Genetically (Dis)Advantaged? Gene-Environment
Interaction, Parental Support and Social Anxiety in
Adolescence
Lisa van der Storm – 10188150University of Amsterdam
Graduate School of Child Development and Education Child Development and Education 2015–2017
Masterthesis
Email: lisavanderstorm@live.nl
Supervisors: prof. dr. Geertjan Overbeek, dr. Terrence D. Jorgensen Second Readers: drs. Milica Nikolic & drs. Machteld Hoeve
Date: 11 July 2017
Word Count Thesis: 6700 Word Count Abstract: 242
Index
Abstract 3
Genetically (Dis)Advantaged? Gene-Environment Interactions of Parental Support and
Social Anxiety Across Adolescence 4
Parenting and Social Anxiety 5
Gene-Environment Interactions 5
Multiple Polymorphic Markers 6
Personality Traits and Social Anxiety 7
Present Study 9 Method 10 Sample 10 Procedure 11 Measures 12 Strategy of Analysis 14 Results 16 Preliminary Analyses 16
Latent Growth Modeling Analyses 16
Sensitivity Analyses 17
Discussion 18
Strengths, Limitations, and Future Research 22
Conclusion 24
References 26
Abstract
This study examined whether the relationship between parental support and social anxiety was moderated by adolescents’ personality traits and genetic factors. It was
hypothesized that children with a high polygenic susceptibility (i.e., with a higher polygenic score on an index comprising the 5-HTTLPR, HTR1A, and HTR2A genes) would show higher levels of social anxiety when they received less parental support, and lower levels of social anxiety when they received more parental support. Further, it was expected that this moderating effect of the polygenic susceptibility would be mediated through adolescents’ Big Five personality traits and behavioral inhibition. Participants were 523 adolescents (56.3% boys; Mage = 13.03 at T1, SD = 0.46) who completed annual self-report questionnaires for six
successive years. A piecewise latent growth curve model analysis showed that the relationship between social anxiety and parental support was moderated by polygenic susceptibility during the first and second wave, in a for-better-and-for-worse manner. This moderating effect of the polygenic susceptibility was mediated by neuroticism. The association between higher
parental support with lower social anxiety was stronger for adolescents with a higher
polygenic susceptibility than for adolescents with a lower polygenic susceptibility, while the association between a lack of parental support and higher social anxiety was also especially strong in children with a higher polygenic susceptibility. The present study encourages labeling people with more putative plasticity alleles as having a higher polygenic susceptibility instead of labeling those individuals as having a higher genetic risk.
Genetically (Dis)Advantaged? Gene-Environment Interactions of Parental Support and Social Anxiety Across Adolescence
Everyone can feel socially anxious at some point. However, for some people, the level of feeling socially anxious is so high that it can hinder their social life in terms of social and relational functioning. Social anxiety refers to the fear or worry of being judged or evaluated in a negative way by others during social interactions or social performance situations. Social anxiety meets the criteria for a social anxiety disorder if the anxiety is excessive and
persistent, and has a substantial negative influence on day-to-day life (DSM-5; American Psychiatric Association, 2013). In children, as well as adolescents and adults, social anxiety disorder is thought to be one of the most prevalent psychiatric illnesses (Fehm, Pelissolo, Furmark, & Wittchen, 2005; Kashani & Orvaschel, 1990; Ruscio et al., 2008; Scaini, Belotti, & Ogliari, 2014). A recent meta-analysis on the contributions of genetic and environmental factors found that most of the individual differences in social anxiety disorder are explained by genetic factors (Scaini et al., 2014). Nevertheless, environmental variables, like parental personality and parental style and skills contribute to the onset of social anxiety disorder as well (Beidel & Turner, 2007). Thus, to identify children at risk for developing high levels of social anxiety symptoms, it is crucial to identify the etiological interplay between genetic and environmental risk factors in the development of social anxiety.
But how do environmental and biological risks interact with each other in predicting social anxiety? The literature proposes two models stipulating the vulnerability to
environmental adversity: the diathesis-stress and the differential susceptibility model (Belsky, Pluess, & Widaman, 2013). According to the diathesis-stress model, some individuals will be more susceptible to the negative consequences of adverse environmental influences than others (Burmeister, McInnis, & Zollner, 2008). However, Belsky and colleagues (Belsky, 1997; Belsky & Pluess, 2009a, 2009b; Belsky, Bakermans-Kranenburg, & van IJzendoorn,
2007; Belsky, Hsieh, & Crnic, 1998) argued that the diathesis-stress approach ignores an important form of gene-environment (G × E) interaction: differential susceptibility. The differential susceptibility model stipulates that some individuals can also be more susceptible to positive environmental influences, not just to negative environmental influences (i.e., ‘for better and for worse; Belsky et al., 2007). This differential vulnerability might be gene based; certain genetic polymorphisms could be rather seen as plasticity factors instead of
vulnerability factors. In the present study, we will focus on G × E interactions in social anxiety according to the differential susceptibility theory.
Parenting and Social Anxiety
An important environmental factor that may influence the development of social anxiety in children is parenting style and parenting behavior. A negative parenting style can contribute to the development of anxiety in children (McLeod et al., 2007). More
specifically, parenting characterized by high levels of control and low levels of warmth and support is associated with social anxiety disorder in children (Knappe, Beesdo, Fehm, Lieb, et al., 2009; Rapee & Spence, 2004). The presence of negative parenting behavior may be strongly related to social anxiety because sensitivity to negative evaluation from significant others (e.g., friends, parents, teachers) is a core feature of social anxiety disorder (Rapee & Spence, 2004). Consistent exposure to parental rejection and a lack of emotional support, which can be characterized by high levels criticism, may cause children to develop a high sensitivity to negative evaluation (Gulley, Oppenheimer, & Hankin, 2014). This may contribute to the development of social anxiety disorder.
