A longitudinal study of personality and major
depression in a population-based sample
of male twins
AYMAN H. FANOUS1,2,3*, MICHAEL C. NEALE3, STEVEN H. AGGEN3
AND KENNETH S. KENDLER3
1Washington VA Medical Center, Washington, DC, USA;2Georgetown University Medical Center,
Washington, DC, USA;3Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth
University, Richmond, VA, USA
Background. The relationship between personality and psychiatric illness is complex. It is not clear
whether one directly causes the other.
Method. In a population-based sample of male twins (n=3030), we attempted to predict major
depression (MD) from neuroticism (N) and extraversion (E) and vice versa, to evaluate the causal,
scar, state, and prodromal hypotheses. In a longitudinal, structural equation twin model, we
decomposed the covariation between N and MD into (a) genetic and environmental factors that
are common to both traits, as well as specific to each one and (b) direct causal effects of N at time
1 on subsequent MD, as well as between MD and subsequent N.
Results. E was negatively correlated with lifetime and one-year prevalence of MD. N predicted the
new onset of MD, and was predicted by both current and past MD. It did not predict the time to
onset of MD. All of the covariation between N and MD was due to additive genetic and individual-
specific environmental factors shared by both traits and a direct causal path between MD and N
assessed later. No genetic factors were unique to either trait.
Conclusions. In men, N may be a vulnerability factor for MD but does not cause it directly.
However, MD may have a direct causal effect on N. The genetic overlap between N and MD in men
may be greater than in women.
The notion that certain personality configur-
ations are associated with more severe psycho-
pathology dates back to Hippocrates (Jackson,
1986). In the twentieth century, Kraepelin
(1936) described personality types that were
predisposed to depression, mania, and psy-
chosis. More recently, the etiological relation-
ship between personality and psychiatric illness
has been of nosological as well as genetic sig-
nificance. This may be due in part to the as-
cendancy of a spectrum concept in the affective
(Akiskal & Pinto, 1999) and psychotic disorders
(Siever et al. 1993; Kendler et al. 1995), ac-
cording to which some Axis I disorders have
personality-based substrates. In addition, the
use of endophenotypes in genetic studies of
psychiatric illness may provide greater power to
detect susceptibility genes (Gottesman & Gould,
2003). Personality traits as endophenotypes im-
proved the power to detect linkage to alcohol-
ism in one study (Czerwinski et al. 1999), while
the inclusion of schizophrenia-related person-
ality disorders in the definition of affection
* Address for correspondence: Ayman H. Fanous, M.D.,
Washington VA Medical Center, 50 Irving St NW, Washington,
DC 20422, USA.
Psychological Medicine, 2007, 37, 1163–1172.
f 2007 Cambridge University Press
First published online 4 April 2007Printed in the United Kingdom
have resulted in higher LOD scores (Riley &
Eysenck proffered a parsimonious theory
of personality comprising the factors neuro-
ticism (N), extraversion (E), and psychoticism
(Eysenck & Eysenck, 1985). Rating scales of N-
and E-like traits have subsequently been devel-
oped within the frameworks of other theories of
personality. N, thought to represent emotion-
ality or the predisposition to experience negative
affect, has been linked to MD in clinical, epi-
demiological, family, and twin studies. It may
predict the later onset of MD in never-ill in-
dividuals (Nystrom & Lindegard, 1975; Boyce
et al. 1991; Kendler et al. 1993), suggesting a
causal relationship. Additionally, N may be
increased in individuals with MD even after
recovery, indicating a‘scar’ effect (Kendler et al.
1993), although two clinical studies could not
confirm this (Zeiss & Lewinsohn, 1988; Duggan
et al. 1991).
