Strong genetic e¡ects on cross-situational antisocial behaviour among 5-year-old children ...

17 Pages·2003·0.28 MB·English

JournalofChildPsychologyandPsychiatry44:6(2003),pp832–848
Strong genetic e¡ects on cross-situational antisocial
behaviour among 5-year-old children according
to mothers, teachers, examiner-observers, and twins(cid:1)
self-reports
Louise Arseneault,1 Terrie E. Moffitt,1,2 Avshalom Caspi,1,2 Alan Taylor,1
Fruhling V. Rijsdijk,1 Sara R. Jaffee,1,2 Jennifer C. Ablow,3
and Jeffrey R. Measelle3
1Social,Genetic,andDevelopmentalPsychiatryResearchCentre,InstituteofPsychiatry,King’sCollegeLondon,UK;
2DepartmentofPsychology,UniversityofWisconsin,Madison,USA;3DepartmentofPsychology,Universityof
Oregon,Eugene,USA
Background: Early childhood antisocial behaviour is a strong prognostic indicator for poor adult
mental health. Thus, information about its etiology is needed. Genetic etiology is unknown because
mostresearchwithyoungchildrenfocusesonenvironmentalriskfactors,andthefewexistingstudiesof
youngtwinsusedonlymothers(cid:1)reportsofbehaviour,whichmaybebiased. Method: Weinvestigated
geneticinfluencesonantisocialbehaviourinarepresentative-plus-high-risksampleof1116pairsof5-
year-old twins using data from four independent sources: mothers, teachers, examiner-observers pre-
viously unacquainted with the children, and the children themselves. Results: Children’s antisocial
behaviour was reliably measured by all four informants; no bias was detected in mothers(cid:1), teachers(cid:1),
examiners(cid:1), or children’s reports. Variation in antisocial behaviour that was agreed upon by all in-
formants, and thus was pervasive across settings, was influenced by genetic factors (82%) and ex-
periences specific to each child (18%). Variation in antisocial behaviour that was specific to each
informant was meaningful variation, as it was also influenced by genetic factors (from 33% for the
children’s report to 71% for the teachers(cid:1) report). Conclusions: This study and four others of very
youngtwinsshowthatgeneticriskscontributestronglytopopulationvariationinantisocialbehaviour
thatemergesinearlychildhood.Incontrast,geneticriskisknowntoberelativelymodestforadolescent
antisocialbehaviour,suggestingthattheearly-childhoodformhasadistinctetiology,particularlyifitis
pervasiveacrosssituations. Keywords:Antisocialbehaviour,environmentalinfluences,genetics,self-
reports, twins.
Children who exhibit antisocial behaviour at ages 3 adult samples to the virtual exclusion of pre-school
to 5 years are significantly more likely to display children(Rhee&Waldman,2002).Thisissurprising
conduct problems and delinquency in adolescence, given the prognostic significance of childhood-onset
to meet diagnostic criteria for antisocial personality antisocial behaviour.
disorder in adulthood, and to be recidivistic and vi- Two opposing predictions are offered about the
olent criminals (Tremblay, Pihl, Vitaro, & Dobkin, genetic and environmental origins of childhood-
1994; Caspi, Moffitt, Newman, & Silva, 1996; Ste- onset antisocial behaviour. One developmental
venson & Goodman, 2001). Although some children theory suggests that environmental factors are
exhibiting antisocial behaviour do not grow up to strongest in early childhood, but then give way to
have antisocial personalities, virtually all antisocial increasing genetic influences as children grow older
children suffer adult maladjustment (Robins, 1966; and are able to select for themselves environments
Farrington, Gallagher, Morley, St. Ledger, & West, that are correlated with their genotypes (Scarr &
1988; Moffitt, Caspi, Harrington, & Milne, 2002). McCartney, 1983). This theory would predict that
Given the strong prognostic significance of early genetic influences might increase after childhood
childhood antisocial behaviour for life-course devel- whenadolescentswhoaregeneticallypredisposedto
opment, it is critical to understand its etiology antisocial activities shape their environment by
(Keenan & Wakschlag, 2002). choosing deviant peers who will encourage their
Most etiological studies of young children’s anti- antisocial behaviour (Rowe & Osgood, 1984). This
socialbehaviourhavefocusedonenvironmentalrisk theory that genetic effects are weakest in childhood
factors, such as poverty or ineffective parenting is known to apply to cognitive abilities. Develop-
(Shaw, Owens, Vondra, Keenan, & Winslow, 1996; mental behavioural genetics research has revealed
Haapasalo & Tremblay, 1994; Keenan, 2001). The that environmental influences account for most of
role of genetic influences has seldom been consid- the variation observed in cognitive abilities during
ered. Until recently, quantitative genetic studies of early childhood, but genetic influence actually in-
antisocialbehaviourhavefocusedonadolescentand creases as children develop from childhood to
(cid:1)AssociationforChildPsychologyandPsychiatry,2003.
