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Testing the prediction from sexual selection of a positive genetic correlationbetween human mate preferences and corresponding traits
Karin J.H. Verweij, Andrea V. Burri, Brendan P. Zietsch
PII: S1090-5138(14)00079-8DOI: doi: 10.1016/j.evolhumbehav.2014.06.009Reference: ENS 5922
To appear in: Evolution and Human Behavior
Received date: 19 April 2013Revised date: 19 June 2014Accepted date: 24 June 2014
Please cite this article as: Verweij, K.J.H., Burri, A.V. & Zietsch, B.P., Testingthe prediction from sexual selection of a positive genetic correlation between humanmate preferences and corresponding traits, Evolution and Human Behavior (2014), doi:10.1016/j.evolhumbehav.2014.06.009
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Testing the prediction from sexual selection of a positive genetic correlation between human
mate preferences and corresponding traits
KARIN J. H. VERWEIJ1,2†
, ANDREA V. BURRI3,4
, BRENDAN P. ZIETSCH1,5 *†
† These authors contributed equally
Affiliations:
1 School of Psychology, University of Queensland, St. Lucia, Brisbane, Queensland,
Australia
2 Department of Developmental Psychology, VU University, Amsterdam, the Netherlands
3 Department of Twin Research and Genetic Epidemiology, St. Thomas' Hospital, King's
College London.
4 Institute of Psychology, Psychopathology and Clinical Intervention, University of Zurich,
Switzerland.
5 Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Herston,
Brisbane, Queensland, Australia.
Running head: Genetic correlation between traits and preferences
Word count: 2805
Key words: twin study, heritability, mate choice, Fisherian runaway, good genes, signal,
display
*Corresponding author: [email protected] School of Psychology, University of
Queensland, St. Lucia, Brisbane, Queensland, Australia, 4072
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Testing the prediction from sexual selection of a positive genetic correlation between
human mate preferences and corresponding traits
Abstract
Sexual selection can cause evolution in traits that affect mating success, and it has thus been
implicated in the evolution of human physical and behavioural traits that influence
attractiveness. We use a large sample of identical and nonidentical female twins to test the
prediction from mate choice models that a trait under sexual selection will be positively
genetically correlated with preference for that trait. Six of the eight preferences we
investigated, i.e. height, hair colour, intelligence, creativity, exciting personality, and
religiosity, exhibited significant positive genetic correlations with the corresponding traits,
while the personality measures ‘easy going’ and ‘kind and understanding’ exhibited no
phenotypic or genetic correlation between preference and trait. The positive results provide
important evidence consistent with the involvement of sexual selection in the evolution of
these human traits.
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1.0 Introduction
Sexual selection is an evolutionary process generated by individual differences in
number and identity of mates (Andersson 1994). These differences can result from
interactions between individuals of the same-sex (intrasexual selection; e.g. male-male
competition) or opposite-sex (intersexual selection; i.e. mate choice). Here we focus on the
latter, in which heritable traits in one sex are subject to selection pressure from mate
preferences of the opposite sex.
Humans are highly selective when choosing a partner, and mate choices are based on
a variety of traits providing cues to quality or compatibility (Buss and Barnes 1986). These
attractive traits can potentially affect reproduction, and it has been suggested that the
consequent sexual selection has played an important role in shaping our physical and
behavioural characteristics (Darwin 1859, 1871; Miller 2000). Sexual selection tends to result
in traits that differ greatly between even closely related species (Darwin 1871; Andersson
1994) and also show wide heritable variation within species (Pomiankowski and Moller
1995). Many defining human characteristics are of known importance in mate selection,
differ greatly from those of other apes, and exhibit large heritable variation between
individuals, begging investigation into the possible role of sexual selection in their evolution.
Such traits include intelligence, creativity, personality characteristics, and
morphological characteristics including body shape and size, and hair colour. As well as
differing distinctly from those of apes, these traits exhibit substantial heritable variation
within humans (Bouchard and McGue 2003; Zietsch et al. 2011), and recent evidence
indicates substantial heritable variation in unconstrained mate preferences for these traits
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(Verweij et al. 2012; Zietsch et al. 2012). Despite these clues, formulating tests of the
historical influence of sexual selection on the traits has proven difficult. However,
evolutionary genetics does provide a testable prediction for traits under sexual selection.
