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ORIGINAL RESEARCH
Heritability of Working in a Creative Profession
Mark Patrick Roeling
1
•Gonneke Willemsen
2
•Dorret I. Boomsma
2
Received: 6 June 2016 / Accepted: 18 November 2016 / Published online: 15 December 2016
ÓThe Author(s) 2016. This article is published with open access at Springerlink.com
Abstract Creativity is the tendency to generate or recog-
nize ideas, alternatives, or possibilities. Following a study
on the genetic contribution to working in a creative pro-
fession, based on polygenic score analysis, we report the
total heritability of this trait in a large sample of adult twins
and their siblings registered with the Netherlands Twin
Register. Data from 6755 twins and 1817 siblings were
analyzed using genetic structural equation modeling.
Working in a creative profession is relatively rare in our
sample (2.6% of twins and 3.2% of siblings). Twin corre-
lations (identical 0.68 and fraternal 0.40) commended a
model with additive genetic factors (full model estimate
0.56), shared (full model estimate 0.12), and unique envi-
ronmental factors (full model estimate 0.32). Genetic
model fitting resulted in a best-fitting model existing of
additive genetic factors and unique environmental factors,
resulting in a heritability of 0.70.
Keywords Creativity Twin study Talent Working
Profession Heritability
Introduction
Creativity is the tendency to generate or recognize ideas,
alternatives, or possibilities that may be useful in solving
problems, communicating with others, and entertaining
ourselves and others (Franken 2006). This broad array of
discrete abilities has a strong cognitive component (Weis-
berg 1992) and creativity correlates with intelligence and
cognitive performance (Guilford 1967; Penke 2006).
There is ample evidence for the influence of genetic
factors but heritability estimates are diverse. Ten early twin
studies were summarized in a review presenting average
correlations of 0.61 for MZ twins and 0.50 for DZ twins
(Nichols 1978), resulting in a rough heritability estimate of
25% (and 38% shared environmental factors; Penke 2006).
A subsequent study into perceptual and esthetic abilities
(Barron and Parisi 1976) also argued in favor of hereditary
influences in creativity. Generally, studies in adolescents
provide lower heritability estimates of creativity-related
traits. One twin study in adolescents (13–19 years) exam-
ined 11 creative ability measures (e.g. recognizing obscure
figures or create story plot titles) and observed only three
scales with significant MZ-DZ differences (Reznikoff et al.
1973). A Russian twin study estimated a heritability of 0.44
in creative thinking (Grigorenko et al. 1992). Compared to
the aforementioned studies in adolescents, heritability
estimates are slightly higher in adults for traits as creative
personality (50–54%; Bouchard et al. 1993; Velazquez
et al. 2015), drawing (38–47%; Velazquez et al. 2015), arts
(60%; Vinkhuyzen et al. 2009), creative writing (83%;
Vinkhuyzen et al. 2009), creative achievement (61%; Piffer
and Hur 2014), perceived (62%) and figural (26%) cre-
ativity (Kandler et al. 2016). Variability in creativity over
age has been interpreted as indicative for cognitive matu-
rity, with children first becoming proficient learners while
Edited by Yoon-Mi Hur.
&Mark Patrick Roeling
mark.roeling@wolfson.ox.ac.uk
1
Department of Computer Science, University of Oxford,
Robert Hooke Building, Parks Road, Oxford OX1 3QD, UK
2
Department of Biological Psychology, Vrije Universiteit
Amsterdam, Amsterdam, The Netherlands
123
Behav Genet (2017) 47:298–304
DOI 10.1007/s10519-016-9832-0
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
the ability to think creatively evolves throughout devel-
opment (Fogarty et al. 2015).
Another aspect of creativity is the phenotypic and
genotypic overlap with traits such as openness, extraver-
sion, and intelligence (Canter 1973; Kandler et al. 2016;
Penke 2006), which extends to extreme expressions of
behavior (Carson 2011) illustrated by the co-occurrence of
creativity with attention problems (Mayseless et al. 2013),
schizophrenia and bipolar disorder (Power et al. 2015). The
latter study linked genetic variants underlying schizophre-
nia and bipolar disorder to creativity, explaining 0.24 and
0.26% of the variance of creativity, respectively. Power
et al. (2015) analyzed creativity as working in a creative
profession, but did not report the overall heritability of this
trait. Therefore, we now use data from 6755 twins and
1817 siblings aged C21 years registered with the NTR
(Willemsen et al. 2013), to estimate the total heritability of
working in a creative profession. Extending the classical
twin design of mono- and dizygotic twins with their sib-
lings gives larger statistical power to estimate both shared
environmental and non-additive genetic effects (Posthuma
and Boomsma 2000).
