Emerging Trends in The Social and Behavioral Sciences

Genetic and Environmental Approaches to Political Science

Item

Title

Genetic and Environmental Approaches to Political Science

Author

Fazekas, Zoltán

Hatemi, Peter K.

Research Area

Special Areas of Interdisciplinary Study

Topic

Genetics, the Individual and Society

Abstract

Over the past decade, a growing interest in the possibility that biological factors, including genes, might contribute to individual differences in political and social behaviors has emerged. Behavioral genetic techniques have provided a variety of approaches to quantify the effects of genetic and nongenetic inheritance. However, until quite recently, these methods were largely unknown to political scientists. In this essay, we review the general approaches to modeling genetic and social influences on differences in complex human social traits. In so doing, we focus on the “genetics of politics,” including attitudes, ideologies, voting, and partisanship. The emergence of this research reflects a paradigm shift in the study of social traits necessitating the inclusion of biological influences, and recognizing the interdependence of genetic, social, and environmental factors in the development of political behaviors over the life course.

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Identifier

etrds0342

extracted text

Genetic and Environmental
Approaches to Political Science
ZOLTÁN FAZEKAS and PETER K. HATEMI

Abstract
Over the past decade, a growing interest in the possibility that biological factors,
including genes, might contribute to individual differences in political and social
behaviors has emerged. Behavioral genetic techniques have provided a variety of
approaches to quantify the effects of genetic and nongenetic inheritance. However,
until quite recently, these methods were largely unknown to political scientists. In
this essay, we review the general approaches to modeling genetic and social influences on differences in complex human social traits. In so doing, we focus on the
“genetics of politics,” including attitudes, ideologies, voting, and partisanship. The
emergence of this research reflects a paradigm shift in the study of social traits necessitating the inclusion of biological influences, and recognizing the interdependence of
genetic, social, and environmental factors in the development of political behaviors
over the life course.

INTRODUCTION
Interest in identifying genetic influences on political traits began in the 1970s
by psychologists and geneticists (Eaves & Eysenck, 1974; Martin et al., 1986)
and has remained a topic of interest since (Bouchard, Lykken, McGue, Segal,
& Tellegen, 1990; Eaves & Hatemi, 2008; Hatemi, Medland, Morley, Heath, &
Martin, 2007). Yet, the last decade witnessed major developments in terms of
integrating behavior genetic (BG) approaches into explicating political traits
in the social sciences (for reviews see Hatemi, Dawes, Frost-Keller, Settle, &
Verhulst, 2011; Hatemi & McDermott, 2012a). A multitude of special issues
focused on behavioral genetic approaches have appeared in The Annals of the
American Academy of Political and Social Science (Hibbing & Smith, 2007), Political Research Quarterly (McDermott, 2009), Social Sciences Quarterly (Lineberry,
2011), Journal of Theoretical Politics (Hatemi, Byrne, & McDermott, 2012), Political Psychology (Hatemi & McDermott, 2012c), and Twin Research and Human
Genetics (Hatemi, 2012).
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

1

2

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

As Hatemi, Byrne, et al. (2012) advocate, in order for BG approaches to
be integrated into political science, it is necessary for scholars to begin
from the same set of starting assumptions regarding the nature and meaning of genetic influences. Thus, we offer a brief description of some of
these approaches, results from recent studies, and their implications for
understanding political behaviors.
FOUNDATIONAL RESEARCH
WHAT IS A GENE?
Genes regulate the cellular environment and create proteins, the main
functional tools in the cell, which in turn instigate or restrict hormonal and
other biological pathways in both state and trait circumstances. Thousands
of genes interact with countless environmental conditions, both inside and
outside the body to produce the chain of mechanisms that lead to a given
trait, which may radically differ across the life span. Thus, whenever genetic
influence is found for a given trait, whether by twin studies that rely on a
latent measure of genetic influence, or molecular studies that rely on specific
markers and their expression, it is implied that the genetic influences are not
fixed, but conditional upon and interacting with environmental conditions,
developmental processes, and other biological mechanisms (Hatemi, Byrne,
et al., 2012).
Influences between genes and behavior are mutual and bidirectional. It is
believed that DNA has some role in indirectly guiding people into certain
environments, and gene expression is affected by and based on exposure
to those environments and one’s own behavior. In this view displayed in
Figure 1, the incorporation of genetic influences on political or social traits
are set in a framework of constant interaction between biological and environmental forces that differ at various stages of one’s lifetime. In addition,
as Hatemi and McDermott (2012a, p. 4) state, “whatever genetic influences
exist probably operate through those emotional, cognitive, or rational processes that are instigated when individuals are asked particular questions
about their attitudes.” Given the complexity of these processes, it is most
likely impossible for any single gene to account for any substantial amount
of variance for any complex social or political trait. Rather, it is the totality of
one’s genetic make up, in combination with social and environmental stimuli, which account for different exposure to and selection into experiences,
emotive and cognitive states, perceptions, and preferences.
Considering the dynamic nature of genetic mechanisms, how can one accurately identify genetic influence? As Box and Draper (1987, p. 74) eloquently
state: “Remember that all models are wrong; the practical question is how
wrong do they have to be to not be useful.” A model that perfectly captures

Genetic and Environmental Approaches to Political Science

3

Figure 1 The Interaction of Biology and Environment Over the Life Course.
Notes: Figure taken from Hatemi, Byrne, et al., 2012 and originally published by
Project Foresight (2008) Mental Capital and Wellbeing Project. London: The
Government Office for Science. Available at www.bis.gov.uk/foresight.

one’s genetic, social, environmental, psychological, and physical factors over
the life course does not exist. To conduct empirical research, whether using
social, environmental, developmental, or biological approaches, or the combination as we advocate, scholars must rely on reductionist models that make
assumptions about the world, and such assumptions shape and limit the
interpretation of results. All statistical models attempt to simplify the vast
complexity of real life in order to allow researchers to test specific hypotheses,
and genetic models remain equally informative and fallible as any social science approach (for a detailed discussion, see Verhulst & Hatemi, 2013). Given
these limitations, we discuss the two most common approaches to explore
genetic influences and how they have been applied to political traits.
CUTTING-EDGE RESEARCH
MODELS OF HERITABILITY
BG analyses guided by biometric theory assume that the variation of a
phenotype or trait (P) can be thought of as a consequence of latent genetic
(G) and environmental factors (E). Twin and kinship models are among the

4

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

most popular approaches to identify the sources of variance on a trait or the
sources of covariation between traits. The conventional twin model notation
is that the total variance of a trait can be decomposed into additive genetic
effects (A) or the sum of the effects of all the individual genetic markers that
influence the trait; shared or common environmental influence (C), which
captures the factors that are perfectly shared between twins and family
members, such as the effects of neighborhood; and unique environmental
influence (E), which captures all environmental stimuli not shared between
twins, including error (P = A + C + E) (Medland & Hatemi, 2009).
This model differs from traditional social science models (SSMs), which
seek to identify systematic relationships between two or more characteristics,
and predict different outcome levels depending on the variables placement
in the regression equation. SSM models assume that all traits, regardless of
position, are environmentally determined; the exclusion of biological factors
is not a decision based on model fitting, but an a priori paradigmatic decision
(Smith & Hatemi, 2013).
Extant literature suggests that the assumption of purely environmental
determinants of political traits is not warranted and genetics plays an
important role in how and why people differ (Hatemi & McDermott, 2012a).
For example, numerous studies conducted across decades and in several
different countries find individual differences in ideology are genetically
influenced (between 0.3 and 0.6 of the variance, see Hatemi et al., 2014).
These findings inform theoretical models involving ideology because they
steer researchers to understand ideology as a psychological disposition
that guides behavior and consequently employ it as a predictor, not an
outcome. Contrary to the recently hypothesized notion that the heritability
of ideology is simply channeled through personality (Mondak, Hibbing,
Canache, Seligson, & Anderson, 2010), a recent stream of research (Verhulst,
Eaves, & Hatemi, 2012; Verhulst, Hatemi, & Martin, 2010) shows that genetic
influences on attitudes and ideologies are not subsumed by other covariates
but specific to ideological differences. The challenge for political science
theories becomes more poignant: it is not only that the assumption of no
genetic influences for political traits is unwarranted but also these genetic
effects are more than some spillover or confounded effects channeled
through related traits. These findings brought about the imperative for an
integrated theory of ideology, that embraces both genes and environment,
yet remain embedded within a developmental framework, that includes
parental investment, social groups, education, cognition, perception, aging,
and all other critical environmental and neurobiological mechanisms (Eaves,
Hatemi, Heath, & Martin, 2011; Fowler & Schreiber, 2008; Hatemi et al., 2009).
In the majority of twin models, the focus is on monozygotic (MZ) and
dizygotic (DZ) twin pairs reared together. MZ twins develop from a single

Genetic and Environmental Approaches to Political Science

5

1.0
1.0

1.0

1.0

E1

C1

A1

e

c

PhTwin1

a

0.5/1.0

1.0

1.0

1.0

A2

C2

E2

a

c

e

PhTwin2

Figure 2 ACE model. Notes: Figure prepared by the authors.

fertilized egg and share 100% of their chromosomal sequence (i.e., “genetically identical”), whereas DZ twins develop from two different eggs
fertilized by two different sperms and share, on average, 50% of their
chromosomal sequence (Medland & Hatemi, 2009). The most valuable information stems from the covariance between twin pairs for each zygosity type.
Different relationships of between twin-pair correlations (r) for MZs and DZs
indicate what sort of transmission should we expect: if rMZ = rDZ , there is no
genetic effect; if rMZ > rDZ genetic and (shared and unique) environmental
factors are present; if rMZ = 2 × rDZ , genetic and unique environmental factors
drive the variation in the phenotype (no shared environmental effects); and
if rMZ >2 × rDZ , the variation in the phenotype is due to additive genetic
effects, nonadditive genetic effects, and unique environmental factors.
Figure 2 shows the ACE variance decomposition and how this decomposition is informed by the properties of twin data. We have information about
the phenotype for each twin from a pair, and these are marked in the rectangles. The model stipulates that P = A + C + E, where the three factors are
unobserved and the relative proportions of variance can be decomposed into
these three components that sum to 1. Working with twins reared together,
we assume that the shared or common environment influences each twin in
the same manner, and thus the C1 and C2 are correlated perfectly, r = 1.0,
where subscripts indicate twin 1 and twin 2. This correlation is independent
of zygosity (i.e., the equal environments assumption). Unique environmental factors are defined to capture why the twins are different, and hence they
are not correlated. The correlation between the genetic factors (A1 and A2 )
reflects the amount of shared genetic material (MZ twins is set to 1.0. DZ is
set to 0.5; for a full description, see Medland & Hatemi, 2009).
To illustrate this method consider, Klemmensen et al. (2012) study of
political participation in Denmark and the United States. The MZ pair
correlation is 0.51, whereas the DZ correlations is 0.32, indicating that

6

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Source of variance

Genetic

SharedEnv

UniqueEnv

1.0

Proportion of variance

0.8

0.6

0.4

0.2

0.0

n
io
at
ic
tif
en
id y
s
rty ut
up
pa c d
ro
al vi
lg
ic ci
s
lit of ism tica
de
Po se ntr poli
tu
n e
tti
Se noc on hts des l a
h s g u a
Et tude s ri attit litic
ti n’ t o
At e en l p s
a
om m n de
W sh tio tu
ni adi tti
s
Pu n tr p a ay ism nce
u c t e
o
N -gro effi rva fer es
ut l se re ud
O itica on y p ttit s
l l c lic a e
Po ua po nse erti
x n e ib
Se eig def d l
r / n
Fo tary s a gy s
ili m lo e
M edo eo itud s
e id tt e
Fr cial s a titud
u
t
So gio c a
i
i
s
el
t
R nom ude es
ou
o tit d s
rn
Ec at itu tic
tu
r
x att oli
e
t
Se ial in p
vo
e)
s
ic
ac t sm
R es ali nd de
at
r
a
rv
te on n itu
sm e
In iti tio att
ni ns n
ad a n
ia o tio
Tr ticip ria
ar l-c ca
rit ra isti
r ita
Pa or st ho ibe h
th tru aut (l op
Au ial ng ogy e/s
c i ol dg
So ht w ide le
ig ll ow
R era kn
v l
O itica
l
Po

Figure 3 Summary of Relative Genetic and Environmental Influences on Political
Traits. Notes: Figure taken from Hatemi and McDermott (2012c, 526).

genetic factors should play an important role in understanding variation
in participatory behavior but shared environmental factors could also exert
some influence. The univariate ACE model results of A = 0.39, C = 0.12, and
E = 0.49 can be interpreted to mean that roughly 39% of the variation (why
individuals differ) in participation is accounted for by genetic factors. The
95% confidence intervals for the shared environment (C) are 0.00 and 0.29,
which means that the shared environmental effects cannot be distinguished
from 0 at the traditional thresholds employed in quantitative analysis, while
the bulk of the variation is accounted for by unique factors, and error.
Scores of political traits have been explored relying on twin studies and
other models of heritability. Hatemi and McDermott (2012a) combined the
findings of all reported twin and kinship studies that estimated genetic and
environmental influences on political traits from 1974 to 2012 and aggregated
them into 26 domains. Figure 3 displays the relative proportion of variance
on each trait explained by additive genetic factors, common environmental
influences, and unique environmental influences.
As displayed in Figure 3, individual differences in ideology, political
knowledge, trust, authoritarianism, and participation and most political
attitudes are accounted for largely by genetic and environmental factors.

Genetic and Environmental Approaches to Political Science

7

However, differences in one’s party identification, sense of duty, and
ethnocentrism are hardly, if at all, influenced by genetic factors.
ACE models can be extended in numerous ways, including research questions involving the genetic and environmental covariance between two or
more variables (Hatemi, McDermott, Eaves, Kendler & Neale, 2013; Neale &
Cardon, 1992). Continuing with the Klemmensen et al. (2012) example, this
approach is suitable for answering whether the genetic influence on political efficacy and political participation is partially shared; or if same latent
genetic or environmental factors account for the covariance between these
traits. That is, rather than focusing on prediction, or the size of the correlation
between traits, multivariate models offer information what is driving the correlation. Is the trait of interest related due to a common genetic factor or due
to similar experience or familial environment? Building on the between-trait
correlations, in this Klemmensen et al. study, those who are politically more
efficacious also participate more in politics, however roughly 80–90% of the
covariation between efficacy and political participation is driven by a common latent genetic factor and not by environmental similarity, a finding that
requires serious rethinking of current theories on how efficacy influences
political behaviors.
Further extensions such as direction of causation models are able to test
directional hypotheses. Verhulst and colleagues (Verhulst & Estabrook,
2012; Verhulst et al., 2010; Verhulst et al., 2012) employed such a model to
explore four causal scenarios: (i) a unidirectional causal model where the
variation in personality traits drives the variation in political attitudes; (ii) a
unidirectional causal model where the set of genes that influence variation
in political attitudes in turn leads to variation in personality traits; (iii)
reciprocal causation, where personality traits and political attitudes have
a nonrecursive causal structure; and (iv) pleiotropy or a common set of
genes that mutually influences both personality traits and political attitudes.
Verhulst et al. pointed toward the fourth scenario or possibly even the
second, finding no causal relationship from personality to political attitudes
(also see Dawes et al., 2014; Hatemi & Verhulst 2015), in direct contrast with
political science theories (Mondak et al., 2010). DoC models can be extremely
powerful tools for testing theories and disentangling causal relationships
that are only asserted but remain empirically untested.
UNDERSTANDING HERITABILITY
Notwithstanding the increasing exposure of political science to these methods through detailed methodological papers (Boardman, 2011; Eaves et al.,
2011; Hatemi, 2013; Medland & Hatemi, 2009; Verhulst & Hatemi, 2013),
there remains an erroneous understanding of how genes operate, what