PublishedbyBlackwellPublishing,9600GarsingtonRoad,OxfordOX42DQ,UKand350MainStreet,Malden,MA02148,USA
Antisocialbehaviourin5-year-oldtwins 833
adulthood (McGue, Bouchard, Iacono, & Lykken, with their children’s behavioural problems (Patter-
1993; Plomin, Fulker, Corley, & DeFries, 1997). son&Yoerger,1997).Theseconcernshaveledsome
In contrast to this theory predicting weak genetic child development experts to claim that mothers(cid:1)
influences for early relative to later antisocial beha- reports of behaviour problems are of limited value
viour, a taxonomic theory of antisocial development because these parental measures are suffused by
has suggested the opposite; that antisocial beha- multiple forms of rating bias (Kagan, 1998). Al-
viour evident in early childhood should have strong thoughthisclaimmaybeaslightexaggerationofthe
genetic influences (Moffitt, 1993; DiLalla & Gottes- known facts (Simonoff et al., 1995, 1998; van der
man, 1989). According to this theory, antisocial be- Valket al.,2001),itiscertainlytruethatknowledge
haviour that begins in childhood has its origins in about the genetic and environmental origins of
neurodevelopmental vulnerabilities known to be childhood-onset antisocial behaviour is currently
heritable (e.g., undercontrolled temperament, lan- restricted to knowledge about the genetics of par-
guage delay), and is likely to become life-course ental ratings.
persistent. In contrast, antisocial behaviour that In the present epidemiological twin study, we in-
beginsin adolescenceisrelatively transientandhas vestigategeneticandenvironmentalinfluenceson5-
its origins in peer social processes. This develop- year-oldchildren’santisocialbehaviour,asassessed
mental theory would thus predict strong heritability by four different sources of information: the chil-
for early childhood samples. The goal of the present dren’s mothers, their teachers, examiner-observers
study is to test these two competing hypotheses by previously unacquainted with the children, and the
examining antisocial behaviour among 5-year-old children themselves.
children.
Increasing evidence suggests that genetic factors
do play an important role in children’s early-emer- Method
ging antisocial behaviour (Schmitz, Fulker, & Mra-
Sample
zek, 1995; van den Oord, Verhulst, & Boomsma,
1996; van den Oord, Boomsma, & Verhulst, 2000; Participants are members of the Environmental Risk
van der Valk, Verhulst, Stroet, & Boomsma, 1998; (E-risk) Longitudinal Twin Study, which investigates
Dionne, Tremblay, Boivin, Laplante, & Pe´russe, genetic and environmental factors shaping children’s
2003; van der Valk, van den Oord, Verhulst, & development. The E-risk sampling frame was two con-
Boomsma, 2001). However, to date, no epidemiolo- secutive birth cohorts (1994 and 1995) in the Twins(cid:1)
Early Development Study (TEDS), a birth register of
gicalstudyhasbeenabletoestablishthegeneticand
twinsborn inEngland andWales (Trouton, Spinath,&
environmental origins of antisocial behaviour in
Plomin, 2002). The full register is administered by the
earlychildhoodwithoutbeingconstrainedbytheuse
government’s Office of National Statistics (ONS), which
of parent reports only. Alternative sources of in-
invitedparentsofalltwinsbornin1994–95toenrol.Of
formation about children’s behaviour problems are
15,906 twin pairs born in these two years, 71% joined
hard to come by: pre-school children are generally theregister.Oursamplingframeexcludedopposite-sex
thought to be unable to report on their own beha- twin pairs and began with the 73% of register families
viour problems (Edelbrock, Costello, Dulcan, Kalas, havingsame-sex twins.
& Conover, 1985; Boyle et al., 1993; Schwab-Stone, The E-risk Study sought a sample size of 1100 fam-
Fallon, Briggs, & Crowther, 1994); reports from ilies to allow for attrition in future years of the longi-
teachers are usually not available because, in most tudinal study while retaining statistical power. An
initiallistof1210familieswasdrawnfromtheregister
countries, children under age 5 years have not
totargetforhomevisits,a10%oversampletoallowfor
started school; and observational measures are dif-
nonparticipation. The probability sample was drawn
ficult and costly to collect in the context of the large
using a high-risk stratification strategy. High-risk
samples required by behavioural genetic research
families were those in which the mother had her first
designs. As a result, in genetic epidemiology,
birthwhenshewas20yearsofageoryounger.Weused
mothers(andintwostudies,fatherscontributedtoa this sampling to replace high-risk families who were
parental rating score) are the sole informants about selectively lost to the register via non-response and to
pre-school children’s antisocial behaviour. This re- ensure sufficient base rates of problem behaviours
liance on mothers(cid:1) reports may be problematic be- given the low base rates expected for 5-year-old chil-
causeconcernshavebeenraisedaboutthereliability dren. Early first childbearing was used as the risk-
and validity of mothers(cid:1) reports. First, mothers(cid:1) re- stratification variable because it was present for virtu-
ally all families in the register, it is relatively free of
ports may be biased by mothers(cid:1) own psychopa-
measurement error, and it is a known risk factor for
thologyorcriminalhistory(Dumas&Wekerle,1995;
children’s problem behaviours (Maynard, 1997; Moffitt
Fergusson, Lynskey, & Horwood, 1993; Chilcoat &
& the E-Risk Study Team, 2002). In the final sample,
Bresleau, 1997). Second, mothers may have limited
two-thirds of Study mothers accurately represent all
information about what is normative for young chil-
mothers in the general population (aged 15–48) in
dren. Third, mothers(cid:1) reporting skill is subject to England and Wales in 1994–95. The other one-third of
variation in their intellectual abilities and educa- Study mothers (younger only) constitute a 160% over-
tionalbackground,whichareknowntobecorrelated sampleofmotherswhowereathighriskbasedontheir
