Genetic influences on the cognitive biases associated with anxiety and depression symptoms in adolescents

Journal of Affective Disorders 124 (2010) 45–53

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Journal of Affective Disorders j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / j a d

Research report

Genetic influences on the cognitive biases associated with anxiety and depression symptoms in adolescents Helena M.S. Zavos a,⁎, Frühling V. Rijsdijk a, Alice M. Gregory b, Thalia C. Eley a a b

Social, Genetic, & Developmental Psychiatry Centre, Institute of Psychiatry, King′s College London, United Kingdom Department of Psychology, Goldsmiths, University of London, United Kingdom

a r t i c l e

i n f o

Article history: Received 22 January 2009 Received in revised form 30 October 2009 Accepted 31 October 2009 Available online 28 November 2009 Keywords: Anxiety Depression Anxiety sensitivity Attributional style Twins

a b s t r a c t Background: There is a substantial overlap between genes affecting anxiety and depression. Both anxiety and depression are associated with cognitive biases such as anxiety sensitivity and attributional style. Little, however, is known about the relationship between these variables and whether these too are genetically correlated. Methods: Self-reports of anxiety sensitivity, anxiety symptoms, attributional style and depression symptoms were obtained for over 1300 adolescent twin and sibling pairs at two time points. The magnitude of genetic and environmental influences on the measures was examined. Results: Strongest associations were found between anxiety sensitivity and anxiety ratings at both measurement times (r = .70, .72) and between anxiety and depression (r = .62 at both time points). Correlations between the cognitive biases were modest at time 1 (r = − .12) and slightly larger at time 2 (r = − .31). All measures showed moderate genetic influence. Generally genetic correlations reflected phenotypic correlations. Thus the highest genetic correlations were between anxiety sensitivity and anxiety ratings (.86, .87) and between anxiety and depression ratings (.77, .71). Interestingly, depression ratings also showed a high genetic correlation with anxiety sensitivity (.70, .76). Genetic correlations between the cognitive bias measures were moderate (− .31, − .46). Limitations: The sample consists primarily of twins, there are limitations associated with the twin design. Conclusions: Cognitive biases associated with depression and anxiety are not as genetically correlated as anxiety and depression ratings themselves. Further research into the cognitive processes related to anxiety and depression will facilitate understanding of the relationship between bias and symptoms. © 2009 Elsevier B.V. All rights reserved.

Anxiety and depression are among some of the most prevalent psychiatric problems in children and adolescents (Costello et al., 2005; Hankin et al., 1998) and co-occur at a rate much greater than chance (Angold et al., 1999). As symptoms in adolescence often persist into adulthood (Gregory et al., 2007; Pine et al., 1999) taking a developmen-

⁎ Corresponding author. Box P080, SGDP Centre, Institute of Psychiatry, De′Crespigny Park, London, SE5 8 AF, United Kingdom. Tel.: +44 20 7848 0687; fax: +44 20 7848 0866. E-mail address: [email protected] (H.M.S. Zavos). 0165-0327/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jad.2009.10.030

tal approach to their investigation is necessary. Moreover, early intervention may prevent difficulties from persisting into adulthood. A wealth of quantitative genetic studies over the past two decades has provided evidence for moderate genetic influences on anxiety and depression across the lifespan (Eaves et al., 1997; Eley and Stevenson, 1999a; Thapar and Rice, 2006). As symptoms of anxiety and depression are highly comorbid, they are often studied in combination and one of the most consistent genetic findings is that they are influenced largely by the same genes (Eley and Stevenson, 1999a; Thapar

