The genetic architecture of effortful control and its interplay with psychological adjustment in adolescence

Accepted Manuscript The Genetic Architecture of Effortful Control and its Interplay with Psychological Adjustment in Adolescence Corrado Fagnani, Emanuela Medda, Guido Alessandri, Davide Delfino, Cristina D'Ippolito, Nancy Eisenberg, Maria Antonietta Stazi PII: DOI: Reference:

S0092-6566(17)30023-5 http://dx.doi.org/10.1016/j.jrp.2017.03.003 YJRPE 3632

To appear in:

Journal of Research in Personality

Received Date: Revised Date: Accepted Date:

8 March 2016 19 January 2017 18 March 2017

Please cite this article as: Fagnani, C., Medda, E., Alessandri, G., Delfino, D., D'Ippolito, C., Eisenberg, N., Stazi, M.A., The Genetic Architecture of Effortful Control and its Interplay with Psychological Adjustment in Adolescence, Journal of Research in Personality (2017), doi: http://dx.doi.org/10.1016/j.jrp.2017.03.003

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

RUNNING HEAD:THE GENETIC BASES OF EC IN ADOLESCENCE

The Genetic Architecture of Effortful Control and its Interplay with Psychological Adjustment in Adolescence

Fagnani Corrado,1* Medda Emanuela,1* Alessandri Guido,2 Delfino Davide,1 D’Ippolito Cristina,1 Eisenberg Nancy,3 Stazi Maria Antonietta1

* Theses authors contributed equally to this work

1

Istituto Superiore di Sanità, Centre for Behavioural Sciences and Mental Health, Rome, Italy

2

Department of psychology, Sapienza, University of Rome, Italy

3

Department of psychology, Arizona State University, USA

1

Abstract The role of genes and environment in the relation between self-regulation and adjustment is unclear. We investigated, with the twin design, genetic and environmental components of the association between effortful control (EC) and indicators of psychological adjustment using adolescents’ and parents’ reports for 774 twins. Genetic factors explained a substantial proportion of variance in EC (58%) and the outcome variables of optimism (55%), general self-esteem (45%), happiness (48%), and self-derogation (29%). Perceived competence had no significant genetic component. Aside from perceived competence, uncorrelated with EC, phenotypic correlations of EC with measures of well-being/adjustment were moderate and predominantly explained by shared genetic effects. Results suggest a significant genetic contribution in adolescents’ EC and in its relation to various aspects of adjustment.

Keywords: self-regulation; self-control; effortful control; twins; genetic modeling.

2

1. Introduction Researchers have frequently explored the potential role of stable individual characteristics in social adjustment and emotional competence in adolescence (JaffariBimmel, Juffer, IJzendoorn, Bakermans-Kranenburg, & Mooijaart, 2006). Often researchers have stressed the assumed causal link between temperamentally-based individual differences in emotion-related self-regulation and adolescents' psychological and social maladjustment (Eisenberg, et al.,2001; Eisenberg et al., 2003; Silk, Steinberg, & Morris, 2003) under the assumption that emotion-related self-regulation and its component skills are basic characteristics, rooted in the genetic endowment of the individuals, that can foster positive development (Eisenberg, Spinrad, & Eggum, 2010). However, the heritability of emotionrelated self-regulation has been investigated mostly in infancy or early childhood, and, of particular importance, the roles played by heredity and the environment in explaining the broad role of self-regulation in adjustment are unclear. We examined these issues using a sample of 774 twins for whom a measure of emotion-related self-regulation (henceforth labeled self-regulation) was obtained from both the adolescent and a parent. The aims of this paper were twofold. First, we estimated the genetic and environmental (shared and nonshared) components of observed variability in measures of regulation and indicators of emotional psychological well-being (i.e., happiness, optimism, and general self-esteem, perceived general competence, and low self-derogation; see Houben, Van Den Noortgate, & Kuppens, 2015).Second, we estimated the degree to which genetic and environmental factors account for the correlations- often reported in literature - between self-regulation and indicators of psychological adjustment in adolescence. One cannot assume that the role of genetics and the environment in the correlation between self-regulation and well-being is the same at all ages. Moreover, given changes during adolescence in prefrontal regulatory processes and other related, potentially opposing sub-

3

cortical processes (such as sensation seeking or impulsivity) that affect the expression of selfregulation (and appear to actually decrease self-regulation during part of adolescence; e.g., Casey, 2015; Luciana, Wahlstrom, Porter, & Collins, 2012; Steinberg, 2010), one cannot assume that the role played by genetic versus environmental factors is the same in adolescence as during other periods of life. 1.1 Effortful self-regulation Loosely speaking, self-regulation represents a broad construct entailing attentional, cognitive, physiological, and behavioral processes that operate in concert to ensure an appropriate level of emotional, motivational, and cognitive arousal (Blair & Diamond, 2008). From a developmental perspective, researchers often investigate self-regulation from a temperament framework using measures of effortful control (Rothbart, Derryberry, & Posner, 1994; Rothbart, Ellis, & Posner, 2011; Rueda, Posner, & Rothbart, 2005). The construct of effortful control (EC) reflects the temperamentally-based component of emotion-relevant selfregulation and captures a set of relatively deliberate control functions needed for voluntary and goal-directed behavior (Rothbart & Bates, 2006). EC pertains to dispositional differences in the abilities to effortfully modulate attention, behavior, and emotion and involves some executive functioning capacities (i.e., planning, detecting errors, assimilating information, etc.; Eisenberg, Hofer, Sulik, & Spinrad, 2014).As highlighted by Rothbart and Derryberry (2002), the constitutional temperamental basis of EC refers to the relatively enduring “makeup” of the organism, influenced over time by heredity, maturation, and experience. The capabilities that are part of EC can be viewed as tools available for self-regulation of emotion and behavior in specific contexts; thus, EC provides the temperamentally based capacities for self-regulation (Eisenberg et al., 2014). Developmentally, EC represents an early appearing component of child temperament (Kochanska, Murray, & Harlan, 2000; Rothbart & Bates, 1998; Rothbart & Rueda, 2005).

4

Children relatively high in EC, compared to those who are lower, exhibit better social and emotional competence (Bjorklund & Kipp, 1996; Derryberry & Rothbart, 1997; Eisenberg et al., 2003; Kochanska, Murray, & Coy, 1997; Kopp, 1982, 1989), are less likely to develop internalizing and externalizing problems (Eisenberg et al., 2001, 2003; Eisenberg & Spinrad, 2004), and perform better at school (Brock, Rimm-Kaufman, Nathanson, & Grimm, 2009; Eisenberg, Valiente, & Eggum, 2010). In empirical studies, EC has been associated with peers’ (Eisenberg, Fabes,& Murphy, 1996) or teachers’ reports (Diener & Kim, 2004) of children’s and adolescents’ prosocial behavior. Based on a person-centered approach, Veenstra, Lindenbeg, Oldehinkel, De Winter, Verhulst, and Ormel (2008) found that clusters of preadolescents characterized by high levels of prosociality had an elevated level of EC. Other researchers have found that, in early adulthood, EC is positively associated with intimate interpersonal relationships and self-esteem (e.g., Busch & Hofer, 2012), and prosociality (Alessandri et al., 2014; Veenstra et al., 2008), as well as life satisfaction and optimism (Fosco, Caruthers, & Dishion, 2012). 1.2 The genetics of effortful self-regulation From a neurobiological point of view, available evidence suggests that EC is under the influence of the executive attention network, which is neuroanatomically centered in the anterior cingulate gyrus and areas in the prefrontal cortex (Botvinick, Nystrom, Fissell, Carter, & Cohen, 1999; Fan, Flombaum, McCandliss, Thomas, & Posner, 2003). The functioning of this network is related to regulating thoughts, emotion, and action (Posner & Petersen, 1990; Posner, Rothbart, Sheese,& Tang, 2007). The executive attention network appears to undergo progressive maturation, starting to emerge in the first year of life and continuing during childhood into adolescence (Rueda, Checa, & Combita, 2012). However, in adolescence, heightened reactivity to emotions and rewards (as reflected in sensation seeking or impulsivity) affects the level of self-regulation and can result in a temporary decline in its

