Assignment: Case Study Analyses–Biological and Evolutionary and Integrative Theories

R E V I E W

The genetics of human personality

S. Sanchez-Roige1 | J. C. Gray2 | J. MacKillop3 | C.-H. Chen4 | A. A. Palmer1,5

1Department of Psychiatry, University of

California San Diego, La Jolla, California

2Center for Deployment Psychology,

Uniformed Services University, Bethesda,

Maryland

3Peter Boris Centre for Addictions Research,

McMaster University/St. Joseph's Healthcare

Hamilton, Hamilton, Ontario, Canada

4Department of Radiology, University of

California San Diego, La Jolla, California

5Institute for Genomic Medicine, University of

California San Diego, La Jolla, California

Correspondence

Prof A. A. Palmer, Department of Psychiatry,

University of California San Diego, 9500

Gilman Drive, Mail Code 0667, BRF2 3A24, La

Jolla, CA 92093.

Email: [email protected]

Funding information

Pilot award, Grant/Award number:

P50DA037844; Interdisciplinary Research

Fellowship in NeuroAIDS (IRFN); Frontiers of

Innovation Scholars Program (FISP); National

Institute of Mental Health, Grant/Award

number: R01MH100351; Peter Boris Chair in

Addictions Research

Personality traits are the relatively enduring patterns of thoughts, feelings and behaviors that

reflect the tendency to respond in certain ways under certain circumstances. Twin and family

studies have showed that personality traits are moderately heritable, and can predict various

lifetime outcomes, including psychopathology. The Research Domain Criteria characterizes psy-

chiatric diseases as extremes of normal tendencies, including specific personality traits. This

implies that heritable variation in personality traits, such as neuroticism, would share a common

genetic basis with psychiatric diseases, such as major depressive disorder. Despite considerable

efforts over the past several decades, the genetic variants that influence personality are only

beginning to be identified. We review these recent and increasingly rapid developments, which

focus on the assessment of personality via several commonly used personality questionnaires

in healthy human subjects. Study designs covered include twin, linkage, candidate gene associa-

tion studies, genome-wide association studies and polygenic analyses. Findings from genetic

studies of personality have furthered our understanding about the genetic etiology of personal-

ity, which, like neuropsychiatric diseases themselves, is highly polygenic. Polygenic analyses

have showed genetic correlations between personality and psychopathology, confirming that

genetic studies of personality can help to elucidate the etiology of several neuropsychiatric

diseases.

KEYWORDS

agreeableness, conscientiousness, EPQ, extraversion, gene, GWAS, NEO, neuroticism,

openness, personality, TPQ

1 | INTRODUCTION

Personality traits are the relatively enduring patterns of thoughts,

feelings, and behaviors that reflect the tendency to respond in certain

ways under certain circumstances.1(p140) Strong phenotypic correla-

tions have been reported over the years between personality traits

and a wide array of psychopathological conditions2; however, it is

unknown whether these correlations have a genetic or environmental

basis.

Personality traits can be closely aligned with domains within the

Research Domain Criteria (RDoC), which views psychiatric disorders

as extremes of normal tendencies, and is intended to foster a biologi-

cal analysis of behavior. While numerous genetic studies have exam-

ined psychiatric diseases, relatively less work has been done on the

genetic basis of RDoC traits such as personality. Twin studies have

showed that personality traits, as measured by self-report

questionnaires,3 are moderately heritable4,5 and have a relatively sta-

ble trajectory over time after early adulthood.6 However, the exact

genetic basis of personality is still poorly understood.

We review findings from twin and family studies of heritability,

followed by linkage studies, candidate gene association studies, and

genome-wide association studies (GWAS). We summarize the most

robust loci associated with personality. We only included genetic

studies of personality if they used validated, standardized, self-report

measures of personality traits (Box 1) in healthy adults of any ethnic

origin. We conclude by discussing polygenic methods, which provide

mounting evidence that the association between personality traits

and psychopathology has a genetic basis. Future directions for

research are also suggested.

Developments in the field, including the availability of large data-

sets such as UK Biobank, and bioinformatics techniques such as gene

pathway analysis, have furthered our understanding of the genetic

Received: 30 August 2017 Revised: 12 October 2017 Accepted: 7 November 2017

DOI: 10.1111/gbb.12439

© 2017 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society

Genes, Brain and Behavior. 2018;17:e12439. wileyonlinelibrary.com/journal/gbb 1 of 13 https://doi.org/10.1111/gbb.12439

etiology of personality. Ultimately, genetic studies of personality may

enhance our understanding of neuropsychiatric diseases and thus fos-

ter novel treatment approaches.13

2 | TWIN AND FAMILY STUDIES: HERITABILITY ESTIMATES

Twin studies of personality have shown that identical twins are more

concordant than nonidentical twins, yielding narrow-sense heritability

estimates in the range of 40%,14–17 suggesting that a moderate pro-

portion for the variance of personality traits can be attributed to

additive genetic effects. Family and adoption studies, which estimate

the resemblance between parent/biological child, or relatives sepa-

rated by adoption, have yielded slightly lower estimates (approxi-

mately 30%18,19).

Heritability does not appear to vary by sex (estimated by com-

paring the resemblance between opposite-sex vs same-sex dizygotic

twins20). For example, a meta-analysis of data from over 29 000 twin

pairs16 showed that the heritability of neuroticism scores was at

48%, and that the same proportion of variance in neuroticism can be

attributed to genetic factors in both in men and women.

Environmental differences can increase or decrease the impor-

tance of genetic factors, and may also reflect gene-environment

interactions.21,22 Twin data show that genetic influences contribute

to personality stability and are relatively constant with age. Personal-

ity stability is defined as the degree to which the relative differences

between individuals are preserved over time, which is typically

assessed with a test-retest correlation. In contrast, the environmental

influence on personality increases with age.23

All questionnaires show roughly similar levels of heritability, albeit

with variable estimates from study to study (Table 1). Moreover, heri-

tability estimates across personality traits do not reflect significantly

different levels of genetic influence. For example, a recent meta-

analysis reported heritability estimates for the Big Five (NEO) traits,

ranging from 31% to 41%17,36. The moderate variability in heritability

estimates is to be expected because heritability is not only a property

of a trait but is also influenced by differences among samples, meth-

odology, including strong theoretical assumptions in twin studies (eg,

assumption of equal environment, exclusion of gene-environment

interactions19,37,38).

