همپوشانی ژنتیکی و محیطی بین رفتار ضد اجتماعی تهاجمی و غیر تهاجمی در کودکان و نوجوانان با استفاده از مصاحبه خود گزارش بزهکاری (SR-DI)

Abstract Purpose This study investigated genetic and environmental commonalities and differences between aggressive and non-aggressive antisocial behavior (ASB) in male and female child and adolescent twins, based on a newly developed self-report questionnaire with good reliability and external validity -- the Self-Report Delinquency Interview (SR-DI). Methods Subjects were 780 pairs of twins assessed through laboratory interviews at three time points in a longitudinal study, during which the twins were: (1) ages 9-10 years; (2) age 11-13 years, and (3) age 16-18 years.

Introduction The genetic and environmental influences on antisocial behavior (ASB) have been studied extensively in twin, adoption and family designs. There is consistent support for the roles both genes and environment play in the development of ASB (Rhee & Waldman, 2002). More recently, however, distinctions have been drawn between aggressive (fighting, weapon-use) and non-aggressive (theft, vandalism) forms of ASB (Burt, 2012a and Burt, 2012b). Genetic studies support this distinction. For example, aggressive ASB shows primarily genetic influence (Edelbrock et al., 1995; Thalia et al., 1999; Ghodsian-Carpey and Baker, 1987 and Hudziak et al., 2003), whereas non-aggressive ASB shows roughly equal influence of genes and shared environment (Bartels et al., 2003, Edelbrock et al., 1995 and Eley et al., 2003). A recent meta-analysis of 103 twin and adoption studies also revealed clear evidence of etiological distinctions between aggressive and non-aggressive ASB (Burt, 2009). Aggressive ASB showed approximately 65% genetic influences with little influence of shared family environment, especially after childhood. In contrast, while genetic influence was also important for non-aggressive ASB, (48% of influences), there was also an important role for shared environmental effects (18% of influences) (Burt, 2009). One weakness in the literature on child and adolescent ASB, however, is that it has relied heavily on measures obtained through parent or teacher reports (Hinshaw & Zupan, 1997). These reports are fallible for several reasons. First, parents and teachers may not be aware of certain behaviors in which the child may engage. These include both covert ASB such as stealing and lying, which may not be observed by anyone, and behaviors which may be overt but unobserved by adults, such as bullying and relational forms of aggression among peers. Additionally, parents do not generally observe behaviors at school, while teachers do not observe behaviors in the child’s home. For these reasons, some ASB may be unnoticed by the adults who are asked to rate children in widely used instruments such as the Child Behavior Checklist (Achenbach, McConaughy, & Howell, 1987). It is also well known that inter-rater correlations for children’s externalizing behavior problems are low to moderate at best, ranging from r = 0.2 (between self-reports and teacher ratings) to 0.3 (between teacher and parent ratings) ( Achenbach et al., 1987). To the extent that children’s behavior varies across situations, opportunities for raters to observe certain behaviors will differ, contributing to low agreement. Parents and teachers also have different reference groups to which the child may be compared (e.g., siblings or a few peers in the neighborhood, vs. a larger group of peers at school), which may influence ratings. One alternative to parent and teacher ratings of ASB is through self-report measures, which have been used successfully in past research on adolescents and adults. Self-report has proved to be a valid and reliable source of information for drug use, sexual behavior, violence, theft, and other illegal behaviors (Elliott and Huizinga, 1989, Loeber et al., 1991, Moffitt et al., 1994, Rowe, 1983 and Turner et al., 1998). These methods have the advantage of detecting covert behaviors that may be known only to the perpetrator, in addition to overt behaviors that are known to other reporters or available in official records. The lack of any published self-report instrument of ASB in children led us to develop such a measure for use in a large-scale, comprehensive twin study of risk factors for ASB: the University of Southern California (USC) Risk Factors for Antisocial Behavior (RFAB) twin study (Baker et al., 2012). In constructing this instrument – the Self-Report Delinquency Interview (SR-DI) - we considered two primary factors: (1) it should include lifetime and recent offending, to aid in the distinction between individual children with life-course persistent behavior and more transient groups who engage in ASB only during specific developmental periods and; (2) it should measure a wide variety of ASB, so that different etiologies may be investigated for different forms of ASB (e.g. aggressive and non-aggressive). Considering past findings of the distinctions between aggressive and non-aggressive ASB (Burt, 2009, Burt, 2012a and Burt, 2012b), it is important to distinguish among forms of ASB so that different etiologies may be investigated. In the present study, we examined the internal and external validity of the SR-DI – a self-report measure specifically developed for the USC RFAB twin study. To date, no study has investigated the developmental changes in genetic and environmental components in self-report ASB over the span of childhood and adolescence. This paper aims to fill this gap with three assessments using the SR-DI when the twins were age 9-10, 11-13, and 16-18 years old. Based on previous studies, we hypothesized high shared environmental overlap and moderate genetic overlap between aggressive and non-aggressive ASB as measured by the SR-DI within each assessment. Additionally, we also hypothesized that shared environment would play a bigger role in non-aggressive ASB, highlighting an etiological distinction between the two forms of ASB.

