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Genetic and environmental influences on the stability
of psychotic experiences and negative symptoms in
adolescence
Laura Havers,
1
Mark J. Taylor,
2
and Angelica Ronald
1
1
Department of Psychological Sciences, Birkbeck, University of London, London, UK;
2
Department of Medical
Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden
Background: Psychotic experiences (PEs) such as paranoia and hallucinations, and negative symptoms (NS) such as
anhedonia and flat affect are common in adolescence. Psychotic experiences and negative symptoms (PENS) increase
risk for later psychiatric outcomes, particularly when they persist. The extent to which genetic and environmental
influences contribute to the stability of PENS in mid-to-late adolescence is unknown. Methods: Using the Specific
Psychotic Experiences Questionnaire (SPEQ) twice across ~9 months in adolescence, N=1,448 twin pairs
[M=16.32 (0.68)] reported experiences of paranoia, hallucinations, cognitive disorganization, grandiosity and
anhedonia, and their parents reported on a range of NS. Individuals were split into low-scoring, decreasing,
increasing and persistent groups for each subscale. Frequencies and mean differences in distress, depression traits
and emotional problems were investigated across groups. Longitudinal structural equation modelling was used to
estimate the aetiological components underlying the stability of PENS. Results: Phenotypic stability was moderate
for all PENS (r=.59–.69). Persistent PENS across 9 months were associated with greater levels of distress (V=0.15–
0.46, for PEs only), depression traits (d=0.47–1.67, except grandiosity) and emotional problems (d=0.47–1.47,
except grandiosity and anhedonia) at baseline compared to groups with transitory or low levels of PENS. At both ages
PENS were heritable and influenced by shared and nonshared environment. Genetic influences contributed 38%–
62% and shared environment contributed 13%–33% to the stability of PENS. Nonshared environment contributed
34%–41% (12% for parent-rated NS). There was strong overlap of genetic and shared environmental influences across
time, and lower overlap for nonshared environment. Imperfect stability of PENS was at least partly due to nonshared
environmental influences. Conclusions: When adolescent PENS persist over time, they are often characterized by
more distress, and higher levels of other psychopathology. Both genetic and environmental effects influence stability
of PENS. Keywords: Adolescence; aetiology; development; mental health; psychosis.
Introduction
Experiences such as paranoia, hallucinations, anhe-
donia and behaviours such as flat affect are reported
in childhood and adolescence, and in general popu-
lation as well as clinical samples (McGrath et al.,
2015; Peters et al., 2016; Wong, Freeman, &
Hughes, 2014). These experiences and behaviours
are grouped together in the study of psychotic or
psychotic-like experiences (PEs), and negative symp-
toms (NS), because in their extreme they are char-
acteristic of psychotic illnesses. Psychotic
experiences and negative symptoms (PENS) show
considerable variability in the general population
and typically show a positively skewed distribution
(e.g., Bebbington et al., 2013; Ronald et al., 2014).
Epidemiological findings suggest that PEs are
common (McGrath et al., 2015), associated with
earlier childhood behaviour problems (Shakoor,
McGuire, Cardno, Freeman, & Ronald, 2018) and
that they are cross-sectionally less prevalent with
increasing age (Kelleher, Connor et al., 2012;
McGrath et al., 2015). For the majority of people,
PEs generally abate (Linscott & van Os, 2013), show-
ing mean-level decline over time (Dominguez, Wich-
ers, Lieb, Wittchen, & van Os, 2011; Mackie,
Castellanos-Ryan, & Conrod, 2011; R€
ossler et al.,
2007). Some PEs may thus be part of typical
behavioural variation (Hanssen, Bak, Bijl, Volle-
bergh, & van Os, 2005; Van Os, Linscott, Myin-
Germeys, Delespaul, & Krabbendam, 2009; Wong &
Raine, 2018; Wong et al., 2014). Longitudinal studies
show that child and adolescent PEs are associated
with increased odds of psychiatric disorders in adult-
hood (Fisher et al., 2013). Furthermore, PEs reported
in mid compared to early adolescence (Bartels-
Velthuis, van de Willige, Jenner, van Os, & Wiersma,
2011; Kelleher et al., 2012), and those which persist
over time (Dominguez et al., 2011; Wigman, Winkel,
Raaijmakers et al., 2011) are associated with rela-
tively increased odds for psychiatric and dysfunc-
tional behavioural outcomes. Compared to PEs, there
are fewer studies on NS in the general population, and
there are no meta-analyses or reviews. Like PEs,
however, NS appear to be common in adolescence in
the general population (Barragan, Laurens, Navarro,
& Obiols, 2011; Ronald et al., 2014). As such,
research on the aetiological factors that influence
the presentation and the persistence of PEs and NS
(PENS) in mid adolescence is informative about
Conflict of interest statement: No conflicts declared.
The copyright line for this article was changed on 21 May 2019
after original online publication.
©2019 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and
Adolescent Mental Health.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited.
Journal of Child Psychology and Psychiatry 60:7 (2019), pp 784–792 doi:10.1111/jcpp.13045
PFI_12mmX178mm.pdf + eps format
typical adolescent development, but may also shed
light on the pathways that lead to mental illness.
