Continuity and Change in Preschool ADHD Symptoms: Longitudinal Genetic Analysis with Contrast Effects

Transcription

1 Behavior Genetics, Vol. 35, No. 2, March 2005 (Ó 2005) Continuity and Change in Preschool ADHD Symptoms: Longitudinal Genetic Analysis with Contrast Effects Thomas S. Price, 1,4 Emily Simonoff, 2 Philip Asherson, 2 Sarah Curran, 2 Jonna Kuntsi, 2 Irwin Waldman, 3,4 and Robert Plomin 2 Received 22 August 2003 Final 14 July 2004 The genetic and environmental mediation of continuity and change in parent-reported ADHD symptoms were investigated in a cohort of over 6000 twin pairs at 2, 3 and 4 years of age. Genetic analyses of the cross-sectional data yielded heritability estimates of at each age, with contrast effects. A common pathway model provided the best fit to the longitudinal data, indicating that genetic influences underlie 91% of the stable variance in ADHD symptomatology. In other words, what is stable for ADHD symptoms is largely genetic. Contrast effects acting in the same direction at different ages contributed to the observed continuity:longitudinal correlations were greater for dizygotic than monozygotic twins. KEY WORDS: ADHD, Attention-Deficit/Hyperactivity Disorder; Child behavior disorders; Genetics; Twins. ABBREVIATIONS: ADHD, Attention-Deficit/Hyperactivity Disorder; TEDS, Twins Early Development Study; MZ, monozygotic; DZ, dizygotic; DZSS, dizygotic same sex; DZOS, dizygotic opposite sex; CBCL, Child Behavior Checklist. INTRODUCTION Attention-Deficit/Hyperactivity Disorder (ADHD) is an early-onset disorder characterized by inattention, impulsivity, and overactivity. ADHD occurs in approximately 3 6% of school-age children, affecting about three times as many males as females (Tannock, 1998). It is a major cause of childhood behavioral problems, including impaired family and social relationships and failure at school. The long-term outcome is poor, with increased risk for social isolation, psychopathology, and antisocial behavior in 1 Wellcome Trust Centre for Human Genetics, University of Oxford, UK. 2 Social, Developmental an Psychiatry Research Centre, Institute of Psychiatry, London, UK. 3 Emory University, Atlanta, GA. 4 To whom correspondence should be addressed at The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt, Drive OX3 7BN, UK. Tel: Fax: tprice@well.ox.ac.uk The Twins Early Development Study is funded by the Medical Research Council. 121 adolescence and adulthood (Mannuzza and Klein, 2000). Research has concentrated on school-age children, and less is known about the emergence of inattentive and overactive behaviors in early childhood and how they may persist, remit, or change with development. Clinically significant impairments are known to ensue from early behavior problems:population samples of children identified as hard-to-manage at age 3 or 4 have a high probability of persisting in overactive and inattentive behavior throughout childhood and into adolescence (Caspi et al., 1995; Richman et al., 1982). The cognitive symptoms of ADHD emerge for these children between the ages of 6 and 9, with impairments in attention and impulse control (Marakovitz and Campbell, 1998). Current evidence points to the emergence of overactive and impulsive symptoms considerably earlier, at around 3 4 years (Barkley, 1997), suggesting that temperamental overactivity during early childhood may be the most important predictor of the developmental emergence of ADHD /05/ /0 Ó 2005 Springer Science+Business Media, Inc.

2 122 Price, Simonoff, Asherson, Curran, Kuntsi, Waldman, and Plomin Genetically informative studies agree that there is a substantial genetic influence on liability to the disorder. ADHD is known to run in families (Hechtman, 1996), and twin studies of ADHD in middle childhood and adolescence, using both population and clinic-based samples, have consistently shown strong genetic influences. Estimates of heritability cluster between 0.6 and 0.9, with the higher estimates tending to come from studies employing symptom rating scales rather than diagnostic categories (reviewed in Faraone and Biederman, 1998; Thapar et al., 1999). Similarly, adoption studies of ADHD diagnoses (Cantwell, 1975; Morrison and Stewart, 1973; Sprich et al., 2000) and measures of attention (Alberts-Corush et al., 1986; van der Valk et al., 1998) suggest genetic influences, despite methodological limitations in some studies. Extreme scores on symptom scales are also liable to strong genetic influences (Gillis et al., 1992; Gjone et al., 1996; Levy et al., 1997; Price et al., 2001; Stevenson, 1992), suggesting that dimensional measures of ADHD symptomatology will provide a useful approach to the identification of genetic variants associated with ADHD, complementary to the study of deemed clinical types (Asherson and Curran, 2001). The pattern of strong genetic influence extends to behaviors related to ADHD in pre-school children. Twin studies have found evidence of strong genetic influences for parent reports and other measures of activity (Cyphers et al., 1990; Rietveld et al., 2003a; Saudino and Eaton, 1991, 1995; Saudino et al., 1996; Stevenson and Fielding, 1985), and extreme overactive and inattentive behavior (Manke et al., 2001; Price et al., 2001). Adoption studies of activity in infancy and childhood, however, have found much smaller parent-offspring correlations than would be expected on the basis of large additive genetic influences (Plomin et al. 1991, 1998). While it seems clear that genetic influences play a large part in the expression of ADHD symptomatology, far less is known about how genetic and environmental factors influence continuity and change in ADHD symptoms during the transition from early childhood to school age. Of particular concern to molecular genetic investigations is the degree to which stable genetic influences mediate the continuity between ADHD in early and middle childhood. Little evidence relates directly to this issue. A Dutch twin study has reported both additive and non-additive genetic contributions to stability between parent-rated Child Behavior Checklist (CBCL; Achenbach, 1991) measures of overactive behavior at age 3 and inattentive behavior at age 7. The additive genetic effects correlated 0.57 (0.60) for males (and females) respectively, and accounted for 0.59 (0.73) of the longitudinal