intellectual interest mediates gene × socioeconomic status interaction on adolescent academic...

8/3/2019 Intellectual Interest Mediates Gene Socioeconomic Status Interaction on Adolescent Academic Achievement

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Intellectual Interest Mediates Gene Socioeconomic Status Interaction on

Adolescent Academic Achievement

Elliot M. Tucker-Drob and K. Paige Harden

University of Texas at Austin

Recent studies have demonstrated that genetic influences on cognitive ability and academic achievement arelarger for children raised in higher socioeconomic status (SES) homes. However, little work has been done todocument the psychosocial processes that underlie this Gene Environment interaction. One process mayinvolve the conversion of intellectual interest into academic achievement. Analyses of data from 777 pairs of17-year-old twins indicated that Gene SES effects on achievement scores can be accounted for by strongerinfluences of genes for intellectual interest on achievement at higher levels of SES. These findings are consis-tent with the hypothesis that higher SES affords greater opportunity for children to seek out and benefit fromlearning experiences that are congruent with their genetically influenced intellectual interests.

Genetic influences are often found to account forupward of 50% of individual differences in bothgeneral cognitive ability and academic achieve-ment at the population level. Although someauthors have argued that this large figure rendersenvironmental explanations for socioeconomicdisparities in cognition implausible (Hernstein &Murray, 1994; Jensen, 1969, 1973), other develop-mental theorists have posited that genetic variancein cognitive ability and academic achievementmay emerge, in part, via childrens transactionswith particular environmental experiences (Bron-

fenbrenner & Ceci, 1994; Dickens & Flynn, 2001).These transactional models of cognitive develop-ment represent an attempt to move beyond afocus on the relative magnitude of genetic versusenvironmental influences, and to move toward amore integrated understanding of how genes andenvironments combine and interact to producecomplex behavioral phenotypes (Anastasi, 1958).In this article, we review the propositions of trans-actional models of cognitive development and dis-cuss how transactional models can provide a

framework for understanding recent findings thatgenetic variance in cognitive outcomes is moder-ated by socioeconomic status (SES). Next, we sug-gest that noncognitive factors, such as motivation,self-concept, and interests, are driving forces inchildrens transactions with their environments,and that a decoupling of childrens intellectualinterest and academic achievement may accountfor the decreased genetic variance in academicachievement for children living in lower SEShomes. Finally, we present evidence from newanalyses of the National Merit Twin Study sup-

porting our hypothesis about the role of intellec-tual interest in Gene SES interaction onacademic achievement.

Transactional Models of Cognitive Development

Plomin, DeFries, and Loehlin (1977) firstdescribed the processes by which genotypes couldcome to be differentially associated with environ-mental exposure, i.e., geneenvironment correla-tion. Passive geneenvironment correlations arisewhen children are raised by their biological par-

ents, such that their rearing environments are influ-enced by some of the same genes that they inherit.Evocative geneenvironment correlations arisewhen childrens genetically influenced traits,features, and characteristics elicit particular envi-ronments from others. Finally, active geneenviron-

Data were obtained from the Henry A. Murray ResearchArchive at Harvard University (http://www.murray.harvard.edu/). The Population Research Center at the University ofTexas at Austin is supported by a center grant from the NationalInstitute of Child Health and Human Development (R24HD042849). The original collectors of the data, the MurrayResearch Archive, and NICHD bear no responsibility for use ofthe data or for interpretations or inferences based upon suchuses. We thank John Loehlin for helpful comments on previousversions of this article.

Correspondence concerning this article should be addressed toElliot M. Tucker-Drob, Department of Psychology, University ofTexas at Austin, 1 University Station A8000, Austin, TX 78712-0187. Electronic mail may be sent to [email protected].

Child Development, xxxxx 2012, Volume 00, Number 0, Pages 115

2012 The Authors

ChildDevelopment 2012Societyfor Research in ChildDevelopment, Inc.

All rights reserved. 0009-3920/2012/xxxx-xxxx

DOI: 10.1111/j.1467-8624.2011.01721.x


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ment correlations arise when children seek out andchoose experiences and environments that are con-sistent with their genetically influenced traits.