8

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

heritability means and the statistical and theoretical assumptions required
to conduct empirical research.
First, an accurate interpretation of genetic influence does not include the
word “determined.” With rare exception, genetic influences are not fixed
or unmalleable. They are mediated and moderated by environmental conditions and change greatly throughout the life cycle. Heritability estimates
account for variance within a population at a given time and are population
specific. Instead of explaining the value of trait, they focus on the difference
of values on a trait within a population. In the case of the above-mentioned
Klemmensen example, it is not that genes explain 39% of political participation; rather, it is that 39% of the variance, or individual differences in political
participation within the population, are accounted for by the aggregate of
genetic influences. Twin or “ACE” models explain how people differ. They
are not to be interpreted to mean that for every person in the population 0.39
of their political participation is due to genes (for more detail, see Hatemi,
Byrne, et al., 2012).
ASSUMPTIONS, LIMITATIONS, AND EXTENSIONS
Akin to any statistical model, univariate classical twin models (CTDs) rely
on several assumptions (for more detail, see Hatemi et al., 2012a; Medland
& Hatemi, 2009); three of them have received the most attention both from
critics and advocates of the CTD: equal environment assumption (EEA), no
assortative mating, and no correlation or interaction between genetic and
environmental influences (rGE/G×E). The EEA stipulates that, on average,
MZ cotwins share equally similar environments as DZ cotwins, or that if
any differences do exits they have no effect on the traits of interest. This
assumption enables researchers to equate the cotwin correlations for the
latent common environmental factor (Figure 2). If this assumption does
not hold (EEA violations), the CTD would bias the genetic effects upward
and the shared environmental influences downward. Many studies have
addressed the EEA issues for political traits (Hatemi et al., 2009; Littvay, 2012;
Smith et al., 2012) and the results consistently suggest that wherever the
similarity in MZ cotwins family environments differ from DZ cotwin pairs,
such differences have no effect on the heritability estimates of political traits.
The basic twin model also assumes that no assortative mating for the trait
exists; that is, parents (spouses) choose each other randomly and not based
on similarities related to the traits under investigation. This assumption
however is often not true for social traits. Indeed, overwhelming evidence
indicates that this assumption does not hold for political traits as well;
spousal correlations for ideology range from 0.62 to 0.68 (Alford, Hatemi,
Hibbing, Martin, & Eaves, 2011; Eaves & Hatemi, 2008, 2011; Eaves et al.,

Genetic and Environmental Approaches to Political Science

9

1999; Eaves et al., 2011; Hatemi et al., 2010). This is important because in the
simplest scenario, if there is assortative mating on the trait of interest and
this trait is heritable, genetic influences will be underestimated in a classical
twin model, and the shared environmental effects will be overestimated
(Eaves & Hatemi, 2008).
This lacuna is easily remedied by simple extensions to the CTD. Indeed,
the effects of assortative mating were estimated in extended kinship models using information on the twins’ parents and twins’ spouses, resulting in
genetic influences accounting for an even greater portion of individual differences on political attitudes and ideology than reported by the CTD (Eaves
& Hatemi, 2008; Hatemi et al., 2010). Extended kinship models offer the possibility to estimate numerous other types of intergenerational transmission
including gene–environment covariance and sibling-specific environmental
influences. For important political traits such as ideology, analyses carried
out on extended family data reinforced the results presented by twin studies, further emphasizing that a serious theoretical discussion of genetic effects
on political traits is inevitable if we want to advance our understanding of
politics and political preference formation (Hatemi et al., 2010).
Univariate twin models provide only the simplest representation of
a highly complex gene to behavior process. That is, similar to simple
regression models, they provide a baseline that allows for the exploration of
increasingly complex modes of phenotypic transmission (for a description of
this progression, see Hatemi et al., 2009). However, numerous extensions to
the CTD exist allowing for more complex analyses and the integration with
traditional political science theories. Indeed, an emerging stream of research
has begun to merge BG theories with psychological and political science
theories (Fowler, Baker and Dawes, 2008; Hatemi, Eaves, & McDermott,
2012; Hatemi & McDermott, 2012b; Hatemi et al., 2007; Hatemi et al., 2009;
Hatemi et al., 2013; Loewen & Dawes, 2012). In one such example, Fazekas
and Littvay (2012) investigate proximity and directional voting principles.
Building on rational choice models and employing an operationalization
rooted in social psychology, they analyze the heritability of the adoption of
these specific voting theories. They use a bivariate twin model to show that
there are shared underlying genetic factors influencing both the strength of
partisanship and which spatial considerations guide individual vote choice.
Another simple extension to the CTD allows for inclusion of specific environmental measures, thus making it possible to estimate gene–environment
interplay. Statistically speaking, when an individual differentially responds
to or selects into an environment (actively or passively) as a function of their
genotype, they induce an interaction or correlation between their genotype
and the environment. Several works have recently provided methodological
and theoretical primers on gene–environment interplay specifically tailored

10

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

for political scientists (Boardman, 2011; Hatemi, 2013; Verhulst & Hatemi,
2013), while others have provided empirical examples, ranging from the
influence of childhood environments on genetic influences of ideology
(Smith et al., 2012) to the import of school nutrition programs on the genetic
influences on childhood obesity and its impact on public policy (Boardman
et al., 2012). Research thus far has demonstrated that for the majority of
political traits, biases from passive gene–environment covariance are not
significant or substantial (Eaves & Hatemi, 2008; Hatemi et al., 2010). However, this was not the case for gene–environment interaction. Hatemi (2013)
found that twins responded to life events differentially based on genetic
similarity. In most cases, he found that for the population of individuals who
experienced financial problems were laid off or fired or were divorced in the
last year, genetic influences on Capitalism and Socialism all but dissipated
(Figure 4). However, for the population of individuals who were laid off or
fired, genetic influences on Property Tax increased (Figure 5).
These studies are only a small representation of an integrative research
framework that has emerged in within political science with has combined
latent models of transmission with the foundation works of our field, extending both the theoretical models and empirical toolkit of the discipline.
GENE MAPPING
Gene mapping methods identify associations between-trait levels and
specific genetic polymorphisms, by analyzing a priori selected genes or by
scanning the entire genome for a genetic marker or chromosomal region that
covaries with the trait of interest or by measuring the expression of specific
genetic marker under certain environmental conditions (e.g., epigenetic).
These approaches identify specific biological mechanisms responsible for
some portion of the variation in behavior.
Candidate gene studies preselect genes that are believed to be susceptible
to be associated with the trait under consideration. Some genes come in
alternate forms at a given chromosomal position—labeled as alleles—that
reflect one (or more) single-nucleotide polymorphisms (SNPs) or are due to
differences in length of specific DNA section (Hatemi, Gillespie, et al., 2011).
“Gene association studies test whether an allele or genotype occurs more
frequently within a group exhibiting a particular trait than those without
the trait” (Fowler & Dawes, 2008, p. 584), this being the case-control design.
Alternatively, family designs can be employed that “compare whether
offspring exhibiting the trait receive a specific allele from their parents more
often than would be expected by chance.” The statistical methods are those
commonly found in most political science research, analysis of variance
(ANOVA), and some form of regression.

Genetic and Environmental Approaches to Political Science

Female

11

Male
C

0.75

A
E

0.25
E

Capitalism

C
0.50

A

0.00
–0.25

E
E

0.50
0.25
A
C

C
A

C

C

0.25

E

E

0.00

A

A

0.00

Property tax

Proportion of variance

0.75

0.75
0.50

Socialism

–0.25

–0.25
0

1

2

3

0

1

2

3

Number of risk events

Figure 4 Change in Source of Variance on Capitalism, Property Tax, and
Socialism When Exposed to One or More Financial Risk Events. Notes: Figure
taken from Hatemi (2013).

This area of research is developing and numerous publications now exist
which explore the relationship between genetic markers and political traits,
including interactions with environmental conditions (Benjamin et al., 2012;
Dawes & Fowler, 2009; Fowler & Dawes, 2008; Fowler, Dawes, & Christakis,
2009; Fowler, Settle, & Christakis, 2011; Hatemi, Gillespie, et al., 2011; Hatemi
et al., 2014; McDermott, Dawes, Prom-Wormley, Eaves, & Hatemi, 2013;
McDermott, Tingley, Cowden, Frazzetto, & Johnson, 2009).

12

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Both sexes

Genetic

0.4
Unique env

Property tax

Proportion of variance

0.6

0.2

Common env

0.0

0.0

1
0 = Employed

1 = Fired or laid off

Figure 5 Changes in Source of Variance on Property Tax When Fired or Laid Off.
Notes: Figure taken from Hatemi (2013).

For example, Fowler and Dawes (2008) build their selection of the MAOA
and 5HTT (serotonin) as candidate genes associated with voter turnout
because these two genes were previously linked to antisocial behavior.
They find that the “high” allele of MAOA and the “long” allele of 5HTT are
significantly associated with higher voter turnout, but only for those who
frequently attend religious services. Similarly, focusing on the role of the
dopamine D2 receptor in forming social attachments, Dawes and Fowler
(2009) report that having an A2 allele in the DRD2 gene is significantly associated with higher probability of partisan attachment. However, yet again, the
authors and Hatemi, Byrne, et al. (2012, p. 319) point out that “the proposed
pathway suggested to influence voting behavior noted above, is certainly
a function of the genotype; that is, certain genotypes appear to have a role
in the greater or lesser release or uptake of hormones, but the regulation of
these hormones are a function of gene expression. [ … ] However, it is critical
to note that most candidate gene studies account for a very small amount of
the variance, and most results fail to withstand efforts at replication.”

Genetic and Environmental Approaches to Political Science

13

Avoiding the bias of preselecting particular genes, genome wide
approaches (GWAs) present themselves as “more empirically rigorous
method [that] scans the entire genome for a genetic marker or chromosomal
region that is significantly related to the trait of interest” (Hatemi, Byrne,
et al., 2012, p. 317). These analyses can “implicate genes that we did not
suspect were influencing a trait of interest and thus reveal novel pathways
to the formation of political orientations” (Hatemi, Gillespie, et al., 2011,
p. 2). Hence, it is an exploratory and data-driven approach.
So far, GWAs have not identified a specific genetic marker related to political traits. Hatemi, Gillespie, et al., 2011 conducted a genome wide linkage
study and found three regions that significantly covary with and account
for up to 13% of ideology. Many genes reside within the 90% or 95% confidence intervals of the identified peaks that were related to similar social
traits. However, in two follow-up studies that employed a more rigorous
genome wide association approach that focuses on specific variants, no specific markers were found to be associated with political ideology (Benjamin
et al., 2012; Hatemi et al., 2014). This, however, was the expected conclusion
because the effects of individual markers on such a complex traits are going
to be extremely small, too small to identify because extremely large samples, possibly in the hundreds of thousands, are needed (Hatemi et al., 2014,
p. 22). Unfortunately, the largest sample available for GWA analyses on political traits numbers less than 14,000. Thus, for political traits, genome wide
association studies are in a far less developed stage compared to models of
latent influence, but technological and methodological expansion is certain
to lead to further advancement.
KEY ISSUES FOR FUTURE RESEARCH
In this extremely abbreviated contribution, we have provided a brief review
of genetic methodologies applied to political traits, using findings from the
recent literature. We view the introduction of these BG approaches as a start,
not a conclusion. Recent findings introduce new questions and continue
BG methodological developments and availability of data accommodate
the possibility of answering these questions. Research began with relatively
simple models of heritability that raised both ontological and theoretical
questions. Extended kinship models reinforced those findings and further
detailed the underlying mechanisms behind the transmission of political
attitudes. Subsequently, the interplay between physiological and environmental forces was incorporated into the scientific inquiry of political traits,
and lately specific genetic markers, and the interaction between genes, and
genes and environments are emerging. All these were necessary because
prior findings posed new challenges and a more complicated picture of

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

political behavior emerged. We expect to see models that focus on gene
expression and developmental and longitudinal designs in the near future
(e.g., Hatemi et al., 2009). Given that exclusively environmental theories
of political behavior offered only partial answers, this more inclusive
description of political behavior appears to resemble more closely the reality
of contemporary politics.
Integrating models of genes and environments contributes to our broader
conception of how individuals develop, select into and react to specific environments, and ultimately decide on crucial matters related to power sharing, social construction, and societal interactions. If the goal of research is
to increase knowledge, the inclusion of BG approaches is indeed a substantive one. This integration, however, means that there is a lot of catching up
to do. Some methodological concerns have been already addressed decades
ago in other disciplines and accepted limitations have been treated as such.
With the integration of research traditions, a detailed understanding of the
underlying principles and limitations are necessary. One such example is the
focus of models of heritability on within-population variance (i.e., individual differences) in contrast with the interest in population mean prediction
in most SSMs. Providing an estimate of why people differ in a population
is not equal to, and should not be equated with, why someone is a liberal
or a conservative. When research is misunderstood, as is often the case with
new material, interpretation of findings become erroneous, which inevitably
has a spillover effect on the integration of the substantive implications and
research approach.
Inclusion of a BG approach shifts research questions toward understanding
mechanisms not simply manifestations. With increased attention, technology, data, and better measurement of traits, we can test our theories in a more
rigorous manner. Forthcoming panel studies and experiments on genetically
informative samples allow researchers to address issues of causality and to
recover changes (or stability) in political traits in novel ways, including the
driving forces behind change. Such models also allow for researchers to better focus on environmental stimuli, by controlling for genetic disposition.
In the quest for understanding the complicated nature of individual political behavior, theoretical and methodological expertise from political science,
genetics, psychology, sociology, and many other fields contribute to asking
better questions and getting more accurate answers and ultimately increase
our knowledge base in a rigorous scientific manner.
REFERENCES
Alford, J. R., Hatemi, P. K., Hibbing, J. R., Martin, N. G., & Eaves, L. J. (2011). The
politics of mate choice. Journal of Politics, 73(02), 362–379.

Genetic and Environmental Approaches to Political Science

15

Benjamin, D. J., Cesarini, D., van der Loos, M. J., Dawes, C. T., Koellinger, P. D.,
Magnusson, P. K., … , Visscher, P. M. (2012). The genetic architecture of economic
and political preferences. Proceedings of the National Academy of Sciences, 109(21),
8026–8031.
Boardman, J. D. (2011). Gene-environment interplay for the study of political behaviors. In P. K. Hatemi & R. McDermott (Eds.), Man is by nature a political animal (pp.
185–207). Chicago, IL: University of Chicago Press.
Boardman, J. D., Roettger, M. E., Domingue, B. W., McQueen, M. B., Haberstick, B.
C., & Harris, K. M. (2012). Gene–environment interactions related to body mass:
School policies and social context as environmental moderators. Journal of Theoretical Politics, 24(3), 370–388.
Bouchard, T. J., Jr., Lykken, D. T., McGue, M., Segal, N. L., & Tellegen, A. (1990).
Sources of human psychological differences: The minnesota study of twins reared
apart. Science, 250(4978), 223–228.
Box, G. E. P., & Draper, N. R. (1987). Empirical model-building and response surfaces.
Wiley series in probability and mathematical statistics. Applied probability and statistics. New York, NY: John Wiley & Sons, Inc.
Dawes, C. T., Cesarini, D., Fowler, J. H., Johannesson, M., Magnusson, P. K. E., &
Oskarson, S. (2014). The relationship between genes, personality traits, and political participation. American Journal of Political Science, 58(4 October 2014), 888–903.
doi:10.1111/ajps.12100
Dawes, C. T., & Fowler, J. H. (2009). Partisanship, voting, and the dopamine D2 receptor gene. The Journal of Politics, 71(03), 1157–1171.
Eaves, L. J., & Eysenck, H. J. (1974). Genetics and the development of social attitudes.
Nature, 249(454), 288–289.
Eaves, L. J., & Hatemi, P. K. (2008). Transmission of attitudes toward abortion and
gay rights: Effects of genes, social learning and mate selection. Behavior Genetics,
38(3), 247–256.
Eaves, L. J., & Hatemi, P. K. (2011). Do we choose our spouse based on our in-laws?
Resolving the effects of family background and spousal choice for educational
attainment, religious practice, and political preference. Social Science Quarterly,
92(5), 1253–1278.
Eaves, L. J., Hatemi, P. K., Heath, A. C., & Martin, N. G. (2011). Modeling biological
and cultural inheritance. In P. K. Hatemi & R. McDermott (Eds.), Man is by nature
a political animal (pp. 101–184). Chicago, IL: University of Chicago Press.
Eaves, L. J., Heath, A. C., Martin, N. G., Maes, H., Neale, M., Kendler, K., & Corey, L.
(1999). Comparing the biological and cultural inheritance of personality and social
attitudes in the virginia 30,000 study of twins and their relatives. Twin Research, 2,
62–80.
Fazekas, Z., & Littvay, L. (2012). Choosing sides: The genetics of why we go with the
loudest. Journal of Theoretical Politics, 24(3), 389–408.
Fowler, J. H., Baker, L. A., & Dawes, C. T. (2008). Genetic variation in political participation. American Political Science Review, 102(2), 233–248.
Fowler, J. & Schreiber, D. (2008). Biology, politics, and the emerging science of human
nature. Science, 322(5903), 912–914. doi:10.1126/science.1158188