While there are no published findings regarding
differing trajectories of NS, several studies have
identified distinct trajectories of PEs through growth
mixture modelling in 10- to 11-year olds (Wigman,
Winkel, Raaijmakers et al., 2011) and 14-year olds
(Mackie et al., 2011), and through latent class anal-
ysis in adults (Wigman, Winkel, Jacobs et al., 2011).
A meta-analysis of cross-age studies reported that
PEs were persistent for ~20% of individuals (Linscott
& van Os, 2013). As well as being associated with
clinical and poor behavioural outcomes, persistence
is associated with a range of risk factors including
cannabis use, trauma, stressful life events and
urban environment in adolescence (Cougnard et al.,
2007; Wigman, Winkel, Raaijmakers et al., 2011).
Despite these findings, no studies to date have
investigated the aetiological influences on the sta-
bility of PENS in mid-to-late adolescence. A study of
adult twins hinted at a substantial genetic contribu-
tion to PE-persistence (NS were not reported in terms
of persistence). Wigman, Winkel, Jacobs et al. (2011)
reported that for monozygotic (MZ) twins who expe-
rienced persistence, 49% of their co-twins also
experienced persistence, compared to 14% for dizy-
gotic (DZ) twins, although twin model-fitting was not
conducted. A different but related conceptualization
of PENS, schizotypy is viewed as being an expression
of psychotic-like behaviour at a personality level (see
Linscott & van Os, 2013). One twin study has
reported on the moderate stability (r=.58) of a
‘schizotypy factor’ over early to mid adolescence
from ages 11–16 in 100 pairs assessed across time
(Ericson, Tuvblad, Raine, Young-Wolff, & Baker,
2011). Genetic and nonshared environmental influ-
ences explained 81% and 19% of this factor, respec-
tively. At the second time point, variance was
explained by both stable and new genetic influences
(36% and 42% respectively), and stable and new
nonshared environmental influences (3% and 19%
respectively). While these results demonstrate that
the aetiological effects influencing psychosis-related
phenotypes are both stable and dynamic, the cross-
time sample size was small for a twin study.
The largest twin study to date on adolescent PENS
at a single time (using the same sample as the
current study) reported heritability estimates of
15%–50% for PEs and 47%–59% for NS (Zavos et al.,
2014). Common environmental influences were evi-
dent for hallucinations and parent-rated negative
symptoms (PRNS) (17%–24%), and the remainder of
variance in PENS was accounted for by nonshared
environment (49%–64%), and to a lesser degree for
PRNS (17%). Using genotype data from unrelated
individuals, SNP-heritability has been estimated as
3%–9% in a recent genome-wide association study
(GWAS) meta-analysis of adolescent PENS, providing
further evidence of genetic effects influencing PENS
(Pain et al., 2018; see also Sieradzka et al., 2015).
The current study builds on existing research by
utilizing a large, representative sample of male and
female twins. It encompasses four specific domains
assessing PEs (paranoia, hallucinations, cognitive
disorganization and grandiosity), and two assessing
NS (self-reported anhedonia, parent-reported NS)
measured over approximately 9 months in mid-to-
late adolescence. The first aim was to estimate the
extent to which genetic and environmental influ-
ences contribute to the stability of adolescent PENS.
It was predicted that genetic effects would explain a
substantial amount of the cross-time covariance,
and that there would be substantial overlap of
genetic effects across time. It was also expected that
the aetiological cross-time correlations would be less
than 1, highlighting the role of time-specific influ-
ences. The second aim was to characterize the
sample in terms of phenotypic persistence by group-
ing individuals according to whether their PENS
persist, increase, decrease or remain low. It was
predicted that persistence would be associated with
higher levels of psychopathology compared to low-
scoring, increasing and decreasing scores.
Methods
Participants
Participants were part of the Longitudinal Experiences and
Perceptions (LEAP) study, which measured PENS at age 16.
LEAP is part of the Twins Early Development Study (TEDS),
which has collected data from twins born during 1994 to 1996
in England and Wales across their childhood (Haworth, Davis,
& Plomin, 2013). In sum, 10,868 families were invited to LEAP,
of which 5,059 twin pairs and 5,076 parents returned data. A
subsample of responding families was invited to LEAP phase 2
approximately 9 months later. Of 1,773 families invited for
phase 2, 1,464 returned data. Demographics of the two
samples are shown in Table S1. In the current study, 1,448
twin pairs have data at both time points (time 1 M=16.32
(0.68), 54.5% female, 36% MZ; time 2 M=17.06 (0.88), 58.1%
female, 35% MZ). Parents and twins gave their informed
consent to take part in these studies. TEDS was granted
ethical approval from the Institute of Psychiatry Ethics Com-
mittee, Kings College London. See Appendix S1 for further
details.
