Genetic co-morbidity between neuroticism,
anxiety/depression and somatic distress
in a population sample of adolescent
and young adult twins
N. K. Hansell1*, M. J. Wright1, S. E. Medland1, T. A. Davenport2, N. R. Wray1, N. G. Martin1
and I. B. Hickie2
1Genetic Epidemiology, Queensland Institute of Medical Research, Brisbane, Australia
2Brain and Mind Research Institute, University of Sydney, Sydney, Australia
Background. Genetic studies in adults indicate that genes influencing the personality trait of neuroticism account
for substantial genetic variance in anxiety and depression and in somatic health. Here, we examine for the first time
the factors underlying the relationship between neuroticism and anxiety/depressive and somatic symptoms during
Method. The Somatic and Psychological Health Report (SPHERE) assessed symptoms of anxiety/depression
(PSYCH-14) and somatic distress (SOMA-10) in 2459 adolescent and young adult twins [1168 complete pairs (35.4%
monozygotic, 53% female)] aged 12–25 years (mean=15.5¡2.9). Differences between boys and girls across ado-
lescence were explored for neuroticism, SPHERE-34, and the subscales PSYCH-14 and SOMA-10. Trivariate analyses
partitioned sources of covariance in neuroticism, PSYCH-14 and SOMA-10.
Results. Girls scored higher than boys on both neuroticism and SPHERE, with SPHERE scores for girls increasing
slightly over time, whereas scores for boys decreased or were unchanged. Neuroticism and SPHERE scores were
strongly influenced by genetic factors [heritability (h2)=40–52%]. A common genetic source influenced neuroticism,
PSYCH-14 and SOMA-10 (impacting PSYCH-14 more than SOMA-10). A further genetic source, independent of
neuroticism, accounted for covariation specific to PSYCH-14 and SOMA-10. Environmental influences were largely
specific to each measure.
Conclusions. In adolescence, genetic risk factors indexed by neuroticism contribute substantially to anxiety/
depression and, to a lesser extent, perceived somatic health. Additional genetic covariation between anxiety/
depressive and somatic symptoms, independent of neuroticism, had greatest influence on somatic distress, where it
was equal in influence to the factor shared with neuroticism.
Received 26 July 2011; Revised 21 September 2011; Accepted 26 September 2011
Key words: Adolescents, anxiety, depression, heritability, somatic distress, SPHERE, twins.
There is consistent evidence that genes influence
individual variation in the personality trait of neur-
oticism, as well as internalizing disorders such as
anxiety and depression, and somatization syndromes
(most commonly presenting as prolonged fatigue
or chronic pain). In adolescents, these measures are
moderately heritable [i.e. y30–60% (Gillespie et al.
2004; Rettew et al. 2006; Lamb et al. 2010; Bartels et al.
2011)], consistent with that for adults (e.g. Kendler
et al. 2007; Vassend et al. 2011). Genetic covariation has
been shown between neuroticism and anxiety and/or
depression (Boomsma et al. 2000; Hettema et al. 2004,
2006; Kendler et al. 2007), neuroticism and somatic
health (Vassend et al. 2011), anxiety and depression
(Gillespie et al. 2000; Kendler et al. 2007) and anxiety,
depression and somatic syndromes (Hickie et al.
1999b; Gillespie et al. 2000; Kato et al. 2009). However,
it remains unclear if the genetic overlap between
anxiety and depression symptoms and common so-
matic complaints such as prolonged fatigue and pain
is due largely to their relationship with neuroticism.
Extensive co-morbidity between depression and
somatoform symptoms is frequently reported (e.g.
* Address for correspondence: Dr N. K. Hansell, Genetic
Epidemiology, Queensland Institute of Medical Research, Post Office,
Royal Brisbane Hospital, Queensland, Australia 4029.
Psychological Medicine, Page 1 of 12.
f Cambridge University Press 2011
Goldberg, 1996; Vaccarino et al. 2009). Previous work,
particularly in health care settings, has indicated
the extent to which the two syndromes can be dis-
tinguished cross-sectionally and, more importantly,
longitudinally (Hickie et al. 1997, 1999c; Gillespie et al.
1999; van der Linden et al. 1999). Both syndromes
appear to have relatively early ages of onset, with
some subjects largely only ever developing one or
other form of illness. Our previous genetic analyses
(Hickie et al. 1999b; Gillespie et al. 2000) also showed
that, while significant genetic risk is shared between
measures of psychological and somatic distress, there
were also independent genetic and environmental risk
factors influencing somatic health. This has also been
shown in a large Swedish study exploring somatic
syndromes in women (Kato et al. 2009). A variety of
different psychosocial and neurobiological paths have
been proposed to explain both the common and dis-
tinctive aspects of the two syndromes (Rief et al. 2010).