Given heritable variation in traits and trait preferences, individuals with stronger-than-
average preference for a certain trait will tend to choose a mate with above-average values of
that trait, with the resulting offspring tending to inherit alleles predisposing to both stronger-
than-average trait and stronger-than-average preference. This co-inheritance leads to linkage
disequilibrium (i.e. correlated allelic values across loci, and therefore genetic correlation)
between a trait and the preference for it. As such, a prediction from mate choice models is
that a trait under sexual selection will be positively genetically correlated with preference for
that trait (Lande 1981; Fuller et al. 2005).
In many animals, apparent sexual displays are only present (or highly exaggerated) in
the male, and female preference for these displays drives sexual selection. As such, animal
studies have generally tested for a genetic correlation between male display and female
preference, since while genes for preference and display are expected to be present in each
individual, phenotypic expression of the genes will only be observed in one or the other sex.
Using designs such as artificial selection and full-sib/half-sib breeding, these studies have
yielded positive genetic correlations between female preferences and corresponding male
traits in some studies (of insects and fish; Bakker 1993; Houde 1994; Wilkinson and Reillo
1994; Blows 1999; Simmons and Kotiaho 2007), but not in other studies (of insects,
caterpillars, fish, and birds; Lofstedt et al. 1989; Breden and Hornaday 1994; Morris et al.
1996; Muhlhauser and Blanckenhorn 2004; Ritchie et al. 2005; Qvarnstrom et al. 2006;
Allison et al. 2008; Zhou et al. 2011). In humans, many of the traits that most contribute
strongly to attractiveness are exhibited in both sexes, and the vast majority of traits that can
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be measured in both sexes (e.g. height, body mass index, personality traits, intelligence) do
not exhibit marked genetic sex-limitation – that is, for most human traits, underlying genes
express similar effects in males and females (Vink et al. 2012). As such, genetic correlation
between trait and preference should be observable within-sex. Only one human study has
tested this, using a relatively small sample of female twins to find significant genetic
correlation between altruism and preference for altruism in a mate al (Phillips et al. 2010).
In the present study we investigate a range of mate preferences for traits of known
salience in human mate selection, along with the corresponding traits themselves, in a large
sample of identical and nonidentical female twins. Using bivariate genetic modelling, we test
the prediction from sexual selection that each trait preference will be positively genetically
correlated with the trait itself.
2.0 Methods
2.1 Participants
We use data on female twins from the UK Adult Twin Registry, a population-based cohort of
Caucasian twins (see Spector and Williams, 2006). The twin registry consists primarily of
females (because the initial focus of the registry was on osteoporosis and osteoarthritis,
conditions with a higher prevalence in females) and the male sample was too small to provide
sufficient power for the equivalent analyses in males. Note that Andrew et al (2001) tested
the representativeness of the sample for a number of diseases, traits and environmental
factors and they found the twin sample to be no different to the UK population or a singleton
population cohort from North-East London. For the preference data, the sample (collected in
2002) consisted of 1,763 full pairs and 2,823 single twins, the latter group retained to increase
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precision of the means. Approximately half (49.1%) of the twins were identical
(monozygotic; MZ) and half (50.9%) nonidentical (dizygotic; DZ), and ages ranged from 19
to 83 (51.0±12.7). The data on personal traits were collected at several time points as part of
various studies between 1999 and 2009. Sample sizes therefore vary considerably between
the various measures, and these are noted when describing the measures below.
Zygosity of the twins was determined based on standardised questions about physical
similarity that have over 95% accuracy when judged against genotyping and when uncertain
checked by genotyping (Peeters et al. 1998). Further details about the sample, data collection,
zygosity determination and on the comparability of the twins to age-matched singleton
populations can be found elsewhere (Spector and Williams, 2006, Andrew et al., 2001). The
data used in this project are available through http://www.twinsuk.ac.uk/data-
access/submission-procedure/.