Gene-Environment Interactions
Both genetic and environmental influences appear to play an important role in explaining differences in levels of social anxiety among children. When the effect of an exposure to an environmental pathogen on people’s health is conditional on their genotype, a
G × E interaction occurs (Caspi & Moffitt, 2006). This interaction approach assumes the magnitude of an environmental effect on a disorder differs across people with different genetic profiles.
There is not much research available on G × E interactions in social anxiety. A possible explanation for this is that anxiety disorders are more “complex” disorders because they reflect the influence of many genetic risk factors (Smoller, Block, & Young, 2009). Each of these genetic risk factors individually may have small effects. There are some studies investigating the interaction of the anxiety-associated serotonin transporter gene (5-HTTPLR) with environmental stressors in predicting general anxiety symptoms in childhood (Gunthert et al., 2007; Klauke et al., 2011; Laucht et al., 2009; Stein et al. 2008). These studies
examined G × E interactions of stressful life events and child maltreatment on the relationship between 5-HTTLPR and general anxiety (symptoms). They found that children carrying a “high-risk” genotype who were exposed to stressful life events such as child maltreatment displayed higher rates of anxiety symptoms than those without either condition. Most studies consider the genotype with one or two short allele(s) (typed SS or SL) of 5-HTTLPR as the genotype that is more strongly associated with anxiety. Remarkably, a study of Laucht et al. (2009) found an interaction with the LL genotype of 5-HTTPLR and anxiety. Furthermore, some studies found no support for a G × E interaction of general anxiety or social anxiety (Lau, Gregory, Goldwin, Pine, & Eley, 2007). Thus, evidence from single candidate gene studies in this field is mixed and inconclusive.
Multiple Polymorphic Markers
A possible explanation for these contradictory findings and an important point of criticism from the field of genetics is that genotyping just one marker in the gene of interest is no longer reflecting the current practice in genetics (Dick, Latendresse, & Riley, 2011). The literature within developmental science is dominated by studies on a small number of
apparently functional polymorphisms from a small number of ‘popular’ genes, such as 5-HTTLPR (Dick et al., 2011). However, genes can exist at multiple polymorphic sites leading to differential functioning, which can contribute to differential susceptibility (McClellan & King, 2010). There are many polymorphic markers available for most of these popular genes. Instead of using just one allele combination, multiple polymorphic markers can be used to examine the differential susceptibility for social anxiety. This can be done with the use of polygenic scoring; points are assigned when a child carries at least one of the putative plasticity alleles. The more putative plasticity alleles individuals carry, the higher their score on a polygenetic plasticity index (Belsky & Beaver, 2011).
With regard to the identification of G × E interactions in social anxiety, it might thus be wise to model cumulative genetic influences on serotonin, instead of just using just 5-HTTPLR marker. In this paper, we argue that a genetic susceptibility score should be defined polymorphically by identifying alleles associated with abnormal serotonin functioning across the candidate genes HTTPLR, HTR1A, and HTR2A). The serotonin transporter protein (5-HTT) can moderate context sensitivity and is responsible for clearing serotonin from the synaptic cleft (Disner, McGeary, Wells, & Ellis, 2014). An abnormal serotonin function has been associated with several anxiety disorders (Owens & Nemeroff, 1994), and decreased levels of serotonin can lead to the expression of dysphoric or anxious symptoms (Booij, Van der Does, Haffmans, Spinhoven, & McNally, 2005; Booij, Van der Does, & Riedel, 2003). Variations in he SLC6A4-linked polymorphic region 5-HTTLPR (rs25531) modulate the expression of 5-HTT (Heils et al., 1996). In addition, variations in the HTR1A (rs6295) and HTR2A (rs6311) receptors have been commonly identified as candidate markers for genetic vulnerability to affective pathology (Disner et al., 2014).
It might be possible that the genetic moderation effect goes via specific susceptibility markers, such as personality. Until now, there is not much research available about the influence of these susceptibility markers on the genetic moderation effect. What we do know is that variations in personality traits of children, adolescents and adults appear early in life. These differences are a direct result of neurobiological factors and can help predict modes of response (Rutter, 1987). Such aspects like interactions with caregivers or siblings are thought to form the core of a child’s personality. A recent meta-analysis found that 40% of individual differences in personality traits are genetically explained (Vukasović & Bratko, 2015). The 5-HTTLPR genotype has been found to be related to personality (Murakami et al., 1999; Sen et al., 2004), and particularly to neuroticism (e.g., Canli & Lesch, 2007; Lesch et al., 1996; Munafò, Clark, Roberts, & Johnstone, 2006; Takano, et al., 2007). The level of serotonin in the brain can influence the development of neural structures that promote differential classes of behavioral responses to emotional stimuli (Munafò et al., 2006). These modes of responses are reflected in psychometrically quantifiable personality traits. Certain childhood personality traits, such as the Big Five (Kotov, Gamez, Schmidt, & Watson, 2010), and behavioral inhibition (Clauss & Blackford, 2012) have been found to be related to related to social anxiety. Therefore, it would be interesting to investigate whether the genetic moderation effect goes via susceptibility markers such as personality.