The typology of passive, evocative, and activegeneenvironment correlation was applied todevelopmental theory by Scarr and McCartney(1983), who argued that children become increas-ingly autonomous over the course of development,such that passive geneenvironment correlationsweaken and active geneenvironment correlationsstrengthen. A plausible, albeit perhaps counterintu-itive, net effect of this process is that genes andenvironments become increasingly correlated withone another over the course of development. Thusgenetically similar people (like monozygotic [MZ]twins) will select increasingly similar environmen-tal niches over time, and it is this convergence ofenvironmental experience that maintains, or even

increases, their phenotypic similarities. That is,Scarr and McCartney suggested that genetic influ-ences on complex psychological phenotypes arereinforced through on the on-going selection ofenvironmental experiences that are correlatedwith motivational, personality, and intellectualaspects of our genotypes (p. 427). Elaborating onthis perspective, Scarr (1992) later hypothesizedthat the process of individuals sorting themselvesinto learning environments depends on peoplehaving a varied environment from which to chooseand construct experiences (p. 9), a requisite thatshe argued is particularly likely to be absent forchildren reared in very disadvantaged circum-stances.

The process by which environmental experiencesproduce phenotypic differences was further elabo-rated on by Bronfenbrenner and Ceci (1994). Theirbioecological model contends that environmentalexperiences are inextricably linked to genetic differ-ences between individuals and that the dynamic bywhich children and their environments mutually actupon one another is central to the realization ofgenetic potential for healthy development. Specifi-cally, Bronfenbrenner and Ceci state that human

development takes place through processes of pro-gressively more complex reciprocal interactions between an active [child] and the person, objectsand symbols in [the childs] immediate environ-ment (p. 572). These reciprocal interactions aretermed proximal processes. A critical aspect of the bio-ecological model is its prediction that proximalprocesses will differ in their availability and qualityacross macroenvironmental contexts, even in therange of good enough environments. This is a sig-nificant point of departure from Scarr (1993; also see

Scarr, 1992, 1996; Scarr & McCartney, 1983), whoargued that environments other than severe depri-vation were functionally equivalent (p. 1337).

Finally, Dickens and Flynn (2001) applied atransactional model of development specifically tothe domain of cognitive abilities. They state the pre-mise of transactional models simply: Higher IQleads one into better environments causing stillhigher IQ, and so on. A major contribution of theDickens and Flynn model is its conceptualization ofhow environmental experience aggregates overtime. They suggest that environmental influencesneed not be large, but simply need to be consistentand recurring over long periods of development inorder to have large effects on cognition andachievement. Moreover, environmental influencesthat are selected based on genetically influencedtraits and preferences, rather than serendipitously

encountered or externally imposed, are most likelyto be consistent over time (Caspi, Roberts, & Shiner,2005; McAdams & Olson, 2010), and thus are pre-cisely those environmental experiences that aremost influential.

Considered together, transactional models ofcognitive development posit that (a) individualsselect (and are selected into) environments that areincreasingly congruent with their own geneticallyinfluenced traits, (b) environments that are congru-ent with ones genotype are most likely to beconsistent and recurring over the course of devel-opment, (c) recurring interactions with high-qualityenvironments are necessary for the realization ofgenetic potential for healthy cognitive outcomes,and (d) macroenvironmental contexts affect theavailability and quality of environmental experi-ences important for cognition.

Gene SES Interactions in Cognitive Ability andAcademic Achievement

One important macroenvironmental context thatmay affect an individuals ability to select andinteract with high-quality environmental experi-

ences necessary for cognitive development andlearning is SES. SES can be conceptualized as repre-senting a familys level of financial capital (mate-rial resources), human capital (nonmaterialresources such as education), and social capital(resources achieved through social connections)(Bradley & Corwyn, 2002, p. 372). The differencesin resources available to high- versus low-SES fami-lies are evident across multiple domains, includingparental responsiveness, parental teaching, andlevel of cognitive stimulation (Bradley, Corwyn,

2 Tucker-Drob and Harden


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McAdoo, & Coll, 2001). Children in higher SEShomes are more likely to have developmentallyappropriate books, be read to by a family member,be taught academic concepts, receive special lessonsto improve specific skills, and be taken to themuseum or theater. SES differences in environmen-tal quality extend outside of the home, too (Kazdin,Kraemer, Kessler, Kupfer, & Offord, 1997). Com-pared to children growing up in higher SES homes,children growing up in lower SES homes are morelikely to attend schools with inadequate instruc-tional materials, fewer advanced placement classes,fewer books and computers, nonfunctional sci-ence labs, and fewer academically oriented orhigh-achieving classmates (Phillips & Chin, 2004).Moreover, lower SES children are perceived morenegatively and receive less attention from teachers(McLoyd, 1998).