Measures
Psychotic experiences and negative symptoms were measured
using the Specific Psychotic Experiences Questionnaire (SPEQ;
Ronald et al., 2014). The SPEQ is a validated self-report and
parent-report assessment tool, comprising six subscales mea-
suring mild-to-more severe experiences of paranoia (15 items),
hallucinations (9 items), cognitive disorganization (11 items),
grandiosity (8 items), hedonia (10 items, reversed to give a
measure of anhedonia) and parent-rated negative symptoms
(PRNS) (10 items). See Ronald et al. (2014), and Appendix S2
for further details. Distress was measured using a single item
following each subscale (Overall, how distressed are you by
these experiences?), with exception of the anhedonia and PRNS
subscales. Depression traits were measured using the 13-item
self-report Short Mood and Feelings Questionnaire (SMFQ;
Angold, Costello, Messer, & Pickles, 1995). Emotional problems
and other psychopathology scales (conduct problems, hyper-
activity and peer problems) were measured using the 5-item
©2019 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
doi:10.1111/jcpp.13045 Stability of psychotic experiences and negative symptoms 785
self-report Strengths and Difficulties Questionnaire subscales
(SDQ; Goodman, 1997).
Design
The twin design aims to disentangle the roles of genetic and
environmental influences on variation in a phenotype, and
on covariation between phenotypes (Boomsma, Busjahn, &
Peltonen, 2002). Initial inferences can be made by comparing
within-pair MZ and DZ correlations. If MZ correlations (rMZ)
are greater than DZ correlations (rDZ), additive genetic
factors (A) are suggested. If rMZ are more than twice rDZ,
nonadditive genetic factors (D) are implicated. Where rDZ are
greater than half rMZ, shared environmental factors (C) are
suggested. The extent to which rMZ are <1 implicates
nonshared environmental influences, including measurement
error (E). These correlations form the basis for quantifying
the relative genetic and environmental contributions using
twin model-fitting.
Analyses
SPSS software was used for all phenotypic analyses, using
data from one randomly selected twin (per pair) with data at
both time points. Untransformed data were used for descrip-
tive statistics and frequency-based analyses. For each SPEQ
subscale, individuals were grouped as follows: ‘Low-scoring’,
time 1 and time 2 scores in the bottom 90% of scores;
‘Decreasing’, time 1 score in the top 10% and time 2 score in
the bottom 90%; ‘Increasing’, time 1 score in the bottom 90%
and time 2 score in the top 10%; ‘Persistent’, time 1 and time 2
scores in the top 10%. Across PENS, the top 10% of the score
distribution was on average 1.41 SD from the mean.
Cohen’s dwas used to compare group differences in PENS,
SMFQ and SDQ scores. dis a measure of the standardized
difference between means (calculated for unequal sample sizes
using; https://www.psychometrica.de/effect_size.html). Fish-
er’s exact test was used to determine distress frequency
associations between groups due to small numbers of obser-
vations in some cells. Cramer’s V was used to measure the
strength of the association of the chi-squared value.
Skewed measures (paranoia, hallucinations, grandiosity
and PRNS) were log-transformed so that all skew statistics
were between 1 and 1. All measures were regressed on age
and sex, and residuals were standardized. A constrained
saturated model, in which the means, variances and pheno-
typic correlation were constrained to be equal across twin
order and zygosity was run using OpenMx (Boker et al., 2011)
within R software (version 3.3) to derive phenotypic and twin
intraclass correlations, and to test for mean and variance
differences in the data.
Prior to performing bivariate twin analysis, the main
assumptions of the twin model were tested using a series of
saturated models, as outlined in Appendix S3. Bivariate
Cholesky decompositions were fitted using OpenMx to inves-
tigate the aetiology of PENS across time points. Bivariate
analysis compares MZ and DZ cross-twin cross-time correla-
tions. Figure S1 shows a bivariate Cholesky decomposition
solution and a correlated factors model. These are mathemat-
ically equivalent solutions and both provide useful statistics
for interpretation (Loehlin, 1996). Bivariate parameter esti-
mates derived from the Cholesky solution reflect the contribu-
tion of ACE factors to covariance (represented by the diagonal
lines in the left-hand figure in Figure S1), and aetiological
correlation coefficients derived from the correlated factors
model (represented by double headed arrows in the right-hand
figure in Figure S1) describe the overlap of A (rA), C (rC), and E
(rE) influences. Opposite sex DZ twins were included in the
models. The Cholesky decomposition quantifies the ACE
effects at time 2 that also influence the time 1 measure, and
those unique to time 2. OpenMx accounts for missing data
through the use of maximum likelihood, therefore individuals
with data only at time 1 were also included (N=4,870 and
N=1,464 pairs at times 1 and 2 respectively).
ACE and ADE models with quantitative and qualitative sex
differences were first fitted and compared to a saturated model.
Only ACE models were run for hallucinations and PRNS
because the twin correlations did not suggest any D influences
on these scales. The 2LL (2 times log-likelihood) value was
used to assess which of the full sex differences models fit the
data best, with lower values indicating a better fit. Whichever
model fit best was used to determine subsequent testing of the
following models: (a) ACE or ADE with quantitative sex
differences only, (b) ACE or ADE without sex differences on
the aetiological correlations and (c) ACE or ADE without sex
differences. Three indices of fit were generated: 2LL, Akaike’s
Information Criterion (AIC) and Bayesian Index Criterion (BIC).
Goodness of fit for these nested models and subsequent
submodels was assessed using BIC because it has been shown
to outperform alternative indices for multivariate models in
larger samples. Lower BIC values indicated a better fit. A BIC
difference of at least 10 between two models indicates that the
model with the lower BIC value is a better fit than the model to
which it is being compared (Raftery, 1995).