Methods
Participants
This study is part of an ongoing study on health, lifestyle
and personality in twins and their family members regis-
tered in the NTR. Every two to three years, registered
families receive surveys on health and lifestyle and the
present study uses from the seventh and eight surveys
respectively collecting data in 2004–2008 and 2009–2012
(described in Willemsen et al. 2013). Informed consent was
obtained from all individual participants included in the
study. Zygosity was determined either through genotyping
or from self- and parental report answers to survey ques-
tions on physical resemblance or confusion of the twins by
other family members and peers. DNA and survey zygosity
agreement reached more than 96%.
Data were available from 8802 participants
(N
TWINS
=6942, N
SIBLINGS
=1860). We removed indi-
viduals with age unknown (N =6) or age below 20 years
(N =3) and families including only one twin (N =95),
multiple twins from a different pair (N =31), or twins
with missing zygosity (N =27). A maximum of two
brothers and two sisters were included in the analyses,
remaining siblings were excluded (N =68). In families
with triplets, we selected two random twins, and if dizy-
gotic, the remaining twin was used as a sibling. In families
with multiple twin pairs, we selected the first twin pair and
used the other twin pairs to extract one random twin as
sibling. This resulted in a total sample of 6755 twins and
1817 siblings from 4734 families, including 999 monozy-
gotic males (MZM), 541 dizygotic males (DZM), 2585
monozygotic females (MZF), 1249 dizygotic females
(DZF), 1381 DZ opposite-sex pairs, with 679 brothers and
1138 sisters. Table 1shows the complete family configu-
ration of the sample. There were 2427 families in which
both members of a twin pair completed the questionnaire,
1901 families in which only one member of the twin pair
completed the questionnaire and 406 families in which
only non-twin siblings completed the questionnaire (added
to DZMales). The mean age of the twins was 38.42 years
[standard deviation (SD) 11.98, range 20–90 years] and the
mean age for their siblings was 41.26 years (SD 12.18,
range 20–90 years).
Measures
Following an earlier study (Power et al. 2015), creativity
(being in a creative profession) scoring relied on surveys
including detailed questions about the participants’ occu-
pations. Using a detailed description, individuals were
asked to report their profession. We then classified these
professions on being creative or not. Creative professionals
were defined as those having positions in the fields of
dance, film, music, theatre, visual arts, or writing. We did
not differentiate whether, within these categories, persons
were more or less creative. When persons were not work-
ing or reported to be a housewife at the moment of data
completion, they were asked for their past occupation and
this was used to assess whether they had a creative pro-
fession. When a person indicated to be a housewife and had
not had another profession in the past, this was coded as not
involved in a creative profession. In the case of full-time or
part-time education, creative profession was regarded
missing.
Statistical analyses
Descriptive analyses were performed in SPSS v.20.
Genetic structural equation model analyses were conducted
in Mx (Mx: statistical modeling; Neale 2006). Given the
categorical nature of the data (yes/no) we fitted a ‘liability’
model (Falconer 1965), in which the categorical variable
was assumed to reflect an imprecise measurement of an
underlying normal distribution of liability. Being a theo-
retical construct, the liability’s scale needs to be defined. In
general, the liability is assumed to be standard normally
distributed with zero mean and unit variance. The threshold
acts as a reference of incidence of the different categories
in the population (Falconer and Mackay 2005).