5

level, at least in some contexts (see Casey, 2015). Given that EC is a component of temperament with neurological correlates, it is not surprising that investigators have assessed the contributions of both genes and environment to EC (Goldsmith, Pollak, & Davidson, 2008).At present, molecular genetic studies suggest that the dopamine D4 receptor gene (Fan, Fossella, Sommer, & Posner, 2003) and the catechol-omethyl transferase gene (Blasi et al., 2005) are two of the candidate genes involved in the behavioral expression of EC. Yet more data are necessary in order to evaluate the relations of genes and the environment to EC at different ages. Previous studies have often been conducted using the classical twin design (LemeryChalfant, Doelger, & Goldsmith, 2008; Yamagata, Takahashi, Kijima, Maekawa, Ono, & Ando, 2005). With this design, under certain assumptions (Neale & Cardon, 1992), the effects of genetic (‘heritability’) and of environmental (both shared within family and individualspecific) factors on one or more traits can be estimated from the comparison between monozygotic (MZ) twins (genetically identical) and dizygotic (DZ) twins (who share half of their genetic background, like ordinary siblings).The results regarding genetic and environmental effects can be extended - within the limitations of each study- to the general population of which the twin population has been shown to be representative in many respects. Although a number of researchers have used behavioral genetic twin studies to examine the relative contribution of genes and environment to EC, the available evidence regarding the first two decades of life is mostly from samples of infants and young children. The upshot of these studies is that individual differences in EC appear to be substantially heritable. Based on the intraclass correlations in MZ (rMZ) and DZ (rDZ) twin pairs reported in these studies and using the formula 2*(rMZ-rDZ), we estimated moderate genetic influence for various self-regulation measures in children up to 2 years of age (Gagne & Goldsmith,

6

2007;Gagne & Saudino, 2006), moderately high genetic effects for children about 3 years old (Gagne & Goldsmith, 2007), and moderately high genetic effects for children about7-years old (Goldsmith, Buss,& Lemery, 1997; Lemery-Chalfant et al., 2008). After early childhood, behavioral genetics estimates of heritability show some variability across studies, and are often based on highly age-heterogeneous samples. For example, Yamagata et al. (2005) reported a heritability estimate of 49% for EC on a sample of twins varying in age from 17 to 32 years. The lack of reliable behavioral genetic data for adolescents seems particularly critical in light of recent neurobiological evidence indicating that the effortful regulatory skills associated with EC fluctuate during adolescence(Casey, 2015), although there is prefrontal cortical development(e.g., in terms of interconnections within; Casey, 2013) relevant to executive functioning/self-regulation (Casey, 2013; Casey, Jones,& Hare, 2008; Steinberg et al., 2009). Genes are generally viewed as responsible not only for the stability but also for change in individuals' characteristics (e.g., Hopwood et al. 2011;Lewis & Plomin, 2015; Plomin, 1986). Thus, a high heritability coefficient for a trait in a specific phase of life indicates that the expression of that trait at that age is at least partly dependent on genetic mechanisms. Yet the fact that genetic factors appear to be a major contributor to the expression of EC does not mean that the observed influence of EC on psychological adjustment is governed only by genes (Goldsmith et al., 2008) or that the genetic influence is stable with age. 1.3 Effortful Control and psychological adjustment in adolescence Over the past several decades, researchers have related both absolute level of EC and rate of change over time in EC to a wide range of developmental outcomes in adolescence such as general self-esteem (Robins, Donnellan, Widaman, & Conger, 2010), self-perceived general competence (DiBiase & Miller, 2012; Rhoades, Greenberg, & Domitrovich, 2009),

7

the tendency to harbor depressive feelings (Davenport, Yap, Simmons, Sheber, & Allen, 2011), happiness (Fosco et al., 2012), and optimism (Lemola, Raikkonen, Matthews, Scheier, Heinonen, Pesonen, Komsi, & Lahti, 2010). Given that the effortful self-regulation/EC is complex in adolescence, and may relate differently to aspects of functioning such as well being in childhood compared to adulthood, it seems important to assess the role of genetic and environmental factors that govern the relation of EC to markers of psychological adjustment in this phase of life. Previous twin studies have obtained significant heritability estimates for each of the above well-being-related constructs: about 50% for self-esteem (although with between-study variability: see Caprara et al., 2009; Kamakura, Ando,& Ono, 2001; Kendler, Gardner,& Prescott, 1998; Roy, Neale,& Kendler, 1995), 25% for optimism (Caprara et al., 2009; Plomin, Scheier, Bergerman, Pedersen, Nesselroade,& McClearn, 1992), 40-50% for subjective happiness (Bartels & Boomsma, 2009; Stubbe, Posthuma, Boomsma,& De Geus, 2005), 40% for personal competence (Luciano, Wainwright, Wright, & Martin, 2006), and 30-40% for depressive thoughts (Johnson, Whisman, Corley, Hewitt, & Friedman, 2014). Unfortunately, to date, the two lines of research focusing on the behavioral and psychological correlates of self-regulation and on their genetic bases have proceeded almost separately. Consequently, there is a paucity of studies addressing if and to what extent the observed phenotypic correlation of EC with meaningful indicators of emotional well-being such as self-esteem, life satisfaction, optimism, or self-derogation (unmotivated negative affectivity toward oneself strongly correlated with depressive symptomatology; see Alessandri, Vecchione, Eisenberg, & Laguna, 2014) should be attributed to shared genetic mechanisms or to shared or nonshared effects of the environment on EC and emotional wellbeing (or effects of the environment on either well-being or EC mediated by the other variable). Genetic effects or environmental effects that account for part of the association

8

between EC and emotional well-being have different implications for understanding why EC and emotional well-being are related. At present, very few previous studies have investigated the possible genetic or environmental mediation of the relation between EC and emotional well-being; these studies mostly have been confined to infancy and reported that the association between EC and emotional well-being was mediated by genes (Lemery-Chalfant et al., 2008). More important, results from these studies have generally provided support for a protective model (Rothbart & Bates, 2006) positing that individuals with high EC (i.e., a predisposition) are naturally better able to deal with stress and environmental difficulties, with the consequence that they are better adjusted psychologically (Lemery-Chalfant et al., 2008). Shared genetic effects are often interpreted as a signal that a common genetic mechanism accounts, at least in part, for the observed phenotypic resemblance between two psychological traits (Plomin, De Fries, & McClearn, 1990). In the case of EC and self-esteem, for example, one might expect that genes leading to the maturation of systems associated with EC in adolescence are also responsible for observed change in this indicator of emotional well being. In this sense, shared genetic effects might be at least partly responsible for the observed normative changes in the individuals' ability to self-regulate and to adapt successfully to the environment during adolescence. The presence of environmental effects common to EC and adaptive outcomes may signal that the observed similarity between two psychological characteristics is a by-product of the encounter between an individual and a specific environment (shared, nonshared) in adolescence. In this regard, Plomin and Daniels (2011), for example, reported that environmental differences between children in the same family (called ‘‘nonshared environment’’) represent the major source of environmental variance for personality, psychopathology, and cognitive abilities (p. 563). Indeed, as reported above, shared effects

9

have rarely been reported for constructs investigated in this study. Thus, following Plomin and Daniels (2011),one might expect that systematic nonshared influences, such as birth order, different parental treatment, differences in extra-familial networks (i.e., teachers’ and peers’ characteristics), and unsystematic nonshared influences (such as illness, accidents, or trauma) might impact similarly on different but related psychological traits, and thus account for part of their observed phenotypic variation. In summary, there are reasons to expect a role for both genetic and environmental factors in explaining the observed phenotypic relation between EC and emotional well-being in adolescence. Although it is not possible to perfectly disentangle the role of genes and environment in complex interaction processes because they are intimately related and affect one another, general indications from suitable study designs are still desirable because they can guide subsequent investigations on these issues.

2. The present paper The first goal of this study was to examine the role of genetics and the environment in explaining the phenotypic expression of EC in adolescence and of selected indicators of emotional well being. On the basis of the limited available evidence from previous studies, we hypothesized genetic and nonshared (i.e., individual-specific) environmental effects on EC and the markers of emotional well-being, but no role of the shared environment (Bartels & Boomsma, 2009; Caprara et al., 2009; Gagne & Goldsmith, 2007; Gagne & Saudino, 2006; Johnson, Whisman, Corley, Hewitt, & Friedman, 2014; Lemery-Chalfant et al., 2008; Li, Chen, Li, & Li, 2014; Luciano et al., 2006; Yamagata et al., 2005; Stubbe et al., 2005). The second (and more important) aim of this study was (a) to investigate the nature of the phenotypic association between EC and the various indicators of psychological adjustment, and (b) to estimate the genetic and environmental components of such relations

10

(if present). We expected significant shared genetic and nonshared environment effects between EC and indicators of emotional wellbeing. In testing this model, we controlled for potential covariates such as gender and age to further refine the obtained estimates. Previous studies have documented mean-level gender differences (favoring males) in measures of selfesteem, subjective happiness, and self-derogation (Alessandri, Vecchione, Eisenberg, & Laguna, 2015; Tesch-Römer, Motel-Klingebiel,& Tomasik, 2008). Furthermore, although our sample was relatively homogeneous in regard to age (ranging from 14 to 17 years), it was still important to empirically control for differences in participants’ age because age could relate to EC and emotional well-being.

