Genetic and Environmental Influences on Female Sexual
Orientation, Childhood Gender Typicality and Adult
Andrea Burri1,2*, Lynn Cherkas2, Timothy Spector2, Qazi Rahman1*
1Biological and Experimental Psychology Group, School of Biological and Chemical Sciences, Queen Mary University of London, London, United Kingdom, 2Department
of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
Background: Human sexual orientation is influenced by genetic and non-shared environmental factors as are two
important psychological correlates – childhood gender typicality (CGT) and adult gender identity (AGI). However,
researchers have been unable to resolve the genetic and non-genetic components that contribute to the covariation
between these traits, particularly in women.
Methodology/Principal Findings: Here we performed a multivariate genetic analysis in a large sample of British female
twins (N=4,426) who completed a questionnaire assessing sexual attraction, CGT and AGI. Univariate genetic models
indicated modest genetic influences on sexual attraction (25%), AGI (11%) and CGT (31%). For the multivariate analyses, a
common pathway model best fitted the data.
Conclusions/Significance: This indicated that a single latent variable influenced by a genetic component and common non-
shared environmental component explained the association between the three traits but there was substantial
measurement error. These findings highlight common developmental factors affecting differences in sexual orientation.
Citation: Burri A, Cherkas L, Spector T, Rahman Q (2011) Genetic and Environmental Influences on Female Sexual Orientation, Childhood Gender Typicality and
Adult Gender Identity. PLoS ONE 6(7): e21982. doi:10.1371/journal.pone.0021982
Editor: Stacey Cherny, University of Hong Kong, Hong Kong
Received March 9, 2011; Accepted June 14, 2011; Published July 7, 2011
Copyright: ? 2011 Burri et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: These authors have no support or funding to report.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org (AB); email@example.com (QR)
Understanding of the origins of sexual orientation can help
narrow competing developmental explanations for behavioral sex
differences in general and is of increasing importance to
researchers concerned with the physical and mental health of
sexual minorities [1,2]. Homosexuality appears to be a stable
sexual phenotype in humans with population-based surveys
suggesting lifetime prevalence of 2–4% in men and 0.5–1.5% in
women when measured as exclusive same-sex ‘‘feelings’’ (e.g.,
homosexual attractions and fantasies) [3,4]. The distribution of the
trait is generally bimodal and this is stronger for men than it is for
women; a first indication of different, albeit overlapping,
developmental pathways towards male versus female sexual
orientation [5,6]. Basic biobehavioral research into female sexual
orientation appears infrequent compared to that performed on
Several early family and twin studies provide evidence for a
genetic component to both male and female sexual orientation
[7,8,9]. Heritability estimates were found to be in the region of
40% to 50%. However, the putative inheritance patterns have
remained unclear. Family pedigree studies in men have suggested
that maternally inherited factors might be involved [10,11,12,13]
although one large study in a carefully ascertained pedigree has
failed to replicate this . Among females, autosomal and sex-
linked routes have been implicated although there has only ever
been one survey study performed here . Two preliminary
linkage studies reported microsatellite marker loci for male
homosexuality on the X chromosome [10,11] with one confirming
linkage for males but not females . However, two independent
reports found no such linkage in males [16,17]. The latest genome-
wide scan reported several new autosomal markers for male sexual
orientation  which again require replication.
Early criticisms of previous studies focused on the possibility that
their reliance on self-selected volunteers (e.g., through advertise-
ments in gay and lesbian press) may have biased the results by
increasing twin resemblance. But it is not clear how this would
inflate concordance rates or overestimate genetic or non-genetic
effects (similarity would be increased for both MZ and DZ twins).
However, two studies of attraction components of sexual
orientation, and one of same-sex sexual behavior, were popula-
tion-based and all reported lower concordance rates than
previously found at around 30% - although Bailey et al. 2000
were unable to resolve genetic, shared and non-shared environ-
mental factors in their univariate models [19,20,21]. One of these
reports supported the notion that developmental pathways
towards homosexuality might be different for men and women
. One further study which modeled several components of
sexual orientation (attractions, attitudes to homosexual sex, and
lifetime same-sex partners) reported stronger evidence for genetic
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influences of between 50% and 60% in females and approximately
30% in males . Despite the inconsistency of findings across
these studies in terms of the magnitude of the heritability estimates,
all of them suggest a genetic component to sexual orientation.
Sexual orientation, like many complex behaviors, comes as a
‘‘package’’ of covarying traits. Critical among these are childhood
gender typicality (or CGT), which are sex-typed behaviors,
activities and interests that are statistically atypical for biological
sex during childhood) and gender identity (psychological gender as
‘‘masculine’’ or ‘‘feminine’’ during adulthood). CGT is robustly
correlated with adult homosexuality as demonstrated in prospec-
tive and retrospective studies and has been observed cross-
culturally [9,23,24,25]. In order to control for possible retrospec-
tive memory biases (based on the argument that homosexuals
might exaggerate nontypicality and heterosexuals understate it)
one study has confirmed the association between CGT and
homosexuality using home videos of childhood behavior .
Adult gender identity (AGI), although not a psychometric
homologue to CGT, also shows an association with sexual
orientation as measured via ratings of self-ascribed masculine or
feminine feelings, traditional personality measures of gender (e.g.,
the Bem Sex Role Inventory) and occupational interests [27,28].