Personality traits such as negative affectivity, or neuroticism, and low positive affectivity, or low extraversion may influence the development of psychological disorders, e.g. social anxiety (Anderson, Veed, Inderbitzen-Nolan, & Hansen, 2010; Chorpita, Plummer, & Moffitt, 2000). Together with three other domains are neuroticism and extraversion
elements of the “Big Five” (Goldberg, 1981). The Big Five model consists of five broad factors describing personality: neuroticism (e.g., experience of negative affect, anger, depression, distress anxiety); extraversion (e.g., experience of positive affect, sociability);
agreeableness (e.g., kindness, warmth, altruistic behavior, trust); conscientiousness (e.g., task orientation, self-control, rule-abiding); and openness (e.g., creativity, acceptance of new ideas, originality) (Kotov et al., 2010). Some dimensions of the Big Five have been found to be more strongly associated with social anxiety than others.
Kotov et al. (2010) conducted a meta-analysis in which they examined the
associations between the Big Five and social anxiety disorder symptoms. Neuroticism was the strongest predictor of social anxiety compared to the remaining Big Five Traits, with a
moderate association. Extraversion displayed significant negative associations with social anxiety. However, these associations were considerably weaker than with neuroticism. Conscientiousness and Openness were negatively associated with social anxiety, whereas agreeableness was not related to social anxiety. The same associations between the Big Five dimensions and social anxiety are found in more recent research. However, after controlling for neuroticism and extraversion, the three remaining traits hardly explained any of the
variance in social anxiety (Naragon-Gainey & Watson, 2011; Watson, Clark, & Stasik, 2011). In addition, behavioral inhibition is one of the most clearly established developmental risk factors for social anxiety symptoms. Behavioral inhibition is characterized by wariness or avoidant behavior as a response to novel persons, places, and objects (Kagan, Reznick,
Snidman, Gibbons, & Johnson, 1988). Behaviorally inhibited children can be described as shy, cautious, and fearful (Kagan & Snidman, 1999; Fox, Henderson, Marschall, Nichols, & Ghera, 2005a). Those children are at substantially increased risk for developing social anxiety disorder during childhood and adolescence (e.g., Biederman et al., 2001; Clauss & Blackford, 2012; Hirschfeld-Becker et al., 2007; Schwartz, Snidman, Kagan, 1999).
Present Study
In this longitudinal study, we had two aims: (1) to investigate whether adolescents’ polygenic susceptibility would moderate the relationship between parental support and social
anxiety, and (2) to assess whether the moderating effect of the polygenic susceptibility would be mediated by adolescents’ Big Five personality traits and behavioral inhibition. Previous research suggested that low levels of parental support could result in higher levels of social anxiety (Knappe et al., 2009). Further, according to the differential susceptibility theory, children with “high-risk” genotypes can be more susceptible or malleable than others to both positive and negative environmental influences (Kochanska, Kim, Barry, & Philibert, 2011). Therefore, with regard to our first aim, we hypothesized that adolescents who carried the most putative plasticity alleles related to higher brain serotonin levels would not only show higher levels of social anxiety when they received less parental support but would also show lower levels of social anxiety when they received more parental support. Further, it has been suggested that personality traits such as the Big Five (Naragon-Gainey & Watson, 2011; Watson et al., 2011), and behavioral inhibition (Clauss & Blackford, 2012) might be important mediators of a serotonin-based gene-environment interaction effect on social anxiety, because both the facets of the Big Five (Loehlin, McCrae, Costa, & John, 1998), and behavioral inhibition (Robinson, Kagan, Reznick, & Corley, 1992) are moderately heritable and appear to be related to serotonergic functioning in the brain. With regard to our second aim, we therefore expected that the moderating effect of the putative plasticity alleles on the relationship between social support and social anxiety would be mediated through personality traits—especially neuroticism and extraversion—and behavioral inhibition.
Method Sample
Data used in the present study were part of the ongoing Research on Adolescent Development And Relationships (RADAR) Young project. Radar is a longitudinal study conducted in the Netherlands that focuses on which factors are associated with healthy and problematic development in adolescence. For this study, we used the first six annual waves of
questionnaire and genetic data. Participants were 523 adolescents (56.3% boys) aged 11 to 15 (M = 13.03, SD = 0.46) who attended the first year of secondary school at the start of the study. Almost all adolescents were born in the Netherlands (95.2%). For 88.9% of the adolescents, the family socioeconomic status (SES) was average-high on parents’ job level.
Sample attrition in the first six waves of RADAR Young was low across waves. Of the 523 adolescents, 449 were still participating at the sixth wave (i.e., a cumulative retention rate of 86%). To test whether there were differences between the remaining participants and those who dropped out from Wave 1 to Wave 6 we performed an attrition analysis. Logistic regression analyses showed that attrition was not associated with measures of age, gender, social anxiety, parental support, behavioral inhibition, extraversion, agreeableness,
conscientiousness, neuroticism, and openness to experience (R2 = .074).
Procedure
The participants in RADAR Young were recruited from randomly selected schools (n = 429) in the central and western regions of the Netherlands. The 522 families were selected using a two-step inclusion phase (teacher screening followed by parent interviews). First, only children identified by the teacher as being of Dutch origin were eligible for participation in the RADAR study, because the complex and intensive data collection required a good knowledge of the Dutch language of all family members. In total, 1544 adolescents were included and approached in the second selection phase. Second, after parent interviews, 538 (34.8%) families refused to participate and 463 (30.0%) adolescents did not meet the
inclusion requirements in the study (i.e., 2-parent families and the presence of a sibling ≥ 10 years). During these interviews, parents received a complete description of the study. In total, 522 parents and adolescents provided their informed consent. During home visits, adolescents and their mothers completed annual self-report questionnaires for seven subsequent years. For every wave, participants received compensation after completing the questionnaires. This
study was approved by the Medical Ethics Committee of the University Medical Centre in Utrecht (METC protocol number 05/159-K).