Boomsma et al. (2000) found that genetic covariance
between measures of anxiety, depression, somatic
anxiety and neuroticism could be attributed to a com-
mon genetic source in adolescents. However, studies
in young adults suggest that genetic covariation be-
tween anxiety and depression measures and somatic
health may exceed that due to the relationship with
neuroticism. Gillespie et al. (2000) showed that 67%
of the genetic variance in somatic distress was due to
sources that also influence measures of depression and
phobic anxiety, whereas Vassend et al. (2011) showed
recently that only 35–48% of the genetic variance in
somatic health appears due to a source influencing
Adolescence is the peak age of onset for all of
the major adult psychiatric phenotypes, with the
emergence of depressive disorders post-puberty being
of greatest significance (Merikangas et al. 2010). Of
prime importance in clinical psychiatry is the identifi-
cation of the earliest phenotypes that emerge during
this key developmental period and the extent to which
they predict transition to the major anxiety, mood
and psychotic disorders in adult life. Such work lies at
the heart of current international efforts to promote
earlier intervention (Hetrick et al. 2008; Hamilton et al.
2011; McGorry et al. 2011) or ‘pre-emptive’ psychiatry
(McGorry, 2011). Characterization of key genetic or
environmental risks (and the extent to which they are
shared or unique) during the same period has the
capacity to inform both the type and timing of more
relevant preventive and early intervention strategies.
Other modelling of risks to depression based on twin
studies in the teenage and early adult years indicate
the extent to which there are likely to be both multiple
relevant time points and changing patterns of both
genetic and environmental risk (Kendler et al. 2008).
In the current study, we explore these relationships
in a primarily adolescent population sample com-
prising twins from the Brisbane Longitudinal Twin
Study (Wright & Martin, 2004). Measures of anxiety/
depression and somatic distress, as well as a measure
of overall mental health and well-being were assessed
by self-report using the Somatic and Psychological
Health Report (SPHERE) questionnaire (Hickie et al.
2001a). The instrument was developed specifically to
explore these types of relationships in those with
common forms of psychological distress, but particu-
larly those with affective syndromes. The subscales
measure somatic and psychological symptoms in-
dependently (van der Linden et al. 1999; Wijeratne
et al. 2006) and characterize symptomatology (mood
and behavioural features) as continuous dimensional
traits, so are advantageous for genetic modelling in
a population sample (i.e. twin studies have greater
power to resolve sources of familial resemblance when
using continuous compared with binary or ordinal
data; Neale et al. 1994).
Given the variations in prevalence and age of onset
of depressive disorders between boys and girls in
the post-pubertal period, we tested for potential dif-
ferences in SPHERE scores by gender and explored
differences across adolescence to young adulthood
(12–25 years). Using a trivariate twin design, we then
investigated the relationship of neuroticism, which
captures trait-based anxiety present from early child-
hood and is likely to be indicative of genetic risk fac-
tors (Kotov et al. 2010), to the co-morbidity found
between anxiety/depression and somatic distress.
The sample comprised 2459 adolescents and young
adult twins [1168 complete pairs, 35.4% monozygotic
(MZ), 53% female], mean age 15.5¡2.9, range 12.0–
25.6 years. Participants are typical of the South East
Queensland adolescent and young adult population
on a range of traits and had taken part in one or
more studies (Fig. 1). Written, informed consent
was obtained from all participants and a parent or
guardian for those aged <18 years. The study was ap-
proved by the Human Research Ethics Committee at
the Queensland Institute of Medical Research.
Three measures were obtained from the 34-item
SPHERE questionnaire (Hickie et al. 2001a,b). Par-
ticipants indicated if they had been troubled by
2N. K. Hansell et al.
symptoms over the past few weeks, making one
of three response choices: sometimes/never (coded
as zero); often; most of the time (each coded as 1).
Items were summed to obtain scores for SPHERE-34
(all 34 items), PSYCH-14 (14 items tapping anxiety/
depression), SOMA-10 (10 items, non-overlapping
with PSYCH-14, tapping somatic distress). Internal
SPHERE-34, 0.84 for PSYCH-14, 0.70 for SOMA-10).
Items missing for 50 individuals (mean=1.3¡0.8
items, ranging 1–6 items, <0.001% of the dataset)
were imputed in PRELIS 2.30 (Scientific Software Inter-
national, UK) based on sex, age and remaining items
(i.e. 28–33 items).
