Person-Specific Non-shared Environmental Influences in Intra-individual Variability: A Preliminary Case of Daily School Feelings in Monozygotic Twins
Behav Genet
Yao Zheng 0 1 2 3 4
Peter C. M. Molenaar 0 1 2 3 4
Rosalind Arden 0 1 2 3 4
Kathryn Asbury 0 1 2 3 4
David M. Almeida 0 1 2 3 4
0 Human Development and Family Studies, The Pennsylvania State University , University Park, PA , USA
1 Child and Family Research Institute , Vancouver, BC , Canada
2 Edited by Gitta Lubke
3 Department of Education, Psychology in Education Research Centre, University of York , York , UK
4 Centre for Philosophy of Natural and Social Science, London School of Economics , London , UK
Most behavioural genetic studies focus on genetic and environmental influences on inter-individual phenotypic differences at the population level. The growing collection of intensive longitudinal data in social and behavioural science offers a unique opportunity to examine genetic and environmental influences on intra-individual phenotypic variability at the individual level. The current study introduces a novel idiographic approach and one novel method to investigate genetic and environmental influences on intra-individual variability by a simple empirical demonstration. Person-specific non-shared environmental influences on intra-individual variability of daily school feelings were estimated using time series data from twenty-one pairs of monozygotic twins (age = 10 years, 16 female pairs) over two consecutive weeks. Results showed substantial inter-individual heterogeneity in person-specific non-shared environmental influences. The current study represents a first step in investigating environmental influences on intra-individual variability with an idiographic approach, and provides implications for future behavioural genetic studies to examine developmental processes from a microscopic angle.
Intra-individual variability; Non-shared environmental influences; Daily diary data; Person- specific; Idiographic approach; Developmental processes
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Social and behavioural science researchers are increasingly
collecting intensive longitudinal data (ILD) on various
physiological, psychological, and behavioural phenotypes
using study designs such as ecological momentary
assessments and daily diary design (Walls and Schafer 2006). As
opposed to conventional longitudinal designs that typically
follow thousands or hundreds of participants a few times
over multiple years, ILD are typically obtained intensively
and repeatedly over a short time period (e.g., days, weeks) in
a relatively smaller sample of participants (e.g., hundreds).
Recently, behavioural genetic researchers are also starting to
collect ILD in twin samples (e.g., Asbury et al. 2008; Kessler
et al. 2004; Klump et al. 2013; Wichers et al. 2007).
ILD offer a unique opportunity to investigate dynamic
developmental processes at a more refined microscopic
level (e.g., days, vs. years) on phenotypes assessed in
realtime (e.g., momentary ambulatory measures, vs. recall past
year behaviours in questionnaires) and ecological or daily
life contexts (e.g., office, vs. laboratory) (Shiffman et al.
2008; Stone et al. 2007). Particularly, ILD enable the
examination of intra-individual variability, which has been
shown to contain important and unique information on
inter-individual differences for a variety of phenotypes
(Diehl et al. 2015; Nesselroade 1991; Nesselroade and Ram
2004; Ram and Gerstorf 2009). As opposed to
intraindividual change measured in conventional longitudinal
studies that typically involves systematic and irreversible
mean changes (e.g., growth of height and intelligence),
intra-individual variability typically involves relatively
short-term fluctuations that are generally reversible without
substantial mean changes (e.g., daily mood fluctuation)
(Nesselroade 1991; Nesselroade and Ram 2004; Ram and
Gerstorf 2009).
Intra-individual variability enables us to examine the
dynamic intra-individual developmental pattern at the
individual level through single-subject analysis such as
dynamic factor analysis (Molenaar 1985). Additionally,
ILD obtained in multiple individuals enable us to examine
inter-individual differences in intra-individual
developmental patterns (Nesselroade 1991; Nesselroade and Ram
2004; Ram and Gerstorf 2009). This presents some novel
and open questions to behavioural genetic research: What
are the genetic and environmental influences on
intra-individual variability at the individual level? How do
individuals differ from each other regarding their respective
genetic and environmental influences on intra-individual
variability? How do results of genetic and environmental
influences on intra-individual variability compare to results
of genetic and environmental influences on inter-individual
differences, or inter-individual differences in
intra-individual changes that typically are examined in conventional
behavioural genetic studies? The few existing empirical
studies that have collected genetically informative ILD did
not specifically examined genetic and environmental
influences on intra-individual variability (e.g., Asbury et al.