The state of being depressed, however, is itself
associated with higher levels of reported N
(Coppen, 1965; Kerr et al. 1970; Hirschfeld
et al. 1983a; Farmer et al. 2002) (henceforth
called the ‘state’ effect), which may be con-
founded with a true trait effect of N on MD
in cross-sectional studies. Furthermore, person-
ality pathology may be associated with a
worse prognosis (Weissman et al. 1978; Boyce
& Parker, 1985; Duggan et al. 1990). There-
fore, clinical samples may be enriched with
patients having high N, introducing bias. These
considerations underscore the importance of
epidemiological samples and longitudinal de-
E measures sociability, liveliness, and the
level of ease and pleasure felt in the company
of others (Eysenck & Eysenck, 1985). Its re-
lationship to MD may be in the opposite direc-
tion to that seen with N, but is probably less
robust (Akiskal et al. 1983; Hirschfeld & Cross,
1983). Several studies support a state effect
(Coppen, 1965; Kerr et al. 1970; Hirschfeld
et al. 1983a; Farmer et al. 2002), whereby sub-
jects in a depressive episode experience lower
levels of E, but two others fail to support
it (Bartholomew, 1959; Mezey et al. 1963). In
two studies, low E did not predict future MD
(Hirschfeld et al. 1989; Boyce et al. 1991). The
confounding of state and trait effects and the
some linkagestudies ofschizophrenia
selection bias of clinical samples mentioned
above for N also apply to E.
The foregoing studies were designed to test
hypotheses about the prediction of personality
from MD and vice versa. However, since as-
sociation alone does not imply causation, these
studies cannot address hypotheses about the
causes of covariation. Genetic-epidemiologic
designs can do this, and are not susceptible to
confounds and biases inherent in within-person
designs (Neale & Cardon, 1992; Neale & Eaves,
1993). Five studies do (Wetzel et al. 1980; Krieg
et al. 1990; Maier et al. 1992, 1995; Lauer et al.
1997), while four do not indicate that N and
MD share common familial factors (Hirschfeld
et al. 1983b; Katz & McGuffin, 1987; Ouimette
et al. 1992; Farmer et al. 2002). Two studies
support the same hypothesis for low E (Wetzel
et al. 1980; Hirschfeld et al. 1983b), while six do
not (Katz & McGuffin, 1987; Krieg et al. 1990;
Maier et al. 1992; Ouimette et al. 1992; Kendler
et al. 1993; Farmer et al. 2002).
twin study in women of personality and MD
(Kendler et al. 1993), we reported evidence
supporting the causal, scar, and state effects of
N using regression analyses. In a longitudinal,
structural equation twin model, most of the
covariation between N and MD was due to
common genetic factors and a small scar effect.
We found no evidence of a significant relation-
ship between E and MD.
There are several reasons to hypothesize that
the causal relationship between personality and
MD may be different in men than in women.
First, the prevalence of MD (Regier et al. 1988;
Weissman et al. 1993; Kessler et al. 1994) and
the mean levels of N are higher, while mean E
is lower (Floderus-Myrhed et al. 1980; Tambs
et al. 1991; Macaskill et al. 1994; Jang et al.
1996), in women than in men. Second, the rates
of substance abuse and antisocial personality
disorders, both of which have been linked to
dimensions of personality (Cox, 1985; Sher &
Trull, 1994), are higher in men (Regier et al.
1988; Kessler et al. 1994). Third, in this sample,
there is evidence of sex-specific genetic factors
for both N and MD (Kendler & Prescott, 1999;
Kendler et al. 2001; Fanous et al. 2002), as well
as sex differences in the extent to which the
correlation between N and MD arises from
genetic factors common to both traits (Fanous
1164 A. H. Fanous et al.
et al. 2002). However, when we previously
examined sex differences in the relationship
between N and MD, we used lifetime measures
of MD and did not model the direct causal effect
of these traits on each other. In this report, we
combine longitudinal and genetic-epidemiologic
designs to clarify the relationship between N
and MD in men, similar to those previously
used in a female sample (Kendler et al. 1993),
with the goal of comparing the genetic and
causal relationships between personality and
MD across the sexes.
This report is based on data from the first
and second waves of interviews in our study of
adult male twins from the Virginia Twin Re-
gistry (now part of the Mid-Atlantic Twin
Registry), details of which have been outlined
previously (Kendler et al. 2000). Briefly, twins
were eligible for participation if one or both
members were successfully matched to state
Division of Motor Vehicles records, and if they
were white, a member of a multiple birth that
included at least one male, and born between
1940 and 1974. After a full explanation of the
research protocol, signed informed consent was
obtained before all face-to-face interviews and
verbal assent before all telephone interviews.
Subjects were aged 20 to 58 years (mean 36.8,
S.D. 9.1 years).