We fitted different sets of models to the raw ordinal data
using maximum likelihood estimation. In the fully
Behav Genet (2017) 47:298–304 299
123
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saturated model, thresholds were allowed to vary as a
function of sex (0 for male and 1 for female) and twin/sib
status. Specifying separate thresholds for twins and siblings
allows to investigate sibling effects (a specific kind of
genotype-environment autocorrelation), which can occur
because the genotype of one sibling or twin is genetically
correlated with the phenotype of the other sibling which is
providing part of the environment, potentially resulting in
different thresholds between siblings (Neale and Maes
2002). Age (standardized and sex specific coefficients) was
modelled as a covariate on the threshold to account for any
remaining variability in incidence of creativity as a func-
tion of age, expecting similar effects of the age bcoeffi-
cient between zygosity groups. Tetrachoric correlations
were estimated for the continuous liability distribution,
with a total of eight correlations for MZMales, DZMales,
MZFemales, DZFemales, DZ opposite-sex pairs, brother–
brother, sister–sister, and brother–sister) to be estimated. In
total, the saturated model comprised of 16 free parameters:
one threshold for male twins and one threshold for female
twins, one threshold for brothers and one for sisters, two
fixed effects, i.e., covariates coefficients (age and sex) for
males and two coefficients for females, and eight correla-
tions estimating the familial resemblance. In a series of
nested models we tested constraints to test the significance
of different parameters and derive the most parsimonious
model. The fit of submodels was evaluated with Log-
likelihood ratio testing, which involves subtracting the
negative log-likelihood (-2LL) for the more general model
from the -2LL of the more restricted model. This gives a
v
2
test with the degrees of freedom (df) equal to the dif-
ference in the number of estimated parameters in the two
models. A significant v
2
(p\0.05) indicates that the
constrained model fits significantly worse than the previous
model. As a result, the previous model is kept as the most
parsimonious model, to which a new model can be com-
pared. Thus, those models that are the most parsimonious
and efficient representations of the data are selected.
Genetic analyses
From the difference in genetic relatedness in MZ and DZ
twins, who share respectively 100 and 50% (on average) of
their segregating genes, the amount of variance can be
estimated and ascribed to genetic and environmental fac-
tors (Boomsma et al. 2002). A higher MZ correlation
compared to the DZ correlation is indicative of genetic
influences. If MZ and DZ correlations are similar, genetic
effects are not suggested.
Quantitative genetic modeling is based on the fact that the
phenotypic variance is a function of genetic, shared, and
unique environmental variance. The expectation for the phe-
notypic variance may be written as: V
(P)
=V
(A)
?-
V
(D)
?V
(C)
?V
(E)
. Genetic variance can be additive (A),
indicating that the effects of multiple alleles are additive, or
nonadditive (dominance, D) meaning that alleles at a
Table 1 Family structures in
dataset Families yielding
No siblings 1 sibling 2 siblings 3 siblings 4 siblings Total
MZM
Families yielding a twin pair 258 81 14 10 1 364
Families yielding a single twin 233 26 11 0 1 271
DZM
Families yielding a twin pair 108 44 831164
Families yielding a single twin 165 36 10 2 0 213
MZF
Families yielding a twin pair 777 220 53 9 5 1064
Families yielding a single twin 400 46 10 1 0 457
DZF
Families yielding a twin pair 300 95 39 8 0 442
Families yielding a single twin 314 43 620365
DOS
Families yielding a twin pair 260 101 24 6 2 393
Families yielding a single twin 498 75 16 6 0 595
Families yielding no twins – 335 62 8 1 406
Total 3315 1102 253 55 11 4734
Family structures in dataset
MZM monozygotic males, DZM dizygotic males, MZF monozygotic females, DZF dizygotic females, DOS
DZ opposite-sex
300 Behav Genet (2017) 47:298–304
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particularlocus interact. When the DZ correlation is morethan
half the MZ correlation, there is evidence for environmental
effects shared by twins from the same family (C) and when the
DZ correlation is less than half the MZ correlation, there is
evidence for non-additive genetic effects. Broad-sense heri-
tability (h
2
) is the proportion of phenotypic variance that is
attributable to genotypic variance (h
2
=(V
(A)
?V
(D)
)/V
(P)
);
narrow-sense heritability is the proportion of variation
explained by additive genetic factors (h
n
2
=V
(A)
/V
(P)
). A
classical twin design only provides information to model
either an ACE model or an ADE model, but adding data from
siblings of twins provides more information and statistical
power to distinguish between additive and dominant genetic
factors. The additive genetic variance is perfectly correlated in
MZ twins, whereas for DZ twins and siblings the cross-twin/
cross-sib correlation between the A factors is 0.5. Again, the
significance of genetic parameters was tested by comparing
submodels against a more general model, using log-likelihood
ratio testing.