3. Methods 3.1 Sample description and power analysis Participants in this study were twins enrolled in the population-based Italian Twin Register (ITR). The procedures that led to the establishment of the ITR are described in detail elsewhere (Stazi et al., 2002). Currently, the ITR contains information on approximately 27,000 twins, and is involved in both general population- and clinical-based studies on various complex phenotypes, with behavioral and psychiatric genetics as major areas of investigation (Brescianini et al., 2013). Study participants were volunteers recruited within a community-based mail survey on health and psychological well-being in adolescence. Parents of all twins aged 14-17 years who were already enrolled in the ITR were contacted by mail and asked if they were willing to participate. In the same contact, they received the psychological questionnaires to be completed and returned. Out of 1642 families for which there was an attempt to contact, 389 (23.7%) agreed to participate and returned the questionnaires. The sample consisted of 774 twins (385 complete pairs, 4 unmatched twins), of whom

11

281 (36.3%) were monozygotic (MZ) and 493 (63.7%) were dizygotic (DZ). The mean age of respondents was 16.3 years (median 16.4 years), and 51.3% of the twins were males. Gender, zygosity, and age of the twins, as well as age of the parents, did not differ between respondent and non-respondent families, whereas parents of the twins participating in the psychological survey were significantly more educated than parents in the families that did not participate (mothers’ university degree: 32.9% vs 21.4%, p<0.001; fathers’ university degree: 32.6% vs 24.1%, p<0.001; see Table 1). Power calculations, conducted by the software Mx (Neale, Boker, Xie, & Maes, 2006), were focused on the heritability of EC, the key parameter estimated. Based on estimates available from the literature (Gagne & Saudino, 2006; Yamagata et al., 2005), the range 0.200.80 for the heritability of EC was considered as a possible scenario for the effect size. Our sample provides power of 33% to estimate, at the 5% significance level, a heritability of 0.20 for EC, and around 1300 twin pairs would be needed to achieve 80% power for this value. For a true heritability of 0.30, the power of our sample was 68%, whereas the conventional power value of 80% is obtained for a heritability as low as 0.35. If true EC heritabilities are 0.40 and 0.50, power estimates would be 93% and 99% respectively. For heritabilities between 0.51 and 0.80, power would be100%. 3.2 Zygosity assessment Zygosity was assessed with the parent-rated Goldsmith (1991) questionnaire. This instrument consists of items about physical similarity and frequency of confusion of the twins by family members and strangers, and it allows zygosity identification by a mathematical algorithm. This is a well-established procedure in twin studies, which is known to be over 90% accurate (van Beijsterveldt, Verhulst, Molenaar,& Boomsma, 2004). The reliability of this method in the ITR population was recently estimated in an independent sample of 20 same-gender twin pairs aged 6-17 years using nine microsatellite markers; 18 pairs (90%)

12

were correctly classified by the questionnaire. 3.3 Measures 3.3.1 Effortful Control Effortful control was assessed by using 8 items from the 9-item measure (one item was excluded because it showed poor psychometric properties in preliminary analyses) used by Luengo-Kanacri, Pastorelli, Eisenberg, Zuffianò, and Caprara, 2013. This 8-item measure (response format: 0 = not true to 2 = very true or often true) captures the construct of EC in the respective subscales of attentional focusing and inhibitory control. Sample items were “He/she does not finish things he/she started”“He/she also talks when it is not his/her turn” and “His/her demands must be fulfilled immediately, easily frustrated.”. The items were reversed as appropriate: high scores indicated high EC. The reliability for the parent-version of the measure was .82. The same items, worded in first person, were administrated to the adolescent (alpha = .74). Additional analyses supporting the psychometric properties of this instrument (including factorial validity, measurement invariance across observers, and convergent validity between the self-and parent-report scales) are presented in the online Appendix. To address empirically the validity of our measure of effortful control, we reported the results of a pilot study that involved a sample of 338 adolescents (60% females) aged from 14 to 16 years (M = 15.10, SD= 1.1). They responded to the above 8-item measure of effortful control (alpha = .78), along with the Effortful control subscales (i.e., Attentional control, Inhibitory control, Activation control, 21 items; alpha = .74) from the Adult Temperament Questionnaire (ATQ; Rothbart, Ahadi, & Evans, 2000), a well-validated measure of effortful control. To check for the convergent validity between the two scales, we computed the Pearson zero-order correlation coefficient. This coefficient was .77, p<. 001, suggesting a high degree of convergent validity between the two measures (also see results for this sample

13

reported in the online Appendix). 3.3.2 Self-esteem, perceived competence, and self-derogation Self-esteem, perceived competence, and self-derogation were extracted from the Rosenberg General Self-Esteem scale (RGSE, Rosenberg, 1965). Although originally introduced as a scale assessing a single factor, there is growing consensus that a multiple factor model should be used to better describe the RGSE factor structure (Alessandri et al., 2015; Marsh, Scalas & Nagengast, 2010). When testing the structure with the bifactor model methods, Alessandri et al. (2015) obtained three uncorrelated factors. A general factor is comprised of loadings of all ten items (negative and positive) assessing global self-esteem. There are also two specific factors. One is comprised of five positively worded items (e.g., "I am able to do things as well as most other people") that load significantly; this factor reflects individuals’ evaluations of their own self-competence, defined as an aspect of self-evaluation linked with the individual’s appraisal of his or her own abilities (Tafarodi & Swann, 1995). The other is comprised of five negatively worded items that load significantly, assessing a general tendency to disparage oneself, and to express intense negative affect toward oneself, named by Kaplan and Pokorny (1969) self-derogation (e.g., "I feel I do not have much to be proud of"). Global self-esteem, self-competence, and self-derogation have been shown to predict distinct psychological outcomes, to be stable across method of assessment (i.e., selfreport, other-report ratings), to be stable over time, and to show cross-cultural invariance at the individual-level (Alessandri et al., 2014 2015; Marsh et al., 2010). Alpha coefficients were not computed because they are potentially inappropriate given the proposed multifactorial structure of the RGSE (Sijtsma, 2009). The estimates of reliability for the overall scale derived from the measurement model presented in the online Appendix was .80. 3.3.3 Optimism Optimism was assessed using the 10-itemset from the “Life Orientation Test” (LOT-

14

R), a valid measure that can distinguish people with high optimism from pessimists (Scheier, Carver, & Bridge, 1994). Specifically, participants rated (1 = strongly disagree; 5 = strongly agree) 6 items regarding their expectations about the future and their general sense of optimism(e.g., “In uncertain times, I usually expect the best”; alpha = .71).The remaining 4 items were filler items. 3.3.4 Happiness Happiness was assessed by using the Subjective Happiness Scale (SHS). This scale is composed of 4 statements describing global subjective happiness (Lyubomirsky & Lepper, 1999). For each item, the participants choose one of seven options that provide a personal observation on the given sentence. Higher scores indicate higher happiness levels (alpha = .80). High reliability and validity of the SHS have been reported in several studies in different countries and for different ages (Lyubomirsky & Lepper, 1999).

3.4 Statistical analyses All of the outcome variables for the following statistical analyses were factor scores derived from psychometric analyses (see the online Appendix). For EC, both selfand parent-report measures were available. To obtain a single individual score on the EC measure, we estimated factor scores for both self- and parent-rated EC in the measurementinvariant cross-observer model presented in the online Appendix; then we averaged the estimated self-report and parental scores for each subject. These averaged scores were used as overall scores in all subsequent twin models (estimated and observed scores on the self- and parent-report version of the EC scale correlated .95 and .97).The correlation between the latent factors representing self- and parent-rated EC was .71 (p < .05). 3.4.1 Descriptive statistics For each construct, mean values of the scores were computed for twins as individuals

15

and were compared across genders and zygosity groups using robust t-tests (as implemented in Stata, version 11.2) to take account of the dependence of twin data within pairs. Robust regressions for clustered data were used to identify variables independently associated with EC. 3.4.2 Correlations The following correlations were estimated and interpreted in accordance with the typical assumptions of the twin design (Neale and Cardon, 1992): (i) between the different constructs within a twin individual (named ‘phenotypic’), (ii) between twin and co-twin for the same construct (named ‘cross-twin/within-trait’) in MZ and DZ twin pairs separately, and (iii) between one construct in a twin and another construct in the co-twin (named ‘crosstwin/cross-trait’) in MZ and DZ twin pairs separately. A higher cross-twin/within-trait correlation in MZ than in DZ pairs supports genetic effects on the expression of the trait. A possible explanation for a significant phenotypic correlation between a pair of traits could be the existence of etiological (genetic or environmental) influences common to the traits. A higher cross-twin/cross-trait correlation in MZ compared to DZ pairs indicates genetic factors shared by the traits that may contribute to the phenotypic correlation. Only those variables showing a phenotypic correlation with EC higher than 0.20 in absolute value were considered in within-pair correlational analyses and subsequent genetic modeling. To estimate the correlations, the maximum-likelihood method as implemented in the software Mx (Neale, Boker, Xie, & Maes, 2006) was used, and a saturated model was fit to MZ and DZ twin pairs. Gender and age were incorporated as covariates in the model. 3.4.3 Genetic modeling A Cholesky decomposition encompassing additive genetic (A), shared (familial) environmental (C), and nonshared (individual-specific) environmental (E) components, and incorporating gender and age as covariates, was fit to psychological constructs scores in MZ