2008; Burt et al. 2015; Cleveland and Almeida 2013;
Jacobs et al. 2006, 2013; Menne-Lothmann et al. 2012;
Neiss and Almeida 2004; Wichers et al. 2007), and thus
could not shed light on these questions.
Classic ergodic theory (e.g., Birkhoff 1931) provides
some insight to the question on the relation between results
of genetic and environmental influences on intra-individual
variability and results of genetic and environmental
influences on inter-individual differences. The classic ergodic
theory is a general mathematical theory that describes the
relations between analyses of inter-individual variation (i.e.,
inter-individual differences) and analyses of
intra-individual variation (i.e., intra-individual variability). According to
the classical ergodic theorems, for Gaussian processes,
results obtained from analyses of inter-individual variation
and of intra-individual variation would be equivalent to each
other only when two major assumptions are met: the
population homogeneity assumption and the stationarity
assumption (Molenaar 2004; Molenaar and Campbell
2009). Inter-individual analyses can show satisfactory
model fit while being completely insensitive to
intra-individual variation (Kelderman and Molenaar 2007). Relating
to behavioural genetic research, the population
homogeneity assumption implies that genetic and
environmental influences are the same for all individuals in the
population, while the stationarity assumption states that
both the mean levels and sequential (co)variances of
phenotypes remain constant over time. As theoretically
elaborated and empirically shown, however, these two
assumptions are typically violated (see Molenaar 2004,
2007, 2010a, 2014). For example, behavioural genetic
studies adopting mixture modelling have demonstrated
population heterogeneity regarding genetic and
environmental influences (e.g., Eaves et al. 1993; Gillespie and
Neale 2006; Muthe´n et al. 2006; Neale 2014). Stochastic
intra-individual developmental noise (e.g., Molenaar et al.
1993; Molenaar and Raijmakers 1999), gene expression and
epigenetic processes (e.g., DNA methylation) can also result
in substantial inter-individual differences in genetic and
environmental influences (e.g., Bell and Saffery 2012; Bell
and Spector 2011; Dolan et al. 2015; Fraga et al. 2005; Kan
et al. 2010; Petronis 2010; Wright et al. 2014). Therefore,
investigation of intra-individual variability could potentially
provide unique and important information regarding genetic
and environmental influences on intra-individual
developmental processes that conventional behavioural genetic
studies focusing on inter-individual differences could not
offer, especially at the individual level.
Some progress of behavioural genetic research on
intraindividual variability has been made that could provide
potential opportunity for researchers to investigate genetic
and environmental influences at the individual level, and to
uncover inter-individual heterogeneity in genetic and
environmental influences. Nesselroade et al. (2007)
recently introduced the concept of idiographic filter (IF) in
measurement, which allows person-specific factor loadings
and residual variances, while defining measurement
equivalence at the factor level by constraining their
intercorrelations to be invariant across individuals. A
hybridized model was later proposed that combines the IF and
biometric model together, acronized as iFACE model, to
model person-specific genetic and environmental
influences on intra-individual variability within twin pairs
(Molenaar 2010a, 2014; Molenaar et al. 2012; Nesselroade
and Molenaar 2010). Upon its recent development, iFACE
model has been applied to single pairs of dizygotic (DZ)
twins on multivariate electroencephalogram (EEG)
recordings data. Results showed considerable
personspecific genetic and environmental influences within the
same twin pair, demonstrating the violation of population
homogeneity assumption (Molenaar et al. 2012). The
primary goal of the current study is to further introduce the
iFACE model and to demonstrate its utility and feasibility
by applying the iFACE model to daily mood data from a
genetically informative short daily diary study with
monozygotic (MZ) twins.
Intra-individual variability of mood has been widely
studied. People’s moods fluctuate over time in substantial
and meaningful ways (Eid and Diener 1999). Research has
shown that factor patterns explaining intra-individual
variability differ from factor patterns explaining
inter-individual differences (e.g., Watson 1988; Zevon and
Tellegen 1982), and has revealed substantial inter-individual
heterogeneity in dynamic intra-individual patterns of mood
(e.g., Ferrer and Nesselroade 2003). Some people’s moods
fluctuate quickly; others’ moods change more slowly.