correlation of 0.4l (Rietveld et al., 2004). Twin studies have also suggested a genetic component to the stability in temperamental overactivity through the pre-school period (Torgersen, 1981; Saudino and Cherny, 2001a, b). The MacArthur Longitudinal Twin Study is noteworthy for including both observational and parentally rated measures of activity. Although observational measures of activity only showed modest continuity it was mediated via stability in genetic rather than environmental influences, with essentially the same genetic influences acting at 14, 20 and 24 months (Saudino and Cherny, 2001b). Parent reports of activity at 14, 20, 24, and 36 months of age showed substantial phenotypic stability and heritability (Saudino and Cherny, 2001a). Correlations between parent-report and observer measures were small, consistent with other research showing relatively little convergence between parent-report and observer measures of temperament (e.g. Siefer et al., 1994). The analysis of phenotypes related to ADHD, including parental ratings of activity and symptom counts based on parent interviews, may however be problematic due to a pattern of results that is often seen in twin studies of activity (Thapar et al., 1999). Contrast effects are seen when the higher the rating is for one twin, the lower the rating is for the other twin. In twin studies, this manifests itself in intraclass correlations that are high for MZ twins but very low or negative for DZ twins, despite their genetic similarity. Ratings also show greater variability for DZ than MZ twins. There is some evidence to suggest that contrast effects in parental ratings of ADHD symptoms reflect rater bias rather than true behavioral interaction between twins (Simonoff et al., 1998) and may be specific to the rating scale employed (Thapar et al., 2000). Further, it has been suggested that contrast effects on parental ratings may be stable rather than age-specific, and so contribute artifactually to stability (Plomin et al., 1993; Saudino and Cherny, 2001a). It seems a priority to test the latter hypothesis, yet developmental behavioral genetic studies have never done so. A previous report on this sample has shown that at ages 2, 3, and 4 parental-report ADHD symptoms are under strong genetic influence, with significant non-shared environmental influences and sibling contrast effects (Price et al., 2001). We aimed

3 Continuity and Change in Preschool ADHD Symptoms 123 to extend this analysis by modeling the continuity and change in ADHD symptomatology through early childhood. By assessing the relative fit of multivariate genetic models with different assumptions about the relationships among our measures, we were able to test the contributions of genetic influences, environmental influences, and contrast effects to continuity and change in ADHD symptoms. In particular, we tested the hypothesis that contrast effects act in a way that increases the age-to-age stability of ADHD symptoms. METHOD Sample As part of the Twins Early Development Study (TEDS; Trouton et al., 2002), the parents of all twins born in England and Wales in 1994 and 1995 were invited to report on their twins behavior at ages 2, 3 and 4. Reply cards were returned by 11,352 parents (71%), of whom 9191 (81%) returned an initial booklet asking for background information about the family. Twin pairs were excluded if there were extreme pregnancy or perinatal difficulties (187 pairs), specific medical syndromes and chromosomal anomalies (161 pairs), or if zygosity could not be assigned (177 pairs). A further 1754 families did not return subsequent booklets, and another 54 families did not complete the behavior questionnaires, leaving 6858 families who reported on their childrens behavior. For the purposes of these analyses, test scores were considered invalid and omitted if they were completed more than 2 months before the target birthday or more than 6 months after the target birthday. A further 18 families were excluded who did not complete the ADHD items, giving a total sample 6840 twin pairs for whom at least one valid measurement was obtained. Full data were obtained for 3279 twin pairs. Despite attrition in the sample over time, both the total sample and families with complete data were reasonably representative of the UK population on a broad range of demographic indicators, including ethnicity, parental education, and parental employment. A parent-rating instrument validated by polymorphic DNA markers was used to assign twin zygosity with 95% accuracy (Price et al., 2000). The total sample included 2312 pairs of identical (MZ) pairs, 2279 non-identical pairs of the same sex (DZSS), and 2249 opposite-sex (DZOS) pairs. Measures At age 2 years, the children were administered the Revised Rutter Parent Scale for Pre-school Children (Behar and Stringfield, 1974; Elander and Rutter, 1996; Hogg et al., 1997). Additional items were added to the scale at age 3, and again at age 4 when the items were slightly altered, using the wording of the items in the Strengths and Difficulties Questionnaire (Goodman, 1997). One parent, in most cases the mother, completed the items by reporting on the frequency of particular behaviors, with often, sometimes and never given scores of 2, 1, and 0, respectively. Exploratory factor analysis of the 2- year data with Varimax rotation revealed a fouritem ADHD factor comprising activity and inattentive behavior items. Similar factor analyses of the 3- and 4-year data also revealed a robust factor of items relating to activity and inattention. For the current purposes only those four items employing the wording very close to that used in the 2-year items were used at 3 and 4 (see Table I). The ADHD symptom scores at each age were calculated as the sum of the item scores divided by the maximum possible score, i.e. twice the number of items, giving a scale between 0 and 1. Data were required for at least three out of four items for a valid score. Analysis Model-fitting Procedures. Structural equation models were fit to the raw data using full-information maximum likelihood estimation, as implemented in the software package Mx 1.51 (Neale et al., 1999). The estimation and standardization of the models were carried out in accordance with standard procedures for modeling twin data. The linear effects of sex and age were incorporated in the analyses as part of the means model. Confidence intervals were estimated using likelihood- Table I. Parent-report ADHD items Ages 2 and 3 Age 4 1. Restless; runs or jumps up and Restless, overactive, down, doesn t keep still cannot stay still for long 2. Squirmy, fidgety Constantly fidgeting or squirming 3. Has poor concentration, Sees tasks through to the or short attention span end, good attention span (reverse coded) 4. Inattentive Inattentive

