructs reflected variance shared among either executive function tasks or behavioral disinhibition measures. A set of updating tasks and a set of shifting tasks were each represented by latent factors. All three types of tasks (updating, shifting, and inhibiting) loaded on a third executive function factor. The behavioral disinhibition factor included conduct disorder, substance use or dependence vulnerability, and the TPQ novelty seeking dimension. The first study showed that genetic influences on the common executive function factor were more highly correlated with genetic influences on behavioral disinhibition when substance use, rather than dependence vulnerability, was included in the model. Results

5 iv from the second study indicated a higher proportion of shared genetic influences between the common executive function factor and age-of-onset than between executive functioning and later problem-use stages. The final study identified a set of TPQ items that reflected disinhibitory personality. Although the new measure predicted antisocial behavior and substance use disorders, it did not show significant improvement over the original novelty seeking dimension commonly used in studies of behavioral disinhibition. Implications for these findings are discussed.

6 Dedication To my parents Chuck and Nancy Vandever For their endless love and encouragement I especially want to thank my mother for the myriad ways in which she actively supported me throughout my education, and for instilling me with a passion for learning.

7 vi Acknowledgements I cannot express how grateful I am to my advisor Michael Stallings. He has taught me much about research in behavioral genetics, professional development, and the importance of a balanced life. I wish to thank Robin Corley, Susan Young, and Naomi Friedman for their assistance with my research. I am also grateful for the guidance and support of my fellow graduate students Melissa Munn-Chernoff, Raven Astrom, Josh Bricker, Rohan Palmer, and Brooke Huibregtse. I want to give special thanks to Hannah Snyder for her friendship. Through ups and downs she has lent a kind ear and has proven to be the perfect hiking companion. Finally I would like to thank my fiancé John Crabtree for being there for me during the writing process and for being my best cheerleader. This work would not have been possible without funding from the National Institutes of Health. The longitudinal sample and data were maintained by a grant from the National Institute of Child Health and Human Development (HD010333). Data collection was supported by grants from the National Institute of Mental Health (MH63207) and the National Institute of Drug Abuse (DA011015). I am also grateful for the support from the National Institute of Child Health and Human Development, which included an institutional training grant awarded to the Institute for Behavioral Genetics (T32 HD007289) and a Research Project Grant awarded to Rand Donald Conger (R01HD ).

8 vii Contents Chapter 1 Introduction 1.1 Genetic influences common to substance use disorders and antisocial behavior Genetic effects on personality contribute to antisocial behaviors and substance use disorders Executive functions and behavioral disinhibition Summary 5 2 Behavioral Disinhibition and Executive Functions: Genetic Correlations are Stronger for Substance Use than Dependence Vulnerability 2.1 Introduction Methods Participants Behavioral disinhibition measures Conduct disorder Substance measures Novelty seeking Data transformation Executive function tasks General procedure Inhibiting tasks Antisaccade Stop signal Stroop 23

9 viii Updating tasks Keep track Letter memory Spatial 2-back Shifting tasks Number-letter Color-shape Category switch Data transformation The twin design Statistical analyses General procedure Modeling Results Preliminary analyses Executive functioning Behavioral disinhibition Substance use vs. dependence vulnerability in behavioral disinhibition Behavioral disinhibition and the common executive function factor Behavioral disinhibition and the updating- and shifting-specific factors Discussion 51

10 ix 3 The Role of Executive Functioning in the Progression from Substance Use to Dependence 3.1 Introduction Methods Participants Measures Age-of-onset Problem use Dependence Executive function tasks Statistical analyses General procedure Modeling Results Substance stages Substance stages and common executive functioning Discussion 81 4 Using Items from the Tridimensional Personality Questionnaire to Assess Behavioral Disinhibition 4.1 Introduction General Methods Samples Community twin sample Selected family sample 88

11 x Participants Measures Personality assessment Behavioral disinhibition measures Study 1: Multivariate twin analysis of the Tridimensional Personality Questionnaire Methods Tridimensional Personality Questionnaire Data transformation Modeling Results Study 2: Exploratory factor analyses of disinhibitory personality Methods Disinhibitory personality Exploratory factor analyses Data preparation EFA specification Results Study 3: Disinhibitory personality in a second community sample Confirmatory factor analyses Disinhibitory personality, novelty seeking and behavioral disinhibition Methods Results 107

12 xi 4.6 Study 4: Disinhibitory personality in a case-control sample selected for antisocial substance dependence Discussion Summary and Conclusions 5.1 Introduction Summary of Results Chapter 2: Behavioral Disinhibition and Executive Functions: Genetic Correlations are Stronger for Substance Use than Dependence Vulnerability Chapter 3: The Role of Executive Functioning in the Progression from Substances Use to Substance Dependence Chapter 4: Using Items from the Tridimensional Personality Questionnaire to Assess Behavioral Disinhibition Conclusions 118 References 120

13 xii List of Tables Table 2.1 Ethnicity (N = 773) Participants with available data Conduct disorder symptoms in males and females Dependence symptoms by substance type Number of substances used repeatedly Number of participants using repeatedly by substance type Novelty seeking item endorsement by subscale Sex differences and age correlations for behavioral disinhibition measures Sex differences and age correlations for executive function tasks Descriptive information for executive function tasks Twin correlations and univariate results for behavioral disinhibition measures Phenotypic correlations among behavioral disinhibition measures Cross-twin cross-trait correlations for behavioral disinhibition measures Model comparison for behavioral disinhibition Standardized path coefficients for behavioral disinhibition independent pathway models Model fitting results for behavioral disinhibition with executive functioning Genetic correlations for behavioral disinhibition and executive functioning Age at which repeated users first tried a substance Age-of-onset by reporting age Age-of-onset for males and females Problem use for male and female users 61

14 xiii 3.5 Dependence for male and female users Number of complete twin pairs for problem use and dependence stages Twin concordance rates for substance stages Polychoric twin correlations and univariate results for substance stages Polychoric correlations among substance stages Cross-trait cross-twin correlations for substance stages Bivariate results for substance stages Model fitting results for common executive functioning and substance stages Genetic correlations between multi-substance stages and common executive functioning Genetic correlations between common executive functioning and substance-specific stages Sample information by study TPQ dimensions and subscales Reliability coefficients for TPQ dimensions Descriptive information for TPQ dimensions Sex difference and age correlations for TPQ dimensions Twin correlations and univariate results for TPQ dimensions Phenotypic correlations for TPQ dimensions Fit indices for independent pathway models Hypothesized subscales for disinhibitory personality Eigenvalues for the sample correlation matrix Model fit for one, four, and eight factors Factor loadings for the eight-factor solution 104

15 xiv 4.13 Factor intercorrelations from the eight-factor solutions Correlation estimates from the seven-factor confirmatory factor analysis Twin correlations and univariate results for novelty seeking and disinhibitory personality Correlations among personality dimensions and behavioral disinhibition measures Results for behavioral disinhibition measures regressed on personality dimensions 110

16 xv List of Figures Figure 2.1 Distributions of substance use and dependence vulnerability before and after log transformation Distributions of conduct disorder and novelty seeking before and after log transformation Distributions of inhibition tasks before and after log transformation Distributions of updating tasks before and after log transformation Distributions of shifting tasks before and after log transformation Univariate twin model Independent pathway model Common pathway model Full behavioral disinhibition and executive function model with substance use Full behavioral disinhibition and executive function model with dependence vulnerability Trivariate Cholesky model Example model with standardized path coefficients for executive functions Trivariate model for multi-substance use Standardized path coefficients for multi-substance stages with Common EF Standardized path coefficients for alcohol stages with Common EF Standardized path coefficients for tobacco stages with Common EF Standardized path coefficients for cannabis age-of-onset and Common EF Standardized path coefficients for the multivariate TPQ model Scree plot for EFA 102

17 1 CHAPTER 1 Introduction Antisocial behavior and substance use disorders are often comorbid in adults (Kessler, Chiu, Demler, & Walters, 2005) and adolescents (Armstrong & Costello, 2002; Bukstein, Brent, & Kaminer, 1989; Disney, Elkins, McGue, & Iacono, 1999). Furthermore, evidence suggests a common underlying liability to substance use disorders, antisocial behavior, and other behaviors such as risky sex. This liability is often referred to as behavioral disinhibition. Behavioral disinhibition has been described as a lack of control of response tendencies, such that immediate rewards are obtained at the expense of long-term gains (Gorenstein & Newman, 1980). Therefore, behavioral disinhibition likely includes personality traits and executive functions that are part of a bottom up mechanism of increased reward seeking, and a top down mechanism related to a lack of control (Iacono et al., 2008). While several studies have examined genetic and environmental influences on behavioral disinhibition, few have explored influences in common with the cognitive and personality traits thought to reflect behavioral disinhibition. The purpose of this dissertation is to better understand the nature of behavioral disinhibition. Twins reared together were used to examine genetic and environmental influences on individual differences in behavioral disinhibition, as well as related personality traits and executive functions. In addition, genetic and environmental influences on the dimensions of the Tridimensional Personality Questionnaire (TPQ) were explored, followed by the development of a new personality dimension characteristic of behavioral disinhibition. A better understanding of the genetic link between disinhibited behavior, personality, and cognition can advise research on the biological underpinnings of risky behaviors.

18 2 This chapter provides an overview of the quantitative-genetic literature on antisocial behavior and substance use disorders. This is followed by an examination of genetic influences on personality and executive functions that contribute to antisocial behavior and substance use disorders. Chapter 2 examines genetic correlations between behavioral disinhibition and a common executive functioning factor composed of inhibiting, updating and shifting tasks. Genetic correlations with updating- and shifting-specific factors are also explored. In addition, differences in genetic correlations were examined when dependence vulnerability versus substance use was included in the behavioral disinhibition construct. Chapter 3 follows up on the findings from Chapter 2 by focusing on the genetic covariance between executive functioning and substance use behavior. It explores whether executive functioning is differentially related to stages along the substance use trajectory. In Chapter 4 a multivariate analysis is used to determine if, in addition to specific influences, there are genetic and environmental influences common to the four dimensions of the TPQ (harm avoidance, novelty seeking, reward dependence, and persistence). Then items from the TPQ are used to create a personality dimension more reflective of behavioral disinhibition than the novelty seeking dimension, which has traditionally been used in studies of behavioral disinhibition. Lastly, Chapter 5 summarizes the results from all four studies and suggests how they can inform future research. 1.1 Genetic influences common to substance use disorders and antisocial behavior Evidence suggests there are common genetic vulnerabilities underlying substance use disorders and antisocial behavior. In a study of parent-offspring similarity, a factor characterized by antisocial personality disorder and substance use disorders was transmitted in families (Kendler, Davis, & Kessler, 1997). In a sample selected for antisocial drug dependence, transmittable family factors for antisocial personality symptom-counts and alcohol problems

19 3 were highly correlated (Stallings et al., 1997). A study using the same sample also found that relatives of probands were more likely to have conduct disorder, antisocial personality disorder and substance abuse than relatives of controls (Miles et al., 1998). These studies were consistent with common genetic influences for antisocial behavior and substance use disorders, but the possibility that the transmission was due to environmental factors could not be ruled out. In a genetically informative sample of adult twins reared apart, common genetic factors were shown to influence substance use problems and antisocial behavior (Grove et al., 1990). Similar results have been found in samples of adult twins reared together (Kendler, Prescott, Myers, & Neale, 2003; Pickens, Svikis, McGue, & LaBuda, 1995; Slutske et al., 1998). Research using our adolescent community twin sample has shown that the covariance between conduct disorder and a non-specific measure of dependence vulnerability can be explained by common genetic influences (35%), shared environmental influences (46%), and non-shared environmental influences (19%; Button et al., 2006). A follow-up study (Button et al., 2007) indicated that the genetic contribution to the comorbidity between alcohol dependence and illicit drug dependence was partially explained by the genetic influences they shared with conduct disorder. In a family study of adolescent twins and their parents, a highly heritable latent liability accounted for most of the familial resemblance in antisocial behavior and substance dependence (Hicks, Krueger, Iacono, McGue, & Patrick, 2004). Overall, the literature suggests common genetic factors contribute to the comorbidity between antisocial behavior and substance use problems in adults and adolescents. 1.2 Genetic effects on personality contribute to antisocial behaviors and substance use disorders Personality dimensions related to sensation seeking, impulsivity, behavioral undercontrol, and social non-compliance have been shown to predict substance problems in adults

20 4 (Jang, Vernon, & Livesley, 2000) and adolescents (Chassin, Flora, & King, 2004; Elkins, McGue, Malone, & Iacono, 2004; Grekin, Sher, & Woods, 2006; Krueger, 1999; Sher & Trull, 1994). Measures of disinhibited personality traits have also been included in studies on the genetic covariance between substance use disorders and antisocial behavior. Slutske and colleagues (2002) found that behavioral under-control (from the TPQ and Eysenck Personality Questionnaire) accounted for the majority of common genetic risk for alcohol dependence and conduct disorder. Genetic influences on the novelty seeking dimension of the TPQ accounted for some of the variance shared among two antisocial disorders (oppositional defiant disorder and conduct disorder) and attention deficit hyperactivity disorder (Hink et al., 2013). Finally, two studies reported heritable behavioral disinhibition factors which included novelty seeking (Young et al., 2000) and the constraint dimension of the Multidimensional Personality Questionnaire (Krueger et al., 2002). Interestingly, genetic influences on constraint (or lack thereof) were shown to contribute less to the variation in substance dependence symptom count with age (Vrieze, Hicks, Iacono, & McGue, 2012). This finding raises the question of whether genetic effects on disinhibited personality have less influence on the covariation between antisocial and substance measures with age. 1.3 Executive functions and behavioral disinhibition Cognitive under-control has been put forth as a component of behavioral disinhibition that increases liability for risky behaviors. Many studies have examined substance dependence in relation to cognitive tasks. Reviews of these studies have concluded that addicts often exhibit deficits in executive functions and that this is true for different types of substances (Hester, Lubman, & Yücel, 2010; Loeber et al., 2012; Montgomery, Fisk, Murphy, Ryland, & Hilton, 2012; Murphy et al., 2012). In most cases it was unclear whether these deficits occurred after