These inter-individual differences in intra-individual
patterns of mood can be predicted by individual-level
characteristics (e.g., Chow et al. 2005). In addition, previous
studies have found associations between daily mood and
daily social interactions and experience (e.g., Clark and
Watson 1988). These findings suggest that intra-individual
processes of mood are subject to significant environmental
influences. Therefore, when examining phenotypes like
mood, it is also important to examine intra-individual
variability at the individual level, as well as
inter-individual differences in these intra-individual patterns.
Twin studies have examined genetic and environmental
influences on inter-individual differences in positive and
negative mood, and generally found them to be affected by
genetic and non-shared environmental influences, although
the estimates vary depending on different sample sizes, age
ranges, and specific measures (e.g., Baker et al. 1992; Gatz
et al. 1992; Riemann et al. 1998). As mentioned earlier, the
few existing empirical studies that have collected genetically
informative ILD on mood did not specifically examined
genetic and environmental influences on intra-individual
variability, but focused instead on inter-individual
differences in constructs aggregated together over multiple
observations, such as intra-individual mean and variation
(e.g., Jacobs et al. 2006, 2013; Menne-Lothmann et al. 2012;
Neiss and Almeida 2004; Wichers et al. 2007). Among those
studies, however, one study that followed 239 female twin
pairs aged 16–25 years over a 7-day period provided some
interesting results that could shed some lights on genetic and
environmental influences on intra-individual variability of
affect (Burt et al. 2015). Besides the finding that both
positive and negative affect were primarily explained by genetic
(25–47 %) and non-shared environmental influences
(54–75 %), it was also found that genetic stability remained
high across days (*.95), whereas the stability of non-shared
environmental influences was low and decreased
monotonically with increasing time intervals (Burt et al. 2015). This
finding suggests that non-shared environmental influences
could consist primarily of idiosyncratic and transient daily
experience, and highlights the importance of examining
intra-individual processes, particularly with regard to mood.
Using data from a genetically informative short daily
diary study with MZ twins, the current study aimed to
demonstrate the utility of iFACE model by examining
person-specific non-shared environmental influences on
intra-individual variability of daily mood. In
intra-individual behavioural genetic analysis of intra-individual
variability, the time series data of a single MZ or DZ twin
pair are considered. As the additive genetic factor is
indistinguishable from the shared environmental factor
among MZ twins, the current study focused on familiality
(additive genetic and shared environmental influences)
versus non-shared environmental influences (see Molenaar
et al. 2012 for an application in DZ twin pairs). We are
particularly interested to know how individuals differ from
each other regarding their person-specific non-shared
environmental influences.
Sample and data collection procedure
The analysis sample consists of twenty-one MZ twin pairs
drawn from the 1994/1995 cohort of the Twins Early
Development Study (TEDS). TEDS is a longitudinal study
of twins born in England and Wales between 1994 and
1996; the twins have been followed since infancy. All pairs
were 10-year-olds and in the penultimate year of
elementary schooling in the United Kingdom. Both twins in each
pair were pupils in the same classroom. The sample mean
SES (-.02) is close to the mean for the full TEDS sample
(0.00), which has been shown to be representative of the
UK population (Haworth et al. 2013). The majority of the
twin pairs were female (16 pairs) and White (19 pairs).
Trained interviewers conducted daily telephone interviews
during the same two consecutive weeks every weekday
evening after school. Each interview lasted 5–10 min per
day for each child. Therefore, each twin provided up to
10 days of diary data, and a total of 402 observations were
collected. Details of sample recruitment and interview
procedure are described elsewhere (Asbury et al. 2008). It
is noteworthy that the original sample includes a total of
sixty-one MZ twin pairs. However, many twin pairs
showed little or no intra-individual variability in their daily
school feelings in either one or both twins in each pair
during the short sampling period. Therefore, they were
excluded from further analysis.
Daily positive school feelings
This construct was measured by combining two sub-scales,
one measuring daily positive feelings about classroom
experiences and the other about playground experiences.
