Emotion Regulation and Parent-Child Mutuality: Gene-Environment Links

Emotion Regulation and Parent-Child Dyadic Mutuality: An Evocative Gene-Environment Correlation Model-Fitting Analyses Introduction • A bivariate Cholesky model (see Figure 4) was used to decompose the variance within, and the covariance between, emotion regulation and dyadic mutuality into genetic, shared environmental and nonshared environmental components. • Most studies of parent-child interaction have focused on the contributions of parents or the child as separate individuals to the interaction, whereas parent-child dyadic mutuality (e.g., mutual responsiveness) has received less attention. • Dyadic mutuality has implications for socioemotional and behavioral adjustment (Kochanska & Thompson, 1997; Kochanska, & Murray, 2000). • In these bivariate analyses, we are able to estimate 1) heritability, and shared and nonshared environmental variance of each domain; 2) genetic and environmental contributions to the phenotypic correlation; and 3) genetic and environmental correlations between emotion regulation and dyadic mutuality. Figure 1. Lab Assessment: Bayley • Dyadic mutuality is positively associated with children’s positive emotionality (Deater-Deckard & O’Connor, 2000). • Little is known about the relation between parent-child dyadic mutuality and child emotion regulation. Results Study Goal Phenotypic Correlation • The present study explored the relation of emotion regulation to parent-child dyadic mutuality and sources of the relation. Figure 2. Lab Assessment: Lab-TAB • The association of emotion regulation with dyadic parentchild mutuality was moderate (r=.37, p< .01). Twin Intraclass and Cross Correlations (Table 1) Methods • For both emotion regulation and dyadic mutuality, MZ intraclass correlations exceeded DZ correlations, suggesting that both were genetically influenced. Sample • 304 same-sex (140 monozygotic, 164 dizygotic) twin pairs assessed within approximately 2 weeks of their 3rd birthday. Procedure • Twins completed two laboratory visits approximately 48 hours apart. On both visits, twins engaged in a variety of behavioral and cognitive assessments. • At the initial visit, one twin was assessed with the Bayley Scales of Infant Development, 2nd edition (BSID-II; Bayley, 1993; Figure 1) and a parent-child interaction task (Figure 3), while the other twin was assessed with the Laboratory Temperament Assessment Battery–Preschool Version (Lab– TAB; Goldsmith, Reilly, Lemery, Longley, & Prescott, 1995; Figure 2). At the second visit, the assessments were reversed for each twin. • The twins were assessed by different testers. In most cases (86.6%), the parent involved in the parent-child interaction task was the mother. Figure 3. Lab Assessment: parent-child interaction Parent-Child Dyadic Mutuality • The Parent–Child Interaction System (PARCHISY; DeaterDeckard, Pylas, & Petrill, 1997; Deater-Deckard, 2000) was used to assess parent-child mutuality during the parent-child interaction task. In this task, parents and children participated in an interactive picture book task (10-15 minutes). • Parent-child mutuality factor of the PARCHISY includes emotional reciprocity, conflict (reversed scored), and cooperation. Items were scored according to a 5-point likert format based on the observed frequency of behaviors (ranging from 1=no occurrence of the behavior, to 5=constant occurrence of the behavior). A1 C1 E1 A2 C2 E2 • The MZ and DZ cross-twin cross-variable correlations (i.e., the correlation between Twin A's score for emotion regulation with Twin B's score for dyadic mutuality and vice versa) were similar in magnitude and hint that shared environmental effects may contribute to the association between emotion regulation and dyadic mutuality, which can be tested by more powerful multivariate genetic model-fitting analyses. Table 1 Twin Intraclass Correlations and Cross-Twin Cross-Trait Correlations Variables Twin zygosity MZ twins DZ twins Emotion Regulation .53* .27* Dyadic Mutuality .28* .18* Cross Correlations .19* .22* Note. MZ=monozygotic; DZ=dizygotic. *p<.05. Measures Model-Fitting Results Emotion Regulation • Figure 5 presents the path estimates from the best fitting model. Variance components are presented in Table 2. • To assess children’s emotion regulation, the Behavior Rating Scales (BRS) of the BSID-II was completed by testers after each visit based on children’s behaviors observed during assessments. • For 3 year-olds, the BRS Emotional Regulation factor comprises 9 items, including negative affect, frustration, and attention, each rated on a 5-point scale. • The BRS scores for the two laboratory visits were averaged to create a composite measure. • Both emotion regulation and dyadic mutuality were influenced by genetic and nonshared environmental factors. Emotion Regulation Dyadic Mutuality Figure 4. Full bivariate Cholesky model. A1, C1, and E1 denote genetic, shared environmental and nonshared environmental factors influencing both emotion regulation and dyadic mutuality. A2, C2, and E2 represent factors specific to dyadic mutuality. • The association between emotion regulation and dyadic mutuality was mediated by both genetic (rg = .61), and nonshared environmental factors (re = .21). In addition, 69% of the phenotypic correlation between emotion regulation and dyadic mutuality was due to common genetic influences with the remainder due to nonshared environmental influences. A1 E1 A2 .35 (.21,.47) -.17 (-.29,-.05) .73 (.64,.79) E2 .45 (.27,.58) -.80 (-.88,-.72) -.69 (-.77,-.61) Emotion Regulation Dyadic Mutuality Figure 5. Path estimates (95% CI) from the best-fitting bivariate Cholesky model. Table 2 Estimates of Genetic and Environmental Variance (95% CI) from the Best Fitting Model c2 e2 Variance h2 Emotion Regulation .53 (.41, .62) -- .47 (.37, .59) Dyadic Mutuality .33 (.18, .46) -- .67 (.55, .82) rg rc re .61(.39, .83) -- .21(.06, .35) Covariance Emotion Regulation — Dyadic Mutuality Note. h2= genetic variance; c2= shared environmental variance; e2= nonshared environmental variance. rg, rc and re denote the genetic, shared environmental, and nonshared environmental correlations, respectively. Conclusions • Consistent with prior research (Deater-Deckard & O’Connor, 2000), our findings suggest that dyadic mutuality, as an “environmental” phenotype of children, is influenced by moderate children-driven genetic effects, which illustrates a possible evocative gene-environment correlation. • The current study extends previous research by clarifying that emotion regulation is a specific child trait that contributes to the genetic effects on dyadic mutuality. • Our study indicates that nonshared environmental influences also contribute to the association between dyadic mutuality and children’s emotion regulation. In other words, some individual-specific experiences, such as different patterns of interactions with their parents, could make children differ in their abilities to regulate emotion and bring about different levels of parent-child dyadic mutuality.