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and McGuffin, 1997). It is becoming increasingly clear that in a broader sense, genetic influences on aspects of child psychopathology tend to be more shared than specific. Environmental factors, conversely, tend to be more specific. This phenomenon is described by some as the ‘generalist genes hypothesis’ (Plomin and Kovas, 2005). In other words, genes affecting closely associated phenotypes are likely to be shared. Comparatively less is known about factors which might mediate these genetic links and explain how the same genes influence different disorders. Important areas of investigation include cognitive biases (e.g. Abramson et al., 1978), genetic polymorphisms (e.g. Lesch et al., 1996) and environmental (e.g. Jaffee et al., 2002) vulnerabilities. The primary aim of this study was to explore how two cognitive biases (anxiety sensitivity and attributional style) associated with anxiety and depression are related, phenotypically and genetically, to each other and to the symptoms themselves. Biased cognitions have been shown to be key components of both anxiety and depression (e.g. Barrett et al., 1996; Hankin et al., 1998; Silverman et al., 1999). Recent focus has considered such biases as diathesis-stress factors (i.e. latent predispositions which are expressed in the face of environmental adversity) helping to explain how environmental events are differentially interpreted and experienced by individuals. Three main levels of bias have been described in cognitive models of anxiety; those of attention, interpretation and memory (reviewed by Muris and Field, 2008). The current study focuses on biases of attention and interpretation which have been shown to be important across all types of anxiety.

One such attentional bias receiving much research interest is anxiety sensitivity (Silverman et al., 1999; Stein et al., 1999). This refers to sensitivity to the physical and emotional symptoms of anxiety (an attentional bias) and the belief that these are harmful (an interpretational bias: Reiss et al., 1986). Although initially developed with respect to panic disorder, subsequent research has provided evidence of a relationship between anxiety sensitivity and not only panic, but anxiety in general (Stein et al., 1999; Taylor et al., 1996; Muris et al., 2001; Silverman et al., 1999) More recently anxiety sensitivity has been investigated in the context of depression, as researchers are beginning to acknowledge the possibility that cognitive vulnerabilities for these two difficulties are not necessarily entirely specific to the disorder being examined. Anxiety sensitivity has been shown to be heritable with estimates of around 35% in children (Eley et al., 2007) and 50% in adults (Stein et al., 1999) with some evidence of a genetic correlation between anxiety sensitivity and anxiety in children (Eley et al., 2007). No such studies have been conducted using an adolescent sample. Thus far, no quantitative genetic studies of anxiety sensitivity and depression have been conducted. In summary, previous research has provided support for a phenotypic relationship between anxiety sensitivity and anxiety and depression. We expect there is to be a similar genetic relationship between these variables in adolescence. With regards to the genetic overlap between anxiety sensitivity and depression, considering the strong phenotypic relationship between them, we would expect some degree of

Fig. 1. Full multivariate ACE model of anxiety sensitivity, anxiety, attributional style and depression.

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genetic overlap. Such a finding would suggest that anxiety sensitivity is less symptom specific and is associated with affective disorders in a broader sense. A key component of cognitive style associated with depression is biased attributions of negative and positive events (Abramson et al., 1978). According to the reformulated helplessness theory, those who attribute negative events to internal, (directed to the self), stable (likely to persist over time) and global (likely to affect all aspects of life) causes and positive events to external, unstable and specific causes, are at risk for depression (Abramson et al., 1978). The importance of attributional style has been supported by cross-sectional (Gladstone and Kaslow, 1995) and prospective (Hankin and Abramson, 2001) studies. More recently, it has been demonstrated that genetic effects are important in the development of attributional style and that genetic effects account for a large proportion of the correlation between attributional style and depression (Lau et al., 2006). Attributional style has yet to be investigated in relation to anxiety, but given its close association with depression it will be particularly interesting to see whether there is a genetic overlap between them. The current study sought to investigate whether the high genetic correlation seen between anxiety and depression is also found between anxiety sensitivity and attributional style. A high genetic correlation would be expected, in line with the generalist genes hypotheses, if anxiety sensitivity and attributional style are found to be phenotypically correlated. A high genetic correlation between the cognitive biases would suggest that anxiety sensitivity and attributional style are acting as a single genetic risk factor for anxiety and depression. In contrast, environmental experiences across anxiety and depression are fairly distinct (Eley and Stevenson, 2000). Thus, we might expect that environmental correlations between the cognitive biases to be low. It is hoped that this research will help clarify the role of cognitive biases in the development of anxiety and depression.

