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Genetic and environmental influences on the stability of psychotic experiences and negative symptoms in adolescence

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Journal of Child Psychology and Psychiatry
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Abstract

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.
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=0.471.67, except grandiosity) and emotional problems (d=0.471.47,
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
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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 1116 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 S5S10 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 S11S16). 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=.771.00)
(Table S18). The high rC estimates across PENS
suggest considerable overlap in C influences
(rC=.591.00). 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 0.380.46
for PEs and 0.540.62 for NS. The proportion of the
phenotypic correlation that was explained by shared
environmental influences was 0.130.33, except for
anhedonia which showed no C. The proportion of the
phenotypic correlation that was explained by non-
shared environmental influences was 0.340.41,
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=1.081.61). 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 (0.150.46) 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=0.471.67, except grandiosity), and emotional
problems (d=0.471.47, 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
S22S24, the same pattern was shown as for
depression traits and emotional problems, with the
persistent group having more conduct problems
(d=0.280.95), hyperactivity (d=0.141.50), and
peer problems (d=0.481.22, 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 1617 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 S5S10. Results of testing for mean and variance
differences in the data.
Table S11S16. 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 S20S21. Mean differences for depression traits
and emotional problems at time 1 by group.
Table S22S24. 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.
792 Laura Havers, Mark J. Taylor, and Angelica Ronald J Child Psychol Psychiatr 2019; 60(7): 784–92
... concurrent and subsequent psychopathology than nonpersistent psychotic experiences (12,13), and are considered part of an extended psychosis phenotype (11,14). In one study, a fifth of clinical psychosis cases were preceded by persistent psychotic experiences (14). ...
... Another study (mean age 11.8 years, range 6-21) not including a control group found that a family history of schizophrenia, bipolar disorder, and major depressive disorder was not differentially associated with the occurrence of psychotic experiences (21). Regarding prodromal symptoms, a study including offspring at familial high risk of schizophrenia and bipolar disorder found higher scores in offspring at familial high risk of schizophrenia with offspring at familial high risk of bipolar disorder intermediate relative to the control group at baseline (mean age 11.7 years, range [6][7][8][9][10][11][12][13][14][15][16][17] and at 2-year follow-up (22). Elevated scores on prodromal symptoms were also reported in a sample including siblings (age 7-16 years) of individuals with schizophrenia within this study (23). ...
... The higher increase in odds of having a disorder in middle childhood in children with three or more early childhood psychotic experiences is in keeping with a large, cross-sectional general population study of preadolescents documenting that higher severity of self-reported psychotic experiences corresponded with stronger associations with concurrent psychopathology (9). It should be noted that definitions of severity vary across studies, and while we used number of psychotic experiences, other indicators of increasing severity such as associated distress also confer increased risk of clinical outcomes and persistence (12,42). The predictive value of early childhood psychotic experiences on middle childhood mental disorders in our population is important since childhood axis I disorders may index an increased risk of transitioning to severe mental illness in children at familial high risk (26,43,44). ...
Article
Objective: Psychotic experiences are common in children and adolescents and are associated with concurrent and subsequent psychopathology. Most findings originate from general population studies, whereas little is known of the clinical outcomes of psychotic experiences in children and adolescents at familial high risk of psychosis. We examined the prevalence of psychotic experiences in middle childhood and whether early childhood psychotic experiences and developmental pathways of psychotic experiences predicted mental disorders in middle childhood in children at familial high risk of schizophrenia (FHR-SZ), bipolar disorder (FHR-BP), and a population-based control group. Methods: In a longitudinal population-based cohort study children at FHR-SZ (N=170), FHR-BP (N=103), and the control group (N=174) were assessed for psychotic experiences and axis I disorders with face-to-face interviews in early and middle childhood (at 7 and 11 years of age). Results: Psychotic experiences were more prevalent in children at FHR-SZ (31.8%, odds ratio 2.1, 95% CI 1.3-3.4) than in the control group (18.4%) in middle childhood. Early childhood psychotic experiences predicted mental disorders in middle childhood after adjusting for early childhood disorders and familial risk (odds ratio 2.0, 95% CI 1.2-3.1). Having three or more psychotic experiences increased odds the most (odds ratio 2.5, 95% CI 1.1-5.7). Persistent psychotic experiences were associated with increased odds of middle childhood disorders (odds ratio 4.1, 95% CI 2.1-8.4). Psychotic experiences were nondifferentially associated with mental disorders across the three familial risk groups. Conclusions: Early childhood psychotic experiences predict mental disorders in middle childhood. Psychotic experiences index vulnerability for psychopathology nondifferentially in children at familial high risk and the control group. Psychotic experiences should be included in mental health screenings including children at familial high risk.