Given the relation between SES and childrensopportunities to select and participate in the envi-ronmental experiences necessary for maximizingability and achievement, transactional models ofcognitive development predict that genetic variancein cognitive outcomes will be higher for childrenliving in higher SES circumstances. Consistent withthis prediction, there is an emerging body ofresearch indicating that genetic influences on cogni-tive outcomes do indeed positively vary with SES. Ina seminal article, Rowe, Jacobson, and van den Oord(1999) demonstrated that the heritability of verbalability in a national sample of adolescents rangedfrom 72% in the most educated families to 26% inthe least educated families. A subsequent reanalysisof this data by Guo and Stearns (2002), who incorpo-rated multiple indices of parental SES, found thatthe moderating effect of SES could be best accountedfor by parental ethnicity and unemployment. Next,using advances in quantitative techniques formodeling Gene Environment interaction, Turkheim-er, Haley, Waldron, DOnofrio, and Gottesman(2003) reported that among low-SES 7-year-olds,heritability of IQ was 10%, whereas among high-SES 7-year-olds, heritability of IQ was 72% (see

also Tucker-Drob, Harden, & Turkheimer, 2009).Harden, Turkheimer, and Loehlin (2007) reported asimilar effect of parental income and parental educa-tion on the heritability of academic achievement in asample of 17-year-olds, and subsequent analysesindicated that their result was robust to assumptionsabout assortative mating and passive geneenviron-ment correlation between adolescent genotype andfamily income (Loehlin, Harden, & Turkheimer,2009). Using a sample of middle-aged males, Kre-men et al. (2005) found that heritability for word

recognition (a component of reading ability) variedpositively with parental education, accounting for21% of the variance at the lowest parental educationand 69% at the highest. This effect, however, wasdriven by decreases in shared environmental vari-ances with increasing education, rather than byincreases in genetic variance. Very recently, Tucker-Drob, Rhemtulla, Harden, Turkheimer, and Fask(2011) demonstrated that Gene SES effects on cog-nitive ability emerge as early as 2 years of age. Theyreported that in a nationally representative sampleof 2-year-old twins born in the United States in 2001,genetic influences on Bayley mental ability testscores approached 0 for low-SES children andapproached 50% for high-SES children. Finally, evi-dence consistent with a Gene SES effect on cogni-tive outcomes has also been found using a moleculargenetic paradigm: Enoch, Waheed, Harris, Albaugh,

and Goldman (2009) found a significant interactionbetween Val158Met, a functional polymorphism ofthe COMTgene, and educational attainment on cog-nitive abilities among adults. They found that,among Met allele carriers, educational attainmenthad a strong positive relation with test scores,whereas among ValVal individuals, the positiverelation between educational attainment and testsscores was much less pronounced. Overall, extantresearch suggests that there are Gene SES inter-actions for cognitive outcomes in childhood, adoles-cence, and adulthood.

Noncognitive Factors as Driving Forces in CognitiveDevelopment

Research on Gene SES interactions is largelyconsistent with the propositions of transactionalmodelsthat children actively select and respondto environmental experiences in accordance withtheir own genetically influenced traits, and that thisprocess is restricted by socioeconomic disadvan-tage, resulting in lower heritability of cognitive out-comes in lower SES homes. However, the specificfactors that govern how a child or adolescent differ-

entially selects or responds to options within theenvironment remain largely unexplored. While theDickens and Flynn (2001) model emphasizes chil-drens preexisting levels of ability and competence,we propose that noncognitive factorsincludinglevels of scholastic motivation, drive for achieve-ment, intellectual self-concept, and intellectualinterestare also critical for the process of selectingenvironmental niches.