SPHERE measures were collected on at least two
occasions for just under half the sample (i.e. 48%;
see Fig. 1). To increase test measurement reliability for
this first analysis, we averaged the data collected on
multiple occasions to give a single measure, together
with an ‘average age’ at assessment. This decision was
further supported by the subtleness and generally
linear nature of change with age and the relative
stability of the data over time (in a subsample of 91
individuals tested twice within 2–6 months, r=0.43 for
SPHERE-34, 0.48 for PSYCH-14, 0.64 for SOMA-10).
Neuroticism was obtained from either 20 items
(scored as yes=1, no=0) from the Junior Eysenck
Personality Questionnaire (JEPQ; Eysenck, 1972;
Eysenck & Eysenck, 1975) and/or 12 items (using a
5-point Likert scale and scored 0–4) from the NEO
Five-Factor Inventory (NEO-FFI) of the NEO-PI-R
(Costa & McCrae, 1992), with more recent assessments
McCrae & Costa, 2004, 2010). Participants with miss-
ing items were excluded and neuroticism was in-
cluded only when SPHERE had been collected on the
same occasion (i.e. 2065 individuals, 84% of the
Distributions for each of the SPHERE measures were
normalized by converting to a proportional scale
before transformation into arcsin values (Freeman &
Tukey, 1950) (e.g. Birley et al. 2006; Wray et al. 2007),
with outliers (four to eight individuals) winsorized to
S.D.¡3.3. Neuroticism was normally distributed with
no outliers. In addition, a single family was found to
be outlying for the three SPHERE measures, using the
%P option in Mx (Neale et al. 2003), which provides a
likelihood statistic for each family conditional on the
genetic model, and this family was excluded from
Modelling, which uses all data points regardless
of missingness, was performed in Mx using a full
Fig. 1. Venn diagram showing study participation numbers
for Somatic and Psychological Health Report (SPHERE)
collection across three studies (Wright & Martin, 2004),
with added components for participants in studies 1A
and 2 only (54 participants) and studies 1B and 3 only
(four participants). Data were collected once only for 1281
participants [52% of total sample (n=2459)], twice for 523
(21%), three times for 526 (21%), four times for 129 (5%).
A measure of neuroticism was available for 84% of the
sample. For approximately one-third of the sample a single
assessment of neuroticism from the NEO was available,
a further third were assessed on the Junior Eysenck
Personality Questionnaire (JEPQ) (one to two occasions)
and the remaining third were assessed on both the
NEO (single occasion) and JEPQ (one to three occasions;
JEPQ and NEO were collected at the same time point for
393 individuals, r=0.71). The summed scores for each
neuroticism measure were standardized (Z-scores:
mean=0¡1) and averaged to produce a composite measure.
For participants with multiple SPHERE measures, a mean
measure (and mean age) was used in analyses. Studies 1
and 2 are ongoing in-person studies of melanocytic naevi
(moles) at age 12 and 14 years (studies 1A and 1B) and
cognition at 16 (Study 2). Study 3 was a mail and phone study
of health and well-being targeting adolescent and young
adult twins. Exclusion criteria for the cognition study at 16
were parental report of head injury, neurological or
psychiatric illness, substance abuse/dependence or current
use of psychoactive medication in either twin. Availability
was the only criterion for the other studies. We determined
zygosity from DNA using a commercial kit (AmpFlSTR
Profiler Plus Amplification Kit; ABI, USA) and this was
later confirmed for >80% of the sample genotyped on a
genome-wide single nucleotide polymorphism (SNP)
genotyping platform [610K Illumina; Illumina Inc., USA
(Medland et al. 2009)].
Comorbidity neuroticism, anxiety/depression, somatic distress3
information maximum likelihood estimator. The fit of
constrained models was compared with the full model
by examining the difference in the x2 log likelihood,
which is distributed as a x2for given degrees of free-
dom. We first assessed homogeneity of sampling by
examining the means and variances for birth order
and zygosity effects as described in McGregor et al.
(1999), as well as the effects of sex, age and sexrage.
Those with significant effects were retained as covari-
ates. We also tested whether the twin correlations for
both MZ and dizygotic (DZ) boys and girls could
be set equal. If not, this is suggestive of magnitude
differences in genetic and/or environmental estimates
for boys and girls. Similarly, if correlations for
opposite-sex DZ pairs are significantly lower than
those of same-sex DZ pairs, this indicates different
sources of influence between boys and girls.
