# Rapid trait evolution drives increased speed and variance in experimental range expansions

Christopher Weiss-Lehman, Ruth A. Hufbauer Gillies, Brett A. Melbourne

► To cite this version:

Christopher Weiss-Lehman, Ruth A. Hufbauer Gillies, Brett A. Melbourne. Rapid trait evolution drives increased speed and variance in experimental range expansions. Nature Communications, 2017, 8, 10.1038/ncomms14303. [https://doi.org/10.1038/ncomms14303]

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INVITED REVIEWS AND SYNTHESES

# Patterns of MHC-dependent mate selection in humans and nonhuman primates: a meta-analysis

J. WINTERNITZ,\*†‡ J. L. ABBATE,§¶ E. HUCHARD,\*\* J. HAVLÍČEK†† and L. Z. GARAMSZEGI‡‡
\*Department of Evolutionary Ecology, Max Planck Institute for Evolutionary Biology, August-Thienemann-Strasse 2, 24306 Ploen, Germany, †Institute of Vertebrate Biology, Czech Academy of Sciences, v.v.i., Květná 8, 603 65 Brno, Czech Republic, ‡Institute of Botany, Czech Academy of Sciences, v.v.i., Lidická 25/27, 657 20 Brno, Czech Republic, §Institute of Ecology and Evolution, University of Bern, Balterstrasse 6, 3006 Bern, Switzerland, ¶INRA - UMR 1062 CBGP (INRA, IRD, CIRAD, Montpellier SupAgro), 755 Avenue du campus Agropolis, 34988 Montferrier-sur-Lez, France, \*\*CEFE UMR5175, CNRS - Université de Montpellier - EPHE, 1919 Route de Mende, 34295 Montpellier Cedex 5, France, ††Department of Zoology, Faculty of Science, Charles University, Viničná 7, 128 44 Prague 2, Czech Republic, ‡‡Department of Evolutionary Ecology, Estación Biológica de Doñana-CSIC, c/Americo Vespucio, s/n, 41092, Seville, Spain

## Abstract

Genes of the major histocompatibility complex (MHC) in vertebrates are integral for effective adaptive immune response and are associated with sexual selection. Evidence from a range of vertebrates supports MHC-based preference for diverse and dissimilar mating partners, but evidence from human mate choice studies has been disparate and controversial. Methodologies and sampling peculiarities specific to human studies make it difficult to know whether wide discrepancies in results among human populations are real or artefact. To better understand what processes may affect MHC-mediated mate choice across humans and nonhuman primates, we performed phylogenetically controlled meta-analyses using 58 effect sizes from 30 studies across seven primate species. Primates showed a general trend favouring more MHC-diverse mates, which was statistically significant for humans. In contrast, there was no tendency for MHC-dissimilar mate choice, and for humans, we observed effect sizes indicating selection of both MHC-dissimilar and MHC-similar mates. Focusing on MHC-similar effect sizes only, we found evidence that preference for MHC similarity was an artefact of population ethnic heterogeneity in observational studies but not among experimental studies with more control over sociocultural biases. This suggests that human assortative mating biases may be responsible for some patterns of MHC-based mate choice. Additionally, the overall effect sizes of primate MHC-based mating preferences are relatively weak (Fisher's Z correlation coefficient for dissimilarity $Z_r = 0.044$, diversity $Z_r = 0.153$), calling for careful sampling design in future studies. Overall, our results indicate that preference for more MHC-diverse mates is significant for humans and likely conserved across primates.

**Keywords:** good genes, HLA, inbreeding avoidance, major histocompatibility complex, mating preference, sexual selection

Received 18 May 2016; revision received 14 October 2016; accepted 19 October 2016

## Introduction

The major histocompatibility complex (MHC), an extraordinarily polymorphic and ancient chromosomal

region shared by virtually all vertebrates, is the most likely candidate for ‘good genes’ – genes with fitness benefits – due to its involvement in both immune defence and mate choice (Potts & Wakeland 1990; Hedrick 1994; Bernatchez & Landry 2003; Milinski 2006). The MHC encodes molecules that bind specific self- and pathogen-derived peptides and present these