4 124 Price, Simonoff, Asherson, Curran, Kuntsi, Waldman, and Plomin based techniques rather than standard errors. Goodness-of-fit was assessed using the Bayesian Information Criterion (BIC; Raftery, 1995). This statistic was the preferred index of fit for the current analyses since it provides a test whose preference for parsimony remains stable at large sample sizes. In contrast, both the Likelihood Ratio test and the AIC(Akaike, 1987) provide goodness-of-fit tests that strongly favor saturated models when samples are large as in the present study. The BIC statistic is calculated as BIC ¼ 2LL d lnðnþ; where LL is the log likelihood of the model, d the number of degrees of freedom, and ln(n) the natural logarithm of the sample size. Differences of more than 10 indicate a strong preference for the model with the more negative BIC value. The current analyses required an approximation to the effective n, given that they were fit to the raw data and thus included subjects who had one or more missing data points. We took n as the number of cases used in the analyses that had full data:in the multivariate genetic analyses this was 3279 twin pairs, corresponding to a significant reduction in fit for one degree of freedom of DBIC 8:10. As this n underestimates the sample size by not counting subjects with partial data, this approximation makes the BIC test somewhat less favorable to parsimonious models. Cross-sectional model fitting. Univariate genetic models were fit to the twin data for ADHD symptom scores at each age. Sex-limited baseline models were tested that allowed the coefficient of additive genetic relatedness a to vary between 0 and 0.5 for opposite-sex pairs, fixed the coefficient of dominance genetic relatedness b to 0.25 for opposite-sex pairs, estimated parameters separately for males and females, and where applicable estimated the sibling contrasts (see below) separately for male-tomale, female-to-female, male-to-female and femaleto-male contrasts. A baseline model incorporated parameters for additive genetic (A), dominance genetic (D), and non-shared environmental (E) influences, plus sibling contrast (S) effects. This model is known to be well-identified in a twin design incorporating all five sex and zygosity groups (Rietveld et al., 2003b). The best-fitting submodel at each age was selected using the BIC statistic. No submodels were tested that estimated a dominance term but no additive genetics parameter:such models are biologically implausible for complex traits such as ADHD symptomatology, and would conflict with the results of family studies showing non-zero parent-offspring correlations for ADHD and related phenotypes. Sex differences were tested using a nested series of constraints, applied in the following sequence:constraining the additive genetic relatedness between opposite-sex twins to 0.5; constraining the male-to-female and female-to-male sibling contrasts for opposite-sex pairs to be equal; constraining all the parameter estimates for males and females to be equal but allowing mean differences and scalar variance differences between males and females; and equating the variances for males and females. Mean sex differences were not tested as these had been established in earlier analyses. Lastly, non-significant pathways were removed. The statistical significance of these constraints was assessed by choosing the model with the lower BIC statistic. Longitudinal Model Fitting. Multivariate models of the longitudinal twin data were used to test hypotheses about the continuity and change in symptom scores over time. Two classes of model were tested. Figure 1. Independent Pathwav Model. The first model is based on an independent pathway or biometric model. Common A, D, and E factors load onto the observed measures accounting for the covariance (stability) among them. Age-specific factors account for the remaining variance on the measures. Reciprocal sibling contrasts act on the observed measures. This model constrains the longitudinal phenotypic correlations to be the same for MZ and DZ twins. Contrast effects act to increase phenotypic dissimilarity, and since the within-pair phenotypic dissimilarity is always greater in DZ than MZ pairs for any heritable phenotype, the model assumes that contrast effects do not contribute to phenotypic stability. Figure 2. Common Pathway Model. The second model is based on a common pathway or psychometric model. A common factor loading onto the observed measures accounts for the covariance (stability) among them, as if the longitudinal measurements were tapping into a stable latent phenotype. Age-specific residual factors account for the remaining variance on the measures. Both common and residual factors are subject to additive genetic, dominance, and non-shared environmental influences. Contrast effects operate on the latent common

5 Continuity and Change in Preschool ADHD Symptoms 125 Fig. 1. Independent pathway model. Legend: a = loading from additive genetic factor (A) to observed ADHD phenotype (P); d = loading from dominance factor (D); e = loading from non-shared environment factor (E); s = sibling contrast; a = coefficient of additive genetic relatedness; b ¼ coefficient of dominance relatedness; F suffix = common factor; R suffix = age-specific residual factor; numerical suffices indicate age of child at measurement. Fig. 2. Common pathway model. Legend: a = loading from additive genetic factor (A); d = loading from dominance factor (D); e = loading from non-shared environment factor (E); f = loading from common factor (F) to observed ADHD phenotype (P); r = loading from age-specific residual factor (R); a = coefficient of additive genetic relatedness; b ¼ coefficient of dominance relatedness; F suffix = loading on common factor; R suffix = age-specific residual factor; numerical suffices indicate age of child at measurement.