16

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Fowler, J. H., & Dawes, C. T. (2008). Two genes predict voter turnout. The Journal of
Politics, 70(03), 579–594.
Fowler, J. H., Dawes, C. T., & Christakis, N. A. (2009). Model of genetic variation
in human social networks. Proceedings of the National Academy of Sciences, 106(6),
1720–1724.
Fowler, J. H., Settle, J. E., & Christakis, N. A. (2011). Correlated genotypes in friendship networks. Proceedings of the National Academy of Sciences, 108(5), 1993–1997.
Hatemi, P. K. (2012). The intersection of behavioral genetics and political science:
Introduction to the special issue. Twin Research and Human Genetics, 15(01), 1–5.
Hatemi, P. K. (2013). The influence of major life events on economic policy attitudes in
a world of gene-environment interplay. American Journal of Political Science, 57(4),
987–1007.
Hatemi, P. K., Byrne, E. M., & McDermott, R. (2012). Introduction: What is a ‘gene’
and why does it matter for political science? Journal of Theoretical Politics, 24(3),
305–327.
Hatemi, P. K., Dawes, C. T., Frost-Keller, A., Settle, J. E., & Verhulst, B. (2011). Integrating social science and genetics: News from the political front. Biodemography
and Social Biology, 57(1), 67–87.
Hatemi, P. K., Eaves, L. J., & McDermott, R. (2012). It’s the end of ideology as we
know it. Journal of Theoretical Politics, 24(3), 345–369.
Hatemi, P. K., Funk, C. L., Medland, S. E., Maes, H. M., Silberg, J. L., Martin, N. G., &
Eaves, L. J. (2009). Genetic and environmental transmission of political attitudes
over a life time. Journal of Politics, 71(3), 1141–1156.
Hatemi, P. K., Gillespie, N. A., Eaves, L. J., Maher, B. S., Webb, B. T., Heath, A. C., … ,
Martin, N. G. (2011). A genome-wide analysis of liberal and conservative political
attitudes. The Journal of Politics, 73(1), 271–285.
Hatemi, P. K., Hibbing, J. R., Medland, S. E., Keller, M. C., Alford, J. R., Smith, K. B.,
… , Eaves, L. J. (2010). Not by twins alone: Using the extended family design to
investigate genetic influence on political beliefs. American Journal of Political Science, 54(3), 798–814.
Hatemi, P. K., & McDermott, R. (2012a). The genetics of politics: Discovery, challenges, and progress. Trends in Genetics, 28(10), 525–533.
Hatemi, P. K., & McDermott, R. (2012b). A neurobiological approach to foreign policy analysis: Identifying individual differences in political violence. Foreign Policy
Analysis, 8(2), 111–129.
Hatemi, P. K., & McDermott, R. (2012c). The political psychology of biology, genetics,
and behavior. Political Psychology, 33(3), 307–312.
Hatemi, P. K., McDermott, R., Eaves, L. J., Kendler, K. S., & Neale, M. C. (2013). Fear
as a disposition and an emotional state: A genetic and environmental approach to
out-group political preferences. American Journal of Political Science, 57(2), 279–293.
Hatemi, P. K., Medland, S. E., Morley, K. I., Heath, A. C., & Martin, N. G. (2007). The
genetics of voting: An Australian twin study. Behavior Genetics, 37(3), 435–448.
Hatemi, P. K., Medland, S. E., Robert Klemmensen, R., Oskarrson, S., Littvay, L.,
Dawes, C. T., … , Martin, N. G. (2014). Genetic influences on political ideologies:

Genetic and Environmental Approaches to Political Science

17

Genome-wide findings on three populations, and a mega-twin analysis of 19 measures of political ideologies from five western democracies. Behavior Genetics, 44(3),
282–294. doi:10.1007/s10519-014-9648-8
Hatemi, P. K., & Verhulst, B. (2015). Political attitudes develop independently of
personality traits. http://journals.plos.org/plosone/article?id=10.1371/journal.
pone.0118106.
Hibbing, J. R., & Smith, K. B. (2007). The biology of political behavior: An introduction. The Annals of the American Academy of Political and Social Science, 614(1),
6–14.
Klemmensen, R., Hatemi, P. K., Hobolt, S. B., Petersen, I., Skytthe, A., & Nørgaard,
A. S. (2012). The genetics of political participation, civic duty, and political efficacy
across cultures: Denmark and the United States. Journal of Theoretical Politics, 24(3),
409–427.
Lineberry, R. L. (2011). Letter from the former editor. Social Science Quarterly, 92(5),
1133.
Littvay, L. (2012). Do heritability estimates of political phenotypes suffer from an
equal environment assumption violation? Evidence from an empirical study. Twin
Research and Human Genetics, 15(1), 6–14.
Loewen, P. J., & Dawes, C. T. (2012). The heritability of duty and voter turnout. Political Psychology, 33(3), 363–373.
Martin, N. G., Eaves, L. J., Heath, A. C., Jardine, R., Feingold, L. M., & Eysenck, H.
J. (1986). Transmission of social attitudes. Proceedings of the National Academy of
Sciences, 83(12), 4364–4368.
McDermott, R. (2009). Mutual interests. Political Research Quarterly, 62(3), 571–583.
McDermott, R., Dawes, C. T., Prom-Wormley, L., Eaves, L., & Hatemi, P. K. (2013).
MAOA and aggression: A gene-environment interaction in two populations. Journal of Conflict Resolution, 57(6), 1043–1064.
McDermott, R., Tingley, D., Cowden, J., Frazzetto, G., & Johnson, D. D. (2009).
Monoamine oxidase a gene (Maoa) predicts behavioral aggression following
provocation. Proceedings of the National Academy of Sciences, 106(7), 2118–2123.
Medland, S. E., & Hatemi, P. K. (2009). Political science, biometric theory, and twin
studies: A methodological introduction. Political Analysis, 17(2), 191–214.
Mondak, J. J., Hibbing, M. W., Canache, D., Seligson, M. A., & Anderson, M. R. (2010).
Personality and civic engagement: An integrative framework for the study of trait
effects on political behavior. American Political Science Review, 104(1), 85–110.
Neale, M. C., & Cardon, L. R. (1992). Methodology for genetic studies of twins and families.
Dordrecht, The Netherlands: Kluwer Academic Publishers.
Project Foresight (2008) Mental Capital and Wellbeing Project. The Government Office
for Science, London. www.bis.gov.uk/foresight
Smith, K. B., Alford, J. R., Hatemi, P. K., Eaves, L. J., Funk, C., & Hibbing, J. R.
(2012). Biology, ideology, and epistemology: How do we know political attitudes
are inherited and why should we care? American Journal of Political Science, 56(1),
17–33.
Smith, K. B., & Hatemi, P. K. (2013). OLS is AOK for ACE: A regression-based
approach to synthesizing political science and behavioral genetics models. Political
Behavior, 35(2), 383–408.

18

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Verhulst, B., Eaves, L. J., & Hatemi, P. K. (2012). Correlation not causation: The relationship between personality traits and political ideologies. American Journal of
Political Science, 56(1), 34–51.
Verhulst, B., & Estabrook, R. (2012). Using genetic information to test causal relationships in cross-sectional data. Journal of Theoretical Politics, 24(3), 328–344.
Verhulst, B., & Hatemi, P. K. (2013). Gene-environment interplay in twin models.
Political Analysis, 21(3), 368–389.
Verhulst, B., Hatemi, P. K., & Martin, N. G. (2010). The nature of the relationship
between personality traits and political attitudes. Personality and Individual Differences, 49(4), 306–316.

ZOLTÁN FAZEKAS SHORT BIOGRAPHY
Zoltán Fazekas is post-doctoral research fellow in the Department of
Political Science and Public Management, University of Southern Denmark.
He was trained in political science at the Central European University
(Budapest, Hungary) and University of Vienna (Austria). In his research, he
strives to understand political behavior on the voter and the elite level, and
how our characteristics as human beings influence political attitude formation and political decision making. Currently, he is working on the research
team designing and implementing political surveys for twins together with
the Danish Twin Registry. Among others, his work has been published in
outlets such as International Journal of Public Opinion Research, Electoral
Studies, Social Science Quarterly, and Journal of Theoretical Politics.
PETER K. HATEMI SHORT BIOGRAPHY
Peter K. Hatemi is Associate Professor of Political Science, Microbiology,
and Biochemistry at the Pennsylvania State University and research fellow
at the United States Studies Centre at the University of Sydney. He was
trained in political science at the University of Nebraska and in genetic
epidemiology at the Queensland Institute of Medical Research (QIMR).
He continued his postdoctoral study in Human Genetics, Psychology, and
Psychiatry at the Virginia Institute for Psychiatric and Behavioral Genetics
(VIPBG) in the Medical College of Virginia. He is primarily interested
in advancing the study of the neurobiological mechanisms of social and
political behaviors and utilizing advanced methods in genetics, physiology,
endocrinology, and neurology in order to better understand human decision
making and preferences in complex and dynamic political environments.
He is also an active member of the Institute for Statskundskab at Syddansk
Universitet, VIPBG, and the genetic epidemiology laboratory at QIMR.
Pete’s recent work on the genetic, physiological, and endocrinological

Genetic and Environmental Approaches to Political Science

19

sources of individual differences in political attitudes, fear dispositions,
mate selection, personality, political violence, and religion has appeared in
the American Journal of Political Science, Behavior Genetics, Demography,
Evolution and Human Behavior, Journal of Politics, Political Psychology,
Science, Social Forces, and Trends in Genetics among other venues. His
recent book, co-edited with Rose McDermott, Man is by Nature a Political
Animal at the University of Chicago Press, offers a comprehensive volume
that includes applications of evolution, genetics, primatology, neuroscience,
and physiology to understand political preferences.
RELATED ESSAYS
Social Epigenetics: Incorporating Epigenetic Effects as Social Cause and
Consequence (Sociology), Douglas L. Anderton and Kathleen F. Arcaro
Telomeres (Psychology), Nancy Adler and Aoife O’Donovan
Kin-Directed Behavior in Primates (Anthropology), Carol M. Berman
The Sexual Division of Labor (Anthropology), Rebecca Bliege Bird and Brian
F. Codding
Genetics and the Life Course (Sociology), Evan Charney
Sexual Behavior (Anthropology), Melissa Emery Thompson
Evolutionary Approaches to Understanding Children’s Academic Achievement (Psychology), David C. Geary and Daniel B. Berch
Genetics and Social Behavior (Anthropology), Henry Harpending and Gregory Cochran
An Evolutionary Perspective on Developmental Plasticity (Psychology),
Sarah Hartman and Jay Belsky
Grandmothers and the Evolution of Human Sociality (Anthropology), Kristen
Hawkes and James Coxworth
Genetic Foundations of Attitude Formation (Political Science), Christian
Kandler et al.
Complexity: An Emerging Trend in Social Sciences (Anthropology), J. Stephen
Lansing
Niche Construction: Implications for Human Sciences (Anthropology), Kevin
N. Laland and Michael O’Brien
From Individual Rationality to Socially Embedded Self-Regulation (Sociology), Siegwart Lindenberg
Evolutionary Perspectives on Animal and Human Personality (Anthropology), Joseph H. Manson and Lynn A. Fairbanks
Born This Way: Thinking Sociologically about Essentialism (Sociology),
Kristen Schilt



Genetic and Environmental
Approaches to Political Science
ZOLTÁN FAZEKAS and PETER K. HATEMI

Abstract
Over the past decade, a growing interest in the possibility that biological factors,
including genes, might contribute to individual differences in political and social
behaviors has emerged. Behavioral genetic techniques have provided a variety of
approaches to quantify the effects of genetic and nongenetic inheritance. However,
until quite recently, these methods were largely unknown to political scientists. In
this essay, we review the general approaches to modeling genetic and social influences on differences in complex human social traits. In so doing, we focus on the
“genetics of politics,” including attitudes, ideologies, voting, and partisanship. The
emergence of this research reflects a paradigm shift in the study of social traits necessitating the inclusion of biological influences, and recognizing the interdependence of
genetic, social, and environmental factors in the development of political behaviors
over the life course.

INTRODUCTION
Interest in identifying genetic influences on political traits began in the 1970s
by psychologists and geneticists (Eaves & Eysenck, 1974; Martin et al., 1986)
and has remained a topic of interest since (Bouchard, Lykken, McGue, Segal,
& Tellegen, 1990; Eaves & Hatemi, 2008; Hatemi, Medland, Morley, Heath, &
Martin, 2007). Yet, the last decade witnessed major developments in terms of
integrating behavior genetic (BG) approaches into explicating political traits
in the social sciences (for reviews see Hatemi, Dawes, Frost-Keller, Settle, &
Verhulst, 2011; Hatemi & McDermott, 2012a). A multitude of special issues
focused on behavioral genetic approaches have appeared in The Annals of the
American Academy of Political and Social Science (Hibbing & Smith, 2007), Political Research Quarterly (McDermott, 2009), Social Sciences Quarterly (Lineberry,
2011), Journal of Theoretical Politics (Hatemi, Byrne, & McDermott, 2012), Political Psychology (Hatemi & McDermott, 2012c), and Twin Research and Human
Genetics (Hatemi, 2012).
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

1

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

As Hatemi, Byrne, et al. (2012) advocate, in order for BG approaches to
be integrated into political science, it is necessary for scholars to begin
from the same set of starting assumptions regarding the nature and meaning of genetic influences. Thus, we offer a brief description of some of
these approaches, results from recent studies, and their implications for
understanding political behaviors.
FOUNDATIONAL RESEARCH
WHAT IS A GENE?
Genes regulate the cellular environment and create proteins, the main
functional tools in the cell, which in turn instigate or restrict hormonal and
other biological pathways in both state and trait circumstances. Thousands
of genes interact with countless environmental conditions, both inside and
outside the body to produce the chain of mechanisms that lead to a given
trait, which may radically differ across the life span. Thus, whenever genetic
influence is found for a given trait, whether by twin studies that rely on a
latent measure of genetic influence, or molecular studies that rely on specific
markers and their expression, it is implied that the genetic influences are not
fixed, but conditional upon and interacting with environmental conditions,
developmental processes, and other biological mechanisms (Hatemi, Byrne,
et al., 2012).
Influences between genes and behavior are mutual and bidirectional. It is
believed that DNA has some role in indirectly guiding people into certain
environments, and gene expression is affected by and based on exposure
to those environments and one’s own behavior. In this view displayed in
Figure 1, the incorporation of genetic influences on political or social traits
are set in a framework of constant interaction between biological and environmental forces that differ at various stages of one’s lifetime. In addition,
as Hatemi and McDermott (2012a, p. 4) state, “whatever genetic influences
exist probably operate through those emotional, cognitive, or rational processes that are instigated when individuals are asked particular questions
about their attitudes.” Given the complexity of these processes, it is most
likely impossible for any single gene to account for any substantial amount
of variance for any complex social or political trait. Rather, it is the totality of
one’s genetic make up, in combination with social and environmental stimuli, which account for different exposure to and selection into experiences,
emotive and cognitive states, perceptions, and preferences.
Considering the dynamic nature of genetic mechanisms, how can one accurately identify genetic influence? As Box and Draper (1987, p. 74) eloquently
state: “Remember that all models are wrong; the practical question is how
wrong do they have to be to not be useful.” A model that perfectly captures

