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Genetic and environmental inuences on adult attention decit
hyperactivity disorder symptoms: a large Swedish population-based
study of twins
H. Larsson, P. Asherson, Z. Chang, T. Ljung, B. Friedrichs, J.-O. Larsson and P. Lichtenstein
Psychological Medicine / Volume 43 / Issue 01 / January 2013, pp 197 - 207
DOI: 10.1017/S0033291712001067, Published online: 16 August 2012
Link to this article: http://journals.cambridge.org/abstract_S0033291712001067
How to cite this article:
H. Larsson, P. Asherson, Z. Chang, T. Ljung, B. Friedrichs, J.-O. Larsson and P. Lichtenstein (2013). Genetic and
environmental inuences on adult attention decit hyperactivity disorder symptoms: a large Swedish population-based study
of twins. Psychological Medicine, 43, pp 197-207 doi:10.1017/S0033291712001067
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Genetic and environmental influences on adult
attention deficit hyperactivity disorder symptoms:
a large Swedish population-based study of twins
H. Larsson
1
,
2
*, P. Asherson
3
, Z. Chang
1
, T. Ljung
1
, B. Friedrichs
4
, J.-O. Larsson
4
and P. Lichtenstein
1
1
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
2
Karolinska Institutet Center of Neurodevelopmental Disorders, Stockholm, Sweden
3
MRC Social Genetic and Developmental Psychiatry, Institute of Psychiatry, King’s College London, UK
4
Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
Background. Attention deficit hyperactivity disorder (ADHD) frequently persists into adulthood. Family and twin
studies delineate a disorder with strong genetic influences among children and adolescents based on parent- and
teacher-reported data but little is known about the genetic and environmental contribution to DSM-IV ADHD
symptoms in adulthood. We therefore aimed to investigate the impact of genetic and environmental influences on the
inattentive and hyperactive–impulsive symptoms of ADHD in adults.
Method. Twin methods were applied to self-reported assessments of ADHD symptoms from a large population-
based Swedish twin study that included data from 15 198 Swedish male and female twins aged 20 to 46 years.
Results. The broad heritability [i.e. A+D, where A is an additive genetic factor and D (dominance) a non-additive
genetic factor] was 37 % (A=11%, D=26 %) for inattention and 38 % (A=18 %, D=20 %) for hyperactivity–
impulsivity. The results also indicate that 52 % of the phenotypic correlation between inattention and hyperactivity–
impulsivity (r=0.43) was explained by genetic influences whereas the remaining part of the covariance was explained
by non-shared environmental influences. These results were replicated across age strata.
Conclusions. Our findings of moderate broad heritability estimates are consistent with previous literature on self-
rated ADHD symptoms in older children, adolescents and adults and retrospective reports of self-rated childhood
ADHD by adults but differ from studies of younger children with informant ratings. Future research needs to clarify
whether our data indicate a true decrease in the heritability of ADHD in adults compared to children, or whether this
relates to the use of self-ratings in contrast to informant data.
Received 8 July 2011 ; Revised 17 April 2012 ; Accepted 23 April 2012 ; First published online 16 August 2012
Key words : ADHD, adults, etiology, genetics, twin study.
Introduction
Attention deficit hyperactivity disorder (ADHD) is a
disorder characterized by developmentally inappro-
priate and impairing levels of inattention, hyper-
activity and impulsivity. Follow-up studies of children
with ADHD into adolescence and early adulthood
show a substantial degree of continuity across time
(Barkley et al. 2008; Biederman et al. 2010a,b), with
around 65% of children with ADHD retaining the full
syndrome or in partial remission by the age of 25 years
(Faraone et al. 2006). Despite an increased interest in
the developmental continuity of ADHD (Wilens et al.
2004), knowledge on the role of genetic and environ-
mental influences on the two DSM-IV ADHD symptom
dimensions (i.e. inattention and hyperactivity–
impulsivity) in adulthood is still limited.
Family studies have shown that children of adults
with ADHD are at increased risk of having ADHD
compared to control groups (Biederman et al. 1995 ;
Faraone et al. 2000). Two large-scale twin studies of
adult ADHD symptoms recently estimated the herita-
bility using self-ratings of the Conners’ Adult ADHD
Rating Scales (CAARS; Boomsma et al. 2010; Saviouk
et al. 2011). The heritability was estimated at 30 % for
total ADHD symptom load (Boomsma et al. 2010),
35% for inattention and 23% for hyperactivity
(Saviouk et al. 2011). These results are largely consist-
ent with a previous twin study of a sample of adults
(aged 18–30) that estimated the heritability of self-
reported attention problems as 40 % (van den Berg
* Address for correspondence: Dr H. Larsson, Karolinska Institutet,
Department of Medical Epidemiology and Biostatistics, PO Box 281,
SE-171 77 Stockholm, Sweden.
