A genome-wide association study of occupational creativity and its relations with well-being and career success

Abstract

Creativity is one defining characteristic of human species. There have been mixed findings on how creativity relates to well-being, and little is known about its relationship with career success. We conduct a large-scale genome-wide association study to examine the genetic architecture of occupational creativity, and its genetic correlations with well-being and career success. The SNP-h² estimates range from 0.08 (for managerial creativity) to 0.22 (for artistic creativity). We record positive genetic correlations between occupational creativity with autism, and positive traits and well-being variables (e.g., physical height, and low levels of neuroticism, BMI, and non-cancer illness). While creativity share positive genetic overlaps with indicators of high career success (i.e., income, occupational status, and job satisfaction), it also has a positive genetic correlation with age at first birth and a negative genetic correlation with number of children, indicating creativity-related genes may reduce reproductive success.

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communications biology Article
https://doi.org/10.1038/s42003-024-06686-5
A genome-wide association study of
occupational creativity and its relations
with well-being and career success
Check for updates
Wen-Dong Li 1, Xin Zhang 2,KailiYu
1,YimoZhu
3, Nianyao Du4,ZhaoliSong
3&QiaoFan 5
Creativity is one defining characteristic of human species. There have been mixed findings on how
creativity relates to well-being, and little is known about its relationship with career success. We
conduct a large-scale genome-wide association study to examine the genetic architecture of
occupational creativity, and its genetic correlations with well-being and career success. The SNP-h2
estimates range from 0.08 (for managerial creativity) to 0.22 (for artistic creativity). We record positive
genetic correlations between occupational creativity with autism, and positive traits and well-being
variables (e.g., physical height, and low levels of neuroticism, BMI, and non-cancer illness). While
creativity share positive genetic overlaps with indicators of high career success (i.e., income,
occupational status, and job satisfaction), it also has a positive genetic correlation with age at first birth
and a negative genetic correlation with number of children, indicating creativity-related genes may
reduce reproductive success.
Creativity plays a crucial role in shaping not only the optimal functioning of
individuals, but also the development and well-being of human societies and
civilizations1–3.Asignificant amount of research endeavors has thus been
devoted to identifying what factors contribute to creativity. Among these
endeavors, individual characteristics have received ample research attention
in the literature, which perhaps dates back to Galton’s landmark research on
the heredity of creative genius4. Indeed, twin studies have shown sizable
genetic influences on creativity5–7. Recent molecular genetics research has
further revealed specific genetic variants that may be responsible for the
heritability of creativity8,9.
Genetic research on creativity has shed light on the notion that crea-
tivity is perhaps one of the few defining characteristics of the human
species10, the importance of which looms large in the new era of artificial
intelligence11. Yet, this line of inquiry is not without limitations. First,
affected by the proposition that creativity is domain specific12,13,theprior
research on adult creativity has primarily concentrated on either specific
occupations (e.g., artists or scientists) or a narrow group of people with
unique creative characteristics with small samples14. This has limited the
scope of creativity research, because creativity matters for a broader spec-
trum of work and occupations beyond artists and scientists13. Second, the
lack of a more inclusive and comprehensive approach to creativity has also
hampered the investigation of the genetic architecture of creativity using
large samples that allow us to tackle important questions in the field, such as
whether and how the genetic architecture of creativity overlaps with that of
health and well-being variables. The stereotype that there may be a positive
correlation between creativity and mental disorder—perhaps dating back to
Aristotle15—suggests a possible negative phenotypic correlation between
creativity and well-being16–19. A recent meta-analysis, however, imposes a
challenge to this stereotype by reporting a small, but significant positive
phenotypic correlation (.14) between creativity and well-being20. Probing
genetic correlations between creativity and well-being may contribute to a
more nuanced understanding of the genetic etiology and the nature of such
relationships21. Third, another stereotype of creative people is that they often
struggle with their careers22,23. This has in fact been supported by census
data24,25 showing that artists often earn less income—an objective indicator
of career success26—than counterparts in other occupations, suggesting a
negative phenotypic correlation between creativity and career success.
Research has also demonstrated that artists experienced higher levels of job
satisfaction—a subjective indicator of career success—than incumbents in
other occupations27, which suggests a positive phenotypic relationship
between creativity and career success. Thus, a large-scale study on the nature
and genetic etiology of the relationship between creativity and a relatively
1Department of Management, CUHK Business School, The Chinese University of Hong Kong, Hong Kong, China. 2Department of Human Resource Management,
School of Business, Shanghai University of Finance and Economics, Shanghai, China. 3Department of Management and Organization, National University of
Singapore, Singapore, Singapore. 4Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore. 5Centre for Quantitative
Medicine, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore. e-mail: oceanbluepsy@gmail.com;xinzhang@sufe.edu.cn;
bizszl@nus.edu.sg;qiao.fan@duke-nus.edu.sg
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more comprehensive spectrum of career success variables (e.g., income,
occupation status, job satisfaction, and reproductive success) may shed light
on the inconsistent findings on the phenotypic relationship between crea-
tivity and career success21.
We thus conducted a large-scale genome-wide association study using
data from the U.K. biobank (N= 219,473) and three independent replica-
tion samples (N= 26,975) to examine the above three questions. The
objective of this research is three-fold. First, we draw from the literatures on
creativity and organizational research28 and developed two more inclusive
and comprehensive creativity measures to capture creativity in a broader
fashion. Creativity is often defined as the generation of ideas, products,
problem solutions, approaches and practices that are both novel and
useful1,3,29. Creativity is a multifaceted construct and has been shown to be
very difficult to assess13,30. Prior research on adult creativity has primarily
concentrated on artistic and scientific creativity14 and thus neglected another
important form of creativity related to novel and useful management
practices and approaches in organizations, which we call managerial crea-
tivity (e.g., the introduction of modern assembly line)31. Indeed, the litera-
ture in organizational research has highlighted that managerial jobs,
particularly those at senior and leadership levels, require job incumbents to
come up with novel and appropriate ideas and practices to initiate changes
in the workplace and to deal with new challenges in the ever-changing
business world28,32–34. Such novel challenges loom large in today’sbusiness
world wherein extant approaches to manage customers’needs, supply
chains, and employee motivation and turnover are constantly interrupted
and modified by broader political, economic, and technological changes34,35.