For each sub-scale, four items were used with possible
responses ranging from 1 ‘‘very’’ to 4 ‘‘not at all’’. An
average score was calculated from the combined
subscales. An example item asked: did you feel proud in the
classroom today? Other adjectives included interested,
excited, and enthusiastic. Items were reverse coded so that
higher scores indicated more positive school feelings
(ranging from 1 to 4).
Daily negative school feelings
This construct was measured by combining two sub-scales,
one measuring daily negative feelings about classroom
experiences and the other about playground experiences. For
each sub-scale, four items were used with possible responses
ranging from 1 ‘‘very’’ to 4 ‘‘not at all’’. An average score
was calculated from the combined sub-scales. An example
item asked: did you feel nervous in the playground today?
Other adjectives included upset, scared, and irritable. Items
were reverse coded so that higher scores indicated more
negative school feelings (ranging from 1 to 4).
Analytic strategy
We tested the data against the idiographic filter ACE
(iFACE) model that combines the idiographic filter (IF) with
a conventional biometric model to decompose
intra-individual phenotypic variability into three independent factors,
additive genetics (A), shared environment (C), and
nonshared environment (E) (Molenaar et al. 2012; Nesselroade
and Molenaar 2010). The iFACE model is analogous to the
standard longitudinal genetic factor model that decomposes
inter-individual phenotypic variation in the population
(Boomsma and Molenaar 1987; Martin and Eaves 1977;
Molenaar and Boomsma 1987). The identification and
interpretation of the genetic and environmental factors are
obtained by their inter-correlation patterns within the pair. A
major difference between the iFACE model and a
longitudinal genetic factor model is that, while the latter constrains
factor loadings and residuals to be invariant both between
twin pairs and between members in the same twin pair, the
iFACE model allows for person-specific factor loadings and
residuals as they are estimated from individual time series
data. This enables the estimation of person-specific genetic
and environmental influences. Applying an iFACE model to
time series data for each member of a twin pair does not
invoke the population homogeneity assumption; it can also
uncover person-specific heterogeneity in intra-individual
genetic and environmental factor loadings.
Because MZ twins share all of their genes, their latent
additive genetic factor is indistinguishable from the latent
shared environmental factor, which is the same within each
twin pair. Therefore, the current analysis can only distinguish
two latent factors: the non-shared environment (E), as well as
the additive genetic and shared environment factor combined
together (AC, or familiality). As shown in Fig. 1, each twin’s
positive and negative school feelings measured at one time t
(e.g., NSF1,t, PSF1,t) have factor loadings on two latent
factors, AC1,t and E1,t (e.g., a1,2 for the AC path on PSF1,t). Each
phenotype also has its residual (e.g., e1,1 for NSF1,t) which
could contain specific genetic and environmental influences.
The same specification applies to the same phenotypes
measured at the next time t ? 1 (e.g., NSF1,t?1, PSF1,t?1),
and the respective factor loadings are constrained to be the
same (e.g., /1,1 for the E path of NSF1,t and NSF1,t?1).
Therefore, genetic and environmental influences estimated in
each individual iFACE model are average effects across each
person’s measured time. A first-order autoregression (factors
at time t ? 1 predicted by factors at the previous time t) is
used to model the stability of the latent AC and E factors with
person-specific autoregressive coefficient for E (c1). The
same specification applies to the co-twin’s time series data,
however, with person-specific factor loadings (e.g., a2,2 for
the AC path on PSF2,t) and residuals (e.g., e2,1 for NSF2,t),
giving person-specific estimates of genetic and
environmental influences. The prediction residual for E (v) was
uncorrelated in the twin pair by definition, and correlated at 1
in the twin pair for AC (n), leading the prediction coefficient
(b), or the stability of AC, to be the same for both twins in the
same pair. The phenotypic residuals (e) and prediction
residuals (v and n) for each twin are assumed to be
multinormally distributed within each individual time series data.
A more detailed explanation for iFACE model in the general
multivariate case with mathematic formula and model
identification proof is provided elsewhere (Molenaar et al. 2012).