1. Method 1.1. Participants The present analyses combine data from the G1219 and G1219Twins longitudinal studies. G1219 began as the adolescent offspring of adults from a large-scale populationbased study (GENESiS: Genetic–Environment Study of Emotional States in Siblings, Sham et al., 2000). Of the 9000 families contacted through GENESiS, a total of 3600 (40%) participated either in this study or another study on hyperactivity (Curran et al., 2003). The G1219Twins are a random selection of live twin births born between 1985 and 1988 identified by the UK Office of National Statistics. Health Authorities and General Practitioners then contacted families (Eley et al., 2004). At wave 1 of the data collection (which took place between 1999 and 2002) 3640 respondents aged between 12 and 19 years participated in the study. Informed consent was obtained from parents/ guardians of all adolescents under 16 years, and from the adolescents themselves when over 16. Ethical approval for different stages of this study has been provided by the Research Ethics Committees

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of the Institute of Psychiatry, South London and Maudsley NHS Trust, and Goldsmiths, University of London. Analyses reported here focus on the second and third waves of the data collection that involved only the G1219Twins sample and sibling pairs from the G1219 study. Data collection for waves 2 and 3 (from hereon referred to as time 1 and time 2 for ease of presentation) respectively took place approximately 8 (range: 0–2 years) and 33 months (range: 2–5) after initial contact. At time 1, data were available from 2651 individuals (73% of the original sample at wave 1) whilst corresponding figures for time 2 were 1597 adolescents (44% of the original sample at wave 1). Zygosity was established through a questionnaire measure completed by mothers at times 1 and 2, assessing physical similarity between twins (Cohen et al., 1975). When zygosity was only available on one or another wave, this rating was used. If there were disagreements between zygosity rating at the two time points, DNA was obtained (N = 26 pairs) before final classifications were made. The entire G1219 sample consists of 118 MZ male twin pairs, 199 MZ female twin pairs, 138 DZ male twin pairs, 190 DZ female pairs, 463 opposite-sex DZ pairs, 109 male sibling pairs, 132 female sibling pairs and 186 opposite-sex sibling pairs. At times 1 and 2, the proportions of girls and boys were 56.1% and 43.9% and 58.7% to 41.3% respectively. The mean age of the sample at time 1 and time 2 were 15 years (range 12–21) and 17 years (range 14–23). Levels of parental education were somewhat higher (39% educated to A-level or above) than in a large nationally represented sample of parents where 32% were educated to A-level or above (Meltzer et al., 2000). G1219 parents were also somewhat more likely to own their own houses (82%) than in the nationally representative sample (68%). To reduce the impact of any initial response bias associated with educational level, the sample was re-weighted to match the distribution of educational qualifications in a nationally representative sample of parents (Meltzer et al., 2000). Predictors of attrition between waves were also examined. Girls and individuals whose parents had higher educational qualifications and were owner–occupiers were more likely to respond at time 1 and time 2. A single weighting variable incorporating both initial response and later attrition biases was created by multiplying weights together, and was included in all statistical analyses. Weighting involves assigning lower weights to individuals from over-represented categories and higher weights to individuals from underrepresented categories in the sample relative to the population distribution. Weights were created to be family-general, such that in model-fitting analyses, the weights did not incur any additional individual-specific effects between members of the same family. 1.2. Measures 1.2.1. Anxiety sensitivity The Child Anxiety Sensitivity Index (CASI: Silverman et al., 1991) is a 16-item questionnaire which measures children′s sensitivity to different symptoms of anxiety. It includes items such as ‘It scares me when my heart beats fast.’ Children rate each item on a 4 point Likert scale (1 = never to 4 = always). A total CASI score can be computed by summing the items.