2.2 Measures
The mate preferences reported here, shown in Table 1, come from a) a scale on which
participants ranked 13 traits according to their relative importance in a long-term mate,
validated by Buss and Barnes (1986) and used in Buss’ landmark study of mate preferences
across 37 cultures (Buss 1989), and b) a range of sexually dimorphic physical traits for which
participants reported the characteristic they were more likely to be attracted to, via
dichotomous response options. The preference measures are described in more detail
elsewhere (Verweij et al. 2012; Zietsch et al. 2012). The subset of preferences used in this
study were those for which a roughly corresponding trait measure was available on the same
sample – those preferences were ‘height’ (tall/short), ‘hair colour’ (brown/blonde),
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‘intelligent’, ‘creative and artistic’, ‘exciting personality’, ‘easygoing’, ‘kind and
understanding’, and ‘religious’.
As for the corresponding personal traits, height (N pairs = 3349) and hair colour (N
pairs = 129) were self-reported, and analysed as ordinal data so as to be able to be analysed
with the corresponding dichotomous preference measures. ‘Hair colour’ was dichotomised
into darker colour (black, brown and chestnut) and lighter colours (blonde) (NB: red,
strawberry and mouse were treated as missing data, the aforementioned N does not include
these). ‘Height’ was converted into six ordinal categories, so it could be analysed with the
dichotomous preference measure of height. ‘Intelligence’ (N pairs = 308) was measured with
the National Adult Reading Test (Nelson 1982), which is a validated measure of intelligence
that is strongly correlated (r≈0.8) with full-scale and verbal IQ (Nelson 1982; Crawford et al.
1989). ‘Creativity’ (N pairs = 2174) was measured with slight variations of an item on which
participants rated their creativity from ‘Not very artistic or creative’, to ‘Extremely artistic or
creative’ on a five-, seven-, or ten-point rating scale, depending on which questionnaire it was
taken from. Similarly, ‘religiosity’ (N pairs = 2796) was measured with an item on which
participants rated their religiosity from ‘Not at all’ to ‘Extremely’ on a ten-point scale
(standardised). ‘Exciting personality’ (N pairs = 1746), ‘easygoing’ (N pairs = 2455), and
‘kind and understanding’ (N pairs = 1750) were measured with the TIPI (Ten-Item
Personality Inventory; Gosling et al. 2003) scales extraversion, emotional stability, and
agreeableness, respectively, plus some additional very similar items. For ‘creativity’,
‘religiosity’, and the personality measures, scores were standardised per-item, and where
scores on multiple items were available for a participant they were averaged. More
information about the measures can be found in the Supplementary Material.
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2.3 Analyses
In accordance with standard genetic analysis of twin data, all analyses employed maximum-
likelihood modelling procedures using the statistical packages Mx (Neale et al. 2006). In
maximum-likelihood modelling, the goodness-of-fit of a model to the observed data is
distributed as chi-square (χ2). By testing the change in chi-square (Δχ
2) against the change in
degrees of freedom (Δdf), we can test whether dropping or equating specific model
parameters significantly worsens the model fit, and can thus test hypotheses regarding those
parameters. In the case of dichotomous and ordinal traits/preferences, it was assumed that one
or more thresholds delimiting the categories overlayed a normally distributed continuum of
liability. Other traits/preferences were analysed as continuous variables and were transformed
(square root, log, or inverse) to improve normality where necessary.
Additive genetic variance (A) is due to summed allelic effects across multiple genes;
because MZ twins share all of their alleles, while DZ twins share on average only half of their
segregating alleles; A influences are correlated at 1.0 for MZ pairs, and 0.5 for DZ pairs.
Shared environmental factors (C) may include shared home environment, parental style, and
uterine environment; C influences are assumed to correlate at 1.0 for both MZ and DZ pairs.
Residual variance (E) includes environmental factors not shared by twin pairs (e.g.
idiosyncratic experiences, such as traumatic events, different peers etc), stochastic biological
effects, and measurement error; E influences are correlated at zero for both MZ and DZ twin
pairs. In reality, observed MZ and DZ twin correlations generally reflect a combination of A,
C, and E influences, and structural equation modelling determines the combination that best
matches the observed data (Posthuma et al. 2003). By using cross-twin cross-trait
correlations, covariance between the preferences and corresponding traits can be partitioned
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into A, C, and E components in the same way as variance within traits. If the cross-twin-
cross-trait correlation is higher for MZ twin pairs than for DZ twin pairs, this indicates that
the correlation between the preference and the corresponding trait is partly explained by
genetic influences. Again, structural equation modelling is used to precisely quantify the
relative contribution of A, C, and E influences. This contribution can be quantified in
different ways. Here we report the genetic correlation, which measures the overlap in the
genetic variance of each trait, with a genetic correlation of zero reflecting no overlap in the
genetic variance and 1 reflecting complete overlap (i.e. the same genetic factors account for
variation in the two traits). Note that a strong genetic correlation does not imply a strong
phenotypic correlation, because the latter is also a function of the heritabilities of the traits. A
more detailed explanation for non-experts can be found in the Supporting Information
(‘Using the classical twin design to decompose the covariance between two traits’) of Zietsch
et al. (2014).