All iFACE models were fit using the Fortran program
MKFM6 (Dolan 2005) that implements Harvey’s
time-invariant Kalman filter algorithm (Harvey 1989) to calculate
normal theory maximum likelihood estimates of
multivariate stationary time series model (The MKFM6 program
is freely available from http://quantdev.ssri.psu.edu/resour
ces). This algorithm seeks to minimize the difference, or
prediction error, between the predicted value based on all
previous observations E[yt|Yt-1], where Yt-1 = {y1,
y2,…,yt-1}, and the actual observation value yt. A
quasiNewton optimization routine using exact gradients is used
to maximize the loglikelihood function; missing values
during weekend days were coded as missing and handled
by the MKFM6 algorithm (Dolan 2005). The iFACE model
can also be equivalently represented in Toeplitz format
summarizing time lag covariance in block Toeplitz matrix
and fit in any structural equation modelling software (e.g.,
Molenaar et al. 2012).
The iFACE model was first fitted to individually
standardized time series data. As a reference for comparison, a
group-level model with the same model specification as in the
Fig. 1 Path diagram of iFACE
model in one monozygotic twin
pair. NSF, negative school
feelings, PSF positive school
feelings, AC additive genetic
and shared environmental
factor, E, non-shared
environmental factor
individual iFACE model but constraining all parameters (e.g.,
factor loadings, residuals) to be the same across all
individuals was also performed, therefore assuming genetic and
environmental influences to be the same for all individuals.
Descriptive statistics
2.80 (SD = 0.62), with individual means ranging between
1.66 and 3.90 out of the potential 1–4 range. The group
mean of negative school feelings was 1.29 (SD = 0.35),
with individual means ranging between 1.03 and 2.00.
Therefore, in general, pupils reported ‘‘quite’’ positive and
‘‘not very’’ negative about school every day.
Group-level and individual-level non-shared
environmental influences
As shown in Table 1, the group grand mean (averaged over
all individuals and days) of positive school feelings was
Group-level results showed that non-shared environmental
influences accounted for 73 % of the intra-individual
variability in positive school feelings (see Table 2).
Children varied in the magnitude of their person-specific
nonshared environmental influences ranging from 0 to almost
1. The average of person-specific non-shared environ
mental influences was 0.52 (SD = 0.32, skewness = -.21,
kurtosis = -1.27; see Fig. 2). Using 60 % as a subjective
cut-off for the estimated magnitudes of person-specific
non-shared environmental influences, the twin concordance
rate regarding person-specific non-shared environmental
influences (non-shared environmental influences of both
twins in the same pair were lower or higher than 60 %) was
48 %. Seven concordant pairs showed the same pattern as
in the group-level results in that their daily positive school
feelings were primarily accounted for by non-shared
environmental influences, ranging from approximately
60 % to almost 100 % (e.g., pair 14). Three concordant
pairs showed the opposite pattern—daily positive school
feelings of both twins primarily explained by familiality,
with non-shared environmental influences ranging from
almost 0 to 25 % (e.g., pair 12).
The remaining eleven discordant pairs demonstrated two
patterns. First, among six discordant pairs, one twin’s daily
positive school feelings were primarily explained by
nonshared environmental influences as in the group-level
result, whereas the co-twin’s was mostly explained by
familiality (e.g., 3 vs. 60 % in pair 4). The remaining five
pairs showed a pattern where one twin’s positive feelings
were either primarily explained by non-shared
environmental influences (e.g., twin 2 in pair 2) or familiality (e.g.,
twin 1 in pair 19), whereas the co-twin’s was explained
about half (*50 %) by non-shared environmental
influences.
Group-level results showed that the intra-individual
variability of negative school feelings was largely
explained by familiality, with only a small portion
attributed to non-shared environmental influences (16 %).
Children also varied in person-specific non-shared
environmental influences, with an average of 0.51 (SD = 0.37,
skewness = .07, kurtosis = -1.62; see Fig. 2). The
concordance rate for negative school feelings was 43 %.
Specifically, five concordant pairs showed the same pattern
as the group-level results, with minimal non-shared
environmental influences ranging from 2 to 39 % (e.g., pair 1).
Another four concordant pairs showed the opposite pattern
in that their daily negative school feelings were mostly
explained by non-shared environmental influences for both
twins, ranging from 66 % to almost 100 % (e.g., pair 21).