Accordingly, with our first measure of occupational creativity,wedis-
tinguished the three types of creativity (artistic, scientific, and managerial) as
they are reflected in the three categories of occupations based on the U.K.
Standard Occupational Classification (SOC) 2000 systems (we also com-
bined the three creative occupations to general a categorial variable of overall
occupational creativity). Furthermore, in order to provide a common metric
to assess creativity across various occupations (because job incumbents in
occupations beyond the three creative occupations may also exhibit high
levels of creativity), we capitalized on the literature on occupational classi-
fication/analyses and constructed an omnibus measure of creativity from
the U.S. Department of Labor’s Occupational Information Network
(O*NET)36 by a crosswalk linking the U.K. SOC system to the O*NET SOC
system37. We adopted three items from O*NET (e.g., tapping into partici-
pants’abilities to generate novel ideas and thinking creatively) to access
occupational creative achievement as a continuous variable. Employing the
two major measures of creativity enablesustofurtherexplorepossible
genetic polymorphisms at the whole genome level in our GWAS. To further
examine the distinction among the three different forms of creativity, we
also examine their different genetic underpinnings and how they are dis-
tinctively related to personal traits (e.g., intelligence, educational achieve-
ment, personality, and physical height) at the whole genome level (also for
well-being and career success variables).
Second, we investigated the genetic correlations between occupational
creativity and a number of health and well-being variables including bipolar
disorder and schizophrenia —traditionally theorized and found as positive
correlates with creativity8,9,38–41—and others (e.g., subjective well-being,
BMI, autism, longevity, cannabis misuse, and alcohol use).
Third, we examined the genetic correlations between occupational
creativity with a wide range of important indicators of career success,
including income, occupation status, job satisfaction, and reproductive
success (number of children and age at first birth). We also compared such
genetic correlations among the three different forms of creativity. We fur-
ther examined the genetic correlations between creativity and well-being
and career success variables after partialling out genetic influences asso-
ciated with intelligence, an important cognitive predictor of creativity(3), as
well as educational achievement. Overall, our investigation contributes to
the scholarship on creativity by expanding its scope with including artistic,
scientific, managerial creativity and a more general metric of creative
achievement, examining the genetic architecture of occupational creativity
in a large-scale study, and probing the genetic correlations between occu-
pational creativity with well-being and career success.
Results
Phenotypical measures of occupational creativity
Due to its multifaceted nature, we assessed creativity with two measures (five
variables) based on the major job responsibilities of participants’occupa-
tions (Table S1). The first was a categorial measure capturing three types of
occupational creativity: artistic, scientific, and managerial, as manifested in
occupations that require generating novel and useful ideas (in total four
binary variables). The literature has primarily focused on two forms of
creativity: artistic and scientific14. It has overlooked another form of crea-
tivity in managerial and leadership jobs—managerial creativity, the
importance of which looms large when organizations face ever-changing
business environments to deal with novel challenges related tosupply-chain,
customer needs, and employee management34,35. We thus further included a
third category: managerial creativity. We captured the three types of
occupational creativity via categorizing participants’occupations based on
the U.K. SOC 2000 system into creative occupations (artistic, scientific, and
managerial) according to the major responsibilities of the occupations that
require generating new and appropriate ideas (Method). In addition to
treating the three types of creative occupations (artistic, scientific, and
managerial occupations versus conventional/noncreative occupations) as
three binary variables, we also combined them to generate a variable of
overall occupational creativity (i.e., 1 =either of the three creative occupa-
tions, 0 = conventional occupations). Thus the first categorical measure
yielded four binary creativity variables: artistic, scientific, managerial, and
overall. In order to assess occupational creativity from a broader scope
beyond the three types of occupational creativity with a common metric
across various types of occupations, our second measure of creativit y gauged
creative achievement using three items from O*NET by linking the U.K.
SOC 2000 occupation codes in the U.K. Biobank (UKB) data to the occu-
pation codes from O*NET37.
In our GWAS analyses with the UKB discovery data, we included
125,803 participants of European ancestry with 67,848 classified as holding
creative occupations that have high levels of occupational creativity, and
57,955 as holding conventional (less creative) occupations (Table 1,S2,S3&
Figure S1). Among participants with creative occupations, 6,625 were
artists, 18,225 scientists, and 42,998 managers. In addition, 219,473 parti-
cipants with available data on the creative achievement phenotype were
included in the discovery stage. The average creative achievement score was
3.24 ranging from 1.03 to 4.74.
In our replication analyses, we included three independent datasets: the
UKB follow-up dataset (N= 23,249), Add Health Wave IV dataset
(N= 1,461), and Wisconsin Longitudinal Study (WLS; N= 2,265). The
mean age (years) of participants was 59.8 in the UKB follow-up dataset
(32.72% male), 37.8 in the Add Health dataset (37.60% males), and 68.4 in
the WLS cohort (35.79% males; Table S5).
GWAS for occupational creativity
We conducted GWAS analyses on 9,804,641 variants that passed quality
control (QC) with a minor allele frequency (MAF) of more than 1% for the
five creativity variables in UKB discovery data (see method and ref)42.The
linkage disequilibrium (LD) score regression intercept, which ranged from
1.004 to 1.049, indicated the expected polygenicity for these traits43 (see
Table S6 and Fig. 1and S2 for Manhattan plots).
For GWAS performed in this study, a locus was defined by an index
lead SNP (P<5×10
−8)withitsflanking 500-kb region in either direction.
Nineteen loci were identified to be significantly linked to one of these five
creativity variables at the P<1.0×10
−8, with P-value accounted for multiple
testing correction for five traits. Six were shared by at least two traits (see
Tables S7–1 and Figures S3 and S4 for QQ plots). Additionally, 12 more loci
displayed conventional genome-wide significance for either of these five
creativity variables, with a P-value of < 5.0 × 10−8(see Tables S7–1). Alto-
gether 31 loci were carried out for the replication.
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We subsequently evaluated associations of these top loci with the
creativity variables in the three independent replication cohorts (see above).