Correspondence: Jamie Winternitz, Fax: +49 4522 763 310; E-mail: jcwinternitz@gmail.com
to T lymphocytes, thus initiating appropriate immune activation (Hughes & Yeager 1998). Class I molecules mainly bind intracellular pathogen peptides (e.g. viruses and bacteria) and are expressed by all nucleated cells, whereas class II molecules mainly bind extracellular parasite peptides (e.g. helminths, ectoparasites) and are expressed by professional immune antigen-presenting cells, such as mononuclear phagocytes or T cells (Knapp 2005). Currently over 9000 human MHC (HLA) class I alleles and over 3000 class II alleles have been recorded across human populations (Robinson et al. 2014). This hyperpolymorphism is attributed to parasite-mediated balancing selection promoting host allelic diversity to defend against a dynamic spectrum of parasites (Bernatchez & Landry 2003; Wegner et al. 2003a; Prugnolle et al. 2005; Šimková et al. 2006; Göüy de Bellocq et al. 2008; Solberg et al. 2008; Garamszegi & Nunn 2011; Eizaguirre et al. 2012; Garamszegi 2014).

Infectious agents are thought to be the strongest selective force shaping human evolutionary history (McMichael 2001; Fumagalli et al. 2011; Karlsson et al. 2014) and continue to have strong effects on fitness. For example, humans are currently known to be infected by over 1400 parasitic species (Taylor et al. 2001) and in 2010 parasites were responsible for nearly 64% of global deaths in children younger than 5 years (Liu et al. 2012). Parasite-mediated balancing selection in humans is identifiable at both broad and fine scales. At the population level, parasite-mediated balancing selection is indicated by spatial patterns of MHC polymorphism increasing with virus species richness (Prugnolle et al. 2005) and greater frequencies of protective MHC alleles in areas with greater parasite risk (Hill et al. 1991). Selection at the individual level is indicated by resistance of rare MHC genotypes to specific strains of pathogens (Thursz et al. 1995; Trachtenberg et al. 2003) with reciprocal selection for pathogen escape mutations to avoid MHC immune control (Goulder et al. 2001).

While it is clear that population MHC allelic diversity is associated with pathogen diversity and prevalence, there is also theoretical and empirical support for the role of sexual selection in maintaining heterozygosity and allelic variation across the MHC (Potts et al. 1991, 1994; Hedrick 1992; Jordan & Bruford 1998; Penn & Potts 1999; Winternitz et al. 2013; Ejsmond et al. 2014). Proximate mechanisms enabling MHC-mediated mate choice include odour and visual cues of MHC composition. Animals can discriminate between the different volatile peptides bound by products of specific alleles that contribute to body odours (Boyse et al. 1983; Potts et al. 1994; Carroll et al. 2002; Penn 2002; Leinders-Zufall et al. 2004; Milinski et al. 2005, 2013). Additionally, visual cues such as the expression of sexually dimorphic conspicuous traits, or even condition-related

behaviour, can be indicators of immune genotype (Hamilton & Zuk 1982; Folstad & Karter 1992).

Ultimate mechanisms for MHC-mediated mate choice can take three nonmutually exclusive forms (Piertney & Oliver 2006). (i) Preferences for MHC diversity (measured in terms of heterozygosity, or the number of MHC alleles) in mates could provide direct fitness benefits (e.g. healthier mates are better providers and less infectious to their mates and offspring) and/or indirect benefits if rare alleles, more likely to be carried by heterozygotes or by individuals harbouring many alleles, can be passed on to offspring [e.g. 'good-genes-as-heterozygosity' (Brown 1997, 1999]), although this remains to be demonstrated empirically. Thus, preferences for MHC diversity in mates could also increase offspring levels of heterozygosity in structured, finite populations (Fromhage et al. 2009). (ii) Preferences for specific MHC genotypes in potential mates could provide the direct benefits mentioned previously and also indirect benefits if genes that are protective against contemporary parasites can be passed to offspring. Protective alleles are thought to be more rare in the population (Slade & McCallum 1992) and so more likely to be carried by heterozygotes. Both preferences for heterozygosity and preferences for specific resistance genotypes can broadly fit under the category of preferences for MHC diversity for our current study. (iii) Preferences for MHC dissimilarity/complementarity in mates would provide indirect fitness benefits by increasing immunodiversity of offspring (Tregenza & Wedell 2000). Alternatively, preferences for MHC-dissimilar mates may be independent of immunogenetic benefits for offspring if MHC alleles serve as markers of relatedness in mates. This would provide indirect fitness benefits by avoiding consequences of close inbreeding (Yamazaki et al. 1988). If preference for dissimilar mates is for immunogenetic indirect benefits, then the extent of dissimilarity of the potential mate may matter. The optimality hypothesis proposes that offspring may benefit most from optimal rather than maximal immunodiversity (Wegner et al. 2003b; Milinski 2006), because having too many different alleles could actually deplete autoreactive T cells that are required for immune response (Nowak et al. 1992; Woelfing et al. 2009). In this case, the degree and composition of optimal MHC diversity for offspring are expected to primarily depend on the diversity of parasites in the environment (Wegner et al. 2003b; Milinski 2006; Eizaguirre et al. 2009).