Results
General descriptives
Descriptive statistics are presented in Tables S2 and
S3. Table S4 shows frequencies of distress associ-
ated with PEs. Of those with some PEs, 11.8%–
37.4% of individuals reported some level of distress.
Between 2.1% and 10.8% reported being quite or
very distressed.
Univariate twin model-fitting
Table 1 shows the univariate twin correlations and
Tables S5–S10 show the results of testing for mean
and variance differences in the data. The univariate
twin estimates are reported from the bivariate twin
models. Across all PENS except anhedonia, bivariate
ACE models without sex differences fit the data best.
An AE model without sex differences fit the data best
for anhedonia (Tables S11–S16). Table 2 shows the
univariate parameter estimates from these models.
At each time point, genetic influences contributed
moderately to the variance in PEs (heritability 22%–
38%), and more so to variance in NS (heritability
45%–47%). Shared environment contributed mod-
estly to variance in PEs (6%–19%), and to a greater
extent to variance in PRNS (36%–38%). Nonshared
environment contributed moderately to the variance
in PENS (51%–59%), but less so for PRNS (17%–
18%).
Bivariate twin model-fitting
Table 1 shows the phenotypic cross-time correla-
tions (r=.59–.69) and cross-twin cross-time corre-
lations. Cross-twin cross-time rMZ were higher than
rDZ for all PENS suggesting genetic influences, and
cross-twin cross-time rMZ were all less than 1,
©2019 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
786 Laura Havers, Mark J. Taylor, and Angelica Ronald J Child Psychol Psychiatr 2019; 60(7): 784–92
suggesting nonshared environment on the cross-
time covariation. For hallucinations and PRNS,
cross-twin cross-time rDZ were greater than half
rMZ, suggesting shared environment on the cross-
time covariance. rDZ were less than half rMZ for
female paranoia, cognitive disorganization and
grandiosity, and across sexes for anhedonia, sug-
gesting some nonadditive genetic effects.
Table S17 shows the Cholesky estimates. Across
PENS, between 42% and 58% of the variance at time
2 was accounted for by aetiological influences car-
ried over from time 1. Specifically, 25%–38% of the
variance in each measure at time 2 was accounted
for by genetic influences carried across time, 0%–
13% was due to shared environment, and 3%–14%
was due to nonshared environment. Aetiological
influences unique to time 2 were highest for non-
shared environment across PENS (42%–49%, except
PRNS, 14%).
Genetic correlations indicated substantial overlap
in genetic influences across time (rA=.77–1.00)
(Table S18). The high rC estimates across PENS
suggest considerable overlap in C influences
(rC=.59–1.00). Moderate rE suggest that E influ-
ences across time partially overlap (rE=.36–.49).
Table 2 shows the bivariate parameter estimates.
The proportion of the phenotypic correlation that
was explained by genetic influences was 0.38–0.46
for PEs and 0.54–0.62 for NS. The proportion of the
phenotypic correlation that was explained by shared
environmental influences was 0.13–0.33, except for
anhedonia which showed no C. The proportion of the
phenotypic correlation that was explained by non-
shared environmental influences was 0.34–0.41,
although less for PRNS (0.12).
Cross-time phenotypic subgroup analysis
Table S19 shows the descriptives of the phenotypic
persistence subgroups. For individuals with high
time 1 PENS, means were significantly higher for
persistent compared to decreasing groups (d=0.31–
0.56, except grandiosity). For individuals with low
time 1 PENS, means were significantly higher for the
increasing compared to low-scoring groups
(d=1.08–1.61). Between 5.5% and 8.1% of individ-
uals had persistently high PENS (Table S19).
Table 3 shows that for all PEs except grandiosity,
point estimates suggested that the persistent group
reported being quite or very distressed more often
than the other groups, and reported being not
distressed to a lesser extent. Fisher’s exact test and
Cramer’s Vstatistics (0.15–0.46) were significant
(p≤.001) for comparisons between the low-scoring
and increasing groups, the persistent and low-scor-
ing groups, and between the persistent and increas-
ing groups, but not between the persistent and
decreasing groups.