16

and DZ twin pairs (Figure 1). Additive genetic influences are associated with the average effect of alleles without allelic or gene-gene interaction. Shared environmental influences relate to experiences (typically operating within the family) that are common to both twins in a pair, thus contributing to within-pair resemblance independently of zygosity. Nonshared environmental factors are specific to each twin individual, and therefore are responsible for within-pair differences; measurement error is also included in this component. Relevant statistics that can be derived from such a decomposition include: (a) the “heritability” of each construct, defined as the proportion of total variance in the construct explained by genetic effects; (b) the “genetic correlation” between constructs, which measures the extent to which the constructs share the same genetic factors; and (c) the “bivariate heritability” of constructs, namely the proportion of phenotypic correlation between the constructs that is due to shared genetic effects (Neale & Cardon, 1992). The Cholesky decomposition modeling was started with the full model incorporating all three sources of variances/covariances (i.e. A, C, E). The full ACE decomposition was then compared with the AE sub-model using the likelihood-ratio chi-square test; when the test was not significant, the AE sub-model was considered as the best parameterization according to the principle of parsimony. Parameter estimates are reported for the best model. To gain preliminary insight into the most suitable model for the observed correlations, the Cholesky decomposition approach was preceded by univariate model-fitting. In this analysis, shared environment and genetic dominance were alternatively considered based on the correlations in MZ and DZ pairs (rMZ and rDZ) for each trait. More precisely, the full model included shared environment (ACE) if rMZ<2rDZ, and included genetic dominance (ADE) if rMZ>2rDZ (Neale & Cardon, 1992). The univariate results are shown in the online Appendix. All genetic modeling was performed using the software Mx (Neale, Boker, Xie, &

17

Maes, 2006).

4. Results 4.1 Descriptive statistics Significant gender differences in the mean factor scores were found for self-esteem, self-derogation and happiness scales, whereas no significant differences between MZ and DZ twins were detected (Table 1). Findings from the regression model showed that EC was significantly higher in females (p=0.03), and slightly (even if not significantly) positively related to age. Consequently, gender and age were covaried in all analyses. 4.2 Correlations When the relations between behavioral variables were explored, EC was significantly positively correlated with optimism, general self-esteem and happiness scales, and negatively correlated with self-derogation (Table 2). A low correlation (r<0.20) was observed between EC and self-perceived competence, and therefore the latter variable was not included in subsequent analyses. Cross-twin/within-trait correlations were higher in MZ pairs (range:0.29 to 0.59) than in DZ pairs (range: 0.14 to 0.31), indicating that genetic factors contributed to individual differences in the phenotypes. In addition, lower cross-twin/cross-trait correlations in DZ pairs compared to MZ pairs provided a first indication of genetic influences on the covariance between EC and the other traits (Table 2). 4.3 Genetic modeling Results from univariate analysis showed no significant effects of shared environment or genetic dominance for any of the scales (see online Appendix). In the full Cholesky model, significant genetic and nonshared environmental influences on all traits were found. The role of shared environment was negligible as the goodness of fit of the AE model was not significantly worse than that of the full model (likelihood-ratio test: χ2(15) = 9.6,p=0.84).

18

Therefore, the AE model was selected as the best model. As shown in Table 3, under the bestfitting AE model, around half of the total variance of EC (58%), optimism (55%), general self-esteem (45%) and happiness score (48%) was attributable to additive genetic factors, whereas a lower heritability (29%) was found for self-derogation. Furthermore, in regard to the covariates included in the model (i.e., gender and age), girls were significantly higher on EC (Beta coefficient, Female vs Male:1.25) and self-derogation (Beta coefficient, Female vs Male: 0.18), whereas boys were higher on self-esteem (Beta coefficient, Female vs Male: 0.36). Age was inversely correlated with happiness (Beta coefficient: -0.06). Table 4 shows the genetic and environmental correlations as well as the proportions of phenotypic correlations of the five scales considered. Genetic correlations were all substantial and significant, suggesting that the traits may be influenced by common sets of genes. These shared genetic factors are likely to account for most of the covariance observed between the traits. The significant negative genetic correlation between EC and self-derogation suggests the existence of shared genetic factors that may simultaneously predispose individuals to higher EC and lower self-derogation (or viceversa). In addition, no significant nonshared environmental correlations emerged between EC and self-esteem, optimism, self-derogation or happiness; the absence of evidence for environmental overlap could suggest that the nonshared environmental factors that are relevant for explaining the phenotypic covariation of EC with various measures of emotional well being, could be specific to each of them.

5. Discussion Although behavioral geneticists have generally abandoned the strict conception of genetic determinism, there is still a need to refine knowledge about the contributions of nature and nurture to the expression of psychological characteristics at different ages. It is generally believed that both genes and environment can account for change as well as stability in

19

psychological characteristics (Lewis & Plomin, 2015; Plomin, 1986) and, consequently, it is useful to quantify their relative role in various aspects of functioning at different ages. In addition, a more recent line of inquiry has been to examine to what degree genetics and the environment account for associations between different aspects of psychological/behavioral functioning, although few of these studies have been conducted with adolescents. In the case of EC, the need to examine such issues is amplified by recent studies (Casey, 2013, 2015; Casey et al., 2008; Collins & Steinberg, 2006; Luciana et al., 2012) suggesting that the regulatory skills associated with EC continue to develop during adolescence because of the maturation of associated neurobiological systems, although selfregulation in some contexts may be out of balance with (and overridden by) subcortically based heightened reactivity to rewards and emotion. These studies suggest that adolescence might be a particularly sensitive period for the development of individual differences in EC and that predictors of EC in adolescence may differ from those in childhood and adulthood. Although modern behavioral geneticists (e.g., Plomin et al.,1990) warn us against assigning to genes the entire responsibility for the expression of a given trait in a given phase of the development, genetic expression is often the key mechanism invoked to explain the expression or the maturation of a specific individual character in a specific developmental phase (Plomin et al.,1990). In our sample of adolescents aged 16 years on average, genes accounted for about 60% of variance in EC. This estimate is in line with estimates already reported in studies using self-and other-report measures to assess EC (Goldsmith et al., 2008; Li et al., 2014). Beside the genetic contribution, nonshared environment was the other developmental force that appeared to be responsible for inter-individual differences in EC. Different individualspecific environmental experiences might help to explain developmental differences in the maturation of EC, or why some children seem to improve their ability to self-regulate more

20

slowly than others, or need more years to reach a mature level of self-regulation. The other heritability estimates obtained in this study are generally in line with those reported in previous works (Caprara et al., 2009; Kamakura et al., 2001; Kendler et al., 1998; Roy et al., 1995), with the exception of optimism, for which the heritability is higher (being about .50) than that reported by both Caprara et al. (2009) and Plomin (1992). It is possible that this difference is partly the result of the younger age of the twins included in our sample. However, given the scarcity of previous studies investigating the genetics of optimism in adolescence, it is difficult to establish the real nature of this discrepancy. One could, for example, speculate that adolescent optimism is more under the influence of genes, and that the environmental influences are likely to increase with increasing age. This idea is in line with a suggestion from a previous study that the impact of environment on positive thinking increases during the course of life (Fagnani, Medda, Stazi, Caprara & Alessandri, 2014). However, more data are necessary to test for age-related changes in this relation. It is probable that the biological systems involved in the maturation of EC are connected with those biological systems underlying domains of adolescent adjustment such as self-esteem, happiness, optimism and depressive thoughts as assessed by self-derogation. Given that only genes accounted for the association between EC and adjustment, it is likely that this genetic association is due to a third variable. For example, one might speculate that genes impact on the brain functionality or connectivity, and that this, in turn, affects both selfregulation and adjustment to a similar degree, as in the case of a poor amygdale-prefrontal cortex connectivity (Botvinick et al., 1999; Fan et al., 2003). In fact, the conjoint functioning of these two areas has often been associated with the functioning of the attention network believed to be responsible to self-regulation of thoughts, emotions, and actions (Posner & Petersen, 1990; Posner et al., 2007). These data reinforce the theoretical assumption that there should be a close connection between temperamentally-based individual differences in self-