Wave One Assessment
Of 9417 eligible individuals for the first wave,
6812 (72.4%) completed initial interviews. At
these interviews, which were conducted by tele-
phone in over 95% of subjects, MD, N, and E
were assessed for the first time. To assess test-
retest reliability, 150 members of male-male
twins were re-interviewed a mean of 4.4 (S.D.
1.1) weeks after their initial interview.
Wave Two Assessment
Those who completed the Wave One Assess-
ment were recontacted by telephone or mail to
schedule a face-to-face interview at least one
year later. This second-wave interview (1994
through 1998), in which MD was assessed again,
was performed face to face in 79.8% of the
sample, and by telephone in the remainder. N
and E were assessed for the second time by a
self-report questionnaire mailed out after the
Wave Two interview.
Our analyses are based on 1517 male-male
pairs [867 monozygotic (MZ) and 650 dizygotic
(DZ)]. Interviewers had a master’s degree in
a mental-health-related field or a bachelor’s
degree in this area plus two years of clinical
experience. Members of a twin pair were inter-
viewed by different interviewers who were
unaware of clinical information about the co-
twin. Zygosity determination was made using
a discriminant function analysis based on
six standard zygosity questions. The algorithm
was developed on 227 twin pairs who under-
went genotyping using eight or more highly
polymorphic DNA markers (Kendler et al.
N and E were assessed using the 12 and 8
items, respectively, of the short form of the
Eysenck Personality Questionnaire (Eysenck
& Eysenck, 1975). The stabilities of N and E
over 19.3 months in this sample were +0.69
and +0.73, respectively. We will refer to N and
E assessed at time 1 and time 2 as N1, E1, N2,
and E2, respectively. Lifetime and last-year pre-
valence of MD was assessed by a structured
(Spitzer et al. 1987). Our assessment differed
from the standard SCID in two major ways.
First, we assessed last-year history of MD
and a lifetime history of MD prior to the
last year in two separate sections. An individual
who was positive for one or more episodes of
MD in either of these sections is here called
positive for lifetime MD. Second, SCID ques-
tions for MD were modified so that, from the
‘A criteria’ for depression, we independently
noted the presence of the 14 disaggregated
symptoms (i.e. separately assessing weight
loss, weight gain, decreased appetite, and in-
creased appetite). Diagnoses were generated
from the interview response data using a
computer algorithm. For the last year, onsets
and offsets of episodes of MD were estimated
in units of a month. Therefore, current MD
means that the individual reported symptoms
meeting criteria for MD in the month of the in-
Personality and major depression1165
Regression analyses were used to describe the
association between personality at two time-
points and the prevalence of MD between these
time-points. Logistic regression was used when
MD was the dependent variable, and linear
regression when personality was the depen-
dent variable. Generalized estimating equations
(GEE) corrected for spuriously low standard
errors caused by the non-independence of two
members of a twin pair. All analyses were im-
plemented in SAS using the procedure GENMOD
(SAS Institute, Cary, NC, USA).
We performed five kinds of regression ana-
lyses, which we termed descriptive, causal,
prodromal, state and scar. In the descriptive
analyses, we sought to describe the overall as-
sociation between N, E and MD and therefore
included all individuals, using their personality
scores to predict lifetime or last-year MD.
In the causal analyses, we eliminated twins
with one or more episodes of MD prior to or at
time 1 and examined whether N1or E1could
prospectively predict a first onset of MD in the
year prior to the time 2 interview.
In the prodromal analyses, our goal was to
determine whether any of the putative causal
relationships between N, E and MD could result
from these personality measures reflecting prod-
romal depressive symptoms. Therefore, we
tested, only in individuals who went on to de-
velop a new onset of MD, whether N1and E1
predicted the time to onset of their episode.
In the state analyses, we sought to determine
whether being in an episode of MD influenced
personality scores. We eliminated twins who
were in an episode of MD at their time 1 as-
sessment (so that this assessment was not in-
fluenced by state effects of MD), and then,
controlling for their time 1 personality score,
determined whether being in an episode of MD
at their time 2 assessment influenced time 2
In the scar analyses, our goal was to de-
termine whether personality was influenced by
prior episodes of MD. Therefore, we eliminated
twins who had had an episode of MD prior to or
at their time 1 assessment (so that their person-
ality was unaffected by possible earlier scar ef-
fects) and twins who were in an episode of MD
at their time 2 assessment (the presence of which
would contaminate the scar and state effects).