834 LouiseArseneaultetal.
young age at first birth (15–20years). To ensure that behaviourofbothtwinswasratedbythesameteacher
thefindingsinthisarticlerepresentunbiasedestimates for 79% of the twins. Scores ranged from 0 to 74
ofthegeneralpopulation,thedatawereweightedforall (M¼ 5.66, SD¼9.10). The internal consistency reli-
analyses.Thisweightingmakestheproportionofyoung abilityof theteacherreport was.95.
mothers in the sample equivalent to the overall pro-
portionin thepopulation(BirthStatistics, 1996). Examiners(cid:1) observations. After the home visit, ex-
Ofthe1210familiestargeted,7werediscoveredtobe aminers rated each twin on the Dunedin Behavioural
ineligible for inclusion in our study because the twins Observation Scale, which includes 9 items measuring
hadmovedoverseas,didnotspeakEnglish,werebeing disruptivebehaviour(e.g.,hostility,lability,roughness)
reared by neither biological parent, or were opposite- (Caspi, Henry, McGee, Moffitt, & Silva, 1995). Each
sex. Of the 1203 eligible families, 1116 (93%) particip- behaviour was defined in explicit terms, and the ex-
ated in home-visit assessments when the twins were aminer evaluated whether each characteristic was ob-
age 5years. The sample includes 56% monozygotic served (0) not at all, (1) somewhat, or (2) definitely.
(MZ) and 44% dizygotic (DZ) twin pairs. Sex is evenly A version if this instrument was initially used in the
distributed within zygosity (49% male). Data were col- American Collaborative Study on Cerebral Palsy, Men-
lected within 120days of the twins(cid:1) fifth birthday. Re- tal Retardation, and Other Neurological Disorders of
searchworkersvisitedeachhomefor2.5to3hours,in InfancyandChildhood(Goldsmith&Gottesman,1981)
teams of two. While one interviewed the mother, the and later modified for use with 3- and 5-year-old chil-
othertestedthetwinsinsequenceinadifferentpartof dren in the Dunedin Multidisciplinary Health and De-
the house. Families were given shopping vouchers for velopment Study. Follow-up of the Dunedin sample
their participation, and children were given colouring showed that these disruptive behaviours predicted
booksandstickers.Allresearchworkershaduniversity adolescent behaviour problems, as well as adult psy-
degrees in behavioural science, and experience in psy- chopathology(Caspi,2000).Scoresrangedfrom0to18
chology, anthropology, or nursing. With parents(cid:1) per- (M¼ 2.22, SD¼3.46). The internal consistency reli-
mission, questionnaires were posted to the children’s ability of the examiner report was .90 and the inter-
teachers,andteachersreturnedquestionnairesfor94% rater reliabilitycoefficient was .70.
of cohortchildren.
A substantial proportion of children in the E-risk
Children’s self-reports. WeusedtheBerkeleyPuppet
sampleshoweddisruptivebehaviouratage5;consistent
Interview (BPI) to obtain self-reports from the twins
with DSM-IV (American Psychiatric Association, 1994)
about their own antisocial behaviour (Measelle, Ablow,
diagnostic criteria, children with 3 or more symptoms
Cowan,&Cowan,1998).TheBPIisadevelopmentally-
could be given a diagnosis of conduct disorder (un-
appropriate instrument designed to investigate symp-
weighted, the prevalence of conduct disorder in the
tomatology in 4- to 8-year-old children. The BPI was
samplewas8.5%;weightedtorepresentthepopulation,
administered to each twin separately. In the BPI, the
it was 6.6%). Within this group, a smaller number of
examinerintroducestwoidenticalfluffyanimalpuppets
childrenwith5ormoresymptomsmetcriteriaforsevere
(IggyandZiggy)tothechild,andthepuppetsinvitethe
conduct disorder (unweighted prevalence ¼ 3.4%,
child to join them in a conversation in which they tell
weightedtorepresentthepopulation,itwas2.5%).
the child things about themselves and the child tells
them about him/herself. The two puppets make oppo-
Antisocial behaviour site statements (e.g., Iggy: (cid:2)I hit kids a lot(cid:1) – Ziggy: (cid:2)I
don’thitkids(cid:1))inacounterbalancedorder.Thechildis
The present study successfully avoided problems
then asked to tell the puppets how he/she behaves.
caused by shared method variance across sources
Children are allowed to respond verbally or non-verb-
(Bank, Dishion, Skinner, & Patterson, 1990): one ex-
allybypointingortouchingthepuppettoindicatetheir
aminer interviewed the mother, another examiner in-
answer. All examiners completed a one-week certifica-
terviewedandratedthechildren,theteacherreturneda
tion-training course designed by Ablow and Measelle
postal questionnaire, and a coder who did not test the
(Ablow &Measelle, 1999).
children scoredtheirvideotaped self-reports.