Measures
Social anxiety symptoms. To assess adolescent social anxiety symptoms, we used the
4-item social anxiety disorder subscale of the Dutch version of the original 38-item Screen for Child Anxiety Related Emotional Disorders, (SCARED;Birmaher et al., 1997; Hale,
Raaijmakers, Muris, & Meeus, 2005). The items were rated on a 3-point scale, ranging from 1 (almost never) to 3 (often). A sample item includes “I feel shy with people I don’t know well.” Previous research has shown that the SCARED is a reliable and valid instrument to assess (social) adolescent anxiety in both clinical samples (Hale et al., 2005) and nonclinical samples (Birmaher et al., 1997). The internal consistency was good with Cronbach’s αs ranging between 0.78 and 0.87 across waves.
Parental support. To measure parental support as perceived by adolescents, we used
the Dutch version of the adolescent version of the 38-item Level of Expressed Emotions Scale (LEE; Cole & Kazarian, 1988; Gerlsma & Hale, 1997). The LEE is a self-report
questionnaire, which contains a 19-item subscale measuring parental support (e.g., “My parents calm me when I am upset”). Items were rated on a 4-point rating scale, ranging from 1 (untrue) to 4 (true). The LEE has demonstrated good psychometric properties in adolescents (Hale, Raaijmakers, van Hoof, & Meeus, 2011). The internal consistency for the emotional support subscale was good with Cronbach’s αs ranging between 0.84 and 0.93 across study waves.
Personality traits. Adolescents’ personality was assessed using a shortened Dutch
version of the Big Five Questionnaire (Goldberg, 1992). With the use of 30 Big Five personality markers, we assessed five personality dimensions (each with six items): neuroticism (e.g., on edge), extraversion (e.g., talkative), agreeableness (e.g., friendly),
conscientiousness (e.g., neat), and openness to experience (e.g., creative). The items were rated on a 7-point rating scale, ranging from 1 (very untrue) to 7 (very true). Internal
consistency of each subscale was acceptable to good with Cronbach’s αs ranging across study years between 0.78 and 0.89 for extraversion, 0.79 and 0.85 for agreeableness, 0.82 and 0.89 for conscientiousness, 0.82 and 0.89 for neuroticism, and 0.72 and 0.77 for openness to experience.
Behavioral inhibition. We used the 7-item Dutch version of the Behavioral Inhibition
Scale (BIS; Carver & White, 1994) to measure adolescent behavioral inhibition, which has adequate reliability and validity (Branje, Van Lieshout, & Gerris, 2007; Goldberg, 1992). The items were rated on a 4-point scale, ranging from 1 (do not agree at all) to 4 (totally agree). A sample item includes “I feel upset when I think or know that somebody is cross with me”. The internal consistency was good with Cronbach’s α’s ranging between 0.64 and 0.81 across waves.
Genotype. The genotyping was performed with the Affymetrix 6.0 array (McCarroll
et al., 2008) using DNA from the whole blood. The genotype calling was performed with the Birdseed 2 algorithm (Korn et al., 2008; McCarroll et al., 2008), with the Affymetrix 3.3 APT software on all samples simultaneously. Samples were removed in case the Affymetrix CQC was lower than 0.40, the genotyping calling rate was lower than 0.90, the heterozygosity F value was different from zero, or in case the DNA gender of the sample did not match the phenotype gender. Additionally, we removed samples if the 10 genetic principal components indicated a CEU ethnic outlier after projection of the study samples on the 1000 genomes reference sample. Fifty-three participants were identified as ethnic outliers and excluded from the analyses. SNPs were filtered using Plink 1.07 (Purcell et al., 2007) based on the following criteria: No or incorrect mapping on Build 37 HG19 of the human genome, inconsistent calls in plate control samples with an error rate > 1%, < 0.95 genotyping rate, MAF < 0.01, HWE p
< 0.000001. After this QC, all SNPs were strand aligned to the 1000 genomes Phase 1 June 2014 reference. The genotype data were subsequently phased with SHAPEIT V2.970 (Delaneau, Zagury, & Marchini, 2013) and imputed to the 1000 genomes reference with IMPUTE 2.3.1 following standard protocols (van Leeuwen et al., 2015). We used SNPs with imputation quality R2 higher than 0.8.
For our analysis, based on the literature we used three different polymorphisms related to the serotonergic system: 5-HTTLPR, 5-HTR1A, 5-HTR2A (Disner et al., 2014). We constructed a cumulative polygenetic index; points are assigned for the putative plasticity alleles, the more putative plasticity alleles, the higher the polygenetic plasticity index (Belsky & Beaver, 2011).The scores to code for polygenic susceptibility, along with the frequencies and percentages of the polymorphisms in the total sample are displayed in Table 1.The cumulative polygenetic score was calculated by counting the susceptibility alleles from the three polymorphisms.
Strategy of Analysis
There were 201 participants with at least one missing value among variables in the analysis. The values for the variables were approximately normally distributed; all values for kurtosis and skewness were < |2|. Model parameters were estimated using full information maximum likelihood (FIML), which allows the use of all available observations to estimate the model. Because there were so many missing data, it is important to take this into
consideration when interpreting the results.
First, descriptive analyses (means and standard deviations) of the variables were calculated for the total sample, for boys and girls, and for the different age groups (11–13,5 year olds and 13,5–16 year olds). Second, we carried out latent growth curve (LGC) modeling analyses to examine the growth trajectories of social anxiety symptoms (SA) in adolescents
over six years using the lavaan package (version 0.5–23; Rosseel, 2012) in R 3.2.3 (R Core Team, 2015).