At the univariate level and using the five zygosity
groups (i.e. MZ females, MZ males, DZ females, DZ
males and opposite-sex pairs), we decomposed the
variance of each variable into additive genetic (A),
common environmental (C) and unique environ-
mental (E) sources of variance. We tested for sex limi-
tation effects relating to the source of genetic influence
by setting the correlation between additive genetic
sources of influence on opposite-sex pairs to 0.5 and
comparing the fit of this model with that of the fully
saturated model in which the correlation was free to
vary. Magnitude effects were examined by setting A,
C, and E influences to be equal for boys and girls and
comparing model fit with the fully saturated model,
which allowed these estimates to vary.
Finally, we examined the covariation between
neuroticism, PSYCH-14 and SOMA-10 in a multi-
variate sex-limitation model and in a model collapsed
over sex. Cholesky decomposition and independent
and common pathway modelling approaches (Neale
& Cardon, 1992) were examined, which provide A, C,
and E variance/covariance matrices from which gen-
etic, common environmental and unshared environ-
mental correlations can be calculated. As Cholesky
decomposition is the standard general approach to
decomposing variance into genetic and environmental
sources, we used this model to test the significance of
A and C influences. Akaike’s Information Criterion
(AIC) was examined to compare model fit between
Means, standard deviations and ranges, as well as sex
and age effects for all measures, are shown in Table 1.
We found no evidence of birth order [Dx2ranged
0.5–7.1, df=4 (i.e. Dx2
4)] or zygosity effects (Dx2
2.8–4.9), but significant sex and sexrage effects were
found. Differences were subtle. Girls scored higher for
SPHERE-34 (8.6 v. 8.2), PSYCH-14 (3.8 v. 3.4) and
neuroticism (NEO: 23.2 v. 21.0; JEPQ: 10.1 v. 9.3).
Post-hoc analyses showed that, for SPHERE-34 and
SOMA-10, scores increased with age for girls (Dx2
17.1 and 24.7 respectively), but decreased for boys
increased with age for girls (Dx2
change significantly for boys (Dx2
effects of sex and age are shown in a cross-sectional
format in Fig. 2, with the sample divided into four
age categories (based on the mean age, and mean
score, for individuals with multiple measures).
Neuroticism was strongly correlated with PSYCH-
14 (Table 2) and to a lesser, but still substantial extent,
with SOMA-10 (r=0.58 with SPHERE-34). While there
was no overlap in items for the SPHERE subscales,
they nevertheless showed a strong phenotypic cor-
1=5.1 and 4.5 respectively). For PSYCH-14, scores
1=19.0), but did not
1=2.9). These subtle
The MZ and DZ twin correlations (Table 1) indicate
additive genetic (A), common environmental (C)
and unique environmental (E) influences on both
SPHERE-34 and PSYCH-14 (i.e. the DZr>0.5rMZr)
(Martin et al. 1988), whereas for SOMA-10 and neur-
oticism, A and E influences are indicated (i.e. the
DZrB0.5rMZr). There was some suggestion of mag-
nitude differences in the heritability for boys and girls
for PSYCH-14 (i.e. reduced A and increased C
suggested for girls compared to boys, as indicated by
DZ correlations being similar to MZs for girls, but less
than MZs for boys) and preliminary univariate sex
limitation modelling showed that PSYCH-14 was sig-
nificantly less heritable for girls compared to boys
(p=0.03). In addition, there was also some indication
that different genetic or environmental factors may
influence individual differences in boys and girls for
both SPHERE-34 and PSYCH-14 (i.e. opposite-sex
correlations significantly lower than same-sex DZ
correlations), but this finding was not supported by
sex limitation modelling, which showed that genetic
sources of influence did not differ significantly for
boys and girls.
Covariation between neuroticism, PSYCH-14, and
SOMA-10, was initially examined in a sex-limited
ACE Cholesky model. While the genetic correlations
suggested neuroticism was more strongly related to
anxiety/depression than somatic distress in girls [0.89
(95% confidence intervals (CI) 0.32–1.0) v. 0.51 (x0.01
to 1.0)] than boys [0.76 (0.17–1.0) v. 0.67 (0.002–1.0)],
the CIs were wide and both genetic and environmental
pathways could be set equal for boys and girls
4N. K. Hansell et al.