Children were administered 19 items covering three
BPI scales that assess antisocial behaviour: Overt Ag-
Mothers(cid:1) reports. Mothers(cid:1) reports of children’s anti- gression/Hostility (e.g., (cid:2)I fight with other kids(cid:1)), Con-
social behaviour were obtained in interviews using the duct Problems (e.g., (cid:2)I take things that don’t belong to
Child Behavior Checklist (CBCL; Achenbach, 1991a). me(cid:1)), and Oppositionality (e.g., (cid:2)I don’t do what my
For this study focusing on antisocial behaviour, we teacher asks me to do(cid:1)). All interviews were videotaped
used the Delinquency and Aggression scales supple- to score the children’s answers later. Each item was
mented with DSM-IV (American Psychiatric Associ- coded on a 7-point Likert scale ranging from 1 (no
ation, 1994) items assessing conduct and oppositional symptom)to7(definitesymptom).Twodifferentcoders
defiant disorder (e.g., (cid:2)spiteful, tries to get revenge(cid:1), scored each interview, with inter-rater reliability ex-
(cid:2)uses force to take something from another child(cid:1)). ceeding.90forallcoders.Scoresrangedfrom31to106
Scores ranged from 0 to 72 (M¼ 15.56, SD¼ 11.40). (M¼ 51.48, SD¼ 13.38), and the internal consistency
Theinternalconsistencyreliabilityofthemotherreport reliability was .82. Further psychometric evaluation
was .92. conducted as part of the McArthur Research Network
on Psychopathology and Development attests to the
Teachers(cid:1) reports. Teachers(cid:1) reports of antisocial be- reliability and validity of the BPI in this age group (Ab-
haviour were obtained using the Teacher Report Form lowetal.,1999).DatafortheBPIweremissingfor353
(TRF; Achenbach, 1991b) supplemented as above. The children leaving valid data for both twins in 901 pairs,
Antisocialbehaviourin5-year-oldtwins 835
81%ofsamplefamilies.Missingnesswascausedbythe settings. In other words, this model isolates from the
child not being able to complete the interview, by the measured variance of each report, the consensus
examiner’s and/or coder’s assessment that the child across informants, and thus represents antisocial be-
didnotunderstandthetask,orbydisruptionorlackof haviour agreed upon by the informants. This model is
privacy for theinterview. moreparsimoniousthanthebiometricmodelbecauseit
estimates fewer parameters.
The rater-bias model (Figure3) partitions the vari-
Statistical methods
ances and the covariances of the four measures of
children’s antisocial behaviour into three components:
Genetic model fitting. We used maximum likelihood
(1)reliabletraitvariancewhichrepresentsthevariance,
estimation of model parameters in univariate and
freeofraterbias,thatissharedbythefourinformants
multivariate genetic models of children’s antisocial be-
(conceptualised as the reliable consensus across in-
haviour (Neale & Cardon, 1992; Plomin, DeFries,
formants); (2) rater bias, which represents the correla-
McClearn, & McGuffin, 2001). When one phenotypic
ted errors across twins, within each informant; and (3)
measure is analysed, models decompose variance in
unreliability due to measurement error, which repre-
children’s antisocial behaviour into latent additive
sentstheresidualsthatarenotcorrelatedacrosstwins.
genetic (A; i.e., the sum of the average effects of indi-
In contrast to both the biometric and psychometric
vidual alleles at all loci), latent shared environmental
models, the rater-bias model assumes that only the
(C),andlatentnonshared environmental(E)factors. In
variance that is shared across informants is valuable
cases where DZ correlations are less than half the MZ
andinformative.Itpositsthatthisreliable,agreed-upon
correlations, it is possible to examine two alternative
trait variance is influenced by a set of common genetic
models. First, a genetic dominance (D; i.e., interaction
and environmental factors. The rest of the variance is
effectsbetweenallelesonthesamelocus)factorcanbe
accounted for by unreliability that is partitioned into a
estimatedinstead of aCfactor (ADEmodel). Second, a
rater-bias component and a measurement-error com-
siblinginteractionmodel(AE )canbefittedtothedata
s ponent. The rater-bias model is more parsimonious
to estimate genetic and environmental parameters free
than either the biometric or psychometric models
of sibling interaction effects (Thapar, Holmes, Poulton,
becauseit estimates fewer parameters.