Results
We observed a low frequency of working in a creative
profession with 175 (2.6%) of 6755 twins and 58 (3.2%) of
1817 siblings reporting positive (see Table 2). For each
family, we indicated the number of individuals within the
family, restricted to twins, siblings and parents. From the
8572 individuals (twins with B4 siblings) included in the
statistical analyses, no data on additional family members
were available in 1331 persons. In the remaining 7241
individuals with data from family members: 4.5% had 1 or
more family members in a creative profession. We then
split the data according to creative profession of the person
itself. Of the persons who were, themselves, not in a cre-
ative profession (N =7042), 3.9% had family members in
a creative profession. In the persons who were in a creative
profession (N =199), 26.1% had family members in a
creative profession.
Table 2 Reported creative
professions N (%) for first profession N (%) for second profession
Architecture 18 (7.6) –
Art teacher 15 (6.4) 7 (21.9)
Art therapy 1 (0.4) –
Artisan 16 (3.8) 6 (18.8)
Cinematography 20 (8.5) 3 (9.4)
Creative director 13 (5.5) 2 (6.3)
Creative writer 5 (2.1) –
Curator 2 (0.8) –
Dance teacher 4 (1.7) 1 (3.1)
Fashion design 2 (0.8) –
Flower design 15 (6.4) –
Game design 1 (0.4) –
Graphical design 41 (17.4) –
Illustrator 2 (0.8) –
Interior design 18 (7.6) –
Landscape architect 3 (1.3) –
Music teacher 9 (3.8) –
Musician 3 (1.3) –
Photography 2 (0.8) 1 (3.1)
Reporter 19 (8.1) –
Set decoration 3 (1.3) –
Singer 1 (0.4) –
Theatre artist 7 (3.0) 1 (3.1)
Theatre teacher 5 (2.1) 1 (3.1)
Web design 7 (3.0) –
Writer 4 (1.7) 9 (28.1)
Costume maker – 1 (3.1)
Total 236 (100) 32 (100)
The Artisan category includes individuals working as a fine artist (painter, art drawing, and ceramics artist).
Three individuals reported two creative professions as first profession
Behav Genet (2017) 47:298–304 301
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Between twins, there was no evidence for birth-order
effects (v2
ð1Þ=0.010, p=0.920). Of the 4734 families,
4523 (95.5%) had no family members who are who are
working, or worked, in a creative profession. Table 3
presents the results of the tests in the saturated model.
Creativity scores, measured as thresholds, were not sig-
nificantly different for male twins and brothers nor for
female twins and sisters (v2
ð1Þ=1.759, p=0.415) sug-
gesting the absence of sibling effects. Thresholds could
also be constrained across gender (v2
ð1Þ=0.032,
p=0.857). In females, there was a significant positive age
effect (v2
ð1Þ=12.309, pB0.001) indicating that older
females more frequently report to work, or to have worked,
in a creative profession. Phenotypic correlations between
twins were 0.54 in MZMales and 0.69 in MZFemales
versus 0.47 in DZMales, 0.25 in DZFemales and 0.31 in
DZ-opposite sex pairs. Sibling correlations were 0.54
between brothers, 0.31 between sisters, and 0.51 between
sisters and brothers. Constraining the phenotypic correla-
tions between twins and siblings (e.g., brother–brother
correlation to DZMales) and between males and females
did not result in a significant deterioration of the model fit,
commending a model where the heritability is equal for
both gender groups. After constraining the correlations, the
constrained MZ (MZMales and MZFemales) correlation
was 0.68 and the constrained DZ (DZ twins and siblings)
correlation was 0.40, suggesting the influence of shared
environmental factors (C) and providing evidence for an
ACE model.
The full model estimated A to be 0.56 (95% CI 0.11–
0.80), C to be 0.12 (95% CI 0–0.45) and E to be 0.32 (95%
CI 0.19–0.50). Dropping the shared environmental factor
from the ACE model did not significantly worsen the
model fit (v2
ð1Þ=0.478, p=0.489), whereas removal of
the additive genetic component did (v2
ð1Þ=5.997,
p=0.014). Therefore, the best fitting model is an AE
model with a heritability estimate of 0.70.