... In terms of genetic factors, several studies have investigated their influence on PENS at single time-points or assessments , and findings from a small number of family studies further suggest that genetic factors are associated with the development of PENS (Ericson, Tuvblad, Raine, Young-Wolff, & Baker, 2011;Havers, Taylor, & Ronald, 2019;Janssens et al., 2016;Wigman et al., 2011a). The prior study with the largest sample size (N = 1448 twin pairs) found that 38-62% of the covariance in separate PENS dimensions measured across two time-points in adolescence was accounted for by genetic influences (Havers et al., 2019). ...
... In terms of genetic factors, several studies have investigated their influence on PENS at single time-points or assessments , and findings from a small number of family studies further suggest that genetic factors are associated with the development of PENS (Ericson, Tuvblad, Raine, Young-Wolff, & Baker, 2011;Havers, Taylor, & Ronald, 2019;Janssens et al., 2016;Wigman et al., 2011a). The prior study with the largest sample size (N = 1448 twin pairs) found that 38-62% of the covariance in separate PENS dimensions measured across two time-points in adolescence was accounted for by genetic influences (Havers et al., 2019). Genome-wide polygenic scores (GPS) can also be used as an index of an individual's polygenic propensity to a given outcome. ...
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Background Psychotic experiences and negative symptoms (PENS) are common in non-clinical populations. PENS are associated with adverse outcomes, particularly when they persist. Little is known about the trajectories of PENS dimensions in young people, nor about the precursory factors associated with these trajectories. Methods We conducted growth mixture modelling of paranoia, hallucinations, and negative symptoms across ages 16, 17, and 22 in a community sample ( N = 12 049–12 652). We then described the emergent trajectory classes through their associations with genome-wide polygenic scores (GPS) for psychiatric and educational phenotypes, and earlier childhood characteristics. Results Three trajectory classes emerged for paranoia, two for hallucinations, and two for negative symptoms. Across PENS, GPS for clinical help-seeking, major depressive disorder, and attention deficit hyperactivity disorder were associated with increased odds of being in the most elevated trajectory class (OR 1.07–1.23). Lower education GPS was associated with the most elevated trajectory class for hallucinations and negative symptoms (OR 0.77–0.91). Conversely for paranoia, higher education GPS was associated with the most elevated trajectory class (OR 1.25). Trajectory class associations were not significant for schizophrenia, obsessive-compulsive disorder, bipolar disorder, or anorexia GPS. Emotional/behaviour problems and life events in childhood were associated with increased odds of being in the most elevated trajectory class across PENS. Conclusions Our results suggest latent heterogeneity in the development of paranoia, hallucinations, and negative symptoms in young people that is associated with specific polygenic scores and childhood characteristics.
... 14 Persistent psychotic experiences may therefore reflect an underlying neurodevelopmental vulnerability, which is phenotypically expressed through neurocognitive impairments, decreased social skills and increased psychopathological risk, as shown in the current study. Evidence from twin studies has indicated a genetic component in the stability of psychotic experiences over time, 31 and it has been reported that cumulative exposure to environmental risk factors (such as trauma, cannabis use and urbanicity) affect the likelihood that psychotic experiences become persistent. 28 Future waves of the Generation R Study will permit further longitudinal assessment of these youth into middle/late adolescence, enabling us to examine the impact of age-specific risk factors, such as substance use and risk-taking behaviour, which may interact with a pre-existing neurodevelopmental vulnerability. ...