Figure 1 is a schematic illustration that repre-sents the proposed role of noncognitive factors in

Intellectual Interest and GE 3


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the selection of environments that influencecognition and achievement. At the corners of thetriangle are three core class of constructs: (a) inter-ests, intentions, and personality; (b) proximal envi-ronments (peer groups, coursework, activities,interpersonal interactions); and (c) cognitive abili-ties and achievement. Along the sides of the trian-gle are mechanisms that illustrate the bidirectionalnature of the relations between the three coreclasses of constructs. For example, interest in intel-lectual and academic pursuits is probabilisticallyrelated to experiencing high-quality proximal envi-ronments (e.g., enrollment in challenging course-work), through processes by which childrenactively seek out these experiences (e.g., an adoles-cent enrolling in advanced placement English inhopes of improving chances of college admission)and by which they evoke these experiences fromothers (e.g., a teacher recommending more difficultmath courses to an engaged and interested stu-dent). In turn, high-quality proximal environmentscan result in further intellectual interest as a result

of socialization processes, while also directly boost-ing achievement through the instructional process.(This schematic is not intended to be fully compre-hensive; there are, of course, many other mecha-nisms that may underlie the relations among thethree core sets of constructs.)

Furthermore, we predict that the strength of themechanisms relating noncognitive factors, achieve-ment, and proximal environments will differsystematically with SES. Only in high-SES circum-stances, where children and adolescents can take

advantage of a wide array of environmental experi-ences, interest and motivation will become tightlycoupled with achievement: Intellectually interestedadolescents will be able to invest more time andeffort into achievement-relevant behaviors (e.g.,additional time studying), and they will preferen-tially select achievement-enhancing proximal envi-ronments (e.g., high-achieving peer groups,challenging coursework). More specifically, geneticdifferences in interest and motivation will becometightly coupled with achievement. This is becauseintellectual interest will result in an advantage forachievement only when it is systematic and recur-ring over long periods of development (Dickens &Flynn, 2001), and genetically influenced aspects ofpersonality are generally more developmentallyconsistent than are aspects of personality influ-enced by the immediate environment (Caspi et al.,

2005). Thus, children and adolescents in high-SEShomes will be able to convert genetic differencesin interest and motivation into achievement, result-ing in higher overall heritability for achievement. Incontrast, intellectual interest and achievement will be decoupled for children in lower SES circum-stances, who have restricted access to achievement-enhancing proximal environments (e.g., feweropportunities for advanced math classes) and whohave fewer resources to devote to achievement-rele-vant behaviors. Without the opportunity to actout genetically influenced interest and motivationin the environment, genetic differences in noncog-nitive factors become irrelevant for academicachievement. The net effect of this process will bereduced influence of genes related to intellectualinterest on achievement, and lower overall herita-bility of achievement, for children living in lowerSES homes.

Hypotheses

This study aims to test the role of intellectualinterest in Gene SES interactions on academicachievement, using a sample of 777 adolescent twin

pairs from the National Merit Twin Study (Loehlin& Nichols, 1976, 2009). Specifically, we test whetherSES moderates the relation between geneticvariance in intellectual interest and academicachievement. Transactional models of cognitivedevelopment predict that in higher SES homes,genetic variance in intellectual interest is stronglycoupled with academic achievement, resulting inhigher overall heritability for academic achieve-ment. Alternatively, in lower SES homes, whereintellectually interested adolescents have restricted

Figure 1. A conceptual model for the mutual relations betweeninterests, proximal environments, and achievement.



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opportunities to select and respond to enrichingproximal environments, the association betweengenetic variance in intellectual interest and achieve-ment is predicted by transactional models to beweakly coupled, resulting in lower overall heritabil-ity of achievement.

Method

Data for the current project come from the NationalMerit Twin Study (Loehlin & Nichols, 1976).Harden et al. (2007) have previously reported thatin these data, heritability of a general achievementfactor was higher for twins growing up in higherincome homes. Here we extend this work by exam-ining the extent to which this Gene Environmentinteraction can be accounted for by socioeconomic

differences in the genetic basis of the interestachievement relation.