Across PENS, the persistent group showed a
pattern of having higher point estimates for both
Table 1 Phenotypic and twin correlations
Paranoia Hallucinations
Cognitive
disorganization Grandiosity Anhedonia PRNS
Phenotypic
Whole sample 0.63 [0.62, 0.65] 0.61 [0.59, 0.63] 0.69 [0.68, 0.71] 0.59 [0.58, 0.61] 0.63 [0.61, 0.64] 0.65 [0.63, 0.66]
Female 0.63 [0.61, 0.65] 0.61 [0.58, 0.63] 0.69 [0.67, 0.71] 0.58 [0.56, 0.60] 0.63 [0.61, 0.65] 0.65 [0.62, 0.67]
Male 0.64 [0.61, 0.67] 0.61 [0.58, 0.64] 0.69 [0.67, 0.72] 0.63 [0.60, 0.66] 0.62 [0.59, 0.65] 0.64 [0.61, 0.67]
Cross-twin time 1
MZM 0.45 [0.40, 0.50] 0.36 [0.30, 0.42] 0.43 [0.37, 0.49] 0.47 [0.42, 0.52] 0.47 [0.41, 0.52] 0.83 [0.81, 0.85]
MZF 0.53 [0.49, 0.57] 0.47 [0.43, 0.52] 0.46 [0.42, 0.51] 0.46 [0.41, 0.50] 0.49 [0.45, 0.54] 0.83 [0.81, 0.84]
DZM 0.26 [0.19, 0.33] 0.28 [0.20, 0.34] 0.29 [0.22, 0.35] 0.25 [0.17, 0.32] 0.21 [0.14, 0.28] 0.50 [0.45, 0.55]
DZF 0.28 [0.24, 0.31] 0.26 [0.23, 0.30] 0.21 [0.17, 0.25] 0.26 [0.22, 0.30] 0.22 [0.18, 0.25] 0.53 [0.50, 0.56]
DZOS 0.26 [0.21, 0.31] 0.23 [0.18, 0.28] 0.23 [0.18, 0.27] 0.23 [0.19, 0.28] 0.18 [0.14, 0.23] 0.50 [0.46, 0.53]
Cross-twin time 2
MZM 0.37 [0.26, 0.47] 0.42 [0.31, 0.51] 0.46 [0.36, 0.55] 0.37 [0.26, 0.46] 0.50 [0.40, 0.58] 0.84 [0.79, 0.87]
MZF 0.54 [0.47, 0.60] 0.59 [0.52, 0.65] 0.50 [0.43, 0.56] 0.51 [0.44, 0.58] 0.48 [0.40, 0.55] 0.84 [0.81, 0.86]
DZM 0.15 [0.02, 0.28] 0.32 [0.20, 0.43] 0.24 [0.12, 0.35] 0.27 [0.13, 0.39] 0.10 [0.02, 0.22] 0.49 [0.39, 0.58]
DZF 0.26 [0.20, 0.32] 0.27 [0.21, 0.33] 0.15 [0.09, 0.21] 0.28 [0.22, 0.34] 0.19 [0.13, 0.25] 0.55 [0.51, 0.59]
DZOS 0.19 [0.11, 0.27] 0.21 [0.13, 0.29] 0.13 [0.05, 0.20] 0.23 [0.15, 0.31] 0.15 [0.07, 0.23] 0.50 [0.45, 0.56]
Cross-twin cross-time
MZM 0.33 [0.26, 0.40] 0.34 [0.27, 0.41] 0.43 [0.36, 0.49] 0.40 [0.32, 0.46] 0.44 [0.37, 0.50] 0.57 [0.54, 0.60]
MZF 0.46 [0.41, 0.51] 0.44 [0.39, 0.49] 0.46 [0.41, 0.50] 0.45 [0.40, 0.50] 0.38 [0.33, 0.43] 0.57 [0.54, 0.59]
DZM 0.19 [0.11, 0.27] 0.24 [0.16, 0.32] 0.28 [0.20, 0.35] 0.20 [0.10, 0.28] 0.13 [0.05, 0.20] 0.29 [0.23, 0.36]
DZF 0.22 [0.18, 0.27] 0.23 [0.19, 0.27] 0.17 [0.12, 0.21] 0.22 [0.18, 0.26] 0.18 [0.14, 0.22] 0.36 [0.32, 0.39]
DZOS 0.18 [0.13, 0.24] 0.19 [0.14, 0.24] 0.17 [0.11, 0.22] 0.20 [0.14, 0.25] 0.17 [0.11, 0.22] 0.32 [0.28, 0.36]
A full constrained saturated model was used to obtain phenotypic intraclass correlations for males and females. A reduced model
was fit to obtain intraclass correlations collapsed by sex. Twin intraclass correlations were obtained from the full constrained
saturated model.
DZF, Dizygotic females; DZM, Dizygotic males; DZOS, Dizygotic opposite sex; MZF, Monozygotic females; MZM, Monozygotic males;
PRNS, Parent-rated negative symptoms.
©2019 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
doi:10.1111/jcpp.13045 Stability of psychotic experiences and negative symptoms 787
depression traits and emotional problems than the
increasing, decreasing and low-scoring groups. The
persistent group had more depression traits
(d=0.47–1.67, except grandiosity), and emotional
problems (d=0.47–1.47, except grandiosity and
anhedonia) compared to low-scorers as indicated
by significantly larger effect sizes, and for some
subscales such as paranoia, differences reached
significance between the persistent and the increas-
ing/decreasing groups (Tables S20 and S21).
In some additional analyses, the persistence
groups were compared for the other psychopathology
subscales of the SDQ, namely hyperactivity, conduct
problems and peer problems. As shown in Tables
S22–S24, the same pattern was shown as for
depression traits and emotional problems, with the
persistent group having more conduct problems
(d=0.28–0.95), hyperactivity (d=0.14–1.50), and
peer problems (d=0.48–1.22, except grandiosity),
compared to low-scorers (indicated by significantly
larger effect sizes), and for some subscales such as
paranoia and cognitive disorganization, significant
differences were apparent between the persistent
and the increasing/decreasing groups. Correlations
between PENS, SMFQ and SDQ scales are shown in
Table S25.