6 126 Price, Simonoff, Asherson, Curran, Kuntsi, Waldman, and Plomin factor, modeling the contribution of contrast effects to continuity (the covariance among the measures). Separate contrast effects act on the age-specific factors, modeling the contribution of contrast effects on change (age-specific variance). This structure allows longitudinal phenotypic correlations to differ between MZ and DZ twins. Contrast effects on the common factor cause greater phenotypic stability for DZ than MZ twins, whereas contrast effects on the age-specific factors decrease phenotypic stability for DZ relative to MZ twins. The matrix algebra for this model is explained in further detail below. A model with one common factor and p observed variables represents the phenotypic variance/covariance matrix as follows: RR T þ FF T ; where R is a p p diagonal matrix of age-specific residuals, F is a p-vector of loadings on the common factor. In the genetic version of this model, additive genetic, dominance, and non-shared environmental influences on the residuals are modeled by p p matrices A R ; D R and E R. The additive genetic, dominance, and environmental influences on the common factor are modeled by the scalar quantities A F ; D F and E F. Additive genetic influences between twins in the same pair are allowed to correlate with coefficient a, dominance influences with coefficient b. (Typically, a and b would take the value 1 for MZ pairs, and 0.5 and 0.25, respectively, for DZ pairs.) Adapting the genetic model further to allow sibling contrasts between the latent common and residual factors at each age results in the following model for the variance/covariance matrix: ði 22 RÞP R ðm A R A T R þ N D RD T R þ I 22 E R ER T Þ PR T ði 22 RÞ T þði 22 F Þ P F ðm A F A T F þ N D F D T F þ I 22 E F EF T Þ PF T ði 22 F Þ T : where the 2p 2p matrix P R ¼ðI 2p2p B R Þ 1, B R being the matrix of sibling contrasts for the residual factors, the 2 2 matrix P F ¼ðI 22 B F Þ 1, where B F is the 2 2 matrix with 0 on the leading diagonal and the sibling contrast coefficients for the common factor, S F, in the off diagonal elements. M and N are 2 2 correlation matrices with a and b respectively, in the offdiagonal elements. Figures 1 and 2 show the observed variables (squares) and latent variables (circles) for these two models. The variables and pathways relating to one twin are printed in continuous lines, and those relating to the second twin are printed in dotted lines. All the latent factors have unit variance. All common factor loadings are constrained to be positive so that the models converge to plausible solutions. Cotwins factor scores are shown linked by reciprocal arrows indicating sibling contrast. Cotwins additive genetic factors are linked by curved arrows indicating the genetic correlation a which takes the value of 1.0 for MZ twin pairs and 0.5 for same-sex DZ pairs. Similarly, dominance genetic factors are linked by curved arrows indicating the dominance correlation between cotwins b which takes the value of 1.0 for MZ twin pairs and 0.25 for same-sex DZ pairs. In the full sex-limited models estimating all parameters separately for males and females, a was allowed to vary between 0 and 0.5 for opposite-sex DZ pairs while b was fixed to Sibling contrast parameters were estimated separately for male-tomale, female-to-female, male-to-female and femaleto-male contrasts. These two baseline models, while of theoretical interest, contain many more parameters than can realistically be estimated in a standard experimental design with MZ and DZ twins. Indeed, at the present time it has not yet been rigorously established whether these models are in fact well-identified, although the authors are confident that this is the case. Variants of the two baseline models were also applied, testing sex differences by applying the same model-fitting procedure used for the univariate models. As in the univariate model-fitting analyses, dominance genetics parameters were not estimated in the absence of the corresponding additive genetics parameter. RESULTS Descriptive Statistics. The saturated common pathway model was used as a baseline model for testing the linear effects of age, sex, and zygosity (MZ, DZSS, and DZOS groups). Although at each age, males appeared to have higher symptom scores than females in every zygosity group, these sex differences were not statistically significant (see Table II). Constraining the means to be equal across sex groups but within zygosity groups did not provide worse fit (DBIC < 0; df ¼ 3). Mean scores for MZ and DZ twins were generally

7 Continuity and Change in Preschool ADHD Symptoms 127 Table II. Means by sex and zygosity for ADHD symptom scores MZ DZSS DZOS N M SD N M SD N M SD Males Age Age Age Females Age Age Age Note: MZ = monozygotic twins; DZSS = same sex dizygotic twins; DZOS = opposite sex dizygotic twins. similar:constraining the means to be equal across zygosity groups but within sex groups did not provide worse fit (DBIC < 0; df ¼ 4). However, DZ twins appeared to have slightly greater variance on each measure than MZ twins, for both sexes, consistent with a hypothesis of sibling contrast effects. The linear effects of age were non-significant (DBIC < 0; df ¼ 1). Although the measures had positive skew and negative kurtosis, the deviations from normality were not sufficiently large to justify transformation prior to analysis. The 2-year measure showed the most negative kurtosis ( 0.55) and the 4-year measure was the most skewed ( 0.57). Attrition. (See Table III). There were no apparent effects of attrition between ages 2 and 3. Children who did not have a valid symptom score after age 2 were no more or less symptomatic than children who had valid scores only at ages 2 and 3. Some effects of attrition were, however, evident between ages 3 and 4. Children without a valid score after age 3 had higher symptom scores than children who had valid scores at all ages, both on Table III. Analysis of attrition. Means and standard deviations of ADHD symptom scores at ages 2 and 3 by presence of valid scores at subsequent ages Year 2,3,4 2,3,4 2,3,4 Present ( p p pp ppp ) or missing () N Mean a a b SD Mean c d SD No significant differences in variance (p > 0:05, 2-tailed, F test). Superscript letters show significant differences in means (p<0:05, 2- tailed, pooled-variance t test). the 2-year and 3-year measures. Although statistically significant in this large sample, the attrition had a very small effect size:the mean differences were less than 0.1 SD. Phenotypic correlations. In the entire sample, the 2-year measure correlated r = 0.55 (N = 8843) with the 3-year measure, and r =.46 (N = 6783) with the 4-year measure. The 3-year measure correlated r =.60 (N = 8070) with the 4-year measure. Interestingly, despite the change in wording of certain test items between the ages of 3 and 4, there is greater stability in symptom scores between ages 3 and 4 than between ages 2 and 3. Consistent with the hypothesis that phenotypic stability is increased by contrast effects, these correlations were, respectively, greater for DZ twins (r = 0.55, 0.48, 0.63; N = 5886, 4862, 5333) than for MZ twins (r = 0.54, 0.42, 0.54; N = 2974, 2493, 2747). The phenotypic correlations also appeared slightly greater for males (r = 0.55, 0.47, 0.60; N = 4334, 3600, 3951) than for females (r = 0.52, 0.44, 0.58; N = 4526,3755, 4129). Twin correlations. As shown in Table IV, the measures showed a pattern of twin correlations typical for parental ratings of ADHD symptoms:high correlations for MZ pairs (0.54 to 0.69) and low or zero correlations for DZ pairs ( 0.03 to 0.21). In conjunction with the higher variance for DZ than for MZ pairs, this pattern suggests high heritability with sibling contrast effects. Twin correlations were similar for males and females and for same-sex and opposite-sex DZ twins. Cross-sectional Model Fitting. At each age, the genetic model that best fitted the symptom data was a model specifying additive genetic influences, nonshared environmental influences, and sibling contrast effects. The fit statistics and parameter estimates of the best-fitting models are shown in Table V. At ages 2 and 3 the best-fitting model Table IV. Pearson twin correlations for ADHD symptoms by sex and zygosity MZM MZF DZM DZF DZOS r N r N r N r N r N Age Age Age Note: MZM = monozygotic male pairs; MZF = monozygotic female pairs; DZM = dizygotic male pairs; DZM = dizygotic female pairs; DZOS = opposite sex pairs.