examine the significance of A and C contributions
in a Cholesky model (Table 2). Small C components,
which accounted for 3% of total variance for neur-
oticism, 11% for PSYCH-14 and 7% for SOMA-10
could be dropped from the model without worsening
1ranged 0.0–3.0). Thus, we collapsed over sex to
essential to maintain fit (Dx2
In examining models containing only A and E
components, both the Cholesky and independent
pathway models containing either one or two common
A and E factors provided a good fit to the data
6=2.3, p=0.89), while A components were
Table 1. Sample demographics: mean, S.D., range, sex and age effects and twin correlations (with 95% CI) for SPHERE-34, PSYCH-14,
SOMA-10 and neuroticism
x0.15¡0.9 (x2.6 to 2.3)
0.03¡0.9 (x2.6 to 2.8)
Sex and age effectsb[Dx2(b)]
Twin correlationsc(95% CI)
MZ (414 pairs)d
DZ (752–753 pairs)
MZF (226 pairs)
MZM (188 pairs)
DZF (214 pairs)
DZM (193 pairs)
DZOS (345–346 pairs)
CI, Confidence intervals; SPHERE, Somatic and Psychological Health Report; MZ, monozygotic; DZ, dizygotic; F, female;
M, male; OS, opposite sex.
aMeans for neuroticism are based on the combined NEO/Junior Eysenck Personality Questionnaire (JEPQ) measure.
NEO raw scores=22.4¡7.2 (2, 45), JEPQ raw scores=9.4¡4.5 (0, 20).
bb weights for sex and age effects are based on standardized covariates and dependent variables. Effects for neuroticism are
for a combined NEO/JEPQ measure, which is described in the method.
cFor all variables, MZM=MZF and DZM=DZF (Dx2
influence based on sex. However, sex limitation modelling suggests that, for PSYCH-14, genetic estimates are larger in boys than
SPHERE-34 (i.e. DZO <DZ same-sex, Dx2
not find significant differences between boys and girls (Dx2
dNeuroticism was available for 84% of the SPHERE sample, for which pair numbers are listed here.
*p<0.05, ** p<0.01, *** p<0.00001.
2ranged 0.4–2.3), suggesting no magnitude differences in genetic
1=4.8, p=0.03). Also, different genetic and/or environmental sources influencing boys and girls are indicated for
1=4.0, p=0.04) and PSYCH-14 (Dx2
1=3.0, p=0.08), but not neuroticism (Dx2
1ranges 0.0–2.0, p ranges 0.16–0.99).
1=4.0, p=0.05) and are suggestive for SOMA-10
1=0.2, p=0.67). Sex limitation modelling further assessed genetic sources, but did
MalesFemales MalesFemalesMales Females
SPHERE scales by sex and age category
Fig. 2. Summed scores for Somatic and Psychological Health Report (SPHERE-34), PSYCH-14 and SOMA-10, meaned separately
for sex and for four age categories.
Comorbidity neuroticism, anxiety/depression, somatic distress5
(Table 2) with the AE Cholesky having the lowest
AIC and thus designated as the best-fitting model. We
then estimated variance specific to neuroticism and
PSYCH-14 by changing the order of the variables and
re-running the model with each of these variables
specified last. Note that this does not change model fit.
Table 2 shows estimates for the best fitting model
with 95% CI (Fig. 3 shows the pathway model). A
Table 2. Model fitting results (best-fitting model shown in bold), plus the additive genetic (A) and unshared environmental (E) estimates
(shown as percentages of total variance with 95% CI) and phenotypic, genetic and unshared environmental correlations derived from
trivariate AE Cholesky analyses of neuroticism, PSYCH-14 and SOMA-10
x2 log likelihood dfAIC
1. Cholesky ACE
2. Cholesky AE
3. Cholesky CE
4. Independent Pathway, 2 Factor A, 2 Factor E, plus specifics
5. Independent Pathway, 1 Factor A, 1 Factor E, plus specifics
6. Common Pathway, 1 Common Factor, plus specifics
% Additive genetic factors (95% CI)
% Unshared environmental factors (95% CI)
Total EA1A2 A3 E1 E2E3
– 40 19 (14–24)41c(37–46)
SOMA-1016 (10–22) 17 (10–24) 10 (04–16)43 04 (02–06) 06 (04–09) 47 (42–53)57
Phenotypic r (rp) Genetic r (rg) Unshared environmental r (re)
CI, Confidence intervals; AIC, Akaike’s Information Criterion.
aIn a Cholesky decomposition, each of the trivariate variance/covariance matrices is decomposed into the product of a lower
triangular matrix and its transpose. This decomposition generates a first factor that influences all variables, a second factor
independent of the first that influences the second and third variables and a third factor independent of the first and second that
influences only the third variable. Independent pathway models allow for one or more genetic or environmental common
factors to be specified, with any remaining variance showing as specific influences. Common pathway models specify genetic
and environmental influence on a latent variable that loads onto each phenotype, with remaining variance showing as specific
bTotal A [heritability (h2)] for Somatic and Psychological Health Report-34, determined from univariate analyses,=49%.