21 5 prolonged use or were present in individuals prior to their exposure to substances. Many studies have also examined the role of executive functions in conduct disorder and attention deficit hyperactivity disorder (e.g. Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). There are relatively few biometrical studies of executive functions as most twin analyses of cognition have focused on IQ. However a highly heritable common factor was shown to account for the covariance among three executive functions in adolescent twins (Friedman et al., 2008). Genetic influences specific to two executive functions, updating and shifting, indicated that these executive functions were also separable to some extent. Furthermore the executive function factors were shown to reflect variation independent of IQ and perceptual speed. One study examined the genetic relations between behavioral disinhibition and inhibition in adolescents (Young et al., 2009). Inhibition is a commonly studied executive function that represents the intentional control of pre-potent responses. In this study it was modeled as a latent factor, which consisted of three laboratory inhibition tasks. Behavioral disinhibition was also a latent factor representing variance shared among substance use, conduct disorder, attention deficit hyperactivity disorder and novelty seeking. Findings indicated that genetic influences on behavioral disinhibition were negatively correlated with inhibition in 12 year-olds (rg = -.60) and 17 year-olds (rg = -.61). 1.4 Summary Genetic influences contribute to the covariance between antisocial behavior and substance use disorders. Part of this covariance is accounted for by genetic influences on personality traits related to novelty seeking, impulsivity, and a lack of control. Antisocial behavior, substance use/dependence and disinhibited personality have been included in latent behavioral disinhibition factors, which have also been shown to be heritable. Furthermore, in an

22 6 adolescent sample genetic influences on behavioral disinhibition were correlated with genetic effects on inhibition (Young et al., 2009). This dissertation sought to address four questions. First, are genetic influences on behavioral disinhibition related to genetic influences on a common executive functioning factor, updating-specific factor, and shifting-specific factor? If so, do the relationships change if behavioral disinhibition consists of different substance measures: substance use versus dependence vulnerability? Third, are genetic influences on the common executive function factor associated with particular stages of substance use? Finally, are there items in the TPQ that better predict antisocial behavior and substance disorders than the novelty seeking dimension alone? By using genetically informative samples we can obtain a better understanding of the complex etiology of behavioral disinhibition, which can then inform studies on the biological underpinnings of risky behavior..

23 7 CHAPTER 2 Behavioral Disinhibition and Executive Functions: Genetic Correlations are Stronger for Substance Use than Dependence Vulnerability 2.1 Introduction Novelty seeking, impulsivity, a lack of persistence, sensitivity to reward, and insensitivity to punishment are thought to be elements of behavioral disinhibition that play a role in the development of antisocial behavior and substance use disorders. Another possible component includes cognitive under-control or poor executive functioning. Executive functions are cognitive processes important in controlled, organized, and goal-directed thoughts and behavior. As the term behavioral disinhibition implies, deficits in inhibition (the ability to stop prepotent responses) likely underlie both antisocial behavior and substance use disorders. This was supported by a twin study in which genetic influences on laboratory measures of inhibition were negatively correlated with genetic influences on behavioral disinhibition (age 12 r = -.60, age 17 r = -.61; Young et al., 2009). Inhibition, however, may simply be an effect of active maintenance of abstract information (such as goals) in the prefrontal cortex (PFC; Munakata et al., 2011). In a factor analysis of three commonly studied executive functions (inhibition, updating, and shifting), inhibition was entirely subsumed under a highly heritable factor common across all nine executive function tasks (Friedman et al., 2008). This common executive function factor (Common EF) may represent active maintenance in the PFC, as all of the executive function tasks required the ability to hold onto the goal and various contexts of the task. The primary aim of this study was to examine whether genetic influences on Common EF were related to genetic influences on behavioral disinhibition.

24 8 In addition to Common EF, the executive function model included updating- and shifting-specific factors (Friedman et al., 2008). Updating takes place when information in working memory is replaced with newer, more relevant information. Shifting occurs when an individual must disengage from one task to take part in another task. The second aim, therefore, was to explore the genetic and environmental relations between these factors and behavioral disinhibition. Lastly, this study examined whether the executive function factors were differentially correlated with a behavioral disinhibition construct that included substance use (Young, Stallings, & Corley, 2000; Young et al., 2009) and one that included substance dependence (Krueger et al., 2002). Behavioral disinhibition may be tapping different liabilities depending on whether substance use or dependence is included, and these liabilities may relate differently to executive functioning. 2.2 Methods Participants Participants were part of the Colorado Longitudinal Twin Study (LTS; Rhea, Gross, Haberstick, & Corley, 2006). Same-sex twins were identified through birth records and included in the study if they were born between 1984 and 1990, had a normal gestational period and birth weight, and lived within a three-hour drive from the Institute for Behavioral Genetics (Rhea et al., 2006). The current study included 773 adolescents: 205 monozygotic (MZ) twin pairs (110 female, 95 male) and 178 dizygotic (DZ) twin pairs (90 female, 88 male). Seven singletons (female: 1 MZ, 3 DZ; male: 2 MZ, 1 DZ) were included in descriptive analyses but did not contribute to the genetic analyses. Twins were mostly White (81.1%, see Table 2.1), which is

25 9 consistent with demographics of the state of Colorado. Hispanics represented the second most common ethnicity (10.0%). Table 2.1 Ethnicity (N = 773) n % White Hispanic American Indian/Alaska Native Native Hawaiian/Pacific Islander More than One Race Unknown Beginning at age 12, LTS twins were included in the Colorado Center for Antisocial Drug Dependence (CADD; PI: John K. Hewitt) funded by the National Institute on Drug Abuse (DA011015). At-home assessments included self-report questionnaires, a clinical interview, and zygosity judgment. Additionally, twins came into the laboratory to complete a battery of nine executive function tasks. Participants who completed the executive function tasks and a second assessment (the questionnaire or interview) between 16 and 18 years of age were included in the current study (N = 773). Table 2.2 shows the number of participants with data for each measure. Eighty-four percent had usable data for all fourteen measures. The average age at assessment was 17 for the interview/questionnaire (M = 17.19, SD = 0.52) and executive function tasks (M = 17.28, SD = 0.48). All participants gave informed consent (if 18 years) or assent (if 17 years or younger) prior to participation. Parents also provided informed consent for participants under age 18. The Institutional Review Board of the University of Colorado approved the study.

26 10 Table 2.2 Participants with available data n Personality (TPQ) 772 Substance use (CIDI SAM) 768 Conduct disorder DISC 747 DIS 24 Executive functions Antisaccade 754 Stop Signal 716 Stroop 734 Keep Track 749 Letter Memory 760 Spatial2back 752 Number-Letter 752 Color-Shape 743 Category Switch 743 Note. TPQ = Tridimensional Personality Questionnaire; CIDI SAM = Composite International Diagnostic Interview Substance Abuse Module; DISC = Diagnostic Interview Schedule for Children (DSM IV); DIS = Diagnostic Interview Schedule (DSM IV). Zygosity was initially determined from a nine-item questionnaire of physical characteristics (Nichols & Bilbro, 1966) in which 85% agreement from at least four raters was required. Ratings were later confirmed using 11 highly informative short tandem repeat (STR) genetic polymorphisms. Concordance across all polymorphisms between co-twins indicated MZ status, while discordant markers for members of a twin pair indicated their DZ origin. Senior staff resolved any discrepancies between the rater judgment and DNA calls, and resampled and genotyped the DNA if necessary Behavioral disinhibition measures Conduct disorder. Conduct disorder was assessed using the DSM IV versions of the Diagnostic Interview Schedule (DIS; Robins et al., 2000) and the Diagnostic Interview

27 11 Schedule for Children (DISC; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000). The DIS was administered to 18-year-olds who no longer lived with their parents (n = 24). All other participants completed the DISC and two-thirds were given a supplemental conduct disorder interview developed through the CADD. Fifteen symptoms, reflecting a range of externalizing behaviors, were scored from interview responses. Six symptoms required a minimum number of instances of the behavior in question, which the DISC and DIS assessed for the past year. In order to measure lifetime conduct disorder, items from the supplement were used to assess whether, depending on the symptom, a participant had ever met the minimum frequency or met the minimum in any one-year period. For example, a participant would meet criteria for bullying if he or she had bullied others five or more times in a one-year period. The most common symptom for participants assessed with the DISC was stealing without confronting the victim (see Table 2.3). For those assessed with the DIS, lying was most common. Participants were diagnosed with conduct disorder if they met DSM criteria for 3 of the 15 symptoms at some time in their life. Conduct disorder diagnoses were low for females (3.2%). The rate for males (10.4%) was consistent with U.S. population estimates (Nock, Kazdin, Hiripi, & Kessler, 2006). For this study conduct disorder was measured as lifetime symptom count. Although scores could potentially range from 0 to 15, scores in the twin sample ranged from 0 to 7 symptoms (M = 0.76, SD = 1.18) with 38% percent of females and 50% of males endorsing at least one symptom. Interview type (DISC vs. DIS) was not a significant predictor of conduct disorder scores, t(769) = 0.8, p =.937.

28 12 Table 2.3 Conduct disorder symptoms in males and females Females (n = 404) Males (n = 367) n % n % Bullies, threatens Initiates fights Fights or threatens with a weapon Physically cruel towards people Physically cruel towards animals Steals with confrontation Forces sex Sets fires Destroys property Breaking and entering Lies Steals without confrontation Stays out late Runs away Truant Substance measures. The Composite International Diagnostic Interview Substance Abuse Module (CIDI SAM; Robins, Cottler, & Babor, 1993), which employs DSM IV criteria, assesses alcohol, tobacco, marijuana, and eight categories of illicit drugs. If participants were not currently using, past substance use behavior was scored. Participants were diagnosed with substance dependence if they met DSM criteria for three or more symptoms for a particular substance. Thirteen percent of participants (14% males, 12% females) met criteria for lifetime dependence, which is consistent with U.S. population estimates (Compton, Thomas, Stinson, & Grant, 2007; Hasin, Stinson, Ogburn, & Grant, 2007; Young et al., 2002). As shown in Table 2.4, DSM IV symptoms are related to tolerance, withdrawal, increased time spent obtaining a substance, and continued use of a substance despite interference with important life activities (American Psychiatric Association, 1994). In the current sample the most common

29 13 symptoms were tolerance, taking larger amounts or over a longer period than was intended, and a persistent desire (or unsuccessful efforts) to cut down or control substance use. Among users, dependence symptoms across substances ranged from 0 to 23 (M = 2.23, SD = 3.88). Nearly onehalf of users (48%) endorsed at least one dependence symptom.

30 Table 2.4 Dependence symptoms by substance type Tolerance Withdrawal Larger amounts longer Unsuccessful quit attempts Increased time obtaining substance Important activities forgone Continued use despite problems Alcohol 63 14% 12 3% 93 21% 78 18% 37 8% 11 2% 24 5% Tobacco 59 13% 57 13% 59 13% % 67 15% 17 4% 22 5% Marijuana 35 8% 19 4% 21 5% 12 3% 21 5% 14 3% 53 12% Other Drugs 16 4% 12 3% 17 4% 6 1% 17 4% 8 2% 30 7% Note. Numbers and percentages are based on the number of users (n = 444). 14

31 15 Two substance variables were used in the analyses: substance use and dependence vulnerability. Substance use was measured as the number of substances used repeatedly as defined by the CIDI SAM. The current version of the CIDI SAM defines repeated use as smoking at least 20 cigarettes, using alcohol at least once, and using tobacco (pipe, cigar, or chewing tobacco) or illicit drugs six or more times. As shown in Table 2.5, the majority of participants reported repeated use for at least one substance (M = 1.08, SD = 1.32), and a substantial proportion reported poly-substance use, or use of two or more substances. Over onehalf of participants had used alcohol and one in five reported repeated use of tobacco and/or at least one illicit substance (see Table 2.6). Table 2.5 Number of substances used repeatedly n % Note. Percentages are based on the number of participants with substance use data (n = 768). Repeated use for illicit drugs is defined as using 6 or more times. Use for alcohol is defined as having ever used. For tobacco, repeated use consists of smoking a pipe or cigar 6 or more times, chewing tobacco 6 or more times, or smoking at least 20 cigarettes.

32 16 Table 2.6 Number of participants using repeatedly by substance type n % Alcohol Tobacco Cannabis Stimulants Sedatives Club Drugs Cocaine Opioids PCP Hallucinogens Inhalants Note. Repeated use for illicit drugs is defined as using 6 or more times. Use for alcohol is defined as having ever used. For tobacco, repeated use consists of smoking a pipe or cigar 6 or more times, chewing tobacco 6 or more times, or smoking at least 20 cigarettes. Dependence vulnerability was calculated by taking the total lifetime dependence symptom count across substances, divided by the number of substances used repeatedly. For example, a participant who used two substances repeatedly and had four dependence symptoms received a score of 2. Participants reporting no substance use were scored 0. Dependence vulnerability scores ranged from 0 to 5.33 (M = 0.47, SD = 0.96). Dependence vulnerability has been shown to be highly heritable and successful in discriminating community controls from cases with substance use disorders (Stallings et al., 2003) Novelty seeking. Participants completed the 18-item novelty seeking dimension from the short form (Cloninger, Przybeck, & Svrakic, 1991) of the Tridimensional Personality Questionnaire (TPQ; Cloninger, 1987). The true-false items assess exploratory and impulsive behavior. Endorsement (response = true) rates were between 20% and 80% for all but the following two items (see Table 2.7): I am slower than most people to get excited about new

33 17 ideas and activities (17.5%) and I like to stay at home better than to travel or explore new places (13.7%). Internal consistency (Cronbach s α =.72; female α =.75; male α = 069) fell within the range reported in psychometric studies (Kuo, Chih, Soong, Yang, & Chen, 2004; Otter, 2003; Sher, Wood, Crews, & Vandiver, 1995). Ten of the items were reverse-scored so higher scores indicated higher novelty seeking. As is common with self-report questionnaires, participants sometimes skipped items or circled both true and false. Therefore, scores were calculated as the mean number of items endorsed instead of the sum of endorsements. Because the items were coded as 0 and 1, mean novelty seeking scores were effectively the proportion of items endorsed for each participant. Participants were required to have answered 16 (~90%) of the items to be included in the analysis (n = 768). Novelty seeking scores ranged from.06 to 1.0 (M = 0.52, SD = 0.19).

