J. Child Psychol. Psychiat. Vol. 39, No. 8, pp. 1153–1158, 1998

Cambridge University Press

' 1998 Association for Child Psychology and Psychiatry

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Life Events and Depressive Symptoms in Childhood—Shared Genes orShared Adversity? A Research Note

Anita Thapar

University of Manchester, Royal Manchester Children’s Hospital, U.K.

Gordon Harold Peter McGuffin

University of Wales, Cardiff, U.K. University of Wales College of Medicine, Cardiff, U.K.

A twin study design was used to examine to what extent genetic and environmental factorsmediate the association between life events and depressive symptoms. Questionnairemeasures (maternally rated) of depressive symptoms and life events were obtained for asystematically ascertained sample of 270 twin pairs aged 8 to 17 years. Bivariate geneticmodel fitting showed that depressive symptoms and some life events (total events, negativeimpact) share a common genetic influence. The covariation of independent life events anddepressive symptoms was explained by a shared environmental influence common to both.At least part of the association between life events and depressive symptoms is mediated byfamilial factors that include both genes and shared environment.

Keywords: Depression, twins, life events, genetics.

Abbreviations: DZ: dizygotic ; MZ: monozygotic.

Introduction

There is now a large volume of research documentingan association between life events and the development ofdepressive symptoms and disorder in childhood(Goodyer, 1995; Gotlib & Hammen, 1992). However, todate, little is known about the mechanisms that mightexplain this association. Much research has focused onthe role of behavioural contingencies such as changes inthe affective quality of the parent–child relationship, forexample following disturbances in the marital relation-ship, while also highlighting the role of children’s cog-nitive appraisals of events in accounting for the de-velopment of depressive symptoms (Erel & Burman,1995; Grych & Fincham, 1990). It has also been notedthat vulnerable children may contribute to the generationof stressful events and that life events may thus be aconsequence as well as a cause of psychopathology(Gotlib & Hammen, 1992). However, the relative lack oflongitudinal data and the failure to take into consider-ation the role of other contributory factors, such asgenetic vulnerability, has limited the ‘‘causal ’’ con-clusions that may be derived from this body of research.Indeed, despite an overall increased interest in genetic

Requests for reprints to: Anita Thapar, Department of Childand Adolescent Psychiatry, University of Manchester, RoyalManchester Children’s Hospital, Pendlebury, Manchester,M27 4HA, U.K.

studies in childhood, the role of familial and geneticfactors in mediating the link between childhood lifeevents and the onset of depressive symptoms has untilrecently been relatively unexplored.

Over the last few years, considerable evidence hasaccumulated suggesting that genetic factors influencemeasures of environment such as parental warmth andsocial support (Plomin, 1994). There have now beenseveral twin studies in which reported life events havebeen found to be heritable in adult life (Kendler, Neale,Kessler, Heath, & Eaves, 1993a; Plomin, 1994) as well asin childhood (Thapar & McGuffin, 1996). Similarly thereis much to suggest the importance of genetic influences ondepressive symptoms (Eaves et al., 1997; Edelbrock,Rende, Plomin, & Thompson 1995; Rende, Plomin,Reiss, & Hetherington, 1993; Thapar & McGuffin, 1994).These findings raise a number of questions. First, giventhat life events have consistently been shown to beassociated with depression (Brown & Harris, 1989), canthis observation be explained by a common risk factor?Second, given that life events (Kendler et al., 1993a;Plomin, 1994; Thapar & McGuffin, 1996) and depressivesymptoms and disorder (Eaves et al., 1997; Edelbrock etal., 1995; Kendler, Neale, Kessler, Heath, & Eaves, 1992;McGuffin, Katz, Rutherford, & Watkins, 1996; Rende etal., 1993; Thapar & McGuffin, 1994) have been shown tobe heritable, is it possible that the association is mediated,at least in part, by genes?

Although the relationship between environmental riskfactors such as life events and psychopathology has longbeen recognised, it is only recently that the possible role

1153

1154 A. THAPAR et al.

of familial and genetic factors in mediating this as-sociation has been acknowledged (Plomin, 1994). In astudy of depressed adult patients, life events and clinicaldepression were found to cluster in the same families(McGuffin, Katz, Aldrich, & Bebbington, 1988). Thisfinding suggested that life events and depression sharecommon family risk factors, which could be genetic orenvironmental.