3 | CANDIDATE GENES ASSOCIATION STUDIES

Twenty years ago, it was commonly assumed that the genetic

architecture of personality might be relatively simple, or at least

simpler than psychiatric diseases, with a few key genes explaining

much of the observed heritable variance.39 This partially explains

the enthusiasm for candidate gene association studies that con-

tinue to be performed even today, despite mounting evidence that

personality is likely to be at least as polygenic as psychiatric dis-

eases.40,41 The candidate gene literature has been dominated by a

small number of genes that were largely selected based on their

involvement in key neurotransmitter systems (eg, dopamine, DA;

serotonin, 5-HT) that mediate the effects of many psychoactive

drugs. These studies have produced inconsistent and inconclusive

findings.42 In an attempt to summarize this complex literature, we

have focused on the major findings obtained from meta-analyses,

which are summarized in Table 2. Rather than exhaustively survey-

ing the candidate gene literature, we have focused on the most

heavily studied genes/polymorphisms, because these allow the

BOX 1

SELF-REPORTED QUESTIONNAIRES TO CAPTURE THE

MULTIFACETED TRAITS OF PERSONALITY

The Eysenck Personality Questionnaire (EPQ7) defines 3 traits

of personality: psychoticism (characterized by aggressiveness

and interpersonal hostility), extraversion (manifested in outgo-

ing, energetic behavior), neuroticism (typified by emotional

stability).

The Tridimensional Personality Questionnaire (TPQ8,9)

consists of 3 traits of personality (or temperaments) that are

based on the biochemical bases of temperament: novelty seek-

ing (or the intense exploration toward novel stimulation or

potentially rewarding cues, and active avoidance of punish-

ment), harm avoidance (or the tendency to respond intensely

to aversive cues, and to learn to avoid punishment and nov-

elty), reward dependence (or the rapid response to rewarding

cues and/or relief of punishment). Later, the Temperament

and Character Inventory (TCI10,11) included a fourth tempera-

ment dimension, persistence (or perseverance in spite of

fatigue or frustration), and 3 character traits—self-directedness

(the ability to modify behavior in order to achieve personal

goals), cooperativeness (the tendency to exhibit agreeable rela-

tions with others) and self-transcendence (associated with

experiencing spiritual aspects of the self ).

The Five Factor model12 is based on biological mecha-

nisms shaping 5 higher-order traits (referred to as “the Big

Five” personality traits): neuroticism (proneness to experience

negative affect), extraversion (motivation to engage with

others), openness to experience (inventive or curious behavior),

agreeableness (friendliness and compassion toward others) and

conscientiousness (attentive and organized behavior). The

NEO-PI questionnaire (NEO; and its derivatives, such as the

NEO-PI-R and NEO-FFI) is the most commonly used question-

naire for genetic studies.

TABLE 1 Overview of narrow-sense heritability estimates for the

most commonly used personality questionnaires

Questionnaire Heritability estimate Reference

EPQ 35%-57% 24,25

TPQ/TCI 30%-60% 24,26–29

NEO-PI 17%-65% 30–35

These studies used a combination of twin and family-based designs.

2 of 13 SANCHEZ-ROIGE ET AL.

most informative meta-analyses. These studies show that associa-

tions between personality traits and the proportion of variance

explained by a specific variant is unlikely to be ≥3%.50 As

described below, GWAS studies have clearly indicated that true

effect sizes are much smaller.

Over 60 studies have investigated whether variation in genes

involved with DA transmission were associated with personality.

Early publications reported associations between SNPs in the D4

dopamine receptor (DRD4) gene and extraversion or novelty

seeking,51–53 but those effects were not replicated in 2 meta-ana-

lyses.44,45 Some argued that the genetic effects may be sex-depen-

dent, as polymorphisms in the tyrosine hydroxylase gene, the rate

limiting enzyme in the biosynthesis of DA, were associated with nov-

elty seeking Temperament and Character Inventory (TCI) in healthy

males but not females.54 On the other hand, others hypothesized that

early environmental exposure and polymorphisms in the DRD4 gene

might either strengthen or dilute the observed associations with nov-

elty seeking52; but these hypotheses have yet to be rigorously tested

in human populations.

Similarly, early publications reported that variation in the seroto-

nin transporter gene 5HTT (SERT or SLC6A4), which is responsible for

reuptake of 5-HT from synapses, was associated with neuroticism46,47

and harm avoidance (Tridimensional Personality Questionnaire, TPQ),

which is related to neuroticism.55 However, meta-analyses of over

40 studies involving 5-HTTLPR variants show that this genotype is not

consistently associated with neuroticism (Eysenck Personality

Questionnaire, EPQ) or harm avoidance (TPQ).48,49 The difficulty of

accurately genotyping of this locus may contribute to these inconsis-

tent results.56

In hindsight, it is easy to identify reasons why candidate gene

association studies have produced unsatisfactory results40,41,57; most

notably, they assumed that the loci under study would have larger

effect sizes than have been observed in more recent GWAS, and

were thus underpowered. Additional reasons for the lack of replica-

tion include confounding due to population stratification and selec-

tive reporting of positive results.58 Furthermore, the assumption that

genes related to key neurotransmitter systems were critical for per-

sonality turned out to be overoptimistic.44,59 Moreover, candidate

gene association studies generally focused on coding regions rather

than regulatory/noncoding regions; however, most GWAS hits impli-

cate noncoding regions.60 Views about the polygenicity of personality

and many other traits continue to evolve, with a recent paper sug-

gesting that even the most important “core genes” for a trait can only

explain a minor fraction of the heritable variance.60

4 | GENOME-WIDE SCANS

4.1 | Linkage studies

Genome-wide linkage studies, which take advantage of close familial

relationships to identify chromosomal regions, have also been used to

study the genetics of personality. Amin et al61 reported significant link-

age results for conscientiousness (NEO) at 20p13. A later meta-analysis

(N = 6149) of 4 genome-wide linkage scans by Amin et al62 identified

11q24 for openness to experience (NEO). Other suggestive linkage sig-

nals have been identified in independent studies for several chromo-

somal regions and different personality traits21,42,61. Nonetheless, only a

TABLE 2 Meta-analyses of the most commonly studied polymorphisms in relation to personality