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Background Psychotic experiences predict adverse health outcomes, particularly if they are persistent. However, it is unclear what distinguishes persistent from transient psychotic experiences. Aims In a large population-based cohort, we aimed to (a) describe the course of hallucinatory experiences from childhood to adolescence, (b) compare characteristics of youth with persistent and remittent hallucinatory experiences, and (c) examine prediction models for persistence. Method Youth were assessed longitudinally for hallucinatory experiences at mean ages of 10 and 14 years ( n = 3473). Multi-informant-rated mental health problems, stressful life events, self-esteem, non-verbal IQ and parental psychopathology were examined in relation to absent, persistent, remittent and incident hallucinatory experiences. We evaluated two prediction models for persistence with logistic regression and assessed discrimination using the area under the curve (AUC). Results The persistence rate of hallucinatory experiences was 20.5%. Adolescents with persistent hallucinatory experiences had higher baseline levels of hallucinatory experiences, emotional and behavioural problems, as well as lower self-esteem and non-verbal IQ scores than youth with remittent hallucinatory experiences. Although the prediction model for persistence versus absence of hallucinatory experiences demonstrated excellent discriminatory power (AUC -corrected = 0.80), the prediction model for persistence versus remittance demonstrated poor accuracy (AUC -corrected = 0.61). Conclusions This study provides support for the dynamic expression of childhood hallucinatory experiences and suggests increased neurodevelopmental vulnerability in youth with persistent hallucinatory experiences. Despite the inclusion of a wide array of psychosocial parameters, a prediction model discriminated poorly between youth with persistent versus remittent hallucinatory experiences, confirming that persistent hallucinatory experiences are a complex multifactorial trait.
Article
Background Mental health problems and traits capturing psychopathology are common and often begin during adolescence. Decades of twin studies indicate that genetic factors explain around 50% of individual differences in adolescent psychopathology. In recent years, significant advances, particularly in genomics, have moved this work towards more translational findings. Methods This review provides an overview of the past decade of genetically sensitive studies on adolescent development, covering both family and genomic studies in adolescents aged 10–24 years. We focus on five research themes: (1) co‐occurrence or comorbidity between psychopathologies, (2) stability and change over time, (3) intergenerational transmission, (4) gene–environment interplay, and (5) psychological treatment outcomes. Results First, research shows that much of the co‐occurrence of psychopathologies in adolescence is explained by genetic factors, with widespread pleiotropic influences on many traits. Second, stability in psychopathology across adolescence is largely explained by persistent genetic influences, whereas change is explained by emerging genetic and environmental influences. Third, contemporary twin‐family studies suggest that different co‐occurring genetic and environmental mechanisms may account for the intergenerational transmission of psychopathology, with some differences across psychopathologies. Fourth, genetic influences on adolescent psychopathology are correlated with a wide range of environmental exposures. However, the extent to which genetic factors interact with the environment remains unclear, as findings from both twin and genomic studies are inconsistent. Finally, a few studies suggest that genetic factors may play a role in psychological treatment response, but these findings have not yet been replicated. Conclusions Genetically sensitive research on adolescent psychopathology has progressed significantly in the past decade, with family and twin findings starting to be replicated at the genomic level. However, important gaps remain in the literature, and we conclude by providing suggestions of research questions that still need to be addressed.
Article
Psychotic experiences (PE) are forms of hallucinations and delusions neither reaching the intensity and functional impairment required to be regarded as full psychotic symptoms nor a psychotic disorder. Here we investigated the ability to predict PE using multiple models (regressions, mediation and moderation) using polygenic risk score for psychotic experiences (PE-PRS), polygenic risk score for schizophrenia (SCZ-PRS), and polyenvironmental risk score (PERS) in youth from a Brazilian sample. The scores were not able to predict outcome, either when both scores were combined (PERS + PE-PRS and PERS + SCZ-PRS) or separately. Our results show that there is no association between PE and PRS or PERS among adolescents in our Brazilian sample. The lack of association may be a result of the absence of better representativeness regarding genetic and environmental factors of our population.