Twin models show that both CGT and AGI are heritable
although the estimates vary. Knafo et al.  reported
heritability estimates of 37% in boys and almost 80% in girls
(3–4 year olds). Iervolino et al.  examined the full range of
normal variation in CGT in the same dataset and found 34%
heritability in boys compared to 57% in girls. Van Beijsterveldt
et al.  reported 70% for CGT in both sexes among 7 and 10-
year old twins. Bailey et al.  reported heritability of 50% for
men and 37% for women in retrospectively recalled CGT. A
similar wide range of estimates applies to twin models of gender
identity. Lippa and Hershberger  reported modest heritability
for their measure based on occupational interests at 53% (no sex
differences) whereas Bailey et al.  reported estimates of 31%
for men and 24% for women. Finally, Bailey et al.  reported
that the covariation between sexual orientation, CGT and AGI
could be explained by a common familial factor although the
model was a poor statistical fit.
Taken together the data suggest that CGT (and to a lesser
extent, AGI) might be considered as a possible ‘‘sex atypicality’’
endophenotype for trait sexual orientation which could be more
powerful in future gene discovery. Trait sexual orientation is
notoriously skewed. Thus, a broad research strategy which
includes CGT and AGI with their more favorable statistical
distributions will enhance our ability to resolve the molecular
genetics of sexuality.
A related conceptual issue concerns the putative etiological
factor(s) which explain the link between CGT, AGI and sexual
orientation and thus may constitute the ‘‘sex atypicality’’
endophenotype. A good candidate is prenatal sexual differentia-
tion under the action of androgens [1,33]. Homosexuals are
viewed as having been subject to atypical levels of prenatal
androgens thus causing sex-atypical differentiation of brain
structures that control direction of sexual preference, gender-
related psychological traits (including CGT and AGI) and related
traits (such as specific cognitive differences). However, there are
very few direct tests of this hormonal link between measures of sex-
atypicality and trait sexual orientation. There are also few tests of
the developmental progression of this process. For example, do
genetic variations contribute to atypical hormonal levels which
shape CGT and then does CGT precedes the development of trait
sexual orientation? Or are CGT and sexual orientation simply
correlated but develop independently? Strong evidence for an
association between prenatal androgen levels, CGT and sexual
orientation comes from studies of women with congenital adrenal
hyperplasia (CAH) who have been exposed to high levels of
prenatal adrenal androgens. Girls (and adolescents) with CAH
show sex-typed behavior and interests in the male-typical
direction, in spite of strong sex-typical parental gender socializa-
tion [34,35,36]. Adult females with CAH also report significantly
more bisexual/homosexual fantasies and attractions relative to
their control sisters [37,38,39]. Prospective studies in non-clinical
populations also suggest that variations in fetal levels of
testosterone (measured via amniotic sampling) are associated with
male-typical gender related behaviors in girls [40,41]. Finally, a
meta-analysis of the relationship between sexual orientation and
the ratio of the 2ndto 4thfinger digits (a somatic marker ascribed to
the prenatal actions of androgen exposure) revealed a significant
association between male-typical digit ratios and sexual orientation
in women . These lines of evidence support the notion of a
developmental coupling between levels of prenatal androgen,
gender-related behaviors and interests, and sexual orientation
In the present study, we analyzed questionnaire data from a
large volunteer register of female twins in the United Kingdom to
test the hypotheses that (1) genetic factors significantly influence
variation in measures of sexual orientation and it’s two covariates
– CGT and AGI; (2) that these three traits correlate significantly at
the phenotypic level; and (3) that the covariation among the three
traits is also due to a genetic correlation. This is the first study of its
kind in a British sample.
The study was approved by the St. Thomas’ Hospital research
ethics committee. All study participants involved in this study
provided informed written consent.
Participants and questionnaire
Subjects were monozygotic (MZ) and dizygotic (DZ) volunteer
female twins drawn from the ‘‘TwinsUK’’ registry . Due to
unavailability of data, no males were included in this study.
Zygosity was established using standardized questions about
physical similarity and confirmed by multiplex DNA genotyping
in cases of uncertainty .
In 2002, twins were sent a questionnaire asking about general
sexual behavior and sexual orientation (referring to ‘‘sexual
attractions’’ with men and women in this study). Of the 8,418
questionnaires sent, 4,725 (56.1%) were returned. In a 2005
follow-up survey, an anonymous questionnaire assessing CGT and
AGI was also sent to 6,934 female twins in the registry and
returned by 4,850 (69.9%). The questionnaires were developed
previously based on scales in the published literature but shortened
for the purposes of practicality within a large twin register. Twins
were not selected on the basis of variables being studied and were
unaware of any hypothesis being tested.
Final questionnaire data relating to sexual orientation and its
psychological correlates, CGT and AGI, was available on a total of
4,426 female twin individuals - a 49% response rate. Females who
reported never having felt sexually attracted to anyone else
(N=44; 0.99%) and/or reported never having had sexual
experiences (N=51; 1.15%) were excluded from the analyses as
were 228 (5.15%) females with missing values for any items
assessing CGT and AGI. Also, 32 (0.72%) women were excluded
because of unknown zygosity. After applying exclusion criteria, a
total of 4,066 women were eligible for analysis, comprising 906
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complete MZ pairs, 806 complete DZ pairs and 642 women
whose co-twins did not participate (15.35%). However, sample
sizes varied somewhat in the different analyses because of missing
Demographic information on all twins including age, marital
status, and years of education were obtained from the TwinsUK
Childhood gender typicality (CGT) and adult gender
The CGT scale consisted of four items
gender identity which are comparable to several published scales
[19,45]. Example items include ‘‘As a child I was called a ‘tomboy’
by my peers’’ and ‘‘As a child I preferred playing with boys rather
than girls’’. Assessment of participants’ self-concepts as masculine
or feminine (AGI) was computed using four items comparable to
those used by Bailey et al. . Example items include ‘‘I don’t
feel very masculine’’ and ‘‘I pride myself on being feminine’’.