Candidate gene Form Comparison Reference Number of studies (total sample size) Trait; Questionnaire (effect size, P)

DRD4 VNTR +7-R 43 14 (N = 2720) Novelty seeking; TPQ, TCI,KSP (d = .00, P > .05)

L/S 12 (N = 1719) Novelty seeking; TPQ, TCI, NEO, KSP (d = .21, P > .05)

S/L 44 17 (N = NA) 10 (N = NA)

Approach-related; NEO, TPQ, EPQ, KSP, TCI (d = .07, P > .05) Avoidance-related; NEO, TPQ, EPQ, KSP, TCI (d = −.08,

P > .05)

+7-R 45 36 (N = NA) Approach-relateda; TCI, TPQ, NEO, KSP (d = .04, P = .14)

C-521 T CC/CT-TT 11 (N = NA) Approach-relateda; TCI, TPQ, NEO, EPI, EPQ (d = .25, P < .001)

5HTT (SERT or SLC6A4)

5HTTLPR ll-sl/ss 44 22 (N = NA) Avoidance-relateda (d = −.11, P = .04)

17 (N = NA) Approach-relateda (d = −.11; P > .05)

sl-ss/ll 46 26 (N = 5629) Neuroticism, Harm Avoidance; NEO, TPQ/TCI (β = 1.68, P = .087)

sl-ss/ll 47 26 (N = 7657) Neuroticism, harm avoidance; NEO, TPQ, EPQ, KSP, TCI, EPI (d = .10, P > .05)

sl-ss/ll 48 55b (N = 3872) Harm avoidance; TPQ, TCI (d = .02, P = .37) Neuroticism; EPQ (d = .01, P = .71) Neuroticism; NEO (d = .18, P < .001)

ll/sl-ss 49 44 (N = 15 476) Neuroticism, harm avoidance; NEO, TCI (d = −.04, P = .03)

Abbreviations: +7-R, presence or absence of a 7-repeat (“long”) allele; 5HTTLPR, 5HTTL polymorphism; 16PF, 16 Personality Factor Scale; BIS/BAS, Behav- ioral Inhibition/Activation Scale; C-521 T, DRD4 promoter region; d, Cohen’s d effect size; KSP, Karolinska Scales of Personality; NA, not listed; S/L, short/long; SSS, Sensation Seeking Scale; VNTR, variable number tandem repeat or presence or absence of a 7-repeat (“long”) allele in exon III. a Calculated from BIS/BAS scale scores, that measures sensitivity to reward and punishment (reward responsiveness, drive, fun seeking). b The total number of studies is 55 in the full meta-analysis; the number of studies for each individual trait may be lower. Only meta-analyses with more than 10 studies are included in the table.

SANCHEZ-ROIGE ET AL. 3 of 13

few genomic regions (eg, 12q) have been consistently reported in multi-

ple studies.63–67 The estimated effect sizes did not suggest variants with

large effects, which is consistent with the results from candidate gene

studies. Although linkage studies are effective at mapping traits with

monogenic or simple genetic architecture, they are suboptimal for the

study of complex traits like personality.68

4.2 | Genome-wide association studies

GWAS interrogate hundreds of thousands to millions of SNPs across

the genome.50 Unlike candidate gene studies, GWAS are a hypothesis

generating approach that do not assume any prior knowledge about

the underlying biology associated with the trait. Unlike linkage stud-

ies, GWAS utilize unrelated individuals, which have the potential to

identify smaller regions that contain one or a few genes. By

convention,69 the threshold for statistical significance is based on a

Bonferroni correction for 1 million comparisons, yielding a threshold

of P < 5.0 × 10−8. This stringent threshold requires large sample sizes

and favors type II over type I errors. Table 3 summarizes all previous

GWAS of personality (N = 16) at the time of our writing. Figure 1

provides an overview of the chronology of GWAS for human person-

ality traits.

The earliest GWAS for personality used samples in the range of

1000 to 5000 subjects, which proved to be underpowered.70–72,88

Terracciano et al73 performed an unsuccessful GWAS for NEO-PI-R;

in the absence of any significant results, the same group74 conducted

a meta-analysis for excitement-seeking (NEO-PI-R), which identified

rs7600563 (P = 2.0 × 10−8). This SNP is located within an intron of

the gene CTNNA2 (cadherin-associated protein alpha 2) and is within

200 kb of the gene LRRTM1; however, this association was not repli-

cated in 3 independent samples.

Several other meta-analyses followed. Service et al76 and Luciano

et al75 did not find any significant results for TPQ and EPQ, respec-

tively. In contrast, de Moor et al77 conducted a meta-analysis of NEO

personality traits that showed significant associations for openness

near the gene RASA1 (rs1477268, P = 2.8 × 10−8), and for conscien-

tiousness in the gene KATNAL2 (rs2576037, P = 4.9 × 10−8). How-

ever, neither association could be replicated.

Bae et al78 conducted a GWAS of NEO-FFI; although a signifi-

cant association for agreeableness was detected, it was not replicated

in an independent cohort. Kim et al79 performed the first GWAS for

personality traits (NEO-PI-R) in a non-European population of young

women; however, none of the results were significant. The same

group subsequently performed a meta-analysis of NEO-FFI data using

4 cohorts of Korean ancestry; again, none of the results were

significant.80

More recent GWAS and meta-analyses of personality traits have

used dramatically larger sample sizes. de Moor et al81 and van den

Berg et al85 conducted meta-analyses of neuroticism and extraver-

sion. Those studies sought to harmonize data obtained from different

personality questionnaires by using “Item Response Theory” (IRT16).

For neuroticism there was a significant association at rs35855737

(P = 9.26 × 10−9). This SNP is located in an intron of the MAGI1

gene, which is expressed in brain, and has been implicated in bipolar

disorder, SCZ, and major depressive disorder (MDD).90–92 However,

the association with MAGI1 (rs35855737) failed to replicate in an

independent cohort. van den Berg et al85 did not identify any signifi-

cant associations with extraversion.

