Division of Psychological Medicine, University of Wales College of Medicine, Heath Park, Cardiff
Institute of Psychiatry, King's College London, De Crespigny Park, London, UK
Correspondence: Dr Neilson Martin, The Wellcome Trust Centre for Human Genetics, Henry Wellcome Building of Genomic Medicine, Roosevelt Drive, Oxford OX3 7BN, UK
Declaration of interest Support from the Medical Research Council.
1 For 12 families, the twins replied, but not the parents; these 12 are
excluded from the analyses presented here.
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ABSTRACT |
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Aims To use multiple informants to assess the extent to which observer effects influence such estimates in an epidemiological sample of twins.
Method Questionnaire packs were sent to the families and teachers of twins aged 5-16 years in the Bro Taf region of South Wales. The twins were ascertained from community paediatric registers.
Results Both parent- and teacher- rated data showed a high degree of heritability for ADHD measured as a symptom dimension, but the correlation between the two types of rater was modest. Bivariate analyses suggested that parent and teacher ratings reflect the effects of different genes. Self-report data from twins aged 11-16 years showed no evidence of genetic effects.
Conclusions Although ADHD is shown to be highly heritable by both parent- and teacher-rated data, the underlying genotypes may be substantially different. This has implications for study designs aiming to find genes that contribute to the disorder.
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INTRODUCTION |
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A reason for these differences may be the choice of rater used in each study. For example, it was found from a sample of male twins that teachers and mothers may rate differently (Sherman et al, 1997). Results from both raters suggested that ADHD was highly heritable, estimated at 89% for mothers and 73% for teachers. It has also been found that what appears to be sibling interaction contributes to heritability in maternal and paternal estimates, but not in teacher estimates (Eaves et al, 1997).
Differences between these ratings may be due to the environment in which the observations are made. The parents are more likely to compare twins with each other, and so may exaggerate differences and similarities. Teachers, on the other hand, can compare each twin with a large number of children of similar age, so ratings may be more objective. Twin confusion may also be a factor, in that a teacher might attribute behaviour to the wrong twin whereas parents would seldom mis-report their own children (Simonoff et al, 1998). Moreover, the twins might behave in a different manner at home and at school (perhaps owing to different situational manifestations of ADHD) so that the raters would truly be observing different behaviours. This suggests that evidence from either rater alone cannot be interpreted as conclusive.
In this study we assess the extent to which there is overlap between the parent-and teacher-rated observations using the same questionnaires, and compare both with results for self-report data in the subset of children aged 11-16 years.
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METHOD |
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An initial total of 3152 individual records were found, this was reduced to 2380 useable records after excluding those aged over 16 years, twins living apart, triplets or quadruplets, or where we were unable to trace the family. This yielded 1190 twin pairs, of whom 20 pairs were used for a pilot study to test the suitability of the questionnaire package. From the 1170 packages sent out in the first mailing, 61 were returned as wrongly addressed and thus 1109 families were left for the main study.
Measures
A six-item twin similarity questionnaire of demonstrated validity
(Thapar et al, 1995)
was used to assign zygosity to each twin pair. This approach has been shown to
have good agreement with zygosity tests using blood groups or other genetic
markers (McGuffin et al,
1994) and was used in the previous Cardiff twin study
(Thapar & McGuffin, 1994).
A short questionnaire adapted from Loehlin & Nichols
(1976) was included to assess
environmental sharing.
Symptoms of ADHD were measured using the abbreviated Conners scale (Conners, 1973) and the Strengths and Difficulties Questionnaire (SDQ) hyperactivity sub-scale (Goodman, 1997), and ratings were obtained from parents usually mothers and teachers. For the SDQ scale, self-reports were also collected in the adolescent sample (those aged 11-16 years).
Analyses
Exploratory analysis of the data was performed using SPSS
(SPSS, 1999). The raw scores
for both measures of ADHD were skewed with a floor effect,
whereby a high proportion of the sample have low scores. To achieve a closer
approximation to normality, the data were transformed by taking square
roots.
Variancecovariance matrices were obtained from the transformed data for monozygotic (MZ) and dizygotic (DZ) twins separately. These matrices were then used in the Mx package (Neale, 1997) to perform model-fitting.