All analyses were adjusted for age, sex and main principal components. For a
total of 43 variants at 31 top loci identified in UKB discovery, 28 variants at
19 loci showed genome-wide significant for different traits at 1.0 × 10−8in
the meta-analysis (see Tables S7–1). Three proxy variants (r2> 0.9 with the
lead variant) were selected since the lead variant was not presented in the
meta-analysis results. Tables S7–2 summarizes findings of our meta-
analyses based on both the discovery and replication samples. No hetero-
geneity was observed for the effect size across cohorts for these replicated
variants (P≥0.0737, see Tables S7–2).
Among the 19 top loci, five were identified as significant for overall
occupational creativity, one for artistic creativity at chromosome 18 gene
CELF4, one for managerial creativity and scientific creativity at
chromosome 6 MIR2113/PNKY, and three for scientific creativity (chro-
mosome 1 NEGR1, chromosome 3 GPX1/USP4/NICN1, chromosome 5
NDUFAF2/PART1/ZSWIM6). Several top loci identified for creativity traits
were overlapped with the genes previously reported for psychiatric traits
such as schizophrenia (ZSWIM6)40, bipolar disorder (micro RNA
MIR2113)41, neuroticism (CELF4)44, and autism (NEGR1)45.
The identified top loci harbor genes involved in various neuronal
functions. For example, gene NEGR1 encodes protein neuronal growth
regulator 1 (Negr1) of 46KDa. Negr1 is highly expressed in the cerebral
cortex, hippocampus, and cerebellum in the brain, and is a member of the
immunoglobulin LON family46. It accumulates in the neurotransmitter
GABAergic inhibitory synapses of neurons47. As neurotransmitters play a
crucial role in creativity48, the mutation of NEGR1 may have implications
for the trait of creativity. Another gene CELF4 (CUGBP,ELAV-likefamily
4) is related to a neural RNA-binding protein, predominantly expressed in
the central nervous system. CELF4 plays a critical role in various neuronal
functions and development, particularly in RNA processing49.
Sex-specific analyses were also conducted, which identified nine novel
loci for either males and females (see Table S8). Among these, one novel
signal was found for the creative achievement variable at gene SALL1 on
chromosome 16 in males, while two novel signals were identified at gene
NSUN3 on chromosome 3 and gene MAPKAP1 on chromosome 9 in
females (also see Figures S5 for Manhattan plots).
To test whether the aggregate estimates of genetic effects are associated
with creativity phenotypes, we constructed polygenic scores (PGS) based on
the GWAS summary statistics in the independent UKB follow-up dataset,
Addhealth,andWLSdatautilizingC+T (clumping +thresholding)50 and
PRS-SC approaches51 (Table S18). The results from both approaches are
Table 1 | Summary of the creativity phenotypes in the UKB
discovery sample
NCase Control Genomic
control λ
Artistic creativity 64,580 6625 57955 1.147
Scientific creativity 76,180 18225 57955 1.147
Managerial creativity 100,953 42998 57955 1.096
Overall occupational
creativity
125,803 67848 57955 1.147
N Mean SD Genomic control λ
Creative Achievement 219,473 3.24 0.79 1.310
Fig. 1 | Manhattan plot of GWAS analysis for creativity traits in the UKB dis-
covery sample. Results are shown for aoverall occupational creativity (n= 125,803),
and bcreative achievement (n= 219,473). The y axis represents -log
10
(p-value) for
association with each phenotype, and the x axis represents genomic position based
on human genome build 37. The cross in red represents independent genome-wide
significant association signals, labelled by names of gene or nearest genes.
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similar. The PGSs were significantly associated with all 5 creativity phe-
notypes in the UK follow-up dataset (model fitting P for PGS ≤6.53 × 10−5).
PGS accounted for a small amount of variance of leadership position, with
an incremental R2 up to 1.34%, on top of age, sex and top PCs. For
Addhealth and WLS, due to the small sample and heterogeneity of the
sample, the PGSs were not significant, except for scientific creativity, and
creative achievement in WLS sample (p≤6.77 × 10−5). The findings of small
predictive power were generally similar to those reported previously in
social sciences20.
Heritability and genetic correlations among the creativity vari-
ables across sex
We calculated common SNP heritability (SNP-h2) for the five creativity
variables in the UKB discovery sample (Table S9 and Fig. 2). Using GWAS
summary statistics, we applied LD-score regression to estimate the pro-
portion of variance in liability to creativity traits that was explained by the
aggregated effect of the SNPs52. The SNP-h2estimates were 0.22 (95% CI
[0.18, 0.25]), 0.18 (95% CI [0.16, 0.21]). 0.08 (95% CI [0.06, 0.09]) and 0.12
(95% CI [0.11, 0.14]), for artistic, scientific, managerial and overall occu-
pational creativity. For creative achievement, the SNP-h2estimate was
0.0939 (95% CI [0.09, 0.10]). The SNP-h2estimates for artistic and scientific
creativity were significantly higher than that for managerial creativity. The
SNP-h2estimatesobservedinthisstudyweresmallerthantheheritability
estimates reported in twin studies5–7. This is probably related to the fact that
creativity is a multifaceted construct and thus it is likely to be affected by
thousands of genetic variants (i.e., polygenity). Yet, the SNP-h2estimates are
similar to those reported for variables studied in social sciences, including
personality traits and subjective well-being21.
The five creativity traits had moderate to strong genetic correlations
among them (Table 2). These findings suggest a possibly similar significant
genetic basis for the different types of creativity variables, and that certain
types of creativity may be more closely related than others. In addition, there
were high genetic correlations of creative phenotypes between males and
females (Table S10).
Genetic correlations between occupational creativity and
personal traits
We then examined the genetic overlaps between creativity variables and
personality traits, well-being, and success variables using summary statistics
from previous GWAS research (See Methods; Table S11). In order to correct
for multiple testing, we set the significance level at a false discovery rate
(FDR) < 0.0553. With respect to personal traits, we included intelligence,
educational achievement, the big five personality traits (openness, neuro-
ticism, extraversion, agreeableness, and conscientiousness), risk tolerance
and physical height. The creativity literature3,14,29,30,32 has theorized and
found significant relationships between creativity with intelligence and
personality traits (e.g., openness, risk tolerance, and extraversion). Physical
height has been shown to affect one’s career success and has also been
factored in important indicators of physical health54.