Smith et al82 performed a meta-analysis of neuroticism (EPQ)

that identified 9 significant loci (Table 3). Examples of the implicated

loci include rs111433752, which is near the gene CRHR1, which regu-

lates release of cortisol and is involved in anxiety-like behaviors in

mice.93,94 Another example is rs490647, which is near the gene

GRIK3 (glutamate receptor kainite 3), a gene that is highly expressed

in postmortem brains of complete suicides.95

Okbay et al83 performed a large meta-analysis of GWAS data for

neuroticism using UK Biobank and Genetics of Personality Consor-

tium (GPC) cohorts. Eleven loci were identified (Table 3); 2 of these

tagged inversion polymorphisms on chromosomes 8 and 17.

Lo et al84 conducted a meta-analysis of the Five Factor Model

that included multiple cohorts using different personality instruments.

They used data from the GPC, UK Biobank and 2 companies:

23andMe, Inc. and deCode Genetics. The authors identified 6 SNPs

exceeded GWAS significance in the meta-analysis (Table 3). For

example, extraversion showed an association at rs6481128, which is

near the gene PCDH15, which encodes a member of the cadherin

superfamily and is important for calcium-dependent cell-cell adhe-

sion. Furthermore, rs9611519, on chromosome 8, was associated

with neuroticism; this locus is located in the L3MBTL2 gene. Addition-

ally, rs2164273, which is an intronic variant of MTMR9, also on Chro-

mosome 8, was associated with extraversion and inversely associated

with neuroticism. This locus resides in the inversion region on chro-

mosome 8 found in the UK Biobank study.83

At the time of our writing, 2 studies using large cohorts from

UK Biobank have identified multiple loci associated with neuroti-

cism; however, these are not yet peer-reviewed and are therefore

only briefly discussed. Luciano et al86 performed a GWAS of neu-

roticism (EPQ) and reported 116 significant independent genetic

loci distributed across the genome (N = 329 821). Two of the SNPs

most strongly associated with neuroticism were rs6981523 and

rs9611519, which were previously reported by Lo et al.84 Further-

more, rs2572431, which is on chromosome 8, was associated with

neuroticism, replicating a finding by Okbay et al83 using UK Bio-

bank participants and 2 additional cohorts (23andMe, GPC), Nagel

et al87 performed the largest meta-analysis of neuroticism ever

conducted (N = 449 484), which identified 136 independent loci.

The authors performed independent GWAS for different neuroti-

cism subclusters (ie, “depressed affect” and “worry”), and identified

that the 2 subclusters showed notable differences in genetic signal,

establishing for the first time the genetic multidimensionality of

neuroticism.

4.3 | Summary of GWAS studies of personality traits

Personality traits are extremely polygenic and are influenced by

many common alleles of small effect96,97 (also see Tables S1 and

S2, Supporting Information). However, their effect sizes on molec-

ular phenotypes can be large.50 Neuroticism is currently the best-

studied personality trait (Table 3); Table S1 shows the list of SNPs

4 of 13 SANCHEZ-ROIGE ET AL.

T A B L E 3

C o m p ar is o n o f p re v io u s G W

A S o f p e rs o n al it y tr ai ts

R e fe re n ce

Q u e st io n n ai re

T ra it

S tu d y d e si g n

D is co

v e ry

sa m p le

G W

as so

ci at io n P < 5 ×

1 0 − 8

N e ar b y g e n e (r e g io n )

R e p lic at io n

sa m p le

R e p lic at e d

7 0

E P Q

(s h o rt )

N e u ro ti ci sm

G W

A S

1 2 2 7 E U R

N —

1 8 8 0 E U R

N

7 1

E P Q

(s h o rt )

N e u ro ti ci sm

G W

A S

2 2 3 5 E U R

N —

N A

—

7 2

T P Q

H A , N S , R D , P

G W

A S

5 1 1 7 E U R

N —

N A

—

7 3

N E O -P I- R

N e u ro ti ci sm

G W

A S

3 9 7 2 E U R (S ar d in ia )

N 5 1 0 5 E U R

N

E xt ra v e rs io n

N

O p e n n e ss

N

A g re e ab

le n e ss

N

C o n sc ie n ti o u sn e ss

N

T P Q

H A

N

7 4

N E O -P I- R

E xc it e m e n t- se e k in g

M e ta -a n al y si s

7 8 6 0 E U R

1 S N P : rs 7 6 0 0 5 6 3 (P

= 2 × 1 0 − 8 )

C T N N A 2

6 0 8 , 1 5 4 5 , 3 0 4 3

E U R

N

7 5

E P Q

N e u ro ti ci sm

M e ta -a n al y si s

6 2 6 8 E U R

N —

5 2 7 -6 0 3 2 E U R

N

E xt ra v e rs io n

N —

7 6

T P Q

H A , N S , R D , P

M e ta -a n al y si s

~ 1 1 0 0 0 E U R

N —

N A

—

7 7

N E O

O p e n n e ss

M e ta -a n al y si s

1 7 3 7 5 E U R

2 S N P : rs 1 4 7 7 2 6 8 (P

= 2 .8

× 1 0 − 8 ), rs 2 0 3 2 7 9 4

(P = 3 .1

× 1 0 − 8 )

R A S A 1

3 2 9 4 E U R

N

C o n sc ie n ti o u sn e ss

1 S N P : rs 2 5 7 6 0 3 7 (P

= 4 .9

× 1 0 − 8 )

K A T N A L2

N

7 8

N E O -F F I

A g re e ab

le n e ss

G W

A S

4 5 9 5 E U R

3 S N P s: rs 9 6 5 0 2 4 1 (P

= 8 .1 2 × 1 0 − 1 0 ),

rs 2 7 0 1 4 4 8 (P

= 9 .4 6 × 1 0 − 1 0 ), k g p 6 0 8 0 0 5 8

(P = 1 .5 7 × 1 0 − 9 )

C h r 8

1 2 7 9 E U R

N

7 9

N E O -P I- R

O p e n n e ss

G W

A S

4 0 0 0 E A S

1 S N P : rs 2 1 4 6 1 8 (P

= 1 .6 7 × 1 0 − 8 )

P T P R D

2 0 9 0 E A S

N

N e u ro ti ci sm

N

8 0

N E O -P I- R

A g re e ab

le n e ss

M e ta -a n al y si s

3 8 9 8 E A S

N —

1 0 2 1 E A S

N

8 1

IR T

N e u ro ti ci sm

M e ta -a n al y si s

6 3 6 6 1 E U R

1 S N P : rs 3 5 8 5 5 7 3 7 ; P = 9 .2 6 × 1 0 − 9

M A G I1

9 7 8 6 E U R

N

8 2

E P Q

(s h o rt )