Article
Purpose of review: The two past decades have seen the production of a vast amount of evidence about the genetic and nongenetic factors that contribute to the onset of psychosis from various fields of research. The present article reviews recent evidence from four of these fields that were shown to be strongly associated with psychosis: proneness, urbanicity, trauma, and cannabis use. Recent findings: The evidence reviewed shows that all four sets of factors investigated here are implicated in the occurrence of psychosis. The specificity and complexity of these associations, however, are not yet clear and recent findings show that the directions of the associations described may be different than we first thought. Summary: It is clear that psychosis is strongly affected by a number of environmental determinants that act in concert with genetic determinants to cause psychotic disorders; however, these influences are complex and their actual impact may be difficult to establish because of poor definitions and specificity. Urbanicity in special is a poorly defined concept that seems to encompass different sets of factors in each study, which hinders discussions and conclusions regarding its impact.
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In this editorial, the author reflects on changes that occurred in the quality of research on developmental psychopathology over the last 35 years. This is illustrated in the increased quality of nine longitudinal studies that are included in the current issue of JCPP. Using approaches that capitalize on the passage of time, ranging from 28 days to 40 years across investigations, these studies employed multiple levels of analysis, used sophisticated statistical methods to control for confounding factors, included measurement at both the biological, cognitive, and behavioral levels, and collectively provided results that allow improved assessment of causality.
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This study aimed to test for overlap in genetic influences between psychotic-like experience traits shown by adolescents in the community, and clinically-recognized psychiatric disorders in adulthood, specifically schizophrenia, bipolar disorder, and major depression. The full spectra of psychotic-like experience domains, both in terms of their severity and type (positive, cognitive, and negative), were assessed using self- and parent-ratings in three European community samples aged 15-19 years (Final N incl. siblings = 6,297-10,098). A mega-genome-wide association study (mega-GWAS) for each psychotic-like experience domain was performed. SNP-heritability of each psychotic-like experience domain was estimated using genomic-relatedness-based restricted maximum-likelihood (GREML) and linkage disequilibrium- (LD-) score regression. Genetic overlap between specific psychotic-like experience domains and schizophrenia, bipolar disorder, and major depression was assessed using polygenic risk scoring (PRS) and LD-score regression. GREML returned SNP-heritability estimates of 3-9% for psychotic-like experience trait domains, with higher estimates for less skewed traits (Anhedonia, Cognitive Disorganization) than for more skewed traits (Paranoia and Hallucinations, Parent-rated Negative Symptoms). Mega-GWAS analysis identified one genome-wide significant association for Anhedonia within IDO2 but which did not replicate in an independent sample. PRS analysis revealed that the schizophrenia PRS significantly predicted all adolescent psychotic-like experience trait domains (Paranoia and Hallucinations only in non-zero scorers). The major depression PRS significantly predicted Anhedonia and Parent-rated Negative Symptoms in adolescence. Psychotic-like experiences during adolescence in the community show additive genetic effects and partly share genetic influences with clinically-recognized psychiatric disorders, specifically schizophrenia and major depression.
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Purpose of the Review This review identifies the early developmental processes that contribute to schizotypy and suspiciousness in adolescence and adulthood. It includes the most recent literature on these phenomena in childhood. Recent Findings The early developmental processes that affect schizotypy and paranoia in later life are complex. In contrast to existing studies of psychiatric patients and clinical/nonclinical adult populations, the study of schizotypy and suspiciousness in young children and adolescents is possible due to new child-appropriate dimensional assessments. New assessments and the advancement of technology (e.g., virtual reality in mental health) as well as statistical modeling (e.g., mediation and latent-class analyses) in large data have helped identified the developmental aspects (e.g., psychosocial, neurocognitive and brain factors, nutrition, and childhood correlates) that predict schizotypy and suspiciousness in later life. Summary Prospective longitudinal designs in community youths can enhance our understanding of the etiology of schizophrenia-spectrum disorders and, in the future, the development of preventive interventions by extending adult theories and interventions to younger populations.