Scores for CGT and AGI were derived by adding the point values
for each of the four scale-specific items together and dividing it by
the number of scale items. Response options were on a 7-point
Likert-type scale, ranging from ‘‘strongly agree’’ (1) to ‘‘strongly
disagree’’ (7). Cronbach’s alpha, a measure of internal consistency,
was 0.62 for CGT and 0.44 for AGI. High scores on each measure
mean more feminine.
Sexual orientation was measured using a
scale, similar in kind to Kinsey-type scales used extensively in
sexuality research assessing sexual attraction (degree of attraction
towards the same or opposite sex. Response options for this
measure ranged from 1 (‘‘only to/with males, never to/with
females’’) to 5 (‘‘only to/with females, never to/with males’’) with a
supplementary option of ‘‘no sexual attraction’’ (numbered 6)
For descriptive and genetic analysis CGT, AGI and sexual
attraction were treated as continuous traits based on women’s
responses to the specific questions. Unpaired t-tests (two-tailed)
were used to examine differences between MZ and DZ twins on
age, years of education, CGT, AGI and mean sexual orientation
scores. Two-sample tests of proportions were used to test for
differences in marital status and SES. Pearson’s correlation
coefficients were used to explore patterns of association between
CGT, AGI and sexual orientation scores.
For all analyses, a P value less than 0.05 or odds ratios with a
95% confidence interval not including ‘‘1’’ were considered
statistically significant, unless stated otherwise. Data handling and
descriptive analyses were undertaken using STATA (Intercooled
Stata for Windows 95, Version 5.0, 1997, StataCorp, College
Station, TX) while all genetic modelling was carried out with Mx
Univariate genetic modelling.
classical twin design where population variance in phenotypes, as
well as covariance between them, can be dissected into genetic and
environmental sources. The twin design assumes that MZ twins
share 100% of both their genes and shared environment, whereas
DZ twins share - on average - 50% of their genes and 100% of
shared environment. Presuming that both types of twins share
equally similar family environments, any greater similarity
between MZ as compared with DZ twin pairs is attributed to
In the present study maximum likelihood genetic modeling was
used to model latent genetic and environmental factors influencing
The present study used the
sibling covariance in CGT, AGI and sexual orientation for MZ
and DZ twins. Bivariate normality was given for the measures of
CGT and AGI after the variables were transformed. However, for
trait such as sexual orientation, normality cannot be achieved.
Genetic model fitting was used to decompose the observed
phenotypic variance (P) into additive (A) and dominant (D) genetic
effects, and shared (C) and non-shared environmental (E) effects
. The shared environmental variance refers to factors shared
between twin pairs such as family environment. The non-shared
environmental variance reflects factors affecting each twin
individually (e.g., specific prenatal events or peer socialization)
and also includes measurement error.
For continuous phenotypes, evidence for a genetic contribution
(heritability or h2) can be obtained by comparing similarities in
scores using intra-class correlation coefficients (ICCs) for MZ and
DZ twin pairs. Depending on the correlations between the MZ
and DZ twins, either an ACE or an ADE model is fitted. For
univariate models in the present study, an ACE model was applied
when DZ correlations were more than half the MZ correlations.
When the DZ correlations were less than half the MZ correlations,
both ACE and ADE were estimated for comparative purposes
. Initial assessment of the components (A, D, C, and E) may
suggest non-significant values in one or more component. Further
analysis can determine the significance of each factor as
components of the observed variance by removing each
sequentially from the full model and testing the deterioration in
fit of the various nested models, using the likelihood ratio test. In
the present study, the fit of the different models was compared by
taking the fit function and the degrees of freedom (df) of the full
model and subtracting it from the fit function and the df of the
nested restricted models. The subtraction gives an x2 value and
associated df that can be tested for significance. In addition, the
Akaike Information Criteria (AIC=x2-2df) was considered, with
lower values indicating the most suitable model. The most
parsimonious model was then used to estimate the heritability.
Note the assumption that trait-related environments are similar to
the same degree in MZ and DZ pairs is valid for trait sexual
orientation (Bailey et al., 2000; Kendler et al., 2000). More
detailed descriptions of twin modeling analyses can be found in
Posthuma et al. .
Multivariate genetic modelling.
cross-trait correlations allows us to partition the covariance
between traits into genetic and environmental components and
therefore permit the quantification of any overlap in the genetic or
environmental correlation between traits. Here we present both
the estimated genetic covariance between the traits as a proportion
of the total phenotypic covariance (bivariate heritability) and the
proportion of the total genetic variance for the traits (genetic
correlation).To test our hypothesis that the covariation among
CGT, AGI, sexual orientation (attraction) can be explained by
genetic correlation between the traits, we further fitted the
following three multivariate models to the data [48,51]: (1) The
Cholesky decomposition provides the correlations between the
three independent genetic and environmental factors (A, C, D, E)
and decomposes the variance for a trait into additive and non-
additive genetic and non-shared environmental effects, providing
the fullest potential explanation of the data. (2) The independent
pathway model is a submodel of the Cholesky model and tests
whether covariance between the traits is explained by a single
underlying genetic factor and a single underlying environmental
factor. (3) The common pathway model assumes that a single
shared latent factor underlies all three measures.
The suitability of the multivariate models was determined by
comparing the models AIC, BIC (Bayesian Information Criterion)
Using cross-twin and
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and their goodness of fit as measured with the likelihood ratio chi-
square test (22LL).
Table 1 displays the participant characteristics for the whole
sample and by zygosity group. The MZ and DZ twin groups were
well matched for sexual orientation, CGT, AGI and most
demographic variables except for marital status where MZ twins
were significantly more often married compared with DZ twins
(44.32% vs. 39.10%; P,0.01). Also DZ twins, more than MZ
twins, reported being in a relationship (35.82% vs. 39.85%;
P,0.05) or being widowed (5.58% vs. 7.36%; P,0.01).