Twin studies allow us to disentangle the effects of genesand shared environmental factors. More recently multi-variate genetic analysis (Neale & Cardon, 1992; Plomin,1994) using a twin study design has been used to examineto what extent the association between environmentalrisk factors and psychopathology can be explained bycommon genetic and environmental factors.

In this paper we present the results of a bivariategenetic analyses of twin data on life events and depressivesymptoms. We hypothesised that :

(1) Genetic factors that influence life events are thesame as those that influence depressive symptoms.

(2) Genetic factors mediate the association betweenreported life events and depressive symptoms.

Method

Sample

All twins born to residents of South Glamorgan between1976 and 1984 were identified from a database of birth records,the Cardiff Births Survey (Andrews, Davies, Chalmers, &Campbell, 1986). The twins were subsequently traced (411 of457 pairs) and then contacted by post.

The characteristics of this twin sample have been describedpreviously (Thapar & McGuffin, 1996). In this phase of thestudy, questionnaires were sent to the 376 families who could betraced and who had not refused to participate.

Measures

Families were sent the questionnaires and two reminders bypost. Depressive symptoms were rated using the Mood andFeelings Questionnaire (Costello & Angold, 1988), a ques-tionnaire specifically designed for children, which has beenshown to be reliable and valid (Wood, Kroll, Moore, &Harrington, 1995). A modified 35-item version of the LifeEvents Checklist (Johnson & McCutcheon, 1980) was used torate life events. This questionnaire allows respondents toendorse whether or not an event has occurred in the last yearand then rate the impact of the event. Maternal ratings wereused to obtain three types of life event ratings. The sum of thetotal number of events over the last year was used to derive atotal life event score. An independent event score was obtainedby only including events that were judged to be independent ofthe person’s symptoms or immediate behaviour. This wouldinclude events such as death of a relative, which would notordinarily be expected to be a consequence of a person’s mentalstate or behaviour. Finally a score for the negative impact of lifeevents was obtained by summing the impact scores that wererated as negative.

The Twin Similarity Questionnaire, which has been shown tohave over 90% accuracy, was used to distinguish betweenmonozygotic and dizygotic twins (Cohen, Dibble, Grawe, &Pollin, 1975). Completed maternal ratings of these question-naires for both twins were available for 109 monozygotic twinsand 161 same-sex and opposite-sex dizygotic twins.

Figure 1. Bivariate model. A¯ additive genes; C¯ sharedenvironment; E¯nonshared environment; ra¯ genetic cor-relation; rc¯ shared environment correlation; re¯nonshared

environment correlation.

Genetic Analysis

The total phenotypic variation of a trait can be decomposedinto variance due to additive genes (h#, heritability), sharedenvironment (c#) (environmental influences shared by familymembers, e.g. social adversity), and nonshared environment(e#). The basis of the twin design is that identical (monozygotic ;MZ) twins are genetically identical whereas nonidentical twins(dizygotic ; DZ) twins share on average 50% of their genes incommon. MZ and DZ twin correlations can then be expressedas follows:

rmz¯h#­c#

rdz¯ "

#h#­c#

Thus for a genetically influenced trait we would expect MZtwins to be more similar for that trait than DZ twins. Twin dataare most commonly analysed using this type of univariategenetic analysis, in which the genetic and environmentalcontribution to the total variation for a given trait is estimated(Neale & Cardon, 1992). Model fitting allows us then toexamine further how well the observed data are explained by afull model incorporating additive genetic (A), common en-vironmental (C), and nonshared environmental (E) factors(ACE model). The goodness of fit of the model is assessed usinga chi-square goodness of fit with a smaller chi-square valueindicating a better fit, and the degrees of freedom are estimatedby subtracting the number of estimated parameters from thenumber of observed statistics. The fit of reduced models wheretwin resemblance is explained only by additive genes (AEmodel), by common environmental factors (CE), and a modelof ‘‘no familial transmission’’ (E), is then compared with the fitof the full model using likelihood ratio chi-square tests. Theaccepted model is then chosen on the basis of goodness of fit andparsimony.