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This measure has good psychometric properties (Silverman et al., 1991). In the current sample the internal consistency was .82 and .86 at times 1 and 2 respectively. 1.2.2. Anxiety The Spence Children′s Anxiety Scale (SCAS: Spence, 1998) is a self-report questionnaire assessing anxiety disorder symptoms in children. The scale contains 38 items tapping into generalized anxiety, separation anxiety, social phobia, panic and agoraphobia, obsessive–compulsive disorder and fears of injury (items include ‘I worry that something bad will happen’). SCAS items are measured on a four-point scale (never to always). SCAS total scores are computed by summing the items. Generally the internal consistency of this measure is high as is its test–re-test reliability (Spence, 1998). The internal consistencies of SCAS in the current sample were .88 and .87 at the two time points. 1.2.3. Attributional style The revised Children′s Attributional Style Questionnaire (CASQ: Thompson et al., 1998) measures 24 forced-choice items that assess the three dimensions of attributional style (internal–external, global–specific and stable–unstable). Each item describes a positive or negative event (e.g. ‘You get A on a test’) followed by two possible causes of the event (e.g. ‘I am clever’ or ‘I am good in the subject the test was in’), from which the individuals must choose. Each set of response options holds constant two of the three dimensions, whilst varying the third, allowing for independent assessment of that dimension. A composite score is computed by summing across all responses. Lower composite scores indicate more negative attributional styles. Moderate internal consistency has been reported ranging from .40 to .60 (Thompson et al., 1998). Test–re-test reliabilities over a six month period have also been modest at .53. Finally criterion-related validity assessed through associations with measures of depression are adequate (r = −.40). Internal consistencies of the current sample were moderate, .61 and .66 at times 1 and 2 respectively. 1.2.4. Depression Depression was rated on the Short Mood and Feelings Questionnaire (SMFQ: Angold, 1995a,b), a self-report measure consisting of 13 items which assess the core depressive symptoms occurring over the past two weeks. A total depression score was created by summing items. At time 1, a four-point response format (never to always) was used to allow for better discrimination at the lower end of the spectrum. The standard three-point scale was used at time 2. The SMFQ has good internal consistency and adequate test– re-test reliability (Costello and Angold, 1988; Costello et al., 1991). It also correlates well with other well-known measures of depression (.67 with the Children′s Depression Inventory and .51 with the depression scores for children) (Angold et al., 1995a,b). The internal consistencies in the current sample were .90 and .88 at times 1 and 2. 1.3. Model-fitting analyses The rationale of the twin and sibling design is to compare the degree of similarity of resemblance among monozygotic

(MZ) twins, who share 100% of their genetic make-up, and dizygotic (DZ) twins, who share on average 50% of their segregating genes. Relative differences in within-pair correlations are then used to estimate additive genetic (A) shared environmental (C) and non-shared environmental (E) effects on measures. Where correlations are higher for MZ twins as compared to DZ and full sibling (FS) pairs, genetic influence is assumed to be playing a role. Within-pair similarity that is not due to genetic factors is accounted for by shared environmental influences (C), which contribute to the resemblance between family members. Non-shared environment (E) accounts for individual-specific factors that create differences among siblings from the same family. These are estimated from within-pair differences between MZ twins. Any measurement error present is included in this term. The structural equation-modelling program Mx (Neale et al., 2006) was used to conduct analyses. Variables were regressed for age and sex as is the standard practise for quantitative genetic model fitting. Variables were transformed to ensure all skew statistics were between the range of −1 and 1 (CASI and SCAS were square rooted at both time points and the MFQ score was log transformed). 1.4. Selecting models of best fit Models were fitted using raw data maximum likelihood, incorporating appropriate weighting corrections. The fit statistics provided by Mx for raw data modelling is minus twice the log likelihood (− 2LL) of the observations. This is not an overall measure of fit, but provides a relative measure of fit, since differences in − 2LL between models are distributed as χ2. Therefore, to examine the overall fit of the genetic model it is necessary to compare the − 2LL to that of a saturated model. Consistent with the principle of parsimony, the fit of sub-models was assessed by χ2 difference tests and the Akaike′s Information Criterion (AIC = χ2 − 2df) with lower χ2 values and more negative AIC values suggesting a better fit. Confidence intervals of parameter estimates were obtained by maximum likelihood. Model-fitting analyses can also test for sex differences in the patterns of aetiological factors by comparing models which vary in their assumptions and specifications of the genetic and environmental parameters in male and females. Using model-fitting techniques, it is possible to examine whether 1) the magnitude of genetic and environmental influences are different in males and females (quantitative sex differences); 2) the extent to which the genetic and environmental factors influencing males are the same as those influencing females (qualitative sex differences); and 3) whether there are variance differences in the variables for males and females (scalar sex differences). 1.5. Univariate models Univariate models examined the influences of additive genetic (A), shared environment (C) and non-shared environmental (E) on anxiety, depression and the cognitive biases. Univariate analyses were performed in order to inform the multivariate models in terms of sex differences. Several models were tested beginning with a saturated model to which the full ACE model was compared. Quantitative sex

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differences were examined by evaluating the significance of fit reduction when male and female variance components were equated. If quantitative sex differences were found, a scalar model was fitted to see if this difference could be due to variance differences in the measure (variance differences between males and females, but not for genetic and environmental parameters). Where there were quantitative differences that were not due to variance differences, qualitative sex differences were also tested by freeing up the genetic correlations between DZOS twins and seeing whether it was a significantly worse fit. Qualitative sex differences were not found so are not discussed further.