34 18 Table 2.7 Novelty seeking item endorsement by subscale Exploratory Excitability 1 - I often try new things just for fun or thrills, even if most people think it is a waste of time. 2 - When nothing new is happening, I usually start looking for something that is thrilling or exciting. 3 - I am slower than most people to get excited about new ideas and activities. (R) 4- I like to stay at home better than to travel or explore new places. (R) All Females Males n % n % n % Impulsivity 5 - I like to think about things for a long time before I make a decision. (R) 6 - I often follow my instincts, hunches, or intuition without thinking through all the details. 7 - I usually think about all the facts in detail before I make a decision. (R) 8 - I nearly always think about all the facts in detail before I make a decision, even when other people demand a quick decision. (R) 9 - I hate to make decisions based on my first impressions. (R) Extravagance 10 - I am much more reserved and controlled than most people (R) 11 - I am better at saving money than most people. (R) I often spend money until I run out of cash or get into debt from using too much credit Because I often spend too much money on impulse, it is hard for me to save money even for special plans like a holiday I enjoy saving money more than spending it on entertainment or thrills. (R) Disorderliness 15 - I often do things based on how I feel at the moment without thinking about how they were done in the past I often break rules and regulations when I think I can get away with it I can usually do a good job at stretching the truth to tell a funnier story or to play a joke on someone I have trouble telling a lie, even when it is meant to spare someone else s feelings. (R) Note. Percentages are based on the available n for each item. R = reverse-scored.

35 Data transformation. Basic t tests were used to examine sex differences for all behavioral disinhibition measures (see Table 2.8). The dependence in the data was accounted for by weighting the scores of complete twin pairs by 0.5 and the scores of singletons by 1.0. On average males had more conduct disorder symptoms than females, which is consistent with the literature on adolescent antisocial behavior (Moffitt, 2001). Males also used more substances repeatedly than females. In a study of adolescents, some of whom overlapped with this sample, there were few differences between male and female rates of substance use and dependence (Young et al., 2002). However, substance use was defined as having ever used, which may explain the discrepancy in findings. Correlations between scores and age at test are also shown in Table 2.8. If age, or age and sex differences were observed, basic regression (within sex) was used to correct for age. When only sex differences were observed, scores were regressed on sex. Conduct disorder scores were corrected within instrument (DIS or DISC) and then combined to create one variable. Non-normal distributions were log transformed and re-standardized to obtain z scores with a mean of zero and a variance of one. Figure 2.1 and Figure 2.2 show distributions of the original and transformed variables.

36 20 Table 2.8 Sex differences and age correlations for behavioral disinhibition measures n M SD t-test (p) Age correlation (p) Conduct disorder DISC M a (<.001).143 (.010) F (.458) Conduct disorder DIS M F Substance use M a (.014).280 (<.001) F (<.001) Dependence vulnerability M (.170).244 (<.001) F (<.001) Novelty seeking M (.393).055 (.297) F (.445) Note. P values are based on tests of means and correlations where the dependence in the data was accounted for, not the actual means and correlations shown. There were not enough participants with DIS conduct disorder scores for meaningful test statistics. M = males; F = females; DISC = Diagnostic Interview Schedule for Children; DIS = Diagnostic Interview Schedule; Age = age at test. a Equal variances could not be assumed under Levene s Test for Equality of Variances; separate variance t-tests were utilized.

37 21 Figure 2.1. Distributions of substance use and dependence vulnerability before and after log transformation a. b. Figure 2.1. Age and sex corrected substance use (a.) and dependence vulnerability (b.).

38 22 Figure 2.2. Distributions of conduct disorder and novelty seeking before and after log transformation a. b. Figure 2.2. Age and sex corrected conduct disorder (a.) and novelty seeking (b.) Executive function tasks General procedure. Participants came into the laboratory and completed nine tasks in PsyScope (Cohen, MacWhinney, Flatt, & Provost, 1993). Inhibition, updating, and shifting were assessed with three tasks each. Stimuli were counterbalanced and randomized, and the order of stimuli within tasks was the same for all participants. Practice trials were included to

39 23 ensure participants understood instructions. Reaction times (RT) were measured using a button box with millisecond accuracy. A headset was attached to the button box to record RTs for verbal responses. Descriptions of each task are provided below (for more details, see Friedman et al., 2008) Inhibiting tasks Antisaccade. Participants were required to override their natural tendency to look at a black square and instead direct their attention to a target stimulus (arrow) and report the direction of the arrow (up, left, or right) (adapted from Roberts, Hager, & Heron, 1994). After 175 ms the target was masked with gray cross-hatching until the participant responded. There were 22 practice trials and 90 target trials. Scores were measured as the proportion of correct responses with higher scores indicating higher inhibition Stop signal. Participants categorized words as either animals or non-animals as quickly as possible (Logan, 1994). During trials with an auditory signal, participants were instructed to withhold their response. The first 48 trials were used to build up a pre-potent categorization response and calculate each participant s average RT. In the remaining four blocks (96 trials each) a signal (tone approximately 100 ms in duration) was emitted on 25% of trials. For each participant the signal occurred equally often at three time points: 225 ms before his or her average RT (long stop-signal delay), 50 ms before his or her average RT (medium stop-signal delay), or 50 ms after the onset of the trial (short stop-signal delay). The dependent measure was the estimated time at which the stopping process finished (averaged across trials), or the stop-signal RT. Slower RTs indicate lower executive functioning Stroop. Participants named the color of the stimulus (string of asterisks, color word, or neutral word) as quickly as possible (Stroop, 1935). Asterisks were in six different

40 24 colors (red, green, blue, orange, yellow, or purple) and were of variable length (corresponding to the lengths of the color words). Color words were sometimes in a different color (e.g. GREEN presented in blue). After 18 practice trials, there were 60 trials each of asterisks, color words, and neutral words. Neutral word trials were not used in the present analysis. The dependent measure was the reaction time difference between trials with asterisks and trials where the word and color were incongruent. This provides an index of inhibition, as participants are required to inhibit their natural tendency to read the word and instead report the color of the word. The RTs for asterisk trials were subtracted to adjust for individual differences in reaction time Updating tasks Keep track. During each trial of the keep track task (adapted from Yntema, 1963) participants were presented with 15 words, one at time, while a list of categories remained at the bottom of the screen. At the end of each trial, participants were asked to report the last word that was presented for each of the categories. Participants were required to recall words for two, three, or four categories, thus creating three levels of difficulty. Before the task, participants were shown the words belonging to each of six categories (animals, colors, countries, distances, metals, and relatives). After three practice trials, participants completed four trials of each difficulty level (12 trials for a total of 36 words). Scores were measured as the proportion of correctly recalled words, with higher scores indicating higher executive functioning Letter memory. In each trial of the letter memory task (adapted from Morris & Jones, 1990) five, seven, or nine letters were presented one at a time and participants were instructed to say the last three letters out loud. Thus, before saying the new list of three letters participants were required to mentally drop the fourth letter back and add the most recent letter. Participants were not required to say the letters in order, just recall the last three letters. There

41 25 were three practice trials and 12 trials (four of each length). Due to a programing error only the first 10 trials were scored. The proportion of all letters correctly recalled was the dependent variable Spatial 2-back. Ten squares were scattered across the screen (Friedman et al., 2008). One at a time boxes appeared to flash (24 flashes per block). Participants reported if the square that flashed was the same square that had flashed two trials earlier. Four blocks followed a practice block. The proportion of correct responses (yes or no) was the dependent measure Shifting tasks. For all three shifting tasks a cue indicating which subtask to perform was presented prior to the stimulus onset. The dependent measure for all switching tasks was the switch cost, or difference between the average RT for trials that required a switch and the average RT for noswitch trials. Smaller RT differences (switch costs) were indicative of higher executive functioning. The RTs immediately following trials with errors were excluded. Each task had four blocks consisting of 24 switch and 24 no-switch trials. In addition there were two practice blocks and six warm-up trials at the beginning of each actual block. The cue was presented 150 ms (block 1 and 3) or 1500 ms (block 2 and 4) prior to the stimulus onset. Only the first and third blocks were scored Number-letter. The cue was the appearance of a box either above or below a line dividing the computer screen in half. A number-letter pair then appeared in the box. When the box was above the line, participants were required to specify whether the number was odd or even. When the box was below the line, participants specified whether the letter was a consonant or a vowel (adapted from Rogers & Monsell, 1995).

42 Color-shape. In each trial a colored rectangle with either a circle or a triangle inside was presented along with a cue (letter C or S above the rectangle; Miyake, Emerson, Padilla, & Ahn, 2004). Participants indicated whether the shape in the rectangle was a circle or a triangle when the cue was S and whether the color of the rectangle was green or red when the cue was C Category switch. When the symbol above the word on the screen was a heart, participants specified whether the word could be described as living or nonliving (adapted from Mayr & Kliegl, 2000). When the symbol was an arrow cross, participants specified whether the word represented something that is smaller or larger than a soccer ball. There were 16 words: table, bicycle, coat, cloud, pebble, knob, marble, snowflake, shark, lion, oak, alligator, mushroom, sparrow, goldfish, and lizard Data transformation. To improve normality of executive function task data, all accuracy data was arcsine transformed and observations three or more standard deviations from the mean were replaced with the value at three standard deviations. Reaction time errors and all RTs below 200 ms were eliminated. Then a within-subject trimming procedure (Wilcox & Keselman, 2003) was applied and observations that were 3.32 times above or below the median value were excluded. Finally, RT measures were reversed so higher scores reflected better performance. Like the behavioral disinhibition measures, executive function scores were corrected for sex differences and age at test using basic regression. On average, females scored significantly higher than males on the stop signal, stroop, number-letter and color-shape tasks (see Table 2.9). Males scored higher on the antisaccade and keep track tasks. Age correlations varied among tasks and between males and females. Non-normal distributions were logtransformed and re-standardized. Figures 2.3 through 2.5 show distributions of the original and

43 27 log-transformed variables for inhibition, updating, and shifting tasks, respectively. Table 2.9 Sex differences and age correlations for executive function tasks n M SD t test (p) Age correlation (p) Antisaccade M (<.001) (.001) F (.479) Stop signal a M b (.010).118 (.000) F (.401) Stroop a M b (<.001).021 (.021) F (.905) Keep track M b (.030) (.008) F (.621) Letter memory M (.947) (.001) F (.880) Spatial 2-back M b (.495) (.052) F (.029) Number-letter a M b (.010) (.603) F (.254) Color-shape a M b (.002) (.808) F (.383) Category switch a M (.237) (.302) F (.310) Note. P values are based on tests of means and correlations where the dependence in the data was accounted for, not the actual means shown. M = males; F = females. a Lower means indicate better performance. b Equal variances could not be assumed under Levene s Test for Equality of Variances; separate variance t-tests were utilized.

44 28 Figure 2.3. Distributions of inhibition tasks before and after log transformation a. b. c. Figure 2.3. Age- and sex-corrected antisaccade (a.), stop signal (b.) and stroop (c.). Because stop signal and stroop were measured using RTs, they were reverse scored so higher scores indicated better executive functioning.

45 29 Figure 2.4. Distributions of updating tasks before and after log transformation a. b. c. Figure 2.4. Age- and sex-corrected keep track (a.), letter-memory (b.) and spatial 2-back (c.).

46 30 Figure 2.5. Distributions of shifting tasks before and after log transformation a. b. c. Figure 2.5. Age- and sex-corrected number-letter (a.), color-shape (b.) and category switch (c.). Because all tasks were measured using RTs, they were reverse-scored so higher scores indicated better executive functioning.

47 The twin design Genetic analyses were conducted using the classic twin design, which uses structural equation modeling to compare the trait resemblance (or covariance) of MZ and DZ twins reared together (for a good tutorial on the twin design see Neale & Cardon, 1992). With the classic twin design the phenotypic variance of a trait can be divided into genetic and environmental variance. When the MZ trait correlation is greater than the DZ correlation, additive genetic effects are implicated. For complex traits additive genetic effects (A) include the effects of many genes, whose alleles act in an additive manner. In the model, the additive genetic correlation is set to 1.0 for MZ twins because they share all of their genes (see Figure 2.6). The genetic correlation for DZ twins is set to.5 because they share half of the additive effects of their segregating genes on average. When the DZ correlation is less than half the MZ correlation there is evidence that nonadditive genetic effects are operating in addition to additive genetic influences. Non-additive genetic effects include interactions between alleles at a given locus. Based on biometrical principles, the non-additive genetic correlations are set to 1.0 for MZ twins and.25 for DZ twins.