Results Descriptive statistics and correlations Mean sex differences were found, with males displaying higher scores on average than females for non-aggressive ASB (Wave 1: t(1215) = 3.58, p < .001; Wave 4: t(909) = 4.39, p < .001), and aggressive ASB (Wave 1: t(1215) = 6.73, p < .001; Wave 2: t(371) = 5.19, p < .001; Wave 4: t(909) = 7.58, p < .001), but not for non-aggressive ASB at Wave 2: t(371) = 1.37, p = .17 ( Table 1). Twin correlations for non-aggressive and aggressive ASB across the three waves are presented in Table 2. In some cases correlations among MZ twins exceeded those for DZ twins—especially in girls but also in boys at the Wave 4 assessment—suggesting some genetic influences. However, MZ and DZ correlations were comparable in other cases, especially in boys during Wave 1 for both ASB scales, and non-aggressive ASB for both males and females in Wave 2. Phenotypic correlations across scales and waves are presented in Table 3. As can be seen, non-aggressive and aggressive ASB are significantly correlated within and across waves. Univariate genetic model Table 5 contains univariate ACE results for aggressive and non-aggressive ASB within each of the three waves. In Wave 1, males and females significantly differed on means of both forms of ASB. Males always exhibited higher means scores than females (M = 0.75 in males, M = 0.66 in females for non-aggressive ASB; M = 0.78 in males, M = 0.56 in females for aggression). Consistent with the pattern of twin correlations shown in Table 2, significant sex differences on ACE estimates for Wave 1 were also found for both non-aggressive ASB, with genetic influences being more important in females, but common environment being more important in males (A = 1%, C = 34%, E = 66% for males; A = 18%, C = 15%, E = 67% for females) and aggression (A = 3%, C = 28%, E = 69% for males; A = 30%, C = 10%, E = 60% for females). Table 5. Univariate results for non-aggressive and aggressive ASB Overall Fit Chi-square difference test Parameter estimates Males Females Wave 1: 9-10 years χ2 df CFI RMSEA BIC Δχ2 Δdf p A C E Mean Non-aggressive 1. males ≠ females on mean and ACE 22.09 16 0.895 0.056 4915.46 Free rc in DZO 1a. Constrain ACE equal 45.36 19 0.544 0.107 4919.52 23.27 3 < .01 1b. Constrain means equal 35.107 17 0.687 0.094 4922.07 13.02 1 < .01 1c.Constrain rc = 1 in DZO 22.09 16 0.895 0.056 4915.46 0.06 1 0.81 0.03 0.58 0.81 0.75 0.42 0.39 0.82 0.66 Aggressive 1. males ≠ females on mean and ACE 31.57 16 0.765 0.090 2401.39 Free rc in DZO 1a. Constrain ACE equal 86.9 19 0.000 0.172 2437.58 55.33 3 < .01 1b. Constrain means equal 69.94 17 0.201 0.160 2433.36 38.37 1 < .01 1c.Constrain rc = 1 in DZO 34.74 17 0.732 0.094 2398.16 3.17 1 0.08 -0.17 0.53 0.83 0.78 0.55 0.32 0.77 0.56 Wave 2: 11-13 years Non-aggressive 1. males ≠ females on mean and ACE 28.63 16 0.330 0.145 1684.71 Free rc in DZO 1a. Constrain ACE equal 29.18 19 0.460 0.119 1669.54 0.55 3 0.91 1b. Constrain means equal 30.48 17 0.285 0.145 1681.32 1.30 1 0.25 1c. Constrain rc = 1 in DZO 32.00 17 0.204 0.153 1682.84 1.52 1 0.22 0.00 0.59 0.81 0.75 Aggressive 1. males ≠ females on mean and ACE 20.39 16 0.727 0.085 836.84 Free rc in DZO 1a. Constrain ACE equal 37.80 19 0.000 0.180 849.01 17.41 3 < .01 1b. Constrain means equal 41.97 17 0.000 0.198 853.18 21.58 1 < .01 1c. Constrain rc = 1 in DZO 20.57 17 0.778 0.075 831.78 0.18 1 0.67 0.66 0.07 0.75 0.76 0.60 0.06 