& Harrington, 1999). When we analysed all four mea-
suresofantisocialbehavioursimultaneously,wetested
three models: abiometric model, a psychometric model, Goodness-of-fit indices. The goal of fitting different
andarater-biasmodel(Hewitt,Silberg,Neale,Eaves,& structural equations to twin data is to account for the
Erickson,1992;vanderValketal.,2001;vandenOord observed covariance structure using fewest possible
etal.,2000).Testingandcomparingthesethreemodels parameters.Incomparingthefitofdifferentmodels,we
enables us to examine the extent of the consensus used four model-selection statistics to have a general
among the four informants, and whether information pictureofthemodelfitbecauseoverallfitstatisticsare
unique to each informant represents measurement affected by large sample size (Bollen, 1989). The first
error, potential bias, or valuable data in the four was the v2 goodness-of-fit statistic. Large values
measures. compared to model degrees of freedom indicate poor
The biometric model (Figure 1) partitions the vari- model fit to the observed covariance structure. When
ances and the covariances of the four measures of twomodelsarenested(i.e.,identicalwiththeexception
children’santisocialbehaviourintotwocomponents:(1) of constraints placed on the sub-model), the difference
commonvariance,whichrepresentsthevariancethatis in fit between them can be evaluated with the v2 dif-
shared by all four measures (conceptualised as the ference,usingasitsdegreesoffreedomthedfdifference
consensus between informants); and (2) unique vari- from the two models. When the v2 difference is not
ance, which represents the variance that is not shared statistically significant, the more parsimonious model
between the four measures (conceptualised as infor- is selected, as the test indicates that additional con-
mation that is unique to each informant). This model straints do not improve the model fit. The second
posits that variation common to the four measures of model-selection statistic was Akaike’s Information Cri-
antisocial behaviouris influenced by aset of (cid:2)common(cid:1) terion (AIC) (Burnham & Anderson, 1998). The AIC is
geneticandenvironmentalfactorsand,inaddition,that foundedonideasfrominformationtheoryandprovides
the variation unique to each four measures is also in- a goodness-of-fit measure that penalises models for
fluencedby(cid:2)unique(cid:1)geneticandenvironmentalfactors. increasingcomplexityandcanbeusedwithnon-nested
This model assumes that each informant reports in- models. When comparing two models, the model with
formation that is shared or common with other in- the lowest AIC value is selected as the best fitting
formants,butalsoreportsunique,valuableinformation model.Thethirdmodel-selectionstatisticwastheRoot
for which genetic and environmental factors can be MeanSquareErrorofApproximation(RMSEA)whichis
estimated. an index of the model discrepancy, per degree of free-
In contrast to the biometric model, the psychometric dom, from the observed covariance structure (MacCal-
model (Figure2) posits that genetic and environmental lum,Browne,&Sugawara,1996).ARMSEAoflessthan
factors influencing variation common to the four or equal to .06 indicates a good fitting model (Hu &
measures do not influence the measures directly; ra- Bentler,1999).Thefourthmodel-selectionstatisticwas
ther, they influence each of the four measures of anti- the Bayesian Information Criterion (BIC), where in-
socialbehaviourviaalatentfactor(Figure 2).Thisfactor creasingly negative values correspond to increasingly
represents the underlying common variation between better-fitting models. In comparing two models, differ-
informants that indexes individual differences in the ences of BIC between 6 and 10 give strong evidence in
liabilitytoantisocialbehaviourthatispervasiveacross favour of the model with the smaller value (Raftery,
836 LouiseArseneaultetal.
(cid:1)examiner-observers,andchildren’sself-reports.Measuredvariablesares;theupperpart(abovethemeasuredvariables)containstheeffectsonthecommonsharedenvironment(C1),andcommonnon-sharedenvironmentc(cid:1)ceuniquetoeachinformant;forexamplewiththemothersratings–uniqueuniquenon-sharedenvironment(E1).Single-headedpathsaretheeffectsm(cid:1)mmongeneticfactoronmothersratingsofchildren’santisocialbehaviour).andardbiometricgenetictheory,thatMZtwinstotallysharebothcommonanduniquegeneticinfluences(.5),andthatbothMZandDZtwinsr¼latentvariableswerefixedtooneformodelidentification
(cid:1)eachers,twopartrtwin1),hevarian1),andmofthecoate,bystcommoncesofall
(cid:1)Figure1Thebiometricmodelforantisocialbehaviouraccordingtomothers,tdepictedbyrectanglesandlatentvariablesbycircles.Themodelisseparatedintovariancecommontoallfourinformants–commongenetic(A1,wherestandsfo1c(E1)–andthelowerpart(belowthemeasuredvariables)containstheeffectsontc(cid:1)genetic(A1wheremstandsformothersreport),uniquesharedenvironment(Cmoflatentvariablesonmeasuredvariables(e.g.,thepathacrepresentstheeffectmDouble-headedpathsrepresentcorrelationsbetweenlatentvariableswhichindicanduniquegeneticinfluences(1.0),thatDZtwinsshareonlyhalfofbothr¼completelysharecommonanduniqueshared-environmentalinfluences.Varian
Antisocialbehaviourin5-year-oldtwins 837
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Antisocialbehaviourin5-year-oldtwins 839
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adsutlinlaffbnefoirdaueasnrrecddaeinsseetisidsntoiamcmlilaaefltaoebnuserlhtemhavvaeeiltaossucuorafrnseacsnabwtleeisistoh.gcieTinnaolesrcebaoxelr.ihrsTeaecvodtiopfutoroorr,vstiwhdexeee ehaviour F .39(.29–.50)1.50(2.72)251
b
pr(BeopiprrtuehlsaeStnitotasnti,sthtwiecesp,wr1oep9igo9hr6tt)ie.odnStaorulflcystoutuarntaiglstemiqcsuotahstoieornsthimenostdhaeemlliUpnlKge ntisocial (cid:1)rsreport /1116 DZ .32 .35(.28–.43) 2.17(3.42)494 M .32(.21–.44)2.83(3.90)243
(pNreoaglrea,m1s99M7)plwuesre(Musuetdhe´tnoa&naMlyustehde´ant,a1fo9r98th)isansdtudMyx. portsofa Examine 1103 F .57(.50–.65)1.68(3.03)314
e
RDTotwfweeessiaenucnnrlitrtpseiMstsoieZvcmeitabwrleliansbnuseclhtwesaewvriaoesussruebe(Tsnataabnclertoi1asl)s,.arCallonfrgoriuenlrgamtfiroeonamssu.br4ee3s- ndchildren’sself-r MZ .63 .61(.56–.66) 2.27(3.50)609 FM .36.65(.25–.47)(.58–.72)3.782.89(5.88)(3.84)240295 eIntervals.variables.