The remaining twelve discordant pairs demonstrated
two patterns. First, for nine of these pairs, one twin’s daily
negative school feelings was primarily explained by
familiality as in the group-level results, whereas the
cotwin’s was primarily explained by non-shared
environmental influences (e.g., 15 vs. 82 % in pair 5). The
remaining 3 pairs showed a pattern in which one twin’s
negative school feelings were either primarily explained by
familiality (e.g., twin 1 in pair 9) or non-shared
environmental influences (e.g., twin 2 in pair 7), whereas the
coTable 1 Group and individual means (SDs) of daily school feelings
Positive school feelings
Negative school feelings
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Fig. 2 Histograms of individual estimates of non-shared environmental influences for positive school feelings (left) and negative school feelings
(right). NSE non-shared environmental influences. PSF positive school feelings, NSF negative school feelings
twin’s was approximately half explained by non-shared
environmental influences.
The group-level results showed a stability of .30 over
1 day lag for non-shared environmental factor. Therefore,
about 9 % of the variance of non-shared environmental
factor overlapped with the previous day’s non-shared
environmental factor. However, individual estimates of this
stability coefficient range from -0.91 to 0.90, with a mean
of -.09 (SD = .56).
For positive and negative school feelings respectively,
individual estimates of non-shared environmental
influences were not significantly correlated with individual time
series mean scores (r = .06 and -.20, ns), individual
variance (r = -.10 and -.08, ns), or individual
standardized and squared time series mean scores (r = -.05 and
-.18, ns). Lastly, the group-level model that fully
constrained all parameters to be the same across individuals
had lower fit indices (-2logliklihood = 1072.54,
AIC = 1088.54, BIC = 1120.59) than the fully
unconstrained model that specified all parameters to be
personspecific (-2logliklihood = 727.76, AIC = 1265.76,
BIC = 2343.47).
The primary goal of the current study was to demonstrate
the utility and feasibility of the iFACE model (Molenaar
et al. 2012; Nesselroade and Molenaar 2010) in examining
genetic and environmental influences on intra-individual
variability at the individual level. By applying the model to
daily mood data from a genetically informative daily diary
study with a small sample of MZ twins, the results
demonstrated substantial inter-individual heterogeneity
regarding person-specific non-shared environmental
influences. Specifically, group-level results showed substantial
non-shared environmental influences for positive school
feelings (73 %), but modest non-shared environmental
influences for negative school feelings (16 %). The
nonshared environmental factor had a low stability (r * .30)
over 1 day lag, suggesting that it changed substantially
across days and may consist primarily of idiosyncratic and
transient experiences (Burt et al. 2015).
The individual-level results revealed substantial
interindividual heterogeneity in person-specific non-shared
environmental influences in that its magnitude varies
greatly across individuals. Group-level estimates could
describe some but not all of the pupils’ intra-individual
patterns in the sample. This clear violation of the
population homogeneity assumption indicates that the ergodic
theorem does not hold in the current situation (Molenaar
2004; Molenaar and Campbell 2009). Therefore,
investigation of genetic and environmental influences on
intraindividual variability can uncover unique information on
intra-individual developmental processes that conventional
behavioural genetic studies could not offer, especially at
the individual level. There are a few possible explanations
to this inter-individual heterogeneity in person-specific
non-shared environmental influences. First, stochastic
intra-individual developmental noise can lead to
inter-individual differences in genetic and environmental
influences as a third source, which however cannot be
distinguished from non-shared environmental influences in
conventional behavioural genetic studies (e.g., Molenaar
et al. 1993; Molenaar and Raijmakers 1999). Second,
epigenetic processes (e.g., DNA methylation) involving
interactions between genes, environment, and other
processes could also affect gene expression to lead to
interindividual heterogeneity (e.g., Bell and Saffery 2012; Bell
and Spector 2011; Dolan et al. 2015; Fraga et al. 2005; Kan
et al. 2010; Petronis 2010; Wright et al. 2014).