Non-additive genetic variation (D) can in principle be estimated in the twin design,
but not at the same time as estimating C. Further, in the twins-only design, there is very little
power to distinguish D from A, because both sources of variance predict similar patterns of
twin correlations, and preliminary analyses (unreported) did not reveal any significant D
variation. Because unparameterized non-additive genetic variation is captured by the A
parameter (Keller et al. 2010), we estimate A, C, and E with the caveat that A may include
non-additive genetic variation and so is best considered as broad-sense heritability (H²).
An assumption of the twin design is that trait-relevant environments are equally
similar for MZ and DZ twins. Tests of this assumption suggest it is valid for a variety of traits
including personality (Plomin et al. 1976; Morrisyates et al. 1990; Borkenau et al. 2002) and
sexual orientation (Kendler et al. 2000). (Note that greater similarity in MZ twin
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environments does not violate this assumption if it is elicited by the greater similarity of the
twins themselves, see Eaves et al. (2003).)
Further details of the twin design, including assumptions, can be found elsewhere
(Neale and Cardon 1992; Posthuma et al. 2003). Age was controlled for by modelling it as a
covariate effect, so that twin pair correlations would not be inflated due to pairs being the
same age.
3.0 Results
We first tested the assumption that MZ and DZ twins are comparable except for their level of
genetic similarity - inequality of means and variances of MZ and DZ twins could suggest
non-random sampling or sibling interaction effects that could bias estimation of variance
components (Carey 1992). There were no significant mean or variance differences between
MZ and DZ twins for preferences/traits after correcting for the number of tests performed. As
such, all preference/trait means and variance were constrained in the model to be equal
between MZ and DZ twins in subsequent analyses.
For all preferences and traits, the MZ twin pair correlation was greater than the DZ
twin pair correlation (Table 1), suggesting widespread genetic variance. To quantify this
genetic variance and test for genetic correlations, bivariate ACE variance-components models
were performed for each preference and its corresponding trait. Table 2 shows the genetic
and environmental components of variance of each trait (i.e. the proportion of variance
accounted for by genetic, shared environmental, and residual factors), as well as the genetic,
shared environmental, and residual correlations between each preference-trait pair. As can be
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seen, preferences for height, hair colour, intelligence, creativity, exciting personality, and
religiosity all yielded significant positive genetic correlations with the corresponding traits, in
accordance with predictions from sexual selection, whereas ‘kind and understanding’ and
‘easygoing’ were not phenotypically or genetically correlated with their respective
preferences.
4.0 Discussion
Most of the mate preferences we examined exhibited significant genetic correlations
with the corresponding preferred traits, in accordance with predictions from sexual selection.
These genetically correlated traits and preferences included height, hair colour, intelligence,
creativity, exciting personality, and religiosity. On the other hand, ‘kind and understanding’
and ‘easygoing’ were not phenotypically or genetically correlated with their respective
preferences.
While our findings of genetic correlations between traits and corresponding trait
preferences are consistent with predictions from sexual selection, the results do not suggest
that the traits evolved exclusively via sexual selection, nor do they definitively confirm that
sexual selection is at all involved in their evolution. First, the action of sexual selection does
not preclude concurrent natural selection; sexually selected traits may also be selected for
their benefits to survival. For example, even if we find the evidence compelling that human
intelligence has been subject to sexual selection (Miller 2000), it also seems likely that
greater intelligence would have enhanced survival prospects. Second, there may be
alternative explanations (i.e. not involving the sexual selection process outlined in the
Introduction) for genetic correlations between traits and corresponding trait preferences. For
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example, ‘genetic similarity theory’ (Rushton 1989) proposes that humans have an evolved
tendency to prefer mates who are genetically similar to themselves, which is enacted via
preferences for phenotypic similarity on heritable traits – according to the theory, this
preference is adaptive because it benefits copies of one’s own genes (i.e. those in the
genetically similar mate) by giving the vehicle of those genes (i.e. the mate) an extra mating.