Discussion
This is the first study to investigate the genetic and
environmental influences on the stability of PENS in
a large sample in mid-to-late adolescence. Over a
period of ~9 months at ages 16–17 years, PENS
showed considerable phenotypic stability as
reflected in the high phenotypic correlations. This
stability was influenced by both genetic and envi-
ronmental factors, with genetic and nonshared
environmental influences explaining a similar pro-
portion of the relationship between PEs across time,
and genetic influences explaining a larger proportion
of the stability of NS. Of the genetic and common
environmental influences that contributed to stabil-
ity, most were shared across time, although overlap
of nonshared environmental effects was much lower.
Nonshared environmental influences at the later
time point contributed to the imperfect stability of
PENS.
Individuals with persistent PENS reported higher
levels of PENS (with exception of grandiosity) than
individuals with PENS that were either increasing,
decreasing or consistently low. The persistent group
also tended to have more distress associated with
their PEs, and higher levels of depression traits and
emotional problems and other psychopathology at
baseline compared to the other groups. The majority
of comparisons showed a significant effect size when
comparing the persistent group with the increasing,
decreasing or low-scoring groups. The direction of
effect was such that the persistent group was the
more impaired or distressed group. It is noted that
Table 2 Parameter estimates for best-fitting bivariate Cholesky solutions
Standardized univariate estimates time 1 Standardized univariate estimates time 2
Bivariate heritability, bivariate shared environment,
bivariate nonshared environment
Proportion of phenotypic correlation explained by A, C
and E
ACEACEACEACE
Paranoia 0.28 [0.22, 0.34] 0.19 [0.15, 0.23] 0.53 [0.50, 0.56] 0.32 [0.22, 0.43] 0.12 [0.05, 0.19] 0.55 [0.50, 0.60] 0.25 [0.19, 0.32] 0.12 [0.07, 0.17] 0.23 [0.20, 0.27] 0.42 [0.31, 0.53] 0.20 [0.12, 0.27] 0.38 [0.32, 0.44]
Hallucinations 0.22 [0.16, 0.28] 0.19 [0.15, 0.23] 0.59 [0.56, 0.63] 0.33 [0.23, 0.43] 0.16 [0.09, 0.22] 0.51 [0.46, 0.57] 0.22 [0.16, 0.29] 0.14 [0.10, 0.19] 0.22 [0.18, 0.25] 0.38 [0.27, 0.49] 0.25 [0.17, 0.32] 0.37 [0.31, 0.43]
Cognitive
disorga-
nization
0.27 [0.21, 0.33] 0.15 [0.11, 0.19] 0.58 [0.55, 0.62] 0.38 [0.30, 0.45] 0.06 [0.02, 0.11] 0.56 [0.51, 0.62] 0.3 [0.24, 0.35] 0.09 [0.05, 0.12] 0.26 [0.23, 0.30] 0.46 [0.37, 0.54] 0.13 [0.08, 0.19] 0.41 [0.36, 0.46]
Grandiosity 0.26 [0.20, 0.32] 0.18 [0.13, 0.22] 0.57 [0.53, 0.60] 0.26 [0.16, 0.36] 0.19 [0.12, 0.25] 0.56 [0.50, 0.62] 0.25 [0.18, 0.31] 0.13 [0.09, 0.18] 0.19 [0.16, 0.23] 0.43 [0.32, 0.54] 0.23 [0.16, 0.30] 0.34 [0.28, 0.40]
Anhedonia 0.47 [0.44, 0.50] - 0.53 [0.50, 0.56] 0.46 [0.40, 0.51] –0.54 [0.49, 0.60] 0.37 [0.33, 0.41] –0.22 [0.19, 0.26] 0.62 [0.57, 0.68] –0.38 [0.32, 0.43]
PRNS 0.46 [0.42, 0.50] 0.36 [0.33, 0.39] 0.18 [0.17, 0.20] 0.45 [0.39, 0.51] 0.38 [0.33, 0.43] 0.17 [0.15, 0.20] 0.34 [0.30, 0.38] 0.21 [0.17, 0.25] 0.08 [0.06, 0.09] 0.54 [0.48, 0.60] 0.33 [0.28, 0.39] 0.12 [0.10, 0.15]
A, Additive genetic effects; C, Common environmental effects; E, Nonshared environmental effects; PRNS, Parent-rated negative symptoms; 95% CI in parentheses.