48 32 Figure 2.6. Univariate twin model Figure 2.6. A = additive genetic influences; C = shared environmental influences; D = nonadditive genetic influences; E = non-shared environmental influences; rmz = monozygotic twin correlation; rdz = dizygotic twin correlation. Variance not accounted for by genetics is due to the environment and measurement error. In the classic twin design the environment is not directly measured. Rather, the influence of the environment is statistically estimated. When the DZ trait correlation is greater than half the MZ correlation, shared environmental effects (C) are implicated. By definition, the shared environment is the environment experienced by both twins that makes them more similar. It can include influences such as family, nutrition, and peer groups. For the shared environment, twin correlations are set to 1.0 for both MZ and DZ twins (see Figure 2.6). Importantly, D and C may

49 33 both influence a trait. However, with just the MZ and DZ covariances only one can be estimated at a time. For complex traits, it is biologically implausible to have D without A (e.g. DCE; Eaves, 1988), so the relationship between the MZ and DZ correlation (described above) is used to decide whether to estimate D or C. The non-shared environmental variance (E) can be calculated by subtracting the MZ covariance from the phenotypic variance. In other words, when the MZ correlation is less than one, non-shared environment is implicated. The non-shared environment includes environments unique to each twin and measurement error, both of which contribute to the dissimilarity of twins. Thus, twin correlations for the non-shared environment are set to 0 in the model (see Figure 2.6). Multivariate models are an extension of univariate models in which the variance and covariation among variables are examined. The multivariate twin model is a direct extension of the basic twin model (see Neale & Cardon, 1992). The extent to which variables share genetic and environmental effects is estimated, as well as influences that are unique to each variable. Multivariate models are useful for understanding the complex etiology of comorbid behaviors Statistical analyses General procedure. Descriptive statistics were obtained with SPSS version 21 (IBM Corp, 2012). Structural equation modeling using the statistical programs Mx (Neale, Boker, Xie, & Maes, 2003) and Mplus (Muthén & Muthén, ) was used to decompose the variance of behavioral disinhibition, executive functioning, and their covariance into genetic and environmental sources. Parameters were calculated using maximum likelihood estimation (ML). Both Mx and Mplus can use raw data files and accommodate missing data (instead of requiring covariance matrices with complete data). This allowed for the maximum use of data for

50 34 each variable. For example, if a twin pair was missing executive functioning data their information was still used to decompose the variance of behavioral disinhibition into genetic and environmental sources. The number of complete twin pairs ranged from 334 to 371 for the executive function tasks and 378 to 381 for the behavioral disinhibition measures. Consistent with most twin studies MZ pairs and female pairs were over-represented. On average there were 24 more MZ than DZ pairs (SD = 6.61) and 24 more female than male pairs (SD = 9.05). Each variable was examined for outlying twin pairs using scatterplots for twin 1 and twin 2 scores. No extreme outliers were observed. Twin 1 and twin 2 variances were also examined (by zygosity and sex) for each variable. When twin 1 variances differ greatly from twin 2 variances there may be a systematic bias in twin-number designation. When such biases occur twin-number can be a confounding variable. Variances were around 1.0 because all variables were standardized. Differences between twin 1 and twin 2 variances ranged from 0.01 to 0.26 for behavioral disinhibition measures (M = 0.11, SD = 0.07) and to for executive function tasks (M = 0.190, SD = 0.102). Therefore, bias due to twin number designation was not of concern Modeling. An advantage of structural equation modeling is its use of latent variables, which make it possible to examine the underlying construct of interest. Latent variables represent only the variance that is shared among variables. For example, the keep track, letter memory and spatial 2-back tasks are thought to require a common cognitive process (updating information in working memory). Variances specific to each measure are modeled as well. The specific variance for an executive function task, for example, may contain measurement error and cognitive processes unique to the task (e.g. visual or auditory processing).

51 35 Miyake et al. (2000) used this latent variable approach in an individual differences study of executive functioning. Their results suggested that three of the commonly studied executive functions (inhibition, updating, and shifting) were separate but correlated constructs. My colleagues followed up on these findings by using genetic analyses to examine why there was variation common and specific to the three executive functions (Friedman et al., 2008). A highly heritable common factor (Common EF) explained the covariance among the three types of executive functions. Genetic influences specific to the updating and shifting factors indicated that there was also variation common to the tasks over and above Common EF. Interestingly, variance in the inhibition tasks was entirely accounted for by Common EF. These findings were represented in a nested factors model, which was used for the executive function component of this study (see Figure 2.9, p. 47). The main goal of this study was to use genetic correlations to examine whether the behavioral disinhibition and the executive function factors (common and specific) shared genetic influences. Then the difference in the proportion of genetic influences shared with executive functioning was examined when behavioral disinhibition included dependence vulnerability versus substance use. Prior to the above analyses the genetic and environmental structure of behavioral disinhibition was examined. An independent pathway model was implemented, followed by a common factor model. In an independent pathway model part of the covariation among variables is explained by a specified number of genetic and environmental factors (see Figure 2.7). Each factor has a unique (independent) path to each variable. Therefore, the effects of A (or C, D, or E) on two variables may differ in magnitude and direction (positive vs. negative). Factors specific to each variable are also estimated. The common pathway model is a more constrained

52 36 version of the independent pathway model in which a phenotypic latent factor (e.g. behavioral disinhibition) explains the covariation among variables (see Figure 2.8). Estimates of genetic and environmental influences on the latent factor and influences specific to each variable are obtained. Figure 2.7. Independent pathway model Figure 2.7. Only twin 1 is depicted. CD = conduct disorder; SU = substance use; DV = dependence vulnerability; NS = novelty seeking; A = additive genetic influences; C = shared environmental influences; E = non-shared environmental influences.

53 37 Figure 2.8. Common pathway model Figure 2.8. Only twin 1 is depicted. CD = conduct disorder; SU = substance use; DV = dependence vulnerability; NS = novelty seeking; A = additive genetic influences; C = shared environmental influences; E = non-shared environmental influences. In all models the variance of the genetic and environmental latent variables was set to one so that the variance components could be obtained by squaring the standardized path coefficients. Information from twin correlations was used to specify the initial models. In followup models non-significant As and Cs were set to zero and the model fit was calculated to determine if their exclusion resulted in a significant decrement in fit. To examine the fit of the models, three goodness-of-fit indices were used: Akaike s information criteria (AIC; Akaike, 1987), the root-mean-square error of approximation (RMSEA; Steiger & Lind, 1980), and the

54 38 Tucker-Lewis index (TLI; Tucker & Lewis, 1973). For AIC, larger negative values indicate better fit. The RMSEA and TLI are good indicators of fit with complex multivariate models because they take into account the degrees of freedom of the model (Hu & Bentler, 1998). An RMSEA <.06 and TL1 >.95 indicate an adequate fit of the model. When comparing the relative fit of nested models, χ 2 difference tests were used. P-values less than.05 indicated significantly worse fit of a reduced/constrained model compared to the original model. 2.3 Results Preliminary analyses Executive functioning. Because over 75% of participants with executive functioning data were used in our prior study of the genetics of executive functions (Friedman et al., 2008), it was important that results for the tasks be similar. Task means and distributions (prior to sex- and age-correction) were consistent with those reported earlier (see Table 2.10). Genetic influences accounted for nearly all the variation in the common executive function factor (96%) and the updating factor (100%; see Figure 2.9, p. 47). Genetic influences explained 71% of the variance in the shifting factor, with non-shared environmental influences accounting for the rest. Specific variance components were also consistent with Friedman et al. (2008) and the standardized individual factor loadings were within at least 0.08.

55 39 Table 2.10 Descriptive information for executive function tasks n M SD Min Max Skew Kurtosis Antisaccade a Stop signal ms Stroop ms Keep track a Letter memory a Spatial 2-back a Number-letter ms Color-shape ms Category switch ms Note. The descriptive information followed trimming procedures (see Data Transformation) but was prior to age- and sex-correction. a Accuracy scores were arcsine transformed Behavioral disinhibition. Monozygotic twin correlations were consistently higher than DZ correlations, indicating genetic influences (see Table 2.11). Dizygotic correlations greater than half the MZ correlations suggested shared-environmental influences on conduct disorder, substance use, and dependence vulnerability. There was evidence for nonadditive genetic influences on novelty seeking. Non-additive effects have been reported for many measures of personality (e.g. Eaves, Heath, Neale, Hewitt, & Martin, 1998; Loehlin, 1992), including novelty seeking (Heiman, Stallings, Young, & Hewitt, 2004; Keller, Coventry, Heath, & Martin, 2005). Variance components and the model fit statistics for the best-fitting univariate models are also shown in Table An AE model adequately described conduct disorder, while the inclusion of shared environmental influences was necessary for the substance measures. This supports many studies in which the shared environment explained a substantial portion of the variance in adolescents (Stallings, Gizer, & Young-Wolff, in press). Correlations suggested a DE model for novelty seeking. Because a DE model is not plausible, an AE model

56 40 was used with the understanding that variance explained by non-additive genetic influences would go into the additive variance component. Table 2.11 Twin correlations and univariate results for behavioral disinhibition measures Females Males Variance Components Model Fit MZ DZ MZ DZ A C E -2LL AIC RMSEA TLI CD SU DV NS Note. CD = conduct disorder; SU = substance use; DV = dependence vulnerability; NS = novelty seeking; MZ = monozygotic; DZ = dizygotic; A = additive genetic; C = shared environment; E = non-shared environment; -2LL = -2 log likelihood; AIC = Akaike s information criteria; RMSEA = Root mean square error approximation; TLI = Tucker Lewis index. It is important to note that the DZ twin correlation for novelty seeking was essentially zero. This suggested that factors other than dominance or additive-additive epistasis influenced this measure. It is possible that complex epistatic interactions occurred, which this study was unable to examine due to the nature of the classic twin design. Also, special MZ twin environments could account for the large difference between the MZ and DZ correlation. Again, this hypothesis could not be tested with the classic twin design. All behavioral disinhibition measures were significantly correlated (see Table 2.12) which supports the implementation of a multivariate model. Novelty seeking had the weakest relationship with the other measures and, as expected, substance use and dependence vulnerability were highly correlated (r =.636). Similar correlation patterns were observed for both males and females. Table 2.13 shows cross-trait cross-twin correlations, which were used to

57 41 determine if the observed covariation was partly due to genetic factors. Higher MZ than DZ correlations indicated genetic covariance between conduct disorder and dependence vulnerability. Correlations among the remaining measures were inconsistent as one twin1/twin2 pairing was higher in MZs while the correlation for the other twin1/twin2 pairing was not. Table 2.12 Phenotypic correlations among behavioral disinhibition measures CD SU DV NS All CD SU.484 DV NS By Sex* CD SU DV NS Note. All correlations are significant at the.01 level (two-tailed) after accounting for dependence in the data (weighted.5 for complete pairs, 1.0 for singletons). CD = conduct disorder; SU = substance use; DV = dependence vulnerability; NS = novelty seeking. *Males and females are above and below the diagonal, respectively.

58 42 Table 2.13 Cross-twin cross-trait correlations for behavioral disinhibition measures MZ Twins DZ Twins CD1 SU1 DV1 NS1 CD1 SU1 DV1 NS1 CD SU DV NS Note. Twin correlations for single traits are on the diagonal in boldface type. Cross-trait crosstwin correlations are on the off diagonals. MZ = monozygotic; DZ = dizygotic; CD = conduct disorder; SU = substance use; DV = dependence vulnerability; NS = novelty seeking. Model fitting results for behavioral disinhibition are shown in Table Because twin correlations for novelty seeking suggested a DE model, a D factor specific to novelty seeking was included. It was not possible to estimate D in the univariate model. Here, however, additive genetic influences (shared with the other measures) were also operating. When the D specific factor was excluded there was no decrement in fit. Therefore it was eliminated in all subsequent models. All models fit significantly worse when the C factor on behavioral disinhibition was dropped, which supported an ACE model. The ACE independent pathway models fit slightly better then the ACE common pathway models. This may suggest that influences shared among substance use behavior, antisocial behavior, and related traits may operate differentially instead of through a common mechanism like behavioral disinhibition. The differences in fit were minor, however. So for completeness, executive functioning was also modeled with behavioral disinhibition characterized by a common pathway model.

59 Table 2.14 Model comparison for behavioral disinhibition Model -2LL df AIC χ 2 df p TLI RMSEA BD SU IP ACDE* ACE AE < CP ACE < BD DV IP ACDE* ACE AE CP ACE < Note. The best-fitting models are indicated in boldface type. The ACE common pathway models were nested under the best-fitting independent pathway models. BD SU = behavioral disinhibition with substance use; BD DV = behavioral disinhibition with dependence vulnerability; IP = independent pathway; CP = common pathway; A = additive genetic component; C = shared environmental component; E = nonshared environmental component; -2LL = -2 log likelihood; AIC = Akaike s information criteria; χ 2 = chi-square difference test; TLI = Tucker Lewis index; RMSEA = Root mean square error approximation. *Non-additive (D) specific effect on novelty seeking. 43

60 Substance use vs. dependence vulnerability in behavioral disinhibition For the independent pathway portion of the full models (with executive functioning) the additive genetic factor loaded highest on substance use and dependence vulnerability, followed by conduct disorder and novelty seeking (see Table 2.15). All loadings were positive suggesting that genetic influences were not acting on the variables in an opposite matter. The additive genetic factor loaded higher on conduct disorder when dependence vulnerability was modeled than when substance use was incorporated. However this difference was questionable as confidence intervals for these estimates overlapped. The full models incorporating the independent pathway structure were modeled in the statistical program Mplus. It is important to note that in this program the residual variance was obtained instead of ACEs specific to each variable.