Most women displayed ‘‘average’’ AGI scores with the peak
score being at 4.25 (30.9% of total sample). A small fraction of
women had values at both extreme ends of the distribution.
Overall, AGI showed less variability compared with CGT.
Whilst only a negligible proportion of subjects reported a high
degree of childhood gender nonconforming behavior, most of
the women scored in the upper third of the distribution, with
peak scores at 5.5 and 7. We also observed the previously
documented general tendency for women to show more non-
heterosexuality at the predominantly heterosexual end of the
two scales (see Table 2).
Intra-class correlations for MZ and DZ twins in the three
measures are reported in Table 3. For all measures MZ twin
correlations were consistently higher compared with DZ twin
correlations, indicating a genetic contribution to the variance in
these traits. However, the correlations were also all modest
indicating a substantial influence of non-shared environmental
factors. In the case of AGI the correlations were very low
militating against a genetic contribution. For all traits, except
AGI, DZ correlations were less than half the MZ correlations
pointing to the involvement of dominant genetic effects
Univariate model fitting
Based on the intra-class correlations, ACE and ADE models
were fitted for all phenotypes. For all measures the best-fitting
model was an AE model (Table 3). The highest heritability was
found for CGT (32%). Heritability was moderate for sexual
attraction (25%) and small for AGI (11%). There was no effect of
dominance on any of the measures. There was a larger
contribution of non-shared environmental factors to AGI than to
CGT. In contrast to previous studies which produced relatively
large confidence intervals [19,20,21], our confidence intervals
were relatively narrow in comparison (see Table 3) although
probably larger than other twin studies of psychological individual
differences traits (such as personality). Nevertheless, they suggest
some stability of our point estimates.
Multivariate model fitting
Genetic and environmental correlations derived from the ADE
Cholesky model are shown in Table 4, along with the phenotypic
correlations. We found significant associations between all three
measures; hence, all measures were included in the multivariate
analyses. The significant correlations ranged from r=20.21 to
r=0.05, with the highest correlation being between CGT and
sexual attraction and the lowest between AGI and sexual
attraction (Table 4). The Cholesky results indicated that a
considerable degree of genetic correlation exists especially between
sexual attraction and CGT and AGI (r=20.42 and r=20.45,
respectively). The bivariate heritability suggested that approxi-
mately 57% of the covariance between CGT and sexual attraction
is due to additive genetic factors with the remaining 43%
attributable to unique environmental effects (Table 4).
When comparing the independent and common pathway
models with the Cholesky model, the common pathway model
was found to offer the most suitable explanation of the data with
the lowest value of AIC at 3871.1 and the lowest BIC at 222609.7
(Table 5). This common pathway model explains the variance in
each variable in terms of unique A, D and E contributions as well
as a contribution from the ‘‘common sexual orientation pheno-
type’’ (Pc). The parameter estimates derived from the common
Table 1. Means (and standard deviations) for continuous demographic variables, CGT, AGI and sexual orientation (attraction),
along with frequency data for discrete demographics for the whole sample and by zygosity group.
Overall (N=4,066)MZ (N=2,098) DZ (N=1,998)
Mean (SD)Range Mean (SD)Range Mean (SD)Range
Age 53.36 (12.65)16–87 53.10 (13.43)16–87 53.66 (11.73)16–810.11
Education in years10.40 (2.91)3–33 10.50 (2.93)6–33 10.35 (2.88)3–320.09
CGT 5.22 (1.25)1–7 5.25 (1.25)1–7 5.19 (1.26) 1–70.12
AGI 4.39 (0.89) 1–7 4.38 (0.89)1–7 4.41 (0.91)1–70.28
Sexual attraction1.13 (0.46)1–5 1.12 (0.41) 1–51.14 (0.50)1–50.16
Single2097.2996 6.86113 7.700.30
Married 1,19441.65 620 44.32574 39.100.00
In relationship 1086 37.8850135.82 58539.85 0.01
Divorced 212 7.391047.43 108 5.990.07
Widowed166 5.79785.58 887.36 0.02
*Unpaired two-tailed t-test and Mann-Whitney U-tests were used to test for mean differences in response frequencies.
**Two-sample test of proportions were used to explore differences in response frequencies.
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pathway model are shown in Figure 1. To obtain the contribution
that Pcand unique A, D and E make to the variance in a trait,
squares of the path coefficient are taken. Thus, the model
postulates the existence of an underlying sexual orientation
phenotype (Pc) with a heritability 24% (0.492) that chiefly explains
the co-occurrence of CGT, AGI and sexual attraction. For CGT
Pc accounts for 43% (0.652) of the variation, for AGI it accounts
for 4% (0.192) and for sexual attraction it accounts for 11%
(20.322) of the variation. The heritability of the variation in the
phenotype that is not accounted for by Pc is 10% (20.322) for
CGT, 10% (0.322) for AGI and 19% (0.442) for sexual attraction.
No influence of D on the variation of Pc and the phenotypes could
be detected. Overall, these results suggest that the common sexual
orientation phenotype does not account for significant variation in
AGI in this model.
Our results show that sexual attraction and CGT are influenced
by genetic factors (accounting for 25% and 32% of the variance
respectively). Genetic contributions as estimated in the univariate
analyses had a much weaker impact on AGI (11%). The effect of
non-shared environmental factors (including measurement error)
on all traits was large. However, there was no effect of the shared
family environment on any trait.