However, there are serious theoretical problems with this idea. One problem is that mating
with the genetically similar individual confers an opportunity cost on that individual (they are
less able to mate with someone else – especially problematic in humans, in which bi-parental
investment in offspring is standard), countervailing the adaptive benefit of such a preference
(Ridley 1989). Another problem is that mating with a genetically similar individual risks the
detrimental effects of inbreeding depression (Charlesworth and Willis 2009). Several more
proximate explanations for attraction to self-similar individuals (‘homophily’) have been
proposed - for example, that similar individuals reinforce the logic and consistency of the
each other’s worlds (Byrne 1971) - but these explanations are inconsistently supported by
evidence (Montoya and Horton 2013) and in any case apply only to attitudinal and
personality traits, so would not predict our findings of genetic correlation between physical
traits (height, hair colour). Social exchange theory (Homans 1961) predicts assortative mating
on overall mate value, but not necessarily on individual traits, and not on traits that are
unrelated to mate value in one or both sexes or oppositely related to mate value in each sex
(e.g. hair colour, height). Lay ideas about preferring similar individuals as a partner because
of ‘matching’ or ‘getting along’ better have not been theoretically well-developed, and any
such alternative explanations would have to apply across our broad range of traits from
intelligence to hair colour. Future research could tease out competing hypotheses by
incorporating parental data into models that could then, given sufficient power, distinguish
between genetic correlation due to linkage disequilibrium (as per the sexual selection process
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described in the Introduction) and genetic correlation due to other causal mechanisms (Keller
et al. 2009). (Note that parental data is not available for the sample used here.)
While previous studies testing the same hypothesis in animals have focussed on
females, our study in humans would ideally have included male preferences and traits as well.
Humans are unusual in the extent to which males as well as females are choosy in mate
selection (Stewart-Williams and Thomas 2013), which is why we were able to test and find
preference-trait genetic correlations within-sex. We might expect similar genetic correlations
between traits and corresponding preferences in men, but there were insufficient male data
with which to test this interesting hypothesis.
There are methodological issues that need to be taken into account when interpreting
the current findings. One potential limitation is the use of self-report to assess mate
preferences. While studies have shown correspondence between self-reported mate
preferences and those revealed by implicit testing (Wood and Brumbaugh 2009), we cannot
rule out the possibility that our self-report measures could be subject to bias that could
influence the results. Further to the general issue of self-report measures, some of the
reported preferences did not refer precisely to the corresponding trait, which may have
reduced or eliminated a true correlation between trait and preference; this issue is particularly
pertinent for ‘kindness’, for which the trait and preference were measured with items using
quite different terms (i.e. preference: ‘kindness’; trait: ‘critical/quarrelsome’ and
‘sympathetic/warm’) and no significant correlation between trait and preference was found.
Overall, these findings provide the first evidence across a range of mating-relevant
human traits for a genetic correlation between preferences and the corresponding preferred
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traits. This is important evidence consistent with the hypothesis that sexual selection has been
involved in the evolution of these traits.
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Acknowledgements
We would like to thank the twins for their voluntary contribution to this research project. We
would also like to thank the staff of the Twin Research Unit (KCL) for their help and support
in undertaking this project. The Wellcome Trust provides core support for Department of
Twin Research. BPZ is supported by a Discovery Early Career Research Award from the
Australian Research Council.
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References
Allison, J. D., D. A. Roff, and R. T. Carde. 2008. Genetic independence of female signal form and
male receiver design in the almond moth, Cadra cautella. J. Evol. Biol. 21:1666-1672.
Andersson, M. 1994. Sexual selection. Princeton University Press, New Jersey.
Andrew, T., D. J. Hart, H. Snieder, M. de Lange, T. D. Spector, and A. J. MacGregor. 2001. Are
twins and singletons comparable? A study of disease-related and lifestyle characteristics in
adult women. Twin Research 4:464-477.
Bakker, T. C. M. 1993. Positive genetic correlation between female preference and preferred male
ornament in sticklebacks. Nature 363:255-257.
Blows, M. W. 1999. Evolution of the genetic covariance between male and female components of
mate recognition: an experimental test. Proceedings of the Royal Society of London Series B-
Biological Sciences 266:2169-2174.