Discussion
This study estimated the heritability of working in a cre-
ative profession in a large sample of Dutch adult twins and
their siblings. The best fitting model yielded a heritability
Table 3 Model fit results for the saturated model
Model Test Versus -2LL df v
2
Ddf p
0 Full model – 2115.081 8572
1 Thresholds male twins =brothers
Threshold female twins =sisters
0 2116.840 8574 1.759 2 0.415
2 Thresholds males =females 1 2116.872 8575 0.032 1 0.857
3 Age effects males =females 2 2122.821 8576 5.949 1 0.015
3a Age effects males =0 2 2116.962 8576 0.090 1 0.764
3b Age effects females =0 2 2129.181 8576 12.309 1 \0.001
4 Sex effects males =females 3a 2117.011 8577 0.049 1 0.825
4a Sex effects males and females =0 4 2117.107 8578 0.096 1 0.757
5 DZ twin correlations =sibling correlations 4a 2117.860 8581 0.753 3 0.861
6 Male correlations =female correlations 5 2118.288 8583 0.428 2 0.807
7Same sex DZ/sibling correlations =opposite sex DZ/sibling correlations 6 2119.074 8584 0.786 1 0.375
Model fit results for the saturated model. The best fitting model had four free parameters (one threshold, MZ correlation, DZ correlation, and age
coefficient females). The best fitting model is printed in bold
-2LL -2 log likelihood, df degrees of freedom, ppvalue, DZ dizygotic
Illustration 1 MZ twins are more often concordant for creativity, as
illustrated by these portraits where Dutch MZ twins David and Pieter
Oyens (successful nineteenth century painters) painted each other
302 Behav Genet (2017) 47:298–304
123
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estimate of 70% with the remaining variance explained by
unique environmental factors. Our finding confirms earlier
smaller studies in adults (Bouchard et al. 1993; Kandler
et al. 2016; Penke 2006; Piffer and Hur 2014; Vinkhuyzen
et al. 2009) presenting evidence for significant genetic
influences underlying creativity. Outcomes of this study are
also in line with twin studies investigating personality traits
where the majority of studies show a heritability of
0.49–0.57 and low to zero shared environmental influences
(Bouchard and McGue 2003). The insignificant prevalence
differences and similar heritability estimates between sexes
conforms to earlier work (Penke 2006; Piffer and Hur
2014) and adds evidence to a field where studies that report
higher creativity scores in females are counterbalanced (in
number) by studies where males score higher (Baer and
Kaufman 2006).
This study is limited in using only self-report data on
profession that is categorized by us as creative or not cre-
ative, and in the low prevalence of working in a creative
profession. Possibly, the focus on working in a creative
profession provides an underestimate of creativity in gen-
eral, as participants can still be creative in their own time
(e.g., during leisure) which is not captured by this study. The
combination of subtle influences of C (ACE model esti-
mate =0.12) and the use of a threshold model in an
unbalanced dataset reduces the power of our method. The
inclusion of (at least one) additional sibling(s) is a proven
strategy to increase the power to accurately estimate vari-
ance components, especially common environmental factors
(Posthuma and Boomsma 2000). From Neale et al. (1994)we
deduce that with our prevalence (between 0.01 and 0.05) a
sample size at least 100,000 twins would be needed to detect
the AE model under a true C of 0.12. Despite this being one
of the largest twin studies focusing on creativity to date,
there may be much to learn about shared environmental
effects that contribute to the decision to pursue a creative
carreer with more powerful samples or methods. From our
work and others, the substantial heritability estimate for the
phenotype indicates that multiple DNA variants, in addition
to those overlapping with schizophrenia and bipolar disorder
are likely to be found in the future (Illustration 1).
Acknowledgements The Netherlands Twin Register thanks all par-
ticipants. The research leading to these results has received support
from the Netherlands Organization for Scientific Research (NWO)
and MagW/ZonMW, BBMRI-NL (184.021.007), the VU University
Institute for Health and Care Research (EMGO ?) and Neuroscience
Campus Amsterdam, the European Science Council (ERC) Genetics
of Mental Illness (230374), the Avera Institute for Human Genetics,
and the Engineering and Physical Sciences Research Council.
Compliance with ethical standards
Conflict of interest Mark Patrick Roeling, Gonneke Willemsen, and
Dorret I. Boomsma declare no conflict of interest.
Human and animal rights The study protocols were approved by
the Central Ethics Committee on Research Involving Human Subjects
of the VU University Medical Centre, Amsterdam, an Institutional
Review Board certified by the US Office of Human Research Pro-
tections (IRB number IRB-2991 under Federalwide Assurance-3703;
IRB/institute codes, NTR 03-180).
Informed consent Twin families are voluntarily registered with the
NTR and the data collection protocol was approved by the Medical
Research Ethics Committee of the VU University Medical Center.
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
link to the Creative Commons license, and indicate if changes were
made.
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