Instead of mediating mate choice preferences, the MHC may be incidentally associated with signals of overall condition and individual vigour that are dependent on genomewide heterozygosity (Brown 1997). Similarly, MHC allele frequencies that vary by population may simply correlate with phenotypic cues of genetic
relatedness. The absence of a correlation between MHC and neutral variation would support MHC-mediated mate choice, although the presence of a correlation would not necessarily preclude the MHC's role.

A recent meta-analysis found broad evidence of MHC-associated mate choice in nonhuman species, with stronger support for diversity preferences than for dissimilarity preferences (Kamiya et al. 2014), but results from human studies have been more equivocal and contentious (reviewed in Havlicek & Roberts 2009; Winternitz & Abbate 2015). This is in large part due to the greater variation inherent in human research because many potentially confounding aspects of the study design are harder to control. The most problematic issue is likely hidden, yet substantial admixture between populations that can result in spurious assortative or disassortative genetic patterns in pairing (Redden & Allison 2006; Solberg et al. 2008; Havlicek & Roberts 2009). These patterns of genetic similarity/dissimilarity between partners can arise because autosomal and MHC genetic variation is structured by ethnicity (Rosenberg et al. 2002; Vina et al. 2012) where the population frequencies of MHC alleles depend on the geographical location and on the level of population heterogeneity (Prugnolle et al. 2005; Solberg et al. 2008; Vina et al. 2012). A second confounding variable unique to humans is technological alterations in biological phenotypes, which include hygiene and make-up routines, surgery and artificial hormones for controlling contraception (Wedekind et al. 1995). However, perfumes appear to be chosen to amplify one's MHC odour profile (Milinski & Wedekind 2001). Lastly, it may sometimes be difficult to standardize experimental designs between studies when using human subjects and these methodological differences can also confound results (Havlicek & Roberts 2009). Several recent reviews have discussed these issues in attempts to reconcile significant and nonsignificant results from over three decades of human MHC mate choice research (Havlicek & Roberts 2009; Winternitz & Abbate 2015). However, only quantitative assessment that explores the magnitude and precision of effects can reveal the ultimate biological importance of phenomena (Nakagawa & Cuthill 2007). Thus, there is a need for a quantitative comparison of human MHC studies. For evolutionary context, nonhuman primates should be relatively free from the confounding biological and technological aspects of human studies and thus should more closely resemble human evolutionary origins than contemporary human populations.

To determine the biological importance of MHC-based mating across human populations and uncover drivers of mate selection, we performed a phylogenetically controlled meta-analysis of published studies. We

tested for biologically significant mate choice for MHC dissimilarity and MHC diversity separately. We aimed to identify consistent relationships between MHC and mating across primates, including nonhuman primates, to put human results in context. In addition, we tried to disentangle potential biological or methodological sources of variation in the observed effect sizes to better understand differences between past studies and minimize differences for future studies.