cAdditive genetic factor A1 for neuroticism (52%) includes specific genetic variance (As1=23%). Remaining variance for
neuroticism (Ac1=29%) represents genetic influences common to all measures. Similarly, the unshared environmental factor
E1 for neuroticism (48%) includes specific environmental variance (Es1=33%) and variance due to environmental factors that
are common to all measures (Ec1=15%). In the same way, additive genetic factor A2 (18%) includes specific genetic variance
(As2=7%) and genetic variance in common with SOMA-10 (Ac2=11%). A similar breakdown can be shown for the unshared
environmental factor E2 (41%; Es2=36, Ec2=5). Variance specific to neuroticism and PSYCH-14 was identified by changing
the order of the variables and running the model with either neuroticism or PSYCH-14 specified last. This does not change
model fit. A3 and E3 in the model above represent variance specific to SOMA-10.
6N. K. Hansell et al.
genetic factor (A1) accounts for all of the genetic vari-
ance in neuroticism, which includes 23% specific
variance for neuroticism and 29% that is in common
with the variance for PSYCH-14 and SOMA-10, plus
22% of the variance in PSYCH-14 and 16% in SOMA-
10. A second genetic factor (A2), independent of
A1, accounts for a further common source of genetic
variance between PSYCH-14 (i.e. 11% of the variance:
18% minus 7% specific genetic variance) and SOMA-
10 (17%). The genetic factor (A3) accounts for specific
genetic variance for SOMA-10 (10%). These common
sources of genetic influence are reflected in the genetic
correlations (as the smaller environmental overlap is
reflected in the unshared environmental correlations;
see Table 2).
In contrast to the strong genetic association among
the three measures, common environmental effects
(i.e. the common influence subsumed in factors E1
and E2) are generally much less, with specific environ-
mental effects accounting for a substantial amount
of the variance (i.e. 33% for neuroticism, 36% for
PSYCH-14, 47% for SOMA-10). As can be seen in
Table 2, E1, after adjusting for the specific environ-
mental variation for neuroticism, accounted for only
15% of the variance for neuroticism and 4% for
SOMA-10, but 19% for PSYCH-14 (i.e. variance ac-
counted by A1 and E1 are approximately the same
for PSYCH-14). A second independent factor (E2)
accounts for further common environmental influ-
ences for PSYCH-14 (5%) and SOMA-10 (6%), which
again is approximately half that accounted for by A2.
In the current study we sought to identify the role of
neuroticism, a known risk factor for common mental
disorders (Kotov et al. 2010), as the major shared risk
that may best explain the co-morbidity between
anxiety/depression and somatic distress. We targeted
adolescents and young adults as they pass through
this key stage of onset of both of these syndrome
sets (Merikangas et al. 2010). All measures from
the SPHERE questionnaire were moderately heritable
(40–49%), consistent with similar measures (Lamb
et al. 2010; Bartels et al. 2011; Vassend et al. 2011) and
accounted for most of the covariation between
neuroticism, anxiety/depression and somatic symp-
toms. However, genetic overlap between anxiety/
depression and somatic symptoms was not solely
due to their relationship with neuroticism. This rep-
resents the first genetic study to examine the role
of neuroticism in the covariation between anxiety/
depressive and somatic symptoms in adolescence.
A strongly influential common factor was ident-
ified, accounting for just over half of the genetic
variance in anxiety/depression and neuroticism and
approximately one-third of the genetic variance in
somatic distress. This factor may reflect a suscepti-
bility to psychological distress (i.e. an increased likeli-
hood of responding to situations with fear, sadness,
embarrassment, anger, guilt and disgust), which is
core to the neuroticism domain, and neuroticism-
related characteristics, such as a proneness to ir-
rational ideas, poor impulse control and poor stress
management (McCrae & Costa, 2010). Further, neur-
oticism is related to a self- or body-focused disposition
(Pennebaker & Watson, 1991), which is known to
correlate with level of somatic symptom reporting
(Robbins & Kirmayer, 1991) and to be an associated
feature of mood and anxiety disorders (APA, 2000).
Cognitive processes may also contribute as difficulty
in discriminating emotional feelings and bodily sen-
sations or in expressing emotions have been related
to neuroticism and depressed mood (Parker et al. 1989)
and may also influence self-report of somatic symp-
toms (Kirmayer et al. 1994).