21

regulation and adolescents' emotional well-being (Eisenberget al., 2001, 2003; Silk et al., 2003). However, our study goes even further by suggesting that the nonshared environmental factors impacting on EC and psychological adjustment act in a rather specific manner. Genes may operate to reinforce the dependence between the ability for self-regulation and adjustment; in contrast, the unique experiences encountered by each individual during development appear to impact only specific areas of functioning. Thus, it is possible for an adolescent to maintain high self-esteem while being generally deregulated, if he or she encounters some environmental experiences that tend to reinforce the sense of oneself and other life circumstances that affect self-regulation. In summary, whereas the effect of genes seems to be that of promoting the mutual interdependency between EC and psychological adjustment, the nonshared environment seems to act as a diverging influence, mostly contributing to differences between EC and psychological adjustment. In addition, there may be some gene-by-environment interactions that contribute to the degree of heredity found in this study (because such interactions may become incorporated into genetic effects); thus, environmental factors, interacting with genetic factors (or activating the expression of genes), might affect the relation of EC to psychological adjustment even though we did not find evidence of purely environmental factors accounting for the association. Among covariates, being male was associated with higher scores in self-esteem, whereas being female was associated with higher EC and a greater tendency to self-derogate. All in all, these results are in line with previous studies suggesting that females tend to be better regulated (in terms of EC: Else-Quest, Hyde, Goldsmith, & van Hulle, 2006) whereas males tend to have higher self-esteem (Byrne & Shavelson, 1987; Hoelter, 1983; Kling, Hyde, Showers, & Buswell, 1999) and lower self-derogation (Alessandri et al., 2015).

22

Finally, we found a nonsignificant correlation between age and EC, despite significant evidence that adolescence is in fact a period in which there might be development of self-regulation (Casey et al, 2016; Luciana et al., 2012; Steinberg et al., 2009). A possible explanation for this result could be the use of a “global” measure of effortful control in this study, while the different facets of the construct (captured by longer scales) may show different rates of change. Indeed, recent evidence regarding the developmental trajectory of EC in early- to mid-adolescence suggests that some markers of this construct decline during this period, whereas other may increase (Casey et al., 2015; Casey et al. 2016; Sternberg et al., 2010). Thus, these opposing directions of change may simply cancel out when observed from a global or aggregate point of view. Future studies using longitudinal data should further investigate and clarify this point. Conclusions The data presented in this study have two main implications. First, they show that genes substantially contribute to the variance in adolescents’ EC and its phenotypic covariance with indicators of psychological adjustment and well-being. Second, these data suggest that environmental factors are likely to be specific to each of the investigated constructs because the environmental overlap among them is very low; therefore, intervention programs may prove to be effective only fora single trait, depending on the specifics of the intervention. Our study relied on a moderately sized sample. This problem did not seem to affect the size of observed phenotypic correlations, which were comparable to those reported in previous studies (Alessandri et al., 2014; Busch & Hofer, 2012; Fosco et al., 2012). Yet low statistical power may have prevented us from detecting shared environmental effects for the happiness scale and possibly also the dominant genetic effects for the optimism scale, although such effects have been suggested by the size of the observed difference between DZ

23

and MZ correlations (Neale & Cardon, 1992). Moreover, we found no phenotypic correlation between EC and self-perceived competence, a result that needs to be replicated in future studies. Finally, using self-report to assess effortful control has potential difficulties that can be minimized, although not totally eliminated, by the use of others’ reports. Further research involving larger samples or different age groups might help to clarify if our findings can be generalized across cultures, socioeconomic groups, and age groups. That said, it is worth emphasizing that our estimates are based on a measure of EC including both self- and parents’ evaluations. Moreover, our findings are strengthened by the evaluation of across-observer measurement invariance, which ensures the comparability of self- and parent-report measures of EC. In addition, the participants in this study were not from North America or an English-speaking country; thus, the results provide needed data on diverse samples of youths. Our twin study design allowed for the control of covariates associated with development of EC, such as family socioeconomic status during early childhood (Lengua, Moran, Zalewski, Ruberry, Kiff,& Thompson, 2015) and parenting behavior (Kiff, Lengua,& Zalewski, 2011), at least when these influences simply act as confounders. Indeed, twins from their home of origin are naturally matched for family background and related influences, and therefore the possible confounding effects of these factors are somewhat neutralized. However, in more complex cases in which family-related factors moderate genetic influences, this simplistic model is still inadequate, and other approaches are needed. These features, along with the use of a reasonably large sample of twins and the use of covariates to adjust for potential confounds (such as gender and age), strengthen confidence in our findings. In conclusion, despite some limitations, our findings provide useful insights in regard to the nature of the association between EC and some aspects of psychological adjustment.

24

Online Appendix. Supplementary material – Psychometric analyses Supplementary material associated with this article can be found in the online version.

Declaration of conflicting interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The authors received no financial support for the research, authorship, and/or publication of this article.

25

References Alessandri, G., Luengo-Kanacri, B. P., Eisenberg, N., Zuffianò, A., Milioni, M., Vecchione, M., & Caprara, G. V. (2014). Prosociality during the transition from late adolescence to young adulthood: The role of effortful control and ego-resiliency. Personality and Social Psychology Bulletin, 40, 1451–1465. doi: 10.1177/0146167214549321. Alessandri, G., Vecchione, M., Eisenberg, N., &Łaguna, M. (2015). On the factor structure of the Rosenberg (1965) general self-esteem scale. Psychological Assessment, 27, 621-635. doi: 10.1037/pas0000073. Bjorklund, D.F., & Kipp, K. (1996). Parental investment theory and gender differences in the evolution of inhibition mechanisms. Psychological Bulletin, 120,163–188. doi:10.1037//0033-2909.120.2.163. Blair, C., & Diamond, A. (2008). Biological Processes in Prevention and Intervention: The Promotion of Self-Regulation as a Means of Preventing School Failure.Development and Psychopathology, 20, 899–911. doi:10.1017/S0954579408000436. Blasi, G., Venkata, S. M., Bertolino, A., Elvevåg, B., Callicott, J. H., Das, S., Kolachana, B. S., Egan, M.F., Goldberg, T. E., & Weinberger, D. R. (2005). Effect of Catechol-OMethyltransferase val 158 met Genotype on Attentional Control. The Journal of Neuroscience, 25, 5038–5045. doi::10.1523/JNEUROSCI.0476-05.2005. Botvinick, M., Nystrom, L. E., Fissell, K., Carter, C. S., & Cohen, J. D. (1999). Conflict monitoring versus selection-for-action in anterior cingulate cortex. Nature,402, 179-81. doi: 10.1038/46035. Brescianini, S., Fagnani, C., Toccaceli, V., Medda, E., Nisticò,L., D'Ippolito, C., Alviti, S., Arnofi, A., Caffari, B., Delfino, D., Ferri, M., Penna, L., Salemi, M., Sereni, S., Serino, L.,Cotichini, R., & Stazi, M.A. (2013). An update on the Italian Twin Register: advances in cohort recruitment, project building and network development. Twin research and

26

human genetics, 16, 190-196. doi: 10.1017/thg.2012.85. Brock, L. L., Rimm-Kaufman, S. E., Nathanson, L., & Grimm, K. J. (2009). The contributions of ‘hot’ and ‘cool’ executive function to children’s academic achievement and learning-related behaviors, and engagement in kindergarten. Early Childhood Research Quarterly, 24, 337-349. doi: 10.1016/j.ecresq.2009.06.001. Busch, H.,& Hofer, J. (2012). Self-regulation and milestones of adult development: Intimacy and generativity. Developmental Psychology, 48, 282-293. doi: 10.1037/a0025521. Byrne, B. M., & Shavelson, R. J. (1987). Adolescent self-concept: Testing the assumption of equivalent structure across gender. American Educational Research Journal, 24, 365-385. doi:10.3102/00028312024003365. Casey, B. J., Jones, R. M., & Hare, T. A. (2008). The adolescent brain. Annals of the New York Academy of Sciences, 1124, 111-126. doi:10.1196/annals.1440.010. Casey, B. J. (2013). The teenage brain. Current Directions in Psychological Science, 22, 114120. doi:10.1177/0963721412471347 Casey, B. J. (2015). Beyond Simple Models of Self-Control to Circuit-Based Accounts of Adolescent Behavior. Annual Review of Psychology, 66, 295–319. doi:10.1146/annurevpsych-010814-015156 Casey, B.J, Galván, A., Somerville, L. H. (2016). Beyond simple models of adolescence to an integrated circuit-based account: A commentary. Developmental Cognitive Neuroscience, 17, 128-130. doi: 10.1016/j.dcn.2015.12.006. Collins, W. A., & Steinberg, L. (2006). Adolescent development in interpersonal context. In W. Damon & R. M. Lerner (Series Eds.) and N. Eisenberg (Vol. Ed.), Handbook of child psychology: Social, emotional, and personality development (6th ed., Vol. 3, pp. 10031067). Hoboken, NJ: Wiley. Davenport, E., Yap, M.B.H., Simmons, J.G., Sheeber, L.B., & Allen, N.B. (2011). Maternal