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logical assumptions regarding covariation between behaviours (Hewitt et al., 1992). 2. Results 2.1. Descriptive statistics Descriptive statistics for all four study variables at both time points are given in Table 1. Females rated their anxiety and depression as higher than males both at time 1 (t(2599) = −12.46, p b 0.01 for anxiety and t(2627) = −9.57, p b 0.01 for depression) and at time 2 (t(1568) = −9.787, p b 0.01 for anxiety and t(1589) = − 7.479, p b 0.01 for depression).

1.6. Multivariate models

2.2. Phenotypic associations

In multivariate models, MZ and DZ correlations are compared across traits, i.e. one twin′s score on a trait is correlated with the co-twin′s score on another trait. If crosstrait twin correlations are higher for MZ twin than DZ twins, it implies that genetic factors contribute to the covariation between traits. Correlations can similarly be estimated for shared and non-shared environmental factors. The extent to which genetic, shared and non-shared environmental factors contribute to the phenotypic correlations can also be calculated. Genetic influences on the correlation can be calculated by multiplying the square root of the heritability of each variable by the genetic correlation. Similar calculations for shared and non-shared environmental correlations reflect the shared and non-shared environmental influences, respectively on the correlation. These are re-expressed as proportions of the correlation by dividing by the overall phenotypic correlation. A triangular (Cholesky) decomposition was first fitted to the data, this assesses the extent to which the shared influences of A, C and E underlying one behaviour also influence the others (see Fig. 1). However, as the ordering of the variables in a Cholesky Decomposition is arbitrary we present a correlated factor solution of the Cholesky model. This is a mathematically equivalent solution of the triangular decomposition, where the variance in each rating is decomposed into A, C and E influences, and the correlations between variance components for each behaviour are estimated. Second, a common pathway model was fitted to the data. In this model the variance in behaviours is decomposed into that which is shared — a single underlying ‘phenotypic’ latent variable, and that which is unique to each behaviour. This latent variable has genetic and environmental components of variance but there are still variable-specific genetic and environmental sources of variances (Rijsdijk and Sham, 2002). This model tests the hypothesis that there is an underlying phenotype that mediates the effects of common genetic and environmental influences on outcome. Finally, an independent pathways model was fitted to the data. In such a model, common genetic and environmental factors influence the observed variables directly without an intermediate higher order factor (Rijsdijk and Sham, 2002). This model tests whether there are common factors which influence the variables accounting for their correlation. Each model is fitted against the saturated model which provides a baseline comparison for subsequent models, as it makes no psycho-

The phenotypic correlations amongst the variables are provided in Table 2. The strongest associations in both sexes and at both times were between anxiety ratings and anxious cognitions (.70–.73). Correlations between anxiety and depression ratings were also high (.57–.62) with the exception of time 1 females (.39). Correlations between the anxiety-related and depression-related cognitive biases were low (−.12 to −.35). Correlations between depression and depressive cognitions were moderate (−.36 to −.47). Correlations between attributional style and the variables are negative as lower scores on this measure represent more negative attributional style, a more negative attributional style is associated with, for example, higher levels of depression and anxiety. 2.3. Univariate genetic analyses The within-pair correlations for the four variables across the five sex-zygosity groups for times 1 and 2 are also given in Table 2. There were no significant differences in the

Table 1 Descriptive statistics for all study variables at times 1 and 2. Anxiety sensitivity