(Email : Henrik.Larsson@ki.se)
Psychological Medicine (2013), 43, 197–207. fCambridge University Press 2012
doi:10.1017/S0033291712001067
ORIGINAL ARTICLE
et al. 2006). Heritability estimates in the range 30–40%
have generally been reported in other studies using
self-reported ADHD symptoms: in a study of ado-
lescent twins and their siblings aged 12–19 years
(Ehringer et al. 2006), and in two adult twin studies of
retrospectively recalled childhood ADHD symptoms
(Schultz et al. 2006; Haberstick et al. 2008), with one
small twin study (286 adolescent twin pairs; Martin
et al. 2002) estimating the heritability of ADHD as
zero using the Strength and Difficulties Questionnaire
(SDQ) hyperactivity subscale (Goodman, 1997). By
contrast, twin studies of ADHD among children
and adolescents, rated by parents or teachers, have
been highly consistent in showing strong genetic in-
fluences, with heritability estimates around 60–90 %
(Faraone et al. 2005; Burt, 2009). The available litera-
ture therefore suggests that ADHD self-ratings yield
lower heritability estimates than do parent and teacher
ratings.
Twin studies of adult ADHD symptoms have
additionally shown that self-ratings have moderate to
high reliability (around 0.66), that the heritability is
similar across gender and is stable from early (average
age 20 years) to late adulthood (average age 55 years),
and also that the stability of self-rated symptoms is
largely due to genetic factors (van den Berg et al. 2006 ;
Boomsma et al. 2010). Less is known about the
genetic and environmental overlap between inatten-
tion and hyperactivity–impulsivity. Childhood and
adolescent studies suggest a strong genetic overlap
between symptoms of inattention and hyperactivity–
impulsivity (Larsson et al. 2006; Schultz et al. 2006;
McLoughlin et al. 2007; Haberstick et al. 2008) but this
has not been studied for ADHD symptoms in adults.
In this study we used self-ratings from more than
15 000 adult twins from the national Swedish Twin
Registry to examine the genetic and environmental
contribution to the variation within inattention and
hyperactivity–impulsivity and to the covariation be-
tween these two components of DSM-IV ADHD. We
also studied the role of sex differences in the genetic
and environmental effects. In addition, because results
from a recent meta-analysis suggest that the magni-
tude of the non-shared environmental component
underlying hyperactivity–impulsivity increases from
childhood to adolescence (Nikolas & Burt, 2010), we
explored the impact of age on the genetic and en-
vironmental contribution.
Method
Sample
A sample of 42 582 Swedish twins was recruited from
the population-representative Swedish Twin Registry.
The inclusion criteria were all twin pairs born in
Sweden between 1959and 1985 where both individuals
survived their first birthday. Of this target sample,
25 321 (59.5 %) individuals took part in the Swedish
Twin study of Adults: Genes and Environment
(STAGE; Lichtenstein et al. 2006). Twins were sent a
letter inviting them to participate in the study and
were given a personal login to the study web page.
Non-responders were approached with up to three
reminders. Twins could also choose to complete the
questionnaire by telephone with a trained interviewer
using a computer-based data collection method, sup-
plemented with a self-administered paper question-
naire for sensitive topics. As all of the twins were born
in Sweden, none of them were first-generation im-
migrants. Of the participants in STAGE, 64% were
married or living with their partner, 5% had a stable
relationship without living together, 27% were single,
and 4% were separated, divorced, widowed or did not
indicate their status. Furthermore, 5% had completed
or attended elementary school, 41% high school, 12%
vocational education, military college or other, and
42% college or university as their highest academic
degree (Furberg et al. 2008), which is consistent with
the total Swedish population (Statistics Sweden, 2011).
The majority of our responders (72%, n=18 327)
chose to answer over the web, 12 % (n=2946) com-
pleted the telephone interview and also sent in the
paper questionnaire, and 16 % (n=4105) completed
the telephone interview but did not return the paper
questionnaire. As the DSM-IV items were included in
the paper questionnaire, we had to exclude those who
only undertook the telephone interview. Although we
cannot rule out the possibility that this subsample
differed from the full sample of twins, lack of response
to the ADHD symptom assessment seemed to be due
mainly to the survey design and a general unwilling-
ness to participate in this rather lengthy survey (Frisell
et al. 2010). The response rate for the ADHD symptom
assessments of STAGE was 72% (n=18 316), of whom
40% (n=7366) were men and 60% (n=10 950) were
women. Participants were between 20 and 46 years old
(mean=33.7, S.D.=7.7) at the time of assessment.
It was not possible to assign zygosity with certainty to
3112 twins, resulting in a sample of 15 198 twins with
known zygosity. Individuals (n=4170) from incom-
plete twin pairs and individuals (n=11 028) from
complete twin pairs were included in the twin analy-
ses resulting in 2091 monozygotic male (MZM) twins,
1437 dizygotic male (DZM) twins, 3660 MZ female
(MZF) twins, 2483 DZ female (DZF) twins and 5527
DZ opposite-sex (DZOS) twins. Zygosity was estab-
lished using standard physical similarity questions
that have been validated previously through geno-
typing (Lichtenstein et al. 2006).