8 128 Price, Simonoff, Asherson, Curran, Kuntsi, Waldman, and Plomin Table V. Cross-sectional model fitting results Age n Model 2LL df BIC a 2 e 2 s SD (males) SD (females) AES y (0.77, 0.81) 0.20(0.19, 0.23) 0.13( 0.15, 0.11) 0.26(0.25, 0.26) 0.26(0.25, 0.26) AES y (0.79, 0.82) 0.19(0.18, 0.21) 0.19( 0.17, 0.20) 0.26(0.26, 0.27) 0.26(0.26, 0.27) AES (0.75, 0.80) 0.22(0.20, 0.25) 0.21( 0.22, 0.18) 0.26(0.25, 0.26) 0.25(0.24, 0.25) Note: n = number of twin pairs with full data at each measurement point; 2LL = log likelihood fit statistic; BIC = Bayesian Information Criterion; df = degrees of freedom; A = additive genetic influences; D = dominance influences; E = non-shared environmental influences; S = sibling contrast; y allowing mean sex differences; allowing sex differences in means and variances; a 2 = proportion of additive genetic variance; e 2 = proportion of non-shared environmental variance; s = sibling contrast; SD = standard deviation of measure. estimated only mean sex differences, whereas at age 4 the best-fitting model estimated small but significant differences in variance between males and females. No sex differences were found in the parameter estimates, nor in contrast effects between opposite-sex and sex-same pairs. As shown in Table V, the best-fitting models showed a strong genetic influence, which accounted for of the variance at each age. The remainder of the variance was accounted for by non-shared environment. Sibling contrast effects were estimated to be in the range 0.13 to These results are almost exactly the same as those reported for a subset of this sample born in 1994, using the same measures ranked and standardized separately by sex (Price et al., 2001). As a result of the large sample size, 95% confidence intervals around these estimates were very small. BIC statistics were calculated on the basis of the number of twin pairs with full data at each measurement point, namely 5500 at age 2, 5412 at age 3, and 4462 at age 4. Longitudinal Model Fitting. The fit statistics for the multivariate models tested are presented in Table VI. Based on the univariate model-fitting results, we expected the multivariate model-fitting to reveal additive genetic influences, non-shared environmental influences, contrast effects, and few if any sex differences, with the possible presence of genetic dominance. For each of the longitudinal models described in the Methods section, a saturated sex-limited baseline model was tested that modeled additive genetic, dominance, and nonshared environmental influences, with contrast effects (models 1A and 2A in Table VI). Sex differences in the parameter estimates were tested by fitting submodels that hypothesised sex differences only in means and scale (models 1B and 2B) and sex differences only in means (1C and 2C). It should be noted that sex differences in scale can give rise to sex differences in phenotypic correlations in these models, since the effect sizes of the sibling interactions are functions of the variances. In each case, the best-fitting model was the one that modeled sex differences only in mean levels of symptomatology. One of the saturated models (1A) could not be satisfactorily estimated since convergence was not achieved. Submodels of 1C and 2C were estimated that were constrained to have no contrast effects (1D, 2D) or no dominance effects (1E, 2E). The statistical significance of the dominance parameters in the independent pathway model (1C) and common pathway model (2C) were tested further by dropping only the common dominance factor(s) (models 1F, 2F) and also by dropping only the age-specific dominance factors (models 1G, 2G). The best fitting of these submodels were 1G and 2E. A submodel of 2E that dropped age 3 specific contrast effect (2H) provided greater parsimony with no deterioration in fit (D 2LL ¼ 0:3; Ddf ¼ 1; p ¼ 0:583; DBIC ¼ 7:8). In contrast, dropping the sibling contrast on the common factor from model2e drastically worsened the fit (D 2LL = 103.6, Ddf ¼ 1; p<0:0001; DBIC ¼ 95:5). Comparison of BIC statistics showed that the common pathway model (2H) provided a large improvement in fit over the independent pathway model (1G) (D 2LL = 24.6, Ddf ¼ 5; DBIC ¼ 15:9; Likelihood Ratio test not possible since the models are not nested). A further analysis, motivated by the observation that the phenotypic correlations among the measures were greater for males than females, aimed to further improve the fit by modifying the common pathway model (3H) to allow loadings from the common factor to differ for females as a scalar function of the male loadings. No such improvement was noted (D 2LL = 4.1, Ddf ¼ 1; DBIC ¼ 4:0), indicating that any sex differences in the phenotypic correlations were not statistically significant.