Participants

Participants were sampled from approximately600,000 American high school students (averageage = 17 years) who took the National Merit Schol-arship Qualifying Test (NMSQT) in 1962. Of these,1,507 pairs of same-sex twins were identified, 850pairs of which agreed to participate. Zygosity wasdetermined by a questionnaire that assessed twinsimilarity in childhood and the frequency withwhich they were confused by others (Nichols &Bilbro, 1966). These determinations were cross-vali-dated using a subsample of 124 twin pairs ofknown zygosity, and found to be over 90% accu-rate. The current analyses were restricted to the 777pairs for whom family income was reported (475MZ pairs and 302 dizygotic [DZ] pairs). It is impor-tant to note that this sample size is comparable tothose of many other twin studies (Boomsma, Bus- jahn, & Peltonen, 2002), but much smaller thanmost epidemiological studies. While power todetect Gene Environment interaction effects is

always a potential concern, previous studies usingthis data set have already found significant Gene Environment interaction effects on academicachievement (Harden et al., 2007), which is a testa-ment to the power of this sample size.

Measures

Academic achievement was measured with theNMSQT, which is composed of five subscales: Eng-lish Usage, Mathematics Usage, Social Science

Reading, Natural Science Reading, and WordUsage. For the purposes of this article the NMSQTselection score, which is a unit-weighted compositeof the five subscale scores, was used as an index ofgeneral academic achievement. The twin pair corre-lation for this score was r = .88 for MZ twins andr = .64 for DZ twins (both p < .01).

Intellectual interest was measured with the Intel-lectual Efficiency scale of the California Psychologi-cal Inventory. According to the Megargee (1972),the manifest content [of the Intellectual Efficiency]scale reflects an interest in and enjoyment in intel-lectual pursuits: I like to read about history[True]; and self-confidence and assurance: I seemto be as capable and smart as most others aroundme [True]. According to McAllister (1996), veryhigh scorers on the intellectual efficiency scale areconceptual and intellectually oriented, tending to

think or talk about problems more than act onthem, whereas low scorers prefer to deal withtangible and concrete issues rather than with con-cepts or abstractions. Importantly, intellectual effi-ciency is a subscale that correlates moderately withobjective indices of cognition and achievement butis based entirely on subjective self-report personal-ity items. In the current data set, this scale corre-lated with academic achievement at .44 (p < .01;based on only 1 twin per pair). The twin pair corre-lation for this scale was r = .52 for MZ twins andr = .33 for DZ twins (both p < .01).

SES was indexed by parental report of familypretax income in a written questionnaire, with sevenresponse categories ranging from less than $5,000per year to over $25,000 per year. This rangeapproximately corresponds to a range of lessthan $31,250 to over $156,250 in 2004 dollars. In thecurrent data set, this index correlated with aca-demic achievement at .23 (p < .01; based on only 1twin per pair), and with intellectual interest at .11(p < .01; based on only 1 twin per pair).

Analytical Methods

Data were analyzed using a series of four struc-tural equation models of increasing complexity.First, we fit univariate main effects models sepa-rately to achievement and intellectual interest, inorder to determine the overall magnitude of geneticinfluences on these phenotypes. Second, we fitunivariate Gene Environment interaction (GE)models separately to achievement and intellectualinterest, in order to determine whether the herit-abilities of these phenotypes were moderated bySES. Third, we fit a bivariate main effects model, in



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order to test the contribution of genes to the associa-tion between intellectual interest and achievement.Finally, we fit a bivariate GE model, in order todetermine whether the relation between geneticvariance in intellectual interest and achievementwas moderated by SES, as would be predicted bytransactional models of cognitive development.These models are described in more detail below.For all analyses, all variables were standardized rel-ative to the means and standard deviationsobserved for the first twin in each pair. Analyseswere carried out using the Mplus computer pro-gram (Muthen & Muthen, 1998-2009) with maxi-mum likelihood estimation.