These findings are broadly consistent with previous population-
level twin studies demonstrating a heritable basis to male and
female sexual orientation. The heritability estimates reported here
for female sexual attractions were larger than those reported by
Bailey et al.  for sexual attraction components (8%). For
attraction, we found no effect of the shared environment in
contrast to Bailey et al.  who reported an estimate of 41%.
Langstrom et al.  reported shared environmental effects of
same-sex sexual behavior of 17%. Kendler et al.  did not
separate their analysis by sex so we cannot compare the findings.
Finally, our genetic estimates were lower than those reported by
Kirk et al.  who attempted to model two components of sexual
orientation - sexual attraction and sexual experience - and
reported estimates for females between 50 and 60%. Whilst Kirk
et al.  did use attractions in their study they supplemented
these with the measures ‘‘attitudes to homosexual sex’’ and
‘‘lifetime same-sex partners’’ (from a range in an extensive sexual
orientation questionnaire) which are not directly comparable to
measure used here.
The effect of E on all traits was large. E includes phenotypic
variation accounted for by non-shared environment and
measurement error. A variety of sources can cause measure-
ment error, including inadequate or imprecise assessment
instruments and phenotype description, and a variety of
response styles, specifically acquiescence,
extreme response, midpoint responding, and noncontingent
responding . The twin modelling approach used in this
study does not allow separation of the two sources, hence,
quantification of the influence of measurement error is
impossible. It is therefore likely that some inconsistency
between our heritability estimates for sexual attraction com-
pared to previous work might be due to different usage of
Kinsey-type scales for measuring trait sexual orientation. Here
we used a 5-item measure; Bailey et al.  used the full 7-item
Kinsey-scale; Langstrom et al.  used number of same-sex
partners; and Kendler et al.  employed a single item with
three response choices (heterosexual, bisexual, and homosexual
in attractions). If we compare our data to Bailey et al. (both
studies focused on attractions and using what approximate
traditional Kinsey-type scales), it is possible that the relatively
small difference in response options between the two studies
(that is, a difference of 2) contributed to the differing heritability
estimates for sexual attraction. Parameter estimates for sexual
orientation might be unusually sensitive to the range of items
used to assess the trait and thus future researchers should be
mindful of the utilizing psychometrically robust scales.
Table 2. Percentage of women that checked each item of sexual attraction along with means (and standard deviations) for their
respective CGT and AGI scores.
Measure: ‘‘I have felt sexually attracted’’…’’% Sexual attractionCGT mean score (SD)AGI mean score (SD)
1 Only to/with males, never to/with females89.92 5.98 (8.01) 4.43 (0.88)
2More to/with males than females 8.56 5.44 (8.74) 4.22 (0.91)
3 Equally to/with males and females0.29 3.90 (1.57) 4.53 (0.55)
4 More to/with females than males0.86 3.89 (1.33)4.33 (0.99)
5 Only to/with females, never to/with males0.36 4.12 (1.41) 4.15 (0.65)
Table 3. Intra-class correlations, cross-twin cross-trait correlations and heritabilities for CGT, AGI and both measures of sexual
CGT twin 1 AGI twin1 Sexual attraction twin1Heritability % (95% CI)
CGT twin 2 0.36/0.020.03
20.02 0.32 (0.26–0.37)
AGI twin 20.03 0.11/0.07
20.02 0.11 (0.05–0.17)
Sexual attraction twin2
20.060.28/0.04 0.25 (0.17–0.33)
Heritability estimates and 95% CIs for all variables are calculated from the best-fitting, most parsimonious univariate AE model.
Note. Twin correlations for MZs/DZs are presented on the diagonal. Cross-twin cross-trait correlations for MZs are presented below the diagonal. Cross-twin cross-trait
correlations for DZs are presented above the diagonal.
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Consistent with several studies the highest heritability was found
for CGT (32%) [19,30]. CGT seems to be the highest heritable
correlate of sexual orientation reported thus far, and furthermore
lies in the region of the h2estimates generally reported for sexual
orientation (measured both behaviorally and psychologically). This
adds further support to the notion that CGT may be the main
heritable component or endophenotype of sexual orientation
[53,54]. However, genetic effects for AGI were negligible
compared to previous work and we are less confident about the
validity of AGI as a robust correlate of sexual orientation .
The inter-correlations with other measures of sexual orientation
were low for AGI compared to CGT and showed little variability.
Compared with CGT items which capture sex-typed behavior,
interests and identity, AGI comprises identity items only and thus
has restricted psychometric precision. Other documented indices
of gender identity such as ‘‘gender diagnosticity’’ e.g.  should
be the focus of future twinstudies if suitable short-form scales canbe
developed. As with attraction, the inconsistencies between the
studies might be attributed to the number of items in the measures
used. Our measure of CGT comprised four items and Bailey et al. ’s
 five items used to check the reliability of self- vs. other-report of
their 24-item measure . The relative comparability here (a
difference between the two studies of only 1 item) provides some
confidence for the validity of heritability estimates reported by both.
However, their measure of AGI comprised seven items and ours
only four (with relatively low internal consistency) which may
explain the differences between studies for this particular measure.
The results from the multivariate analyses presented here
provide evidence of a genetic overlap between CGT and sexual
attraction but less for AGI. We detected a common latent
phenotype with a heritability of 24% underlying sexual orienta-
tion, CGT and AGI as well as moderate phenotype-specific
additive genetic factors and large phenotype-specific non-shared
environmental factor loading on these traits. These data are
supportive of those from Bailey et al.  who also found that a
common pathway ACE model best fitted the available data. Both
studies support the notion that showed that genetic and non-
shared environmental factors markedly contributed to the
covariation among the measures, with all three measured variables
(sexual attraction, CGT and AGI) being good indicators of an
underlying latent factor. Overall, the present results support
previous non-twin evidence for the existence of an intermediate
phenotype for sexual orientation, such as for example ‘‘sex-
atypicality’’ . A likely candidate for this latent phenotype is
prenatal androgen exposure which shapes variations in gender
nonconforming behavior and sexual orientation and the develop-
mental coupling between them [1,33,35]. Nevertheless, specula-
tion about origins of this putative sex hormone-related phenotype
is limited by two candidate gene studies of male sexual orientation
both producing null results: one for the androgen receptor 
and another for aromatase . However, the absence of such
associations in men does not imply a similar null result in women.