Genetic and Environmental Approaches to Political Science

3

Figure 1 The Interaction of Biology and Environment Over the Life Course.
Notes: Figure taken from Hatemi, Byrne, et al., 2012 and originally published by
Project Foresight (2008) Mental Capital and Wellbeing Project. London: The
Government Office for Science. Available at www.bis.gov.uk/foresight.

one’s genetic, social, environmental, psychological, and physical factors over
the life course does not exist. To conduct empirical research, whether using
social, environmental, developmental, or biological approaches, or the combination as we advocate, scholars must rely on reductionist models that make
assumptions about the world, and such assumptions shape and limit the
interpretation of results. All statistical models attempt to simplify the vast
complexity of real life in order to allow researchers to test specific hypotheses,
and genetic models remain equally informative and fallible as any social science approach (for a detailed discussion, see Verhulst & Hatemi, 2013). Given
these limitations, we discuss the two most common approaches to explore
genetic influences and how they have been applied to political traits.
CUTTING-EDGE RESEARCH
MODELS OF HERITABILITY
BG analyses guided by biometric theory assume that the variation of a
phenotype or trait (P) can be thought of as a consequence of latent genetic
(G) and environmental factors (E). Twin and kinship models are among the

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

most popular approaches to identify the sources of variance on a trait or the
sources of covariation between traits. The conventional twin model notation
is that the total variance of a trait can be decomposed into additive genetic
effects (A) or the sum of the effects of all the individual genetic markers that
influence the trait; shared or common environmental influence (C), which
captures the factors that are perfectly shared between twins and family
members, such as the effects of neighborhood; and unique environmental
influence (E), which captures all environmental stimuli not shared between
twins, including error (P = A + C + E) (Medland & Hatemi, 2009).
This model differs from traditional social science models (SSMs), which
seek to identify systematic relationships between two or more characteristics,
and predict different outcome levels depending on the variables placement
in the regression equation. SSM models assume that all traits, regardless of
position, are environmentally determined; the exclusion of biological factors
is not a decision based on model fitting, but an a priori paradigmatic decision
(Smith & Hatemi, 2013).
Extant literature suggests that the assumption of purely environmental
determinants of political traits is not warranted and genetics plays an
important role in how and why people differ (Hatemi & McDermott, 2012a).
For example, numerous studies conducted across decades and in several
different countries find individual differences in ideology are genetically
influenced (between 0.3 and 0.6 of the variance, see Hatemi et al., 2014).
These findings inform theoretical models involving ideology because they
steer researchers to understand ideology as a psychological disposition
that guides behavior and consequently employ it as a predictor, not an
outcome. Contrary to the recently hypothesized notion that the heritability
of ideology is simply channeled through personality (Mondak, Hibbing,
Canache, Seligson, & Anderson, 2010), a recent stream of research (Verhulst,
Eaves, & Hatemi, 2012; Verhulst, Hatemi, & Martin, 2010) shows that genetic
influences on attitudes and ideologies are not subsumed by other covariates
but specific to ideological differences. The challenge for political science
theories becomes more poignant: it is not only that the assumption of no
genetic influences for political traits is unwarranted but also these genetic
effects are more than some spillover or confounded effects channeled
through related traits. These findings brought about the imperative for an
integrated theory of ideology, that embraces both genes and environment,
yet remain embedded within a developmental framework, that includes
parental investment, social groups, education, cognition, perception, aging,
and all other critical environmental and neurobiological mechanisms (Eaves,
Hatemi, Heath, & Martin, 2011; Fowler & Schreiber, 2008; Hatemi et al., 2009).
In the majority of twin models, the focus is on monozygotic (MZ) and
dizygotic (DZ) twin pairs reared together. MZ twins develop from a single

Genetic and Environmental Approaches to Political Science

5

1.0
1.0

1.0

1.0

E1

C1

A1

e

c

PhTwin1

a

0.5/1.0

1.0

1.0

1.0

A2

C2

E2

a

c

e

PhTwin2

Figure 2 ACE model. Notes: Figure prepared by the authors.

fertilized egg and share 100% of their chromosomal sequence (i.e., “genetically identical”), whereas DZ twins develop from two different eggs
fertilized by two different sperms and share, on average, 50% of their
chromosomal sequence (Medland & Hatemi, 2009). The most valuable information stems from the covariance between twin pairs for each zygosity type.
Different relationships of between twin-pair correlations (r) for MZs and DZs
indicate what sort of transmission should we expect: if rMZ = rDZ , there is no
genetic effect; if rMZ > rDZ genetic and (shared and unique) environmental
factors are present; if rMZ = 2 × rDZ , genetic and unique environmental factors
drive the variation in the phenotype (no shared environmental effects); and
if rMZ >2 × rDZ , the variation in the phenotype is due to additive genetic
effects, nonadditive genetic effects, and unique environmental factors.
Figure 2 shows the ACE variance decomposition and how this decomposition is informed by the properties of twin data. We have information about
the phenotype for each twin from a pair, and these are marked in the rectangles. The model stipulates that P = A + C + E, where the three factors are
unobserved and the relative proportions of variance can be decomposed into
these three components that sum to 1. Working with twins reared together,
we assume that the shared or common environment influences each twin in
the same manner, and thus the C1 and C2 are correlated perfectly, r = 1.0,
where subscripts indicate twin 1 and twin 2. This correlation is independent
of zygosity (i.e., the equal environments assumption). Unique environmental factors are defined to capture why the twins are different, and hence they
are not correlated. The correlation between the genetic factors (A1 and A2 )
reflects the amount of shared genetic material (MZ twins is set to 1.0. DZ is
set to 0.5; for a full description, see Medland & Hatemi, 2009).
To illustrate this method consider, Klemmensen et al. (2012) study of
political participation in Denmark and the United States. The MZ pair
correlation is 0.51, whereas the DZ correlations is 0.32, indicating that

6

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Source of variance

Genetic

SharedEnv

UniqueEnv

1.0

Proportion of variance

0.8

0.6

0.4

0.2

0.0

n
io
at
ic
tif
en
id y
s
rty ut
up
pa c d
ro
al vi
lg
ic ci
s
lit of ism tica
de
Po se ntr poli
tu
n e
tti
Se noc on hts des l a
h s g u a
Et tude s ri attit litic
ti n’ t o
At e en l p s
a
om m n de
W sh tio tu
ni adi tti
s
Pu n tr p a ay ism nce
u c t e
o
N -gro effi rva fer es
ut l se re ud
O itica on y p ttit s
l l c lic a e
Po ua po nse erti
x n e ib
Se eig def d l
r / n
Fo tary s a gy s
ili m lo e
M edo eo itud s
e id tt e
Fr cial s a titud
u
t
So gio c a
i
i
s
el
t
R nom ude es
ou
o tit d s
rn
Ec at itu tic
tu
r
x att oli
e
t
Se ial in p
vo
e)
s
ic
ac t sm
R es ali nd de
at
r
a
rv
te on n itu
sm e
In iti tio att
ni ns n
ad a n
ia o tio
Tr ticip ria
ar l-c ca
rit ra isti
r ita
Pa or st ho ibe h
th tru aut (l op
Au ial ng ogy e/s
c i ol dg
So ht w ide le
ig ll ow
R era kn
v l
O itica
l
Po

Figure 3 Summary of Relative Genetic and Environmental Influences on Political
Traits. Notes: Figure taken from Hatemi and McDermott (2012c, 526).

genetic factors should play an important role in understanding variation
in participatory behavior but shared environmental factors could also exert
some influence. The univariate ACE model results of A = 0.39, C = 0.12, and
E = 0.49 can be interpreted to mean that roughly 39% of the variation (why
individuals differ) in participation is accounted for by genetic factors. The
95% confidence intervals for the shared environment (C) are 0.00 and 0.29,
which means that the shared environmental effects cannot be distinguished
from 0 at the traditional thresholds employed in quantitative analysis, while
the bulk of the variation is accounted for by unique factors, and error.
Scores of political traits have been explored relying on twin studies and
other models of heritability. Hatemi and McDermott (2012a) combined the
findings of all reported twin and kinship studies that estimated genetic and
environmental influences on political traits from 1974 to 2012 and aggregated
them into 26 domains. Figure 3 displays the relative proportion of variance
on each trait explained by additive genetic factors, common environmental
influences, and unique environmental influences.
As displayed in Figure 3, individual differences in ideology, political
knowledge, trust, authoritarianism, and participation and most political
attitudes are accounted for largely by genetic and environmental factors.

Genetic and Environmental Approaches to Political Science

7

However, differences in one’s party identification, sense of duty, and
ethnocentrism are hardly, if at all, influenced by genetic factors.
ACE models can be extended in numerous ways, including research questions involving the genetic and environmental covariance between two or
more variables (Hatemi, McDermott, Eaves, Kendler & Neale, 2013; Neale &
Cardon, 1992). Continuing with the Klemmensen et al. (2012) example, this
approach is suitable for answering whether the genetic influence on political efficacy and political participation is partially shared; or if same latent
genetic or environmental factors account for the covariance between these
traits. That is, rather than focusing on prediction, or the size of the correlation
between traits, multivariate models offer information what is driving the correlation. Is the trait of interest related due to a common genetic factor or due
to similar experience or familial environment? Building on the between-trait
correlations, in this Klemmensen et al. study, those who are politically more
efficacious also participate more in politics, however roughly 80–90% of the
covariation between efficacy and political participation is driven by a common latent genetic factor and not by environmental similarity, a finding that
requires serious rethinking of current theories on how efficacy influences
political behaviors.
Further extensions such as direction of causation models are able to test
directional hypotheses. Verhulst and colleagues (Verhulst & Estabrook,
2012; Verhulst et al., 2010; Verhulst et al., 2012) employed such a model to
explore four causal scenarios: (i) a unidirectional causal model where the
variation in personality traits drives the variation in political attitudes; (ii) a
unidirectional causal model where the set of genes that influence variation
in political attitudes in turn leads to variation in personality traits; (iii)
reciprocal causation, where personality traits and political attitudes have
a nonrecursive causal structure; and (iv) pleiotropy or a common set of
genes that mutually influences both personality traits and political attitudes.
Verhulst et al. pointed toward the fourth scenario or possibly even the
second, finding no causal relationship from personality to political attitudes
(also see Dawes et al., 2014; Hatemi & Verhulst 2015), in direct contrast with
political science theories (Mondak et al., 2010). DoC models can be extremely
powerful tools for testing theories and disentangling causal relationships
that are only asserted but remain empirically untested.
UNDERSTANDING HERITABILITY
Notwithstanding the increasing exposure of political science to these methods through detailed methodological papers (Boardman, 2011; Eaves et al.,
2011; Hatemi, 2013; Medland & Hatemi, 2009; Verhulst & Hatemi, 2013),
there remains an erroneous understanding of how genes operate, what

8

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

heritability means and the statistical and theoretical assumptions required
to conduct empirical research.
First, an accurate interpretation of genetic influence does not include the
word “determined.” With rare exception, genetic influences are not fixed
or unmalleable. They are mediated and moderated by environmental conditions and change greatly throughout the life cycle. Heritability estimates
account for variance within a population at a given time and are population
specific. Instead of explaining the value of trait, they focus on the difference
of values on a trait within a population. In the case of the above-mentioned
Klemmensen example, it is not that genes explain 39% of political participation; rather, it is that 39% of the variance, or individual differences in political
participation within the population, are accounted for by the aggregate of
genetic influences. Twin or “ACE” models explain how people differ. They
are not to be interpreted to mean that for every person in the population 0.39
of their political participation is due to genes (for more detail, see Hatemi,
Byrne, et al., 2012).
ASSUMPTIONS, LIMITATIONS, AND EXTENSIONS
Akin to any statistical model, univariate classical twin models (CTDs) rely
on several assumptions (for more detail, see Hatemi et al., 2012a; Medland
& Hatemi, 2009); three of them have received the most attention both from
critics and advocates of the CTD: equal environment assumption (EEA), no
assortative mating, and no correlation or interaction between genetic and
environmental influences (rGE/G×E). The EEA stipulates that, on average,
MZ cotwins share equally similar environments as DZ cotwins, or that if
any differences do exits they have no effect on the traits of interest. This
assumption enables researchers to equate the cotwin correlations for the
latent common environmental factor (Figure 2). If this assumption does
not hold (EEA violations), the CTD would bias the genetic effects upward
and the shared environmental influences downward. Many studies have
addressed the EEA issues for political traits (Hatemi et al., 2009; Littvay, 2012;
Smith et al., 2012) and the results consistently suggest that wherever the
similarity in MZ cotwins family environments differ from DZ cotwin pairs,
such differences have no effect on the heritability estimates of political traits.
The basic twin model also assumes that no assortative mating for the trait
exists; that is, parents (spouses) choose each other randomly and not based
on similarities related to the traits under investigation. This assumption
however is often not true for social traits. Indeed, overwhelming evidence
indicates that this assumption does not hold for political traits as well;
spousal correlations for ideology range from 0.62 to 0.68 (Alford, Hatemi,
Hibbing, Martin, & Eaves, 2011; Eaves & Hatemi, 2008, 2011; Eaves et al.,

Genetic and Environmental Approaches to Political Science

9

1999; Eaves et al., 2011; Hatemi et al., 2010). This is important because in the
simplest scenario, if there is assortative mating on the trait of interest and
this trait is heritable, genetic influences will be underestimated in a classical
twin model, and the shared environmental effects will be overestimated
(Eaves & Hatemi, 2008).
This lacuna is easily remedied by simple extensions to the CTD. Indeed,
the effects of assortative mating were estimated in extended kinship models using information on the twins’ parents and twins’ spouses, resulting in
genetic influences accounting for an even greater portion of individual differences on political attitudes and ideology than reported by the CTD (Eaves
& Hatemi, 2008; Hatemi et al., 2010). Extended kinship models offer the possibility to estimate numerous other types of intergenerational transmission
including gene–environment covariance and sibling-specific environmental
influences. For important political traits such as ideology, analyses carried
out on extended family data reinforced the results presented by twin studies, further emphasizing that a serious theoretical discussion of genetic effects
on political traits is inevitable if we want to advance our understanding of
politics and political preference formation (Hatemi et al., 2010).
Univariate twin models provide only the simplest representation of
a highly complex gene to behavior process. That is, similar to simple
regression models, they provide a baseline that allows for the exploration of
increasingly complex modes of phenotypic transmission (for a description of
this progression, see Hatemi et al., 2009). However, numerous extensions to
the CTD exist allowing for more complex analyses and the integration with
traditional political science theories. Indeed, an emerging stream of research
has begun to merge BG theories with psychological and political science
theories (Fowler, Baker and Dawes, 2008; Hatemi, Eaves, & McDermott,
2012; Hatemi & McDermott, 2012b; Hatemi et al., 2007; Hatemi et al., 2009;
Hatemi et al., 2013; Loewen & Dawes, 2012). In one such example, Fazekas
and Littvay (2012) investigate proximity and directional voting principles.
Building on rational choice models and employing an operationalization
rooted in social psychology, they analyze the heritability of the adoption of
these specific voting theories. They use a bivariate twin model to show that
there are shared underlying genetic factors influencing both the strength of
partisanship and which spatial considerations guide individual vote choice.
Another simple extension to the CTD allows for inclusion of specific environmental measures, thus making it possible to estimate gene–environment
interplay. Statistically speaking, when an individual differentially responds
to or selects into an environment (actively or passively) as a function of their
genotype, they induce an interaction or correlation between their genotype
and the environment. Several works have recently provided methodological
and theoretical primers on gene–environment interplay specifically tailored

10

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

for political scientists (Boardman, 2011; Hatemi, 2013; Verhulst & Hatemi,
2013), while others have provided empirical examples, ranging from the
influence of childhood environments on genetic influences of ideology
(Smith et al., 2012) to the import of school nutrition programs on the genetic
influences on childhood obesity and its impact on public policy (Boardman
et al., 2012). Research thus far has demonstrated that for the majority of
political traits, biases from passive gene–environment covariance are not
significant or substantial (Eaves & Hatemi, 2008; Hatemi et al., 2010). However, this was not the case for gene–environment interaction. Hatemi (2013)
found that twins responded to life events differentially based on genetic
similarity. In most cases, he found that for the population of individuals who
experienced financial problems were laid off or fired or were divorced in the
last year, genetic influences on Capitalism and Socialism all but dissipated
(Figure 4). However, for the population of individuals who were laid off or
fired, genetic influences on Property Tax increased (Figure 5).
These studies are only a small representation of an integrative research
framework that has emerged in within political science with has combined
latent models of transmission with the foundation works of our field, extending both the theoretical models and empirical toolkit of the discipline.
GENE MAPPING
Gene mapping methods identify associations between-trait levels and
specific genetic polymorphisms, by analyzing a priori selected genes or by
scanning the entire genome for a genetic marker or chromosomal region that
covaries with the trait of interest or by measuring the expression of specific
genetic marker under certain environmental conditions (e.g., epigenetic).
These approaches identify specific biological mechanisms responsible for
some portion of the variation in behavior.
Candidate gene studies preselect genes that are believed to be susceptible
to be associated with the trait under consideration. Some genes come in
alternate forms at a given chromosomal position—labeled as alleles—that
reflect one (or more) single-nucleotide polymorphisms (SNPs) or are due to
differences in length of specific DNA section (Hatemi, Gillespie, et al., 2011).
“Gene association studies test whether an allele or genotype occurs more
frequently within a group exhibiting a particular trait than those without
the trait” (Fowler & Dawes, 2008, p. 584), this being the case-control design.
Alternatively, family designs can be employed that “compare whether
offspring exhibiting the trait receive a specific allele from their parents more
often than would be expected by chance.” The statistical methods are those
commonly found in most political science research, analysis of variance
(ANOVA), and some form of regression.

