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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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http://dx.doi.org/10.1016/j.evolhumbehav.2014.06.009

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ACCEPTED MANUSCRIPT1

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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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

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