9 Continuity and Change in Preschool ADHD Symptoms 129 Table VI. Longitudinal model fit statistics 2LL df BIC Model 1 1A:ADE (factors), Did not converge ADE (residuals), S 1B:ADE (factors), ADE (residuals), S 1C:ADE (factors), ADE (residuals), S y 1D:ADE (factors), ADE (residuals) y 1E:AE (factors), AE (residuals), S y 1F:AE (factors), ADE (residuals), S y 1G:ADE (factors), AE (residuals), S y Model 2 2A:ADES (factor), ADES (residuals) 2B:ADES (factor), ADES (residuals) 2C:ADES (factor), ADES (residuals) y 2D:ADE (factor), ADE (residuals) y 2E:AES (factor), AES (residuals) y 2F:AES (factor), ADES (residuals) y 2G:ADES (factor), AES (residuals) y 2H:AES (factor), AES (residuals) yz Note: sex-limited model; allowing sex differences in means and variances; y allowing sex differences in means; z AES residuals at 2 and 4 years, AE residual at 3 years. Description of Best-fitting Model (2H). The parameter estimates for the best-fitting model are shown in Fig. 3. Proportions of variance and 95% confidence intervals are reported in Table VII. The common factor is influenced overwhelmingly by additive genetic influences (a 2 F ¼ 0:91; 95% CI:0.90, 0.93). As the common factor mediates the correlations between the measures at different time points, this means that the continuity in ADHD symptoms from age to age is mediated 91% by additive genetic influences. Strong sibling contrast effects operate at the level of the common factor (S F ¼ 0:26). At ages 2 and 4, the majority of the residual variance is mediated genetically (a 2 R2 ¼ 0:69, a2 R4 ¼ 0:58); at age 3, a majority of the residual variance is mediated by non-shared environment (e 2 R3 ¼ 0:63). The contrast effects on the residual variances at 2 and 4 are comparatively small ( 0.09 and 0.11, respectively). The best-fitting model estimates more stability for DZ than MZ twins, due to the strong contrast effects on the common factor. The expected phenotypic correlations for MZ twins are 0.49 between ages 2 and 3, 0.41 between ages 2 and 4, and 0.56 between ages 3 and 4. The corresponding expected phenotypic correlations for DZ twins are 0.55 between ages 2 and 3, 0.47 between ages 2 and 4, and 0.62 between ages 3 and 4. Likewise, the genetic correlations between ADHD symptoms at different ages are higher for DZ than MZ twins. The genetic correlations for MZ twins are 0.57 between ages 2 and 3, 0.48 between ages 2 and 4, and 0.66 between ages 3 and 4. For DZ twins, the genetic correlations are.64 between ages 2 and 3, 0.55 between ages 2 and 4, and 0.73 between ages 3 and 4. DISCUSSION The current study investigated the genetic and environmental origins of ADHD symptoms in a large longitudinal population sample of pre-school age twins. As previously reported, parent-reported ADHD symptoms were highly heritable with nonshared environmental influences and sibling contrast effects (Price et al., 2001). ADHD symptoms were moderately stable:the measures at ages 2, 3, and 4 intercorrelated about 0.5. Model-fitting analyses indicated that the longitudinal data were best explained by a common pathway model, in which the common factor and residual variances were subject to genetic influences, non-shared environmental influences, and sibling contrast effects. The best-fitting common pathway model indicated that stability in the ADHD symptoms was 91% genetically mediated. Sibling contrast effects contributed to both continuity and change in parent-reported ADHD symptoms. Contrast effects were strongest on the common factor, resulting in significantly greater phenotypic stability for DZ than MZ twins. Sibling contrast effects may represent genuine behavioral interaction between siblings, or measurement bias. Therefore, contrast effects on the common factor may represent stable measurement biases that inflate the correlations between ADHD symptom scores at different ages. It should be noted, however, that contrast effects on such a heritable phenotype contribute relatively little to the variance or stability of MZ twins. The extent of any inflation in age-to-age correlations due to contrast

10 130 Price, Simonoff, Asherson, Curran, Kuntsi, Waldman, and Plomin Fig. 3. Results of best-fitting longitudinal model. effects may be tested further in future analyses on the TEDS sample by comparing the ratings of twins with ratings of their younger siblings. The possibility of contrast effects as stable measurement bias also has implications for the design of molecular genetic studies of ADHD and related phenotypes. Association designs typically require the selection of extreme-scoring subjects as probands, and linkage designs using siblings gain most of their power from the inclusion of extreme-scoring sibling pairs, usually concordant high probands or discordant pairs (Dolan and Boomsma, 1998; Eaves and Meyer, 1994; Gu et al., 1996; Risch and Zhang, 1995; Zhang and Risch, 1996). Contrast effects at the extremes may adversely affect proband selection by exaggerating discordance between twins, leading to low rates of concordance for DZ pairs. The substantive findings should be considered in the context of a number of limitations. The most important concerns the measurements of the phenotype, which assessed a limited number of behaviors on a single scale. Although we have not attempted to validate our measures against clinical diagnoses, a recent multivariate analysis of a large sample of twins aged between 8 and 16 found both convergent and discriminant validity for the maternal report Rutter A questionnaire, on which our measures are based, as a measure of ADHD symptomatology. Different ADHD symptom ratings were highly genetically related (Nadder et al., 2001). The change in wording of the test items between ages 3 and 4 may also hamper the interpretation of the current results. Another salient limitation is our reliance on a single parental informant, since the clinical disorder requires that problem behaviors are pervasive across situations. The overwhelming majority of ratings used in the current study were supplied by mothers rather than fathers; teacher ratings were naturally unavailable for preschool children. There Parameter Table VII. Results of best fitting longitudinal model Estimate (95% confidence interval) a 2 F 0.91 (0.90, 0.93) e 2 F 0.09 (0.07, 0.10) s F 0.26 ( 0.27, 0.22) a 2 R (0.64, 0.73) e 2 R (0.27, 0.36) s R ( 0.12, 0.06) a 2 R (0.30, 0.44) e 2 R (0.56, 70) a 2 R (0.51, 0.65) e 2 R (0.35, 0.49) s R ( 0.14, 0.07) f (0.63, 0.67) f (0.84, 0.87) f (0.72, 0.75) r (0.74, 0.78) r (0.49, 0.54) r (0.66, 0.70)