Step 1: Univariate main effects model.. A conven-tional biometric model for twins reared togetherspecifies that a given phenotype is influenced bythree statistically additive and independent unob-

served components: additive genes (A); the sharedenvironment (C), that is, environmental influencesthat make children raised in the same home similarto each other; and the nonshared environment (E),that is, environmental influences that make childrenraised in the same home different from each other,plus measurement error (see Neale & Cardon, 1992,for more details regarding the parameterization oftwin models). This basic model can be expanded toinclude measured covariates. Figure 2 illustratesthe univariate main effects model for intellectualinterest, including SES as a measured family-levelcovariate. In this model, the variances of the A, C,and E components are fixed to 1, and the correla-tion between A components in the first and second

members of each twin pair is fixed according togenetic theory (rs = 1.0 in MZ twins and 0.5 in DZtwins). The paths from the A, C, and E componentsare freely estimated, and the square of these pathsindicate the proportion of variance in the pheno-type attributable to genes, the shared environment,and the nonshared environment. Thus the squareof the a path gives the familiar heritability statistic(h2)the proportion of variance in the phenotypeattributable to additive genes. It is important tonote that because SES was measured at the familylevel, it was by definition an aspect of the sharedenvironment. That is, controlling for SES reducesour estimate of the variance accounted for by theshared environment. Because SES is controlled forin all of our models, C should therefore be inter-preted as family-level influences that are incre-mental to family-level SES. Univariate main

effects models were fit separately for intellectualinterest and academic achievement, in order totest the overall magnitude of additive genetic,shared environmental, and nonshared environ-mental influences on these phenotypes at thepopulation level.

Step 2: Univariate interaction model.. As described by Purcell (2002), the conventional univariate twinmodel can be easily expanded to test forGene Environment interaction. The univariateGE model for intellectual interest is shown inFigure 3. This model is identical to the model illus-trated in Figure 2, except that the paths represent-ing the influence of additive genetic, sharedenvironmental, and nonshared environmental influ-

1 1 1 1 1 1

1.0 or 0.5 1.0

A1

C1

E1

A2

C2

E2

I t ll t l ll l

ci

ai ei

ci

ai

ei

Intellectual

Interest

(Twin 1)

Intellectual

Interest

(Twin 2)

si s

SES

2

si si

s

Figure 2. A path diagram for the univariate main effects modelof intellectual interest in twins.

ciSES

1 1 1 1 1 1

1.0 or 0.5 1.0

A1

C1

E1

A2

C2

E2

e + e SESSES e + e SESSES

ci

+

I t ll t l ll l

i+ i SESai + ai SES i + i SESai + ai SES

Intellectual

Interest

(Twin 1)

Intellectual

Interest

(Twin 2)

si s

SES

2

si

s

si

ci

+ ci

SES

Figure 3. A path diagram for the univariate interaction model ofintellectual interests in twins.



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ences are allowed to vary with SES. For example,the path from the additive genetic components tointellectual interest is modeled as (a + a SES),where a significant value for a would indicate thatthe amount of variance in intellectual interest attri-butable to genes varies with SES.

Step 3: Bivariate main effects model.. The univariatemain effects models fit in Step 1 test the magnitudeof genetic influence on each phenotype separately,but do not test the extent to which genes contributeto the association between intellectual interest andachievement. That is, to the extent that higher intel-lectual interest is correlated with higher achieve-ment, is this association attributable to commongenetic influences? In order to test this, we fit a bivariate main effects model, shown in Figure 4.There are multiple ways to parameterize bivariatetwin data; the current project uses a Cholesky

model, which specifies an a priori ordering of thevariables. Specifically, this model estimates A, C,and E components for intellectual interest; achieve-ment is specified as a dependent variable that isregressed on the A, C, and E components of intel-lectual interest; and residual variance in achieve-ment that is independent of intellectual interest isdecomposed into a second set of A, C, and E com-ponents. Thus, in Figure 4, the ai, ci, and ei parame-ters represent the contributions of genes, the sharedenvironment, and the nonshared environment tointerest; the ab, cb, and eb parameters represent theextent to which genetic, shared environmental, andnonshared environmental influences on intellectualinterest also predict academic achievement; and theaa, ca, and ea parameters represent the contributionsof genes, the shared environment, and the non-

shared environment to the variance in achievementthat is independent of interest.

Step 4: Bivariate interaction model.. Just like theunivariate models, the bivariate main effects modelcan be expanded to allow the genetic, shared envi-ronmental, and nonshared environmental influ-ences to be moderated by SES. Such a bivariateinteraction model is shown in Figure 5. It is impor-tant to note that this model for tests for three differ-ent types of Gene SES interaction: (a) whetherSES moderates genetic influence on intellectualinterest (ai + ai SES), (b) whether SES moderatesthe association between genetic variance inintellectual interest and academic achievement(ab + ab SES), and (c) whether SES moderatesgenetic influence on academic achievement that isindependent of intellectual interest (aa + aa SES).