Borkenau, P., R. Riemann, A. Angleitner, and F. M. Spinath. 2002. Similarity of childhood
experiences and personality resemblance in monozygotic and dizygotic twins: a test of the
equal environments assumption. Personality and Individual Differences 33:261-269.
Bouchard, T. J., and M. McGue. 2003. Genetic and environmental influences on human psychological
differences. J. Neurobiol. 54:4-45.
Breden, F., and K. Hornaday. 1994. Test of indirect models of selection in the Trinidad guppy.
Heredity 73:291-297.
Buss, D. M. 1989. Sex-differences in human mate preferences: Evolutionary hypotheses tested in 37
cultures. Behavioral and Brain Sciences 12:1-14.
Buss, D. M., and M. Barnes. 1986. Preferences in human mate selection. Journal of Personality and
Social Psychology 50:559-570.
Byrne, D. 1971. The attraction paradigm. Academic Press, New York.
Carey, G. 1992. Twin imitation for antisocial behaviour: Implications for genetic and family
environment research. Journal of Abnormal Psychology 101:18-25.
ACC
EPTE
D M
ANU
SCR
IPT
ACCEPTED MANUSCRIPT17
Charlesworth, D., and J. H. Willis. 2009. The genetics of inbreeding depression. Nat. Rev. Genet.
10:783-796.
Crawford, J. R., L. E. Stewart, R. H. B. Cochrane, D. M. Parker, and J. A. O. Besson. 1989. Construct
validity of the National Adult Reading Test: a factor analytic study. Personality and
Individual Differences 10:585-587.
Darwin, C. R. 1859. On the origin of species by means of natural selection, or the preservation of
favoured races in the struggle for life. John Murray, London.
Darwin, C. R. 1871. The descent of man, and selection in relation to sex. John Murray, London.
Eaves, L., D. Foley, and J. Silberg. 2003. Has the "equal environments" assumption been tested in
twin studies? Twin Research 6:486-489.
Fuller, R. C., D. Houle, and J. Travis. 2005. Sensory bias as an explanation for the evolution of mate
preferences. American Naturalist 166:437-446.
Gosling, S. D., P. J. Rentfrow, and W. B. Swann. 2003. A very brief measure of the Big-Five
personality domains. Journal of Research in Personality 37:504-528.
Homans, G. C. 1961. Social behavior: Its elementary forms.
Houde, A. E. 1994. Effect of artificial selection on male color patterns on mating preference of female
guppies. Proceedings of the Royal Society of London Series B-Biological Sciences 256:125-
130.
Keller, M. C., S. E. Medland, and L. E. Duncan. 2010. Are extended twin family designs worth the
trouble? A comparison of the bias, precision, and accuracy of parameters estimated in four
twin family models. Behavior Genetics 40:377-393.
Keller, M. C., S. E. Medland, L. E. Duncan, P. K. Hatemi, M. C. Neale, H. H. M. Maes, and L. J.
Eaves. 2009. Modeling extended twin family data I: Description of the cascade model. Twin
Research and Human Genetics 12:8-18.
Kendler, K. S., L. M. Thornton, S. E. Gilman, and R. C. Kessler. 2000. Sexual orientation in a US
national sample of twin and nontwin sibling pairs. American Journal of Psychiatry 157:1843-
1846.
ACC
EPTE
D M
ANU
SCR
IPT
ACCEPTED MANUSCRIPT18
Lande, R. 1981. Models of speciation by sexual selection on polygenic traits. Proceedings of the
National Academy of Sciences of the United States of America-Biological Sciences 78:3721-
3725.
Lofstedt, C., B. S. Hansson, W. Roelofs, and B. O. Bengtsson. 1989. No linkage between genes
controlling female pheromone production and male pheromone response in the European corn
borer, Ostrinia-nubilalis Hubner (Lepidoptera; Pyralidae). Genetics 123:553-556.
Miller, G. F. 2000. The mating mind: How sexual choice shaped the evolution of human nature.
Doubleday, New York.
Montoya, R. M., and R. S. Horton. 2013. A meta-analytic investigation into the processes underlying
the similarity effect. Journal of Social and Personal Relationships 30:64-94.