Our analyses (Fig. 3and Table S12) revealed significant and relatively
large genetic correlations between the two major creativity variables (overall
occupational creativity combining all the three types of creativity and
creative achievement) and intelligence, as well as educational achievement.
The two creativity variables also shared significant and moderate to large
genetic overlaps with openness. We also recorded findings of moderate
genetic correlations of creativity with neuroticism and height.
We observed significant differences in genetic correlations across the
three different types of occupational creativity (Tables S12 and S13; Fig. 4).
Scientific creativity (r
g
= 0.68, 95% CI = [0.62, 0.74]) had a larger genetic
correlation with intelligence than artistic creativity (r
g
= 0.46, 95% CI =
Fig. 2 | Summary of common SNP heritability estimations for creativity phe-
notypes from GWAS results in UKB data. Liability-scale h2for artistic creativity,
scientific creativity, managerial creativity, and overall occupational creativity,
Observed-scale h2for creative achievement. Vertical bars represent 95% CIs.
Asterisks denote the comparison of heritability between artistic creativity, scientific
creativity, and managerial creativity at Bonferroni-adjusted p-values < 0.01667.
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[0.39, 0.53]; for comparison: P=3.72×10
−6) and managerial creativity
(r
g
= 0.46, 95% CI = [0.38, 0.54]; P=1.43×10
−5). Managerial creativity had
a larger genetic correlation with risk tolerance than artistic creativity and
scientific creativity. Artistic creativity had a lower genetic correlation (in
absolute value) with neuroticism than managerial creativity.
Genetic correlations between occupational creativity and
well-being
We selected well-being variables that are either phenotypically or
genetically related to creativity as reported previously in examining
their genetic correlations with occupational creativity (Fig. 3and
Table S12). We found positive genetic correlations of creativity with
autism—an indicator of low levels of well-being, and with an indicator
of high levels of well-being: subjective well-being. Creativity also had
negative genetic correlations with indicators of low well-being: BMI
and non-cancer illness.
Similar to previous research, creativity was also genetically and posi-
tively correlated with bipolar disorder, schizophrenia, alcohol misuse,
cannabis use, and age at first sextual intercourse. It also had a significant and
negative genetic correlation with ADHD.
Our analyses also revealed significant differences in such genetic cor-
relations across the three types of occupational creativity
(Tables S12 and S13; Fig. 4). For example, artistic creativity had a larger
positive genetic correlation with schizophrenia than managerial creativity.
Managerial creativity had a larger genetic correlation with subjective well-
being than artistic creativity. Scientific creativity had a larger genetic cor-
relation with autism than managerial creativity.
Genetic correlations between occupational creativity and career
success
The relationships of creativity with career success have received little
research attention in the literature. We found (Fig. 3and Table S12) sig-
nificant and positive genetic correlations between creativity and indicators
of high levels of career success including income, and occupational status.
The genetic correlation between overall occupational creativity and job
satisfaction was significant.
With respect to reproductive success, both indicators of number of
children (number of children for men and for women) had negative genetic
correlations with creativity. Age of first birth had a positive genetic corre-
lation with creativity.
Table 2 | Genetic correlations for creativity phenotypes in the UKB sample
Overall Genetic Correlation (S.E.) Artistic creativity Scientific creativity Managerial creativity Overall occupational creativity
Scientific creativity 0.7691 (0.0403)
Managerial creativity 0.6395 (0.0506) 0.6603 (0.0344)
Overall occupational creativity 0.8205 (0.031) 0.8809 (0.0148) 0.9271 (0.0081)
Creative achievement 0.8235 (0.028) 0.9235 (0.0156) 0.8347 (0.0223) 0.9608 (0.0108)
Fig. 3 | Genetic correlations of creativity phenotypes with personality traits,
health and well-being, and career success. A Overall occupational creativity and
creative achievement before partialling out genetic variance related to intelligence
and educational achievement. BOverall occupational creativity and creative
achievement after partialling out genetic variance related to intelligence. COverall
occupational creativity and creative achievement after partialling out genetic var-
iance related to educational achievement. Pvalues for the genetic correlations are
reported above each dot. Horizontal bars represent 95% CIs. Yellow as terisks denote
the genetic correlations at FDR < 0.05.
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Regarding the difference in genetic correlations across the three types
of occupational creativity, we found (Tables S12 and S13; Fig. 4)thatboth
managerial creativity and scientific creativity had greater positive genetic
correlations with income than artistic creativity. Scientific creativity had a
larger genetic correlation with age at first birth than both artistic and
managerial creativity. The genetic correlations (in absolute value) between
number of children and scientific creativity were larger than those with
managerial creativity.
Genetic correlations of occupational creativity with well-being
and career success after partialling out genetic influences
associated with intelligence and educational achievement
Thus, it is important to partial out genetic influences associated with
intelligence or educational achievement when examining the above genetic
correlations. Such analyses shed light on the role of non-cognitive factors
underlying such genetic correlations. To estimate the genetic correlations
after partialling out the genetic variance of intelligence or educational
attainment, we used the Genomic Structure Equation Modeling (SEM)
(Genomic SEM)55. Our results (See Methods; Tables S14 and S15; Fig. 3)
show that partialling out genetic variance associated with intelligence did
not significantly alter most of the genetic correlations with a few exceptions.
With intelligence controlled for, the significant genetic correlations of
occupational creativity and creative achievement with autism became non-
significant; the genetic correlation between creative achievement and
longevity also became non-significant. Such findings suggest that the
observed significant genetic correlations between creativity and autism as
well as longevity may be shaped mostly by their genetic overlap with
intelligence.
After partialling out genetic influences related to intelligence, the
previously observed significant difference in genetic correlations across the
three types of occupational creativity with other variables generally did not
change significantly; The changes mainly either involved genetic correla-
tions for scientific creativity or for well-being and success variables heavily
affected by intelligence.