First, univariate genetic models were tested for each type of rater
(parent, teacher and adolescent) and for each of the ADHD measures. These
analyses provided estimates of broad sense heritability and the extent of
contributions from genetic and environmental effects. Next, bivariate
modelling was performed to investigate to what extent phenotypes based on the
observations of parents and teachers were influenced by the same factors or by
different factors. In all cases, the full ACE model was
fitted to the data first: this tests for additive genetic effects
(A), common environmental effects (C) and non-shared
environmental effects (E). Models lacking one or more of these
parameters are then fitted to see whether or not they can explain the data
equally well. Where C can be dropped, it is then possible to test for
non-additive genetic effects (D) in the models. As sibling
interaction (i) has been found to be a contributing factor to the
variance in ADHD in previous studies
(Thapar et al, 1995; Silberg et al, 1996;
Eaves et al, 1997;
Nadder et al, 1998), it was also explored here. Nested models were compared using chi-squared
differences and Akaike's information criterion (AIC), where
AIC=2-(2 x d.f.)
(Neale & Cardon,
1992).
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RESULTS |
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Zygosity, age and gender
The distribution of zygosity and gender in the study population is shown in
Table 1. In total there were
278 MZ pairs (42%), 378 DZ pairs (56%) and 14 pairs in whom zygosity could not
be assigned (2%). There were 223 pairs of male twins, of whom 124 pairs were
MZ and 99 DZ; 235 female pairs of whom 154 pairs were MZ and 81 DZ; and 198
male/female pairs. This means there were 654 boys (49%) and 686 girls
(51%).
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Tests were carried out to explore whether zygosity had an effect on the
mean or variance of the scores. It appeared not to have any effect on mean
scores (MannWhitney MZ v. DZ, Z=-1.416,
P=0.157 for parent-rated Conners data, Z=-0.079,
P=0.937 for parent-rated SDQ data, tests also performed separately
for males only and females only) and a KruskalWallis one-way analysis
of variance (ANOVA) showed that variance is also unaffected by zygosity
(2=2.006, P=0.157, MZ variance 31.710, DZ 39.952 for
parent-rated Conners data, and
2=0.006, P=0.937, MZ
variance 6.362, DZ 8.087 for parent-rated SDQ data).
As mentioned above, self-report data were collected from twins aged 11 years and over. These data were compared with parent and teacher ratings on the same sample to determine whether any age effects existed. When the mean scores were compared differences were found (Z=-3.102, P=0.002, and Z=-7.244, P<0.001, respectively). This suggests that the adolescents rate themselves as having more symptoms of hyperactivity than do their parents or teachers.
Age effects were further explored using regression analysis. The results showed that there was no significant relationship between age and SDQ hyperactivity score (ß=-0.071, P=0.011) but there was a modest, significant inverse relationship between age and Conners scores (ß=-0.114, P<0.001). This may account for some of the differences between the self-report data and the parent and teacher data.
Environmental sharing
Environmental sharing was statistically significantly greater in MZ than DZ
twins (t=10.398, P<0.001, d.f.=618). Consequently, in
order to test whether greater environmental sharing in MZ than in DZ pairs was
likely to invalidate the equal environments assumption, a
regression analysis was performed separately on the Conners scale and SDQ
sub-scale hyperactivity scores (both parent-rated). For the Conners scale, the
variance in difference in scores between twin 1 and twin 2 of each pair
explained by environmental sharing (r2) was -0.002, and
the standardised regression coefficient (ß) was -0.014
(P=0.731). For the SDQ sub-scale r2 was -0.002,
and standardised ß was -0.006 (P=0.886). In both cases the
effects are small and not statistically significant, so that differences in
environmental sharing between MZ and DZ pairs (at least as reflected by this
particular measure) are unlikely to perturb the assumption of equal
environments in subsequent model-fitting on the data obtained from the Conners
and SDQ questionnaires.
Univariate model-fitting
The results of univariate model-fitting on the parent-rated ADHD measures
are summarised in Table 2. The
correlations at the top of each section of the table show that the MZ
correlation (rMZ) is more than twice that of DZ
(rDZ) pairs for both SDQ and Conners data. This suggests
that the best-fitting model will include additive and non-additive genetic
factors, or sibling interaction effects.