Morris, M. R., W. E. Wagner, and M. J. Ryan. 1996. A negative correlation between trait and mate
preference in Xiphophorus pygmaeus. Anim. Behav. 52:1193-1203.
Morrisyates, A., G. Andrews, P. Howie, and S. Henderson. 1990. Twins: A test of the equal-
environments assumption. Acta Psychiatrica Scandinavica 81:322-326.
Muhlhauser, C., and W. U. Blanckenhorn. 2004. The quantitative genetics of sexual selection in the
dung fly sepsis cynipsea. Behaviour 141:327-341.
Neale, M. C., S. M. Boker, G. Xie, and H. H. Maes. 2006. Mx: Statistical modeling. Department of
Psychiatry, VCU Box 900126, Richmond, VA 23298.
Neale, M. C., and L. R. Cardon. 1992. Methodology for genetic studies of twins and families. Kluwer,
Boston.
Nelson, H. E. 1982. The National Adult Reading Test (NART): The manual. NFER-Nelson, Windsor.
Phillips, T., E. Ferguson, and F. Rijsdijk. 2010. A link between altruism and sexual selction: Genetic
influence on altruistic behaviour and mate preference towards it. British Journal of
Psychology 101:809-819.
Plomin, R., L. Willerman, and J. C. Loehlin. 1976. Resemblance in appearance and the equal
environments assumption in twin studies of personality traits. Behavior Genetics 6:43-52.
Pomiankowski, A., and A. P. Moller. 1995. A resolution of the lek paradox. Proceedings of the Royal
Society of London Series B-Biological Sciences 260:21-29.
ACC
EPTE
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Posthuma, D., A. L. Beem, E. J. C. de Geus, G. C. M. van Baal, J. B. von Hjelmborg, I. Lachine, and
D. I. Boomsma. 2003. Theory and practice in quantitative genetics. Twin Research 6:361-376.
Qvarnstrom, A., J. E. Brommer, and L. Gustafsson. 2006. Testing the genetics underlying the co-
evolution of mate choice and ornament in the wild. Nature 441:84-86.
Ridley, M. 1989. When does natural selection favor assortative mating? Behavioral and Brain
Sciences 12:539-539.
Ritchie, M. G., M. Saarikettu, and A. Hoikkala. 2005. Variation, but no covariance, in female
preference functions and male song in a natural population of Drosophila montana. Anim.
Behav. 70:849-854.
Rushton, J. P. 1989. Genetic similarity theory, human altruism, and group selection. Behavioral and
Brain Sciences 12:503-517.
Simmons, L. W., and J. S. Kotiaho. 2007. Quantitative genetic correlation between trait and
preference supports a sexually selected sperm process. Proceedings of the National Academy
of Sciences of the United States of America 104:16604-16608.
Stewart-Williams, S., and A. G. Thomas. 2013. The ape that thought it was a peacock: Does
evolutionary psychology exaggerate human sex differences? Psychological Inquiry 24:137-
168.
Verweij, K. J. H., A. V. Burri, and B. P. Zietsch. 2012. Evidence for genetic variation in human mate
preferences for sexually dimorphic physical traits. PLos One 7:e49294.
Vink, J. M., M. Bartels, T. van Beijsterveldt, J. van Dongen, J. van Beek, M. A. Distel, M. H. M. de
Moor, D. J. A. Smit, C. C. Minica, L. Ligthart, L. M. Geels, A. Abdellaoui, C. M.
Middeldorp, J. J. Hottenga, G. Willemsen, E. J. C. de Geus, and D. I. Boomsma. 2012. Sex
differences in genetic architecture of complex phenotypes? PLos One 7.
Wilkinson, G. S., and P. R. Reillo. 1994. Female choice response to artificial selection on an
exaggerated male trait in a stalk-eyed fly. Proceedings of the Royal Society of London Series
B-Biological Sciences 255:1-6.
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EPTE
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Wood, D., and C. C. Brumbaugh. 2009. Using revealed mate preferences to evaluate market force and
differential preference explanations for mate selection. Journal of Personality and Social
Psychology 96:1226-1244.
Zhou, Y. H., J. K. Kelly, and M. D. Greenfield. 2011. Testing the fisherian mechanism: examining the
genetic correlation between male song and female response in waxmoths. Evol. Ecol. 25:307-
329.