198 H. Larsson et al.
The project has been reviewed and approved by the
regional ethics committee of the Karolinska Institutet.
All subjects provided informed consent electronically
during the web-based survey or orally during the
telephone interview.
Measures
Adult ADHD symptoms were assessed by using a self-
report questionnaire containing the 18 DSM-IV symp-
toms, consisting of nine inattentive, six hyperactive
and three impulsive items. Each item had a three-point
answer format (0=‘no’, 1=‘yes, to some extent’ and
2=‘yes’). The 18 DSM-IV items, which were slightly
modified to fit adults and also expressed to assess
current ADHD symptoms, are presented in the online
Appendix (Table A1). As expected from a general
population sample, many individuals reported having
no ADHD problems. The symptoms were summed to
create two scales of inattention and hyperactivity–
impulsivity. The distribution of the two sum scores
(range 0–18) is shown in the online Appendix
(Fig. A1). About one-third of the twins reported
no inattention (32.74%) or hyperactivity–impulsivity
symptoms (32.30%). Values for the reliabilities of
the inattention and hyperactivity–impulsivity scales
were a=0.79 and a=0.77 respectively. The two
scales were positively skewed (skewness: inattention,
2.70; hyperactivity–impulsivity, 2.80) and were there-
fore independently transformed [log 10(x+1)] before
analyses to increase the normality of their distri-
butions (skewness: inattention, 0.33; hyperactivity–
impulsivity, 0.34). We conducted sensitivity analyses
by excluding extreme values (i.e. 4 standard devi-
ations from the mean) in the inattention and hyper-
activity–impulsivity scales and by applying threshold
models to predict inattention and hyperactivity–
impulsivity categories (i.e. cut-off imposed at 2.0 and
1.0 standard deviations above the mean of the scales).
Similar results were obtained, suggesting that bias due
to non-normal scales is of limited importance (data not
shown).
A subset of 54 twins in STAGE was assessed again
with the World Health Organization (WHO) Adult
ADHD Self-report Scale (ASRS; Kessler et al. 2006)
after a minimum 18-month follow-up period (mean
follow-up time=28.06 months, S.D.=4.16, range
18–35). The ASRS includes 18 questions about the fre-
quency of recent DSM-IV Criterion A symptoms of
adult ADHD. The correlation between the total score
of the initial STAGE ADHD measure (sum score of the
18 DSM-IV symptoms) and the total ASRS score at
follow-up (sum score of the 18 ASRS items) was esti-
mated as 0.63 (p<0.0001). This relatively high stability
coefficient corresponds to the results reported in other
longitudinal studies of self-reported ADHD symp-
toms in adults (van den Berg et al. 2006 ; Boomsma et al.
2010) and parent-reported ADHD symptoms in chil-
dren (Larsson et al. 2004; Rietveld et al. 2004; Kuntsi
et al. 2005).
Statistical analyses
Mean differences across sex and age intervals were
estimated using linear mixed effect models in SAS
version 9.2 (SAS Institute Inc., USA), which allowed us
to account for the dependent nature of the twin ob-
servations.
The twin method is a natural experiment that relies
on the different levels of genetic relatedness between
MZ and DZ twins. MZ twins are almost genetically
identical whereas DZ twins share on average 50 % of
the polymorphic genetic variation. We used the twin
method to decompose the variance of each pheno-
type and also the covariation between phenotypes
into additive genetic factors (A) reflecting additive
effects of different alleles, non-additive genetic
factors (dominance, D) reflecting interaction effects
between alleles at the same gene locus, and non-
shared environmental factors (E) reflecting experi-
ences that make sibling pairs dissimilar (Plomin et al.
2008).
Twin correlations (i.e. within-twin pair maximum
likelihood correlations) were used for an initial exam-
ination of the relative contributions of A, D and E.
Specifically, MZ correlations higher than DZ corre-
lations indicate A whereas E is indicated by the extent
to which MZ correlations are <1. DZ correlations
lower than half the MZ correlations suggest D or sib-
ling interaction effects (usually labeled ‘ s’). Sibling
interaction effects and D effects can be distinguished
by making use of the fact that sibling interaction effects
lead to differences in variances in MZ and DZ twins
but non-additive genetic effects do not. Thus, lack of
significant variance differences between MZ and DZ
twins suggests that the presence of sibling interaction
effects is not plausible.