To obtain parameter estimates and evaluate the goodness of fit of the model,
maximum likelihood was used. Maximum likelihood provides a chi-square (χ2) test of model fit. All parameter estimates and test statistics were evaluated at the significance level alpha (α = .05). A significant χ2 value indicates a discrepancy between the model implied and the observed covariance matrices, indicating that the model does not fit the data. However, because the overall χ2 test can be a very rigorous measure of fit with large sample sizes, it can have very much power and will be nearly always significant (Kline, 2005). Further, we do not expect that the hypothesized model fits the data perfectly because there are more variables that are related to anxiety and the other latent variables. Therefore, in addition to the χ2 test, the root mean square error of approximation (RMSEA; Steiger & Lind, 1980) and the comparative fit index (CFI; Bentler, 1990) were used as measures of overall goodness-of-fit. RMSEA values above .10 indicate poor fit, values below.08 indicate satisfactory fit, and values below .05 indicate close fit. Ideally, the lower value of the 90% confidence interval (CI) of the RMSEA is close to zero and the upper value lower than .08. CFI values above .95 indicate good fit (Kline, 2005).
First, an initial LGC model for social anxiety was fit to the data without including the predictors of parental support, the polygenic susceptibility score and adolescents’ personality traits and behavioral inhibition in the model. Second, we tested whether adolescents’
polygenic susceptibility score (G) and adolescents’ personality traits and behavioral inhibition (P) moderate the relationship between parental support (S) and social anxiety. G and P are thus moderators. Because G also affects P (which affects social anxiety), the moderating effect of G is partially mediated by P. The model we described can be represented in the path model shown in Figure 1. This subsequent growth model including predictors was fit to the
data if significant individual differences in the intercept and slope of social anxiety could be identified. We ran the model for each of the five personality traits and for behavioral
inhibition separately.
Results Preliminary Analyses
The descriptive statistics for boys and girls, and for early and middle adolescents are summarized in Table 2. Girls had significantly higher scores on behavioral inhibition, t(511) = −4.77, p <.001, neuroticism, t(445.27) = −2.50, p = .013, and social anxiety, t(447.71) = −3.75, p < .001, than boys. Adolescents in the younger age group did not significantly differ from adolescents in the older age group on predictor and outcome variables, all ps > .05. As expected, the Pearson correlation matrix presented in Table 2 shows that only extraversion, neuroticism and behavioral inhibition―but not agreeableness, conscientiousness, and openness―were significantly related to social anxiety (over all six waves).
Latent Growth Modeling Analyses
Exploring the mean change of social anxiety over time, we found that social anxiety decreased from the first to the second wave, after which the mean scores of social anxiety stayed constant (see Figure 2). Because the decrease in social anxiety was not constant, and the first wave level of social anxiety was different from the level of social anxiety at all subsequent waves, we decided to use a piecewise growth curve model with two intercepts and one slope. The first intercept representing the mean level of social anxiety at wave one, and the second intercept representing the mean level of social anxiety at wave 2. The slope
represents the change of social anxiety over time from wave 2 to wave 6. The piecewise latent growth curve model is shown in Figure 3. The fit of the specified model was satisfactory, χ2
(13) = 39.115, p < .001, RMSEA = .062, 90% CI [0.040; 0.085], CFI = .981. The RMSEA indicated satisfactory fit, and the CFI indicated good fit. Significant intercept and slope
variances indicated that there was heterogeneity among the initial levels and growth curves of social anxiety among adolescents. Accordingly, in a subsequent LGC model we included parental support, polygenic susceptibility, personality traits, and behavioral inhibition to predict the development of social anxiety in the model.
The fit of the second model using neuroticism was satisfactory, χ2(25) = 64.269, p < .001, RMSEA = .055, 90% CI[ 0.038; 0.072], CFI = .988. The unstandardized estimates of all direct effects and the proportions of explained variance are depicted in Table 4. The
unstandardized indirect effect of parental support on the level of social anxiety symptoms via neuroticism was significant for both intercept1 and intercept2, but not for the slope (βintercept1=
−0.07, p = .006; βintercept2= −0.06, p = .008; βslope = 0.001, p = .776). This means that this
indirect effect was only related to the initial levels of social anxiety at the first and second wave, but not related to the linear change in social anxiety. In addition, we found that the relation between parental support and levels of social anxiety symptoms via neuroticism was stronger for adolescents with a high polygenic susceptibility for both intercepts, (βhigh_intercept1=
−0.32, p = .003; βhigh_intercept2= −0.27, p = .004), than for children with a low polygenic
susceptibility (βlow_intercept1= −0.04, p = .061; βlow_intercept2= −0.03, p = .064). The difference
between adolescents with a low and high polygenic susceptibility was significant for both intercepts, (βlow_intercept1− βhigh_intercept1 = 0.28, p = .006; βlow_intercept2− βhigh_intercept2 = 0.24, p =
.008).
The fit of the models using the other four personality traits (extraversion,
agreeableness, conscientiousness, and openness to experience) was satisfactory. However, the effect of parental support on the level of social anxiety symptoms via these personality traits, and via behavioral inhibition were not significant, all ps > .05.
We found that the relationship between social anxiety and parental support was moderated by adolescents’ polygenic susceptibility, and that this G×E was mediated by neuroticism. To determine the generalizability of these results―it has been found that including children who vary in their ethnicity can be problematic when studying G×E (Propper et al., 2007)―we compared these results from a total sample analysis to the results from a model with only adolescents who were born in the Netherlands. Our results did not differ using a subsample of only adolescents born in the Netherlands, a multiple-group model was not possible given the small size of the non-Dutch sample.