Cfm(.fsfr7ecoooco9arrmttlerhec(edfsheloa.ri(r2lsTtbdi4(cid:1)ayotrebernaen(lstfepch’osbhor1eeres)tt.recswsslTh)fq(cid:1)e.-hiurelreTednaephrpsrDeeoieogn-rsZrrnt’etsossitfiowoctcsoiofanefratnlsrfrnaet-awtnrlcnaieetsotpsiroisrfeoocroorneimrcatlsmisalao)tbaldwioteetbeinhosoretsaneh,v.fra3ionaoov5ronufiotbgru(ot)ifanhrtotfh)oger-. (cid:1)aminer-observers,a (cid:1)Teachersreport 1035/1116 DZ .27 .31(.23–.39) 5.22(7.81)470 FM .74.27(.69–.79)(.15–.39)4.516.67(7.89)(9.16)287230 male;CIConfidenc¼antisocialbehaviour
MabgeZgtwraeengeadntiDtohnZeftMowrZianansntiedssoDtcaZibalclisobhrerehedalavttihiooauntrs.thpTerhoreevdidiisfefdefarremonuiclgieahsl (cid:1)chers,ex MZ .79 .78(.75–.81) 6.06(10.11)565 M .83(.79–.86)7.65(11.76)278 e;FFe¼nsformed
edwcwtohmisoneaoiaetrtrvnsinhriiminv,eotiaenladusaapdl-iutrtvppei.egbioarsdfgoynTfiueeroxhsccfaficeottmalotisrlnhrdaciermigeiutsefaeflplhsclearayuoheretterriflibtnotminwestangaccteni.aecbta2cesnAeis(edltrsiuititnhayoiMrsnleefhcoZaohotcsf—hdwfihialzeddarinenlinrddtDtieoiit-nivsniZfsfsefop’T)etsocheragwcieaceabeihnainnllfiDleectebcirebZts1eeieohce,bchcrnateoeiahvtviarfvsweiifrlroieeeooMtbuclheunaetrnZser---, (cid:1)ndDZtwinsformothers,tea (cid:1)Mothersreport 1109/1116 DZ .35 .38(.30–.46) 15.84(11.58)498 FMF .70.41.34(.64–.75)(.31–.52)(.24–.45)14.0217.7413.93(10.20)(12.77)(9.88)315248250 Dizygotictwinpairs;MMal¼SD)arereportedfortheuntra
ttsrhwoeneminmbMcelaZonnrtrtcaewellaiinanttiflotcrunoiebrtnruhecteleaaosbtr.ileoetnitcoaalnglyednre1etp.i0cr,easbenendctassuhasalelrettwdhieennMrveiZ-- weenMZa MZ .67 .70(.66–.74) 15.31(11.25)611 M .70(.64–.76)16.66(12.12)296 airs;DZ¼deviation(
lfssswSeooimiivzulWctveeahriaslaelsl,mlp(2o(dobrre0b)afeea0svnahsae1isguaorn)sivvru.ntoeieDisogcssdueio,afrscrrsmtep,oieiasgimdaretlneelaeiwbg.rfis3aeceicct0rhhxhahdanidtllv(oetdtiMihfsorf.sseeoe3uesrnf6rxofiee)fnt,e(stdTcx,zweieahyfxhsfCbgiebiodliarceniseishtfine1pmftedyic)is.r,e.eeamscAnTRnocohcuinnlrreeetoesstvsiemeesaswrflt,efnseeeaatnrco&inle-ttfl Table1Correlationsbet Informantspairs/MaximumpairsN foruntransformedrantisocialbehaviour(95%CI)forsquarerootrantisocialbehaviour M*(SD)pairsN (95%CI)forsquarerootrantisocialbehaviourM(SD)pairsN MZMonozygotictwinp¼*Means(M)andstandard
840 LouiseArseneaultetal.
antisocial behaviour, there were no significant sex of the variance in children’s antisocial behaviour.