Our results (See Methods; Tables S16 and S17; Fig. 3)showthatpar-
tialling out genetic variance associated with educational achievement, a
number of the significant genetic correlations with other variables became
non-significant, but not for other significant genetic correlations, for
example, with risk tolerance, height, income, job satisfaction, and occupa-
tional status, for instance. In summary, the findings show that the genetic
correlations of occupational creativity with the other variables cannot be
entirely attributed to the genetic overlap with intelligence or educational
achievement.
Discussion
Using a large-scale GWAS approach, we investigated the genetic archi-
tecture of occupational creativity and distinguished three important forms
of creativity: artistic, scientific, and managerial. We further probed the
genetic correlations of occupational creativity with psychological traits,
health and well-being variable, and career success. Our findings suggest the
three types of occupational creativity are associated with distinct genetic
variants and shared different genetic overlaps with theoretically relevant
psychological traits. Furthermore, occupational creativity bore different
genetic overlaps with different well-being and career success variables,
suggesting some possible paradoxical influences of genetic variants asso-
ciated with creativity on important career and life outcomes.
Our study identified some associated novel genes. We also found that
different genes were associated with different types of creativity (e.g., artistic,
scientific, and managerial), which one may use as further evidence that
creativity is domain specific. Yet, we caution against such a simplistic
explanation, because the different findings might also be caused by chance
and the statistical power was limited so that one cannot expect the same loci
to be significant in different GWASs.
In addition to corroborating prior findings of twin studies that intel-
ligence, risk tolerance, and openness share the same genetic endowments
with creativity30,56, our research revealed positive genetic correlations
between occupational creativity and physical height and negative genetic
correlations with neuroticism. Physical height and neuroticism have
received little research attention in the creativity literature. Organizational
research suggests that taller employees are likely to be perceived as more
competent, intelligent and attractive, which in turn may bring about more
advantages to come up with and experiment novel ideas54. Less neurotic
Fig. 4 | Genetic correlations of artistic, scientific, and managerial creativity with outcomes. Vertical bars represent 95% CIs. The error bars are imposed on R2. Asterisks
denote the genetic correlations at FDR < 0.05 and the comparison of genetic correlations at Bonferroni-adjusted p-values <0.00152.
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employees are emotionally more stable, which in turn may also be beneficial
for employees to implement new ideas30.Notethatfindings on significant
genetic correlations do not imply causal relationships from one variable to
the other. Such significant genetic correlations may also be caused by
assortative mating57. Future research should look into these phenotypic
correlations and their genetic linkages in greater depth.
Findings of the genetic correlations between occupational creativity
and well-being variables suggest that there are positive genetic overlaps
between creativity with both positive and negative well-being indicators.
Interestingly, occupational creativity had positive genetic correlations with
autism, longevity, subjective well-being, and age at first sexual intercourse.
Given the mixed findings on the phenotypic relationship between creativity
and autism58,ourfinding contributes to the literature by revealing that
occupational creativity and autism may share the same genetic make-up,
suggesting that environmental influence may play an important role in
shaping their phenotypic correlation. We also found that occupational
creativity had negative genetic correlations with ADHD, and BMI—indi-
cators of low levels of well-being. Together, our findings suggest that the
genetic variants positively correlated with occupational creativity may also
enhance the chance for employees to have low levels (e.g., autism and
bipolar disorder) and high levels of well-being (e.g., longevity and subjective
well-being).
Perhaps the most intriguing results of this research lie in our revealing
genetic correlations between occupational creativity with career success,
which may be related to how creativity was measured in this research.
Contrary to the stereotype22,23, we found positive genetic correlations
between occupational creativity with income and occupational status. Genes
positively related to occupational creativity may also enhance the chance for
employees to earn high levels of income and have high status occupations—
indicators of greater extrinsic career success. It is interesting that we
observed a significant positive genetic correlation with only overall occu-
pational creativity, but not with creative achievement. Considering the likely
negative phenotypic correlation between creativity and income24,25 and the
phenotypic positive relationship between creativity and job satisfaction27,
the findings suggest that environmental influences may play a crucial role in
shaping such relationships at the phenotypic level. With respect to repro-
ductive success, occupational creativity had a positive genetic correlation
with age at first birth and a negative genetic correlation with number of
children. Genetic variants positively related to occupational creativity may
reduce the chance for people to have high levels of reproductive success. This
offers some clues to answer the age-old question: would creative genius “die
out”over time17? Interestingly, a recent study59 found unique genetic var-
iants related to creativity may partially explain why modern Homo sapiens
survived and dominated over other hominids (e.g., Neanderthals and
chimpanzees) in human evolution. Such seemingly paradoxical genetic
influences merits future research attention.
The different genetic correlations of the different types of occupational
creativity with other variables point to the importance of differentiating
creativity into different domains. We found that scientific creativity shared
more genetic overlap with intelligence than artistic and managerial
creativity38,39. Managerial creativity had a higher genetic correlation with risk
tolerance than artistic and scientific creativity, which is in consistent with
findings of organizational research that risk tolerance is important for
management and leadership roles60. Managerial creativity bore a higher
positive genetic correlation with subjective well-being than artistic creativity.
Taken together, the findings seem to suggest that compared to artistic and
scientific creativity, genetic variants associated with managerial creativity
appear to be related to better well-being outcomes.
We observed different genetic correlations with career success variables
across the three types of occupational creativity. Artistic creativity had a
lower positive genetic correlation with income than managerial and scien-
tific creativity. Scientific creativity had a higher positive genetic correlation
with age at first birth than the other two forms of creativity, but its negative
genetic correlation with number of childrenwashigherthanmanagerial
creativity. Although genetic variants related to creativity tend to reduce the
chance for people to have children, this tendency seems more salient for
scientific creativity.
Our GWAS results indicate that creativity phenotypes are significantly
heritable traits but are highly polygenic. This complexity presents sub-
stantial challenges in accurately estimating the true “causal”effect sizes of
variants within the discovery dataset, thereby reducing the efficiency of PGS
using these effect estimates. Our findings underscore the polygenicity of the
genetic architecture underlying creativity phenotypes.