## Materials and methods

### Literature search

Data set compilation methods are described in detail in Winternitz & Abbate (2015). Briefly, studies were compiled from the reviews by Havlicek & Roberts (2009) and Setchell & Huchard (2010), from the meta-analysis by Kamiya et al. (2014), and additional studies were identified up from 2009 to January 2015 via Web of Science using the topic 'MHC' and 'Major Histocompatibility Complex' and 'mate choice' or 'mate selection' or 'mate preference' and searching within results for 'human' and 'primate'. Studies were listed as testing for human or primate preferences, and for preferences for MHC dissimilarity or diversity/heterozygosity. Studies were included if MHC genotypes [or their approximations via single nucleotide polymorphisms (SNPs), e.g. HapMap data] were obtained for the individuals tested. Studies were excluded if we could not extract the full set of effect sizes that related to the question of MHC influence on mating preferences [e.g. Giphart & D'Amaro (1983) did not provide test statistics for pairwise tests]. We only considered classical MHC class I and class II genes since nonclassical MHC genes, although also involved in immunity, usually have tissue-specific expression and much less diversity indicating different selection schemes (reviewed in Rodgers & Cook 2005). For example, Khankhanian et al. (2010) provided data for nonclassical HLA-E, which we did not use. Similarly, Laurent et al. (2012) only presented data for nonclassical HLA-L and HLA-J genes. Lists of full references, including references we could not use and explanations for exclusion, are provided in the supplementary materials.

### Data extraction and effect size calculation

We chose *r* effect size (correlation coefficients) as the measure of the association between MHC target (dissimilarity or diversity) and the strength of mating preference/outcome. Studies had mostly measured dissimilarity as categories of allele sharing (e.g. none
and = >1) and occasionally as allele sequence diver-
gence. Diversity was mostly measured categorically
(i.e. homozygous at one or more loci vs. heterozygous)
and occasionally continuously (mean heterozygosity
over all loci considered). Test statistics other than mea-
sures of correlation were converted to *r* effect sizes fol-
lowing (Nakagawa & Cuthill 2007); while there are
limitations to converting *r* from summary statistics
other than bivariate correlations (Aloe 2015), we stress
that these limitations will introduce noise and not bias.
Results from Yang *et al.* (2014) could not be directly
converted to effect sizes so we calculated values for
the data points (points were extracted from Fig. 5
using software DATATHIEF (Tummers 2006)) and fit cor-
relation models to obtain effect size estimates in the
desired scale. When studies provided multiple effect
sizes that we could not independently evaluate with
moderator variables (e.g. results from multiple loci or
MHC allele and supertype data), we calculated
weighted means by first converting measures to *r* and
then weighting them by the underlying sample sizes.
We accounted for nonindependence of multiple effect
sizes extracted from the same study that remain after
calculating weighted means by accounting for the
specific hierarchical structure of the data in the appro-
priate statistical models (see the structure of the mixed
model in the next section). For studies that listed effect
sizes for MHC similarity preferences, we reversed the
sign (i.e. resulting in a negative association for dissimi-
larity) to align data for preference for dissimilar mates
according to the predictions of our focal biological
hypothesis. The number of raters was recorded to test
for potential effects of sample size on the resulting
effect size. The number of individuals rated (number
of independent repeats) in the study was recorded to
calculate the variance in effect size (variance = 1/
(N<sub>study rated</sub> - 3)). When weighted effect size means
were calculated, we also recorded the mean number of
individuals rated and used this estimate to calculate
the variance of the weighted mean. Raw data and con-
verted effect sizes were checked by independent
extraction (JA), and any inconsistency was discussed
until a consensus was reached (between JW, JA and
LG). We converted effect sizes into Fisher's Z (Zr) to
stabilize variance across effect sizes, and Zr and its
variance (defined above) were used for meta-analyses.
The full data set, comprising 58 effect sizes from 30
studies across seven species, and effect size extractions
and conversions are provided in the electronic supple-
mentary material. The data set was split to test for
MHC dissimilarity and diversity-mediated mating pref-
erences across primates (N = 41 and 17, respectively).
Human studies greatly outnumber the other species,
which may lead to biases in the observed effect size

patterns. Therefore, we also analyse them separately
from nonhuman primates.