0.79 0.52 Wave 4: 14-16 years Non-aggressive 1. males ≠ females on mean and ACE 13.90 16 1 0 4997.76 Free rc in DZO 1a. Constrain ACE equal 27.483 19 0.941 0.068 4992.85 13.58 3 < .01 1b. Constrain means equal 30.333 17 0.907 0.091 5008.03 16.43 1 < .01 1c. Constrain rc = 1 in DZO 13.982 17 1 0.000 4991.68 0.18 1 0.62 0.51 0.60 1.21 0.46 0.63 0.63 1.12 Aggressive 1. males ≠ females on mean and ACE 26.598 16 0.787 0.083 3146.37 Free rc in DZO 1a. Constrain ACE equal 60.008 19 0.176 0.151 3161.28 33.41 3 < .01 1b. Constrain means equal 76.222 17 0.000 0.191 3189.82 49.62 1 < .01 1c. Constrain rc = 1 in DZO 27.864 17 0.782 0.082 3141.47 1.26 1 0.26 0.57 0.35 0.74 0.93 0.20 0.55 0.81 0.63 Note. Non-significant parameters were underscored. Table options In Wave 2, males and females showed the only environmental influences (both shared and non-shared) and for non-aggressive ASB and these were not significantly different across sex (A = 0%, C = 35%, E = 65%). In contrast, aggressive ASB was mainly under the influences of genetic and unique environmental factors with a significant sex difference (A = 44%, C = 1%, E = 55% for males; A = 36%, C = 1%, E = 63% for females). Significant sex differences were found on means of both ASB (M = 0.75 in males. M = 0.66 in females for non-aggressive ASB; M = 0.76 in males, M = 0.52 in females for aggression), indicating that males were more likely to be aggressive than females at age 11-13 years old. The best fitting model in Wave 4 supported sex differences for means and ACE components in both forms of ASB. This suggested that again males committed more (both non-aggressive and aggressive) ASB than females (M = 1.21 in males, M = 1.12 in females for non-aggressive ASB; M = 0.93 in males, M = 0.63 in females for aggression). Non-aggressive ASB was more heritable in males than in females (A = 38%, C = 26%, E = 36% for males; A = 26%, C = 37%, E = 37% for females). For aggression in females, there were greater influences from environmental factors (A = 4%, C = 30%, E = 66%), while there genetic influences were greater in males (A = 18%, C = 16%, E = 66%). Bivariate genetic model Next a series of bivariate models were fit to the data to examine the genetic and environmental overlap between non-aggressive and aggressive ASB within each of the three waves, Table 6. The best fitting model for Wave 1 was model 3, while for Waves 2 and 4, it was model 2. Table 6. Fit Indices for Multivariate Cholesky Models for Non-Aggressive and Aggressive ASB Overall Fit Chi-square difference test χ2 df CFI BIC RMSEA Δχ2 Δdf P Wave 1: 9-10 years 1 males ≠ females on ACE of non-aggressive ASB males ≠ females on ACE of aggression males ≠ females on means 78.08 48 0.94 6949.54 0.072 2 males = females on ACE of non-aggressive ASB males ≠ females on ACE of aggression males ≠ females on means 107.63 51 0.891 6959.88 0.096 29.55 3 < .01 3 males = females on ACE of both ASB males ≠ females on means 157.28 57 0.808 6971.10 0.121 79.2 9 < .01 4 Males = females on all 202.61 59 0.725 7003.62 0.142 124.53 11 < .01 Model 1 + Drop NS paths 87.37 53 0.93 6926.81 0.073 9.29 5 0.09 Wave 2: 11-13 years 1 males ≠ females on ACE of non-aggressive ASB males ≠ females on ACE of aggression males ≠ females on means 56.74 48 0.948 2397.18 0.070 2 males = females on ACE of non-aggressive ASB males ≠ females on ACE of aggression males ≠ females on means 57.68 51 0.96 2382.41 0.059 0.94 3 0.84 