We used the structural equation modeling program
Mx (Neale et al. 2003) to perform univariate and
bivariate model-fitting analyses by the method of raw
maximum likelihood estimation. This method allows
the inclusion of singletons, where information from
only one twin in a pair is available. In the univariate
and bivariate model-fitting analyses, the following
combinations of variance components were consi-
dered: ADE, AE, ADEs and AEs. Three sex-limitation
models were fitted to the data. The full sex-limitation
model allows for qualitative differences (i.e. sex-
specific genetic parameters) and quantitative differ-
ences (i.e. differences in the magnitudes of the genetic
Twin study of ADHD symptoms in adults 199
and environmental parameters across sex) (Neale et al.
2006). The common effects sex-limitation model allows
quantitative sex differences between males and fe-
males, but no qualitative differences. Finally, the null
model equates all genetic and environmental par-
ameter estimates for males and females, testing the
hypothesis that there are no sex differences.
The bivariate model estimates the additive genetic
(r
a
), dominance genetic (r
d
) and non-shared (r
e
)
environmental correlations, which vary from x1.0 to
+1.0 and indicate the extent to which genetic and
environmental influences in one phenotype overlap
with those of another phenotype.
Goodness of fit for the different twin models was
assessed by a likelihood-ratio x
2
test. Akaike’s Infor-
mation Criterion (AIC=x
2
– 2 df) was also computed ;
a lower AIC value indicates better fit of the model to
the observed data.
Results
Inattention scores were significantly lower in females
than in males (F
1
,
8716
=16.28, p<0.001) whereas hyper-
activity–impulsivity scores were similar across gen-
der (F
1
,
871
=0.84, p=0.36). Inattention (F
2
,
8932
=33.88,
p<0.001) and hyperactivity–impulsivity (F
2
,
8965
=28.90,
p<0.001) were significantly associated with age.
Age-stratified means and standard deviations for
the non-transformed inattention and hyperactivity–
impulsivity scales are presented in Table 1, which
shows that, for both scales, mean symptom scores
decreased with age.
The twin correlations of inattention and hyper-
activity–impulsivity scores suggest significant addi-
tive and non-additive genetic influences for both
inattention and hyperactivity–impulsivity because DZ
correlations tended to be half or less than half of the
MZ correlations for both sexes (Table 2). All MZ cor-
relations were <1, suggesting non-shared environ-
mental influences (including measurement error) for
both inattention and hyperactivity–impulsivity.
The MZ and DZ cross-trait cross-twin (CTCT) cor-
relations (one twin’s score on inattention correlated
with their co-twin’s score on hyperactivity–impulsivity
score) are also presented in Table 2. MZ CTCT corre-
lations were higher than DZ CTCT correlations, sug-
gesting genetic influences for the overlap between
inattention and hyperactivity–impulsivity. Non-shared
environmental influences were also evident because
MZ CTCT correlations were almost half the pheno-
typic correlation (i.e. <1).
Twin and CTCT correlations were similar for males
and females, which suggests no quantitative genetic
and environmental differences for the variation within
inattention and hyperactivity–impulsivity, and also
for the covariation between these two components of
ADHD. In addition, both intra-class and CTCT corre-
lations were similar for same-sexed DZ and DZOS
twins, suggesting no qualitative sex differences.
The potential importance of sibling interaction ef-
fects was tested by examining variance differences
between MZ and DZ twins. We observed no statisti-
cally significant birth order, sex or zygosity effect
on the variances of inattention and hyperactivity–
impulsivity. Thus, for inattention (Dx
2
=4.84, df=9,
p=0.85) and hyperactivity–impulsivity (Dx
2
=4.19,
df=9, p=0.90), variances could be equated across sex
and zygosity without a significant decrease in fit.
Univariate and bivariate model fitting
As expected from the magnitude of the difference
between MZ and DZ correlations (Table 2) and the
lack of significant variance differences across zygosity
groups, the univariate ADEs (where ‘s’ is the sibling
interaction term) models provided a poor fit to the
data compared to ADE models (data not shown).
Table 3 displays the model-fitting results of ADE and
AE sex-limitation models for inattention and hyper-
activity–impulsivity and shows that the ADE models
without sex differences (the null model) provided the
best fit to the data. For inattention, the additive gen-
etic, dominant genetic and non-shared environmental
factors explained 11% [95% confidence interval (CI)
6–17], 25% (95% CI 10–31) and 63% (95% CI 60–67) of
the variance respectively. The corresponding estimates
Table 1. Means and standard deviations (S.D.)for inattention and hyperactivity–impulsivity symptoms scales, by age group
Age 20–28 years
(n=4618)
Age 29–37 years
(n=4997)
Age 38–46 years
(n=5583) Significant effects
Mean S.D. Mean S.D. Mean S.D.Fdf p
Inattention 2.53 2.88 2.26 2.67 2.07 2.56 33.88 2, 8932 <0.001
Hyperactivity–
impulsivity
2.59 2.89 2.48 2.89 2.15 2.71 28.90 2, 8965 <0.001
200 H. Larsson et al.
for hyperactivity–impulsivity were 18% (A; 95%
CI 4–33), 20% (D; 95% CI 5–35) and 62% (E ; 95 % CI
58–65). Thus, the broad-sense heritability (A+D)
was 36% for inattention and 38% for hyperactivity–
impulsivity. We also refitted the ADE and AE
sex-limitation models but now using an ADHD total
score (sum score of all 18 DSM-IV symptoms).