Biological and methodological differences between
studies have been shown to predict variation in MHC-
mediated mating patterns in human and nonhuman
populations (Havlicek & Roberts 2009; Setchell &
Huchard 2010; Kamiya et al. 2014). We accounted for
these potential sources of heterogeneity by considering
various moderator variables for the partition of the
between-study variance in effect sizes. The following
data were extracted from each study as methodological
predictors: (i) study ID and (ii) year of publication for
publication bias testing, (iii) choice cue used for mating
preference (i.e. facial attractiveness, odour attractiveness
or mate choice outcome), (iv) the number of individual
raters (N of rater), (v) multilocus or single locus, (vi)
level of population heterogeneity (dichotomously classi-
fied as ethnically homogeneous or heterogeneous). This
moderator was included to control for artefactual pat-
terns of MHC similarity or dissimilarity that can arise
from the pooling of different ethnic groups together in
the same sample (Rosenberg et al. 1983). Populations
were classified as ‘ethnically homogeneous’ if the study
samples fell into single ethnic groups according to the
ethnic categories defined by the CIAfactbook (https://
www.cia.gov/library/publications/the-world-factbook/
index.html), or based on detailed genealogical (i.e. Ober
et al. 1997) or anthropological records (i.e. Hedrick &
Black 1997). All other human populations were classi-
fied as ‘ethnically heterogeneous’. All nonhuman pri-
mate populations were considered ‘homogeneous’ as
they most likely represented single isolated populations
(Setchell & Huchard 2010). (vii) Contraceptive pill use
can potentially reverse previous preferences (Wedekind
et al. 1995) so we ran all models excluding pill-use
effect sizes (N = 4), but results remained essentially
unchanged when all effect sizes were used (both sets of
results provided in all tables). We included pill use as a
moderator for human odour preference studies (female-
pill users, female nonpill users). Biological predictors
included (viii) species, (ix) choosy sex (i.e. the unit of
investigation: males, females or pairs), (x) MHC class
(class I, class II or both) and (xi) relative testes size as a
proxy for mating system (Harcourt et al. 1981, 1995;
Dixson & Anderson 2004; Figure S1, Supporting infor-
mation). Mating system strongly impacts individual
mating strategies and population genetic structure
(Sugg et al. 1996) and so could influence the expression
of MHC-mediated mate choice (Setchell & Huchard
2010).

Unfortunately, background genetic measures of dis-
similarity and diversity were not available for the
majority of studies in our data set, and thus, we could
not include this potentially important moderator in our
analyses. However, we were able to extract a limited number of effect sizes based on neutral markers, which were found to show positive but nonsignificant correlations with MHC-based effect sizes (MHC dissimilarity correlation (95% highest posterior density, HPD) = 0.164 (−0.421 to 0.747), *N* = 11; MHC diversity correlation (95% HPD) = 0.110 (−0.708 to 0.942), *N* = 5, Figure S11, Supporting information). See Supplementary Text for analysis details). This suggests that MHC-mediated effects were largely independent of genome-wide effects.

## Statistical analyses

**Meta-analytic procedures.** There were three causes of nonindependence in our data sets: (i) more than one effect size was extracted from a study, (ii) multiple effect sizes were available for the same species, and (iii) species share evolutionary history making effect sizes confounded by the phylogeny of species. We used phylogenetic mixed-effects modelling that includes random effects to account for nonindependence caused by study-, species- and phylogeny-specific effects (Hadfield 2010; Hadfield & Nakagawa 2010; Nakagawa & Santos 2012). A phylogenetic tree was obtained by trimming the primate 10KTree (Arnold et al. 2010) using the *drop.-tip* function from the *APE* package version 3.4 (Paradis et al. 2004). The deviance information criterion (DIC) was computed for all models considering different random-effect structure (Table 1), and top model selection was based on DIC values, where the lowest DIC is considered the best, models within 2 DIC units are considered equivalent, and a change in DIC of 4 or more significantly improves prediction (Spiegelhalter et al. 2002). We calculated phylogenetic heritability or phylogenetic signal, *H*², as the proportion of total variance in Zr that can be explained by phylogenetic variance (Hadfield & Nakagawa 2010; de Villemereuil & Nakagawa 2014), equivalent to Pagel's *λ* (Pagel 1999). Our data sets of MHC dissimilarity and MHC diversity were limited (dissimilarity *N* = 41; diversity *N* = 17) and were comprised of 28 and 11 studies, respectively, and seven species each. Preliminary exploratory analysis of the data revealed that the factors study ID and species were strongly associated, with each study focusing on a single species. We compared models for all combinations of the random factors: study ID, species and phylogeny. We found that including study ID greatly improved model fit, but models adding species, phylogeny or both were all within 1 deviance information criterion (DIC) from the top model (Table 1) and essentially equivalent in terms of prediction (Spiegelhalter et al. 2002). Therefore, to avoid potentially overfitting the models, we chose to include only study ID and

phylogeny to control for phylogenetic pseudoreplication in multispecies models, and to include study ID in human-only models.