Zietsch, B. P., R. Kuja-Halkola, H. Walum, and K. J. Verweij. 2014. Perfect genetic correlation
between number of offspring and grandoffspring in an industrialized human population.
Proceedings of the National Academy of Sciences 111:1032-1036.
Zietsch, B. P., K. J. H. Verweij, and A. V. Burri. 2012. Heritability of preferences for multiple cues of
mate quality in humans. Evolution 66:1762-1772.
Zietsch, B. P., K. J. H. Verweij, A. C. Heath, and N. G. Martin. 2011. Variation in human mate
choice: Simultaneously investigating heritability, parental influence, sexual imprinting, and
assortative mating. American Naturalist 177:605-616.
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Table 1. Identical (MZ) and nonidentical (DZ) twin pair correlations (and 95% confidence
intervals) for each trait and preference for that trait.
MZ twin pair correlation
(95% CI)
DZ twin pair correlation
(95% CI)
Heighta
Trait 0.90 (0.89, 0.91) 0.55 (0.51, 0.58)
Preference 0.31 (0.13, 0.48) 0.12 (-0.10, 0.34)
Hair coloura
Trait 1.00 (0.98, 1.00) 0.26 (-0.18, 0.63)
Preference 0.28 (0.14, 0.41) -0.12 (-0.27, 0.04)
Intelligentb
Trait 0.73 (0.64, 0.79) 0.33 (0.21, 0.44)
Preference 0.36 (0.28, 0.43) 0.28 (0.20, 0.35)
Creative and artisticb
Trait 0.46 (0.42, 0.50) 0.13 (0.06, 0.19)
Preference 0.23 (0.14, 0.31) 0.16 (0.08, 0.24)
Religiousb
Trait 0.54 (0.51, 0.57) 0.38 (0.33, 0.42)
Preference 0.29 (0.21, 0.36) 0.22 (0.13, 0.29)
Exciting personalityb
Trait 0.46 (0.41, 0.50) -0.01 (-0.08, 0.06)
Preference 0.25 (0.17, 0.32) 0.05 (-0.04, 0.13)
Kind and
understandingb
Trait 0.29 (0.23, 0.34) 0.13 (0.06, 0.20)
Preference 0.24 (0.16, 0.32) 0.08 (0.00, 0.16)
Easy goingb
Trait 0.35 (0.30, 0.39) 0.09 (0.02, 0.15)
Preference 0.20 (0.11, 0.28) 0.13 (0.05, 0.22)
a. Preferences obtained from dichotomous forced-choice items regarding morphological
characteristics (tall/short, brown/blonde)
b. Preferences obtained from a scale on which participants ranked 13 traits according to their
relative importance in a long-term mate
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Table 2. Variance components of traits and corresponding preferences, as well as phenotypic, genetic,
shared environmental, and residual correlations between traits and corresponding preferences.
*P<.05, ** P <.01, *** P <.001
NB: All models include A, C and E parameters, but where one of these parameters is zero for a
trait or corresponding preference, the corresponding A, C, or E correlation is meaningless and
therefore not reported.
Trait Preference
N
pa
irs
wi
th
bo
th
tra
its
A
Genes
(H²)
C
Shared
enviro
nment
E
Resi
dual
A
Genes
(H²)
C
Shared
enviro
nment
E
resi
dual
Pheno
typic
correl
ation
Genet
ic
correl
ation
Shared
environ
mental
correlat
ion
Resid
ual
correl
ation
Height 12
20
.72**
* .19*** .10 .26 .04 .70
.35**
* .53** 1.00 .16
Hair colour 52 1.00*
** .00 .00 .20* .00 .80 .34* .67*
Intelligent 12
5
.73**
* .00 .27 .19 .18* .63
.36**
*
1.00*
** -.04
Creative and
artistic
87
6
.44**
* .00 .56 .13 .10 .77
.23**
*
.69**
* .09*
Religious 10
00
.33**
* .21*** .46 .18 .13 .69
.57**
*
.87**
* .14**
.38**
*
Exciting
personality
74
9
.40**
* .00 .60 .22** .00 .78
.14**
* .27** .08*
Kind and
understandin
g
75
0
.28**
* .00 .72 .23** .00 .77 .02 -.06 .05
Easygoing 89
8
.32**
* .00 .68 .10 .09 .81 .00 -.01 -.02