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In keeping with this, the fit of the SDQ models containing only additive
genetic effects (ACE or AE) is poor. The fit of the
ADE model, in contrast, is satisfactory (2=3.127,
P=0.372) but the additive genetic component was estimated at its
lower boundary value of zero. However, it is unlikely in nature that
non-additive genetic factors occur in the absence of additive factors. Next, a
test for sibling inter-action effects was carried out as denoted by the
parameter i. This brought no change in
2 compared
with the AE model and i was estimated at zero. Therefore on
grounds of parsimony and goodness of fit, the ADE model offers the
best explanation of the data. Since additive effects were estimated at zero we
could go on to drop these from the model and achieve even greater
statistical parsimony; however, it could be argued that such a model
is biologically implausible. We therefore accept an estimate of broad
sense heritability of 72% with no common environment effects.
For the Conners scale scores the ACE model gives an acceptable fit
(2=4.783, AIC=-1.217, d.f.=3, P=0.188), the shared
environment (c2) being estimated at zero. Consequently,
dropping C from the model results in the same
2, and,
because there is one more degree of freedom, a lower AIC, but dropping
A to give a CE model (no genetic transmission) results in a
significant deterioration in fit (difference in
2=37.393 for 1
d.f. when compared with the ACE model). Next, the presence of
dominance was tested for and an ADE model was fitted. The AE
model is a sub-model of ADE so a direct comparison can be made
between the two. The ADE is a better fit (difference in
2=4.783 for 1 d.f., AIC better by 2.783). A model with sibling
interaction cannot be fitted as the model would be unidentified (i.e. we would
be trying to estimate too many parameters from the given data). On grounds of
parsimony and goodness of fit, the ADE is accepted as the best fit,
showing the broad sense heritability to be 74% and consisting of both additive
genetic effects (24%) and non-additive genetic effects (50%).
The results of the univariate model-fitting on the teacher-rated data are summarised in Table 3.
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From the teacher ratings there is less suggestion of non-additive effects
than for the parent ratings, in that the DZ correlations are just under half
of the size of the MZ correlations. For the SDQ data, the ACE model
fits well (2=1.150, d.f.=3, AIC=-4.850, P=0.765) but
C is estimated at zero. Dropping C from the model gives a
better fit (AIC decreased by 2) and a simpler model. Removing A for
the CE model gives a significant deterioration in fit (for 1 d.f.,
2 increased by 50.823, AIC increases by 48.863). In contrast,
adding either dominance or sibling interaction effects produced no significant
change in
2, which means that the AE model is
accepted as the best explanation of the data.
For the Conners data, the pattern is identical with the AE model
being accepted (2=0.178, AIC=-7.822, P=0.996), giving
a heritability of 80%.
The results of the univariate model-fitting on the adolescent self-report data are summarised in Table 4.
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Looking at the correlations for the adolescent data, a model with common
environment would be expected to fit best. The ACE model gives a good
fit (2=0.016, AIC=-5.984, P=0.999) and additive
genetic factors are estimated at zero. To test for additive genetic effects,
C was dropped. This resulted in a small worsening of fit (both AIC
and
2 increased). The CE model was then fitted which
gave a superior fit in terms of AIC, but a change of only 2.292 in
2. Finally, a no transmission (E only)
model was tested. This resulted in a much worse fit, and the CE model
is accepted on the grounds of having the lowest AIC. This gives a variance of
29% due to shared environment.
Bivariate model-fitting
Before fitting the models, a test was performed to compare teacher-rated
scores with parent-rated ones. The differences in mean scores are larger than
you would expect by chance alone for both Conners and SDQ ratings (for
Conners, Z=-9.414, P<0.001; for SDQ, Z=-4.419,
P<0.001). This suggests either that there are differences in the
way parents and teachers rate the children, with parents tending to report
more symptoms, or that the children are behaving differently in school and
home settings. Alternatively, a selection bias in the teacher data might
result from only the parents of children with low scores giving permission to
contact teachers. This was explored using a MannWhitney test between
scores from parents who had allowed us to contact teachers, and those who had
not. No significant difference in the means were found (for Conners,
Z=-0.938, P=0.348; for SDQ, Z=-0.587,
P=0.557), suggesting that such a selection bias is not present.
The results of bivariate model-fitting on the parent-rated and teacher-rated ADHD measures are summarised in Table 5. The model-fitting was carried out using the psychometric or common pathway model (Neale & Cardon, 1992). Here it is assumed that both parent and teacher ratings are measuring the same latent phenotype (Fig. 1).