Anxiety ratings

Attributional style

Depression ratings

2630 1586 28.73 25.65 5.55 5.71

2603 1570 28.85 20.62 13.66 12.80

2563 1571 4.30 4.37 3.31 3.50

2631 1591 8.08 6.25 6.65 5.33

Time 1 Time 2 Time 1 Time 2 Time 1 Time 2

1148 629 27.60 24.29 5.05 5.19

1137 624 20.17 16.84 12.41 11.20

1114 622 4.13 4.50 3.32 3.27

1152 633 6.69 5.04 5.51 4.78

Females N Time 1 Time 2 Mean Time 1 Time 2 SD Time 1 Time 2

1480 957 29.61 26.54 5.76 5.87

1464 946 26.71 23.11 13.90 13.17

1447 949 4.43 4.29 3.30 3.64

1477 958 7.15 7.05 7.24 5.52

Whole sample N Time 1 Time 2 Mean Time 1 Time 2 SD Time 1 Time 2 Males N Mean SD

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pathway or common pathway model (χ2 = 363.79, df = 311, p = .052, AIC = − 258.21). The implication is that correlations amongst the variables are due to latent factors which influence each of them. However, as the shared environment effects estimated in this model were all non-significant, as in the univariate analyses, the AE model (see Fig. 2) was run and found to provide a better fit (χ2 = 367.71, df = 321, p = .04, AIC = − 274.29). Similarly, at time 2, a scalar AE Cholesky model provided the best fit (χ2 = 659.31, df = 320, p = .06, AIC = − 280.69; further details of model-fitting available from the first author on request). Parameter estimates from the final models (times 1 and 2) are presented in Table 4 with 95% confidence intervals in brackets. In Table 4, the genetic and environmental influences on each variable are given on the diagonal of each section. For example, the heritability of anxiety sensitivity was .43 at time 1, and .34 at time 2. The genetic and environmental correlations are given above the diagonal. A genetic correlation indicates the extent to which genetic influences on one trait overlap with those on another trait (regardless of their heritabilities). Thus, the genetic correlations between anxiety sensitivity and anxiety (shown as rA(AnxS–Anx) in Fig. 2) are .86 at time 1 and .87 at time two, non-shared environmental correlations between anxiety sensitivity and anxiety ratings (rE(AnxS–Anx ) in Fig. 2) are .55 and .64 at times 1 and time 2 respectively. Finally, below the diagonal of each section of the table the proportion of phenotypic correlation due to genes or environment is provided. Thus, for example, the proportion of the phenotypic correlations between anxiety and depression ratings due to genes are .63 and .56 at time 1 and time 2 respectively. Overall, heritability estimates were moderate for all 4 variables at both time points (.34–.53). Genetic correlations were strongest between anxiety sensitivity and anxiety ratings (.86), but there were also strong genetic correlations between both types of cognitive bias (anxiety sensitivity and attributional style) and depression ratings, and between anxiety and depression ratings. The genetic correlation between attributional style and both anxiety sensitivity and anxiety ratings was modest. Thus these genetic correlations closely reflect the phenotypic associations described above (see Table 2). Non-shared environmental correlations amongst the variables were more mixed, ranging from .64 for anxiety sensitivity and anxiety ratings to just −.10 for attributional style and anxiety sensitivity. The proportion of covariation between each pair of variables was accounted for primarily by genetic influences, and ranged from .48 (anxiety sensitivity and anxiety ratings) to .69 (anxiety sensitivity and depression ratings).

Table 2 Phenotypic and within-pair twin and sibling correlations amongst the variables by sex (time 1 and time 2). Anxiety Anxiety Attributional Depression sensitivity ratings style ratings Anxiety sensitivity Anxiety ratings Attributional style Depression ratings

Time 1 Time 2 Time 1 Time 2 Time 1 Time 2 Time 1 Time 2

– – 0.70 0.73 − 0.25 − 0.35 0.50 0.57

0.70 0.72 – – − 0.32 − 0.35 0.39 0.57

− 0.12 − 0.31 0.19 − 0.36 – – − 0.44 − 0.47

0.44 0.56 0.62 0.62 − 0.36 0.37 – –

MZ males

Time 1 Time 2 Time 1 Time 2 Time 1 Time 2 Time 1 Time 2 Time 1 Time 2

0.41 0.36 0.17 0.24 0.46 0.29 0.23 0.35 0.25 0.10

0.53 0.54 0.17 0.14 0.52 0.51 0.39 0.29 0.29 0.10

0.34 0.32 0.29 0.21 0.47 0.52 0.32 0.24 0.18 0.24

0.43 0.49 0.25 0.26 0.53 0.43 0.35 0.28 0.25 0.18

DZ males MZ females DZ females DZ opp-sex

Note. Correlations for males above the diagonal, for females below. Correlations were run on transformed data.