The best-fitting model (i.e. ADE model without
sex differences) suggested that the additive genetic,
dominant genetic and non-shared environmental
Table 2. Twin correlations and cross-twin cross-trait (CTCT)correlations for inattention
and hyperactivity–impulsivity symptoms scales in 15 198 twins (5514 complete twin pairs)
Inattention
(95% CI)
Hyperactivity–impulsivity
(95% CI)
CTCT between inattention and
hyperactivity–impulsivity
(95% CI)
MZM 0.34 (0.28–0.40) 0.37 (0.31–0.42) 0.23 (0.18–0.27)
DZM 0.11 (0.02–0.19) 15 (0.07–0.23) 0.07 (0.01–0.14)
MZF 0.38 (0.34–0.42) 0.40 (0.35–0.43) 0.23 (0.20–0.26)
DZF 0.15 (0.09–0.21) 0.17 (0.11–0.23) 0.14 (0.10–0.19)
DZOS 0.10 (0.04–0.15) 0.12 (0.06–0.17) 0.06 (0.02–0.10)
MZM, Monozygotic male ; DZM, dizygotic male ; MZF, MZ female ; DZF, DZ
female ; DZOS, DZ opposite-sex ; CI, confidence interval.
Table 3. Model-fitting results of univariate analysis of inattention and hyperactivity–impulsivity
Models
Fit of model compared to saturated model
x2LL df x
2
Ddf pAIC
Inattention
Saturated model 6158.60 10 807 – – – –
1. ADE univariate
Full sex-limitation model
a
6186.87 10 820 28.27 13 0.01 2.27
Common effects sex-limitation model
b
6186.96 10 821 28.37 14 0.01 0.37
Null model
c
6189.65 10 824 31.06 17 0.02 x2.95
2. AE univariate
Full sex-limitation model
a
6190.18 10 822 31.59 15 0.01 1.59
Common effects sex-limitation model
b
6197.46 10 823 38.86 16 0.01 6.86
Null model
c
6199.79 10 825 41.19 18 0.01 5.19
Hyperactivity–impulsivity
Saturated model 6565.53 10 816 – – – –
1. ADE univariate
Full sex-limitation model
a
6580.89 10 829 15.36 13 0.29 x10.64
Common effects sex-limitation model
b
6581.70 10 830 16.18 14 0.30 x11.83
Null model
c
6583.55 10 833 18.02 17 0.39 x15.98
2. AE univariate
Full sex-limitation model
a
6582.09 10 831 16.56 15 0.35 x13.44
Common effects sex-limitation model
b
6588.39 10 832 22.86 16 0.12 x9.14
Null model
c
6589.97 10 834 24.44 18 0.14 x11.56
LL, Log likelihood ; df, degrees of freedom ; AIC, Akaike’s Information Criterion.
a
The full sex-limitation model allows quantitative and qualitative differences in the parameter estimates between males
and females.
b
The common effects sex-limitation model allows quantitative sex differences between males and females but no
qualitative differences.
c
The null model equates all genetic and environmental parameter estimates for males and females, testing the hypothesis
that there are no sex differences.
Best-fitting models indicated in bold.
Twin study of ADHD symptoms in adults 201
factors explained 20% (95% CI 6–34), 22% (95 % CI
6–37) and 58% (95% CI 57–62) of the variance re-
spectively.
Table 4 displays the model-fitting results for the
ADE and AE bivariate sex-limitation models. As can
be seen from the AIC values, the full ADE model
without sex differences (the null model) provided the
best fit to the data, indicating that the genetic and en-
vironmental contribution to the variation within inat-
tention and hyperactivity–impulsivity and to the
covariation between these two components of ADHD
could be estimated to be the same in both sexes.
Table 5 provides parameter estimates for the addi-
tive genetic, dominant genetic and non-shared en-
vironmental influences for the overlap between
inattention and hyperactivity–impulsivity. The addi-
tive genetic correlation was estimated at 1.00 (95 % CI
0.39–1.00), suggesting a substantial genetic overlap
between inattention and hyperactivity–impulsivity.
The dominant genetic (0.37, 95% CI 0.12–0.71) and
non-shared environmental correlations (0.33, 95% CI
0.30–0.36) were also significant but substantially
lower. Table 5 also indicates that the phenotypic
correlation between inattention and hyperactivity–
impulsivity (r=0.43, 95% CI 0.42–0.44) was explained
by additive genetic (33%, 95% CI 8–47), dominant
genetic (19%, 95% CI 3–50) and non-shared environ-
mental (48%, 95% CI 43–52) influences. Thus, 52%
(95% CI 48–57) of the phenotypic covariance was
explained by broad-sense genetic influences (bivariate
a
2
+bivariate d
2
).