These person-specific non-shared environmental
influences offer a unique angle to examine twin concordance,
not in their phenotypes per se (e.g., levels of happiness),
but in their respective individual patterns of how
nonshared environment influences them. In other words, there
are between-pair differences with regard to the similarity
between twins of their person-specific non-shared
environmental influences. The finding that individual estimates
of non-shared environmental influences were not
significantly correlated with conventional phenotypes (e.g.,
individual means, variance) suggests that they could
provide unique information about development above and
beyond conventional phenotypes. By viewing
personspecific non-shared environmental influence as a new
phenotype that contains unique information on
intra-individual developmental processes (the extent to which one’s
daily mood is influenced by non-shared environmental
experience), one is able to further examine inter-individual
differences in intra-individual patterns. For example, the
finding that twin pair concordance in person-specific
nonshared environmental influences could be summarized by a
few general patterns suggests that there could be finite
homogeneous subgroups of twin pairs regarding
withinpair patterns in the population. Additionally,
personspecific non-shared environmental influences offer another
perspective on why twins growing up in the same family
can be so different (Plomin 2011; Plomin and Daniels
1987). They differ not only in their non-shared
environmental experiences, but also in the degree to which their
daily unique environmental experiences influence them.
The current study has a few limitations that warrant
caution and consideration in future studies. First, and most
important, the time series data are short. Therefore, we do
not over-emphasize our substantive results, but focus more
on the implications that our results offer regarding the roles
of genetic and environmental influences in intra-individual
developmental processes. The key point here is to show
empirically that this alternative approach provides new
insights that take us beyond conventional behavioural
genetic studies to ask questions about particular individuals
as well as genetic and environmental contributions to
intraindividual processes. The few existing studies that have
collected ILD in twin samples, including the one from
which we drew the data for analyses (Asbury et al. 2008;
Kessler et al. 2004; Klump et al. 2013; Wichers et al.
2007), were not intended to examine genetic and
environmental influences on intra-individual variability and all
employed designs of a short time length, typically ranging
for a few days. Another issue related to the short time
series data is that many children in the current sample did
not demonstrate any or much intra-individual variability to
be included in the analysis, an issue the few previous
studies encountered as well (e.g., Jacobs et al. 2013;
Menne-Lothmann et al. 2012). Additionally, as opposed to
conventional multivariate behavioural genetic models
where common and specific genetic and environmental
loadings can be specified for each phenotype as in
Cholesky decomposition (Loehlin 1996), the current iFACE
model only modelled the communal part of positive and
negative school feelings. The residuals of positive and
negative school feelings can still contain their specific
genetic and environmental influences. However, such
model would require more parameters and is beyond the
capability of the data. With sufficiently long time series
data, however, iFACE model could be extended to examine
common and specific genetic and environmental influences
in multiple phenotypes. It is important to note that
measurement errors could lead to biased estimates of individual
non-shared environmental influences and could possibly
explain part of the inter-individual heterogeneity as well.
With sufficiently long time series data, measurement errors
could be more efficiently dealt with by adopting a
psychometric common pathway model with a latent construct
loading on item-level data, whose variance represents
intraindividual variability that could be further decomposed
with iFACE model. Rigorous studies design collecting ILD
among twin samples is beyond the scope of the current
study, but we refer interested readers to further readings for
more information (Diehl et al. 2015; Shiffman et al. 2008;
Stone et al. 2007; Walls and Schafer 2006). Simulation
studies are needed to look into the optimal sample size and
length of time series data needed, as well as potential
influences of various signal-to-noise ratio, to reach
satisfactory power and to guide future study design.
Second, while the generalizability issue is usually
emphasized, it is important to note that this alternative
idiographic approach does not mean single-subject analysis
(Molenaar 2010b). The idiographic approach introduced in
this study starts by analysing one specific individual’s
intra-individual variability to extract the dynamic pattern
that characterizes the individual’s development. Next, the
same procedure is applied to many individuals’
intra-individual variability separately. Individuals with the same or
similar developmental patterns are grouped together to
reach subgroup-level results. This idiographic behavioural
genetic approach is capable of capturing inter-individual
differences in the dynamic intra-individual patterns.