In addition, we found that girls reported higher levels of neuroticism and social
anxiety than boys, which might indicate that the G×E we found in the original model might be different for boys and girls as well. Therefore, we first ran a multigroup model without any constraints. Second, we ran several multigroup models and constrained different paths to equality across groups to test whether those effects were further moderated by sex. We compared models where individual effects were constrained one path at a time to a model without any constraints. None of the effects differed across sex, all ps > .05, showing there were no differences between boys and girls in the G×E of neuroticism and the polygenetic score on the relationship between parental support and social anxiety.
Discussion
The purpose of this 6-year longitudinal study was (a) to investigate whether adolescents’ polygenic susceptibility would moderate the relationship between parental support and social anxiety, and (b) to assess whether the moderating effect of adolescents’ polygenic susceptibility would be mediated by adolescents’ personality traits and behavioral inhibition. Our results showed that the indirect effect of parental support on the level of social anxiety was indeed moderated by the polygenic susceptibility for the first and second wave, and that this moderating effect was mediated via neuroticism. Specifically, adolescents with a
high polygenic susceptibility compared to adolescents with a low polygenic susceptibility were at a disadvantage when they received low levels of parental support, but also benefited more from high levels of parental support in relation to their immediate level of social anxiety, and their level of social anxiety one year later. We found no significant mediated moderation effects on linear change in social anxiety, nor any significant mediated
moderation effects via the other Big Five personality traits (i.e., extraversion, agreeableness, conscientiousness, and openness) or behavioral inhibition.
The outcome that both positive and negative parenting plays an important role in complements the findings of previous studies reporting associations between negative parenting and the development of social anxiety (e.g., Knappe et al., 2009; Rapee & Spence, 2004; Gulley et al., 2014). Gulley and colleagues (2014) discuss that a lack of parental support can make children more sensitive to negative evaluations, which in turn can contribute to the development of social anxiety. Our findings now suggest that positive parenting characterized by high levels of support might also make children more sensitive to positive evaluations (or less sensitive to negative evaluations), which might act as a buffer against the development of social anxiety. In addition, the finding that adolescents’ polygenic susceptibility scores moderated the relationship between parental support and social anxiety shows that there is evidence for a G × E interaction in the development of social anxiety. In correspondence with the theory of Caspi and Moffit (2006), the exposure to an environmental pathogen (i.e., parental support) was shown to have a conditional effect on adolescents’ mental health (i.e., development of social anxiety) based on their genotype (i.e., polygenic susceptibility score).
Importantly, the associations for genetically highly susceptible adolescents with both high and low levels of parental support confirm the differential susceptibility theory proposed by Belsky and colleagues (2007) that putatively vulnerable children are especially susceptible
to both negative and positive rearing experiences, for better and for worse. In order to demonstrate differential susceptibility, the moderation effect should reflect a cross-over interaction covering both positive and negative environmental influences. In addition, the slope for the high polygenic susceptibility group should not only be significantly different from zero, but also significantly steeper than the slope for the low polygenic susceptibility group (Belsky et al., 2007). In our study these two preconditions were met: adolescents with a high polygenic susceptibility were indeed susceptible to both high and low levels of parental support (i.e., reflecting a cross-over interaction), whereas the susceptibility for parental support of adolescents with a low polygenic susceptibility not be distinguished from zero, and the slope differed significantly from the high polygenic susceptibility group.
We found that the genetic moderation effect on the relationship between parental support and social anxiety was mediated by neuroticism. This study is the first in line finding support for a genetic moderation effect that is explained by a personality trait (i.e.,
neuroticism) as a susceptibility marker. Because neuroticism has been found to be the
strongest predictor of social anxiety compared to the four other Big Five factors (extraversion, agreeableness, conscientiousness, and openness) (Kotov et al., 2010), it was perhaps not surprising that the personality trait of neuroticism mediated the genetic moderation effect. The lack of significant mediation effects, or even correlations with social anxiety (see Figure 2) for agreeableness, conscientiousness, and openness could be expected because previous research demonstrated that those Big Five Factors explain only little variance in social anxiety (Naragon-Gainey & Watson, 2011; Watson et al., 2011). Our results overall suggest that it is possible that a high polygenic susceptibility influences the development of neural structures in such ways that help promote behavioral response to emotional stimuli, which can be reflected in personality traits such as neuroticism. Subsequently, elevated levels of neuroticism may
strengthen the effect of both low and high parental support on the development of social anxiety.
However, it is surprising that we did not find any significant mediated moderation effects via extraversion and behavioral inhibition, whereas we did find significant correlations for both personality traits with the level of social anxiety across all six waves. It is possible that specific facets of extraversion and behavioral inhibition in children and adolescents interact differently with parenting, or are not as strongly related serotonin functioning in the brain as neuroticism. The majority of research examining associations between personality and serotonin activity in the brain only report significant associations with neuroticism (e.g., Canli & Lesch, 2007; Munafò et al., 2006; Takano, et al., 2007). Additionally, there is one study that reported a G × E interaction of social support and 5-HTTLPR in predicting observed behavioral inhibition (Fox et al., 2005b), suggesting that there is an association between the serotonin functioning in the brain and behavioral inhibition as well. However, to assess adolescents’ behavioral inhibition, we used the BIS scale (Carver & White, 1994). This scale is based on a theory in which the behavioral inhibition system is related to the
motivational system of sensitivity to punishment (Gray, 1972), which differs from the theory of Kagan, Reznick, and Snidman (1987) that defines behavioral inhibition as a fear for unfamiliar events, which is often measured using observations. The items of the BIS scale of Carver and White (1994) do not assess fear in novelty situations, but general worry/fear of things not going well, which is assumed to be related to social anxiety as well. Nevertheless, this measure of behavioral inhibition in relation to sensitivity of punishment might be differently associated with adolescents’ polygenic susceptibility related to brain serotonin levels than we expected.