27

and adolescent temperament as predictors of maternal behavior during mother-adolescent interactions. Journal of Adolescence, 34, 829-839. doi: 10.1016/j.adolescence.2011.02.003. Derryberry, D., & Rothbart, M. K. (1997). Reactive and effortful processes in the organization of temperament. Development and Psychopathology, 9, 633-652. DiBiase, R., & Miller, P. M. (2012). Predicting Feelings of Cognitive Competence in Head Start Preschoolers. The Journal of Genetic Psychology, 173, 23-40. doi: 10.1080/00221325.2011.560976. Diener, M.L.,& Kim, D. Y. (2004). Maternal and child predictors of preschool children's social competence. Journal of Applied Developmental Psychology, 25,3–24. doi: 10.1016/j.appdev.2003.11.006. Eisenberg, N., Fabes, R. A., & Murphy, B. C. (1996). Parents' reactions to children's negative emotions: Relations to children's social competence and comforting behavior. Child Development, 67, 2227–2247. doi: 10.1111/j.1467-8624.1996.tb01854.x. Eisenberg, N., Gershoff, E. T., Fabes, R. A., Shepard, S. A., Cumberland, A. J., Losoya, S.H., Guthrie, I. K., & Murphy, B. C. (2001). Mother's emotional expressivity and children's behavior problems and social competence: Mediation through children's regulation. Developmental Psychology, 37, 475–490. doi: 10.1037/0012-1649.37.4.475. Eisenberg, N., Valiente, C., Morris, A. S., Fabes, R. A., Cumberland, A., Reiser, M., Gershoff, E. T., Shepard, S. A., & Losoya, S. (2003). Longitudinal relations among parental emotional expressivity, children's regulation, and quality of socioemotional functioning. Developmental Psychology, 39, 3–19. Eisenberg, N., & Spinrad, T. L. (2004). Emotion-related regulation: Sharpening the definition. Child Development,75, 334–339. doi: 10.1111/j.1467-8624.2004.00674.x. Eisenberg, N., Spinrad, T. L., & Eggum, N. D. (2010). Emotion-related self-regulation and its relation to children's maladjustment. Annual Review of Clinical Psychology, 6, 495-525.

28

doi: 10.1146/annurev.clinpsy.121208.131208. Eisenberg, N., Hofer, C., Sulik, M., & Spinrad, T. (2014). The development of prosocial moral reasoning and a prosocial orientation in young adulthood: Concurrent and longitudinal correlates. Developmental psychology, 50, 58-70. doi: 10.1037/a0032990. Eisenberg, N., Valiente, C., & Eggum, N. D. (2010). Self-regulation and school readiness. Early Education and Development. Special issue: Overlaps between Socio-emotional and Academic Development, 21, 681-698. doi: 10.1080/10409289.2010.497451 Else-Quest, N. M., Hyde, J. S., Goldsmith, H. H., & Van Hulle, C. A. (2006). Gender differences in temperament: A meta-analysis. Psychological Bulletin, 132, 33-72. Fagnani, C., Medda, E., Stazi, M.A., Caprara, G.V., &Alessandri, G. (2014). Investigation of age and gender effects on positive orientation in Italian twins. International Journal of Psychology, 49, 453-461. doi: 10.1002/ijop.12053. Fan, J., Flombaum, J. I., McCandliss, B. D., Thomas, K. M., & Posner, M. I. (2003). Cognitive and brain consequences of conflict. Neuroimage, 18, 42-57. Fan, J., Fossella, J., Sommer, T., Wu, Y., & Posner, M. I. (2003). Mapping the genetic variation of executive attention onto brain activity. Proceedings of the National Acadademy of Science USA, 100, 7406-7411. doi:10.1073/pnas.0732088100. Fosco, G. M., Caruthers, A. S., & Dishion, T. J. (2012). A six-year predictive text of adolescent family relationship quality and effortful control pathways to emerging adult social and emotional health. Journal of Family Psychology, 26, 565575.doi:10.1037/a0028873. Gagne, J. R., & Saudino, K. J. (2006). A multivariate twin analysis of inhibitory control and behavior problems at 24 months [Abstract]. Behavior Genetics, 36, 964–965. Gagne, J. R., & Goldsmith, H. H. (2007). A twin analysis of inhibitory behavior from 12-36 months of age. Paper presented at the annual meeting of the Behavior Genetics

29

Association, Amsterdam. Goldsmith, H. H. (1991). A zygosity questionnaire for young twins: A research note. Behavior Genetics, 21, 257–269. doi: 10.1007/BF01065819. Goldsmith, H. H., Buss, K. A., & Lemery, K. S. (1997). Toddler and childhood temperament: expanded content, stronger genetic evidence, new evidence for the importance of environment. Developmental Psychology, 33, 891-905. doi: 10.1037//0012-1649.33.6.891. Goldsmith, H. H., Pollak, S. D., & Davidson, R. J. (2008). Developmental neuroscience perspectives on emotion regulation. Child Development Perspectives, 2, 132-140. doi: 10.1111/j.1750-8606.2008.00055.x. Hoelter, J. W. (1983). Factorial Invariance and Self-Esteem: Reassessing Race and Sex Differences. Social Forces, 61, 835-846. doi: 10.1093/sf/61.3.834. Kamakura, T., Ando, J., &Ono, Y. (2001). Genetic and environmental influences on selfesteem in a Japanese twin sample. Twin Research and Human Genetics, 4, 439–442. doi: 10.1375/twin.4.6.439. Kaplan, H., &Pokorny, A. (1969). Self-derogation and psychosocial adjustment. Journal of Nervous and Mental Disease, 149,421-434.doi:10.1097/00005053-196911000-00006 Kendler, K. S., Gardner, C. O., &Prescott, C. A. (1998). A population-based twin study of self-esteem and gender. Psychological Medicine, 28, 1403–1409. Kiff, C.J., Lengua, L.J., &Zalewski, M. (2011).Nature and nurturing: parenting in the context of child temperament. Clinical Child and Family Psychology Review, 14, 251-301. doi: 10.1007/s10567-011-0093-4. Kling, K. C., Hyde, J. S., Showers, C. J., & Buswell, B. N. (1999). Gender differences in selfesteem: A meta-analysis. Psychological Bulletin, 125, 470–500. Kochanska, G., Murray, K., & Coy, K. C. (1997). Inhibitory control as a contributor to conscience in childhood: From toddler to early school age. Child Development, 68, 263–

30

277. doi: 10.2307/1131849. Kochanska, G., Murray, K. T., & Harlan, E. T. (2000). Effortful control in early childhood: Continuity and change, antecedents, and implications for social development. Developmental Psychology, 36, 220–232. doi: 10.1037//0012-1649.36.2.220. Kopp, C. B. (1982). Antecedents of self-regulation: A developmental perspective. Developmental Psychology, 18, 199–214. Kopp, C. B. (1989). Regulation of distress and negative emotions: A developmental view. Developmental Psychology, 25, 343–354. doi: 10.1037/0012-1649.25.3.343. Hopwood, C. J., Donnellan, M. B., Blonigen, D. M., Krueger, R. F., McGue, M., Iacono, W. G., & Burt, S. A. (2011). Genetic and environmental influences on personality trait stability and growth during the transition to adulthood: A three wave longitudinal study. Journal of Personality and Social Psychology, 100(3), 545–556. http://doi.org/10.1037/a0022409 Houben M., Van Den Noortgate W., Kuppens P. (2015). The relation between short-term emotion dynamics and psychological well-being: a meta-analysis. Psychological Bulletin, 141, 901–930. doi:0.1037/a0038822 Jaffari-Bimmel, N., Juffer, F., Van IJzendoorn, M. H., Bakermans-Kranenburg, M.J., & Mooijaart, A. (2006). Social Development from Infancy to Adolescence: Longitudinal and Concurrent Factors in an Adoption Sample. Developmental Psychology, 42,1143-1153. doi: 10.1037/0012-1649.42.6.1143. Johnson, D. P., Whisman, M. A., Corley, R. P., Hewitt, J. K., & Friedman, N. P. (2014). Genetic and environmental influences on rumination and its covariation with depression. Cognition and Emotion, 28, 1270–1286. doi:10.1080/02699931.2014.88132. Lemery-Chalfant, K., Doelger, L.,& Goldsmith, H. H. (2008). Genetic relations between effortful and attentional control and symptoms of psychopathology in middle childhood.