3 males = females on ACE of both ASB males ≠ females on means 111.63 57 0.675 2404.95 0.160 54.89 9 < .01 4 Males = females on all 139.13 59 0.523 2421.97 0.190 82.39 11 < .01 Model 2 + Drop NS paths 58.02 56 0.99 2356.58 0.031 0.34 5 1 Wave 4: 16-18 years 1 males ≠ females on ACE of non-aggressive ASB males ≠ females on ACE of aggression males ≠ females on means 59.78 48 0.976 7860.27 0.051 2 males = females on ACE of non-aggressive ASB males ≠ females on ACE of aggression males ≠ females on means 74.09 51 0.954 7856.08 0.069 14.31 3 < .01 3 males = females on ACE of both ASB males ≠ females on means 97.02 57 0.92 7842.02 0.086 37.24 9 < .01 4 Males = females on all 147.64 59 0.823 7880.32 0.126 87.86 11 < .01 Model 1 + Drop NS paths 60.56 50 0.98 7848.72 0.047 0.78 2 0.68 Table options Figs. 1a, b (Wave 1: males and females separately), 2a, b (Wave 2: males and females separately) and 3a, b (Wave 3: males and females separately) display standardized estimates for the best-fitting bivariate models. Squaring the standardized parameter estimates presented in Figs. 1a,b, 2a,b, and 3a,b provides the relative contributions to the phenotypic variance to non-aggressive and aggressive ASB. a and b. Best fitting model for bivariate genetic analysis between ... Fig. 1. a and b. Best fitting model for bivariate genetic analysis between non-aggressive and aggressive ASB, for males and females respectively, at ages 9-10 years. A = additive genetic variance, C = shared environmental variance, E = non-shared environmental variance. Figure options a and b.Best fitting model for bivariate genetic analysis between non-aggressive ... Fig. 2. a and b. Best fitting model for bivariate genetic analysis between non-aggressive and aggressive ASB, for males and females respectively, at ages 11-13 years. A = additive genetic variance, C = shared environmental variance, E = non-shared environmental variance. Figure options a and b.Best fitting model for bivariate genetic analysis between non-aggressive ... Fig. 3. a and b. Best fitting model for bivariate genetic analysis between non-aggressive and aggressive ASB, for males and females respectively, at ages 16-18 years. A = additive genetic variance, C = shared environmental variance, E = non-shared environmental variance. Figure options For males at Wave 1, about 60% of the shared environmental effects and 21% for non-shared environmental effects in aggression were due to factors also common to non-aggressive ASB. For girls at Wave 1, about 55% of genetic effects in aggression were due to factors also common to non-aggressive ASB, while it was only 16% for non-shared environmental effects. For males at Wave 2, all of the shared environmental effects in aggression were due to factors also common to non-aggressive ASB, while it was about 20% for non-shared environmental effects. The same pattern held for girls at Wave 2, with shared environmental effects of aggression completely overlapping with those of non-aggressive ASB, but only 33% for non-shared environmental effects. For males at Wave 4, all of the genetic effects in aggression were due to factors also common to non-aggressive ASB, while it was about 28% for shared environmental effects and 16% for non-shared environmental effects. For girls, 56% of shared environmental variations and 12% of unique environmental variations in aggression were from factors also common to non-aggressive ASB.