In the remaining sample, we determined
whether having had an episode of MD in the
year between the time 1 and time 2 assessments
influenced the time 2 personality scores, con-
trolling for the time 1 personality scores.
We constructed a longitudinal, structural equa-
tion twin model for the relationship between
personality, as assessed at two times of measure-
ment, and the one-year prevalence of MD that
occurs between these times. We assume a
multifactorial-threshold model for MD, the
strengths and limitations of which have been
discussed elsewhere (Neale, 1992). Owing to the
substantially lower prevalence of last-year than
lifetime MD, there was substantially reduced
power to detect a genetic effect in this sample.
We therefore used a more inclusive defini-
tion of an episode of MD, which differed from
standard DSM-III-R criteria in the following
ways only: subjects were positive if they endor-
sed three or more symptoms for 1 week or
more (n=286, prevalence=9.43%), rather than
at least five symptoms for 2 weeks or more (n=
106, prevalence=3.49%). Criteria were other-
wise identical to those for standard DSM-III-R
The full model (model 1) incorporated two
sets of additive genetic (A), common environ-
mental (C), and individual specific environmen-
tal factors (E): those common to all three
observed variables (level 1), and those specific to
each one (level 2). In addition, it included two
causal paths (level 3): one from N1to MD, (a)
and another from MD to N2(b). This causal
effect reflects the combined impact of both
‘scar’ effects of episodes of MD in the year be-
tween time 1 and time 2 and ‘state’ effects of
individuals who are in an episode of MD at the
time 2 assessment. In addition, in order to as-
sure identification of the two causal paths, we
assume that the genetic and environmental fac-
tors produce an equal influence on time 1 and
time 2 N. This is a reasonable assumption be-
cause this is a sample in early to mid-adult life
and the time period between the two points of
measurement is relatively short. In one study,
the heritability of N declined with age (Viken
et al. 1994). Furthermore, we added MD prior
1166A. H. Fanous et al.
to time 1 as an additional latent variable in
order to equalize as much as possible the inputs
to N1and N2. This model is depicted graphically
in Fig. 1. To choose submodels to test, we
systematically eliminated paths at each of the
three levels sequentially, and used the 95%
confidence intervals (CI) of the parameter esti-
mates of each of these paths in the full model to
Models were fitted directly to raw data
matrices by maximum likelihood using the com-
puter program Mx (Neale et al. 1999). Twelve
thresholds were specified for the N variable,
corresponding to the 12 levels of the scale.
Alternative models were evaluated by compar-
ing the difference in their x2values relative to
the difference in their degrees of freedom, ac-
cording to the principle of parsimony – models
with fewer parameters are preferable if they
do not provide significantly worse fit. We oper-
ationalize parsimony by using the Akaike
Information Criterion (AIC) (Akaike, 1987),
calculated as x2minus twice the degrees of
freedom. The goal was to produce the model
with the lowest (i.e. largest negative) value for
In the epidemiologic, within-person analyses,
the following relationships were observed: (a)
N1 predicted the onset of MD between time 1
and time 2, (b) the presence of MD predicted
N at time 2, while adjusting for N at time 1, and
(c) the occurrence of an episode of MD between
time 1 and time 2 predicted subsequent N at
time 2, again, while adjusting for N at time 1.
These findings supported the causal, state,
and scar hypotheses, respectively. There was
no evidence of the prodromal hypothesis, how-
ever, as N at time 1 did not predict the time to
onset of MD. There was an overall negative
correlation between E and MD although there
was no evidence of causal, scar, state, or prod-
romal effects. Regression coefficients, relative
risks, and significance levels are presented in
points (designated herein as N1and N2) and the 1-year prevalence of major depression (MD) occurring between these two times of
measurement. The model contains three ‘levels’ of paths, represented as arrows: (1) those from the additive genetic, common
environmental, and individual-specific environmental factors at the top of the figure that commonly influence N as assessed at both
times (ACN, CCN, and ECN, respectively) as well as 1-year prevalence of MD (ACMD, CCMD, and ECMD, respectively); (2) those
from the additive genetic, common environmental and individual-specific environmental factors at the bottom of the figure that
specifically influence N at one of the two time-points (ASN, CSN, and ESN, respectively) or MD (ASMD, CSMD, and ESMD,
respectively); and lastly, (3) direct causal paths connecting N1to MD (a) and MD to N2(b). Latent variables are depicted in circles,
while observed variables are depicted in squares.