61 Table 2.15 Standardized path coefficients for behavioral disinhibition independent pathway models Model Variable a c e Residual variance BD SU CD.28 [.08,.47].44 [.30,.58].25 [.06,.44].67 [.56,.77] SU.53 [.38,.68].69 [.58,.79].14 [.03,.25].23 [.18,.27 NS.32 [.16,.48].55 [.21,.90].59 [.21,.97] BD DV CD.40 [.22,.58] DV.55 [.38,.73] NS.39 [.20,.58].36 [.18,.53].65 [.50, 80].18 [-.49,.86].02 [-.08,.12].59 [-1.5, 2.7].69 [.45,.93].30 [.24,.35].56 [-2.0, 2.9] Note. Estimates are from the behavioral disinhibition portion of the full models with executive functioning. Boldface type indicates significance at the.05 level (two-tailed). Values in brackets are the 95% confidence intervals. BD SU = behavioral disinhibition with substance use; BD DV = behavioral disinhibition with dependence vulnerability; CD = conduct disorder; SU = substance use; DV = dependence vulnerability; NS = novelty seeking; a = additive genetic; c = shared environment; e = non-shared environment. 45

62 46 Figure 2.9 shows executive functioning and behavioral disinhibition with substance use. The results for behavioral disinhibition with dependence vulnerability are presented in Figure The behavioral disinhibition factor was more heritable when dependence vulnerability (h 2 =.79) was used than when substance use (h 2 =.28) was included. An opposite pattern was observed for the proportion of variance explained by the shared environment. Substance use loaded higher (.83) on behavioral disinhibition than dependence variability (.64). The loading for conduct disorder was higher when dependence vulnerability was included in the model (.69) than when substance use was used (.59), which supported findings from the independent pathway model.

63 Figure 2.9. Full behavioral disinhibition and executive function model with substance use % 59% 13% 96% 0% 4% 100% 0% 0% 71% 0% 29% A C E A C E A C E A C E BD Common EF Updating Specific Shifting Specific CD SU NS Anti Stop Stroop Keep Letter S2ba Num Col Cat A C E A C E A E A C E A C E A C E A C E A C E A C E A C E A C E A C E Figure 2.9. Numbers on arrows and underneath the lower ACEs are standardized path coefficients. Proportions of variance of the latent variables are represented as percentages. Double-headed arrows are correlation coefficients. Solid lines indicate p <.05. Dashed lines indicate non-significance (p >.05). BD = behavioral disinhibition; CD = conduct disorder; SU = substance use; NS = novelty seeking; Common EF = common executive function factor; Anti = antisaccade; Stop = stop signal; Keep = keep track; Letter = letter memory; S2ba = spatial-2back; Num = number-letter; Col = color-shape; Cat = category switch. 47

64 Figure Full behavioral disinhibition and executive function model with dependence vulnerability % 19% 2% A C E 96% 0% 4% A C E 100% 0% 0% A C E 70% 0% 30% A C E BD Common EF Updating Specific Shifting Specific CD DV NS Anti Stop Stroop Keep Letter S2ba Num Col Cat A C E A C E A E A C E A C E A C E A C E A C E A C E A C E A C E A C E Figure Numbers on arrows and underneath the lower ACEs are standardized path coefficients. Proportions of variance of the latent variables are represented as percentages. Double-headed arrows are correlation coefficients. Solid lines indicate p <.05. Dashed lines indicate non-significance (p >.05). BD = behavioral disinhibition; CD = conduct disorder; DV = dependence vulnerability; NS = novelty seeking; Common EF = common executive function factor; Anti = antisaccade; Stop = stop signal; Keep = keep track; Letter = letter memory; S2ba = spatial 2back; Num = number-letter; Col = color-shape; Cat = category switch. 48

65 Behavioral disinhibition and the common executive function factor For the full models with behavioral disinhibition and executive functioning, RMSEA indicated adequate fit, while TLI suggested a somewhat poorer fit (see Table 2.16). When substance use was included there was a significant genetic correlation between the common executive function factor and behavioral disinhibition (rg = -.54, see Figure 2.9). Again, the results for behavioral disinhibition with dependence vulnerability are presented in Figure Although genetic effects accounted for more of the variance in behavioral disinhibition (79%) the genetic correlation with common executive functioning was smaller (rg = -.23). The difference between the two correlations was not significant, χ 2 = 3.78, df = 1, p =.052. This difference in genetic correlations was also observed when behavioral disinhibition was structured as an independent pathway model (see Table 2.17). The negative direction of the correlations suggested that genetic effects contributing to disinhibited behavior were related to genetic effects that conferred poor executive functioning. And this was especially the case when the behavioral disinhibition measure included substance use instead of dependence vulnerability.

66 50 Table 2.16 Model fitting results for behavioral disinhibition with executive functioning Model 2LL χ 2 df p AIC TLI RMSEA CP: BD SU CP: BD DV IP: BD SU < IP: BD DV * < SU DV Note. CP = common pathway; IP = independent pathway; BD SU = behavioral disinhibition with substance use; BD DV = behavioral disinhibition with dependence vulnerability; SU = substance use; DV = dependence vulnerability; -2LL = -2 log likelihood; χ 2 = chi-square test of model fit; AIC = Akaike s information criteria; TLI = Tucker Lewis index; RMSEA = Root mean square error approximation. *The residuals for novelty seeking could not be equated so four parameters were estimated (MZ- NS1, MZ-NS2, DZ-NS1, DZ-NS2) instead of one (NS). Table 2.17 Genetic correlations for behavioral disinhibition and executive functioning Common EF Updating Shifting CP: BD-SU CP: BD-DV IP: BD-SU IP: BD-DV SU DV Note. Boldface type indicates significance at the.05 level. CP = common pathway model; IP = independent pathway model; BD SU = behavioral disinhibition with substance use; BD DV = behavioral disinhibition with dependence vulnerability; SU = substance use; DV = dependence vulnerability; EF = executive functioning.

67 51 To examine whether substance use and dependence vulnerability were indeed driving the difference, genetic correlations were obtained between common executive functioning and each of the substance variables alone (see Table 2.17). The genetic correlation was significantly larger for substance use (rg = -.43) than dependence vulnerability (rg = -.17), χ 2 = 4.52, df = 1, p =.034. Therefore, results were consistent with the idea that the type of substance measure included in behavioral disinhibition was largely responsible for different genetic correlations with Common EF Behavioral disinhibition and the updating- and shifting-specific factors For the full models with behavioral disinhibition structured as both a common pathway and an independent pathway, genetic correlations with the updating-specific factor ranged from -.04 to.13 (see Table 2.17). Genetic correlations were similar when just substance use or dependence vulnerability was included in the model. All estimates were non-significant. For most models genetic correlations were slightly higher with shifting, but still non-significant. Although the shifting factor had measurable non-shared environmental influences (E.30), they were not significantly correlated with non-shared environmental influences on behavioral disinhibition or on the individual substance measures. 2.4 Discussion This chapter used biometrical analyses of adolescent twins from a nonclinical sample to examine (a) the genetic relationships between behavioral disinhibition and factors common and specific to executive functions, and (b) how these relationships changed when measures of substance use versus dependence vulnerability were used in the behavioral disinhibition construct.

68 52 First, multivariate twin analyses were used to examine the structure of behavioral disinhibition with either substance use or dependence vulnerability. Fit indices indicated better model fit for the independent pathway models. The magnitude of additive genetic effects on the substance measures was similar. Conduct disorder loaded highest on (a) the genetic factor when dependence vulnerability was included, and (b) the shared environmental factor when behavioral disinhibition contained substance use. Variance in behavioral disinhibition modeled with substance use may reflect aspects of antisocial behavior that are influenced by the shared environment. For example, peer pressure. Whereas behavioral disinhibition modeled with dependence vulnerability may represent antisocial behavior more highly influenced by genetics. When behavioral disinhibition was modeled as a latent construct (common pathway model), heritability was higher when dependence vulnerability was used and sharedenvironmental effects were higher when substance use was included. These findings suggest that the etiology of behavioral disinhibition is complex and it may reflect somewhat different processes depending on the type of substance measure used. Research on the decision to try substances and on the role of the shared environment in behavioral disinhibition may benefit from using a measure of substance initiation or substance use. On the other hand, substance problems or dependence may be more appropriate if a maximally heritable behavioral disinhibition construct is desired. For both representations of behavioral disinhibition, there were no significant genetic or environmental correlations with the updating- and shifting-specific factors. This indicated that the variance shared among updating tasks and among shifting tasks (controlling for variance in common with all executive function tasks) was not related to individual differences in behavioral disinhibition. For executive functioning common across inhibition, updating, and shifting

69 53 (Common EF), negative genetic correlations were observed. Despite lower heritability, genetic influences on behavioral disinhibition with substance use were more highly correlated with Common EF. The same pattern was observed when substance use and dependence vulnerability were each modeled separately with executive functioning. These results suggest that individual differences or deficits in Common EF may be more important for behaviors relating to the initiation and regular use of substances than for the development of problem use and/or dependence. Genetic influences shared with executive functions likely play some role in the transition from regular use to dependence. However, given our findings of a smaller genetic correlation with the more highly heritable dependence vulnerability, their contribution to substance dependence variation may be less than that for other genetic factors. For example, evidence suggests homeostasis in the reward system is altered with heavy substance use (Koob & Le Moal, 2001), and individual differences in susceptibility to such changes may contribute more to substance dependence variance than genetic influences in common with executive functioning. Long term substance use has also been shown to alter brain circuitry associated with executive functioning (George & Koob, 2010; Goldstein & Volkow, 2011). And reviews focused on various substances report evidence for executive function deficits in addicts (Hester, Lubman, & Yücel, 2010; Loeber et al., 2012; Montgomery, Fisk, Murphy, Ryland, & Hilton, 2012; Murphy et al., 2012). Therefore one important limitation of this study was the timing of executive function tasks and the diagnostic interview. Some participants had used substances for a considerable amount of time before they completed the executive function tasks. Therefore, it is impossible to know whether any observed deficits in Common EF were present prior to

70 54 substance use or a result of prolonged substance exposure. Future research would benefit from obtaining executive function data prior to the age of risk for substances use. Another limitation of this study was the reliance on estimated genetic correlations between variables. Genetic correlations may have represented identical polymorphisms that influenced both behavioral disinhibition and executive functioning. However, genetic correlations may also reflect spatial and statistical associations between different polymorphisms in different variables. More sophisticated genetic analyses are needed to understand the biological underpinnings of the observed negative genetic correlation between behavioral disinhibition and Common EF. Also of concern was the dependence vulnerability measure. Participants in our community sample had less variation on this measure (s 2 = 0.931) than on substance use (s 2 = 1.79). This suggests a more cautious interpretation of how meaningful the genetic correlation is between dependence vulnerability and Common EF. Another issue to consider was the inclusion of non-users (score = 0) in the dependence vulnerability measure. It has been argued that nonusers should be included in measures of dependence because part of the protective factor against dependence includes the decision not to use substances in the first place. Another perspective is that non-users have not been exposed to substances and therefore their risk of becoming dependent on one of them is unknown. (For a good summary on this issue see Palmer et al., 2012.) Participants in this study were adolescents who hadn t passed through the period in life when most adult users begin to use substances. Therefore, it may have been beneficial to only score dependence vulnerability among users. Finally, there are potential limitations that stem from assumptions inherent in the twin design. Estimates may have been biased if assortative mating, a special twin environment, or

71 55 gene-environment correlations existed for the any of the variables studied. Also, results may not generalize to non-twin individuals or individuals outside of the adolescent age range. A strength of this study was the use of latent variables. Genetic and environmental influences were estimated for the variance shared among variables, which was of theoretical interest and less likely to contain error. Other strengths were the large sample size and use of a variety of measures including laboratory tasks, diagnostic interviews, and a self-report questionnaire. In summary, results suggest additive genetic influences on behavioral disinhibition are related to additive genetic influences on Common EF. This finding is consistent with a study of behavioral disinhibition by my colleagues (Young et al., 2009) in which genetic effects were correlated with inhibition. The inhibition factor was identical to that used in this study, which was subsumed under Common EF. Therefore, executive functions that influence individual differences in behavioral disinhibition may include more than an inhibition component. A second important finding was that negative genetic correlations with Common EF were higher for behavioral disinhibition with substance use than for behavioral disinhibition with dependence vulnerability. It may be that individual differences in Common EF are more predictive of initiation and the number of substances used than problem substance use. There were also differences in the genetic and environmental structure of behavioral disinhibition with substance use versus dependence vulnerability. Therefore the type of substance measure used should be considered in future studies of behavioral disinhibition.

72 56 CHAPTER 3 The Role of Executive Functioning in the Progression from Substance Use to Dependence 3.1 Introduction Findings from Chapter 2 indicate that for processes common to executive functions (Common EF factor containing inhibiting, updating, and shifting tasks; Friedman et al., 2008) the proportion of genetic influences shared with substance use was larger that that for dependence vulnerability. The same pattern was observed for genetic correlations with behavioral disinhibition containing either dependence vulnerability or substance use. These results suggest that genetic factors influencing executive functioning may be more important for particular substance behaviors. Executive functions likely play a role from substance initiation through the development of substance dependence. However, individual differences in executive functions may have more consequences for (and be better able to predict) behaviors associated with earlier stages of the substance-use trajectory. The goal of this chapter was to follow up on the findings from Chapter 2 by exploring the genetic relationship between Common EF and specific substance use stages. The implementation of a stage model addressed the concern from Chapter 2 that non-users were included in the dependence vulnerability measure. In a stage-transition model only individuals who meet the set threshold of a stage are included in the following stage. For example, participants who didn t use substances repeatedly would not be scored on dependence vulnerability. Of course this model has limitations as well. In Chapter 2 the definitions of repeated substance use, for alcohol (ever tried) and tobacco (20+ cigarettes) were different from the other drugs. The high endorsement rates may

73 57 have skewed the results for substance use and dependence vulnerability. In this study, therefore, it was of special interest to examine these substances separately. Model results were compared for alcohol, tobacco, cannabis, and multi-substance use. 3.2 Methods Participants See Chapter 2 for a description of the sample and information on zygosity determination Measures The Composite International Diagnostic Interview Substance Abuse Module (CIDI SAM; Robins, Cottler, & Babor, 1993), which uses DSM IV criteria, was used to assess substance behaviors. Lifetime information was obtained on alcohol, tobacco, cannabis, and eight categories of illicit drugs. Only participants with repeated use on at least one substance were questioned about their substance-related behavior. See Chapter 2 for more details on repeated substance use. A majority of participants (tested between ages 16 and 18) reported repeated use of at least one substance (57.8%). The most commonly used substances were alcohol (56.9%), tobacco (20.7%), and cannabis (20.4%). To capture different stages of the substance use trajectory, three stage-variables were created: early versus late age-of-onset, progression to problem use, and progression to dependence. Because the prevalence of most illicit drugs was low, these stages were examined only for alcohol, tobacco, cannabis and multi-substance use. Multi-substance use was defined as repeated use of two or more substances (including alcohol, tobacco, cannabis and the eight illicit drugs) Age-of-onset. Table 3.1 shows the average age when repeated users first tried a particular substance. For the multi-substance use category the youngest age at first use was

74 58 selected. Age-of-onset was used instead of a dichotomous initiation variable (no use/ever used) because genetic and environmental parameters for two dichotomous variables are likely to be biased in the context of substance initiation and problem substance use. (Heath, Martin, Lynskey, Todorov, & Madden, 2002). An individual can t simultaneously be a non-user and a problem user, so the correlation between non-shared environmental influences can t be estimated. Because substances tend to be more available to high school-age adolescents, participants who started using at age 15 or older were designated as late users and given a score of 1. Participants using before age 15 were considered early users and given a score of 2. Evidence suggests that early users are at greater risk for developing substance use problems (Agrawal et al., 2006; Lando et al., 1999; Lewinsohn, Rohde, & Seeley, 1996). Thus, scoring followed the likely level of risk from non-users (0) to early users (2). The age cut-off for alcohol was 14 instead of 15 because alcohol use prevalence was high and the average age of onset was on the younger end of the spectrum (M = 14.64, SD = 1.66).