31

Infant and Child Development, 17, 365-385. doi: 10.1002/icd.581. Lemola, S., Raikkonen, K., Matthews, K. A., Scheier, M., Heinonen, K., Pesonen, A., Komsi, N., & Lahti, J. (2010). A new measure for dispositional optimism and pessimism in young children. European Journal of Personality, 24, 71-84.doi:10.1002/per.742. Lengua, L.J., Moran, L., Zalewski, M., Ruberry, E., Kiff, C., &Thompson, S. (2015). Relations of growth in effortful control to family income, cumulative risk, and adjustment in preschool-age children. Journalof Abnormal Child Psychology, 43, 705-720. doi: 10.1007/s10802-014-9941-2. Lewis, G.J.,& Plomin, R. (2015). Heritable influences on behavioural problems from early childhood to mid-adolescence: evidence for genetic stability and innovation. Psychological Medicine, 13, 1-9. doi: 10.1017/S0033291715000173. Li, M., Chen, J., Li, N., &Li, X. (2014). A twin study of problematic internet use: its heritability and genetic association with effortful control. Twin Research and Human Genetics, 17, 279-287. doi: 10.1017/thg.2014.32. Luciana, M., Wahlstrom, D., Porter, J. M., & Collins, P. F. (2012). Dopaminergic Modulation of Incentive Motivation in Adolescence: Age-Related Changes in Signaling, Individual Differences, and Implications for the Development of Self-Regulation. Developmental Psychology, 48, 844–861. doi: 10.1037/a0027432. Luciano, M., Wainwright, M. A., Wright, M. J., & Martin, N. G. (2006). The heritability of conscientiousness facets and their relationship to IQ and academic achievement. Personality and Individual Differences,40, 1189–1199. doi: 10.1016/j.paid.2005.10.013. Luengo-Kanacri, B.P., Pastorelli, C., Eisenberg, N., Zuffianò, A., & Caprara, G.V. (2013). The Development of prosociality from adolescence to early adulthood: The role of effortful control. Journal of Personality, 81, 302-312. doi: 10.1111/jopy.12001. Lyubomirsky, S.,& Lepper, H.S. (1999). A measure of subjective happiness: Preliminary

32

reliability and construct validation. Social Indicators Research, 46, 137-155. doi: 10.1023/A:1006824100041. Marsh, H.W., Scalas, L.F., & Nagengast, B. (2010). Longitudinal tests of competing factor structures for the Rosenberg Self-Esteem Scale: Traits, ephemeral artefacts, and stable response styles. Psychological Assessment, 22, 366-381.doi: 10.1037/a0019225 Neale, M. C. & Cardon, L. R. (1992). Methodology for genetic studies of twins and families. Kluwer Academic Publishers, Dordrecht. doi: 10.1002/sim.4780130211. Neale, M. C., Boker, S. M., Xie, G., & Maes, H. H. (2006). Mx: statistical modeling (7th edition), Ed. Richmond, VA: Department of Psychiatry, Virginia Commonwealth University. Plomin, R. (1986). Multivariate analysis and development behavioral genetics: developmental change as well as continuity. Behavior Genetics, 16, 25-43. doi: 10.1007/BF01065477. Plomin, R., De Fries, J.C., & McClearn, G.E. (1990). Behavioral Genetics. A Primer. New York: WH. Freeman. Plomin, R., Scheier, M. F., Bergerman, C. S., Pedersen, N. L., Nesselroade, J. R., & McClearn, G. E. (1992). Personality and Individual Differences, 13, 921-930. doi: 10.1016/0191-8869(92)90009-E. Plomin, R., & Daniels, D. (2011). Why are children in the same family so different from one another? International Journal of Epidemiology, 40, 563-582. doi:10.1093/ije/dyq148. Posner, M. I., & Petersen, S. E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25-42. doi: 10.1146/annurev.ne.13.030190.000325. Posner, M. I., Rothbart, M. K., Sheese, B. E., & Tang, Y. (2007). The anterior cingulate gyrus and the mechanisms of self regulation. Journal of Cognitive, Affective and Social Neuroscience, 7, 391-395. doi: 10.3758/CABN.7.4.391. Rhoades, B. L., Greenberg, M. T., & Domitrovich, C. E. (2009). The contribution of

33

inhibitory control to preschoolers’ social-emotional competence. Journal of Applied Developmental Psychology, 30, 310-320. doi: 10.1016/j.appdev.2008.12.012. Robins, R. W., Donnellan, M. B., Widaman, K. F., & Conger, R. D. (2010). Evaluating the link between self-esteem and temperament in Mexican origin early adolescents. Journal of Adolescence, 33, 403-410. doi: 10.1016/j.adolescence.2009.07.009. Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press. Rothbart, M. K., Ahadi, S. A., & Evans, D. E. (2000). Temperament and personality: Origins and outcomes. Journal of Personality and Social Psychology, 78, 122–135. doi: 10.1037/0022-3514.78.1.122 Rothbart, M. K., Derryberry, D., & Posner, M. I. (1994). A psychobiological approach to the development of temperament. In Bates J. E. & Wachs T. D. Eds., Temperament: Individual differences at the interface of biology and behavior,(pp. 83-116). Washington, DC: American Psychological Association. Rothbart, M. K., & Bates, J. E. (1998). Temperament. In W. Damon (Series Ed.) & N. Eisenberg (Vol. Ed.), Handbook of child psychology. Vol. 3. Social, emotional and personality development (5th ed., pp. 105-176). New York: Wiley. Rothbart, M. K., & Derryberry, D. (2002). Temperament in children. In C. von Hofsten & L. Bäckman (Eds.), Psychology at the turn of the millennium. Vol. 2: Social, developmental, and clinical perspectives, (pp. 17-35). East Sussex, UK: Psychology Press. Rothbart, M. K., & Rueda, M. R. (2005). The development of effortful control. In U. Mayr, E. Awh, & S. Keele (Eds.), Developing individuality in the human brain: A tribute to Michael I. Posner (167-188). Washington, DC: American Psychological Association. Rueda, Checa, & Combita. Rothbart, M. K. & Bates, J. E. (2006). Temperament. In W. Damon & R. Lerner (Series Eds.),

34

& N. Eisenberg (Vol. Ed.), Handbook of child psychology, Vol. 3. Social, emotional, and personality development (6th ed., pp. 99-166). New York: Wiley. Rothbart, M. K., Ellis, L. K., & Posner, M. I. (2011). Temperament and self-regulation. In K. D. Bohs & R. F. Baumeister (Eds.), Handbook of self-regulation: Research, theory, and Applications (2nd ed., pp. 441–460). New York, NY: The Guilford Press. Roy, M. A., Neale, M. C., & Kendler, K. S. (1995). The genetic epidemiology of self-esteem. The British Journal of Psychiatry, 166, 813–20. doi: 10.1192/bjp.166.6.813. Rueda, M. R., Posner, M. I., & Rothbart, M. K. (2005). The development of executive attention: Contributions to the emergence of selfregulation. Developmental Neuropsychology, 28, 573–594. doi: 10.1207/s15326942dn2802_2. Rueda, M. R., Checa, P.,& Cómbita, L. M. (2012). Enhanced efficiency of the executive attention network after training in preschool children: Immediate changes and effects after two months. Developmental Cognitive Neuroscience, 2S, S192-S204. doi: 10.1016/j.dcn.2011.09.004. Scheier, M.F., Carver, C.S.,& Bridges M.W. (1994). Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): A re-evaluation of the Life Orientation Test. Journal of Personality and Social Psychology, 67, 1063-1078. doi: 10.1037//0022-3514.67.6.1063. Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach's alpha. Psychometrika, 74, 107-120.doi:10.1007/s11336-008-9101-0. Silk, J. S., Steinberg, L., & Morris, A. S. (2003).Adolescents’ emotion regulation in daily life: Links to depressive symptoms and problem behavior. Child Development, 74, 1869-1880. doi: 10.1046/j.1467-8624.2003.00643.x. Stazi, M. A., Cotichini, R., Patriarca, V., Brescianini, S., Fagnani, C., D’Ippolito, C., Cannoni, S., Ristori, G., & Salvetti, M. (2002). The Italian Twin project: from the personal

35

identification number to a national twin registry. Twin Research, 5, 382–386. doi: 10.1375/136905202320906138. Steinberg, L. (2010). A behavioral scientist looks at the science of adolescent brain development. Brain and Cognition, 72, 160–164. doi: 10.1016/j.bandc.2009.11.003. Steinberg, L., Graham, S., O'Brien, L., Woolard, J., Cauffman, E., & Banich, M. (2009). Age Differences in Future Orientation and Delay Discounting. Child Development, 80, 28-44. doi: 10.1111/j.1467-8624.2008.01244.x. Stubbe, J.H., Posthuma, D., Boomsma, D.I., & De Geus, E. J. C. (2005). Heritability of life satisfaction in adults: A twin-family study. Psychological Medicine, 35, 1581-1588. Tafarodi, R.W., & Swann, W.B., Jr. (1995). Self-liking and self-competence as dimensions of global self-esteem: Initial validation of a measure. Journal of Personality Assessment, 65, 322-342.doi:10.1207/s15327752jpa6502_8 Tesch-Römer, C., Motel-Klingebiel, A., & Tomasik, M. J. (2008). Gender differences in subjective well-being: comparing societies with respect to gender equality. Social Indicators Research, 82, 329-349. doi: 10.1007/s11205-007-9133-3. van Beijsterveldt, C. E. M., Verhulst,F. C., Molenaar, P. C. M.,& Boomsma, D. I. (2004). The genetic basis of problem behavior in 5-year-old Dutch twin pairs. Behavior Genetics, 34, 229–242. doi: 10.1023/B:BEGE.0000017869.30151.fd. Veenstra, R., Lindenbeg, S., Oldehinkel, A. J., De Winter, A. F., Verhulst, F. C., & Ormel, J. (2008). Prosocial and antisocial behavior in preadolescence. International Journal of Behavioral Development, 32, 243-251.doi:10.1177/0165025408089274. Yamagata, S., Takahashi, Y., Kijima, N., Maekawa, H., Ono, Y.,& Ando, J. (2005). Genetic and environmental etiology of Effortful Control. Twin Research and Human Genetics,8, 300-306. doi: 10.1375/1832427054936790.