The bivariate interaction model allows us to dis-

tinguish between at least two different theoreticalscenarios. If the influence of genetic variance inintellectual interest on achievement is positivelymodified by SES, then Gene SES interaction forachievement would be due to greater geneticcoupling between interest and achievement in high-SES homes. Such a scenario would be consistentwith our hypothesis that higher SES environmentsallow for greater opportunities to select and interactwith experiences congruent with ones own geneti-cally influenced traits, leading to greater geneticcoupling of intellectual interest and academicachievement. However, if only the genetic compo-nent of achievement that is independent of interestis modified by SES, then the Gene SES interactioncannot be accounted for by a decoupling betweeninterest and achievement in low- SES homes.

1 1 1 1 1 1

eb

Ai

Ci

Ei

Aa

Ca

Ea

b

ab

ci

ai ei

cb caaa ea

Intellectual

InterestAchievement

si s

SES

2

sa

s

si

Figure 4. A path diagram of the bivariate Choleksy model ofintellectual interest and academic achievement.Note. Only one twin per pair is shown.

1 1 11 1 1

Ai

Ci

Ei

Aa

Ca

Ea

ca +

ca

SES

aa +

aa

SES ea + eaSESc

i +c

iSES

ai +

aiSES e

i+ e

iSES

Intellectual

InterestAchievement

si s

SES

2

si sa

s

Figure 5. A path diagram of the bivariate interaction model forintellectual interest and academic achievement.Note. Only one twin per pair is shown.



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Results

Step 1: Univariate Main Effects Models

Parameter estimates from the univariate maineffects models for academic achievement and intel-lectual interest are presented in the first and thirdcolumns of Table 1. It can be seen that there werestatistically significant genetic, shared environmen-tal, and nonshared environmental influences onboth outcomes. For academic achievement additivegenetic influences were estimated to account for45% of the variance, shared environmentalinfluences for 36%, and nonshared environmentalinfluences (plus measurement error) for 13% (SESaccounted for the remaining 6%). There were sub-stantially greater within-twin pair differences forintellectual interest than for achievement. For intel-lectual interest additive genetic influences were

estimated to account for 27% of the variance,shared environmental influences for 21%, andnonshared environmental influences for 49% (SESaccounted for the remaining 2%).

Step 2: Univariate Interaction Models

The parameter estimates for the univariate inter-action models for achievement and intellectualinterest are shown in the second and fourth col-umns of Table 1. As previously reported by Hardenet al. (2007), the univariate interaction model foracademic achievement fit the data significantly bet-ter than the univariate main effects (see Table 2 fora summary of model comparisons). Inspection ofparameter estimates from the univariate interactionmodel indicates that the only interaction parameterthat is significant is athe moderating effect ofSES on additive genetic influence. This parameterwas estimated to equal 0.12 (SE = 0.05), indicatingthat genetic influences on academic achievementare higher at higher levels SES. Figure 6 plots thevariance in academic achievement attributable toadditive genetic, shared environmental, and non-shared environmental influences as functions of

SES, as implied by parameter estimates. It can beseen that the variance in achievement attributableto genes increases substantially from low to highSES levels. The same pattern can be observed in theAppendix, which reports MZ and DZ correlationsfor low-, middle-, and high-SES families.

In contrast to the positive finding of a Gene SES interaction on academic achievement, therewas no statistically significant evidence for such aninteraction on intellectual interest: The univariateinteraction model for interest did not fit signifi-

cantly better than the univariate main effectsmodel, and none of the interaction parameters (a,c, or e) were significantly different from zero.Based on these results, we cannot conclude thatgenetic influences are any more or less importantfor intellectual interest in advantaged environmentsversus disadvantaged environments.