75 59 Table 3.1 Age at which repeated users first tried a substance n Min Max M SD Alcohol Tobacco Cannabis Cocaine Stimulants Hallucinogens Club drugs Opioids Sedatives Inhalants PCP 0 Note. Repeated use for illicit drugs was defined as using 6 or more times. Alcohol use was defined as having ever used. For tobacco, repeated use consisted of smoking a pipe or cigar 6 or more times, chewing tobacco 6 or more times, or smoking at least 20 cigarettes. Participants were interviewed at either age 16, 17, or 18. With each successive age group the proportion of late users was larger and non-users was smaller (see Table 3.2). This was expected given that participants in the older cohorts had more time to have started using. The proportion of early users (< 15 years) also increased with reporting age, which suggested possible cohort differences in age-of-onset. More males than females had early-onset substance use (see Table 3.3).

76 60 Table 3.2 Age-of-onset by reporting age Sixteen (n = 267) Seventeen (n = 432) Eighteen (n = 69) Multi-substance < % 27.8% 37.7% % 30.3% 55.1% No use 51.7% 41.9% 7.2% Alcohol < % 9.7% 13.0% % 47.5% 78.3% No use 52.4% 42.8% 8.7% Tobacco < % 12.7% 29.0% % 9.7% 18.8% No use 89.1% 77.6% 52.2% Cannabis < % 3.2% 10.1% % 14.6% 31.9% No use 88.8% 82.2% 58.0% Note. Reporting age = age at the time of the CIDI SAM interview. Table 3.3 Age-of-onset for males and females Males (n = 364) Females (n = 404) n % n % Multi-substance Age-of-onset < Alcohol Age-of-onset < Tobacco Age-of-onset < Cannabis Age-of-onset <

77 Problem use. Participants meeting criteria for at least one dependence symptom were considered problem users. As discussed in Chapter 2, nearly one-half of users (48%) had at least one dependence symptom. Problem use scores from 0 to 4 equaled zero to four symptoms and a score of 5 represented five or more symptoms. Problem use was contingent on the age-of-onset variable as only users were scored (non-users = missing). Table 3.4 shows the distribution of problem use for males and females. A majority of users had no dependence symptoms and each category had a similar number of males and females. Importantly, the problem use variable was only used in models of multi-substance use. Originally problem use was scored for individual substances (zero symptoms = 0, one/two symptoms = 1). However, for each substance there were cells in the cross-twin cross-trait correlations with missing data. This prevented the use of multivariate genetic models. Table 3.4 Problem use for males and female users Males (n = 227) Females (n = 217) n % n % Note. Problem use was only scored for multi-substance use Dependence. Participants with one or two dependence symptoms were assigned a 0 and those with three or more a score of 1. Multi-substance use participants were scored a 1 only if they had at least three symptoms for the same substance. For example, one alcohol

78 62 symptom and two tobacco symptoms would result in a score of 0. Dependence was contingent on problem use (multi-substance) or age-of-onset (individual substances). Substance dependence rates were similar for males and females (see Table 3.5). Table 3.5 Dependence for male and female users Males (n = 227) Females (n = 217) n % n % Multi-substance Alcohol Tobacco Cannabis Note. Dependence = 3 or more dependence symptoms Executive function tasks. Inhibition, updating, and shifting were assessed with three laboratory tasks each. See Chapter 2 for procedural information and a description of the individual tasks. Executive function scores were corrected for sex and age differences and transformed to improve normality. This study focused on the Common EF factor on which all the tasks loaded (for more details see Friedman et al., 2008) Statistical analyses General procedure. Descriptive statistics were obtained with SPSS version 21 (IBM Corp, 2012). The structural-equation modeling program Mplus (Muthén & Muthén, ) was used to estimate the proportion of the variances and covariances due to genetic and environmental influences. This required assumptions based on the biometrical principles of the twin design (see Chapter 2). Mplus was chosen for its ease in incorporating continuous (executive function) and categorical (substance stage) variables in the same model. Each

79 63 ordinal/categorical variable was associated with a continuous underlying latent response variable. Thresholds designated the points along the continuous latent distribution where one measured category ended and another began. Parameters were calculated with mean- and variance-adjusted weighted least squares (WLSMV) estimation. Mean- and variance-adjusted estimation was used because it is more robust to small sample sizes than regular WLS (Muthén, du Toit, & Spisic, 1997). The number of complete twin pairs ranged from 334 to 371 for the executive function tasks. Complete twin pairs for the substance stages are shown in Table 3.6. No outliers were observed for twin 1 and twin 2 scores on the executive function tasks and differences between twin 1 and twin 2 variances ranged from to (M = 0.190, SD = 0.102). For substance stages (for each substance type), the counts of twin 1 and twin 2 participants in each category were compared. Differences for age-of-onset variables ranged from 0 to 9 (M = 4.17, SD = 2.48). For problem use categories the number of twin 1s differed from twin 2s by 4.17 on average (SD = 3.86, range 0 to 12). Differences ranged from 1 to 13 (M = 4.56, SD = 3.74) for dependence variables. This descriptive information indicated that low twin-pair coverage and bias due to twin number designation were not of concern.

80 64 Table 3.6 Number of complete twin pairs for substance stages MZ DZ Multi-substance Age-of-onset Problem use Dependence Alcohol Age-of-onset Dependence Tobacco Age-of-onset Dependence Cannabis Age-of-onset Dependence Note. MZ = monozygotic; DZ = dizygotic Modeling. First, the variance and covariance structures of the substance stages were specified with univariate and Cholesky bi- and tri-variate models. In a Cholesky model the number of genetic factors (A) equals the number of variables (n). All variables load on the first factor, n-1 variables load on the second factor, etc. (see Figure 3.1). Environmental variation (C, E) is modeled the same way. Second, Cholesky models were used to examine whether genetic influences on particular substance stages were also operating on Common EF. Dummy latent variables were created for the substance stages so genetic and environmental factors were at the same level as those for executive functioning (see Figure 3.2, p. 66). It is important to note that in a Cholesky model the order of the variables should be theoretically driven because the questions addressed by the paths will change with a different order. Common EF followed the substance stages, which were ordered to reflect the progression from onset to dependence.

81 65 Figure 3.1. Trivariate Cholesky model Figure 3.1. Only twin 1 is depicted. Non-shared environmental factors are not shown but the loading pattern is identical to that for additive genetic effects (A) and shared environmental effects (C). Onset = early vs. late age-of-onset; Prb = problem use; Dep = dependence.

82 Figure 3.2. Example model with standardized path coefficients for executive functions A 1 A 2 A 3 A 4 a 41 a 22 a 32 a 42 a 33 a 43 a 44 a 11 a 21 a 31 Age-of-onset Problem Use Dependence Common EF 1 1 Updating Specific Shifting Specific Onset Prb Dep.47 Anti Stop Stroop Keep Letter S2ba Num Col.48 Cat δ δ δ δ δ δ δ δ δ Figure 3.2. The proportion of variance accounted for by residual variance is shown below each executive function task. Environmental effects followed the same pattern but are not shown. Only twin 1 is depicted. A = additive genetic effects; Common EF = common executive functioning; Anti = antisaccade; Stop = stop signal; Keep = keep track; Letter = letter memory; S2ba = spatial-2back; Num = number-letter; Col = color-shape; Cat = category switch. 66

83 67 To calculate the total genetic covariance between Common EF and a particular substance stage, covariances through all relevant factors were added. For example, the genetic covariance between Common EF and problem use was equal to their covariance through A1 plus their covariance through A2 (see Figure 3.2). Previous results indicated that for both updating- and shifting-specific factors there were no significant correlations with substance measures (see Chapter 2). Therefore, only the covariation between Common EF and substance stages was examined. However, for an accurate representation of Common EF it was necessary to include the updating- and shifting-specific factors in the model. 3.3 Results Substance stages For all substance categories MZ concordance rates were higher than DZ rates, indicating genetic influences (see Table 3.7). This was supported by polychoric twin correlations (see Table 3.8). Polychoric correlations are estimated for ordinal variables with two or more levels and assume a continuous-normal underlying distribution. For most substances the relative magnitude of the MZ and DZ correlations suggested shared environmental influences. Model fit indices indicated adequate fit for tobacco and cannabis age-of-onset, multi-substance problem use, and multi-substance and cannabis dependence. For the remaining models there were mixed conclusions regarding fit (see Table 3.8). Genetic influences accounted for 23% to 47% of the variance in age-of-onset variables and 23% to 91% percent in dependence variables. For age-ofonset and problem use variables the variance accounted for by the shared environment was equal to or greater than that for genetic influences. In contrast the influence of the shared environment on the dependence variables was negligible. This is consistent with Chapter 2 and other studies

84 68 in which the shared environment had a smaller effect on dependence than it did on less severe substance-related behaviors (Stallings, Gizer, & Young-Wolff, in press). Table 3.7 Twin concordance rates for substance stages Multi-substance Alcohol Tobacco Cannabis Age-of-onset MZ DZ Problem use MZ.48 DZ.44 Dependence MZ DZ Note. MZ = monozygotic; DZ = dizygotic.

85 69 Table 3.8 Polychoric twin correlations and univariate results for substance stages Correlations Variance Components Model Fit MZ DZ A C E χ 2 df p RMSEA TLI Onset Multi Alcohol Tobacco Cannabis Prb Multi Dep Multi Alcohol Tobacco Cannabis Note. Boldface type indicates significance at the.05 level (two-tailed). Onset = early versus late age-of-onset; Prb = problem use; Dep = dependence; Multi = multisubstance use; MZ = monozygotic; DZ = dizygotic; A = additive effects; C = shared environmental effects; E = non-shared environmental effects; χ 2 = chi-square test of model fit; RMSEA = Root mean square error approximation; TLI = Tucker Lewis index. Polychoric correlations indicated covariation between some substance stages (see Table 3.9). For multi-substance use, age-of-onset was more highly correlated with the subsequent stage, problem use, than with dependence. Cannabis age-of-onset and dependence were not correlated so they were examined separately in the full executive function models. Cross-trait cross-twin correlations were necessary to determine if the observed covariation was partly due to genetic factors. For multi-substance age-of-onset and problem use, higher MZ than DZ cross-trait crosstwin correlations indicated genetic covariance (see Table 3.10). The rest of the correlations were inconclusive as the correlation for one twin1/twin2 pairing was higher in MZs while the correlation for the other twin1/twin2 pairing was not.

86 70 Table 3.9 Polychoric correlations among substance stages Onset Problem Use Dependence Multi-substance Onset Problem Use.424 Dependence Alcohol Dependence.328 Tobacco Dependence.076 Cannabis Dependence.020 Note. Onset = early versus late age-of-onset. Table 3.10 Cross-trait cross-twin correlations for substance stages MZ Twins DZ Twins Onset1 Prb1 Dep1 Onset1 Prb1 Dep1 Multi-substance Onset Prb Dep Alcohol Onset Dep Tobacco Onset Dep Note. Univariate twin correlations are in boldface type. Onset = early vs. late age-of-onset; Prb = problem use; Dep = dependence; MZ = monozygotic; DZ = dizygotic. Results from the bivariate models are shown in Table The shared environment appeared to contribute to the covariance between the age-of-onset and dependence stages,

87 71 however the cross paths (c21) were not significant. For multi-substance use, the shared environment contributed to the covariance between (a) age-of-onset and problem use, and (b) problem use and dependence. Genetic cross paths (a21) were substantial but not significant. Similar results were observed for the multi-substance trivariate model (see Figure 3.3). Adequate or near-adequate fit was observed for the bivariate (see Table 3.11) and trivariate models, χ 2 = 51.3 (44), p =.209; RMSEA =.029; TLI =.99. Table 3.11 Bivariate results for substance stages Standardized Path Coefficients Model Fit a11 a21 a22 c11 c21 c22 e11 e21 e22 χ 2 (df) p RMSEA TLI Onset w/ Dep Multi (15) Alc (15) Tob (17) Onset w/ Prb Multi (31) Prb w/ Dep Multi (29) Note. Boldface type indicates significance at the.05 level. Multi = multi-substance; Alc = alcohol; Tob = tobacco; Onset = early versus late age-of-onset; Prb = problem use; Dep = dependence; a = additive genetic effects; c = shared environmental effects; e = non-shared environmental effects; χ 2 = chi-square test of model fit; RMSEA = Root mean square error approximation; TLI = Tucker Lewis index.