Genetic and Environmental Approaches to Political Science

Female

11

Male
C

0.75

A
E

0.25
E

Capitalism

C
0.50

A

0.00
–0.25

E
E

0.50
0.25
A
C

C
A

C

C

0.25

E

E

0.00

A

A

0.00

Property tax

Proportion of variance

0.75

0.75
0.50

Socialism

–0.25

–0.25
0

1

2

3

0

1

2

3

Number of risk events

Figure 4 Change in Source of Variance on Capitalism, Property Tax, and
Socialism When Exposed to One or More Financial Risk Events. Notes: Figure
taken from Hatemi (2013).

This area of research is developing and numerous publications now exist
which explore the relationship between genetic markers and political traits,
including interactions with environmental conditions (Benjamin et al., 2012;
Dawes & Fowler, 2009; Fowler & Dawes, 2008; Fowler, Dawes, & Christakis,
2009; Fowler, Settle, & Christakis, 2011; Hatemi, Gillespie, et al., 2011; Hatemi
et al., 2014; McDermott, Dawes, Prom-Wormley, Eaves, & Hatemi, 2013;
McDermott, Tingley, Cowden, Frazzetto, & Johnson, 2009).

12

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Both sexes

Genetic

0.4
Unique env

Property tax

Proportion of variance

0.6

0.2

Common env

0.0

0.0

1
0 = Employed

1 = Fired or laid off

Figure 5 Changes in Source of Variance on Property Tax When Fired or Laid Off.
Notes: Figure taken from Hatemi (2013).

For example, Fowler and Dawes (2008) build their selection of the MAOA
and 5HTT (serotonin) as candidate genes associated with voter turnout
because these two genes were previously linked to antisocial behavior.
They find that the “high” allele of MAOA and the “long” allele of 5HTT are
significantly associated with higher voter turnout, but only for those who
frequently attend religious services. Similarly, focusing on the role of the
dopamine D2 receptor in forming social attachments, Dawes and Fowler
(2009) report that having an A2 allele in the DRD2 gene is significantly associated with higher probability of partisan attachment. However, yet again, the
authors and Hatemi, Byrne, et al. (2012, p. 319) point out that “the proposed
pathway suggested to influence voting behavior noted above, is certainly
a function of the genotype; that is, certain genotypes appear to have a role
in the greater or lesser release or uptake of hormones, but the regulation of
these hormones are a function of gene expression. [ … ] However, it is critical
to note that most candidate gene studies account for a very small amount of
the variance, and most results fail to withstand efforts at replication.”

Genetic and Environmental Approaches to Political Science

13

Avoiding the bias of preselecting particular genes, genome wide
approaches (GWAs) present themselves as “more empirically rigorous
method [that] scans the entire genome for a genetic marker or chromosomal
region that is significantly related to the trait of interest” (Hatemi, Byrne,
et al., 2012, p. 317). These analyses can “implicate genes that we did not
suspect were influencing a trait of interest and thus reveal novel pathways
to the formation of political orientations” (Hatemi, Gillespie, et al., 2011,
p. 2). Hence, it is an exploratory and data-driven approach.
So far, GWAs have not identified a specific genetic marker related to political traits. Hatemi, Gillespie, et al., 2011 conducted a genome wide linkage
study and found three regions that significantly covary with and account
for up to 13% of ideology. Many genes reside within the 90% or 95% confidence intervals of the identified peaks that were related to similar social
traits. However, in two follow-up studies that employed a more rigorous
genome wide association approach that focuses on specific variants, no specific markers were found to be associated with political ideology (Benjamin
et al., 2012; Hatemi et al., 2014). This, however, was the expected conclusion
because the effects of individual markers on such a complex traits are going
to be extremely small, too small to identify because extremely large samples, possibly in the hundreds of thousands, are needed (Hatemi et al., 2014,
p. 22). Unfortunately, the largest sample available for GWA analyses on political traits numbers less than 14,000. Thus, for political traits, genome wide
association studies are in a far less developed stage compared to models of
latent influence, but technological and methodological expansion is certain
to lead to further advancement.
KEY ISSUES FOR FUTURE RESEARCH
In this extremely abbreviated contribution, we have provided a brief review
of genetic methodologies applied to political traits, using findings from the
recent literature. We view the introduction of these BG approaches as a start,
not a conclusion. Recent findings introduce new questions and continue
BG methodological developments and availability of data accommodate
the possibility of answering these questions. Research began with relatively
simple models of heritability that raised both ontological and theoretical
questions. Extended kinship models reinforced those findings and further
detailed the underlying mechanisms behind the transmission of political
attitudes. Subsequently, the interplay between physiological and environmental forces was incorporated into the scientific inquiry of political traits,
and lately specific genetic markers, and the interaction between genes, and
genes and environments are emerging. All these were necessary because
prior findings posed new challenges and a more complicated picture of

14

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

political behavior emerged. We expect to see models that focus on gene
expression and developmental and longitudinal designs in the near future
(e.g., Hatemi et al., 2009). Given that exclusively environmental theories
of political behavior offered only partial answers, this more inclusive
description of political behavior appears to resemble more closely the reality
of contemporary politics.
Integrating models of genes and environments contributes to our broader
conception of how individuals develop, select into and react to specific environments, and ultimately decide on crucial matters related to power sharing, social construction, and societal interactions. If the goal of research is
to increase knowledge, the inclusion of BG approaches is indeed a substantive one. This integration, however, means that there is a lot of catching up
to do. Some methodological concerns have been already addressed decades
ago in other disciplines and accepted limitations have been treated as such.
With the integration of research traditions, a detailed understanding of the
underlying principles and limitations are necessary. One such example is the
focus of models of heritability on within-population variance (i.e., individual differences) in contrast with the interest in population mean prediction
in most SSMs. Providing an estimate of why people differ in a population
is not equal to, and should not be equated with, why someone is a liberal
or a conservative. When research is misunderstood, as is often the case with
new material, interpretation of findings become erroneous, which inevitably
has a spillover effect on the integration of the substantive implications and
research approach.
Inclusion of a BG approach shifts research questions toward understanding
mechanisms not simply manifestations. With increased attention, technology, data, and better measurement of traits, we can test our theories in a more
rigorous manner. Forthcoming panel studies and experiments on genetically
informative samples allow researchers to address issues of causality and to
recover changes (or stability) in political traits in novel ways, including the
driving forces behind change. Such models also allow for researchers to better focus on environmental stimuli, by controlling for genetic disposition.
In the quest for understanding the complicated nature of individual political behavior, theoretical and methodological expertise from political science,
genetics, psychology, sociology, and many other fields contribute to asking
better questions and getting more accurate answers and ultimately increase
our knowledge base in a rigorous scientific manner.
REFERENCES
Alford, J. R., Hatemi, P. K., Hibbing, J. R., Martin, N. G., & Eaves, L. J. (2011). The
politics of mate choice. Journal of Politics, 73(02), 362–379.

Genetic and Environmental Approaches to Political Science

15

Benjamin, D. J., Cesarini, D., van der Loos, M. J., Dawes, C. T., Koellinger, P. D.,
Magnusson, P. K., … , Visscher, P. M. (2012). The genetic architecture of economic
and political preferences. Proceedings of the National Academy of Sciences, 109(21),
8026–8031.
Boardman, J. D. (2011). Gene-environment interplay for the study of political behaviors. In P. K. Hatemi & R. McDermott (Eds.), Man is by nature a political animal (pp.
185–207). Chicago, IL: University of Chicago Press.
Boardman, J. D., Roettger, M. E., Domingue, B. W., McQueen, M. B., Haberstick, B.
C., & Harris, K. M. (2012). Gene–environment interactions related to body mass:
School policies and social context as environmental moderators. Journal of Theoretical Politics, 24(3), 370–388.
Bouchard, T. J., Jr., Lykken, D. T., McGue, M., Segal, N. L., & Tellegen, A. (1990).
Sources of human psychological differences: The minnesota study of twins reared
apart. Science, 250(4978), 223–228.
Box, G. E. P., & Draper, N. R. (1987). Empirical model-building and response surfaces.
Wiley series in probability and mathematical statistics. Applied probability and statistics. New York, NY: John Wiley & Sons, Inc.
Dawes, C. T., Cesarini, D., Fowler, J. H., Johannesson, M., Magnusson, P. K. E., &
Oskarson, S. (2014). The relationship between genes, personality traits, and political participation. American Journal of Political Science, 58(4 October 2014), 888–903.
doi:10.1111/ajps.12100
Dawes, C. T., & Fowler, J. H. (2009). Partisanship, voting, and the dopamine D2 receptor gene. The Journal of Politics, 71(03), 1157–1171.
Eaves, L. J., & Eysenck, H. J. (1974). Genetics and the development of social attitudes.
Nature, 249(454), 288–289.
Eaves, L. J., & Hatemi, P. K. (2008). Transmission of attitudes toward abortion and
gay rights: Effects of genes, social learning and mate selection. Behavior Genetics,
38(3), 247–256.
Eaves, L. J., & Hatemi, P. K. (2011). Do we choose our spouse based on our in-laws?
Resolving the effects of family background and spousal choice for educational
attainment, religious practice, and political preference. Social Science Quarterly,
92(5), 1253–1278.
Eaves, L. J., Hatemi, P. K., Heath, A. C., & Martin, N. G. (2011). Modeling biological
and cultural inheritance. In P. K. Hatemi & R. McDermott (Eds.), Man is by nature
a political animal (pp. 101–184). Chicago, IL: University of Chicago Press.
Eaves, L. J., Heath, A. C., Martin, N. G., Maes, H., Neale, M., Kendler, K., & Corey, L.
(1999). Comparing the biological and cultural inheritance of personality and social
attitudes in the virginia 30,000 study of twins and their relatives. Twin Research, 2,
62–80.
Fazekas, Z., & Littvay, L. (2012). Choosing sides: The genetics of why we go with the
loudest. Journal of Theoretical Politics, 24(3), 389–408.
Fowler, J. H., Baker, L. A., & Dawes, C. T. (2008). Genetic variation in political participation. American Political Science Review, 102(2), 233–248.
Fowler, J. & Schreiber, D. (2008). Biology, politics, and the emerging science of human
nature. Science, 322(5903), 912–914. doi:10.1126/science.1158188

16

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Fowler, J. H., & Dawes, C. T. (2008). Two genes predict voter turnout. The Journal of
Politics, 70(03), 579–594.
Fowler, J. H., Dawes, C. T., & Christakis, N. A. (2009). Model of genetic variation
in human social networks. Proceedings of the National Academy of Sciences, 106(6),
1720–1724.
Fowler, J. H., Settle, J. E., & Christakis, N. A. (2011). Correlated genotypes in friendship networks. Proceedings of the National Academy of Sciences, 108(5), 1993–1997.
Hatemi, P. K. (2012). The intersection of behavioral genetics and political science:
Introduction to the special issue. Twin Research and Human Genetics, 15(01), 1–5.
Hatemi, P. K. (2013). The influence of major life events on economic policy attitudes in
a world of gene-environment interplay. American Journal of Political Science, 57(4),
987–1007.
Hatemi, P. K., Byrne, E. M., & McDermott, R. (2012). Introduction: What is a ‘gene’
and why does it matter for political science? Journal of Theoretical Politics, 24(3),
305–327.
Hatemi, P. K., Dawes, C. T., Frost-Keller, A., Settle, J. E., & Verhulst, B. (2011). Integrating social science and genetics: News from the political front. Biodemography
and Social Biology, 57(1), 67–87.
Hatemi, P. K., Eaves, L. J., & McDermott, R. (2012). It’s the end of ideology as we
know it. Journal of Theoretical Politics, 24(3), 345–369.
Hatemi, P. K., Funk, C. L., Medland, S. E., Maes, H. M., Silberg, J. L., Martin, N. G., &
Eaves, L. J. (2009). Genetic and environmental transmission of political attitudes
over a life time. Journal of Politics, 71(3), 1141–1156.
Hatemi, P. K., Gillespie, N. A., Eaves, L. J., Maher, B. S., Webb, B. T., Heath, A. C., … ,
Martin, N. G. (2011). A genome-wide analysis of liberal and conservative political
attitudes. The Journal of Politics, 73(1), 271–285.
Hatemi, P. K., Hibbing, J. R., Medland, S. E., Keller, M. C., Alford, J. R., Smith, K. B.,
… , Eaves, L. J. (2010). Not by twins alone: Using the extended family design to
investigate genetic influence on political beliefs. American Journal of Political Science, 54(3), 798–814.
Hatemi, P. K., & McDermott, R. (2012a). The genetics of politics: Discovery, challenges, and progress. Trends in Genetics, 28(10), 525–533.
Hatemi, P. K., & McDermott, R. (2012b). A neurobiological approach to foreign policy analysis: Identifying individual differences in political violence. Foreign Policy
Analysis, 8(2), 111–129.
Hatemi, P. K., & McDermott, R. (2012c). The political psychology of biology, genetics,
and behavior. Political Psychology, 33(3), 307–312.
Hatemi, P. K., McDermott, R., Eaves, L. J., Kendler, K. S., & Neale, M. C. (2013). Fear
as a disposition and an emotional state: A genetic and environmental approach to
out-group political preferences. American Journal of Political Science, 57(2), 279–293.
Hatemi, P. K., Medland, S. E., Morley, K. I., Heath, A. C., & Martin, N. G. (2007). The
genetics of voting: An Australian twin study. Behavior Genetics, 37(3), 435–448.
Hatemi, P. K., Medland, S. E., Robert Klemmensen, R., Oskarrson, S., Littvay, L.,
Dawes, C. T., … , Martin, N. G. (2014). Genetic influences on political ideologies:

Genetic and Environmental Approaches to Political Science

17

Genome-wide findings on three populations, and a mega-twin analysis of 19 measures of political ideologies from five western democracies. Behavior Genetics, 44(3),
282–294. doi:10.1007/s10519-014-9648-8
Hatemi, P. K., & Verhulst, B. (2015). Political attitudes develop independently of
personality traits. http://journals.plos.org/plosone/article?id=10.1371/journal.
pone.0118106.
Hibbing, J. R., & Smith, K. B. (2007). The biology of political behavior: An introduction. The Annals of the American Academy of Political and Social Science, 614(1),
6–14.
Klemmensen, R., Hatemi, P. K., Hobolt, S. B., Petersen, I., Skytthe, A., & Nørgaard,
A. S. (2012). The genetics of political participation, civic duty, and political efficacy
across cultures: Denmark and the United States. Journal of Theoretical Politics, 24(3),
409–427.
Lineberry, R. L. (2011). Letter from the former editor. Social Science Quarterly, 92(5),
1133.
Littvay, L. (2012). Do heritability estimates of political phenotypes suffer from an
equal environment assumption violation? Evidence from an empirical study. Twin
Research and Human Genetics, 15(1), 6–14.
Loewen, P. J., & Dawes, C. T. (2012). The heritability of duty and voter turnout. Political Psychology, 33(3), 363–373.
Martin, N. G., Eaves, L. J., Heath, A. C., Jardine, R., Feingold, L. M., & Eysenck, H.
J. (1986). Transmission of social attitudes. Proceedings of the National Academy of
Sciences, 83(12), 4364–4368.
McDermott, R. (2009). Mutual interests. Political Research Quarterly, 62(3), 571–583.
McDermott, R., Dawes, C. T., Prom-Wormley, L., Eaves, L., & Hatemi, P. K. (2013).
MAOA and aggression: A gene-environment interaction in two populations. Journal of Conflict Resolution, 57(6), 1043–1064.
McDermott, R., Tingley, D., Cowden, J., Frazzetto, G., & Johnson, D. D. (2009).
Monoamine oxidase a gene (Maoa) predicts behavioral aggression following
provocation. Proceedings of the National Academy of Sciences, 106(7), 2118–2123.
Medland, S. E., & Hatemi, P. K. (2009). Political science, biometric theory, and twin
studies: A methodological introduction. Political Analysis, 17(2), 191–214.
Mondak, J. J., Hibbing, M. W., Canache, D., Seligson, M. A., & Anderson, M. R. (2010).
Personality and civic engagement: An integrative framework for the study of trait
effects on political behavior. American Political Science Review, 104(1), 85–110.
Neale, M. C., & Cardon, L. R. (1992). Methodology for genetic studies of twins and families.
Dordrecht, The Netherlands: Kluwer Academic Publishers.
Project Foresight (2008) Mental Capital and Wellbeing Project. The Government Office
for Science, London. www.bis.gov.uk/foresight
Smith, K. B., Alford, J. R., Hatemi, P. K., Eaves, L. J., Funk, C., & Hibbing, J. R.
(2012). Biology, ideology, and epistemology: How do we know political attitudes
are inherited and why should we care? American Journal of Political Science, 56(1),
17–33.
Smith, K. B., & Hatemi, P. K. (2013). OLS is AOK for ACE: A regression-based
approach to synthesizing political science and behavioral genetics models. Political
Behavior, 35(2), 383–408.

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Verhulst, B., Eaves, L. J., & Hatemi, P. K. (2012). Correlation not causation: The relationship between personality traits and political ideologies. American Journal of
Political Science, 56(1), 34–51.
Verhulst, B., & Estabrook, R. (2012). Using genetic information to test causal relationships in cross-sectional data. Journal of Theoretical Politics, 24(3), 328–344.
Verhulst, B., & Hatemi, P. K. (2013). Gene-environment interplay in twin models.
Political Analysis, 21(3), 368–389.
Verhulst, B., Hatemi, P. K., & Martin, N. G. (2010). The nature of the relationship
between personality traits and political attitudes. Personality and Individual Differences, 49(4), 306–316.

ZOLTÁN FAZEKAS SHORT BIOGRAPHY
Zoltán Fazekas is post-doctoral research fellow in the Department of
Political Science and Public Management, University of Southern Denmark.
He was trained in political science at the Central European University
(Budapest, Hungary) and University of Vienna (Austria). In his research, he
strives to understand political behavior on the voter and the elite level, and
how our characteristics as human beings influence political attitude formation and political decision making. Currently, he is working on the research
team designing and implementing political surveys for twins together with
the Danish Twin Registry. Among others, his work has been published in
outlets such as International Journal of Public Opinion Research, Electoral
Studies, Social Science Quarterly, and Journal of Theoretical Politics.
PETER K. HATEMI SHORT BIOGRAPHY
Peter K. Hatemi is Associate Professor of Political Science, Microbiology,
and Biochemistry at the Pennsylvania State University and research fellow
at the United States Studies Centre at the University of Sydney. He was
trained in political science at the University of Nebraska and in genetic
epidemiology at the Queensland Institute of Medical Research (QIMR).
He continued his postdoctoral study in Human Genetics, Psychology, and
Psychiatry at the Virginia Institute for Psychiatric and Behavioral Genetics
(VIPBG) in the Medical College of Virginia. He is primarily interested
in advancing the study of the neurobiological mechanisms of social and
political behaviors and utilizing advanced methods in genetics, physiology,
endocrinology, and neurology in order to better understand human decision
making and preferences in complex and dynamic political environments.
He is also an active member of the Institute for Statskundskab at Syddansk
Universitet, VIPBG, and the genetic epidemiology laboratory at QIMR.
Pete’s recent work on the genetic, physiological, and endocrinological

Genetic and Environmental Approaches to Political Science

19

sources of individual differences in political attitudes, fear dispositions,
mate selection, personality, political violence, and religion has appeared in
the American Journal of Political Science, Behavior Genetics, Demography,
Evolution and Human Behavior, Journal of Politics, Political Psychology,
Science, Social Forces, and Trends in Genetics among other venues. His
recent book, co-edited with Rose McDermott, Man is by Nature a Political
Animal at the University of Chicago Press, offers a comprehensive volume
that includes applications of evolution, genetics, primatology, neuroscience,
and physiology to understand political preferences.
RELATED ESSAYS
Social Epigenetics: Incorporating Epigenetic Effects as Social Cause and
Consequence (Sociology), Douglas L. Anderton and Kathleen F. Arcaro
Telomeres (Psychology), Nancy Adler and Aoife O’Donovan
Kin-Directed Behavior in Primates (Anthropology), Carol M. Berman
The Sexual Division of Labor (Anthropology), Rebecca Bliege Bird and Brian
F. Codding
Genetics and the Life Course (Sociology), Evan Charney
Sexual Behavior (Anthropology), Melissa Emery Thompson
Evolutionary Approaches to Understanding Children’s Academic Achievement (Psychology), David C. Geary and Daniel B. Berch
Genetics and Social Behavior (Anthropology), Henry Harpending and Gregory Cochran
An Evolutionary Perspective on Developmental Plasticity (Psychology),
Sarah Hartman and Jay Belsky
Grandmothers and the Evolution of Human Sociality (Anthropology), Kristen
Hawkes and James Coxworth
Genetic Foundations of Attitude Formation (Political Science), Christian
Kandler et al.
Complexity: An Emerging Trend in Social Sciences (Anthropology), J. Stephen
Lansing
Niche Construction: Implications for Human Sciences (Anthropology), Kevin
N. Laland and Michael O’Brien
From Individual Rationality to Socially Embedded Self-Regulation (Sociology), Siegwart Lindenberg
Evolutionary Perspectives on Animal and Human Personality (Anthropology), Joseph H. Manson and Lynn A. Fairbanks
Born This Way: Thinking Sociologically about Essentialism (Sociology),
Kristen Schilt


Genetic and Environmental
Approaches to Political Science
ZOLTÁN FAZEKAS and PETER K. HATEMI

Abstract
Over the past decade, a growing interest in the possibility that biological factors,
including genes, might contribute to individual differences in political and social
behaviors has emerged. Behavioral genetic techniques have provided a variety of
approaches to quantify the effects of genetic and nongenetic inheritance. However,
until quite recently, these methods were largely unknown to political scientists. In
this essay, we review the general approaches to modeling genetic and social influences on differences in complex human social traits. In so doing, we focus on the
“genetics of politics,” including attitudes, ideologies, voting, and partisanship. The
emergence of this research reflects a paradigm shift in the study of social traits necessitating the inclusion of biological influences, and recognizing the interdependence of
genetic, social, and environmental factors in the development of political behaviors
over the life course.

INTRODUCTION
Interest in identifying genetic influences on political traits began in the 1970s
by psychologists and geneticists (Eaves & Eysenck, 1974; Martin et al., 1986)
and has remained a topic of interest since (Bouchard, Lykken, McGue, Segal,
& Tellegen, 1990; Eaves & Hatemi, 2008; Hatemi, Medland, Morley, Heath, &
Martin, 2007). Yet, the last decade witnessed major developments in terms of
integrating behavior genetic (BG) approaches into explicating political traits
in the social sciences (for reviews see Hatemi, Dawes, Frost-Keller, Settle, &
Verhulst, 2011; Hatemi & McDermott, 2012a). A multitude of special issues
focused on behavioral genetic approaches have appeared in The Annals of the
American Academy of Political and Social Science (Hibbing & Smith, 2007), Political Research Quarterly (McDermott, 2009), Social Sciences Quarterly (Lineberry,
2011), Journal of Theoretical Politics (Hatemi, Byrne, & McDermott, 2012), Political Psychology (Hatemi & McDermott, 2012c), and Twin Research and Human
Genetics (Hatemi, 2012).
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

1

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

As Hatemi, Byrne, et al. (2012) advocate, in order for BG approaches to
be integrated into political science, it is necessary for scholars to begin
from the same set of starting assumptions regarding the nature and meaning of genetic influences. Thus, we offer a brief description of some of
these approaches, results from recent studies, and their implications for
understanding political behaviors.
FOUNDATIONAL RESEARCH
WHAT IS A GENE?
Genes regulate the cellular environment and create proteins, the main
functional tools in the cell, which in turn instigate or restrict hormonal and
other biological pathways in both state and trait circumstances. Thousands
of genes interact with countless environmental conditions, both inside and
outside the body to produce the chain of mechanisms that lead to a given
trait, which may radically differ across the life span. Thus, whenever genetic
influence is found for a given trait, whether by twin studies that rely on a
latent measure of genetic influence, or molecular studies that rely on specific
markers and their expression, it is implied that the genetic influences are not
fixed, but conditional upon and interacting with environmental conditions,
developmental processes, and other biological mechanisms (Hatemi, Byrne,
et al., 2012).
Influences between genes and behavior are mutual and bidirectional. It is
believed that DNA has some role in indirectly guiding people into certain
environments, and gene expression is affected by and based on exposure
to those environments and one’s own behavior. In this view displayed in
Figure 1, the incorporation of genetic influences on political or social traits
are set in a framework of constant interaction between biological and environmental forces that differ at various stages of one’s lifetime. In addition,
as Hatemi and McDermott (2012a, p. 4) state, “whatever genetic influences
exist probably operate through those emotional, cognitive, or rational processes that are instigated when individuals are asked particular questions
about their attitudes.” Given the complexity of these processes, it is most
likely impossible for any single gene to account for any substantial amount
of variance for any complex social or political trait. Rather, it is the totality of
one’s genetic make up, in combination with social and environmental stimuli, which account for different exposure to and selection into experiences,
emotive and cognitive states, perceptions, and preferences.
Considering the dynamic nature of genetic mechanisms, how can one accurately identify genetic influence? As Box and Draper (1987, p. 74) eloquently
state: “Remember that all models are wrong; the practical question is how
wrong do they have to be to not be useful.” A model that perfectly captures

Genetic and Environmental Approaches to Political Science

3

Figure 1 The Interaction of Biology and Environment Over the Life Course.
Notes: Figure taken from Hatemi, Byrne, et al., 2012 and originally published by
Project Foresight (2008) Mental Capital and Wellbeing Project. London: The
Government Office for Science. Available at www.bis.gov.uk/foresight.

one’s genetic, social, environmental, psychological, and physical factors over
the life course does not exist. To conduct empirical research, whether using
social, environmental, developmental, or biological approaches, or the combination as we advocate, scholars must rely on reductionist models that make
assumptions about the world, and such assumptions shape and limit the
interpretation of results. All statistical models attempt to simplify the vast
complexity of real life in order to allow researchers to test specific hypotheses,
and genetic models remain equally informative and fallible as any social science approach (for a detailed discussion, see Verhulst & Hatemi, 2013). Given
these limitations, we discuss the two most common approaches to explore
genetic influences and how they have been applied to political traits.
CUTTING-EDGE RESEARCH
MODELS OF HERITABILITY
BG analyses guided by biometric theory assume that the variation of a
phenotype or trait (P) can be thought of as a consequence of latent genetic
(G) and environmental factors (E). Twin and kinship models are among the

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

most popular approaches to identify the sources of variance on a trait or the
sources of covariation between traits. The conventional twin model notation
is that the total variance of a trait can be decomposed into additive genetic
effects (A) or the sum of the effects of all the individual genetic markers that
influence the trait; shared or common environmental influence (C), which
captures the factors that are perfectly shared between twins and family
members, such as the effects of neighborhood; and unique environmental
influence (E), which captures all environmental stimuli not shared between
twins, including error (P = A + C + E) (Medland & Hatemi, 2009).
This model differs from traditional social science models (SSMs), which
seek to identify systematic relationships between two or more characteristics,
and predict different outcome levels depending on the variables placement
in the regression equation. SSM models assume that all traits, regardless of
position, are environmentally determined; the exclusion of biological factors
is not a decision based on model fitting, but an a priori paradigmatic decision
(Smith & Hatemi, 2013).
Extant literature suggests that the assumption of purely environmental
determinants of political traits is not warranted and genetics plays an
important role in how and why people differ (Hatemi & McDermott, 2012a).
For example, numerous studies conducted across decades and in several
different countries find individual differences in ideology are genetically
influenced (between 0.3 and 0.6 of the variance, see Hatemi et al., 2014).
These findings inform theoretical models involving ideology because they
steer researchers to understand ideology as a psychological disposition
that guides behavior and consequently employ it as a predictor, not an
outcome. Contrary to the recently hypothesized notion that the heritability
of ideology is simply channeled through personality (Mondak, Hibbing,
Canache, Seligson, & Anderson, 2010), a recent stream of research (Verhulst,
Eaves, & Hatemi, 2012; Verhulst, Hatemi, & Martin, 2010) shows that genetic
influences on attitudes and ideologies are not subsumed by other covariates
but specific to ideological differences. The challenge for political science
theories becomes more poignant: it is not only that the assumption of no
genetic influences for political traits is unwarranted but also these genetic
effects are more than some spillover or confounded effects channeled
through related traits. These findings brought about the imperative for an
integrated theory of ideology, that embraces both genes and environment,
yet remain embedded within a developmental framework, that includes
parental investment, social groups, education, cognition, perception, aging,
and all other critical environmental and neurobiological mechanisms (Eaves,
Hatemi, Heath, & Martin, 2011; Fowler & Schreiber, 2008; Hatemi et al., 2009).
In the majority of twin models, the focus is on monozygotic (MZ) and
dizygotic (DZ) twin pairs reared together. MZ twins develop from a single

Genetic and Environmental Approaches to Political Science

5

1.0
1.0

1.0

1.0

E1

C1

A1

e

c

PhTwin1

a

0.5/1.0

1.0

1.0

1.0

A2

C2

E2

a

c

e

PhTwin2

Figure 2 ACE model. Notes: Figure prepared by the authors.

fertilized egg and share 100% of their chromosomal sequence (i.e., “genetically identical”), whereas DZ twins develop from two different eggs
fertilized by two different sperms and share, on average, 50% of their
chromosomal sequence (Medland & Hatemi, 2009). The most valuable information stems from the covariance between twin pairs for each zygosity type.
Different relationships of between twin-pair correlations (r) for MZs and DZs
indicate what sort of transmission should we expect: if rMZ = rDZ , there is no
genetic effect; if rMZ > rDZ genetic and (shared and unique) environmental
factors are present; if rMZ = 2 × rDZ , genetic and unique environmental factors
drive the variation in the phenotype (no shared environmental effects); and
if rMZ >2 × rDZ , the variation in the phenotype is due to additive genetic
effects, nonadditive genetic effects, and unique environmental factors.
Figure 2 shows the ACE variance decomposition and how this decomposition is informed by the properties of twin data. We have information about
the phenotype for each twin from a pair, and these are marked in the rectangles. The model stipulates that P = A + C + E, where the three factors are
unobserved and the relative proportions of variance can be decomposed into
these three components that sum to 1. Working with twins reared together,
we assume that the shared or common environment influences each twin in
the same manner, and thus the C1 and C2 are correlated perfectly, r = 1.0,
where subscripts indicate twin 1 and twin 2. This correlation is independent
of zygosity (i.e., the equal environments assumption). Unique environmental factors are defined to capture why the twins are different, and hence they
are not correlated. The correlation between the genetic factors (A1 and A2 )
reflects the amount of shared genetic material (MZ twins is set to 1.0. DZ is
set to 0.5; for a full description, see Medland & Hatemi, 2009).
To illustrate this method consider, Klemmensen et al. (2012) study of
political participation in Denmark and the United States. The MZ pair
correlation is 0.51, whereas the DZ correlations is 0.32, indicating that