©2019 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
788 Laura Havers, Mark J. Taylor, and Angelica Ronald J Child Psychol Psychiatr 2019; 60(7): 784–92
Table 3 Frequency differences for distress at time 1 by group
N with PE score >0 and
distress data
PE mean score
at time 1 (SD) Not distressed A bit distressed Quite/very distressed Comparison Fisher’s exact test (p) Cramer’s V (p)
Paranoia
Low-scoring (LS) 854 11.61 (8.65) 620 (72.6%) 196 (23.0%) 38 (4.4%) LS versus P 125.56 (<.001)** 0.45 (<.001)**
Increasing (I) 46 23.25 (8.63) 17 (37.0%) 22 (47.8%) 7 (15.2%) LS versus I 25.97 (<.001)** 0.18 (<.001)**
Decreasing (D) 53 42.91 (6.02) 14 (26.4%) 24 (45.3%) 15 (28.3%) D versus P 5.34 (.07) 0.21 (.07)
Persistent (P) 66 45.73 (8.28) 9 (13.6%) 26 (39.4%) 31 (47.0%) I versus P 15.15 (.001)** 0.36 (.001)**
Hallucinations
Low-scoring (LS) 572 5.25 (4.23) 484 (84.6%) 78 (13.6%) 10 (1.7%) LS versus P 83.76 (<.001)** 0.43 (<.001)**
Increasing (I) 63 11.31 (4.54) 41 (65.1%) 21 (33.3%) 1 (1.6%) LS versus I 14.34 (.001)** 0.16 (.001)**
Decreasing (D) 71 22.65 (5.42) 36 (50.7%) 25 (35.2%) 10 (14.1%) D versus P 4.26 (.11) 0.18 (.11)
Persistent (P) 65 24.20 (5.97) 22 (33.8%) 28 (43.1%) 15 (23.1%) I versus P 20.10 (<.001)** 0.39 (<.001)**
Cognitive disorganization
Low-scoring (LS) 765 4.00 (2.11) 556 (72.7%) 172 (22.5%) 37 (4.8%) LS versus P 142.58 (<.001)** 0.46 (<.001)**
Increasing (I) 59 6.53 (1.61) 29 (49.2%) 21 (35.6%) 9 (15.3%) LS versus I 16.66 (<.001)** 0.15 (.001)**
Decreasing (D) 77 9.61 (0.69) 23 (29.9%) 33 (42.9%) 21 (27.3%) D versus P 5.66 (.06) 0.18 (.06)
Persistent (P) 96 10.09 (0.76) 17 (17.7%) 38 (39.6%) 41 (42.7%) I versus P 20.92 (<.001)** 0.37 (<.001)**
Grandiosity
Low-scoring (LS) 810 4.54 (2.99) 699 (86.3%) 89 (11.0%) 22 (2.7%) LS versus P 1.29 (.54) 0.03 (.67)
Increasing (I) 40 7.72 (2.93) 28 (70.0%) 10 (25.0%) 2 (5.0%) LS versus I 7.89 (.02)*0.10 (.02)*
Decreasing (D) 58 16.66 (2.92) 51 (87.9%) 5 (8.6%) 2 (3.4%) D versus P 0.60 (.79) 0.07 (.79)
Persistent (P) 66 17.16 (2.71) 55 (83.3%) 8 (12.1%) 3 (4.5%) I versus P 3.08 (.22) 0.17 (.28)
N, Number of individuals; One randomly selected twin per pair included in analyses; Data shown for sample included in phenotypic analyses who provided data at both time points;
Fisher’s exact test of independence; Cramer’s V measure of effect size (square root of the x2 statistic divided by the sample size multiplied by the lesser number of categories in either
variable minus 1); Monte Carlo pvalues based on 10,000 sampled tables.
D, Decreasing group; I, Increasing group; LS, Low-scoring group; P, Persistent group; PE, Psychotic experiences.
**p<.001;*p<.05
©2019 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
doi:10.1111/jcpp.13045 Stability of psychotic experiences and negative symptoms 789
not all comparisons had a significant effect size, and
in particular, being in the group characterized by
persistence of grandiosity was not associated with
more distress and psychopathology. This is broadly
in line with past findings suggesting that grandiosity
does not always link with other psychopathology at
this age (Ronald et al., 2014). The grandiosity scale
appeared similar to the other PENS scales in terms of
its high internal consistency and its positive skew.
Future work should explore whether this distinct
pattern shown by grandiosity is specific to adoles-
cence.
Our finding that genetic influences contribute
moderately to the stability of PENS in adolescence
is broadly in line with findings by Ericson et al.
(2011), who reported a strong genetic component
contributing to the stability of schizotypy (a related
phenotype), albeit on a smaller and younger sample.
Unlike the Ericson et al. study, we also identified
modest shared environmental influences and mod-
erate nonshared environmental influences for all
PEs. Our study also extended this work by reporting
on stability across specific psychotic experiences
and negative symptoms.
The results highlight the role of environmental
factors in influencing how adolescent PENS develop,
which adds to existing research that has shown the
importance of environmental factors at single time
points (Hur, Cherny, & Sham, 2012; Zavos et al.,
2014; Zhou et al., 2018). Of particular interest in
this context are our results that nonshared environ-
ment contributes to more than a third of the stability
of PENS (except PRNS). These findings concur
broadly with findings that specific environmental
risks such as trauma, cannabis use and stressful life
events are associated with persistent PEs in adoles-
cence (Cougnard et al., 2007; Wigman, Winkel,
Raaijmakers et al., 2011). Furthermore, our results
suggest that some nonshared environmental influ-
ences are time-specific. Whilst estimates for non-
shared environment also include measurement
error, the results suggest that in part, PENS are
influenced by time-specific factors not shared
between family members. This is in line with findings
suggesting that some nonshared environmental
effects at least prior to adulthood are transitory, in
contrast to shared environmental and genetic effects
which are more stable over time (Burt, Klahr, &
Klump, 2015).
The modest contribution of shared environment to
stability of most of the PENS studied (notably not
anhedonia) can be considered in the light of epi-
demiological findings that have identified urbanicity
as a risk factor for persistence in individuals in the
general population reporting PEs at baseline (Coug-
nard et al., 2007). Whilst the findings cannot be
used to draw conclusions about the exact nature of
common environmental influences, they are more
generally reflective of findings that shared environ-
ments explain less variance in behavioural
phenotypes than nonshared environments (Plomin,
2011). The higher proportion of phenotypic stability
explained by shared environment for PRNS may be
influenced by the effect of having the same rater
across twins.