11 Continuity and Change in Preschool ADHD Symptoms 131 is currently no evidence that categories defined with reference to both parent and teacher ratings have a different pattern of genetic and environmental influences than categories defined only by parent report (Goodman and Stevenson, 1989; Sherman et al., 1997; Thapar et al., 2000). However, data from different informants tend to correlate only modestly, so that multivariate analyses can be expected to reveal both shared and informant-specific genetic effects, as in a recent multivariate study of adolescent twins (Martin et al., 2002). This could reflect differences in the way that parents and teachers report similar behaviors, or true differences in children s behavior in the contexts of home and school. The findings underline the advantages of using multiple measures of the phenotype for studies that aim to find genes for ADHD. While the current analyses found no evidence for non-additive genetic effects, their existence remains a plausible hypothesis because the sibling contrast effects that we observe could in part mask non-additive genetic effects. The power of twin studies to detect non-additive genetic effects in the presence of contrast effects can be greatly increased by adding unrelated sibling pairs or non-twin siblings to the sample (Rietveld et al., 2003b). Contrast effects may also mask shared environmental influences, whose presence has been suggested in some studies of older children. Existing studies have had low power to resolve these etiological issues. REFERENCES Achenbach, T. M. (1991). Manual for the Child Behavior Checklist /4 18 and 1991 Profile Burlington, VT: Author. Akaike, H. (1987). Factor analysis and the AIC. Psychometrika 52: Alberts-Corush, J., Firestone, P., and Goodman, J. T. (1986). Attention and impulsivity characteristics of the biological and adoptive parents of hyperactive and normal control children. Am. J. Orthopsychiatry 56: Asherson, P., and Curran, S. (2001). Approaches to gene mapping in complex disorders and their application in child psychiatry and psychology. Br. J. Psychiatr. 179: Barkley, R. A. (1997). ADHD and the nature of self-control New York: Guildford Press. Behar, L., and Stringfield, S. (1974). A behavior rating scale for the preschool child. Dev. Psychol. 10: Cantwell, D. P. (1975). Genetic studies of hyperactive children: Psychiatric illness in biologic and adoptive parents. In R. R. Fieve, D. Rosenthal, and H. Brill (eds.), Genetic research in psychiatr., Baltimore: John Hopkins University Press, pp Caspi, A., Henry, B., McGee, R. O., Moffitt, T. E., and Silva, P. A. (1995). Temperamental origins of child and adolescent behavior problems: From age three to age fifteen. Child. Dev. 66: Cyphers, L. H., Phillips, K., Fulker, D. W., and Mrazek, D. A. (1990). Twin temperament during the transition from infancy to early childhood. J. Am. Acad. Ch. Adolesc. Psychiatr. 29: Dolan, C. V., and Boomsma, D. I. (1998). Optimal selection of sib-pairs from random samples for linkage analysis of a QTL using the EDAC test. Behav. Genet. 28: Eaves, L. J., and Meyer, J. M. (1994). Locating human quantitative trait loci: Guidelines for the selection of sibling pairs for genotyping. Behav. Genet. 24: Elander, J., and Rutter, M. (1996). Use and development of the Rutter parents and teachers scales. Int. J. Meth. Psychiatr. Res. 6: Faraone, S. V., and Biederman, J. (1998). Neurobiology of attention-deficit hyperactivity disorder. Biol. Psychiatr. 44: Gillis, J. J., Gilger, J. W., Pennington, B. F., and DeFries, J. C. (1992). Attention deficit disorder in reading disabled twins: Evidence for a genetic etiology. J. Abnorm. Child Psychol. 20: Gjone, H., Stevenson, J., and Sundet, J. M. (1996). Genetic influence on parent-reported attention-related problems in a Norwegian general population sample. J. Am. Acad. Ch. Adolesc. Psychiatr. 35: Goodman, R. (1997). The Strengths and Difficulties Questionnaire: A research note. J. Child. Psychol. Psychiatr. 38: Goodman, R., and Stevenson, J. (1989). A twin study of hyperactivity, II: The aetiological role of genes, family relationships and perinatal adversity. J. Child Psychol. Psychiatr. 30: Gu, C., Todorov, A., and Rao, D. C. (1996). Combining extremely concordant sibpairs with extremely discordant sibpairs provides a cost effective way to linkage analysis of quantitative trait loci. Genet. Epidemiol. 13: Hechtman, L. (1996). Families of children with attention deficit hyperactivity disorder: A review. Can. J. Psychiat. 41: Hogg, C., Rutter, M., and Richman, N. (1997). Emotional and behavioural problems in children. In I. Sc1are I (ed.), Child psychology porifolio Windsor: NFER-Nelson. Levy, F., Hay, D., McStephen, M., Wood, C., and Waldman, I. (1997). Atention-deficit hyperactivity disorder: A category or a continuum? Genetic analysis of a large-scale twin study. J. Am. Acad. Ch. Adolesc. Psychiat. 36: Manke, B., Saudino, K. J., and Grant, J. D. (2001). Extremes analyses of observed temperament dimensions. In R. N. Emde and J. K. Hewitt (eds.), Infancy to early childhood: Genetic and environmental influences on developmental change Oxford: Oxford University Press, pp Mannuzza, S., and Klein, R. (2000). Longterm Prognosis in Attention-Deficit/Hyperactivity Disorder. Child Adolesc. Psychiatr. Clin. N Am. 9: Marakovitz, S. E., and Campbell, S. B. (1998). Inattention, impulsivity, and hyperactivity from preschool to school age: Performance of hard- to-manage boys on laboratory measures. J. Child. Psychol. Psychiatr. 39: Martin, N., Scourfie1d, J., and McGuffin, P. (2002). Observer effects and heritability of childhood attention-deficit hyperactivity disorder symptoms. Br. J. Psychiatr. 180: Morrison, J. R., and Stewart, M. A. (1973). The psychiatric status of the legal families of adopted hyperactive children. Arch. Gen. Psychiatr. 28: Nadder, T. S., Silberg, J. L., Rutter, M., Maes, H. H., and Eaves, L. J. (2001). Comparison of multiple measures of ADHD symptomatology: A multivariate genetic analysis. J. Child Psychol. Psychiatr. 42: Neale, M. C., Boker, S. M., Xie, G., and Maes, H. (1999). Mx: Statistical modeling, 5th edn Box 126 MCV, Richmond, VA 23298: Department of Psychiatry.