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Background: Childhood emotional and behaviour problems are antecedents for later psychopathology. This study investigated genetic and environmental influences shaping the longitudinal association between childhood emotional and behaviour problems and specific PEs. Method: In a community-based twin sample, parents reported on emotional and behaviour problems when twins were ages 7 and 12 years. At age 16 years, specific PEs were measured using self-reports and parent reports. Structural equation model-fitting was conducted. Results: Childhood emotional and behaviour problems were significantly associated with paranoia, cognitive disorganisation and parent-rated negative symptoms in adolescence (mean r = .15-.38), and to a lesser extent with hallucinations, grandiosity and anhedonia (mean r = .04-.12). Genetic influences on childhood emotional and behaviour problems explained significant proportions of variance in adolescent paranoia (4%), cognitive disorganisation (8%) and parent-rated negative symptoms (3%). Unique environmental influences on childhood emotional and behaviour problems explained ≤1% of variance in PEs. Common environmental influences were only relevant for the relationship between childhood emotional and behaviour problems and parent-rated negative symptoms (explaining 28% of variance) and are partly due to correlated rater effects. Conclusions: Childhood emotional and behaviour problems are significantly, if weakly, associated with adolescent PEs. These associations are driven in part by common genetic influences underlying both emotional and behaviour problems and PEs. However, psychotic experiences in adolescence are largely influenced by genetic and environmental factors that are independent of general childhood emotional and behaviour problems, suggesting they are not merely an extension of childhood emotional and behaviour problems.
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IMPORTANCE: The onset of psychosis is usually preceded by psychotic experiences (PE). Little is known about the etiology of PE and whether the degree of genetic and environmental influences varies across different levels of severity. A recognized challenge is to identify individuals at high risk of developing psychotic disorders prior to disease onset. OBJECTIVES: To investigate the degree of genetic and environmental influences on specific PE, assessed dimensionally, in adolescents in the community and in those who have many, frequent experiences (defined using quantitative cutoffs). We also assessed the degree of overlap in etiological influences between specific PE. DESIGN, SETTING, AND PARTICIPANTS: Structural equation model-fitting, including univariate and bivariate twin models, liability threshold models, DeFries-Fulker extremes analysis, and the Cherny method, was used to analyze a representative community sample of 5059 adolescent twin pairs (mean [SD] age, 16.31 [0.68] years) from England and Wales. MAIN OUTCOMES AND MEASURES: Psychotic experiences assessed as quantitative traits (self-rated paranoia, hallucinations, cognitive disorganization, grandiosity, and anhedonia, as well as parent-rated negative symptoms). RESULTS: Genetic influences were apparent for all PE (15%-59%), with modest shared environment for hallucinations and negative symptoms (17%-24%) and significant nonshared environment (49%-64%) for the self-rated scales and 17% for parent-rated negative symptoms. Three empirical approaches converged to suggest that the etiology in extreme-scoring groups (most extreme scoring: 5%, 10%, and 15%) did not differ significantly from that of the whole distribution. There was no linear change in heritability across the distribution of PE, with the exception of a modest increase in heritability for increasing severity of parent-rated negative symptoms. Of the PE that showed covariation, this appeared to be due to shared genetic influences (bivariate heritabilities, 0.54-0.71). CONCLUSIONS AND RELEVANCE: These findings are consistent with the concept of a psychosis continuum, suggesting that the same genetic and environmental factors influence both extreme, frequent PE and milder, less frequent manifestations in adolescents. Individual PE in adolescence, assessed quantitatively, have lower heritability estimates and higher estimates of nonshared environment than those for the liability to schizophrenia. Heritability varies by type of PE, being highest for paranoia and parent-rated negative symptoms and lowest for hallucinations.