In bivariate genetic analysis (Neale & Cardon, 1992) we havedata for two observed phenotypes and examine to what extentgenetic and environmental factors account for the covariationof these two measures, i.e. life event scores and depressivesymptoms. That is, the cross covariance for trait A and trait Bcan be decomposed into genetic and environmental componentsin the same way as described for univariate genetic analysis.

Thus if genetic factors mediate the correlation between twotraits, we would expect cross-trait correlations for MZ twins tobe greater than those for DZ twins.

Raw scores (x) were transformed to approximate normalityusing f(x)¯ ln (1­x) and genetic models were fitted tocovariance matrices with the program Mx (Neale, 1991) usingmaximum likelihood estimation procedures. We first fitted a fullbivariate ‘‘correlated factors ’’ genetic model (Loehlin, 1996;Neale & Cardon, 1992), which is shown in Fig. 1. This full

1155LIFE EVENTS AND DEPRESSIVE SYMPTOMS IN CHILDHOOD

Table 1Bivariate Model Fitting for Life Events and Depression Scores

Lifeevents Depression r

arc

re

χ# df p

Total events1. ACE ACE fr fr fr 6±20 11 ±8602. ACE CE – fr fr 28±65 13 ±0073. CE ACE – fr fr 22±43 13 ±0494. ACE AE fr – fr 18±23 13 ±1495. AE ACE fr – fr 59±93 13 ±0006. ACE ACE [1] fr fr 6±20 12 ±9067. ACE ACE [0] fr fr 19±96 12 ±0688. ACE ACE [1] [1] fr 17±48 13 ±1789. ACE ACE [1] [0] fr 10±89 13 ±620

10. ACE ACE [1] fr [1] 11684 13 ±00011. *ACE ACE [1] fr [0] 7±34 13 ±884

Independent events1. ACE ACE fr fr fr 7±00 11 ±7992. ACE CE – fr fr 15±49 13 ±2773. CE ACE – fr fr 13±91 13 ±3804. ACE AE fr – fr 24±24 13 ±0295. AE ACE fr – fr 108±90 13 ±0006. ACE ACE [1] fr fr 13±90 12 ±3077. ACE ACE [0] fr fr 7±00 12 ±8578. ACE ACE [0] [1] fr 17±26 13 ±1889. ACE ACE [0] [0] fr 18±27 13 ±148

10. ACE ACE [0] fr [1] 5324 13 ±00011. *ACE ACE [0] fr [0] 7±01 13 ±901

Negative impact1. ACE ACE fr fr fr 14±23 11 ±2202. ACE CE – fr fr 40±36 13 ±0003. CE ACE – fr fr 52±40 13 ±0004. ACE AE fr – fr 25±90 13 ±0185. AE ACE fr – fr 28±21 13 ±0086. ACE ACE [1] fr fr 16±18 12 ±1837. ACE ACE [0] fr fr 36±60 12 ±0008. ACE ACE [1] [1] fr 29±59 13 ±0059. ACE ACE [1] 0 fr 18±69 13 ±133

10. ACE ACE [1] [0] [1] 9777 14 ±00011. *ACE ACE [1] [0] [0] 18±69 14 ±177

*¯ accepted model.A¯ additive genes; C¯ shared environment; E¯nonshared environment.ra¯ additive genetic correlation; r

c¯ shared environment correlation; r

e¯nonshared

environment correlation.fr¯parameter freed to take on any value.[0]¯parameter set at 0; [1]¯parameter set at 1.

model incorporates genetic influences (A), shared environ-mental (C), and nonshared environmental (E) factors thatinfluence depressive symptoms and life events. The model alsoincludes correlations between the genetic and environmentalfactors for depressive symptoms and those influencing lifeevents ; that is, a genetic correlation r

a, a shared environmental

correlation rc, and a nonshared environment correlation r

e.