88 72 Figure 3.3. Trivariate model for multi-substance use A 1 A 2 A Onset Prb Dep C 1 1 C 2 1 C 3 Onset Prb Dep E 1 1 E 2 1 E 3 Figure 3.3. Solid lines indicate significance at the.05 level. Dashed lines indicate nonsignificance (p >.05). Onset = age-of-onset; Prb = problem use; Dep = dependence; A = additive genetic factor; E = non-shared environmental factor; C = non-shared environmental factor Substance stages and common executive functioning The substance stages with Common EF had adequate fit to the data (see Table 3.12). The minimum covariance coverage was not met for cannabis dependence and Common EF, so only

89 73 cannabis age-of-onset with Common EF is shown. Figures 3.4 through 3.7 show standardized path estimates for each model. For all substances except cannabis a significant path from the ageof-onset genetic factor (A1) to Common EF was observed. Interestingly, genetic factors specific to Common EF were only observed for alcohol and tobacco. For multi-substance use, shared and non-shared environmental influences contributed to the covariation between age-of-onset and problem use. Table 3.12 Model fitting results for common executive functioning and substance stages χ 2 df p RMSEA TLI Multi-substance Alcohol Tobacco Cannabis a Note. χ 2 = chi-square test of model fit; RMSEA = Root mean square error approximation; TLI = Tucker Lewis Index. a Cannabis age-of-onset with Common EF.

90 74 Figure 3.4. Standardized path coefficients for multi-substance stages with Common EF 1 A 1 1 A 2 1 A 3 1 A Age-of-onset Problem Use Dependence Common EF E 1 1 E 2 1 E 3 1 E C 1 1 C 2 1 C 3 Figure 3.4. Only latent variables for twin 1 are pictured. See Figure 3.2 for the full factor model with observed and latent variables. Solid lines indicate significance at the.05 level. Dashed lines indicate non-significance (p >.05). A = additive genetic factor; E = non-shared environmental factor; C = non-shared environmental factor.

91 75 Figure 3.5. Standardized path coefficients for alcohol stages with Common EF 1 A 1 1 A 2 1 A Alcohol Age-of-onset Alcohol Dependence Common EF E 1 E 2 E C 1 C Figure 3.5. Only latent variables for twin 1 are pictured. See Figure 3.2 for the full factor model with observed and latent variables. Solid lines indicate significance at the.05 level. Dashed lines indicate non-significance (p >.05). A = additive genetic factor; E = non-shared environmental factor; C = non-shared environmental factor.

92 76 Figure 3.6. Standardized path coefficients for tobacco stages with Common EF 1 A 1 1 A 2 1 A Tobacco Age-of-onset Tobacco Dependence Common EF E 1 E 2 E C 1 C Figure 3.6. Only latent variables for twin 1 are pictured. See Figure 3.2 for the full factor model with observed and latent variables. Solid lines indicate significance at the.05 level. Dashed lines indicate non-significance (p >.05). A = additive genetic factor; E = non-shared environmental factor; C = non-shared environmental factor.

93 77 Figure 3.7. Standardized path coefficients for cannabis age-of-onset with Common EF 1 A 1 1 A Cannabis Age-of-onset Common EF E 1 E C 1 Figure 3.7. Only latent variables for twin 1 are pictured. See Figure 3.2 for the full factor model with observed and latent variables. Solid lines indicate significance at the.05 level. Dashed lines indicate non-significance (p >.05). A = additive genetic factor; E = non-shared environmental factor; C = non-shared environmental factor.

94 78 Genetic correlations were calculated for multi-substance use (Table 3.13) and individual substances (Table 3.14). See Figure 3.2 on p.66 for a visual representation of the additive genetic path labels. For all substance types, genetic influences on age of onset were negatively correlated with genetic influences on Common EF. And for multi-substance use, a substantial portion of genetic effects on age-of-onset was shared with genetic effects on problem use (see Figure 3.4). Additive genetic effects accounted for a large proportion (.83 to.99) of the total covariance between age-of-onset and Common EF. This was not surprising given that Common EF had very little non-shared environmental influences. Overall these findings suggest that genetic factors contributing to better executive functioning in adolescence may also contribute to no use/later age-of-onset and fewer substance use problems.

95 79 Table 3.13 Genetic correlations between multi-substance stages and common executive functioning Variables Expected Expected rg with Variance Common EF Formula ONSET.25 a11 2 PRB.28 a a22 2 DEP.56 a a a33 2 CEF.94 a a a a44 2 ONSET -.45 (a11 x a41) / (a 2 ONSET x a 2 CEF) PRB -.39 (a21 x a41) / (a 2 PRB x a 2 CEF) common w/onset PRB -.08 (a22 x a42) / (a 2 PRB x a 2 CEF) DEP.04 (a31 x a41) / (a 2 DEP x a 2 CEF) common w/onset DEP -.14 (a32 x a42) / ( a 2 DEP x a 2 CEF) common w/prb DEP.41 (a33 x a43) / (a 2 DEP x a 2 CEF) Note. rg = genetic correlation; ONSET = age-of-onset; PRB = problem use; DEP = dependence; CEF = common executive functioning; a = additive genetic effects.

96 80 Table 3.14 Genetic correlations between common executive functioning and substance-specific stages Model Variable Expected Genetic Expected rg with Variance Common EF Formula Alcohol ONSET.36 a11 2 DEP.78 a a22 2 CEF.85 a a a33 2 ONSET -.36 (a11 x a31) / (a 2 ONSET+ a 2 CEF) DEP common w/onset -.17 (a21 x a31) / ( a 2 DEP x a 2 CEF) DEP.02 (a22 x a32) / ( a 2 DEP x a 2 CEF) Tobacco ONSET.45 a11 2 DEP.79 a a22 2 CEF.96 a a a33 2 ONSET -.47 (a11 x a31) / (a 2 ONSET+ a 2 CEF) DEP common w/ onset.07 (a21 x a31) / ( a 2 DEP x a 2 CEF) DEP.22 (a22 x a32) / ( a 2 DEP x a 2 CEF) Cannabis ONSET.27 a11 2 CEF.96 a a22 2 ONSET -.54 (a11 x a21) / ( a 2 ONSET x a 2 CEF) Note. rg = genetic correlation; ONSET = age-of-onset; DEP = dependence; CEF = common executive functioning; a = additive genetic effects. There were substantial genetic influences specific to alcohol dependence (a =.78, Figure 3.5). However these effects were not correlated with genetic effects on Common EF. Genetic

97 81 influences specific to tobacco dependence and multi-substance dependence on the other hand, were positively correlated with genetic influences on Common EF (tobacco rg =.22; Multi rg =.41). These correlations suggest that some genetic effects contribute to better executive functioning and also to the development of substance dependence. However the correlations were likely non-significant and should be interpreted with caution. For all substance types there were substantial shared environmental influences on age-of-onset. And shared-environmental influences on multi-substance age-of-onset were correlated with problem use. 3.4 Discussion The aim of this chapter was to examine genetic and environmental covariation between Common EF and specific substance use stages. The first stage was age-of-onset, which represented no use, late use, and early use. Then early and late users were included in the problem use stage, which represented the number of dependence symptoms across substance type. Participants with at least one dependence symptom were included in the third and final stage dependence. Individuals received a score of 1.0 on dependence if they met criteria for at least three dependence symptoms on the same substance. Another goal of the study was to expand on Chapter 2 by examining these stages for specific substances. Alcohol, cannabis, and tobacco were modeled in addition to multi-substance use. For both alcohol and tobacco, additive genetic influences were higher on dependence (AAlcohol =.78, ATobacco =.79) than on age-of-onset (AAlcohol =.36, ATobacco =.45) There were no shared environmental influences specific to dependence, but shared environmental effects on age-of-onset contributed to the covariance between the stages. Genetic influences also accounted for some of the covariance in alcohol.

98 82 Negative genetic correlations between age-of-onset and Common EF were observed for alcohol (rg = -.36), tobacco (rg = -.47), and cannabis (rg = -.54). These correlations were consistent with multi-substance use (rg = -.45). Genetic influences on age-of-onset accounted for 83% of the covariance between Common EF and problem use, but only 7% of the covariance between Common EF and dependence. Even though repeated use was defined differently for alcohol/tobacco than for other drugs, the pattern of results for these substances was consistent with multi-substance use. However the multi-substance use results may have been partly driven by alcohol and tobacco. Overall these results suggest that genetic influences shared with Common EF may play more of a role in initiation/age-of-onset than in substance dependence. This is consistent with Chapter 2, however the extent to which age-of-onset captured the same variance as substance use (number of substances used repeatedly) is not clear. As in Chapter 2, the direction of causality between substance use and executive function deficits could not be determined. Another limitation of this study was the lack of power to detect significant effects in the later substance stages. Decreasing sample size is an inherent part of stage models as fewer and fewer participants are included in each subsequent stage. This effect is magnified in genetic studies because participants are further categorized by zygosity. In this study the number of participants decreased by at least half with each stage (in multi-substance use for example: nage-ofonset = 762, nproblem use = 358, ndependence = 140), which may partly explain the many non-significant path coefficients. A third concern was that participants were only asked about age of onset if they met criteria for repeated use. Therefore, age-of-onset was the age of onset for individuals who eventually used the substance repeatedly. Future research would benefit from a model with a

99 83 persistence stage (experimentation vs. repeated use) between the age-of-onset and problem use stages. Age-of-onset would then reflect the age at which an individual first tried a substance, independent from continued use. In summary, additive genetic influences that contributed to an earlier age of onset for substance use also contributed to poor Common EF. Furthermore, these genetic influences accounted for the majority of the covariance observed between Common EF and later substance stages. Individual differences in executive functions may be more important in earlier stages of the substance use trajectory than in the progression to dependence.

100 84 CHAPTER 4 Using Items from the Tridimensional Personality Questionnaire to Assess Behavioral Disinhibition 4.1 Introduction Novelty seeking was included with antisocial behavior and substance use as an indicator of behavioral disinhibition both in Chapter 2, and in studies by my colleagues (Palmer et al., 2010; Young, Stallings, & Corley, 2000; Young et al., 2009). The dimension, which is part of the Tridimensional Personality Questionnaire (TPQ; Cloninger, 1987), was developed so that individuals high in novelty seeking express excitement and exploratory activity in response to novel and appetitive stimuli. Novelty seeking consists of four subscales: exploratory excitability, impulsivity, extravagance, and disorderliness (individual items are listed in Chapter 2, Table 2.7). The subscales reflect an overall lack of control, which is a significant aspect of behavioral disinhibition. However, it has also been suggested that the liability to behavioral disinhibition includes a lack of persistence, inattention, insensitivity to social constraints and an inability to learn from consequences (Gorenstein & Newman, 1980; Iacono, Malone, & McGue, 2008). The goal of this chapter was to identify a set of TPQ items that reflected these traits and, when used with novelty seeking items, provided a personality measure more representative of behavioral disinhibition. An example of a broader personality measure of behavioral disinhibition is the constraint dimension of the Multidimensional Personality Questionnaire (MPQ; Tellegen & Waller, 2001), which has been used in studies by the Minnesota Twin and Family Study (Iacono, McGue, & Krueger, 2006). Constraint (or lack thereof) contains three subscales: control, harm avoidance, and traditionalism. (Traditionalism addresses the extent to

101 85 which individuals have a sense of morality and respect and obey social norms.) A study using factor analyses indicated that TPQ novelty seeking was related to MPQ constraint, mostly through the high loading on the control scale (Waller, Lilienfeld, Tellegen, & Lykken, 1991). Participants high in novelty seeking tended to have low scores on control. Also, the fearlessness subscale of TPQ harm avoidance was related to the harm avoidance subscale in MPQ constraint. These findings supported the idea that items from TPQ dimensions other than novelty seeking could be used to measure disinhibitory personality. TPQ dimensions (other than novelty seeking) from which to draw items were harm avoidance, reward dependence and persistence. Harm avoidance was created to reflect the behavioral inhibition system, such that individuals high on the dimension respond strongly to aversive stimuli and are quick to inhibit behavior associated with negative outcomes. Reward dependence reflects variation in the behavioral maintenance system. Individuals high in reward dependence respond strongly both to positive and negative reinforcement and are resistant to extinction of rewarded behavior. Persistence addresses how long/hard individuals work to finish tasks and their desire for achievement. When the TPQ was created, emphasis was placed on the relations between the factors. For example, individuals high in novelty seeking and low in harm avoidance were described as impulsive, danger seeking, and aggressive. Still, the dimensions were thought to be largely uncorrelated (i.e. specific profiles did not occur more often than others). Correlations among the TPQ dimensions would provide initial support for using items from different dimensions in the same measure. Indeed, results from exploratory and confirmatory factor analytic studies have been mixed in their support of the TPQ structure (Howard, Kivlahan, & Walker, 1997).

102 86 Evidence of shared genetic influences among TPQ dimensions would further support the creation of a disinhibitory personality factor. In his original theory Cloninger (1986) proposed that each dimension was genetically independent. This was supported by findings from a multivariate twin study of the four dimensions, at least in men (Stallings, Hewitt, Cloninger, Heath, & Eaves, 1996). There were significant genetic correlations between harm avoidance and novelty seeking, and novelty seeking and reward dependence in women. The average age for this study was 67 years old. Negative genetic correlations between harm avoidance and novelty seeking/reward dependence were observed (for males and females) in a large Australian adult twin sample (Heath, Cloninger, & Martin, 1994). They also found positive genetic correlations between novelty seeking and reward dependence. To our knowledge, no multivariate analysis of the TPQ has been reported for adolescents. This chapter consists of four studies. First, a multivariate twin-analysis was used to examine the extent to which genetic and environmental influences contributed to the covariation among TPQ dimensions. In the second study TPQ items thought to reflect behavioral disinhibition were selected and analyzed using an exploratory factor analysis. The resulting factor structure was used in a confirmatory factor analysis in Study 3, which consisted of a different sample. Next, characteristics of the newly created disinhibitory personality dimension were compared to those of novelty seeking. This was followed by an examination of the extent to which both dimensions predicted conduct disorder and substance use. In the final study regression analyses were used to determine if disinhibitory personality was predictive of casecontrol status (over and above novelty seeking) in a sample selected for antisocial substance dependence.