12 132 Price, Simonoff, Asherson, Curran, Kuntsi, Waldman, and Plomin Plomin, R., Coon, H., Carey, G., DeFries, J. C., and Fulker, D. W. (1991). Parent offspring and sibling adoption analyses of parental ratings of temperament in infancy and childhood. J. Pers. 59(4): Plomin, R., Corley, R., Caspi, A., Fulker, D. W., and Defries, J. (1998). Adoption results for self-reported personality: evidence for nonadditive genetic effects? J. Pers. Soc. Psychol. 75(1): Plomin, R., Emde, R. N., Braungart, J. M., Campos, J., Corley, R., Fulker, D. W., Kagan, J., Reznick, S., Robinson, J., Zahn-Wexler, C., and DeFries, J. C. (1993). Genetic change and continuity from fourteen to twenty months: The MacArthur Longitudinal Twin Study. Child. Dev. 64: Price, T. S., Freeman, B., Craig, I., Petrin, S. A., Ebersole, L., and Plomin, R. (2000). Infant zygosity can be assigned by parental report questionnaire data. Twin Res. 3: Price, T. S., Simonoff, E., Waldman, I., Asherson, P., and Plomin, R. (2001). Hyperactivity in pre-school children is highly heritable. J. Am. Acad. Ch. Adolesc. Psychiatr. 40: Raftery, A. (1995). Bayesian model selection in social research. Sociol. Methodol. 25: Rietveld, M. J. H., Hudziak, J. J., Bartels, M., van Beijsterveldt, C. E., and Boomsma, D. I. (2003a). Heritability of attention problems in children: I. cross-sectional results from a study of twins, age 3 12 years. Am. J. Med. Genet. 117 (1): Rietveld, M. J. H., Hudziak, J. J., Bartels, M., van Beijsterveld, C. E. M., and Boomsma, D.I. (2004). Heritability of attention problems in children: II Longitudinal results from a study of twins, age 3 to 12. J. Child Psychol. Psychiatr. 45: Rietveld, M. J. H., Posthuma, D., Dolan, C. V., and Boomsma, D. I. (2003b). ADHD: Sibling interaction or dominance: an evaluation of statistical power. Behav. Genet. 33: Richman, N., Stevenson, J., and Graham, P. J. (1982). Pre-school to school: A behavioural study London:Academic Press. Risch, N., and Zhang, H. (1995). Extreme discordant sib pairs for mapping quantitative trait loci in humans. Science 268: Saudino, K. J., and Cherny, S. S. (2001a). Parental ratings of temperament in twins. In R. N. Emde, and Hewitt, J. K. (eds.), Infancy to early childhood: Genetic and environmental influences on developmental change Oxford: Oxford University Press, pp Saudino, K. J., and Cherny, S. S. (2001b). Sources of continuity and change in observed temperament. In R. N. Emde, and J. K. Hewitt (eds.), Infancy to early childhood: Genetic and environmental influences on developmental change, Oxford: Oxford University Press pp Saudino, K. J., and Eaton, W. O. (1991). Infant temperament and genetics: An objective twin study of motor activity level. Child. Dev. 62: Saudino, K. J., and Eaton, W. O. (1995). Continuity and change in objectively assessed temperament: A longitudinal twin study of activity level. Br. J. Dev. Psychol. 13: Saudino, K. J., Plomin, R., and DeFries, J. C. (1996). Tester-rated temperament at 14,20 and 24 months: Environmental change and genetic continuity. Br. J. Dev. Psychol. 14: Sherman, D. K., McGue, M. K., and Iacono, W. G. (1997). Twin concordance for Attention Deficit Hyperactivity Disorder: A comparison of teachers and mothers reports. Am. J. Psychiatr. 154: Siefer, R., Sameroff, A. J., Barrett, L. C., and Krafchuck, E. (1994). Infant temperament measured by multiple observations and mother reports. Child. Dev. 65: Simonoff, E., Pickles, A., Hervas, J. L., Silberg, J. L., Rutter, M., and Eaves, L. J. (1998). Genetic influences on childhood hyperactivity: Contrast effects imply parental rating bias, not sibling interaction. Psychol. Med. 28: Sprich, S., Biederman, J., Crawford, M. H., Mundy, E., and Faraone, S. V. (2000). Adoptive and biological families of children and adolescents with ADHD. J. Am. Acad. Child Adolesc. Psychiatr. 39: Stevenson, J. (1992). Evidence for a genetic etiology in hyperactivity in children. Behav. Genet. 22: Stevenson, J., and Fielding, J. (1985). Ratings of temperament in families of young twins. Brit. J. Dev. Psychol. 3: Tannock, R. (1998). Attention deficit hyperactivity disorder: Advances in cognitive, neurobiological, and genetic research. J. Child Psychol. Psychiatr. 165: Thapar, A., Harrington, R., Ross, K., and McGuffin, P. (2000). Does the definition of ADHD affect heritability? J. Am. Acad. Child Adolesc. Psychiatr. 39: Thapar, A., Holmes, J., Poulton, K., and Harrington, R. (1999). Genetic basis of attention deficit and hyperactivity. Br. J. Psychiatr. 174: Torgersen, A. M. (1981). Genetic factors in temperamental individuality. A longitudinal study of same-sexed twins from two months to six years of age. J. Am. Acad. Child Adolesc. Psychiatr. 20: Trouton, A., Spinath, F. M., and Plomin, R. (2002). Twins Early Development Study (TEDS): A multivariate, longitudinal genetic investigation of language, cognition and behavior problems in childhood. Twin Res. 5: van der Valk, J. C., Verhulst, F. C., Neale, M. C., and Boomsma, D. I. (1998). Longitudinal genetic analysis of problem behaviours in biologically related and unrelated adoptees. Behav. Genet. 28: Zhang, H., and Risch, N. (1996). Mapping quantitative trait loci by use of extreme concordant sib pairs: selected sampling by parental phenotypes. Am. J. Hum. Genet. 59: Edited by Dorret Boomsma