Thus it can be seen that we are decomposing the covariationbetween depressive symptoms and life events into a geneticcomponent and environmental components. In the full modelthese correlations are freed to take on any value between 0 and1. We then fitted nested, reduced models in which genetic andenvironmental factors were dropped in turn and the correlationacross the factors were set at 0 or 1. The goodness of fit ofeach reduced model was compared to the fit of the full modelusing likelihood ratio chi-square tests, that is, subtracting thedifferences in chi-square goodness of fit. This difference is

asymptotically a chi-square distribution with the degrees offreedom (df ) equal to the df of the reduced model ®df of the fullmodel. The model accepted as providing the most satisfactoryfit was chosen on the basis of goodness of fit and parsimony ofthe model.

Results

Table 1 shows the results of the model fitting. Cor-relation matrices for MZ twins and DZ twins have beenincluded in the Appendix (correlation rather than co-variance matrices have been shown for ease of interpret-ation). It can be seen from Table 1 that the full model(Model 1) gives a reasonably good fit for the data on totallife events and depression scores [χ#(11)¯ 6±20, p¯±86].

1156 A. THAPAR et al.

We then fitted models in which the genetic factors weredropped in turn (Models 2 and 3) and then the sharedenvironmental factors were dropped (Models 4 and 5).These factors could not be dropped without a significantdeterioration in the chi-square goodness of fit. Thus theA, C, and E factors were all included and we then fittedmodels in which the genetic and environment correlationswere fixed in turn at 1 or 0 (Models 6 to 11). It can be seenthat r

acan be set at 1 without a significant change in fit

(Model 6) but rccould not be set to 1 or 0 (Models 8 and

9). The fit does not worsen when reis also set to 0 (Model

11). Thus on the grounds of goodness of fit and parsimonywe accept Model 11, which includes additive genes,shared and nonshared environment, a genetic correlationof 1, and a shared environment correlation of ±30.

We tested models in the same way for independentevents and depression scores and these results are alsoshown in Table 1. It can be seen that once again theadditive genetic (A), shared environment (C), and non-shared environment components cannot be droppedwithout a significant deterioration in fit (Models 2 to 5).The additive genetic correlation r

aand the nonshared

environment correlation re

can be set at 0 without asignificant change in the goodness of fit. Thus the mostacceptable model (11) is one which includes additivegenes, shared, and nonshared environment, where theonly correlation between factors is a modest correlationbetween the shared environmental factors (r

c¯±31).

It can be seen from Table 1 that the most acceptablemodel for negative impact and depression scores (Model11) is an ACE model where the correlation between thegenetic factors is 1.

Discussion

In summary, the results of our bivariate geneticanalyses suggest that for certain types of life events,namely total events and negative impact scores, the samegenes that influence reported life events also influencedepressive symptoms. Our results should be regarded aspreliminary evidence given certain limitations. First,although the twins were systematically ascertained, thesample size is small, which gives us limited power todistinguish between competing models. Second, we haveused parent-rated questionnaire measures of both de-pressive symptoms and life events, rather than directstandardised interviews. Nevertheless the finding that theassociation of some types of life events and depressivesymptoms is explained by a common genetic influence isan important one, which now needs replication in a largerstudy. These findings suggest that those genes thatinfluence depressive symptoms are the same ones thatinfluence reported life events. This common geneticinfluence may be mediated indirectly, for example byeffects on personality that we know to be heritable (Eaves,Eysenck, & Martin, 1989). This needs to be exploredfurther in longitudinal studies, particularly as there isevidence to suggest that some types of personality traitsare associated both with reported life events (Fergusson& Horwood, 1987; Poulton & Andrews, 1992; Magnus,Diener, Fujita, & Pavot, 1993) and depressive symptoms(Block, Gjerde, & Block, 1991). Our previous findings

suggested that individual perceptions are an importantinfluence in reporting life events (Thapar & McGuffin,1996) and in predicting responses to adversity (Harold &Conger, 1997). Thus another possibility is that genesinfluence a cognitive style, which in turn has a commoninfluence on reported life events and depressivesymptoms. This is less likely to be so in the present study,where maternal ratings, rather than the subjects’ ownquestionnaire responses, were analysed.

A correlation between the shared environmentalfactors for total life events and depression was alsofound, which suggests that a small component of thecovariation is explained by shared adversity, but themagnitude of this was small.