differences in the MZ or DZ twin correlations (as Child-specific environmental effects (plus measure-
shownbytheoverlappingconfidenceintervalsofthe ment error) accounted for between 24% and 39% of
correlations). This suggests that genetic and en- the variance in mother, teacher, and examiner re-
vironmentalinfluencesonantisocialbehaviourwere ports of antisocial behaviour. Child-specific envi-
of the same magnitude for the two sexes. ronmental effects accounted for 58% of the variance
in children’s self-reports of their antisocial beha-
viour, possibly reflecting the greater measurement
Univariate model fitting
errorinherentin5-year-oldchildren’sself-reportsor
Looking at each informant’s report separately, children’s awareness of their own behaviour across
model-fitting revealed three noteworthy results settings.
(Table 2). First, it revealed the important contribu-
tionofgeneticfactorstoantisocialbehaviouramong
Correlations among the four informants
5-year-old children. Second, it revealed the signifi-
and multivariate model fitting
cant contribution of child-specific environmental
experiencestochildren’santisocialbehaviour.Third, The correlations between the four measures of anti-
it revealed only a small contribution of shared- social behaviour ranged from .14 to .28 (Table 3).
environmental experiences to children’s antisocial These correlations may seem low but are in keeping
behaviour accounting for a maximum of 12% of the with cross-informant correlations reported in previ-
variation,whichwouldrequireamuchlargersample ous studies of children’s psychiatric problems
for statistical significance. For all four measures of (Achenbach, McConaughy, & Howell, 1987; van der
antisocialbehaviour,moreparsimoniousAEmodels Ende, 1999); meta-analyses reveal that the mean
providedabetterfittothedatathanfullACEmodels. parent-teacher correlation is .28 (we report .28) and
BecauseDZtwincorrelationswerelessthanhalfthe the mean correlation between subjects themselves
MZ twin correlations for the teachers(cid:1) reports, we and adult informants is .22 (we report between .18
also fitted a model testing a dominance factor (ADE and.21).Aprincipalaxisfactoranalysisyieldedone
model) and a sibling interaction-effects model (AE factor,witheigenvaluesgreaterthan1.0,suggesting
s
model), but these models did not fit any better than that each of the four measures is a valid, but im-
theAEmodel.Dependingonthemeasureexamined, perfectindicatorofaliabilitytoantisocialbehaviour.
genetic influences accounted for between 42% (in Fit statistics appeared to be affected by our large
children’sself-reports)and69%(inmothers(cid:1)reports) sample size. However, the psychometric model had
Table2 Univariateestimatesofgeneticandenvironmentalcontributionstoantisocialbehaviourformothers(cid:1),teachers(cid:1),examiner-
observers(cid:1)andchildren’sself-reports
Variancecomponents
Genetic Environmental Overallmodelfit Modeldifferencetest
Shared Non-shared
h2(95%CI) c2(95%CI) e2(95%CI) v2 df p AIC RMSEA BIC Dv2 Ddf p
Mother-report(N¼1109)
ACE .61(.46–.72) .08(.00–.22) .31(.27–.35) .840 3 .840 )5.160 .000 )20.194
AE .69(.66–.73) .31(.27–.34) 1.922 4 .750 )6.078 .000 )26.123 1.081 1 .298
Teacher-report(N¼1035)
ACE .76(.64–.79) .00(.00–.11) .24(.22–.28) 16.674 3 .001 10.674 .094 )4.152
AE .76(.72–.79) .24(.22–.28) 16.674 4 .002 8.674 .078 )11.095 .000 1 1
ADE(cid:3) .62(.27–.78) .13(.00–.49) .24(.21–.28) 16.076 3 .001 10.076 .092 )4.750
AEz .75(.69–.80) .01(–.04–.06) .25(.20–.31) 16.599 3 .001 10.599 .078 )4.227
s
Examiner-report(N¼1103)
ACE .48(.32–.64) .12(.00–.27) .40(.35–.45) .836 3 .841 )5.164 .000 )20.181
AE .61(.56–.65) .39(.35–.44) 2.970 4 .563 )5.030 .006 )25.053 2.134 1 .144
Child-report(N¼901)
ACE .30(.08–.47) .10(.00–.29) .59(.53–.67) 3.037 3 .386 )2.963 .009 )17.374
AE .42(.35–.48) .58(.52–.65) 4.079 4 .395 )3.921 .007 )23.135 1.042 1 .307
Nreferstothenumberoftwinpairswithcompletedata.
AIC¼Akaike’sInformationCriterion;RMSEA¼RootMeanSquareErrorofApproximation;BIC¼BayesianInformationCriterion.
CI¼ConfidenceIntervals.