It is important to note that the mediated moderating effect of the polygenic
(wave 1), and the level of social anxiety one year later (wave 2). Children can already be diagnosed with social anxiety disorder as young as eight years old (Beidel & Turner, 1998), which might indicate that the influence of parental support on the development of social anxiety occurs much earlier (i.e., already in childhood). The mean age in our sample during the first wave was 13 years old, and the development of social anxiety symptoms might already be quite stable at this age. In addition, adolescents attended the first year of secondary school at the start of our study; at this age the importance of peer group acceptance increases (Brennan, 1982). It has been found that during early adolescence, the levels of social support from peers are related to social anxiety symptoms (La Greca & Lopec, 1998). Therefore, it might be interesting to examine either whether there is a genetic moderation effect on the relationship between parental support and changes in the development of social anxiety during childhood, or examine the genetic moderation effect on the relationship between peer support and social anxiety during adolescence.
When looking specifically at the direct effects of the polygenic susceptibility score and parental support on social anxiety in our model, it is noteworthy that we did not find any significant effects. This may indicate that the effects of parental support and the polygenic susceptibility score are not partially, but fully mediated by neuroticism. However, as Samuels (1989) wrote, “non-rejection of H0 is not the same as acceptance of H0… Non-rejection of H0
indicatesthat the data are compatible with H0, but the data may also be quite compatible with
HA.” Therefore, it would be interesting to test a full mediation against partial mediation of
these associations in future research.
Strengths, Limitations, and Future Research
Our results should, however, be considered in light of some limitations. First, our study sample was characterized by adolescents from relative high SES families mainly from the Dutch indigenous population. Therefore, our results may not be generalizable to more
heterogeneous population. It is important that future research focuses on these associations among ethnic minorities, different family constellations, and groups of low SES as well. However, it is important to keep in mind that when studying G × E interactions one should aim at a homogeneous population because variations in ethnicity can be problematic in estimating unbiased G×E (Propper et al., 2007). Second, for all variables except for the polygenic susceptibility score, we exclusively relied on adolescent self-reports. It would be interesting to use a multi-informant approach (e.g., including parent reports on parental support), which would make it also possible to study interactions between parent gender and child gender. It has been shown that interactions can differ between mothers and fathers with their children, depending on the children’s gender as well (Russel & Saebel, 1997). Third, genetic and environmental factors may be correlated; parenting is confounded with shared genetic factors in children as well as parents within a family (Bakermans-Kranenburg & Van IJzendoorn, 2015). We cannot rule out associations between adolescents’ polygenetic
susceptibility and the levels of parental support of their parents. Therefore, it would be interesting to test differential susceptibility in G × E interactions by using experimental designs to test variations in response to for example low and high levels of parental support (Bakermans-Kranenburg & Van IJzendoorn, 2015; Pluess & Belsky, 2013).
An important strength of the present study is the genetically informed six-year prospective longitudinal design, covering early to late adolescence with an adequate sample size. Because we were using a longitudinal design with a sophisticated analytic strategy, we were able to examine developmental processes between parental support and adolescents’ social anxiety symptoms throughout adolescence. Additionally, we focused on two factors that are considered to be an individual risk for the development of social anxiety symptoms (i.e., levels of parental support and polygenic susceptibility scores), including any possible influences of susceptibility markers (i.e., personality) on the genetic moderation effect as
well. Taking this together with the fact that we used a more sophisticated way of investigating G × E interactions using cumulative polygenic susceptibility scores, the findings of our study might make a substantial contribution to the understanding of the effect of parental support on the development of social anxiety in adolescents.
Do our findings regarding differences in the susceptibility for low and high levels of parental support for children with a high and low polygenic susceptibility have any
implications for clinical practice? We think they do, because our findings contribute to knowledge about specific needs (i.e., high levels of parental support) for specific subgroups (i.e., children and adolescents with a high polygenic susceptibility). This can provide crucial input for the identification of child characteristics and parenting characteristics that need to be targeted for optimal prevention and treatment results (Reiss & Leve, 2007). Our findings in relation to different endophenotypes may already help to determine the reactions of children to parental behavior in the socialization process (Overbeek, Weeland, & Chhangur, 2012; Fischer & Skowron, 2017). However, in the long run there are conditions under which G × E research must comply to be informative for clinical practice (Overbeek, 2017). When the underlying biological mechanisms of G × E interactions are clear by investigating
physiological measures as well (e.g., serotonin levels in the brain, stress reactivity, and blushing), and if replication studies find the same G × E effects with sufficiently large effect sizes, using different experimental approaches (i.e., randomized controlled trials, interrupted time series, and micro-trials), clinicians may benefit greatly.
Conclusion
In research on G × E interactions there has been only limited, to no attention to the question whether genetic moderation effects can be explained by susceptibility markers on the level of personality traits. Our results are among the first to show that show that there is a higher gene-based susceptibility to both positive and negative environmental influences―in
our case, pertaining to parental support―in some adolescents, and that this gene-based susceptibility works through heightened neuroticism in adolescents. Interestingly, adolescents with a high polygenic susceptibility reported more social anxiety with a lack of parental support, but also reported lower social anxiety with higher parental support. Thus, instead of labeling people with more putative plasticity alleles as having a higher genetic risk, or being genetically disadvantaged, we should label those individuals as having a higher genetic susceptibility.