Meta-analyses were conducted with generalized linear mixed-effects models with Markov chain Monte Carlo techniques using the R package *MCMCGLMM* (Hadfield 2010). We present details on *MCMCGLMM* model specification and diagnostics in the supplementary material. Briefly, all models were fit using an uninformative inverse gamma prior for all random effects and residuals and were checked for sensitivity to prior specification and convergence across independent model runs (following Wilson *et al.* 2010). Each model was run for 3 million iterations, sampling every 500 after discarding one-million, and this process was repeated for each model to confirm stability of results. We first ran intercept-only mixed models (with random effects) to determine the mean effect size across all studies and for humans and nonhuman primates separately. We tested whether specifying priors based on the effect size estimates from mammalian MHC mate choice (Kamiya *et al.* 2014) would improve model fit and reduce variance around posterior estimates (Garamszegi *et al.* 2009), but results were essentially identical to those obtained with uninformative priors (Tables S1, S2, S6, Supporting information).

**Heterogeneity estimation.** Variation in observed effect sizes between studies is composed of both real differences in mating outcome (effect size heterogeneity) as well as random error. To estimate effect size heterogeneity, we used *I*² (Higgins & Thompson 2002; Higgins *et al.* 2003) modified for multilevel meta-analytic models (Nakagawa & Santos 2012). Low, moderate and high heterogeneities refer to *I*² of 25%, 50% and 75%, respectively (Higgins *et al.* 2003). The intercept-only models indicated high heterogeneity (>75%) in effect sizes, and while this was mostly explained by the random effects of study ID and phylogeny, substantial residual heterogeneity persisted (26% for dissimilarity, 14% for diversity, Table 2).

We next constructed a series of meta-regression models to identify the most important moderators (listed above) that explained substantial residual heterogeneity in effect sizes (Nakagawa & Santos 2012). We conducted univariate fixed-effect mixed models to estimate the mean effect size for each moderator separately (we avoided complex models with multiple predictors given the limited sample size). Models with categorical moderators were run without the intercept to test each trait against no effect. Parameter estimates were based on posterior means and estimates with highest posterior density (HPD) intervals that do not cross zero are inferred to represent real effects. All effect sizes are
<table><caption>**Table 1** Comparison of models with varying random effects and their deviance information criteria (DIC) values for effects of MHC dissimilarity and diversity on mate choice for all species, only humans, and only nonhuman primates. All models include the intercept and the listed random effects. Values in bold specify the random effects used in subsequent models. Models within 2 DIC units are essentially equivalent (Spiegelhalter *et al.* 2002), so we chose to include random effects that would account for study and phylogenetic nonindependence without overfitting the models</caption><thead><tr><th rowspan="2">Model ID</th><th rowspan="2">Random effects</th><th colspan="2">All</th><th colspan="2">Humans</th><th colspan="2">Nonhuman primates</th></tr><tr><th>DIC</th><th>ΔDIC</th><th>DIC</th><th>ΔDIC</th><th>DIC</th><th>ΔDIC</th></tr></thead><tbody><tr><td colspan="8"><strong>MHC dissimilarity</strong></td></tr><tr><td>Model0</td><td>No random</td><td>-8.76</td><td>43.00</td><td>-4.20</td><td>34.53</td><td>-14.76</td><td>0.54</td></tr><tr><td><strong>Model1</strong></td><td>ID</td><td>-51.09</td><td>0.68</td><td><strong>-38.73</strong></td><td><strong>0.00</strong></td><td>-15.30</td><td>0.00</td></tr><tr><td>Model2</td><td>Species</td><td>-8.26</td><td>43.51</td><td>.</td><td>.</td><td>-15.30</td><td>0.01</td></tr><tr><td>Model3</td><td>Phylogeny</td><td>-8.57</td><td>43.20</td><td>.</td><td>.</td><td>-15.13</td><td>0.17</td></tr><tr><td><strong>Model4</strong></td><td>ID + Phylogeny</td><td><strong>-51.77</strong></td><td><strong>0.00</strong></td><td>.</td><td>.</td><td><strong>-15.28</strong></td><td><strong>0.02</strong></td></tr><tr><td>Model5</td><td>ID + Species</td><td>-51.72</td><td>0.05</td><td>.</td><td>.</td><td>-15.26</td><td>0.04</td></tr><tr><td>Model6</td><td>ID + Species + Phylogeny</td><td>-51.69</td><td>0.08</td><td>.</td><td>.</td><td>-15.16</td><td>0.14</td></tr><tr><td colspan="8"><strong>MHC diversity</strong></td></tr><tr><td>Model0</td><td>No random</td><td>-45.12</td><td>1.26</td><td>-25.05</td><td>0.00</td><td>-19.85</td><td>0.00</td></tr><tr><td><strong>Model1</strong></td><td>ID</td><td>-45.73</td><td>0.65</td><td>-24.82</td><td><strong>0.23</strong></td><td>-19.32</td><td>0.54</td></tr><tr><td>Model2</td><td>Species</td><td>-46.06</td><td>0.32</td><td>.</td><td>.</td><td>-19.37</td><td>0.48</td></tr><tr><td>Model3</td><td>Phylogeny</td><td>-46.38</td><td>0.00</td><td>.</td><td>.</td><td>-19.61</td><td>0.24</td></tr><tr><td><strong>Model4</strong></td><td>ID + Phylogeny</td><td><strong>-46.31</strong></td><td><strong>0.07</strong></td><td>.</td><td>.</td><td><strong>-19.33</strong></td><td><strong>0.53</strong></td></tr><tr><td>Model5</td><td>ID + Species</td><td>-46.11</td><td>0.27</td><td>.</td><td>.</td><td>-19.24</td><td>0.61</td></tr><tr><td>Model6</td><td>ID + Species + Phylogeny</td><td>-46.17</td><td>0.21</td><td>.</td><td>.</td><td>-19.35</td><td>0.51</td></tr></tbody></table>