103 General Methods Samples Community twin sample. Twins were drawn from two samples in the Colorado Twin Registry (Rhea, Gross, Haberstick, & Corley, 2006). Twins born in 1968 forward who attended primary school or were born in the state of Colorado were invited to participate in the Community Twin Sample (CTS). For the Longitudinal Twin Sample (LTS) same-sex twins were identified through Colorado birth records. Twins born between 1984 and 1990 were included in the study if they had a normal gestational period and birth weight, lived within a three-hour drive from the Institute for Behavioral Genetics, and were available for initial testing at 14 months. Inperson assessments with different interviewers for each twin were conducted in the participants homes. They also completed a set of self-report questionnaires, which included the TPQ. There were multiple waves of data collection; therefore, a small number of twins had completed the TPQ twice between the ages of 16 and 18. For these individuals the most recent data was analyzed. All participants gave informed consent (if 18 years) or assent (if 17 years or younger) prior to participation. Parents also provided informed consent for participants under age 18. The Institutional Review Board of the University of Colorado approved the study. Zygosity was determined from a nine-item questionnaire of physical characteristics based on Nichols and Bilbro (1966). For twins recruited through the school system, initial zygosity was based on two questions: how frequently are the twins mistaken for each other by people who know them? and are they as alike as two peas in a pod? An 85% agreement from at least four raters was required for assigning monozygotic (MZ) or dizygotic (DZ) status. Zygosity was confirmed using DNA collected from cheek swabs. Eleven highly informative short-tandem repeat (STR) genetic polymorphisms were genotyped using standard polymerase chain reaction

104 88 technology. Concordance across all polymorphisms between twins indicated MZ status. Discordance indicated DZ status. A panel of researchers resolved discrepancies between the initial zygosity judgment and genotyping. If necessary, DNA was resampled Selected family sample. Participants were also drawn from the Colorado Adolescent Substance Abuse (ASA) family study (Miles et al., 1998; Stallings et al., 2003). The ASA is a selected sample of adolescent probands, matched adolescent controls, and all consenting first-degree biological relatives. Probands were recruited beginning in 1993 from one of three treatment facilities for substance abuse. The treatment facilities were located in the Denver metropolitan area and operated by the Division of Substance Dependence of the University of Colorado School of Medicine. The majority of probands were referred to the treatment centers by juvenile justice and social service agencies. To be included in the sample, they were required to have an IQ score greater than 80, exhibit no current psychotic symptoms, and pose no imminent danger to themselves or others. Controls were matched within 1 year of age, for sex, for ethnicity, and for geographic location (zip code). Participants underwent diagnostic interviews and completed a set of self-report questionnaires, including the TPQ. The Institutional Review Board of the University of Colorado approved the study Participants For all four studies, participants were included if they had completed the TPQ between 16 and 18 years of age. Table 4.1 shows descriptive information for the participants in each study. Participants in the community samples were largely White (CTS = 72.6%, LTS = 81.8%). Hispanics represented the second most common ethnicity (CTS = 11.03%, LTS = 9.7%). This pattern was reversed in the ASA family sample (Hispanic = 34.8%, White = 19.7%).

105 89 Table 4.1 Sample information by study Study Sample N % Female Mean Age (SD) 1-TPQ multivariate twin analysis CTS, LTS (0.80) 2-DP: exploratory factor analyses CTS (0.89) 3-DP: relations with NS and BD LTS (0.56) 4-DP: cases compared to controls ASA (0.73) Note. TPQ = tridimensional personality questionnaire; DP = disinhibitory personality; NS = novelty seeking; BD = behavioral disinhibition; CTS = community twin sample; LTS = longitudinal twin sample; ASA = adolescent substance abuse family study Measures Personality assessment. Participants completed the 54-item version (Cloninger, Przybeck, & Svrakic, 1991) of the TPQ (Cloninger, 1987). Dimensions originally consisted of 18 true-false items. Persistence was a subscale of reward dependence before it was considered a separate factor. Therefore, persistence and reward dependence consisted of 5 and 13 items, respectively. Table 4.2 shows the four dimensions along with their subscales. Ten of the items were reversed so higher scores indicated higher harm avoidance, novelty seeking, etc. Table 4.2 TPQ dimensions and subscales Harm avoidance Novelty seeking Reward dependence Persistence Anticipatory worry Exploratory excitability Sentimentality Fear of uncertainty Impulsivity Attachment Shyness with strangers Extravagance Dependence Fatigability Disorderliness Note. Persistence was originally the fourth subscale under reward dependence.

106 Behavioral disinhibition measures. The DSM IV version of the Diagnostic Interview Schedule (DIS; Robins et al., 2000) was used to assess conduct disorder in 18-yearolds who no longer lived with their parents. All other participants completed the DSM IIIR and DSM IV versions of the Diagnostic Interview Schedule for Children (DISC; Shaffer et al., 1996; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000). Substance use was measured with the DSM III-R and DSM IV version of the Composite International Diagnostic Interview Substance Abuse Module (CIDI SAM; Robins, Cottler, & Babor, 1993). The CIDI SAM assesses alcohol, tobacco, cannabis, and eight categories of illicit drugs. Past substance use behavior was scored if participants were not currently using. 4.3 Study 1: Multivariate twin analysis of the Tridimensional Personality Questionnaire Methods Tridimensional Personality Questionnaire. Internal consistency (see Table 4.3) was comparable to that reported in psychometric studies of the TPQ (Kuo, Chih, Soong, Yang, & Chen, 2004; Otter, 2003; Sher, Wood, Crews, & Vandiver, 1995). Item endorsement rates were between 20% and 80% for 47 of the 54 true-false items, which suggests good variance. The most commonly endorsed (response = true) items were: People find it easy to come to me for help, sympathy, and warm understanding (84.1%) and Usually I am more worried than most people that something might go wrong in the future (79.1%).

107 91 Table 4.3 Reliability coefficients for TPQ dimensions Cronbach α All Female Male Harm avoidance Novelty seeking Reward dependence Persistence Participants sometimes skipped items or circled both true and false. Therefore, the mean number of items endorsed was calculated instead of the sum. Items were coded so false = 0 and true = 1, which resulted in scores that were essentially the proportion of items endorsed. Only participants who answered all five persistence items were scored on that dimension (excluded n = 33). To be scored on the other three personality dimensions participants were required to answer ~90% of the items (maximum excluded n = 14). See Table 4.4 for a summary of each dimension. Table 4.4 Descriptive statistics for TPQ dimensions N Min Max M SD Harm avoidance Novelty seeking Reward dependence Persistence Data transformation. As shown in Table 4.5 females scored higher on harm avoidance and reward dependence on average, which is consistent with many TPQ studies (Miettunen, Veijola, Lauronen, Kantojärvi, & Joukamaa, 2007). Males scored higher on novelty

108 92 seeking, which supports the normative data collected for the TPQ (Cloninger et al., 1991). However, there have been inconsistent results for sex differences in novelty seeking. Basic regression was used to correct for sex. Then non-normal distributions were log transformed and re-standardized. Table 4.5 Sex differences and age correlations for TPQ dimensions n M SD t-test (p) Age correlation (p) Harm Avoidance M a (<.001).056 (.049) F (.652) Novelty Seeking M (.003) (.796) F (.030) Reward Dependence M a (<.001) (.595) F (.184) Persistence M (.070) (.508) F (.588) Note. P values are based on means and correlations where the dependence in the data was accounted for (weighted.5 for complete pairs, 1.0 for singletons), not the actual means and correlations shown. M = male; F = female. a Equal variances could not be assumed under Levene s Test for Equality of Variances Modeling. Genetic analyses were conducted using the classic twin design (see Chapter 2). Univariate models were examined first to determine if the dimensions were heritable. Then phenotypic correlations were used to inform the structure of the multivariate models. An independent pathway model was used to determine if there were genetic influences

109 93 shared across the four TPQ dimensions. Paths unique to each variable made it possible to examine whether shared genetic factors differentially influenced the personality dimensions. Analyses were performed with the structural-equation modeling program Mx (Neale, Boker, Xie, & Maes, 2003), which uses maximum likelihood estimation. Akaike s information criteria (AIC; Akaike, 1987), the root-mean-square error of approximation (RMSEA; Steiger & Lind, 1980), and the Tucker-Lewis index (TLI; Tucker & Lewis, 1973) were used to examine the fit of the models. Non-significant paths were set to zero in follow-up models and χ 2 difference tests were used to determine if their exclusion resulted in a decrement in fit. There were 923 same-sex twin pairs (472 MZ, 336 DZ) and 145 opposite-sex dizygotic pairs (OS). Thirty-eight singletons were used in descriptive analyses but did not contribute to the genetic analyses. No extreme outliers were observed when scatterplots for twin 1 and twin 2 scores were examined, and differences between twin 1 and twin 2 variances were minor Results Univariate results indicated that all four dimensions were heritable and genetic influences explained about 25% to 40% of the variation (see Table 4.6). Twin correlations suggested nonadditive genetic influences (D) for novelty seeking. This is consistent with findings in Chapter 2 and other studies of the TPQ (Heiman, Stallings, Young, & Hewitt, 2004; Keller, Coventry, Heath, & Martin, 2005). When D was included in the model, however, the estimate for additive genetic effects was zero. Only additive influences (A) were estimated in subsequent models because (a) genetic factors with entirely non-additive effects are improbable and (b) the twin design is not able to tease apart the sources of non-additive variation.

110 94 Table 4.6 Twin correlations and univariate results for TPQ dimensions Zygosity Variance Components Model Fit MZ DZ OS A E -2LL AIC RMSEA TLI HA NS RD PS Note. HA = harm avoidance; NS = novelty seeking; RD = reward dependence; PS = persistence; MZ = monozygotic; DZ = dizygotic; OS = opposite sex dizygotic; LL = log likelihood; AIC = Akaike s information criteria; RMSEA = Root mean square error approximation; TLI = Tucker Lewis index. Phenotypic correlations, although small, were consistent with the possibility of shared genetic influences (see Table 4.7). Harm avoidance and novelty seeking were negatively correlated, which suggested that individuals high on harm avoidance tended to have lower scores on novelty seeking. Harm avoidance and persistence were both correlated around.1 or higher with reward dependence and novelty seeking, but not with each other. Therefore, two additive genetic factors were included in the independent pathway model. Harm avoidance, novelty seeking, and reward dependence loaded on the first factor. Persistence, novelty seeking, and reward dependence loaded on the second. Finally, the magnitude of cross-trait cross-twin correlations was compared for MZ and DZ twins. Eight of the 12 comparisons suggested genetic influences contributed to the covariation between dimensions.

111 95 Table 4.7 Phenotypic correlations for TPQ dimensions HA NS RD PS HA NS RD PS Note. Correlations in boldface type are significant at the.01 level (two-tailed) after accounting for dependence in the data (weighted.5 for complete pairs, 1.0 for singletons). HA = harm avoidance; NS = novelty seeking; RD = reward dependence; PS = persistence. The hypothesized independent pathway model with two additive-genetic factors fit significantly better than a model where all dimensions loaded on a single genetic factor (see Table 4.8). Standardized parameter estimates from the best fitting model (AAE Model in Table 4.8) are presented in Figure 4.1. Path coefficients that could not be dropped from the model, without causing a significant decrement in fit, are represented by solid lines. Dashed lines indicate non-significant paths. The first genetic factor was shared by harm avoidance and novelty seeking and it appeared to contribute to a high harm avoidance/low novelty seeking or a low harm avoidance/high novelty seeking profile. The later of which is consistent with high behavioral disinhibition. Additive genetic influences accounted for 32% of the phenotypic correlation between harm avoidance and novelty seeking. For the second factor, significant path coefficients were observed for all the dimensions. The proportions of the phenotypic correlations due to the second set of genetic effects were between 70% and 89%. Although the correlations among the four personality dimensions were modest, these results suggested that shared genetic influences played a substantial role in their covariation.

112 96 Table 4.8 Fit indices for independent pathway models Model -2LL df AIC χ 2 df p TLI RMSEA AAE AE < Note. The best-fitting model is indicated in boldface type. AAE represents the model with two additive genetic factors. A = additive genetic component; C = shared environmental component; E = non-shared environmental component; -2LL = -2 log likelihood; AIC = Akaike s information criteria; χ 2 = chi-square difference test; TLI = Tucker Lewis index; RMSEA = Root mean square error approximation.

113 97 Figure 4.1. Standardized path coefficients for the multivariate TPQ model 1 A 1 A Harm Avoidance Novelty Seeking Reward Dependence Persistence E 1 Figure 4.1. Solid lines indicate p <.05. Dashed lines indicate non-significance (p >.05). Doubleheaded arrows signify that the variance of the genetic and environmental factors was set to one. 4.4 Study 2: Exploratory factor analyses of disinhibitory personality Methods Disinhibitory personality. In Study 1 there were phenotypic and genetic correlations among the four dimensions of the TPQ. These results supported the overall goal of this chapter, which was to obtain items from the TPQ that together would reflect an underlying disinhibitory personality factor. The purpose of this study was to (a) identify items, and (b) examine the validity of using items from different TPQ dimensions. Items were chosen if they fit into one of three recognized components of behavioral disinhibition: lack of control, the pursuit of potentially harmful experiences/environments, and disregard for social convention. All