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Individuals reporting persistent psychotic experiences (PEs) in the general population, but without a “need for care”, are a unique group of particular importance in identifying risk and protective factors for psychosis. We compared people with persistent PEs and no “need for care” (non-clinical, N=92) with patients diagnosed with a psychotic disorder (clinical, N=84) and controls without PEs (N=83), in terms of their phenomenological, socio-demographic and psychological features. The 259 participants were recruited from one urban and one rural area in the UK, as part of the UNIQUE (Unusual Experiences Enquiry) study. Results showed that the non-clinical group experienced hallucinations in all modalities as well as first-rank symptoms, with an earlier age of onset than in the clinical group. Somatic/tactile hallucinations were more frequent than in the clinical group, while commenting and conversing voices were rare. Participants in the non-clinical group were differentiated from their clinical counterparts by being less paranoid and deluded, apart from ideas of reference, and having fewer cognitive difficulties and negative symptoms. Unlike the clinical group, they were characterized neither by low psychosocial functioning nor by social adversity. However, childhood trauma featured in both groups. They were similar to the controls in psychological characteristics: they did not report current emotional problems, had intact self-esteem, displayed healthy schemas about the self and others, showed high life satisfaction and well-being, and high mindfulness. These findings support biopsychosocial models postulating that environmental and psychological factors interact with biological processes in the aetiology of psychosis. While some PEs may be more malign than others, lower levels of social and environmental adversity, combined with protective factors such as intact IQ, spirituality, and psychological and emotional well-being, may reduce the likelihood of persistent PEs leading to pathological outcomes. Future research should focus on protective factors and determinants of well-being in the context of PEs, rather than exclusively on risk factors and biomarkers of disease states.
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Occurrence of psychotic experiences is common amongst adolescents in the general population. Twin studies suggest that a third to a half of variance in adolescent psychotic experiences is explained by genetic influences. Here we test the extent to which common genetic variants account for some of the twin-based heritability. Psychotic experiences were assessed with the Specific Psychotic Experiences Questionnaire in a community sample of 2152 16-year-olds. Self-reported measures of Paranoia, Hallucinations, Cognitive Disorganization, Grandiosity, Anhedonia, and Parent-rated Negative Symptoms were obtained. Estimates of SNP heritability were derived and compared to the twin heritability estimates from the same sample. Three approaches to genome-wide restricted maximum likelihood (GREML) analyses were compared: (1) standard GREML performed on full genome-wide data; (2) GREML stratified by minor allele frequency (MAF); and (3) GREML performed on pruned data. The standard GREML revealed a significant SNP heritability of 20 % for Anhedonia (SE = 0.12; p < 0.046) and an estimate of 19 % for Cognitive Disorganization, which was close to significant (SE = 0.13; p < 0.059). Grandiosity and Paranoia showed modest SNP heritability estimates (17 %; SE = 0.13 and 14 %; SE = 0.13, respectively, both n.s.), and zero estimates were found for Hallucinations and Negative Symptoms. The estimates for Anhedonia, Cognitive Disorganization and Grandiosity accounted for approximately half the previously reported twin heritability. SNP heritability estimates from the MAF-stratified approach were mostly consistent with the standard estimates and offered additional information about the distribution of heritability across the MAF range of the SNPs. In contrast, the estimates derived from the pruned data were for the most part not consistent with the other two approaches. It is likely that the difference seen in the pruned estimates was driven by the loss of tagged causal variants, an issue fundamental to this approach. The current results suggest that common genetic variants play a role in the etiology of some adolescent psychotic experiences, however further research on larger samples is desired and the use of MAF-stratified approach recommended.
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Community-based surveys find that many otherwise healthy individuals report histories of hallucinations and delusions. To date, most studies have focused on the overall lifetime prevalence of any of these psychotic experiences (PEs), which might mask important features related to the types and frequencies of PEs. To explore detailed epidemiologic information about PEs in a large multinational sample. We obtained data from the World Health Organization World Mental Health Surveys, a coordinated set of community epidemiologic surveys of the prevalence and correlates of mental disorders in representative household samples from 18 countries throughout the world, from 2001 through 2009. Respondents included 31 261 adults (18 years and older) who were asked about lifetime and 12-month prevalence and frequency of 6 types of PEs (2 hallucinatory experiences and 4 delusional experiences). We analyzed the data from March 2014 through January 2015. Prevalence, frequency, and correlates of PEs. Mean lifetime prevalence (SE) of ever having a PE was 5.8% (0.2%), with hallucinatory experiences (5.2% [0.2%]) much more common than delusional experiences (1.3% [0.1%]). More than two-thirds (72.0%) of respondents with lifetime PEs reported experiencing only 1 type. Psychotic experiences were typically infrequent, with 32.2% of respondents with lifetime PEs reporting only 1 occurrence and 31.8% reporting only 2 to 5 occurrences. We found a significant relationship between having more than 1 type of PE and having more frequent PE episodes (Cochran-Armitage z = -10.0; P < .001). Lifetime prevalence estimates (SEs) were significantly higher among respondents in middle- and high-income countries than among those in low-income countries (7.2% [0.4%], 6.8% [0.3%], and 3.2% [0.3%], respectively; χ22 range, 7.1-58.2; P < .001 for each) and among women than among men (6.6% [0.2%] vs 5.0% [0.3%]; χ21 = 16.0; P < .001). We found significant associations with lifetime prevalence of PEs in the multivariate model among nonmarried compared with married respondents (χ22 = 23.2; P < .001) and among respondents who were not employed (χ24 = 10.6; P < .001) and who had low family incomes (χ23 = 16.9; P < .001). The epidemiologic features of PEs are more nuanced than previously thought. Research is needed that focuses on similarities and differences in the predictors of the onset, course, and consequences of distinct PEs.