36

Figure 1 Path diagram of the Cholesky decomposition for Effortful control, Selfesteem, Self-derogation and Happiness in one twin.

Note. Rectangles denote observed variables. Circles indicate latent sources of variance and covariance. Ai and Ei represent additive genetic and nonshared environmental influences, respectively.aij and eij are path coefficients (reported only for the first two Effortful Control and optimism for simplicity). Latent genetic factors correlate 1 between monozygotic twins and 0.5 between dizygotic twins. Although model fitting also included shared environmental influences (Ci), the corresponding latent sources were not shown in the diagram for reasons of clarity. As an example, the bivariate heritability of Effortful Control and optimism was calculated as a11a21/(a11a21 + e11e21) and the genetic correlation between the same variables was estimated as a11a21/[a112(a212 + a222)]1/2

37

38

Table 1 Demographic characteristics and raw scores of behavioral traits according to gender and zygosity. Monozygotic Males

Dizygotic Females

Males

Females

Males from opposite sex pairs

Females from opposite sex pairs

N

mean±SD

N

mean±SD

N

mean±SD

N

mean±SD

N

mean±SD

N

mean±SD

Age, years

146

16.4±1.2

136

16.3±1.3

142

16.1±1.2

134

16.3±1.1

108

16.3±1.2

108

16.3±1.2

Effortful control (EC) - Self report

146

4.48±2.75

136

4.96±2.97

142

5.23±3.34

134

4.67±3.18

107

5.02±3.13

108

4.52±2.81

Effortful control (EC) - Parents report

142

2.93±3.22

128

3.14±3.04

132

3.54±3.37

126

2.83±2.78

103

4.11±3.57

104

2.64±2.68

Life orientation test (LOT-R)

145

33.77±6.09

135

31.04±6.89

140

33.08±6.21

132

31.51±7.36

104

32.46±6.67

105

33.13±6.76

Rosenberg General self-esteem (RGSE)

145

31.75±3.91

136

28.90±4.01

142

31.79±4.10

134

29.12±4.02

107

30.80±4.04

108

30.36±4.49

Self-perceived competence

145

16.01±1.81

136

14.92±1.96

142

16.03±1.83

134

15.06±2.03

107

15.82±1.94

108

15.74±2.03

Self-derogation

145

15.75±2.67

136

13.97±2.59

142

15.76±2.69

134

14.06±2.51

107

14.98±2.71

108

14.62±2.97

Subjective Happiness scale (SHS)

140

22.51±4.06

124

20.69±4.59

135

22.05±4.14

128

21.01±4.91

98

21.64±4.45

100

22.09±4.51

39

Table 2 Observed twin correlations. Cross-twin/within-trait correlations Effortful

MZ

DZ

control

Optimism

Self-esteem

Self-derogation

Happiness

0.59

0.59

0.48

0.29

0.44

(0.47, 0.68)

(0.47, 0.67)

(0.34, 0.58)

(0.14, 0.42)

(0.29, 0.55)

0.29

0.19

0.21

0.14

0.31

(0.17, 0.40)

(0.06, 0.31)

(0.09, 0.32)

(0.02, 0.26)

(0.19, 0.42)

Phenotypic correlations Optimism

Self-esteem

Self-derogation

Happiness

0.22

0.23

-0.26

0.23

(0.14, 0.29)

(0.16, 0.30)

(-0.33, -0.18)

(0.16, 0.31)

Effortful control

Cross-twin/cross-trait correlations Optimism

Self-esteem

Self-derogation

Happiness

0.23

0.26

-0.22

0.20

(0.13, 0.33)

(0.16, 0.35)

(-0.32, -0.11)

(0.09, 0.29)

0.11

0.19

-0.05

0.17

(0.01, 0.20)

(0.10, 0.27)

(-0.14, 0.04)

(0.08, 0.25)

MZ Effortful control DZ

Note. Only variables showing a phenotypic correlation with Effortful control higher than 0.20 in absolute value are reported.

40

Table 3 Standardized genetic and environmental components of variance as estimated from the best-fitting Cholesky model. Genetic (A) and unique environmental (E) proportions of variance A

E

Effortful control

0.58 (0.47, 0.67)

0.42 (0.33, 0.53)

Optimism

0.55 (0.44, 0.65)

0.45 (0.35, 0.56)

Self-esteem

0.45 (0.33, 0.55)

0.55 (0.45, 0.67)

Self-derogation

0.29 (0.16, 0.41)

0.71 (0.59, 0.84)

Happiness

0.48 (0.36, 0.58)

0.52 (0.42, 0.64)

Note. Effortful control (EC); Optimism, Life Orientation Test (LOT-R); Self-esteem, Rosenberg General Self-esteem scale (RGSE); Self-derogation, Rosenberg scale; Happiness, Subjective Happiness scale (SHS). Results are adjusted by gender and age. Beta Coefficients Gender (female vs male): LOT-R -0.02 ns, GSE -0.36 (p < .05), Self-derogation 0.18 (p < .05), Effortful control 1.25 (p < .05), Happiness scale -0.11 ns; Beta Coefficients age: LOT-R -0.05 ns, GSE 0.002 ns, Self-derogation -0.04 ns, Effortful control 0.02 ns, Happiness scale 0.06 (p < .05). Values given in parentheses indicate 95% confidence intervals.

41

Table 4 Standardized components of covariance and proportions of phenotypic correlations as estimated from the best-fitting AE Cholesky model. Genetic (ra) and unshared environmental (re) correlations Optimism

Self-esteem

Self-derogation

Happiness

Effortful ra

0.42

0.56

-0.46

0.44

control

(0.25, 0.59)

(0.37, 0.75)

(-0.68, -0.24)

(0.26, 0.62)

-0.04

-0.11

-0.13

0.007

(-0.20, 0.12)

(-0.25, 0.04)

(-0.26, 0.02)

(-0.14, 0.15)

re

Genetic (A) and unshared environmental (E) proportions of phenotypic correlations Optimism

Self-esteem

Self-derogation

Happiness

Effortful A

1.08

1.22

0.73

0.99

control

(0.76, 1.46)

(0.92, 1.63)

(0.39, 1.04)

(0.68, 1.33)

-0.08

-0.22

0.27

0.01

(-0.46, 0.24)

(-0.63, 0.08)

(-0.04, 0.61)

(-0.33, 0.32)

E

Note. Effortful control (EC); Optimism; Life Orientation Test (LOT-R); Self-esteem, Rosenberg General Self-esteem scale (RGSE); Self-derogation, Rosenberg scale; Happiness, Subjective Happiness scale (SHS). Values in parentheses are 95% confidence intervals. The ‘genetic correlation’ between two traits is the ratio of the genetic covariance of the two traits (the covariance due to genetic influences) to the product of genetic standard deviations. Because the genetic covariance (the numerator of the aforementioned ratio) can be negative, the genetic correlation can also be negative, indicating genetic influences that tend to increase one trait and to decrease the other. Similarly for the ‘environmental correlation’. The ‘genetic proportion of phenotypic correlation’ of two traits (‘bivariate heritability’) is the ratio of the genetic covariance to the total covariance (genetic + environmental covariance) of the traits. If the environmental

42

covariance is negative, the numerator of the aforementioned ratio is higher than the denominator, and the bivariate heritability is > 1. Similarly for the ‘environmental proportion of phenotypic correlation’.

43

Highlights



Genes and environment shape adolescent effortful control and adjustment.



Effortful control is moderately associated with adjustment in adolescence.



Adolescent effortful control is mainly genetically associated with adjustment.

44