N e u ro ti ci sm

M e ta -a n al y si s

9 1 3 7 0 E U R

(U K B io b an

k )

6 6 5 9 E U R ; 8 6 8 7 E U R

9 S N P s: rs 4 9 0 6 4 7 (P

= 3 .8

× 1 0 − 8 ), rs 4 6 5 3 6 6 3

(P = 2 .0

× 1 0 − 8 ), rs 1 2 6 3 7 9 2 8 (P

= 4 .3

× 1 0 − 8 ),

rs 6 2 3 5 3 2 6 4 (P

= 3 .7

× 1 0 − 8 ), rs 1 2 6 8 2 3 5 2

(P = 1 .5

× 1 0 − 1 5 ), rs 1 2 3 7 8 4 4 6 (P

= 9 .4

× 1 0 − 9 ),

rs 4 9 7 7 8 4 4 (P

= 3 .2

× 1 0 − 8 ), rs 1 1 1 4 3 3 7 5 2

(P = 9 .3

× 1 0 − 1 2 ), rs 1 1 8 7 2 6 4 (P

= 1 .2

× 1 0 − 8 )

G R IK 3 , E N A H , S R P 9 ,

P V R L3

, T M E M 1 9 2 ,

K LH

L2 , M S M O 1

> 1 0 g e n e s ch

r 8 ,

P T R D , E LA

V L2

, > 1 0

g e n e s ch

r 1 7

(e g , C R H R 1 ), C E LF 4 ,

re sp e ct iv e ly

N A

—

8 3

IR T , E P Q

(s h o rt )

N e u ro ti ci sm

M e ta -a n al y si s

1 7 0 9 1 1 E U R

(G P C , U K B io b an

k )

1 1 S N P s: rs 2 5 7 2 4 3 1 (P

= 4 .2

× 1 0 − 1 6 ), rs 1 9 3 2 3 6 0 8 1

(P = 6 .3

× 1 0 − 1 1 ), rs 1 0 9 6 0 1 0 3 (P

= 2 .1

× 1 0 − 1 0 ),

rs 4 9 3 8 0 2 1 (P

= 4 .0

× 1 0 − 1 0 ), rs 1 3 9 2 3 7 7 4 6

(P = 2 .6

× 1 0 − 9 ), rs 1 5 5 7 3 4 1 (P

= 5 .6

× 1 0 − 9 ),

rs 1 2 9 3 8 7 7 5 (P

= 8 .5

× 1 0 − 9 ), rs 1 2 9 6 1 9 6 9

(P = 2 .2

× 1 0 − 8 ), rs 3 5 6 8 8 2 3 6 (P

= 2 .4

× 1 0 − 8 ),

rs 2 1 5 0 4 6 2 (P

= 2 .7

× 1 0 − 8 ), rs 1 2 9 0 3 5 6 3

(P = 2 .9

× 1 0 − 8 )

— Y a

Y (C o n ti n u es )

SANCHEZ-ROIGE ET AL. 5 of 13

that replicated across studies. Figure 2 shows that the number of

significant associations has increased as a function of sample size,

which is consistent with a polygenic model.

Because of the need for ever-larger sample sizes, meta-analyses

are widely used because they can integrate GWAS results obtained

with similar but not identical measurement scales. However, meta-

analyses potentially introduce their own problems such as differences

in personality scales or properties of the cohorts, including sex, age

and environmental factors.98 For example, performing a meta-

analysis of neuroticism that includes different scales would be prob-

lematic if the traits were not strongly genetically correlated. How-

ever, Okbay et al83 reported a perfect genetic correlation

(>100% � 14%) between measures of neuroticism from the UK Bio- bank83 and the GPC study.81 Furthermore, using IRT, different per-

sonality scales can be transformed into a common phenotype, which

can then be subjected to GWAS. A newer approach does not focus

on transforming phenotypes but instead jointly analyzes GWAS

results from putatively similar phenotypes (multi-trait analysis of

GWAS, MTAG99). MTAG was recently used to analyze GWAS results

from neuroticism, depressive symptoms and subjective well-being,

and allowed identification of 66 new loci for neuroticism; the top

SNP replicated in an independent sample (N = 8197) and in previous

studies (Table S1).

5 | POLYGENIC STRATEGIES FOR STUDYING THE GENETICS OF PERSONALITY

While the primary goal of GWAS for personality traits has been to

identify specific loci, polygenic analyses can be performed using both

significant and nonsignificant GWAS results. In this section, we will

discuss these polygenic approaches (see Box 2).

T A B L E 3

(C o n ti n u e d )

R e fe re n ce

Q u e st io n n ai re

T ra it

S tu d y d e si g n

D is co

v e ry

sa m p le

G W

as so

ci at io n P < 5 ×

1 0 − 8

N e ar b y g e n e (r e g io n )

R e p lic at io n

sa m p le

R e p lic at e d

8 4

N E O -F F I

(2 3 an

d M e );

N E O , IR T , E P Q

E xt ra v e rs io n

M e ta -a n al y si s

1 2 2 8 8 6 E U R

(2 3 an

d M e an

d G P C )

4 S N P s: rs 5 7 5 9 0 3 2 7 (P

= 1 .2 6 × 1 0 − 9 ), rs 2 1 6 4 2 7 3

(P = 1 .6 1 × 1 0 − 9 ), rs 6 4 8 1 1 2 8 (P

= 5 .4 4 × 1 0 − 1 0 ),

rs 1 4 2 6 3 7 1 (P

= 9 .5 4 × 1 0 − 1 5 )

G B E 1 , M T M R 9 ,

P C D H 1 5 , W S C D 2

3 9 5 0 0 E U R

(2 3 an

d M e )

7 1 0 0 (d e C O D E )

Y

N e u ro ti ci sm

2 S N P s: rs 6 9 8 1 5 2 3 (P

= 3 .1 7 × 1 0 − 2 4 ), rs 9 6 1 1 5 1 9

(P = 9 .1 6 × 1 0 − 9 )