A longitudinal, structural-equation twin model for the interrelationship between neuroticism as assessed at two time-
Personality and major depression1167
With zygosity, age and the twin’s current de-
pressive status as control variables, co-twin’s
lifetime history of MD significantly predicted
the level of N (b=0.304, p=0.02) but not E
(b=x0.147, p=0.18). When lifetime history
of MD was used as a control variable instead of
current status, the results were similar, but
more significant: co-twin’s lifetime history of
MD predicted the relative’s level of N (b=
0.397, p=0.003) but not E (b=x0.152, p=
As the parameter estimates in model 1 for the
common C paths were close to zero and their
95% CIs included zero, these paths were
eliminated in model 2, which had a minimally
worse fit than model 1 (x2=+0.008, df=2), but
was preferable (AIC=x3.992). All subsequent
models were submodels of model 2. Working
at level 2, we eliminated all possible combi-
nations of the following paths: specific A to N,
specific A to MD, specific C to N, and specific C
to MD, yielding models 3–15 (details available
on request). Among these, the best-fitting model
was one in which we eliminated the paths of
specific A to N, specific A to MD, and specific C
to N. This model (model 9) fit slightly worse
than model 1 (x2=+0.603), but was preferable
because of its parsimony (AIC=x9.397).
Working then from model 9, we sequentially
eliminated paths at level 3. First, we eliminated
a, yielding model 16, which fit slightly worse
than model 9, but was preferable owing to its
parsimony (df=6, AIC=x10.785). We could
not improve on this model, as eliminating b to
yield model 17 caused substantial worsening
of fit (x2=24.384, AIC=+10.383). Finally, we
eliminated the specific C path to MD from
model 9 to yield model 18. This model fit slightly
worse (x2=+3.780), and although it was more
parsimonious (df=7), it was not significantly
different than model 16, differing by less than
one unit of AIC (AIC=x10.220). Therefore,
model 16 was the best-fitting model. However,
the difference in fit between it and the fuller
model, model 9, was only 1.4 units. Following
the guidelines of Burnham & Anderson (1998),
we are therefore unable to reject model 9 as
a difference in AIC of less than 2 suggests that
the fuller model is preferable. Nevertheless, in
model 9, the path that was removed, a, had a
lower 95% confidence interval of 0. We were
therefore able to reject this model on the
grounds that it both had a less negative AIC
and a non-significant path, compared with
model 16. Parameter estimates and 95% confi-
dence intervals for model 9 and the full model,
model 1, are presented in Table 2.
The results we obtained in the epidemiological
analyses of N and MD were broadly similar to
those from the study of female twins. Consistent
with three prior studies (Nystrom & Lindegard,
1975), N predicted the new onset of MD (Tambs
et al. 1991; Kendler et al. 1993). MD predicted
N2 controlling for N1, consistent with a scar
effect, as did our previous study (Kendler et al.
1993), while two clinical studies failed to provide
evidence for it (Duggan et al. 1991; Zeiss &
Table 1. Epidemiological analyses of neuroticism, extraversion, and major depression
Analysis Exclusionary criteria
Key variablesNeuroticism Extraversion
Any previous MD at time 1
1-y MD at time 1
1-y MD at time 1 or MD
at time 2
No 1-y MD
Descriptive and causal analyses used logistic regression; state, scar and prodromal analyses used linear regression.
LT, Lifetime; 1-y: one-year; P1and P2, personality at times 1 and 2 respectively; MD2, MD at time 2; TTO, time to onset.
Bold values denote p<0.05.
1168A. H. Fanous et al.
Lewinsohn, 1988). While controlling for N1, we
were able to isolate the effect of the state of
being depressed on concurrent N at time 2. An
ongoing episode of MD at time 2 strongly and
significantly predicted N2. The effect size in
this analysis (RR 2.52) was greater than that
for both the causal (RR 1.85) and scar ef-
fects (RR 1.88) and confirms the long-held no-
tion that personality is significantly altered
by clinical mood states, as previously reported
(Coppen, 1965; Kerr et al. 1970; Hirschfeld
et al. 1983a; Farmer et al. 2002), validating the
DSM-IV’s caution against diagnosing Axis II
disorders during acute episodes of Axis I dis-
orders. Although we found no evidence sup-
porting the prodromal hypothesis, in which
individuals with high N would have a shorter
time to onset of MD, our sample size was re-
duced to only 5.8% of the original sample when
we admitted only individuals with MD. Because
of the consequent loss of power, we cannot
confidently rule out the possibility that N does
in fact represent prodromal symptoms of MD.