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Appendices
Table 1. Alleles used to Code for Genetic Risk.
Genotype Score = 2 n (%) Score = 1 n (%) Score = 0 n (%) 5-HTTLPR (rs25531) S/S 1 (0.3) S/L 19 (4.8) L/L 374 (94.4) HTR1A (rs6295) C/C 93 (23.6) C/G 186 (47.2) G/G 115 (29,2) HTR2A (rs6311) C/C 135 (34.3) C/T 194 (49.2) T/T 65 (16.5) Note. Total N is 394.
Table 2. Descriptive Statistics for Boys and Girls and for Adolescents aged 11–13,5 and 13,5–16 at Wave 1. † p < .100;* p < .050;** p < .010;*** p < .001 Estimates Total (N = 522) Boys (n = 294) Girls (n = 228) Early Adolescence (11–13.5 yrs.) (n =265) Middle Adolescence (13.5–16 yrs.) (n =257) M (SD) M (SD) M (SD) M (SD) M (SD) Age adolescent 13.03 (0.46) 13.05 (0.48) 13.00 (0.44) 12.92*** (0.34) 13.93*** (0.35) Age mother 44.40 (4.45) 44.72† (4.74) 43.99† (4.01) 44.36†** (4.37) 44.78†** (5.11) Age father 46.74 (5.10) 47.25* (5.32) 46.10* (4.76) 46.71*** (5.01) 47.02*** (5.94) Caucasian % 95.20 96.30 93.90 95.00*** 98.00*** SES family % Medium or high % low 88.90 11.10 92.00* 8.00* 85.00* 15.00* 89.60 10.00 88.20 17.00 Behavioral inhibition (1–4) 2.47 (0.48) 2.38*** (0.45) 2.58*** (.49) 2.48 (0.48) 2.42 (0.50) Extraversion (1–7) 5.05 (1.05) 5.01 (1.04) 5.11 (1.05) 5.08 (1.03) 4.80 (1.20) Agreeableness (1–7) 5.48 (0.76) 5.44 (0.77) 5.53 (0.74) 5.46 (0.76) 5.58 (0.71) Conscientiousness (1–7) 3.99 (1.11) 3.99 (1.05) 3.99 (1.18) 3.98 (1.12) 4.03 (1.06) Neuroticism (1–7) 3.63 (1.11) 3.52* (1.04) 3.77* (1.18) 3.64 (1.09) 3.50 (1.32) Openness (1–7) 4.90 (0.95) 4.90 (0.96) 4.90 (0.94) 4.89 (0.95) 4.97 (0.95) Emotional support (1–4) 3.38 (0.39) 3.39 (0.38) 3.38 (0.41) 3.39† (0.39) 3.28† (0.39) Social anxiety (1–3) 1.65 (0.51) 1.57*** (0.48) 1.74*** (0.53) 1.66 (0.24) 1.55 (0.46) Polygenetic index (0–6) 2.18 (1.00) 2.11 (1.00) 2.26 (1.00) 2.21† (1.01) 1.89† (0.91)
Table 3. Pearson Correlations between Parental Support, Genetic risk, Big Five Personality Traits, Behavioral Inhibition for the First Wave, and
Social Anxiety for all Six Waves.
2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 1. S1 −.05*** −.17*** −.39*** −.15*** −.08† −.11*** −.09†* −.12*** −.07*** −.16** −.11*** −.07** −.06** 2. G −.01 −.02 −.04 −.01 −.01 −.04 −.03 −.03 −.02 −.08 −.03 −.06 3. E1 −.16*** −.19*** −.50*** −.00 −.23*** −.31*** −.26*** −.29*** −.32*** −26*** −.25*** 4. A1 −.28*** −.07 −.51*** −.03 −.07 −.06 −.01 −.04 −.02 −.01 5. C1 −.19*** −.26*** −.07 −.04 −.03 −.05 −.07 −.05 −.06 6. N1 −.23*** −.58*** −.36*** −.26*** −.31*** −.33*** −.27*** −.26*** 7. O1 −.04 −.05 −.03 −.04 −.01 −.03 −.03 8. BI1 −.41*** −.34*** −.32*** −.35*** −.31*** −.25*** 9. SA1 −.51*** −.47*** −.42*** −.36*** −.42*** 10. SA2 −.61*** −.56*** −.55*** −.52*** 11. SA3 −.65*** −.59*** −.56*** 12. SA4 −.71*** −.64*** 13. SA5 −.75*** 14. SA6 † p < .100;* p < .050;** p < .010;*** p < .001.
Table 4. Unstandardized Estimates of Parental Support, Genetic Risk, the interaction of both,
and Neuroticism for the Piecewise LGC Model
Intercept1 Intercept2 Slope N1
Estimate SE Estimate SE Estimate SE Estimate SE S −0.046*** 0.143 −0.108*** 0.139 −0.008*** 0.038 −0.645†** 0.334 G −0.109*** 0.228 −0.018*** 0.221 −0.010*** 0.060 −1.522*** 0.531 GxS −0.036*** 0.067 −0.003*** 0.064 −0.005*** 0.018 −0.447*** 0.155 N1 −0.157*** 0.019 −0.132*** 0.019 −0.002*** 0.005 −−*** −−* α2 1.258*** 1.389*** 0.004*** 1.427*** σ2 0.223*** 0.148*** 0.007*** 1.195*** R2 0.136*** 0.144*** 0.018*** 0.028*** † p < .100;* p < .050;** p < .010;*** p < .001.