Insofar as our genetic estimates are additive, these data do not
suggest a major role for epistatic or dominant allelic effects. Our
results are also silent on the sources of non-shared environmental
effects. However, what is clear from several twin studies, including
the present, is that shared factors such as the home environment
and parenting styles have little impact on human sexual
orientation. Nevertheless, as we cannot be certain that our
measures, particularly AGI, were robust (given the sizable loading
of a common non-shared environmental factor on each trait) there
is a necessary degree of imprecision to our parameter estimates
and the model fitting results should be treated with caution. Future
studies should be particularly careful in using measures of AGI.
Several other limitations weaken overly strong conclusions from
the present study. The response rate was lower than three other
large twin studies (49%, compared to 53.8% in Bailey et al. 
60% in Kendler et al. ; and 59.6% in Langstrom et al. )
although it is not clear how this could systematically bias the
parameter estimates reported . Also, the response rate here is
in fact comparable to other epidemiological surveys of female
sexual behavior [58,59]. The representativeness of our twin
sample also diminishes any putative selection biases, as shown by a
large comparative study demonstrating that our twin population is
very similar to singletons on a wide range of common health and
lifestyle factors . A comparison of the sample characteristics in
Table 4. Phenotypic, genetic and non-shared environmental correlations among CGT, AGI and sexual orientation (attraction).
CGT-AGI CGT-Sexual attraction AGI-Sexual attraction
proportion of rPdue to:
Table 5. Multivariate analysis of three models showing
change in model fit (x2) and degrees of freedom (df) when
specified parameters are dropped from full ADE model (best
fitting models in bold).
Model dfAIC BIC
Cholesky ADE9008 3875.84
IndependentADE 9011 3876.25
AIC=Akaike Information Criterion. AIC describes the model with best
goodness-of-fit combined with parsimony. BIC=Bayesian Information Criterion.
22LL=likelihood ratio chi-square test as a measure of goodness of fit.
The Genetics of Sexual Orientation
PLoS ONE | www.plosone.org6 July 2011 | Volume 6 | Issue 7 | e21982
Table 1 show that the MZ and DZ twins did not differ significantly
on most demographic variables, arguing against the tendency for
MZ to be more alike possibly due to shared upbringing. The
comparably low internal consistencies for CGT and AGI may
further reflect the heterogeneous nature of the constructs,
suggesting that more items are needed to capture the range of
manifestations of the constructs. We also used retrospective
measures of CGT which may be influenced by recall biases.
However, prospective studies confirm the predictive psychometric
validity of measures of CGT that are comparable to the one used
here as do studies of maternal reports of proband-recalled CGT
and studies of childhood home videos [23,33].
Due to our considerably large sample size we had enough power
to detect a rather small contribution of non-additive genetic
factors, had it been present (1,800 twin pairs are needed to reject
an AE model with a power of 80% when an ADE model is the true
model, with respective contributions of additive genetics ef-
fects=0.50, dominant genetic effects=0.30 and non-shared
environmental effects=0.20) . Nonetheless, there remained
insufficient numbers of non-heterosexual participants to guarantee
a high degree of statistical power in the genetic and environmental
analyses. This is a well-known problem, as sexual orientation-
related data are notoriously skewed .
In summary, we found genetic influences on female sexual
orientation as measured via attractions and on CGT (a key
developmental correlate of sexual orientation). A moderate effect
of a common latent phenotype suggests that there are some
overlapping mechanisms which may be responsible for sexual
orientation. However, stronger conclusions are not warranted at
this stage because of substantial measurement error. Future
research efforts should focus on ‘‘sex-atypicality’’ as a possible
intermediate phenotype for trait sexual orientation which may be
more amenable to gene-mapping approaches.
We thank Professor Michael C. Neale (Virginia Commonwealth
University) for helpful discussion regarding the multivariate analysis.
Conceived and designed the experiments: AVB QR. Performed the
experiments: AVB LC. Analyzed the data: AVB. Wrote the paper: AVB
QR . Internal reviewers: LC TS.
1. Rahman Q (2005) The neurodevelopment of human sexual orientation.
Neuroscience and Biobehavioral Reviews 29: 1057–1066.
2. Sandfort TG, Bakker F, Schellevis FG, Vanwesenbeeck I (2006) Sexual
orientation and mental and physical health status: findings from a Dutch
population survey. American Journal of Public Health 96: 1119–1125.
3. Sell RL, Wells JA, Wypij D (1995) The prevalence of homosexual behavior and
attraction in the United States, the United Kingdom and France: results of
national population-based samples. Archives of Sexual Behavior 24: 235–248.
4. Johnson AM, Mercer CH, Erens B, Copas AJ, McManus S, et al. (2001) Sexual
behaviour in Britain: partnerships, practices, and HIV risk behaviours. Lancet
5. Baumeister RF (2000) Gender differences in erotic plasticity: the female sex drive
as socially flexible and responsive. Psychological Bulletin 126: 347–374.
6. Bailey JM (2009) What is sexual orientation and do women have one? Nebraska
Symposium on Motivation 54: 43–63.