These epidemiologic analyses clearly demon-
strate significant associations between N and
MD consistent with previous studies. However,
they cannot determine whether one has a direct
causal effect on the other, or whether they
merely share a common familial liability. The
genetic-epidemiologic regression analyses, in
which either trait predicted the other across
twins, suggest that at least a portion of the as-
sociation between N and MD is a result of
shared familial factors. They cannot, however,
differentiate genetic from common environ-
mental sources of covariation.
To more definitively resolve these issues, our
longitudinal twin model incorporated genetic
and environmental factors that are shared by N
and MD, those that are specific to each trait,
and direct causal paths between N at time 1 and
MD, as well as between MD and N at time 2. In
the best-fitting model, all of the association be-
tween N and MD was the result of additive
genetic and individual specific environmental
factors shared by both traits, as well as a scar
path between MD and N2. Although in our re-
gression analyses, N1 predicted the one-year
prevalence of MD at time 2, this association was
explained in the twin model by common factors
influencing both traits. This tells against a direct
causal effect of N on MD, but supports the no-
tion that N is an index of the vulnerability to
MD resulting from genetic and environmental
Table 2. Parameter estimates in longitudinal structural equation twin model of neuroticism (N)
and major depression (MD) in males
Parameter Full modelBest-fitting model
A. Paths commonly influencing N and MD
Additive genetic path to N (ACN)
Additive genetic path to MD (ACMD)
Common environmental path to N (CCN)
Common environmental path to MD (CCMD)
path to N (ECN)
path to MD (ECMD)
B. Paths specific to N or MD
Additive genetic path to N (ASN)
Additive genetic path to MD (ASMD)
Common environmental path to N (CSN)
Common environmental path to MD (CSMD)
path to N (ESN)
path to MD (ESMD)
C. Direct causal paths
From N to MD (a)
From MD to N (b)
0.18 (x0.10–0.41)0.29 (0.15–0.40)
0.84 (0.83–0.94)0.83 (0.73–0.92)
See Fig. 1 for graphic representation of model. Path estimates are standardized regression coefficients, so they must be squared to equal the
proportion of variance in the dependent variable accounted for by the dependent variable. Ninety-five per cent confidence intervals are in
parentheses. Dashes indicate that a path was eliminated in its respective model.
Personality and major depression1169
factors and suggests that N may be useful as a
potential endophenotype in genetic studies of
MD. In a recent linkage study of N, regions
on chromosomes 1q and 12q were implicated.
These two regions overlapped with those im-
plicated, respectively, in either alcoholism or
MD (Nurnberger et al. 2001), and MD alone
(Abkevich et al. 2003). However, rigorous
methods for evaluating the overlap of specific
molecular genetic factors for N and MD have
yet to be employed.
There were similarities and differences be-
tween the current analyses and the study of fe-
male twins previously reported (Kendler et al.
1993) that may provide clues about the nature of
the sex difference in MD. In both genders, there
was evidence of a scar effect, but no evidence
that N directly causes MD, suggesting that the
causal relationship between the two traits does
not differ across the sexes (Kendler & Prescott,
1999). The sex difference seen in MD, therefore,
is not likely due to the higher mean N found
in women (Floderus-Myrhed et al. 1980; Tambs
et al. 1991; Macaskill et al. 1994; Jang et al.
1996), as we find no evidence of a causal effect
of N on MD in either sex. It is more likely that
higher N in women is a consequence of the
higher prevalence of MD (Regier et al. 1988;
Weissman et al. 1993; Kessler et al. 1994),
mediated by a scar effect operant in both sexes.
One major difference was that there was evi-
dence of specific genetic factors influencing
either trait in women only. In men, N and MD
completely shared genetic factors. We pre-
viously showed that a model in which the gen-
etic overlap between N and lifetime MD was
higher in men could not be rejected (Fanous
et al. 2002). Taken together, these results sug-
gest that there may be greater genetic hetero-
geneity in women, which could possibly be
related to their greater susceptibility to MD.