We were unable to ascertain the causal effects of genetic variants and
the direction of causality between occupational creativity with psychological
traits and well-being and success variables. Psychological and organizational
research on the phenotypic relationshipssuggeststhatitispossiblethat
psychological traits and well-being variables that share the same genetic
architecture with creativity may influence occupational creativity3,16,32.It
appears also possible that creative achievement in one job may also affect job
incumbents’well-being and career success32,61.Ourfindings may also be
affected by the way that occupational creativity was coded and measured.
For example, one person may exhibit multiple forms of creativity (e.g.,
artistic and scientific simultaneously), and there are other forms of creativity
beyond the three types. Therefore, future research may examine such
direction of causality issues and the neurobiological pathways linking the
genetic variants related to creativity measured with other approaches to
brain functions, psychological traits, and well-being and success.
Materials and Methods
Study samples
Our main analyses were based on the U.K. Biobank (UKB) data, which were
drawn from a population-based study of about 500,000 participants who
were 40 years old or older in the United Kingdom42,62,63. Participants’
information on their jobs and occupations was obtained through interviews
during their visits to the study centers. UKB staffs coded such job and
occupation information (e.g., job titles and major responsibilities) into the
four-digit U.K. Standard Occupational Classification (SOC) system37 (ver-
sion 2000; Field identifier: 132). Our analyses included participants of
European ancestry with available information on their occupation codes
and genotype data that passed quality control (Figure S7).
Replication samples included participants from the UKB follow-up
cohort (N= 23,249), the Add Health Wave IV cohort64,65 (N=1461), and
Wisconsin Longitudinal Study (WLS) cohort66 (N= 2265). Between June to
September 2015, about 102,000 UKB participants completed an online
follow-up assessment of their employment history. We selected those who
had past employment information but were not included in the discovery
phase because of lacking baseline job information. The Add Health study is a
U.S. longitudinal study of adolescents67. We used data from the Add Health
Wave IV survey that was conducted between 2007 to 2009, and included
unrelated Caucasian participants with valid occupation information and
genotype data in analyses. The Wisconsin Longitudinal Study (WLS) cohort
is a large U.S. longitudinal study of a random sample of people who grad-
uated from Wisconsin high schools in 1957 and of their randomly selected
siblings66. Six rounds of data collection were conducted from 1957 to 2011.
The occupation information was collected in a random sample in the last
four rounds. More detailed information about the data preparation of the
replication samples is presented in Supplementary notes and Figures S8-10.
Phenotype definitions and measures
Because multifaceted nature, creativity has been very challe ngingto measure
and thus it has been assessed in various fashions in the literature13,30.Inthis
research, we adopted the previous approach8,38,68 and captured creativity
primarily based on job tasks and responsibilities of participants’occupations
with two measures (five variables, Table S1). The first measure was cate-
gorical, encompassing an overall measure of occupational creativity and its
three domain-specific types: artistic creativity, scientific creativity, and
managerial creativity. The second measure was a continuous variable,
creative achievement, which served as a common metric of creativity across
various occupations.
https://doi.org/10.1038/s42003-024-06686-5 Article
Communications Biology | (2024) 7:1092 7
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Occupational creativity. The first measure of occupational creativity
was derived from the major responsibilities of participants’occupations
either from the U.K. SOC 2000 occupational codes (for the UKB data) or
from the U.S. Occupational Information Network (O*NET, https://
www.onetonline.org/) occupational codes (for the WLS and Add Health
data). Specifically, based on the literature on creativity13,14, we identified
three groups of occupations from the U.K. SOC 2000 or the U.S. O*NET
systems that require high levels of creativity (i.e., artistic, scientific, and
managerial) and one control group (i.e., conventional occupations) that
require low levels of creativity based on the major tasks and responsi-
bilities of participants’occupations. This generated four binary variables
with three reflecting the three domain-specific creativity (e.g., for artistic
creativity, 1 = artist occupation and 0 = conventional occupation). For
the fourth variable reflecting overall occupational creativity, we coded all
the artistic, scientific, and managerial occupations as creative occupa-
tions (i.e., as 1) and used less creative/conventional occupations as the
reference group (coded as 0). Below we provide a detailed description of
how each group of domain-specific creativity was operationalized. For
the control group, we drew from Holland’s research on occupational
interest69 and coded conventional occupations as those with low levels of
creativity, because their primary tasks are more structured, orderly, and
routinized, and thus require less novel ideas69,70. Sample occupational
titles of the control group include counter clerks, plastics process
operatives, and filing assistants/clerks. Occupations that do not clearly
fall into the four categories of artisitic, scientific, manegrial, and con-
ventional jobs were coded as missing values.
Artistic creativity. Artists have long been treated as creative
professionals14. Following previous research on artistic occupations8,71,
we included the following occupations as those with high levels of artistic
creativity: actors, dancers, entertainers, musicians, visual artists, chor-
eographers and writers.
Scientific creativity. Prior research has also identified scientific occu-
pations as creative occupations8,71, because scientists are required to
generate innovative and useful ideas in conducting academic research.
Such scientific occupations are not limited to those in natural and bio-
logical sciences, but also include those in social sciences, engineering, and
mathematics. We thus operationalized scientific creativity as those
occupations whose primary job tasks and responsibilities involve
undertaking research in various sciences and conceiving engineering
designs. Sample occupation titles were chemists, biological scientists, civil
engineers, and scientific researchers.
Managerial creativity. Drawing on previous literature on creativity and
leadership28,29, we proposed a new category of creative professions:
managerial occupations. We treated senior managers and officials as
creative occupations because incumbents of such occupations are
increasingly required to generate novel ideas, practices, and solutions to
direct and coordinate the functioning of organizations and work teams,
particularly when faced with challenges and uncertainties brought about
by the changing business environment28. Sample occupation titles
include directors and chief executives of major organizations, marketing
and sales managers, and senior officers in fire, ambulance, prison and
related services.