7. Bailey JM, Pillard RC (1991) A genetic study of male sexual orientation.
Archives of General Psychiatry 48: 1089–1096.
8. Bailey JM, Pillard RC, Neale MC, Agyei Y (1993) Heritable factors influence
sexual orientation in women. Archives of General Psychiatry 50: 217–223.
9. Whitam FL, Mathy RM (1991) Childhood cross-gender behavior of homosexual
females in Brazil, Peru, the Phillipines, and the United States. Archives of Sexual
Behavior 20: 151–170.
Figure 1. Best fitting common pathway model. The figure shows standardized parameter estimates for the path coefficients of the common
pathway model, selected as the most appropriate depiction of the data. The squares of the path coefficients provide an estimate of the variance
explained by common and specific genetic and environmental components.
The Genetics of Sexual Orientation
PLoS ONE | www.plosone.org7 July 2011 | Volume 6 | Issue 7 | e21982
10. Hamer DH, Hu S, Magnuson VL, Hu N, Pattatucci AML (1993) A linkage Download full-text
between DNA markers on the X chromosome and male sexual orientation.
Science 261: 321–327.
11. Hu S, Pattatucci AML, Patterson C, Li L, Fulker DW, et al. (1995) Linkage
between sexual orientation and chromosome Xq28 in males but not in females.
Nature Genetics 11: 248–256.
12. Camperio-Ciani A, Corna F, Capiluppi C (2004) Evidence for maternally
inherited factors favoring male homosexuality and promoting female fecundity.
Proceedings of the Royal Society of London Series B Biological Sciences 271:
13. Rahman Q, Collins A, Morrison M, Orrells JC, Cadinouche K, et al. (2008)
Maternal inheritance and familial fecundity factors in male homosexuality.
Archives of Sexual Behavior 37: 962–969.
14. Bailey JM, Pillard RC, Dawood K, Miller MB, Farrer LA, et al. (1999) A family
history study of male sexual orientation using three independent samples.
Behavior Genetics 29: 79–86.
15. Pattatucci AML, Hamer DH (1995) Development and familiality of sexual
orientation in females. Behavior Genetics 25: 407–420.
16. Rice G, Anderson C, Risch N, Ebers G (1999) Male homosexuality: absence of
linkage to microsatellite markers at Xq28. Science 284: 665–667.
17. Sanders AR, Cao Q, Zhang J, Badner JA, Goldin LR, et al. (1998) Genetic
linkage study of male homosexual orientation. Poster presented at the meeting of
the American Psychiatric Association, Toronto, Canada.
18. Mustanski BS, DuPree MG, Nievergelt CM, Bocklandt S, Schork NJ, et al.
(2005) A genomewide scan of male sexual orientation. Human Genetics 116:
19. Bailey JM, Dunne MP, Martin NG (2000) Genetic and environmental influences
on sexual orientation and its correlates in an Australian twin sample. Journal of
Personality and Social Psychology 78: 524–536.
20. Kendler KS, Thornton LM, Gilman SE, Kessler RC (2000) Sexual orientation
in a US national sample of twin and non-twin sibling pairs. American Journal of
Psychiatry 157: 1843–1846.
21. Langstrom N, Rahman Q, Carlstrom E, Lichtenstein P (2010) Genetic and
environmental effects on same-sex sexual behavior: a population study of twins
in Sweden. Archives of Sexual Behavior 39: 75–80.
22. Kirk KM, Bailey JM, Dunne MP, Martin NG (2000) Measurement models for
sexual orientation in a community twin sample. Behavior Genetics 30: 345–356.
23. Bailey JM, Zucker KJ (1995) Childhood sex-typed behavior and sexual
orientation: a conceptual analysis and quantitative review. Developmental
Psychology 31: 43–55.
24. Bartlett NH, Vasey PL (2006) A retrospective study of childhood gender atypical
behavior in Samoan fa’afafine. Archives of Sexual Behavior 35: 659–666.
25. Cardoso FL (2009) Recalled sex-typed behavior in childhood and sports
preferences in adulthood of heterosexual, bisexual, and homosexual men from
Brazil, Turkey, and Thailand. Archives of Sexual Behavior 38: 726–736.
26. Rieger G, Linsenmeier JAW, Gygax L, Bailey JM (2008) Sexual orientation and
childhood gender nonconformity: evidence from home videos. Developmental
Psychology 44: 46–58.
27. Lippa RA (2002) Gender-related traits of heterosexual and homosexual men and
women. Archives of Sexual Behavior 31: 83–98.
28. Lippa RA, Tan FD (2001) Does culture moderate the relationship between
sexual orientation and gender-related personality? Cross Cultural Research 35:
29. Knafo A, Iervolino AC, Plomin R (2005) Masculine girls and feminine boys:
genetic and environmental contributions. Journal of Personality and Social
Psychology 88: 400–412.
30. Iervolino AC, Hines M, Golombok SE, Rust J, Plomin R (2005) Genetic and
environmental influences on sex-typed behavior during the preschool years.
Child Development 76: 826–840.
31. van Beijsterveldt CEM, Hudziak JJ, Boomsma DI (2006) Genetic and
environmental influences on cross-gender behavior and relation to behavior
problems: a study of Dutch twins at ages 7 and 10 years. Archives of Sexual
Behavior 35: 647–658.
32. Lippa RA, Hershberger S (1999) Genetic and environmental influences on
individual differences in masculinity, femininity, and gender diagnosticity:
analysing data from a classic twin study. Journal of Personality 67: 127–155.
33. Ellis E, Ames MA (1987) Neurohormonal functioning and sexual orientation: a
theory of homosexuality-heterosexuality. Psychological Bulletin 101: 233–258.