reported as Fisher's normalized correlation coefficients (Zr) with 95% confidence intervals. In ecological literature, $r \approx 0.1$ (Zr $\approx 0.10$) is generally considered a small effect, $r \approx 0.3$ (Zr $\approx 0.31$) a medium effect and $r \approx 0.5$ (Zr $\approx 0.55$) a strong effect (Cohen 1988; Møller & Jennions 2002).

*Publication bias and power analysis.* We tested for publication bias using four different approaches given that they have different advantages and disadvantages. First, we applied Egger's regression (Egger *et al.* 1997) on meta-analytic residuals instead of effect sizes, which can better distinguish between publication bias and other sources of heterogeneity (Egger *et al.* 1997; Sutton *et al.* 2011; Kamiya *et al.* 2014). If the regression of the standard normal deviate (residuals divided by the standard error) on precision has an intercept different from zero at 90% confidence, then there is evidence of bias favouring publication of less precise yet significant results (Egger *et al.* 1997). Second, we tested for temporal bias in publication results (e.g. if nonsignificant studies are suppressed immediately after the first significant publication) by including the publication year of the study as a moderator in the meta-analytic model. We also used Spearman's rank to test for significant correlations between effect size and year of publication. Third, to assess the impact of publication bias and test the robustness of our results, we used the nonparametric trim and fill method (Duval & Tweedie 2000a,b) in

the *METAFOR* R package (Viechtbauer 2010). This method adjusts the mixed-model intercept for potentially missing studies and the difference is added to the original meta-analysis model intercept (and credible interval; following Sutton *et al.* 2011). Fourth and finally, as bias for publications with significant results can rely more on the *P*-value than on the effect size, we used the *P*-curve method to test whether the distribution of significant *P*-values, the 'P-curve', indicates that our studies have evidential value and are free from 'p-hacking' (Simonsohn *et al.* 2014a, b). While problems in identifying publication bias using the *P*-curve method have been identified (Bishop & Thompson 2016; Bruns & Ioannidis 2016), we controlled for false-negative results by ensuring that all data entered into analysis met the three required assumptions set by Simonsohn *et al.* (2014b). Specifically, these assumptions are that *P*-values are (i) associated with the hypothesis of interest, (ii) statistically independent from other selected *P*-values, and (iii) distributed uniformly under the null hypothesis of no bias.