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For the Conners data, the ACE model gives a good fit
(2=4.464, AIC=-17.536, P=0.954), but C is
estimated at zero and consequently dropping it from this model results in no
change in fit and a lower AIC (
2=4.464, AIC=-23.536,
P=0.992). However, dropping A gives rise to a serious
deterioration in fit (
2=75.871, AIC=47.871,
P=0.0001). The full ADE model when tested gave a little
improvement in the fit and an increase in the AIC. Therefore the AE
model provides the most acceptable explanation of the data, with parent and
teacher ratings being explained by the same additive genetic factors
accounting for 31% of variance. However, there were specific additive genetic
effects of 41% for parent ratings and 50% for teachers. This suggests that
despite both the teacher- and parent-observed phenotypes being strongly
influenced by genetic factors, these to a substantial extent involve different
genes.
From the SDQ data, the overall fit of models is similar to that for the
Conners data, but a modified ADE model turns out to be the most
satisfactory (2=4.56, AIC=-23.44, P=0.991). This
variance of 38% is explained by shared non-additive genetic factors. For
parent ratings there is a specific 13% of variance due to non-additive genetic
factors, and for teacher ratings a specific 35% due to additive genetic
effects.
In addition to the fitting shown in
Table 5, models were fitted for
both sets of data but with the shared additive genetic effect fixed at 1
(meaning that all covariation is due to common genetic factors). Both these
tests failed, however, giving 2 values of over 10 000. These
results again imply that what the parents and teachers observe with respect to
SDQ hyperactivity items is influenced to a significant extent by different
genes.
A previous study (Simonoff et al, 1998) found correlational differences between twins rated by the same teacher or by different teachers. In the present sample only 39 pairs (9.2%) of the teacher reports were made by a different teacher for each twin, hence this has not been explored.
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DISCUSSION |
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Observer effects
The most striking difference in our results based on simple univariate
model-fitting was between those from the adolescent twins' own ratings and
those from parent or teacher ratings. Self-rated scores from adolescents
resulted in equal correlations in MZ and DZ twins and the most acceptable
model was one that had zero heritability. It could be argued that ADHD is an
externalising disorder and that therefore its symptoms would be
more accurately reported by others rather than by subjects themselves.
However, this seems unlikely on its own to account for the absence of genetic
effects, since another externalising trait, mild antisocial behaviour, has
been found to be heritable in adolescents in an earlier twin sample from South
Wales (McGuffin & Thapar,
1997).
Our other major finding on observer effects comes from the bivariate analyses where we applied a model with the assumption that both parents and teachers are rating the same underlying phenotype. Each type of measure can then be thought of as a reflection of one latent variable. In fact we did find evidence of commonality, with the same genetic factors explaining some of the variance in parent and teacher ratings (31% using the Conners scale, 38% with the SDQ scale), but there were also sizeable specific genetic components for parents and teachers, suggesting that although both types of report result in high heritabilities there may be different sets of genes underlying what is observed. Unfortunately, the limitation of sample size precluded a trivariate analysis attempting to further explore ratings by parent, teacher and self-report.
Implications for genetic studies
This finding of observer effects has serious implications for molecular
studies attempting to find causative genes for ADHD. Given the same
population, if a study selected one sample for quantitative trait locus
analysis purely on the basis of teacher-rated scores, and another study
selected a sample for analysis based on only parent-rated scores, the results
might be very different. The two studies might both detect a gene or genes
contributing to the shared 31% of heritability, in which case it would be
reasonably safe to accept the quantitative trait locus as being associated
with ADHD. On the other hand, the studies might detect different genes
involved in the specific or non-overlapping portions of the heritability, but
neither group would be able to replicate the other's results and so both loci
would be rejected and regarded as false positives. Thus, different definitions
of what is apparently the same phenotype complicate the task of finding the
causative genes.
The findings of the present study must be seen in the light of rater bias described in previous studies (Eaves et al, 1997; Simonoff et al, 1998). The results may indicate that although both raters are observing the same phenotype, they are scoring it differently because of their own particular biases. Another possible explanation is that the children are truly behaving differently at home from the way they do at school. This means that the raters would be scoring phenotypes for which the differences are real to an extent. To date, most studies attempting to find genetic marker associations in ADHD have focused on categorical clinical samples, but most of the justification for performing such studies has come from research on general population samples, mainly using dimensional measures. Future studies aimed at finding genes involved in ADHD should incorporate multiple informants, and dimensional as well as clinical diagnostic measures in their design.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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REFERENCES |
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Received for publication November 16, 2000. Revision received August 8, 2001. Accepted for publication August 14, 2001.
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