Psychological difficulties such as distress, depres-
sion traits and emotional problems and other psy-
chopathology were elevated at baseline in those who
followed a persistent path in terms of PENS. This
suggests that individuals who go on to experience
high levels of PENS over time are more likely to be
suffering with current psychological disturbance as
well as being at increased risk of later psychopathol-
ogy (Dominguez et al., 2011; Wigman, Winkel, Raai-
jmakers et al., 2011).
Strengths and limitations
It is a key strength of this study that data from over
4,800 twin pairs was used, building on existing
research that has relied on smaller samples. Further,
the study utilized a validated measurement tool
encompassing measurement of four individual
dimensions of PEs and two of NS. In the light of this,
it is a limitation that the time 2 sample was smaller
than the time 1 sample, and that not more time points
were available. However, our results broadly concur
with other findings that modelled data on younger and
older samples assessed across three time points
(Wigman, Winkel, Jacobs et al., 2011; Wigman,
Winkel, Raaijmakers et al., 2011). Future work
should seek to employ both researcher- and data-
driven methods in order to cross-validate the results.
Conclusion
Both genetic and environmental influences con-
tribute to the considerable stability of adolescent
PENS in mid-to-late adolescence. There are also
some dynamic influences particularly via nonshared
environments. Individuals who will go on to report
persistent PENS are more likely to experience other
psychological difficulties such as distress, depres-
sion traits and other psychopathology. In conjunc-
tion with epidemiological findings in the field, the
findings presented here speak of the importance of
measuring adolescent PENS over time.
Supporting information
Additional supporting information may be found online
in the Supporting Information section at the end of the
article:
Appendix S1. Study details.
Appendix S2. The Specific Psychotic Experiences
Questionnaire (SPEQ; Ronald et al., 2014).
Appendix S3. Assumptions testing.
Figure S1. Bivariate Cholesky decomposition solution
(left-hand figure) and correlated factors model (right-
hand figure) path diagrams.
©2019 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
790 Laura Havers, Mark J. Taylor, and Angelica Ronald J Child Psychol Psychiatr 2019; 60(7): 784–92
Table S1. Frequency and mean differences in demo-
graphics of main sample and follow-up sample.
Table S2. Descriptives for psychotic experiences and
negative symptoms subscales.
Table S3. Descriptives for psychotic experiences and
negative symptoms subscales split by sex and zygosity.
Table S4. Frequencies of distress associated with
psychotic experiences.
Table S5–S10. Results of testing for mean and variance
differences in the data.
Table S11–S16. Bivariate twin model statistics.
Table S17. Cholesky estimates.
Table S18. Genetic and environmental correlations for
best-fitting bivariate models.
Table S19. Descriptives and mean differences for
psychotic experiences and negative symptoms sub-
scales at time 1 by group.
Table S20–S21. Mean differences for depression traits
and emotional problems at time 1 by group.
Table S22–S24. Mean differences for conduct prob-
lems, hyperactivity and peer problems at time 1 by
group.
Table S25. Correlations for psychotic experiences and
negative symptoms with depression traits and SDQ
emotional problems and other SDQ subscales.
Table S26. Descriptive statistics for depression traits
and psychopathology subscales.
Acknowledgements
This work was funded by Medical Research Council
grant G1100559 to A.R. TEDS is funded by Medical
Research Council grant MR/M021475/1 to Robert
Plomin. L.H. was funded by an ESRC PhD studentship.
M.J.T. received research funding from the Fredrik och
Ingrid Thurings stiftelse. In the 36 months prior to
submission of this work, A.R. also received funding
from the Swedish Foundation for Humanities and
Social Sciences and the Wellcome Trust ISSF
fund; book royalties from Springer, New York; payment
for brief consultancy work from the National Childbirth
Trust; fees for PhD examining from Cardiff University
and King’s College London. A.R. acts as an action editor
for JCPP for which she receives an honorarium. The
authors thank the TEDS participants, and Robert
Plomin and Andrew McMillan for the collaboration.
The authors have declared that they have no competing
or potential conflicts of interest.
Correspondence
Angelica Ronald, Department of Psychological Sciences,
Centre for Brain and Cognitive Development, Birkbeck,
University of London, Malet Street, London WC1E 7HX,
UK; Email: a.ronald@bbk.ac.uk
Key points
Persistence of psychotic experiences and negative symptoms (PENS) is known to reflect heightened risk for
psychiatric disorders, but the causes of this persistence are unknown.
PENS were found to be largely stable over a period of 9 months in adolescence.
Persistent PENS tended to be associated with greater levels of distress and other psychopathology at
baseline compared to groups with transitory or low levels of PENS.
Genetic and environmental influences contributed to the stability of PENS in adolescence.
Time-specific effects acted primarily via nonshared environment. The imperfect stability of PENS was at
least partly due to new nonshared environmental influences occurring over time.
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Accepted for publication: 12 February 2019
First published online: 7 April 2019
©2019 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
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