To ensure the reliability and validity of our coding, we developed a
coding scheme based on previous literature that defined four occupation
groups8,69,71. Two independent raters coded all the occupations by carefully
reviewing the structure of U.K. SOC 2000, the U.S. O*NET and SOC sys-
tems, and the detailed descriptions of the tasks and duties of each occupation
described in the occupation classification systems, as well as the cross-
country crosswalk on the basis of International Labor Organization’s
International Standard Classification of Occupations (ISCO-88 and ISCO-
08) to ensure accuracy and reliability of the coding results. Furthermore, the
raters also referred to the list of creative industries and occupations released
by the U.K. Department for Digital, Culture, Media and Sport72,73 to ensure
that their coding was consistent with the major consensus in the creative
industry in the world. The initial interrater agreement was approximately
90%. Discrepancies were resolved through discussion among all the raters
and authors.
Creative achievement. Our second measure of creativity—creative
achievement—was assessed with three items obtained from the U.S.
O*NET database with a crosswalk by linking the U.K. SOC 2000 occu-
pation codes in the UKB data to the occupation codes in the O*NET
system. For the Add Health and WLS data, given that participants’
occupation codes were based on the U.S. SOC system, we extracted
information on creative achievement from O*NET directly by matching
participants’occupation codes with the O*NET occupation codes. The
three items tap into core characteristics of creativity: fluency of ideas,
originality, and thinking creatively. Sample items include “What level of
thinking creatively is needed to perform your current job?”and “What
level of originality is needed to perform your current job?”All the items
used a seven-point scale, ranging from the lowest (1) to the highest (7)
level of creative achievement. The internal consistency of this scale
(Cronbach’sα) was 0.92.
Genotyping and imputation
We used imputed genotypes released by U.K. Biobank (bgen files; imputed
data v3 –released in March 2018). The quality control and imputation were
done by U.K. Biobank42.Briefly, genotyped variants were filtered based on
batch effects, plate effects, departures from Hardy Weinberg equilibrium
(HWE), genotype platform, and discordance across control replicates.
Participant samples were excluded based on missing rate larger than 5%,
inconsistencies in reported versus genetic sex, and excessive heterozygosity
based on a set of 605,876 high quality autosomal markers. Genotypes were
phased and the imputation was performed using IMPUTE4 with the
Haplotype Reference Consortium (HRC) data, UK10K and 1000 Genomes
Phase 3 dataset used as the reference set. A European subset was identified
by projecting the UKB participants onto the 1000 Genome Project principal
components coordination. For this study, we e xcluded genetic variants with
MAF < 1%, and poorly imputed markers (IMPUTE info < 0.3), resulting in
9,804,641 autosomal variants imputed or genotyped on individuals of
European ancestry. The genotyping, imputation, and filtering procedure
were similar across the UKB discovery sample and the follow-up sample.
For the Add Health cohort, the genotypes were imputed on the Hap-
lotype Reference Consortium, with quality controls detailed previously74.
Genetic variants were included with MAF > 1% and IMPUTE Info < 0.3.
Analyses were limited to individuals of European-ancestry and cryptically
related individuals were dropped from analyses. For the WLS cohort, the
genotypes were imputed using the 1000 Genomes Phase 3 dataset. Repli-
cation analyses included only unrelated European participants.
Genome-wide association analyses
We assumed an additive genetic model where the dosage of each SNP was a
continuous variable ranging from 0 to 2 for the effect allele. For UKB
GWAS, a linear mixed model accounting for genetic relatedness was con-
ducted to determine its association with the phenotypes. The analyses were
conducted with the software BOLT-LMM v.2.3.2 (https://data.
broadinstitute.org/alkesgroup/BOLT-LMM/downloads)75. The association
analyses were adjusted for age, sex, genotyping array, and top 20 principal
components (PCs). The GWASs were also conducted separately by sex.
Significant independent variants and their surrounding genomic loci
were identified using LD-clumping in PLINK v.2.00 (https://www.cog-
genomics.org/plink/2.0/). The LD structure was estimated from the Eur-
opean panels in the 1000 Genome Project of phase 3 as the reference
population. For GWAS conducted for 5 traits in this study, we set
P<1×10
−8as the genome-wide significance. The index lead SNP was
identified (P<1×10
−8), independent from other variants (r2< 0.01), at each
locus. Here a locus was defined by an index SNP with the region flanking
https://doi.org/10.1038/s42003-024-06686-5 Article
Communications Biology | (2024) 7:1092 8
Content courtesy of Springer Nature, terms of use apply. Rights reserved

500 kb on both sides. The coordinates and variant identifiers were reported
on the NCBI B37 (hg19) genome build. The functional annotation and gene
mapping were performed using ANNOVAR (v.2018Apr16, https://doc-
openbio.readthedocs.io/projects/annovar/en/latest/user-guide/download/),
including types of intronic, exonic, intergenic, 5’-UTR, and 3’-UTR, etc.76.
Regional plots of each identified locus were made by LocusZoom (http://
locuszoom.org/), and the 1000 Genome of the European population was
used to estimate LD information.
Replication analyses
Genome-wide significant SNPs at the top loci were evaluated in the UKB
follow-up dataset, the Add Health dataset, and the WLS cohort. We meta-
analyzed results with both discovery and replication samples using the
inverse-variance weighted fix-effect model with METAL software (https://
genome.sph.umich.edu/wiki/METAL). For the four binary creative vari-
ables (artistic, scientific, and managerial, and overall creativity), the coeffi-
cients obtained from the linear mixed model in the UKB discovery sample
were on a standardized scale. Therefore, we transformed these coefficients to
make them comparable with the observations in all samples. We rescaled the
beta coefficients with the following formula: β
s
=β/k*(1 −k),where kis the
prevalence of creative occupations in the UKB; the ORs were calculated
accordingly using the scaled beta coefficient β
s
.
Common SNP heritability
We employed the software LD-score regression (LDSC) v.1.0.1 (https://
github.com/bulik/ldsc) with GWAS summary statistics in our estimation of
common SNP-h2for the creativity phenotypes for the whole sample and
sub-samples by sex in the UKB discovery data52. We used LDSC to estimate
the proportion of variance in liability to creativity traits that was explained
by the aggregated effect of the SNPs. From GWAS summary-level data, we
included SNPs that were presented in the European panels in the 1000
Genome Project, with the exclusion of the major histocompatibility com-
plex (MHC) region on chromosome 6. SNPs with INFO < 0.8 were not
included in the LDSC regression analyses.