X K R 6 , L3

M B T L2

/C H A D L

3 9 5 0 0 E U R

(2 3 an

d M e )

7 1 0 0 (d e C O D E )

9 1 3 7 0

(U K B io b an

k )

Y

8 5

IR T

E xt ra v e rs io n

M e ta -a n al y si s

6 3 0 3 0 E U R

N —

9 7 8 3 E U R

—

8 6

E P Q

(s h o rt )

N e u ro ti ci sm

G W

A S

3 2 9 8 2 1 E U R

1 1 6 S N P s

Y Y

8 7

E P Q

(s h o rt ),

N E O , N E O -F F I

N e u ro ti ci sm

M e ta -a n al y si s

4 4 9 4 8 4 E U R

(U K B io b an

k ,

2 3 an

d M e , G P C 1 )

1 3 6 S N P s

N A

—

a “Q

u as i- re p lic at io n ”; se e O k b ay

e t al .8 5

BOX 2

MEASURING SNP HERITABILITY, GENE PATHWAYS AND

GENETIC OVERLAP

SNP heritability (or “chip heritability”; hg 2) is the proportion of

phenotypic variance explained by additive contributions from

large sets of genotyped and imputed SNPs, measured in a set

of unrelated individuals. Using a Genomic-relatedness-matrix

Restricted Maximum Likelihood (GREML) analysis, the Genetic

Complex Trait Analysis software (GCTA100) estimates the pro-

portion of variation in a phenotype that is due to all the SNPs

tested, and exploits the fact that genotypic similarity (ie,

“relatedness,” measured using genotyped SNPs from unrelated

individuals) will be correlated with phenotypic similarity for

heritable traits. Linkage Disequilibrium Regression Score

(LDSC) is a more recent method that provides estimates of

SNP heritability and takes linkage disequilibrium (LD) into

account101,102. An advantage of LDSC is that it only requires

the results of the GWAS (the “summary statistics”) rather than

6 of 13 SANCHEZ-ROIGE ET AL.

5.1 | SNP heritability

The earliest estimates of the heritability of personality traits came

from twin studies (Table 1). More recently, the heritable variance

attributable to measured SNPs (see Box 2) has been used to estimate

the heritability of personality traits; this so-called SNP heritability

ranges from 5% to 18% (Table 4). Whereas twin studies provide heri-

tability estimates for both common and rare alleles, SNP heritability is

based on available SNP information from unrelated individuals, which

is typically limited to relatively common alleles100 excluding other fac-

tors such as dominance effects, gene-gene and gene-environment

interactions; therefore, SNP heritability is expected to be lower than

the narrow sense heritability obtained using twins115.

5.2 | Gene-set and pathway-based analyses

Gene-set and pathway-based analyses are intended to perform tests

at the gene or pathway levels, rather than at the level of individual

SNPs and are described in Box 2. The first 2 attempts to apply gene-

based analyses to personality traits did not identify significant find-

ings, likely due to insufficient power.72,76 Using GWAS results from

larger datasets, Luciano et al75 identified 6 genes for neuroticism

(EPQ), 5 of which were in a region of high linkage disequilibrium on

chromosome 15. One of those genes, SCAMP2 (P = 1.0 × 10−6),

which is involved in norepinephrine transporter function and is a drug

target of mood disorders.111 That same year, de Moor et al77

reported that the gene KATNAL2, which is involved in neurodevelop-

ment (neuronal migration, axonal growth, dendritic pruning), was

implicated in conscientiousness (P = 2.0 × 10−6). For extraversion,

van den Berg et al85 found one significant association for a long non-

coding RNA site (P = 2.87 × 10−6). Using a large cohort

(N = 329 000), Luciano et al86 identified 249 genes significantly asso-

ciated with neuroticism (P < 2.77 × 10−6). Intriguingly, 3 of these

genes (XKR6, L3MBTL2 and CHADL) are near SNPS that have been

previously associated with neuroticism.84

Gene-set analyses have repeatedly implicated genes involved in

synapse and cell communication. For example, Kim et al112 identified

an association between axon guidance gene sets and neuroticism

(NEO); similarly, pathways comprising cell adhesion molecules were

enriched for neuroticism. More recently, Luciano et al86 implicated

5 pathways (P = 1.21 × 10−6) in processes for neuron spine and dif-

ferentiation and cell adhesion in neuroticism (EPQ); Nagel et al87

identified 3 specific pathways including neurogenesis

(P = 4.4 × 10−9), behavioral response to cocaine (P = 1.84 × 10−7),

and axon guidance (P = 5.26 × 10−8). Pathways that implicate synap-

tic communication were also implicated when using meta-analyzed

data for depressive symptoms, neuroticism and subjective well-

being99; this relationship was strongest for depressive symptoms.

5.3 | Genomic profile risk scoring

There are numerous studies showing correlations between personal-

ity traits and various forms of psychopathology. All of these correla-

tions, which we term “phenotypic correlations,” are based on

measurements of both traits in the same subjects and cannot easily

separate the environmental vs the genetic basis of the correlations.

For example, neuroticism is correlated with anxiety, depressive and

substance use disorders.113 Twin studies have extended these obser-

vations, and showed that the overlap between personality traits and

psychopathology has a genetic basis. For example, comorbidity

between neuroticism and both MDD and anxiety disorders is attrib-

uted to shared genetic vulnerability.22,114

Genomic profile risk scoring (PRS) is a method for investigating

the polygenic nature of traits like personality and can also be used to

explore the genetic basis of correlations between traits like personal-

ity and psychopathology (Box 2). Reassuringly, PRS for neuroticism

significantly predicted neuroticism in independent cohorts.75,81,86,87

Recently, polygenic scores based on joint GWAS for neuroticism, sub-

jective well-being and depressive symptoms showed greater power

at predicting neuroticism scores.99 Early evidence based on a meta-

analysis of GWAS data115 showed that both PRS for neuroticism, and

PRS for extraversion, predicted BP status. Furthermore, PRS for neu-

roticism predicted MDD,81,86,115 and depressive symptoms.75 Simi-

larly, PRS for neuroticism obtained using European subjects could

predict MDD in Chinese women109; showing that PRS for neuroti-

cism can even be useful when applied to populations with different

ancestry. Lastly, PRS of neuroticism (calculated using summary data

from the GPC) also predicted more general health risk outcomes,

including high BMI, smoking, and coronary artery disease.116 In all

these examples, the 2 traits being examined are not measured in the

the individual-level phenotype and genotype data that is

required by GCTA.