6

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Source of variance

Genetic

SharedEnv

UniqueEnv

1.0

Proportion of variance

0.8

0.6

0.4

0.2

0.0

n
io
at
ic
tif
en
id y
s
rty ut
up
pa c d
ro
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lg
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n e
tti
Se noc on hts des l a
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ti n’ t o
At e en l p s
a
om m n de
W sh tio tu
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s
Pu n tr p a ay ism nce
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o
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l l c lic a e
Po ua po nse erti
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Se eig def d l
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Fo tary s a gy s
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M edo eo itud s
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So gio c a
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at
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ar l-c ca
rit ra isti
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Au ial ng ogy e/s
c i ol dg
So ht w ide le
ig ll ow
R era kn
v l
O itica
l
Po

Figure 3 Summary of Relative Genetic and Environmental Influences on Political
Traits. Notes: Figure taken from Hatemi and McDermott (2012c, 526).

genetic factors should play an important role in understanding variation
in participatory behavior but shared environmental factors could also exert
some influence. The univariate ACE model results of A = 0.39, C = 0.12, and
E = 0.49 can be interpreted to mean that roughly 39% of the variation (why
individuals differ) in participation is accounted for by genetic factors. The
95% confidence intervals for the shared environment (C) are 0.00 and 0.29,
which means that the shared environmental effects cannot be distinguished
from 0 at the traditional thresholds employed in quantitative analysis, while
the bulk of the variation is accounted for by unique factors, and error.
Scores of political traits have been explored relying on twin studies and
other models of heritability. Hatemi and McDermott (2012a) combined the
findings of all reported twin and kinship studies that estimated genetic and
environmental influences on political traits from 1974 to 2012 and aggregated
them into 26 domains. Figure 3 displays the relative proportion of variance
on each trait explained by additive genetic factors, common environmental
influences, and unique environmental influences.
As displayed in Figure 3, individual differences in ideology, political
knowledge, trust, authoritarianism, and participation and most political
attitudes are accounted for largely by genetic and environmental factors.

Genetic and Environmental Approaches to Political Science

7

However, differences in one’s party identification, sense of duty, and
ethnocentrism are hardly, if at all, influenced by genetic factors.
ACE models can be extended in numerous ways, including research questions involving the genetic and environmental covariance between two or
more variables (Hatemi, McDermott, Eaves, Kendler & Neale, 2013; Neale &
Cardon, 1992). Continuing with the Klemmensen et al. (2012) example, this
approach is suitable for answering whether the genetic influence on political efficacy and political participation is partially shared; or if same latent
genetic or environmental factors account for the covariance between these
traits. That is, rather than focusing on prediction, or the size of the correlation
between traits, multivariate models offer information what is driving the correlation. Is the trait of interest related due to a common genetic factor or due
to similar experience or familial environment? Building on the between-trait
correlations, in this Klemmensen et al. study, those who are politically more
efficacious also participate more in politics, however roughly 80–90% of the
covariation between efficacy and political participation is driven by a common latent genetic factor and not by environmental similarity, a finding that
requires serious rethinking of current theories on how efficacy influences
political behaviors.
Further extensions such as direction of causation models are able to test
directional hypotheses. Verhulst and colleagues (Verhulst & Estabrook,
2012; Verhulst et al., 2010; Verhulst et al., 2012) employed such a model to
explore four causal scenarios: (i) a unidirectional causal model where the
variation in personality traits drives the variation in political attitudes; (ii) a
unidirectional causal model where the set of genes that influence variation
in political attitudes in turn leads to variation in personality traits; (iii)
reciprocal causation, where personality traits and political attitudes have
a nonrecursive causal structure; and (iv) pleiotropy or a common set of
genes that mutually influences both personality traits and political attitudes.
Verhulst et al. pointed toward the fourth scenario or possibly even the
second, finding no causal relationship from personality to political attitudes
(also see Dawes et al., 2014; Hatemi & Verhulst 2015), in direct contrast with
political science theories (Mondak et al., 2010). DoC models can be extremely
powerful tools for testing theories and disentangling causal relationships
that are only asserted but remain empirically untested.
UNDERSTANDING HERITABILITY
Notwithstanding the increasing exposure of political science to these methods through detailed methodological papers (Boardman, 2011; Eaves et al.,
2011; Hatemi, 2013; Medland & Hatemi, 2009; Verhulst & Hatemi, 2013),
there remains an erroneous understanding of how genes operate, what

8

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

heritability means and the statistical and theoretical assumptions required
to conduct empirical research.
First, an accurate interpretation of genetic influence does not include the
word “determined.” With rare exception, genetic influences are not fixed
or unmalleable. They are mediated and moderated by environmental conditions and change greatly throughout the life cycle. Heritability estimates
account for variance within a population at a given time and are population
specific. Instead of explaining the value of trait, they focus on the difference
of values on a trait within a population. In the case of the above-mentioned
Klemmensen example, it is not that genes explain 39% of political participation; rather, it is that 39% of the variance, or individual differences in political
participation within the population, are accounted for by the aggregate of
genetic influences. Twin or “ACE” models explain how people differ. They
are not to be interpreted to mean that for every person in the population 0.39
of their political participation is due to genes (for more detail, see Hatemi,
Byrne, et al., 2012).
ASSUMPTIONS, LIMITATIONS, AND EXTENSIONS
Akin to any statistical model, univariate classical twin models (CTDs) rely
on several assumptions (for more detail, see Hatemi et al., 2012a; Medland
& Hatemi, 2009); three of them have received the most attention both from
critics and advocates of the CTD: equal environment assumption (EEA), no
assortative mating, and no correlation or interaction between genetic and
environmental influences (rGE/G×E). The EEA stipulates that, on average,
MZ cotwins share equally similar environments as DZ cotwins, or that if
any differences do exits they have no effect on the traits of interest. This
assumption enables researchers to equate the cotwin correlations for the
latent common environmental factor (Figure 2). If this assumption does
not hold (EEA violations), the CTD would bias the genetic effects upward
and the shared environmental influences downward. Many studies have
addressed the EEA issues for political traits (Hatemi et al., 2009; Littvay, 2012;
Smith et al., 2012) and the results consistently suggest that wherever the
similarity in MZ cotwins family environments differ from DZ cotwin pairs,
such differences have no effect on the heritability estimates of political traits.
The basic twin model also assumes that no assortative mating for the trait
exists; that is, parents (spouses) choose each other randomly and not based
on similarities related to the traits under investigation. This assumption
however is often not true for social traits. Indeed, overwhelming evidence
indicates that this assumption does not hold for political traits as well;
spousal correlations for ideology range from 0.62 to 0.68 (Alford, Hatemi,
Hibbing, Martin, & Eaves, 2011; Eaves & Hatemi, 2008, 2011; Eaves et al.,

Genetic and Environmental Approaches to Political Science

9

1999; Eaves et al., 2011; Hatemi et al., 2010). This is important because in the
simplest scenario, if there is assortative mating on the trait of interest and
this trait is heritable, genetic influences will be underestimated in a classical
twin model, and the shared environmental effects will be overestimated
(Eaves & Hatemi, 2008).
This lacuna is easily remedied by simple extensions to the CTD. Indeed,
the effects of assortative mating were estimated in extended kinship models using information on the twins’ parents and twins’ spouses, resulting in
genetic influences accounting for an even greater portion of individual differences on political attitudes and ideology than reported by the CTD (Eaves
& Hatemi, 2008; Hatemi et al., 2010). Extended kinship models offer the possibility to estimate numerous other types of intergenerational transmission
including gene–environment covariance and sibling-specific environmental
influences. For important political traits such as ideology, analyses carried
out on extended family data reinforced the results presented by twin studies, further emphasizing that a serious theoretical discussion of genetic effects
on political traits is inevitable if we want to advance our understanding of
politics and political preference formation (Hatemi et al., 2010).
Univariate twin models provide only the simplest representation of
a highly complex gene to behavior process. That is, similar to simple
regression models, they provide a baseline that allows for the exploration of
increasingly complex modes of phenotypic transmission (for a description of
this progression, see Hatemi et al., 2009). However, numerous extensions to
the CTD exist allowing for more complex analyses and the integration with
traditional political science theories. Indeed, an emerging stream of research
has begun to merge BG theories with psychological and political science
theories (Fowler, Baker and Dawes, 2008; Hatemi, Eaves, & McDermott,
2012; Hatemi & McDermott, 2012b; Hatemi et al., 2007; Hatemi et al., 2009;
Hatemi et al., 2013; Loewen & Dawes, 2012). In one such example, Fazekas
and Littvay (2012) investigate proximity and directional voting principles.
Building on rational choice models and employing an operationalization
rooted in social psychology, they analyze the heritability of the adoption of
these specific voting theories. They use a bivariate twin model to show that
there are shared underlying genetic factors influencing both the strength of
partisanship and which spatial considerations guide individual vote choice.
Another simple extension to the CTD allows for inclusion of specific environmental measures, thus making it possible to estimate gene–environment
interplay. Statistically speaking, when an individual differentially responds
to or selects into an environment (actively or passively) as a function of their
genotype, they induce an interaction or correlation between their genotype
and the environment. Several works have recently provided methodological
and theoretical primers on gene–environment interplay specifically tailored

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

for political scientists (Boardman, 2011; Hatemi, 2013; Verhulst & Hatemi,
2013), while others have provided empirical examples, ranging from the
influence of childhood environments on genetic influences of ideology
(Smith et al., 2012) to the import of school nutrition programs on the genetic
influences on childhood obesity and its impact on public policy (Boardman
et al., 2012). Research thus far has demonstrated that for the majority of
political traits, biases from passive gene–environment covariance are not
significant or substantial (Eaves & Hatemi, 2008; Hatemi et al., 2010). However, this was not the case for gene–environment interaction. Hatemi (2013)
found that twins responded to life events differentially based on genetic
similarity. In most cases, he found that for the population of individuals who
experienced financial problems were laid off or fired or were divorced in the
last year, genetic influences on Capitalism and Socialism all but dissipated
(Figure 4). However, for the population of individuals who were laid off or
fired, genetic influences on Property Tax increased (Figure 5).
These studies are only a small representation of an integrative research
framework that has emerged in within political science with has combined
latent models of transmission with the foundation works of our field, extending both the theoretical models and empirical toolkit of the discipline.
GENE MAPPING
Gene mapping methods identify associations between-trait levels and
specific genetic polymorphisms, by analyzing a priori selected genes or by
scanning the entire genome for a genetic marker or chromosomal region that
covaries with the trait of interest or by measuring the expression of specific
genetic marker under certain environmental conditions (e.g., epigenetic).
These approaches identify specific biological mechanisms responsible for
some portion of the variation in behavior.
Candidate gene studies preselect genes that are believed to be susceptible
to be associated with the trait under consideration. Some genes come in
alternate forms at a given chromosomal position—labeled as alleles—that
reflect one (or more) single-nucleotide polymorphisms (SNPs) or are due to
differences in length of specific DNA section (Hatemi, Gillespie, et al., 2011).
“Gene association studies test whether an allele or genotype occurs more
frequently within a group exhibiting a particular trait than those without
the trait” (Fowler & Dawes, 2008, p. 584), this being the case-control design.
Alternatively, family designs can be employed that “compare whether
offspring exhibiting the trait receive a specific allele from their parents more
often than would be expected by chance.” The statistical methods are those
commonly found in most political science research, analysis of variance
(ANOVA), and some form of regression.

Genetic and Environmental Approaches to Political Science

Female

11

Male
C

0.75

A
E

0.25
E

Capitalism

C
0.50

A

0.00
–0.25

E
E

0.50
0.25
A
C

C
A

C

C

0.25

E

E

0.00

A

A

0.00

Property tax

Proportion of variance

0.75

0.75
0.50

Socialism

–0.25

–0.25
0

1

2

3

0

1

2

3

Number of risk events

Figure 4 Change in Source of Variance on Capitalism, Property Tax, and
Socialism When Exposed to One or More Financial Risk Events. Notes: Figure
taken from Hatemi (2013).

This area of research is developing and numerous publications now exist
which explore the relationship between genetic markers and political traits,
including interactions with environmental conditions (Benjamin et al., 2012;
Dawes & Fowler, 2009; Fowler & Dawes, 2008; Fowler, Dawes, & Christakis,
2009; Fowler, Settle, & Christakis, 2011; Hatemi, Gillespie, et al., 2011; Hatemi
et al., 2014; McDermott, Dawes, Prom-Wormley, Eaves, & Hatemi, 2013;
McDermott, Tingley, Cowden, Frazzetto, & Johnson, 2009).

12

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Both sexes

Genetic

0.4
Unique env

Property tax

Proportion of variance

0.6

0.2

Common env

0.0

0.0

1
0 = Employed

1 = Fired or laid off

Figure 5 Changes in Source of Variance on Property Tax When Fired or Laid Off.
Notes: Figure taken from Hatemi (2013).

For example, Fowler and Dawes (2008) build their selection of the MAOA
and 5HTT (serotonin) as candidate genes associated with voter turnout
because these two genes were previously linked to antisocial behavior.
They find that the “high” allele of MAOA and the “long” allele of 5HTT are
significantly associated with higher voter turnout, but only for those who
frequently attend religious services. Similarly, focusing on the role of the
dopamine D2 receptor in forming social attachments, Dawes and Fowler
(2009) report that having an A2 allele in the DRD2 gene is significantly associated with higher probability of partisan attachment. However, yet again, the
authors and Hatemi, Byrne, et al. (2012, p. 319) point out that “the proposed
pathway suggested to influence voting behavior noted above, is certainly
a function of the genotype; that is, certain genotypes appear to have a role
in the greater or lesser release or uptake of hormones, but the regulation of
these hormones are a function of gene expression. [ … ] However, it is critical
to note that most candidate gene studies account for a very small amount of
the variance, and most results fail to withstand efforts at replication.”