Follow-up analyses
First, age-stratified model-fitting results revealed
similar estimates of broad heritability (A+D) for
inattention and hyperactivity–impulsivity across age.
To maximize power in the analyses of age-dependent
genetic and environmental influences, we compared
the CIs around the age-stratified non-shared environ-
mental estimates. The results suggest similar non-
shared environmental (E) estimates for inattention
(age 20–28 years: E=0.61, 95% CI 0.56–0.64; age 29–37
years: E=0.62, 95% CI 0.57–0.67; age 38–46 years:
E=0.68, 95% CI 0.62–0.74) and hyperactivity–
impulsivity (age 20–28 years: E=0.58, 95% CI 0.56–
0.64; age 29–37 years: E=0.63, 95% CI 0.58–0.68; age
38–46 years: E=65, 95% CI 0.59–0.70). Thus, the non-
shared environmental contribution to inattention and
hyperactivity–impulsivity symptoms did not increase
as a function of age, and therefore the proportion of
the variance explained by genetic influences also re-
mains stable across the different age groups.
Second, given that hyperactivity (measured by the
six DSM-IV symptoms of ADHD) and impulsivity
(measured by the three DSM-IV symptoms of ADHD)
may represent separate components of ADHD (Sandra
Kooij et al. 2008), we also applied a trivariate Cholesky
Table 4. Bivariate model-fitting results of inattention and hyperactivity/impulsivity symptoms
Model
Fit of model compared to saturated model
Compared
to model
Difference in fit of models
x2LL df x
2
Ddf pAIC Dx
2
Ddf p
Saturated model 10579.17 21 603 – – – – – – – –
ADE bivariate
Full sex-limitation model
a
10639.83 21 645 60.66 42 0.031 x23.34 – – – –
Common effects sex-limitation
model
b
10644.82 21 648 65.65 45 0.024 x24.35 –
a
4.99 3 0.17
Null model
c
10648.94 21 657 69.77 54 0.073 x38.23 –
b
4.12 9 0.90
AE bivariate
Full sex-limitation model
a
10649.87 21 651 70.70 48 0.018 x25.30 – – – –
Common effects sex-limitation
model
b
10666.06 21 654 86.88 51 0.001 x15.12 –
a
16.18 3 0.01
Null model
c
10666.88 21 660 87.71 57 0.001 x26.30 –
a
17.01 9 0.05
LL, Log likelihood ; df, degrees of freedom ; AIC, Akaike’s Information Criterion.
a
The full sex-limitation model allows quantitative and qualitative differences in the parameter estimates between males
and females.
b
The common effects sex-limitation model allows quantitative sex differences between males and females but no
qualitative differences.
c
The null model equates all genetic and environmental parameter estimates for males and females, testing the hypothesis
that there are no sex differences.
Best-fitting model indicated in bold.
202 H. Larsson et al.
model to allow for potential differences in the genetic
and environmental contribution underlying these two
symptom components. These analyses showed that
broad heritability estimates for hyperactivity (0.36)
and impulsivity (0.31) were similar to the corre-
sponding estimate of hyperactivity–impulsivity (0.38).
Thus, we do not find evidence for differences in the
magnitude of the heritability underlying hyperactive
and impulsivity, suggesting that the main results of
this study are robust across both the two- and three-
component definitions of ADHD.
Discussion
In accordance with studies using current or retro-
spective self-rated measures of ADHD symptoms
from childhood or adolescence (Ehringer et al. 2006 ;
Schultz et al. 2006; Haberstick et al. 2008), we found a
moderate broad heritability of ADHD symptoms in
adults. There was no evidence for sex differences in
the genetic and environmental effects underlying the
two DSM-IV symptom dimensions of ADHD. Overall,
we conclude that although self-ratings in adults give
lower heritabilities than those derived from parent
and teacher reports of ADHD symptoms in children,
the overall pattern of the variance components in re-
lation to age and the degree of shared genetic effects
between the two symptom domains of inattention and
hyperactivity–impulsivity are similar to previous stu-
dies of ADHD symptoms in children and adolescents.
The finding that self-rated symptoms of inatten-
tion and hyperactivity–impulsivity in adulthood are
moderately heritable is in line with results from three
adult twin studies of self-reported ADHD symptoms/
attention problems (van den Berg et al. 2006 ; Boomsma
et al. 2010; Saviouk et al. 2011), an adolescent twin
study of self-reported ADHD symptoms (Ehringer
et al. 2006) and two twin studies of retrospectively self-
reported childhood ADHD symptoms (Schultz et al.