magnitude of the correlations between DZ and sibling pairs so data from these groups were combined for twin analyses. For all traits DZ correlations were close to or above half the MZ correlation indicating additive genetic variance with modest shared environment. There were some indications of sex differences in aetiology, as indicated by different patterns of MZ and DZ correlations for male versus female pairs for some variables. Of note were the low MZ female correlations for anxiety sensitivity (.29) which account for the decreased heritability of anxiety sensitivity at time 2, and low DZ male correlation for anxiety symptoms (.14) at time 2. Univariate model-fitting analyses (Table 3) confirmed these initial interpretations. All univariate ACE models were not significantly worse than the saturated models. At both waves results indicated moderate genetic and non-shared environment for all measures but minimal shared environment. There were scalar (variance) sex differences for anxiety and depression ratings and for anxiety sensitivity, but no sex differences for attributional style. At time 2 scalar sex differences were found for all variables. 2.4. Multivariate genetic analyses At time 1, a full ACE cholesky scalar model was found to provide a better fit to the data than either an independent

Table 3 Parameter estimates with 95% confidence intervals of the full univariate models of anxiety sensitivity, anxiety symptoms, attributional style and depression symptoms. A (95%CI)

C (95%CI)

46 42 34 45

0 (0–15) 10 (0–24) 7 (0–22) 7 (0–41)

E (95%CI)

A (95%CI)

C (95%CI)

54 48 59 48

18 50 43 43

12 0 2 0

Time 1 Anxiety sensitivity Anxiety symptoms Attributional style Depressive symptoms

(26–52) (23–58) (12–49) (27–58)

E (95%CI)

Time 2 (48–62) (42–55) (51–68) (52–55)

(0–40) (35–58) (15–54) (17–52)

(0–29) (0–10) (0–21) (0–20)

70 50 55 56

(60–82) (42–59) (46–67) (48–67)

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Fig. 2. Path diagram depicting additive genetic (A) and non-shared environmental influences (E) on anxiety sensitivity (AnxS), anxiety (Anx), attributional style (AttS) and depression (Dep) in one member of a twin pair. The genetic correlation between anxiety sensitivity and depression represented as rA(AnxS–Dep) and non-shared environment as rE(AnxS–Dep). Correlations between other variables are expressed in a similar fashion.

3. Discussion 3.1. Summary In contrast to the high genetic correlations between anxiety and depression our study found only moderate genetic correlations between anxiety sensitivity and attributional style. Genetic correlations between cognitive biases were, however, higher than non-shared environmental correlations suggesting genetic rather than environmental factors drive more of the covariation between these cognitive biases. What was notable from the univariate analysis was the degree of similarity in the genetic and environmental

estimates on the variables across the two time points. This was also evident in the multivariate analyses at the two time points. For all variables, genetic factors were important in explaining variance in the phenotype. Although we have little power to detect shared environment (Burt, 2009), environmental influences seem to be via the non-shared route in the current sample as shared environmental influences were very small in the multivariate model. This is fairly typical in psychiatric genetics with shared environment proving more important in childhood as compared to other age groups (Bergen et al., 2007). Interestingly, no sex differences were found in genetic and environmental estimates for all variables.

Table 4 Parameter estimates from multivariate modelling at times 1 and 2 (with 95% confidence intervals). Genetic and environmental influences on each variable given in bold on the diagonal of each table section. Genetic and environmental correlations given above the diagonal and shaded in gray, and proportions of correlations due to genes and environment given below the diagonal.