Step 3: Bivariate Main Effects Model

The bivariate main effects model extends theresults from the univariate main effects model byestimating the genetic, shared environmental, andnonshared environmental influences on each phe-notype and on the association between interest andachievement. Parameter estimates for the bivariatemain effects model are shown in the fifth column ofTable 1. The ab, cb, and eb parameters were all sig-

nificantly different from zero, indicating that bothgenetic and environmental factors contribute to theassociation between interest and achievement. Spe-cifically, genetic influences on interest accountedfor 8% of the variance in achievement, and sharedenvironmental influences on interest accounted for17% of the variance in achievement. Although sig-nificant, the effect of the nonshared environmentalcomponent of interest on achievement was verysmall, accounting for less than 1% of the variancein achievement after controlling for SES. Theseresults demonstrate that it is genetic and sharedenvironmental components of interest that accountfor the vast majority of the interestachievementassociation. It is important to observe, however,that there was substantial genetic and environmen-tal variation in achievement that was independentof interest, as indicated by the statistically signifi-cant aa, ca, and ea parameters. Specifically, geneticinfluences independent of interest accounted for37% of the variance in achievement, shared envi-ronmental influences independent of interestaccounted for 18% of the variance in achievement,and nonshared environmental influences indepen-dent of interest accounted for 12% of the variance

in achievement.

Step 4: Bivariate Interaction Models

The bivariate interaction model examines whetherSES moderates the extent to which academicachievement is influenced by the genetic, sharedenvironmental, and nonshared environmental com-ponents of intellectual interest. Moreover, it testswhether the genetic variance in academic achieve-ment that is independent of intellectual interest is



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Table1

ParameterEstimatesFromUnivariate

andBivariateTwinModels

Parameter

Univariatemodels

Bivariatemodels

(Interestfi

Achievement)

Achievement

Interest

1.

Maineffects

2.

Interaction

3.

Maineffects

4.

Interaction

5.M

aineffects

6.

Interaction

7.

Finalmodel

Estimate

SE

Estimate

SE

Estimate

SE

Estimate

SE

Estimate

SE

Estimate

SE

Estimate

SE

ai

0.5

23

0.1

10

0.5

03

0.1

27

0.5

38

0.1

06

0.4

90

0.1

24

0.5

36

0.0

95

ai

0.1

01

0.0

68

0.1

11

0.0

68

ci

0.4

59

0.1

13

0.4

60

0.1

23

0.4

45

0.1

17

0.4

70

0.1

09

0.4

47

0.1

04

ci

)0.1

00

0.0

83

)0.1

03

0.0

7

ei

0.7

00

0.0

22

0.6

99

0.0

23

0.6

98

0.0

22

0.7

00

0.0

22

0.6

99

0.0

21

ei

)0.0

37

0.0

20

)0.0

4

0.0

2

ab

0.2

74

0.1

07

0.2

83

0.1

05

0.2

81

0.0

86

ab

0.1

35

0.0

68

0.1

22

0.0

43

cb

0.4

12

0.1

38

0.3

81

0.1

08

0.4

08

0.1

06

cb

0.0

14

0.0

8

eb

0.0

61

0.0

16

0.0

57

0.0

16

0.0

59

0.0

16

eb

)0.0

23

0.0

15

aa

0.6

68

0.0

44

0.6

55

0.0

45

0.6

09

0.0

56

0.5

86

0.0

55

0.5

89

0.0

55

aa

0.1

20

0.0

46

0.0

55

0.0

45

ca

0.5

96

0.0

55

0.5

92

0.0

59

0.4

30

0.1

30

0.4

47

0.0

98

0.4

41

0.1

03

ca

)0.0

79

0.0

64

)0.1

03

0.0

79

ea

0.3

59

0.0

12

0.3

58

0.0

12

0.3

53

0.0

11

0.3

52

0.0

11

0.3

53

0.0

11

ea

)0.0

12

0.0

11

)0.0

10

0.0

11

si

0.1

27

0.0

30

0.1

27

0.0

31

0.1

30

0.0

30

0.1

35

0.0

31

0.1

35

0.0

30

sa

0.2

43

0.0

32

0.2

51

0.0

34

0.2

42

0.0

32

0.2

53

0.0

34

0.2

53

0.0

34

Note.Parametersinboldaresignifi

cantatp

intellectual interest mediates gene × socioeconomic status interaction on adolescent academic...

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