2006; Haberstick et al. 2008). Our data also indicate
that a large proportion of the broad heritability is due
to genetic dominance, which is consistent with pre-
vious childhood twin studies of ADHD (Burt, 2009)
but in contrast to the above-mentioned studies. Prior
research indicates that the balance between additive
and dominant genetic effects for ADHD might differ
as a function of age and informants (Rietveld et al.
2003). However, age- and/or informant-dependent
differences do not provide a good explanation for the
difference between our study and the previous studies
of ADHD symptoms in adults (van den Berg et al.
2006; Boomsma et al. 2010) because all the studies were
based on self-ratings and the age distributions of the
different samples were similar. Several twin studies
have explored the influence of different ADHD rating
Table 5. Parameter estimates (95%CI)from the best-fitting bivariate model
Genetic/environmental
contribution to variance
Genetic/environmental
correlations
% of phenotypic
correlation due to genetic/
environmental effects
ADE r
a
r
d
r
e
Bivariate a
2
Bivariate d
2
Bivariate e
2
Inattention 0.11 (0.02–0.23) 0.26 (0.13–0.35) 0.63 (0.61–0.66) – – – – – –
Hyperactivity–impulsivity 0.18 (0.05–0.32) 0.20 (0.05–0.32) 0.62 (0.59–0.65) – – – – – –
Inattention–hyperactivity–
impulsivity covariance
– – 1.00 (0.39–1.00) 0.37 (0.12–0.71) 0.33 (0.30–0.36) 33 (8–47) 19 (3–50) 48 (43–52)
CI, Confidence interval ; r
a
, additive genetic correlation ; r
d
, dominance genetic correlation ; r
e
, non-shared environmental correlation ; a
2
, proportion of phenotypic correlation due to
genetic effects ; d
2
, proportion of phenotypic correlation due to dominance genetic effects ; e
2
, proportion of phenotypic correlation due to non-shared environmental effects.
Twin study of ADHD symptoms in adults 203
scales on the genetic and environmental estimates
(Freitag et al. 2010). For example, a twin study found
evidence for dominant genetic effects when ADHD
was assessed using the DuPaul Rating Scale but not
when the Rutter A Scale was used (Thapar et al. 2000).
We therefore suggest that the observed difference in
the proportion of dominant genetic effects might be
explained by the use of different measures to assess
ADHD. Future studies should therefore consider these
differences in the selection of rating scales for the in-
vestigation of genetic effects on ADHD in adults.
Our results regarding the etiology of the covariation
between inattention and hyperactivity–impulsivity
suggest a substantial genetic overlap between the two
symptom dimensions of ADHD, with some unique
genetic effects on each dimension. In line with several
earlier twin studies using parent ratings of childhood
ADHD symptoms (Larsson et al. 2006; McLoughlin
et al. 2007), we report a strong additive genetic corre-
lation (1.00) for inattention and hyperactivity–
impulsivity. The dominant genetic correlation was
substantially lower, indicating that the genetic overlap
between inattention and hyperactivity–impulsivity is
mainly due to additive genetic effects. Together, our
results suggest that future molecular genetic studies of
ADHD in adults (and in children) should expect both
‘dimension-general’ and ‘dimension-specific’ genetic
risk markers.
We further extended previous studies of ADHD in
adults by investigating potential genetic and environ-
mental sex differences for adult DSM-IV ADHD
symptom dimensions. Sex effects are difficult to study
because their reliable detection requires large samples,
which might explain why childhood studies have
produced mixed results, with evidence both for (Rhee
et al. 1999) and against (Hudziak et al. 2005) sex dif-
ferences underlying the etiology of ADHD. We found
no significant sex differences in the genetic and
environmental factors for inattention and hyper-
activity–impulsivity, a result that is congruent with
the two prior twin studies of adult ADHD symptoms/
attention problems (van den Berg et al. 2006 ; Boomsma
et al. 2010). Thus, the same genetic effects are operating
in men and women.
Limitations
This study of self-reported DSM-IV ADHD symptoms
from more than 15 000 adult twins from the national
Swedish Twin Registry should be interpreted in the
context of two main limitations. First, the response
rate of the STAGE questionnaire was relatively low
(59.5%), partly because of its length. Non-participants
of STAGE were more likely than participants to be
male, less educated, have at least one parent born
outside of Sweden, to have been convicted of any type
of crime and diagnosed with a psychiatric disorder
(Furberg et al. 2008). We also compared the ADHD
symptom scores of complete and incomplete twin
pairs because they provide an indication of the
extent to which missing values are non-random.