Genetic and Environmental Approaches to Political Science

13

Avoiding the bias of preselecting particular genes, genome wide
approaches (GWAs) present themselves as “more empirically rigorous
method [that] scans the entire genome for a genetic marker or chromosomal
region that is significantly related to the trait of interest” (Hatemi, Byrne,
et al., 2012, p. 317). These analyses can “implicate genes that we did not
suspect were influencing a trait of interest and thus reveal novel pathways
to the formation of political orientations” (Hatemi, Gillespie, et al., 2011,
p. 2). Hence, it is an exploratory and data-driven approach.
So far, GWAs have not identified a specific genetic marker related to political traits. Hatemi, Gillespie, et al., 2011 conducted a genome wide linkage
study and found three regions that significantly covary with and account
for up to 13% of ideology. Many genes reside within the 90% or 95% confidence intervals of the identified peaks that were related to similar social
traits. However, in two follow-up studies that employed a more rigorous
genome wide association approach that focuses on specific variants, no specific markers were found to be associated with political ideology (Benjamin
et al., 2012; Hatemi et al., 2014). This, however, was the expected conclusion
because the effects of individual markers on such a complex traits are going
to be extremely small, too small to identify because extremely large samples, possibly in the hundreds of thousands, are needed (Hatemi et al., 2014,
p. 22). Unfortunately, the largest sample available for GWA analyses on political traits numbers less than 14,000. Thus, for political traits, genome wide
association studies are in a far less developed stage compared to models of
latent influence, but technological and methodological expansion is certain
to lead to further advancement.
KEY ISSUES FOR FUTURE RESEARCH
In this extremely abbreviated contribution, we have provided a brief review
of genetic methodologies applied to political traits, using findings from the
recent literature. We view the introduction of these BG approaches as a start,
not a conclusion. Recent findings introduce new questions and continue
BG methodological developments and availability of data accommodate
the possibility of answering these questions. Research began with relatively
simple models of heritability that raised both ontological and theoretical
questions. Extended kinship models reinforced those findings and further
detailed the underlying mechanisms behind the transmission of political
attitudes. Subsequently, the interplay between physiological and environmental forces was incorporated into the scientific inquiry of political traits,
and lately specific genetic markers, and the interaction between genes, and
genes and environments are emerging. All these were necessary because
prior findings posed new challenges and a more complicated picture of

14

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

political behavior emerged. We expect to see models that focus on gene
expression and developmental and longitudinal designs in the near future
(e.g., Hatemi et al., 2009). Given that exclusively environmental theories
of political behavior offered only partial answers, this more inclusive
description of political behavior appears to resemble more closely the reality
of contemporary politics.
Integrating models of genes and environments contributes to our broader
conception of how individuals develop, select into and react to specific environments, and ultimately decide on crucial matters related to power sharing, social construction, and societal interactions. If the goal of research is
to increase knowledge, the inclusion of BG approaches is indeed a substantive one. This integration, however, means that there is a lot of catching up
to do. Some methodological concerns have been already addressed decades
ago in other disciplines and accepted limitations have been treated as such.
With the integration of research traditions, a detailed understanding of the
underlying principles and limitations are necessary. One such example is the
focus of models of heritability on within-population variance (i.e., individual differences) in contrast with the interest in population mean prediction
in most SSMs. Providing an estimate of why people differ in a population
is not equal to, and should not be equated with, why someone is a liberal
or a conservative. When research is misunderstood, as is often the case with
new material, interpretation of findings become erroneous, which inevitably
has a spillover effect on the integration of the substantive implications and
research approach.
Inclusion of a BG approach shifts research questions toward understanding
mechanisms not simply manifestations. With increased attention, technology, data, and better measurement of traits, we can test our theories in a more
rigorous manner. Forthcoming panel studies and experiments on genetically
informative samples allow researchers to address issues of causality and to
recover changes (or stability) in political traits in novel ways, including the
driving forces behind change. Such models also allow for researchers to better focus on environmental stimuli, by controlling for genetic disposition.
In the quest for understanding the complicated nature of individual political behavior, theoretical and methodological expertise from political science,
genetics, psychology, sociology, and many other fields contribute to asking
better questions and getting more accurate answers and ultimately increase
our knowledge base in a rigorous scientific manner.
REFERENCES
Alford, J. R., Hatemi, P. K., Hibbing, J. R., Martin, N. G., & Eaves, L. J. (2011). The
politics of mate choice. Journal of Politics, 73(02), 362–379.

Genetic and Environmental Approaches to Political Science

15

Benjamin, D. J., Cesarini, D., van der Loos, M. J., Dawes, C. T., Koellinger, P. D.,
Magnusson, P. K., … , Visscher, P. M. (2012). The genetic architecture of economic
and political preferences. Proceedings of the National Academy of Sciences, 109(21),
8026–8031.
Boardman, J. D. (2011). Gene-environment interplay for the study of political behaviors. In P. K. Hatemi & R. McDermott (Eds.), Man is by nature a political animal (pp.
185–207). Chicago, IL: University of Chicago Press.
Boardman, J. D., Roettger, M. E., Domingue, B. W., McQueen, M. B., Haberstick, B.
C., & Harris, K. M. (2012). Gene–environment interactions related to body mass:
School policies and social context as environmental moderators. Journal of Theoretical Politics, 24(3), 370–388.
Bouchard, T. J., Jr., Lykken, D. T., McGue, M., Segal, N. L., & Tellegen, A. (1990).
Sources of human psychological differences: The minnesota study of twins reared
apart. Science, 250(4978), 223–228.
Box, G. E. P., & Draper, N. R. (1987). Empirical model-building and response surfaces.
Wiley series in probability and mathematical statistics. Applied probability and statistics. New York, NY: John Wiley & Sons, Inc.
Dawes, C. T., Cesarini, D., Fowler, J. H., Johannesson, M., Magnusson, P. K. E., &
Oskarson, S. (2014). The relationship between genes, personality traits, and political participation. American Journal of Political Science, 58(4 October 2014), 888–903.
doi:10.1111/ajps.12100
Dawes, C. T., & Fowler, J. H. (2009). Partisanship, voting, and the dopamine D2 receptor gene. The Journal of Politics, 71(03), 1157–1171.
Eaves, L. J., & Eysenck, H. J. (1974). Genetics and the development of social attitudes.
Nature, 249(454), 288–289.
Eaves, L. J., & Hatemi, P. K. (2008). Transmission of attitudes toward abortion and
gay rights: Effects of genes, social learning and mate selection. Behavior Genetics,
38(3), 247–256.
Eaves, L. J., & Hatemi, P. K. (2011). Do we choose our spouse based on our in-laws?
Resolving the effects of family background and spousal choice for educational
attainment, religious practice, and political preference. Social Science Quarterly,
92(5), 1253–1278.
Eaves, L. J., Hatemi, P. K., Heath, A. C., & Martin, N. G. (2011). Modeling biological
and cultural inheritance. In P. K. Hatemi & R. McDermott (Eds.), Man is by nature
a political animal (pp. 101–184). Chicago, IL: University of Chicago Press.
Eaves, L. J., Heath, A. C., Martin, N. G., Maes, H., Neale, M., Kendler, K., & Corey, L.
(1999). Comparing the biological and cultural inheritance of personality and social
attitudes in the virginia 30,000 study of twins and their relatives. Twin Research, 2,
62–80.
Fazekas, Z., & Littvay, L. (2012). Choosing sides: The genetics of why we go with the
loudest. Journal of Theoretical Politics, 24(3), 389–408.
Fowler, J. H., Baker, L. A., & Dawes, C. T. (2008). Genetic variation in political participation. American Political Science Review, 102(2), 233–248.
Fowler, J. & Schreiber, D. (2008). Biology, politics, and the emerging science of human
nature. Science, 322(5903), 912–914. doi:10.1126/science.1158188

16

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Fowler, J. H., & Dawes, C. T. (2008). Two genes predict voter turnout. The Journal of
Politics, 70(03), 579–594.
Fowler, J. H., Dawes, C. T., & Christakis, N. A. (2009). Model of genetic variation
in human social networks. Proceedings of the National Academy of Sciences, 106(6),
1720–1724.
Fowler, J. H., Settle, J. E., & Christakis, N. A. (2011). Correlated genotypes in friendship networks. Proceedings of the National Academy of Sciences, 108(5), 1993–1997.
Hatemi, P. K. (2012). The intersection of behavioral genetics and political science:
Introduction to the special issue. Twin Research and Human Genetics, 15(01), 1–5.
Hatemi, P. K. (2013). The influence of major life events on economic policy attitudes in
a world of gene-environment interplay. American Journal of Political Science, 57(4),
987–1007.
Hatemi, P. K., Byrne, E. M., & McDermott, R. (2012). Introduction: What is a ‘gene’
and why does it matter for political science? Journal of Theoretical Politics, 24(3),
305–327.
Hatemi, P. K., Dawes, C. T., Frost-Keller, A., Settle, J. E., & Verhulst, B. (2011). Integrating social science and genetics: News from the political front. Biodemography
and Social Biology, 57(1), 67–87.
Hatemi, P. K., Eaves, L. J., & McDermott, R. (2012). It’s the end of ideology as we
know it. Journal of Theoretical Politics, 24(3), 345–369.
Hatemi, P. K., Funk, C. L., Medland, S. E., Maes, H. M., Silberg, J. L., Martin, N. G., &
Eaves, L. J. (2009). Genetic and environmental transmission of political attitudes
over a life time. Journal of Politics, 71(3), 1141–1156.
Hatemi, P. K., Gillespie, N. A., Eaves, L. J., Maher, B. S., Webb, B. T., Heath, A. C., … ,
Martin, N. G. (2011). A genome-wide analysis of liberal and conservative political
attitudes. The Journal of Politics, 73(1), 271–285.
Hatemi, P. K., Hibbing, J. R., Medland, S. E., Keller, M. C., Alford, J. R., Smith, K. B.,
… , Eaves, L. J. (2010). Not by twins alone: Using the extended family design to
investigate genetic influence on political beliefs. American Journal of Political Science, 54(3), 798–814.
Hatemi, P. K., & McDermott, R. (2012a). The genetics of politics: Discovery, challenges, and progress. Trends in Genetics, 28(10), 525–533.
Hatemi, P. K., & McDermott, R. (2012b). A neurobiological approach to foreign policy analysis: Identifying individual differences in political violence. Foreign Policy
Analysis, 8(2), 111–129.
Hatemi, P. K., & McDermott, R. (2012c). The political psychology of biology, genetics,
and behavior. Political Psychology, 33(3), 307–312.
Hatemi, P. K., McDermott, R., Eaves, L. J., Kendler, K. S., & Neale, M. C. (2013). Fear
as a disposition and an emotional state: A genetic and environmental approach to
out-group political preferences. American Journal of Political Science, 57(2), 279–293.
Hatemi, P. K., Medland, S. E., Morley, K. I., Heath, A. C., & Martin, N. G. (2007). The
genetics of voting: An Australian twin study. Behavior Genetics, 37(3), 435–448.
Hatemi, P. K., Medland, S. E., Robert Klemmensen, R., Oskarrson, S., Littvay, L.,
Dawes, C. T., … , Martin, N. G. (2014). Genetic influences on political ideologies:

Genetic and Environmental Approaches to Political Science

17

Genome-wide findings on three populations, and a mega-twin analysis of 19 measures of political ideologies from five western democracies. Behavior Genetics, 44(3),
282–294. doi:10.1007/s10519-014-9648-8
Hatemi, P. K., & Verhulst, B. (2015). Political attitudes develop independently of
personality traits. http://journals.plos.org/plosone/article?id=10.1371/journal.
pone.0118106.
Hibbing, J. R., & Smith, K. B. (2007). The biology of political behavior: An introduction. The Annals of the American Academy of Political and Social Science, 614(1),
6–14.
Klemmensen, R., Hatemi, P. K., Hobolt, S. B., Petersen, I., Skytthe, A., & Nørgaard,
A. S. (2012). The genetics of political participation, civic duty, and political efficacy
across cultures: Denmark and the United States. Journal of Theoretical Politics, 24(3),
409–427.
Lineberry, R. L. (2011). Letter from the former editor. Social Science Quarterly, 92(5),
1133.
Littvay, L. (2012). Do heritability estimates of political phenotypes suffer from an
equal environment assumption violation? Evidence from an empirical study. Twin
Research and Human Genetics, 15(1), 6–14.
Loewen, P. J., & Dawes, C. T. (2012). The heritability of duty and voter turnout. Political Psychology, 33(3), 363–373.
Martin, N. G., Eaves, L. J., Heath, A. C., Jardine, R., Feingold, L. M., & Eysenck, H.
J. (1986). Transmission of social attitudes. Proceedings of the National Academy of
Sciences, 83(12), 4364–4368.
McDermott, R. (2009). Mutual interests. Political Research Quarterly, 62(3), 571–583.
McDermott, R., Dawes, C. T., Prom-Wormley, L., Eaves, L., & Hatemi, P. K. (2013).
MAOA and aggression: A gene-environment interaction in two populations. Journal of Conflict Resolution, 57(6), 1043–1064.
McDermott, R., Tingley, D., Cowden, J., Frazzetto, G., & Johnson, D. D. (2009).
Monoamine oxidase a gene (Maoa) predicts behavioral aggression following
provocation. Proceedings of the National Academy of Sciences, 106(7), 2118–2123.
Medland, S. E., & Hatemi, P. K. (2009). Political science, biometric theory, and twin
studies: A methodological introduction. Political Analysis, 17(2), 191–214.
Mondak, J. J., Hibbing, M. W., Canache, D., Seligson, M. A., & Anderson, M. R. (2010).
Personality and civic engagement: An integrative framework for the study of trait
effects on political behavior. American Political Science Review, 104(1), 85–110.
Neale, M. C., & Cardon, L. R. (1992). Methodology for genetic studies of twins and families.
Dordrecht, The Netherlands: Kluwer Academic Publishers.
Project Foresight (2008) Mental Capital and Wellbeing Project. The Government Office
for Science, London. www.bis.gov.uk/foresight
Smith, K. B., Alford, J. R., Hatemi, P. K., Eaves, L. J., Funk, C., & Hibbing, J. R.
(2012). Biology, ideology, and epistemology: How do we know political attitudes
are inherited and why should we care? American Journal of Political Science, 56(1),
17–33.
Smith, K. B., & Hatemi, P. K. (2013). OLS is AOK for ACE: A regression-based
approach to synthesizing political science and behavioral genetics models. Political
Behavior, 35(2), 383–408.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Verhulst, B., Eaves, L. J., & Hatemi, P. K. (2012). Correlation not causation: The relationship between personality traits and political ideologies. American Journal of
Political Science, 56(1), 34–51.
Verhulst, B., & Estabrook, R. (2012). Using genetic information to test causal relationships in cross-sectional data. Journal of Theoretical Politics, 24(3), 328–344.
Verhulst, B., & Hatemi, P. K. (2013). Gene-environment interplay in twin models.
Political Analysis, 21(3), 368–389.
Verhulst, B., Hatemi, P. K., & Martin, N. G. (2010). The nature of the relationship
between personality traits and political attitudes. Personality and Individual Differences, 49(4), 306–316.

ZOLTÁN FAZEKAS SHORT BIOGRAPHY
Zoltán Fazekas is post-doctoral research fellow in the Department of
Political Science and Public Management, University of Southern Denmark.
He was trained in political science at the Central European University
(Budapest, Hungary) and University of Vienna (Austria). In his research, he
strives to understand political behavior on the voter and the elite level, and
how our characteristics as human beings influence political attitude formation and political decision making. Currently, he is working on the research
team designing and implementing political surveys for twins together with
the Danish Twin Registry. Among others, his work has been published in
outlets such as International Journal of Public Opinion Research, Electoral
Studies, Social Science Quarterly, and Journal of Theoretical Politics.
PETER K. HATEMI SHORT BIOGRAPHY
Peter K. Hatemi is Associate Professor of Political Science, Microbiology,
and Biochemistry at the Pennsylvania State University and research fellow
at the United States Studies Centre at the University of Sydney. He was
trained in political science at the University of Nebraska and in genetic
epidemiology at the Queensland Institute of Medical Research (QIMR).
He continued his postdoctoral study in Human Genetics, Psychology, and
Psychiatry at the Virginia Institute for Psychiatric and Behavioral Genetics
(VIPBG) in the Medical College of Virginia. He is primarily interested
in advancing the study of the neurobiological mechanisms of social and
political behaviors and utilizing advanced methods in genetics, physiology,
endocrinology, and neurology in order to better understand human decision
making and preferences in complex and dynamic political environments.
He is also an active member of the Institute for Statskundskab at Syddansk
Universitet, VIPBG, and the genetic epidemiology laboratory at QIMR.
Pete’s recent work on the genetic, physiological, and endocrinological

Genetic and Environmental Approaches to Political Science

19

sources of individual differences in political attitudes, fear dispositions,
mate selection, personality, political violence, and religion has appeared in
the American Journal of Political Science, Behavior Genetics, Demography,
Evolution and Human Behavior, Journal of Politics, Political Psychology,
Science, Social Forces, and Trends in Genetics among other venues. His
recent book, co-edited with Rose McDermott, Man is by Nature a Political
Animal at the University of Chicago Press, offers a comprehensive volume
that includes applications of evolution, genetics, primatology, neuroscience,
and physiology to understand political preferences.
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Genetic and Environmental Approaches to Political Science