This also suggests that N may be more useful as
a proxy endophenotype of MD in men than in
women. The heritability of last-year MD was
substantially lower in men than in women (12%
v. 46%). This is consistent with an analysis
of the same sample, in which the diagnosis of
MD was determined on two occasions to correct
for the unreliability of assessment. In that
analysis, the heritability of lifetime MD was
30% greater in women than in men (Kendler
et al. 2001).
The male and female samples also differed in
the relationship between E and MD. In the
current study of males, there was no evidence
supporting previous reports of state effects
(Coppen, 1965; Kerr et al. 1970; Hirschfeld
et al. 1983a; Farmer et al. 2002), or evidence of
causal, scar, or prodromal effects of E on MD.
However, there was an overall inverse relation-
ship between E and MD in the descriptive
analyses, which was not found in the female
sample. This difference could be a result of
the greater power to detect an effect in the male
sample, as it was substantially larger. As the
descriptive analyses did not exclude any sub-
jects, they may also have had more power to
detect a true effect than did the other analyses,
which excluded different portions of the sample.
Of note, the causal, state, and scar analyses of E
and MD had regression slopes in the expected
direction, but they did not reach statistical sig-
nificance, although in the causal analysis, there
was a trend towards significance (p=0.07).
A sex difference in the relationship between E
and MD may be a result of greater emotional
expression in women (Kring & Gordon, 1998),
leading to a greater degree of social interaction
in women with MD than in their male counter-
The results of this report should be inter-
preted in the context of five potentially import-
ant methodological limitations. First, as the
criteria for MD used in the twin models pre-
sented here were less stringent than those used in
female twins, i.e. three versus five symptoms (see
Method section), differences between these
models and those presented for female twins
should be interpreted with caution. However, in
the female sample, the number of depressive
symptoms in an episode was related to the gen-
etic risk of MD in a monotonic fashion, telling
against the validity of the current requirement in
DSM-IV that five symptoms be present to meet
criteria for an episode of MD (Kendler &
Gardner, 1998). Second, our elimination of dif-
ferent segments of the sample in the regression
analyses to isolate the causal, state, scar, and
prodromal effects is susceptible to selection
effects. Furthermore, as the prevalence of MD
differs in men and women, the selection effects
may also be different, further complicating the
comparison between the male and female
samples. Third, our use of the word ‘causal’
1170 A. H. Fanous et al.
should be understood with the proviso that we
cannot directly assess causal effects of one trait
onanother using thecurrent design, oranyother
are inferred by the covariance of N and MD not
accounted for by genetic and environmental
factors common to both traits, and by the
‘causative’ variable temporally preceding the
‘caused’ variable. Fourth, the diagnoses of MD
were derived from face-to-face interviews in
79.8% and by telephone interviews in the re-
mainder. However, there was little difference
in the assessment of psychiatric disorders or in
the heritability of MD by telephone as opposed
to face-to-face interviews in a previous study
of female twins (Kendler et al. 1992). Fifth, in-
clusion of twins with a wide range of ages makes
it difficult to detect potential difference in the
relationship between personality and MD in
different age groups. One study reported a de-
cline in the genetic influences on N with age
(Viken et al. 1994), although it has not been re-
plicated. Lastly, the one-year interval between
interviews, as well as the fact that the sample
is of adults, makes it difficult to test more de-
velopmental hypotheses about the relationship
between personality and MD, for example,
whether N in childhood or adolescence predicts
MD in adult life.
This work was supported by NIH grants MH-
40828, MH/AA/DA-49492, IT-32 MH-20030,
DA-11287 and AA-09095. We acknowledge
the contribution of the Virginia Twin Registry,
now part of the Mid-Atlantic Twin Registry
(MATR), to ascertainment of subjects for
this study. The MATR, directed by Dr Judy L.
Silberg, has received support from the National
Institutes of Health, the Carman Trust and
the W. M. Keck, John Templeton and Robert
Wood Johnson Foundations. Sarah Burns,
M.A., Frank Butera, M.S., and Patsy Waring
directed data collection. Indrani Ray, B.S.,
provided database support. Dr Carol Prescott
played a major role in the collection of this data.
DECLARATION OF INTEREST
There is no conflict of interest with regard to this
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