For the binary trait of creative occupations (artistic, scientific, and
managerial, and overall creativity), the estimated heritability was trans-
formed to the liability scale using the approach derived by Lee et al.77.Asthe
exact prevalence is unknown, we assumed the percentage of creative
occupations in the UKB sample under current analysis was equal to the
population prevalence, an approach adopted in calculating SNP-h2on the
liability scale for other dichotomous traits using the UKB data (http://www.
nealelab.is/uk-biobank).
Genetic correlations of occupational creativity with personal,
well-being, and success variables
We computed the genetic correlation among creativity variables (for the
overall sample and sub-samples by sex) using the GWAS summary statistics
from the UKB discovery sample. We adopted the bivariate LDSC method,
by regressing the product of testing statistics (z statistics) from each phe-
notype against the LD scores78.
We assessed the genetic correlations of creativity phenotypes with
personal traits, well-being, and success variables using summary statistics
from GWASs of European ancestry, with the detailed information of sample
sizes, phenotypes, and GWAS summary data resources presented in
Table S11. We used summary statistics from the previously published large-
scale GWAS or GWAS results based on the UKB data, including, for
example, subjective well-being79, job satisfaction (UKB data 4537), depres-
sive symptom79, neuroticism80,longevity
81, number of cancer illness (UKB
data 134), number of noncancer illness (UKB data 135), BMI82,height
82,and
intelligence83. Note that for the illustration purpose, we recoded job satis-
factionsothathigherscoresindicatehigherlevelsofjobsatisfaction.
To estimate the genetic correlations after partialling out the genetic
variance of intelligence or educational attainment, we used the Genomic
SEM55. For each genetic correlation between creativity and other pheno-
types, we fitted the Genomic SEM model including three traits: creativity
trait (X), the other phenotype (Y), and intelligence (Z). In the path diagram,
there was a bidirectional arrow between two traits of X and Y, and uni-
directional arrows from Z to X and Z to Y. The genetic effect of Z was
regressed out from the variance of X and Y affecting the genetic correlation.
The genetic covariance matrix of X, Y, and Z was produced by the LDSC
method implemented in Genomic SEM. The process was repeated for each
of the personal traits, well-being and success variables.
Polygenic scores (PGS) analyses
Using the GWAS results from creativity traits, we generated PGSs in the
three replication samples. The polygenic scores were constructed in
PRSice (see URL: https://choishingwan.github.io/PRSice), a method
shown to have decent prediction accuracy involving LD pruning followed
by p-value thresholding50, and PRS-CS (see URL: https://github.com/
getian107/PRScs), a method which generates posterior SNP effect size
estimates using Bayesian regressions with continuous shrinkage priors51.
For the PRSice method, besides automatically optimizing p-value cutoff
as default, variants were also selected at p-value thresholds: p< 0.01 and
p<1×10
−4. Independent lead variants associated with the phenotype
were identified by the “clumping and thresholding”approach, removing
those within 500 kb and in linkage disequilibrium r2≥0.01 with the lead
variant in the region. An individual’s polygenic score is a weighted sum
of the genotypes across all independent variants. The weighting factor is
the estimated additive effect size, beta coefficient, at each variant from the
GWAS summary statistics. Prediction accuracy was based on an ordinary
least squares (OLS) regression of the creativity phenotypes on the
polygenic score and a set of standard covariates, including age, sex, and
the top genetic PCs. The R2or McFadden pseudo-R2(for binary out-
come) for PGS was calculated as the incremental variance for creativity
variables, i.e., the R2of the model including polygenic scores and cov-
ariates minus the R2of the model including only covariates. For the PRS-
CS method, the 1000 Genomes Project Phase 3 European sample is used
as the external LD reference panel. The parameters in the gamma-
gamma prior are set as 1 and 0.5 respectively and the global shrinkage
parameter is automatically learned from GWAS summary statistics using
a fully Bayesian approach. The posterior SNP effect size estimates were
concatenated across all chromosomes, which was used as weight for
calculating individual’s polygenic score in each cohort.
Reporting summary
Further information on research design is available in the Nature Portfolio
Reporting Summary linked to this article.
Data availability
The UKB GWAS summary statistics for the five creativity phenotypes will
be available on the NHGRI-EPI Catalog of human GWAS upon publica-
tion. The accession codes for occupational creativity, artistic creativity,
scientific creativity, managerial creativity, and creative achievement are
GCST90444391, GCST90444392, GCST90444393, GCST90444394,
GCST90444395, respectively. The summary statistics of the top index var-
iants were presented in the article or supporting information, along with the
data analyzed in this study.
Received: 10 December 2023; Accepted: 6 August 2024;
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Acknowledgements
This study was supported by the Singapore Ministry of Education (MOE)
grant R-317-000-138-115. Accessing data resources to conduct this study
was approved by the U.K. Biobank (application reference # 37334), Add
Health Study (application reference # 11030901), and Wisconsin
Longitudinal Study (application reference #98636). We are grateful to the
study’s participantsand staff for data collection. The computational workfor
this study was performed on resources of the National Supercomputing
Centre, Singapore (https://www.nscc.sg).
Author contributions
W.-D.L., Z.S., and Q.F. designed research; W.-D.L., X.Z., Z.S., and Q.F.
performed research; Q.F and Z.S. contributed to source data collation and
maintenance; X.Z., K.Y., Y.Z., N.D., Z.S., and Q.F. analyzed data;and W.-
D.L., X.Z., K.Y. Y.Z., N.D., Z.S., and Q.F. wrote the paper.
Competing interests
The authors declare no competing interests. Dr. Qiao Fan is an Editorial
Board Member for Communications Biology, but was not involved in the
editorial review of, nor the decision to publish this article.
Additional information
Supplementary information The online version contains
supplementary material available at
https://doi.org/10.1038/s42003-024-06686-5.
Correspondence and requests for materials should be addressed to
Wen-Dong Li, Xin Zhang, Zhaoli Song or Qiao Fan.
Peer review information Communications Biology thanks the anonymous
reviewers for their contribution to the peer review of this work. Primary
Handling Editors: George Inglis and Benjamin Bessieres.
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