34. Berenbaum SA (1999) Effects of early androgens on sex-typed activities and
interests in adolescents with congenital adrenal hyperplasia. Hormones and
Behavior 35: 102–110.
35. Cohen-Bendahan CC, van de Beek C, Berenbaum SA (2005) Prenatal sex
hormone effects on child and adult sex-typed behavior: methods and findings.
Neuroscience and Biobehavioral Reviews 29: 353–384.
36. Servin A, Nordenstro ¨m A, Larsson A, Bohlin G (2003) Prenatal androgens and
gender-typed behavior: a study of girls with mild and severe forms of congenital
adrenal hyperplasia. Developmental Psychology 39: 440–450.
37. Zucker KJ, Bradley SJ, Oliver G, Blake J, Fleming S, et al. (1996) Psychosexual
development of women with congenital adrenal hyperplasia. Hormones and
Behavior 30: 300–318.
38. Hines M, Brook C, Conway GS (2004) Androgen and psychosexual
development: core gender identity, sexual orientation and recalled childhood
gender role behavior in women and men with congenital adrenal hyperplasia
(CAH). Journal of Sex Research 41: 75–81.
39. Meyer-Bahlburg HF, Dolezal C, Baker SW, New MI (2008) Sexual orientation
in women with classical or non-classical congenital adrenal hyperplasia as a
function of degree of prenatal androgen excess. Archives of Sexual Behavior, 37:
40. Hines M, Golombok S, Rust J, Johnston KJ, Golding J (2002) Avon
Longitudinal Study of Parents and Children Study Team. Testosterone during
pregnancy and gender role behavior of preschool children: a longitudinal,
population study. Child Development 73: 1678–1687.
41. Auyeung B, Baron-Cohen S, Ashwin E, Knickmeyer R, Taylor K, et al. (2009)
Fetal testosterone predicts sexually differentiated childhood behavior in girls and
in boys. Psychological Sciences 20: 144–148.
42. Grimbos T, Dawood K, Burriss RP, Zucker KJ, Puts DA (2010) Sexual
orientation and the second to fourth finger length ratio: a meta-analysis in men
and women. Behavioral Neuroscience 124: 278–287.
43. Spector T, Williams F (2006) The UK Adult Twin Registry (TwinsUK). Twin
Research and Human Genetics 9: 899–906.
44. Ooki S, Yamada K, Asaka A, Hayakawa K (1999) Zygosity diagnosis of twins by
questionnaire. Acta Geneticae Medicae et Gemellologiae 39: 109–115.
45. Meyer-Bahlburg HF, Dolezal C, Zucker KJ, Kessler SJ, Schober JM, et al.
(2006) The recalled childhood gender questionnaire-revised: a psychometric
analysis in a sample of women with congenital adrenal hyperplasia. Journal of
Sex Research 43: 364–367.
46. Wilson GD, Rahman Q (2005) Born gay: the psychobiology of sex orientation.
London: Peter Owen.
47. Neale MC, Boker SM, Xie G, Maes HH (2006) Mx: Statistical modeling. 7th
edn. Richmond: Virginia Commonwealth University.
48. Neale MC, Cardon LR (1992) Methodology for genetic studies of twins and
families. Dordrecht, the Netherlands: Kluwer Academic.
49. Martin NG, Eaves LJ, Kearsey MJ, Davies P (1978) The power of the classical
twin design. Heredity 40: 97–116.
50. Posthuma D, Beem AL, de Geus EJC, van Baal GCM, von Hjelmborg JB, et al.
(2003) Theory and practice in quantitative genetics. Twin Research 6: 361–376.
51. Williams MK, Cherkas LF, Spector TD, MacGregor AJ (2004) A common
genetic factor underlies hypertension and other cardiovascular disorders. BMC
Cardiovascular Disorders 4: 20–26.
52. Baumgartner H, Steenkamp JB (2001) Response Styles in Marketing Research:
A Cross-National Investigation. Journal of Marketing Research 143: 143–156.
53. Bem DJ (1996) Exotic becomes erotic: a developmental theory of sexual
orientation. Psychological Review 103: 320–335.
54. Dunne MP, Bailey JM, Kirk KM, Martin NG (2000) The subtlety of sex
atypicality. Archives of Sexual Behavior 29: 549–565.
55. Macke JP, Hu N, Hu S, Bailey M, King VL, et al. (1993) Sequence variation in
the androgen receptor gene is not a common determinant of male sexual
orientation. American Journal of Human Genetics 53: 844–852.
56. Dupree MG, Mustanski BS, Bocklandt S, Nievergelt C, Hamer DH (2004) A
candidate gene study of CYP19 (aromatase) and male sexual orientation.
Behavior Genetics 34: 243–250.
57. Heath AC, Madden PAF, Martin NG (1998) Assessing the effects of cooperation
bias and attrition in behavioral genetic research using data-weighting. Behavior
Genetics 28: 415–427.
58. Hayes RD, Dennerstein L, Bennett CM, Sidat M, Gurrin LC, et al. (2008) Risk
factors for female sexual dysfunction in the general population: exploring factors
associated with low sexual function and sexual distress. Journal of Sexual
Medicine 5: 1681–1693.
59. Witting K, Santtila P, Varjonen M, Jern P, Johansson A, et al. (2008) Female
sexual dysfunction, sexual distress, and compatibility with partner. Journal of
Sexual Medicine 5: 2587–2599.
60. Andrews T, Hart DJ, Snieder H, de Lange M, Spector TD, et al. (2001) Are
twins and singletons comparable? A study of disease- related and lifestyle
characteristics. Twin Research 4: 464–477.
The Genetics of Sexual Orientation
PLoS ONE | www.plosone.org8 July 2011 | Volume 6 | Issue 7 | e21982