A¼h2¼genetic variance component; C¼c2¼shared environment variance component; E¼e2¼non-shared environment
variance;D¼d2¼dominancevariancecomponent;s¼siblinginteractioneffect
(cid:3)Deffectisinthesecondcolumn;(cid:4)seffectisinthesecondcolumn.
Antisocialbehaviourin5-year-oldtwins 841
Table3 Internal consistency reliability and correlations be- viour, representing the consensus among inform-
tweenmothers(cid:1),teachers(cid:1),examiner-observers(cid:1),andchildren’s ants,wasinfluencedbygeneticfactors(82%)andby
self-reportsofantisocialbehaviour
child-specific environmental factors (18%). In addi-
Informant tion, the variation in children’s antisocial behaviour
that was uniquely reported by each informant was
Mothers Teachers Examiners Children notsimplymeasurementerror;rather,itrepresented
Mothers .92 meaningful variation between children and it reflec-
Teachers .28 .95 ted the influence of unique genetic factors (ranging
Examiners .14 .21 .90 from 28% of the total variance of children’s self-re-
Children .18 .21 .20 .82
portsto51%ofthetotalvarianceofmothers(cid:1)reports;
Table 5, 7th row) and unique child-specific environ-
Internal consistency reliability coefficients (Cronbach alpha)
arereportedinthediagonal. mental factors including measurement error (ran-
Nchildren¼1680. ging from 56% for children’s self-reports to 20% for
teachers(cid:1) reports; Table 5, 8th row). Parallels be-
the best overall combination of fit to the data and tween common and unique genetic estimates were
parsimony (Table 4). The biometric model had a established by comparing genetic estimates of the
smaller chi-square and AIC than the other models, common latent factor (82%) to the proportion of the
and an equivalent RMSEA. However, it is not as unique variance accounted for by genetic factors,
parsimoniousasthepsychometricmodelwhichalso ranging from 33% (for the children’s self-reports) to
showedafairfittothedataasindicatedbyallmodel- 71% (for the teacher’s reports) (Table 5, 9th row).
selection statistics, and especially by the large neg- This comparison shows that genetic factors greatly
ative BIC (the difference in BIC between the two influencedvariationinantisocialbehaviourthatwas
models was greater than 10). Next, we tested common to the four informants, and also variation
whether the full psychometric model could be sim- thatwasspecifictoeachinformant(toalesserextent
plified by eliminating various parameters of the the children’s reports). Further sensitivity analyses
model. Table 4 shows that dropping the common C were conducted by re-estimating the psychometric
parameter and the four unique C parameters resul- model using raw data rather than standardised,
ted in the most parsimonious model. transformed variables; the results were unchanged
The results of the best-fitting model are shown in (these results are available upon request, as are all
Table 5. The best-fitting psychometric model analysis scripts).
revealed that children’s antisocial behaviour was When looking back at each informant’s report
reliably measured by four different informants, as separately, we observed no significant sex
indicated by factor loadings on each of the four differences in the twin correlations (Table 1).
measures ranging from .39 to .56 (Table 5, 1st row; Nevertheless, we tested a sex-specific version of the
21% of the variance of the measures was explained best-fitting multivariate model (Neale et al., 1992).
by a common latent factor). The variation in the The sex-specific version fit better than a sex-inde-
common latent factor of children’s antisocial beha- pendentmodel(v2 ¼ 80.74,df ¼ 59,p ¼ .03).Closer
Table4 Fit indices of multivariate models for mothers(cid:1), teachers(cid:1), examiner-observers(cid:1), and children’s self-reports of antisocial
behaviour
Overallmodelfit Modeldifferencetest
v2 df p AIC RMSEA BIC Dv2 Ddf p
Overallmodel
Biometricmodel 72.497 48 .013 )23.503 .035 )250.706
Psychometricmodel 92.489 54 .001 )15.511 .040 )271.115
Raterbiasmodel 161.843 58 .000 43.843 .065 )228.694 69.354a 4 .000
Simplificationofbestfittingmodelb
NocommonA 121.123 55 .000 11.123 .053 )249.214 28.634 1 .000
NocommonC 92.489 55 .001 )17.511 .040 )277.848 .000 1 1
NouniqueAsc 161.843 58 .000 45.843 .065 )228.694 69.354 4 .000
NouniqueCs 99.012 58 .001 )16.988 .040 )291.525 6.523 4 .163
NocommonA+nouniqueAs 243.133 59 .000 125.133 .085 )154.138 150.644 5 .000
NocommonC+nouniqueCs 99.012 59 .001 )18.988 .039 )298.259 6.523 5 .259
N¼840twinpairswithcompletedataonalldifferentdatasources.
A¼geneticvariancecomponent;C¼sharedenvironmentvariancecomponent.
AIC¼Akaike’sInformationCriterion;RMSEA¼RootMeanSquareErrorofApproximation;BIC¼BayesianInformationCriterion.
aThechi-squaredifferenceisbetweenthepsychometricandtheraterbiasmodels.
bThefullpsychometricmodelisthecomparisonmodel.
cThisconstrainedmodelistheequivalentoftheraterbiasmodel.