At a neurobiological level, there are likely to be a
variety of paths that are relevant to the expression of
both depression and somatic symptoms. For example,
serotonergic activity appears to influence both de-
pression and somatization, although with differing
relevance to each (Rief et al. 2004) and has also been
(52%). Genetic sources
0.43 0.640.19 0.25 0.69
Fig. 3. Parameter estimates for the trivariate AE Cholesky
model showing covariation between neuroticism, PSYCH-14
and SOMA-10. The model includes additive genetic
(A1, A2, A3) and unshared environmental (E1, E2, E3)
sources. Estimates are standardized such that when squared
they indicate the percentage of variance accounted for.
The factors A1 and E1 account for all of the variance for
neuroticism [i.e. they include specific genetic (23%) and
environmental (33%) variance for neuroticism], while the
factors A2 and E2 are independent of neuroticism
[Note: A2 and E2 include specific genetic (7%) and
environmental (36%) variance for PSYCH-14]. Heritability
(h2) is shown for each variable.
Comorbidity neuroticism, anxiety/depression, somatic distress7
linked to neuroticism (Frokjaer et al. 2010). Disturbed
circadian function (Hickie & Rogers, 2011) appears to
be a major risk factor to onset of both mood disorders
and related somatic syndromes (particularly pro-
longed fatigue) and treatments targeting melatonin
secretion may well provide novel treatment strategies.
We have investigated extensively the relationships
between exposure to infective agents and onset of both
somatic and affective syndromes (Hickie et al. 2006).
Disturbed immune function may also be a factor
and one that we have previously investigated in
adult twin samples (Hickie et al. 1999a). For example,
and induce symptoms such as fatigue, depressed
mood and altered cognition (Vollmer-Conna et al.
2004; Dimsdale & Dantzer, 2011), although con-
centrations of immune parameters can differ between
patients with depression and those with somatization
(Rief et al. 2010). Further, levels of pro-inflammatory
cytokines have been associated with neuroticism
(Sutin et al. 2010).
We also identified a second common genetic factor,
independent of neuroticism and specific to anxiety/
depression and somatic distress. It is unclear what this
genetic factor may represent, but given the complex
psychological and biological processes involved as we
discuss above, it is plausible that there may be some
genetic effects in common with anxiety/depression
and somatic distress that are independent of neuroti-
cism. Interestingly, while this second genetic factor
has a strong influence on somatic distress, accounting
for more than one-third of the genetic variance, it
has less influence on anxiety/depression, where it
accounts for approximately one-quarter of the genetic
variance. As the complex physiology underlying
somatic symptoms is increasingly recognized as im-
mune related (Dimsdale & Dantzer, 2011), this factor
may reflect cytokine activity specific to these traits.
Our results extend those found in adult samples
and are consistent with reported relationships be-
tween neuroticism and generalized anxiety disorder
(Hettema et al. 2006; Kendler et al. 2007), major
depression (Hettema et al. 2006; Kendler et al. 2007)
and somatic health (Vassend et al. 2011). The identi-
fication of a second genetic factor is in contrast
with Boomsma et al. (2000), where a single common
factor was found to account for all genetic covariation
between measures of depression, anxiety, somatic
anxiety and neuroticism. Notably, we found an inde-
pendent pathway model allowing only a single com-
mon genetic source to have only a slightly worse
fit than our best-fitting Cholsky model; thus, both
modelsare worthy of
in contrast to the independent pathway model, all
elements of the Cholesky model were significant,
adding to confidence in this model, which had the best
In contrast to the genetic influences, unshared
environmental influences were largely specific to each
variable, (rgranges 0.61–0.87 while reranges 0.25–0.56).
These reflect environmental risk factors unique to the
individual and to the trait and at least a moderate
degree of trait-specific measurement error [based on
reliability reports for similar symptom scales (e.g.
Vallejo et al. 2007) and our own estimates of stability
over 2–6 months]. Even so, the finding of small over-
lapping unshared environmental factors suggests
that some environmental risk factors are relevant to
all measures or are independent of neuroticism and
common to anxiety/depression and somatic distress.
For example, exposure to stressors can promote pro-
inflammatory processes (Raison et al. 2006), which
could potentially influence multiple related traits.
However, common unshared environmental factors
may also include correlated measurement error,
including state effects.
In addition to the finding of a major role of genetic
factors on the relationship between neuroticism
and anxiety/depressive and somatic symptoms in
adolescence, the subtle differences for girls and boys
across this period of adolescence are worth noting. In
girls, we found that, for each of the SPHERE measures,
the number of symptoms increased slightly over time
(from 12 to 25 years), whereas in boys the symptom
scores either decreased or showed no significant
change. Although not all studies are in agreement
(Bartels et al. 2011), similar patterns have been re-
ported previously for depression in adolescents
(Sund et al. 2001; Angold et al. 2002; Lamb et al. 2010).