Gene set and pathway analyses incorporate multiple

genetic signals relevant to a specific gene that would not oth-

erwise reach GWAS significance.103 This method requires a

lower significance threshold (P < 2.8 × 10−6) because it only

needs to correct for the number of genes being tested

(approximately 18 000104).

Genomic profile risk scoring methods sum the effects of

multiple SNPs to predict individual risk for a given trait.103,104

This approach can be used to assess the shared genetic basis

of a target sample (eg, personality) and a “discovery sample”

(eg, psychiatric disorder). Because the discovery sample is

independent from the target sample, any detected observation

must be genetically driven as there are no shared environmen-

tal factors.

Genetic correlations estimate how much of the genetic

influence on 2 traits/diseases is common to both. Unlike phe-

notypic correlations, which can be due to a combination of

genetic and nongenetic factors, genetic correlations can only

measure genetically driven correlations. GCTA uses bivariate

GREML analysis to estimate the genetic variance of 2 traits

and the genetic covariance between them that is captured by

all SNPs tested.105 LDSC calculates pairwise genetic correla-

tions for autosomal SNPs between 2 traits using the summary

results of GWAS from 2 independent datasets.101

SANCHEZ-ROIGE ET AL. 7 of 13

same individuals. Instead, the correlation is based on genetic similar-

ity among SNPs between 2 independent cohorts. This approach

removes confounding environmental influences and thus disentangles

genetic and environmental factors.

5.4 | Genetic correlations between personality traits, and overlap with psychopathology

Linkage disequilibrium regression score (LDSC) is a method for per-

forming genetic correlations101 (see Box 2) that has been widely

adopted, in part because it only requires the more readily available

summary statistics produced by a GWAS. Smith et al82 used LDSC to

show a positive genetic correlation between neuroticism (EPQ) and

MDD (64% � 7%), and a much smaller genetic correlation with schizophrenia (SCZ; 22% � 5%). Similarly, moderate and high genetic correlations were later identified between neuroticism (NEO, EPQ)

and MDD, at 56%, 68% (�7%) and 68%,84,86,87 consistent with genetic correlations found in twin research.117,118 Moreover, Okbay

et al83 identified a negative genetic correlation between neuroticism

and subjective well-being, at 75% (�3%); and a positive genetic cor- relation between neuroticism and depressive symptoms, at 75%

(�3%), as has also been recently identified by Luciano et al86 The lat- est meta-analysis of neuroticism87 identified genetically distinguish-

able subclusters, namely “depressed affect” and “worry,” both

showing strong positive genetic correlations with major depressive

(66%-58%, respectively) and anxiety disorders (67%-74%), and nega-

tive with subjective well-being (−55%-52%).

Power and Pluess108 reported intercorrelation values between

different personality traits (the International Personality Item Pool,

which is highly correlated with the NEO inventory, rs = .70-.82), iden-

tifying a strong genetic correlation between neuroticism and open-

ness (82% � 39%, P < .05). Using LDSC, Lo et al84 identified positive intercorrelation values between different personality traits (NEO),

which were generally no higher than 40%.84 Lo et al84 subsequently

conducted a principal component analysis to extract components of

genetic variation, derived from the genetic correlation matrix

between all personality traits and neuropsychiatric disorders. They

found that all psychiatric disorders had positive correlations with the

first principal component, while agreeableness and conscientiousness

had negative correlations with the first principal component. The

extraversion-introversion axis aligned with the second principal com-

ponent. In general, they found that most personality traits (conscien-

tiousness, agreeableness and extraversion) clustered together.

Furthermore, some of the personality traits clustered with several

neuropsychiatric disorders. For example, openness clustered with BP

and SCZ. Intriguingly, ADHD clustered with personality traits more

than psychiatric disorders, showing an especially high genetic correla-

tion with extraversion (30%, SE not reported). This indicates that

ADHD, or at least some ADHD subtypes, is genetically coincident

with an extreme form of extraversion. Considering the heterogeneity

of the ADHD disorder (eg, subtypes and differences between child

and adult forms), genetic studies of personality may particularly help-

ful for characterizing the etiology of different ADHD subtypes.119

More recently, a positive genetic correlation between neuroti-

cism and loneliness (40% � 9%120), and delay-discounting (18% � 8%121), have been reported. Correlations between genetic variants for personality traits and other traits may thus improve our

understanding of the biological basis of psychiatric diseases and

inform novel diagnostic approaches.122

6 | LIMITATIONS AND FUTURE DIRECTIONS

GWAS have furthered our understanding of the etiology of person-

ality traits. The number of specific loci discovered will increase as

ever-larger sample sizes are obtained. However, some attention

should also be given to the limitations of these studies that cannot

be addressed by sample size alone. First, the methods used to date

are dominated by GWAS, which focus on additive effects, at the

2008 2009 2010 2011 2012 2013 2014 2015 2016

First GWAS for neuroticism (EPQ)

First GWAS for TPQ traits

First personality GWAS using

imputed

genotypes

First GWAS for NEO traits

First study showing SNP-

heritability for

personality

dimensions

First meta-analysis GWAS for

excitement-seeking

First evidence for polygenicity

between

personality and

psychopathology

First meta- analysis GWAS

for TPQ

First meta- analysis GWAS

for EPQ

First meta-analysis

openness (NEO)

First two reports using

gene-base

analyses

First study to show that phenotypic

variance of

extraversion (EPQ)

explained by each chromosome is

proportional to its

length

First GWAS to

agreeableness

(NEO)

Genetics of Personality

one hit for

neuroticism (IRT)