Notably, based upon model fit indices, the fully constrained
group-level model provided a more parsimonious fit to the
overall data with lower AIC and BIC. This is not surprising
in some way because the fully unconstrained
individuallevel models are highly parameterized in that each twin has
his/her own person-specific parameter. In other words, the
relative accuracy of person-specific individual-level
models describing individual time series data comes with the
trade-off of estimating more parameters than the
grouplevel (i.e., fixed effect) model that more parsimoniously
describes group data. More advanced statistical techniques
are to be developed to describe and model these
personspecific estimates with fewer and more sufficient
parameters. For example, mixture modelling can be incorporated
in future studies to identify latent subgroups with similar
genetic and environmental influences on intra-individual
variability (e.g., Hunter et al. 2014). Random effect models
could also be considered to relate individual-level with
population-level genetic and environmental influences
(Dolan et al. 2015). Recent development in dynamical
systems models can also be used to examine genetic and
environmental influences on dynamic system parameters
that describe unique intra-individual developmental pattern
(e.g., Boker et al. 2014).
For the 21 pairs included in the analyses, each individual
time series data were standardized, which essentially
removed the intercept in the iFACE model. Therefore,
estimates for each twin time series data represented
nonshared environmental influences on intra-individual
variability without the potential confounding of
inter-individual differences in mean levels of each twin time series data
(e.g., one individual generally had higher levels of negative
school feelings than another). Correspondingly, the
grouplevel analysis estimated non-shared environmental
influences on intra-individual variability across all 21 twin pairs
without confounding of inter-individual differences in
mean levels while assuming homogeneity. As noted, 40
MZ pairs were excluded from analyses because of the lack
of intra-individual variability (e.g., reporting no negative
school feelings at all across all 10 days, or only 1 or 2 days
out of 10 days, with some levels of negative school
feelings). This suggests that for these 40 MZ pairs, their
interindividual differences lie more in the mean levels of school
feelings than in intra-individual variability. In this case,
using the aggregated mean scores over the assessed period
would be a better way (e.g., Asbury et al. 2008; Cleveland
and Almeida 2013; Neiss and Almeida 2004), which would
produce non-shared environmental influences as in
conventional behavior genetic studies using discordant
monozygotic twin control design.
Behavioural genetic studies have made a tremendous
contribution to our understanding of the interplay of
genetics and environment in shaping human development.
These studies generally focus on inter-individual
phenotypic difference, or inter-individual differences in
intraindividual change, which are informative at a macroscopic
level—at the population level and at the macro time scale
(e.g., years). As developmental science is moving toward a
more refined understanding of the dynamic developmental
process accompanied by the growing collection of ILD in
social and behavioural science, the current study introduces
a complementary idiographic approach that focuses on
intra-individual phenotypic variability. Resonant with the
call for personalized treatment and medicine (Collins 2010)
and personalized education (Asbury and Plomin 2013)
based on each individual’s specific genomics that
emphasize intra-individual process at the individual level, this
idiographic approach aims to unravel the interplay of genes
and environments at a more microscopic level—at the
individual level and at the micro time scale (e.g., daily).
After all, developmental change does not happen overnight
but occurs under the continuous coaction of genes and
environment in real time at every month, week, day, hour,
and second in real daily life contexts. This novel approach
poses new questions to the theory of behavioural genetics
in intra-individual developmental processes, new
challenges to study designs, data collection methods, and
analytic techniques in behavioural genetic research, as well
as new opportunities to disentangle genetic and
environmental influences in human development at a new and
exciting level of analysis. It is our ultimate intention, by
introducing this novel idiographic behavioural genetic
approach to intra-individual variability, together with an
analytic method among others suitable for this type of data,
to raise awareness of this approach to more behavioural
genetic researchers. We envision and are optimistic that the
field of behavioural genetics will offer a lot to our
understanding of the interplay of genes and environment in
intraindividual variability.
Acknowledgments The authors gratefully acknowledge the
ongoing contribution of the participants in the Twins Early Development
Study (TEDS) and their families. TEDS is supported by a programme
grant from the UK Medical Research Council [G0901245; and
previously G0500079], with additional support from the US National
Institutes of Health [HD044454; HD059215].
Compliance with Ethical Standards
Conflict of Interest Yao Zheng, Peter C. M. Molenaar, Rosalind
Arden, Kathryn Asbury, and David M. Almeida declare that they have
no conflict of interest.
Human and Animal Rights and Informed Consent All families
provided informed consent and understood that they were free to
withdraw from the data collection at any time. None chose to do so.
All procedures were approved by, and performed in accordance with
the Institutional Review Board at King’s College London.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://crea
tivecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
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