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As expected, we reported a high genetic correlation between anxiety and depression providing further evidence for genetic mediation in the relationship between anxiety and depression in an adolescent sample (Thapar and McGuffin, 1997; Eley and Stevenson, 1999b). Also in line with previous research, anxiety sensitivity was found to be more strongly correlated, genetically and phenotypically, with anxiety than with depression and attributional style more strongly with depression than anxiety (Lau et al., 2006; Eley et al., 2007). Our results strengthen previous research showing a relationship between anxiety sensitivity and depression (Muris et al., 2001; Weems et al., 1997) and further such work by indicating that genetic influences account for much of this correlation. Attributional style has not, to our knowledge, been investigated in relation to anxiety in an adolescent sample. We found that, unlike anxiety sensitivity, attributional style was more specific exhibiting a moderate phenotypic and genetic correlation with depression but low phenotypic and genetic correlations with anxiety. 3.2. Implications The current study furthers our theoretical understanding of risk factors involved in the development of anxiety and depression. Our finding that the cognitive biases associated with anxiety and depression is only moderately genetically correlated suggests that they do not act jointly as a shared genetic diathesis. Despite a degree of shared reflection of symptoms, the entire breadth of the symptoms is more specific to the cognitive biases individually. Although the modest correlation between anxiety sensitivity and attributional style implies that they do not seem to act as a single genetic risk factor for symptoms of anxiety and depression, our finding that anxiety sensitivity has a strong genetic correlation with depression and anxiety suggests that this may be acting as a more general genetic risk factor for anxiety and depression. This finding certainly warrants further investigation and it may be that anxiety sensitivity is more trait-like in origin hence explaining the high phenotypic and genetic correlations with both anxiety and depression. Factor analysis has found the mental concerns subscale in particular (e.g. when I′m nervous, I worry I might be mentally ill) predicted increases in depression longitudinally (Zinbarg et al., 2001). However, other studies have found that the physical concerns subscale (e.g. it scares me when my heart beats fast) predicted depressive symptoms (Grant et al., 2007). Attributional style, conversely, is much more symptom specific exhibiting only a low genetic correlation with anxiety. The low correlation between attributional style and anxiety implies that the correlation between anxiety sensitivity and depression is not simply a result of the covariation between anxiety and depression. Environmental influences, as expected, seem to be more variable-specific with moderate to low non-shared environmental correlations between all variables. Previous research has emphasised the differences in environmental factors influencing anxiety and depression. For example, one study exploring life events and experiences in young twins found that loss events (such as loss of an attachment figure or a valued idea), school work stressors, family relationships problems and friendship problems were all associated with

depression but not anxiety (Eley and Stevenson, 2000). Conversely, threat events (including the risk of losing an attachment figure or experiencing trauma as a witness) were associated with anxiety and not depression. This environmental specificity is also seen between anxiety sensitivity and attributional style. The low non-shared environmental correlation suggests cognitive biases reflect different learning experiences. Overall the cognitive biases, together, do not seem to be accounting for what is shared between anxiety and depression. Our findings do, however, further support the idea that genes are not specific to the development of one behaviour problem, instead genetic influence seems to be more general. We found that genes were shared to some extent between anxiety sensitivity, anxiety and depression. 3.3. Limitations Although the large sample is a strength of the study, there are some limitations that need to be addressed. The analyses were conducted on data collected from questionnaires which therefore may fail to capture complexities of the phenotypes in question. There was also evidence of selective response bias, as discussed previously. This limitation was taken into account to some extent by using a weight which assigned more value to those who were under-represented in the sample and less to those who were over represented. The sample consists primarily of twins and there are limitations associated with their use. Concerns surrounding the twin methodology to be considered include: chronicity, the equal environment assumption and generalizability. These limitations are likely to only have small effects in different directions, and as such, derived estimates of heritability and environmental influences should be taken only as indicative rather than absolute (for a more comprehensive discussion of limitations of twin studies, see Plomin et al., 2000). In spite of these caveats, we were able to demonstrate that the cognitive biases associated with anxiety and depression are not as correlated as the ratings themselves. The modest correlations suggest that they do not act jointly as general risk factors and cannot fully explain the high genetic correlation seen between anxiety and depression. The strong genetic correlations between anxiety sensitivity, anxiety and depression do, however, suggest that anxiety sensitivity is more of a general risk factor than attributional style which is more specific to depression. Further research is required to fully understand the relationship between cognitive biases and symptoms of anxiety and depression but our study supports the relevance of studying them multivariately. Role of funding source The G1219 study was supported by the WT Grant Foundation, the University of London Central Research fund and a Medical Research Council Training Fellowship and Career Development Award to Thalia Eley. The G1219 study is currently supported by a research grant from the UK Economic and Social Research Council (RES-000-22-2206) and a grant from the Institute of Social Psychiatry to Alice Gregory who is currently supported by a Leverhulme Research Fellowship. Helena Zavos is supported by a Medical Research Council doctoral studentship.

Conflict of interest All authors declare that they have no conflict of interest.

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