Large meta-

several hits

for neuroticism (Okbay et al 2016, Smith et

al 2016, Lo et al 2016),

extraversion and conscientiousness

Using polygenic methods, Okbay et

al. (2016) identify

personality traits

are enriched for the central nervous

system

van den Oord et al 2008

Terraciano et al 2010

Calboli et al 2010

Verweij et al 2010

Calboli et al 2010

Terraciano et al 2011

de Moor et al 2012

Middeldorp et al 2011

Luciano et al 2012

Service et al 2012

Bae et al 2013

Vinkhuyzen et al 2012

de Moor et al 2015 Lo et al 2016

Service et al 2012

Verweij et al 2012

2017

GWAS hits for neuroticism

Pathways for neural and cell

adhesion

molecules are

enriched for personality traits

Nagel et al 2017

Luciano et al 2017

High genetic correlations are

revealed between

personality traits

and psychopathology

Lo et al 2016

FIGURE 1 Timeline of major findings in personality genetics using GWAS (top panels) and polygenic (bottom panels) methods. Not all

references in Table 3 are included in this figure

8 of 13 SANCHEZ-ROIGE ET AL.

expense of nonadditive genetic variance, such as dominance and

epistasis. Additionally, by focusing on SNPs, genetic contributions

from structural and rare variants are not captured. Moreover, longi-

tudinal twin studies123 showed a stable genetic foundation through-

out adulthood, with more prominent environmental influences

during early-middle adulthood. This observation suggests that both

genetic and nonshared environmental factors influence personality

traits,98 but this has not been adequately tested. Third, even though

polygenic methods have identified potential overlap between per-

sonality traits and psychopathology, additional methods are needed

to identify the specific variants. For example, in a recent Phenome

Wide Association Study, which tests the contribution of a genetic

variant across multiple traits, the genes CADM2 and MSRA were

associated with risk taking behavior and irritability, respectively, and

aspects of the Big Five traits.124 In addition, a “proxy-phenotype”

method97 has also been used to identify genetic overlap between

personality and psychopathology. Okbay et al83 identified a set of

170 candidate SNPs with suggestive evidence of association

(P < 1 × 10−4) with subjective well-being, and tested these candi-

dates for association with neuroticism. Using this method, the

authors identified 4 SNPs showing overlap between neuroticism and

subjective well-being.

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

1

10

100

Agreeableness

Conscientiousness

Excitement-seeking

Extraversion

HA, NS, RD, P

Neuroticism

Openness

Year

G W

A S

h it s (

lo g 1 0 )

FIGURE 2 GWAS hits (log10

transformed) discovered as a function of sample size and personality trait. HA, Harm Avoidance, NS, Novelty Seeking, RD, Reward Dependence, P, Persistence

TABLE 4 SNP heritability estimates of personality traits

Reference Questionnaire Trait Study design Discovery sample GWAS association P < 5 × 10−8

SNP-heritability (%, SE)

106 EPQ Extraversion GCTA ~12 000 EUR N 6% (3%)

Neuroticism 12% (3%)

107 TPQ HA NS RD Persistence

GCTA ~8000 EUR NA 6.6% (3.7%) 9.9% (3.6%) 4.2% (3.6%) 8.1% (3.7%)

108 NEO-PI-R Neuroticism GCTA 5011 EUR NA 15% (8%)

Openness 21% (8%)

81 IRT Neuroticism GCTA 63 661 EUR (NTR, QIMR cohorts) 1 SNP 14.7% (5.5%) 15.7% (8.3%)

85 IRT Extraversion GCTA 3597 and 3369 (NTR, QIMR cohorts) N 4.9% (0.8%)

83 EPQ, NEO Neuroticism LDSC 170 911 EUR 11 SNPs 9.1% (0.7%)

82 EPQ (short) Neuroticism GCTA ~106 000 EUR (UK Biobank) 9 SNPs 15% (0.7%)

84 EPQ, NEO-FFI Extraversion LDSC 59 176 EUR (23andMe) 5 SNPs 18.1% (1.0%)

Neuroticism 2 SNPs 11.9% (1.6%)

Conscientiousness Agreeableness Openness

1 SNP 9.6% (0.9%) 8.5% (0.9%) 10.7% (0.9%)

109 EPQ Neuroticism GCTA 9633 EAS (MDD and control women) NA 10% (3%)

86 EPQ (short) Neuroticism LDSC 329 821 EUR (UK Biobank) 116 SNPs 10.8% (0.5%)

87 EPQ (short), NEO, NEO-FFI

Neuroticism LDSC 449 484 EUR (UK Biobank, 23andMe, GPC1)

136 SNPs 10.0% (0.3%)

SANCHEZ-ROIGE ET AL. 9 of 13

GWAS associations do not unambiguously identify specific

genes and thus do not directly lead to novel biological insights. Path-

way analyses of GWAS will likely become more useful as pathway

annotations continue to improve. The integration of data from

GWAS and gene expression will present new opportunities and chal-

lenges in the discovery of the genes underlying implicated loci and

pathways associated with personality.125 Using imputed transcripts

from GWAS and comparing those with drug-induced changes in

gene expression, So et al13 recently showed that the effectiveness

of psychiatric medications was strongly associated with genomic

information based on GWAS data for several psychiatric conditions.

Aggregating genetic findings based on GWAS of personality traits

may ultimately influence the development of novel medications for

psychiatric conditions that are characterized by, for example, high

levels of neuroticism.

7 | CONCLUSION

Over the past decade, findings from GWAS and polygenic analyses

have made major progress toward unraveling the genetic etiology of

personality traits. And yet, the path from GWAS to biology and, ulti-

mately, psychology is not straightforward. Functional validation of

some of the identified potential candidate genes will prove difficult,

as there are no direct animal analogues of personality traits. None-

theless, continued inquiry into the genetic basis of personality has

the potential to yield insight into the biology of psychiatric

disease risk.

ACKNOWLEDGMENTS

J.M.'s contributions were partially supported by the Peter Boris Chair

in Addictions Research. C.-H.C. is supported by National Institute of

Mental Health R01MH100351. S.S.-R. is supported by the Frontiers

of Innovation Scholars Program (FISP), the Interdisciplinary Research

Fellowship in NeuroAIDS (IRFN), and Pilot award from

P50DA037844.

Conflict of interest

The authors declare no conflict of interest.

Disclosure of interests

The opinions and assertions expressed herein are those of the

author(s) and do not necessarily reflect the official policy or position

of the Uniformed Services University or the Department of Defense.

Author contribution

SSR wrote the manuscript; AAP, CHC, JCG, JM edited the

manuscript.

ORCID

S. Sanchez-Roige http://orcid.org/0000-0001-6137-5699

A. A. Palmer http://orcid.org/0000-0003-3634-0747

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