For independent life events and depression the findingswere somewhat different, in that covariation wasexplained by a shared environmental factor common toboth. This suggests that the association between in-dependent life events and depressive symptoms ismediated via familial adversity rather than by genes.However, given that these analyses are based on parentalratings, we cannot rule out the possibility of rater biascontributing to the shared environment component. In aprevious analysis (Thapar & McGuffin, 1996), we foundthat self-reported life events in adolescents appeared to bemore strongly influenced by genes than parental ratingsof the same events. Unfortunately our present sample istoo small to carry out a separate bivariate analysis of self-report data.

The association between life events and depression isusually viewed in terms of cause and effect but, as hasfrequently been pointed out, events could also begenerated as a consequence of psychopathology(Hammen, 1991). For this reason, Brown and Harris(1978) have emphasised the importance of a distinctionbetween dependent events arising from the subject’s ownactions and independent events over which the subject isunlikely to have any control. The distinction is probablymade more finely by an interview measure such as the LifeEvents and Difficulties Schedule (Brown & Harris, 1978)than by a questionnaire, as employed here. Neverthelessour findings would, to some extent, be in keeping with theconventional view in that it is independent events thatshow an environmentally mediated association withdepressive symptoms. On the other hand, the familystudy of McGuffin et al. (1988) concentrated specificallyon independent events and found them, as in the presentstudy, to show familiality but to be only weaklyassociated with depression in the relatives of depressedprobands. The very fact that supposedly independentevents show familiality therefore challenges the con-ventional view that such events are random misfortunesor ‘‘acts of God’’.

In conclusion, our findings are novel but need to bereplicated using a larger sample size with other measures,which ideally should include a child-based interviewmeasure of life events. It will also be important in futuregenetic studies to incorporate measures of possiblemediating traits such as personality and cognitive style.For example, neuroticism may in part mediate theinfluence of genes on clinical depression in adults(Kendler, Neale, Kessler, Heath, & Eaves, 1993b). Itremains to be seen what sorts of heritable traits account

1157LIFE EVENTS AND DEPRESSIVE SYMPTOMS IN CHILDHOOD

for the common mediating influence of genes on bothreported life events and depression and whether thesegenes can be identified.

Acknowledgements–Peter Lewis and Jackie Bethel, Depart-ment of Medical Computing, University of Wales College ofMedicine assisted in using the Cardiff Births Survey. We thankMichael Rutter, Institute of Psychiatry, for his advice. Thiswork was carried out whilst Anita Thapar was supported by aResearch Training Fellowship from the Medical ResearchCouncil (U.K.).

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Manuscript accepted 25 February 1998

1158 A. THAPAR et al.

Appendix: Correlation Matrices (and Standard Deviations)

Total Number of Events and Depression Scores Independent Life Events and Depression Scores

TOT1 DEP1 TOT2 DEP2 IND1 DEP1 IND2 DEP2

MZ twins MZ twinsTOT1 1±000 IND1 1±000DEP1 ±301 1±000 DEP1 ±202 1±000TOT2 ±838 ±295 1±000 IND2 ±921 ±220 1±000DEP2 ±365 ±730 ±316 1±000 DEP2 ±253 ±730 ±281 1±000

SD ±644 ±852 ±672 ±869 SD ±484 ±852 ±477 ±869

DZ twins DZ twinsTOT1 1±000 IND1 1±000DEP1 ±440 1±000 DEP1 ±254 1±000TOT2 ±737 ±290 1±000 IND2 ±874 ±303 1±000DEP2 ±315 ±464 ±371 1±000 DEP2 ±058 ±464 ±056 1±000

SD ±645 ±907 ±616 ±920 SD ±431 ±907 ±451 ±920

Negative Impact Scores and Depression Scores

NIM1 DEP1 NIM2 DEP2

MZ twinsNIM1 1±000DEP1 ±414 1±000NIM2 ±850 ±465 1±000DEP2 ±435 ±730 ±431 1±000

SD ±797 ±852 ±740 ±869

DZ twinsNIM1 1±000DEP1 ±569 1±000NIM2 ±602 ±261 1±000DEP2 ±209 ±464 ±331 1±000

SD ±744 ±907 ±783 ±920