Hyperactivity–impulsivity scores were significantly
higher in incomplete pairs than in complete pairs
(p<0.01) whereas no significant difference was ob-
served for inattention scores (p=0.10). Thus it is
possible that the variations in ADHD symptoms at the
extremes are truncated. Importantly, we have pre-
viously reported that twins in STAGE with high
ADHD scores are at increased risk for co-morbid psy-
chiatric disorders and stressful life events. Previous
childhood studies (Levy et al. 1997) have suggested
that the genetic and environmental etiology of ADHD
scores at the extreme end is no different from the
etiology of scores across the normal range. However,
because non-responders might have higher levels of
behavioral problems, our results may not be general-
izable to the most extreme ADHD cases.
Second, although some studies have shown that
DSM-IV-based self-report questionnaires are reliable
sources of information when making the diagnosis of
ADHD in adults (Murphy & Schachar, 2000 ; Adler
et al. 2006; Sandra Kooij et al. 2008), others highlight
the importance of using multiple informants (Barkley
et al. 2002). In addition, confirmatory factor analyses of
the 18 DSM-IV symptoms indicate that hyperactivity
and impulsivity may represent separate dimensions of
ADHD in adults (Sandra Kooij et al. 2008). However,
our results suggest that heritability estimates for
hyperactivity and impulsivity were similar to those
of the collapsed hyperactivity–impulsivity scale. In
addition, we did not have information regarding im-
pairment caused by ADHD symptoms in different
settings such as social or occupational environments.
Moreover, information regarding age of onset of
ADHD was not available. Hence, our results may not
be extrapolated directly to clinical settings.
Conclusions
Overall, our findings are consistent with the previous
but limited literature on self-rated ADHD symptoms
in older children and adolescents, and retrospective
and current reports of self-rated childhood ADHD
by adults. Our data suggest a clear discrepancy in
the estimated heritability rates between self and in-
formant ratings. However, because there are no pub-
lished informant data on adult twins and no studies
that investigate the continuity of genetic influences
from adolescence into adulthood, we are unable to
determine whether our data indicate a true fall in the
204 H. Larsson et al.
magnitude of genetic influences on ADHD in adults
compared to children, or whether this relates to the
use of self-ratings in contrast to informant data.
As indicated by results from a recent meta-analysis
(Nikolas & Burt, 2010), lower heritability estimates for
ADHD symptoms in adults may reflect an increase in
the contribution of non-shared environmental factors
as a function of age. However, the results of our fol-
low-up analyses provide little support for such an
explanation, as non-shared environmental estimates of
inattention and hyperactivity–impulsivity were found
to be stable across age, a result that has also been
reported in a meta-analysis of childhood and ado-
lescent ADHD (Bergen et al. 2007).
Another possible explanation for the observed
discrepancy is that self-ratings for adult ADHD
symptoms may have lower reliability compared to
informant reports. This explanation is, however, un-
likely because our DSM-IV ADHD self-report ques-
tionnaires show high internal consistency and also a
high cross-time correlation that corresponds closely to
stability results observed in younger populations
using other informants (Larsson et al. 2004; Rietveld
et al. 2004; Kuntsi et al. 2005).
Lower heritability estimates for ADHD symptoms
in adults could also arise if adult-onset conditions that
give rise to similar symptoms confound the ratings in
adults. This may occur because all the studies to date
have used cross-sectional data and have not taken the
developmental course of ADHD into account. There
are, however, two points against this view. First, our
data are consistent with other self-rated studies of
ADHD symptoms during adolescence. Second, the
overall pattern of findings is similar to that seen for
parent and teacher ratings of ADHD among children
and adolescents, including a substantial and similar
degree of genetic overlap between inattention and
hyperactivity–impulsivity.
Finally, the observed discrepancy in heritability
may be explained by the fact that ratings of ADHD in
childhood are usually based on informant reports
whereas ratings of ADHD in adults are often based on
self-reports. Such an explanation is in line with the
well-established discrepancies among parent ratings
and self-ratings of psychopathology (Loeber et al.
1991; De Los Reyes & Kazdin, 2005) and also with
previous studies of parent–offspring ADHD showing
greater parent–offspring associations with informant
report or cognitive performance data than self-report
data (Alberts-Corush et al. 1986; Epstein et al. 2000;
Curko Kera et al. 2004). Further work is required to
determine whether alternative measures such as in-
formant ratings, neurocognitive measures or im-
proved descriptions of ADHD symptoms in adults
provide more heritable measures related to the ADHD
phenotype in adults, or whether there is a greater
impact of the non-familial environment on ADHD
during the transition from adolescence to adulthood.
Supplementary material
For supplementary material accompanying this paper
visit http://dx.doi.org/10.1017/S0033291712001067.
Acknowledgments
This research was supported by grants from the
Swedish Council for Working Life and Social Research
and from the Swedish Research Council. H. Larsson
was supported by grants from the Swedish Research
Council (2010-3184), Swedish Brain Foundation and
Karolinska Institutet Center of Neurodevelopmental
Disorders. All authors had full access to all the data
in the study. H. Larsson takes responsibility for the
integrity of the data and the accuracy of the data
analysis.
Declaration of Interest
None.
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