For neuroticism, while girls had higher scores than
boys, as has been found previously (McCrae et al.
2002), we detected no change in scores across ado-
lescence in either girls or boys. This is consistent with
what has been shown in adulthood (Terracciano et al.
2006), while varying age effects have been reported for
adolescents. McCrae et al. (2002) found no age affects
in American high school students, but found small
increases for girls with age in a replication sample of
Flemish adolescents and, interestingly, in a sample of
gifted students, perhaps suggesting a cognitive com-
ponent to the increases found. It is possible that
increases over age in girls compared to boys, as found
for our anxiety/depressive and somatic symptoms,
may also reflect differing cognitive styles.
A limitation of this study, despite the large sample
(n=2459), was the lack of power to explore sex differ-
ences underlying covariation between the traits.
8 N. K. Hansell et al.
Although not significant, our results suggested neur-
oticism had a stronger genetic relationship with
anxiety/depression than somatic distress and this was
more prominent in girls than boys. Fanous et al. (2002)
hypothesized that neuroticism and depression may be
more genetically correlated in females, but, in an adult
sample, they found correlations to be higher, although
not significantly, in males. At a univariate level, we
did find significantly higher heritability in boys com-
pared to girls for anxiety/depression (PSYCH-14).
A recent review of childhood and adolescent anxiety
and depression reports small to negligible sex differ-
ences in genetic aetiology (Franic et al. 2010), but,
interestingly, a higher heritability for boys has been
found for self-rated depression in children and ado-
lescents, although no difference was found for par-
ental ratings of depression in the same individuals
(Rice et al. 2002). Most recently, a higher heritability for
adolescent girls for self-reported anxiety/depression
and somatic complaints was found (Bartels et al. 2011),
a finding also reported for teacher ratings of depress-
ive symptoms in young adolescents (Happonen et al.
2002) and for mother-rated separation anxiety in
children and adolescents (Feigon et al. 2001). Clearly,
further research using large samples is required to
clarify the role of sex in the genetic aetiology of child-
hood and adolescent anxiety and depression and the
role of factors such as phenotype definition and par-
The use ofdimensional
than categorical measures of anxiety/depression and
somatic distress is also a potential limitation. Cat-
egorical classification is optimal when no meaningful
clinical variation exists among those diagnosed posi-
tive or among those negative (Kraemer, 2007). How-
ever, the range in symptom count found for anxiety/
depression (0–14) and somatic distress (0–10) suggests
that the full range of variability would not be captured
in a select number of categories. Supporting the use
of a dimensional measure, at least for depression, are
studies showing a linear relationship between symp-
tom count and impairment or disability (Ustun and
Sartorius, 1995; Sakashita et al. 2007) and others
indicating depression is best conceptualized as one
latent continuous dimension (Ruscio & Ruscio, 2000;
Slade & Andrews, 2005).
A further limitation may be the conceptualization
of anxiety/depression and somatic distress as distinct
syndromes, given the relative lack of empirical data
to support a clear separation. Somatic syndromes,
together with anxiety and depression, may be con-
sidered part of a broader spectrum of internalizing
disorders (Krueger et al. 2003). Nevertheless, factor
analytic studies show a clear separation (Gillespie et al.
1999; Kirk et al. 1999), consistent with studies showing,
for example, that not all patients with somatic dis-
orders meet criteria for other psychological disorders
(Hickie et al. 1990) and that patients with fatigue
do not show specific response to antidepressant phar-
macotherapy (Vercoulen et al. 1996).
We must also acknowledge that we are not yet
sufficiently powered to detect common environmental
influences. Based on our current sample size and twin
correlations, we have >75% power to detect additive
genetic influences in a univariate model, but only
5–37% power to identify common environmental in-
fluences (i.e. 37% for SPHERE-34, 25% for PSYCH-14,
5% for SOMA-10). Therefore, our genetic estimates
from the AE model may be slightly inflated. However,
negligible C estimates are consistent with the literature
for self-rated depression and somatic complaints
(Happonen et al. 2002; Rice et al. 2002; Bartels et al.
Although our study design precluded examination
of longitudinal relationships, data collection is on-
going and future analyses will address age-related
changes in genetic and environmental influence. Here,
we show that, in adolescents, the genetic risk factors
indexed by neuroticism do not fully account for the
genetic overlap found between measures of psycho-
logical and somatic health.
This work was supported by grants from the
Australian Research Council (ARC), the National
Health & Medical Research Council (NHMRC) and
Beyond Blue, Australia.
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