Article
Background: We validated the Social Mistrust Scale (SMS) and utilized it to examine the structure, prevalence, and heritability of social mistrust in a large sample of Chinese children and adolescents. Methods: In Study 1, a large sample of healthy twins (N=2094) aged 8 to 14 years (M=10.27 years, SD=2) completed the SMS. Structural equation modelling (SEM) was conducted to assess the structure of the SMS and to estimate the heritability of social mistrust in a sub-sample of twins (n=756 pairs). In Study 2, 32 adolescents with childhood-onset schizophrenia were compared with 34 healthy controls on levels of suspiciousness and clinical symptoms to examine the associations between the SMS and the Positive and Negative Syndrome Scale (PANSS). Results: We found a three-factor structure for social mistrust (home, school, and general mistrust). Social mistrust was found to be moderately - heritable (19% -40%), with mistrust at home most strongly influenced by genetic factors. Compared with 11.76% of the healthy controls, 56.25% of the adolescents with early-onset schizophrenia exhibited very high levels of social mistrust on all three subscales on the SMS. The SMS exhibited good discriminant validity in distinguishing adolescents with childhood-onset schizophrenia from healthy controls, and showed associations with a broad range of symptoms assessed by the PANSS. Conclusions: Social mistrust assessed by the SMS may be heritable. The SMS demonstrates good discriminant validity with clinical diagnoses of schizophrenia. However, it seems to be correlated with multiple aspects of psychopathology in the schizophrenia group, rather than being specific to delusional ideation/paranoia.
Article
Background: We validated the Social Mistrust Scale (SMS) and utilized it to examine the structure, prevalence, and heritability of social mistrust in a large sample of Chinese children and adolescents. Methods: In Study 1, a large sample of healthy twins (N=2094) aged 8 to 14years (M=10.27years, SD=2) completed the SMS. Structural equation modeling (SEM) was conducted to assess the structure of the SMS and to estimate the heritability of social mistrust in a sub-sample of twins (n=756 pairs). In Study 2, 32 adolescents with childhood-onset schizophrenia were compared with 34 healthy controls on levels of suspiciousness and clinical symptoms to examine the associations between the SMS and the Positive and Negative Syndrome Scale (PANSS). Results: We found a three-factor structure for social mistrust (home, school, and general mistrust). Social mistrust was found to be moderately - heritable (19%-40%), with mistrust at home most strongly influenced by genetic factors. Compared with 11.76% of the healthy controls, 56.25% of the adolescents with early-onset schizophrenia exhibited very high levels of social mistrust on all three subscales of the SMS. The SMS exhibited good discriminant validity in distinguishing adolescents with childhood-onset schizophrenia from healthy controls and showed associations with a broad range of symptoms assessed by the PANSS. Conclusions: Social mistrust assessed by the SMS may be heritable. The SMS demonstrates good discriminant validity with clinical diagnoses of schizophrenia. However, it seems to be correlated with multiple aspects of psychopathology in the schizophrenia group, rather than being specific to delusional ideation/paranoia.