We tested the robustness of our results by conducting retrospective power analyses to evaluate whether our sample size (number of effect sizes) was sufficient to have a high chance of detecting a biologically significant effect. We used a prespecified effect size of 0.15 (explaining 2.2% of the variation in mating patterns) which fell within the observed range of effect size estimates from the meta-analysis of Kamiya *et al.* (2014)
<table><caption>**Table 2** Heterogeneity estimates and deviance information criteria (DIC) for a set of random-effect-only meta-analytical models for MHC dissimilarity and diversity. The heterogeneity ($I^2$) value is the percentage variance from a particular random factor over the sum of all variance components plus the mean variance and was calculated from posterior means. The total $I^2$ (and the 95% HPD) is the sum of all variance components. The mode total variance is shown for comparison (where similar values indicate stable models). Phylogenetic heritability ($H^2$) is the proportion of variance that can be explained by phylogenetic variance (Hadfield & Nakagawa 2010). The final random-effect models used in subsequent analyses are highlighted in bold. Rationale for using the bold models is explained in the methods. Model ID refers to models from Table 1</caption><thead><tr><th>Data set</th><th>N</th><th>Model ID</th><th>DIC</th><th>Study I<sup>2</sup></th><th>Residual I<sup>2</sup></th><th>Phylogeny I<sup>2</sup></th><th>Mean total I<sup>2</sup> (HPD)</th><th>Mode total I<sup>2</sup></th><th>Mean H<sup>2</sup> (HPD)</th><th>Mode H<sup>2</sup></th></tr></thead><tbody><tr><td colspan="11"><strong>Dissimilarity</strong></td></tr><tr><td>All</td><td>41</td><td>Model0</td><td>-8.77</td><td></td><td>85.49</td><td></td><td>85.49 (77.66-92.73)</td><td>86.74</td><td></td><td></td></tr><tr><td></td><td></td><td><strong>Model4</strong></td><td><strong>-51.77</strong></td><td><strong>29.20</strong></td><td><strong>32.09</strong></td><td><strong>27.02</strong></td><td><strong>88.32 (78.90-97.50)</strong></td><td><strong>88.80</strong></td><td><strong>0.30 (0.01-0.78)</strong></td><td><strong>0.04</strong></td></tr><tr><td>All (no pill)</td><td>37</td><td>Model0</td><td>-7.88</td><td></td><td>86.88</td><td></td><td>86.88 (79.17-93.52)</td><td>88.53</td><td></td><td></td></tr><tr><td></td><td></td><td><strong>Model4</strong></td><td><strong>-60.99</strong></td><td><strong>37.01</strong></td><td><strong>25.72</strong></td><td><strong>26.57</strong></td><td><strong>89.30 (80.63-98.11)</strong></td><td><strong>88.68</strong></td><td><strong>0.29 (0.004-0.77)</strong></td><td><strong>0.04</strong></td></tr><tr><td>Human</td><td>35</td><td>Model0</td><td>-4.20</td><td></td><td>87.69</td><td></td><td>87.69 (80.17-94.37)</td><td>89.51</td><td></td><td></td></tr><tr><td></td><td></td><td><strong>Model1</strong></td><td><strong>-38.73</strong></td><td><strong>39.07</strong></td><td><strong>47.13</strong></td><td></td><td><strong>86.21 (76.84-93.97)</strong></td><td><strong>87.72</strong></td><td></td><td></td></tr><tr><td>Human (no pill)</td><td>31</td><td>Model0</td><td>-3.56</td><td></td><td>89.01</td><td></td><td>89.01 (81.73-95.08)</td><td>89.81</td><td></td><td></td></tr><tr><td></td><td></td><td><strong>Model1</strong></td><td><strong>-47.08</strong></td><td><strong>51.54</strong></td><td><strong>36.31</strong></td><td></td><td><strong>87.85 (78.79-94.73)</strong></td><td><strong>89.4</strong></td><td></td><td></td></tr><tr><td>Nonhuman primate</td><td>6</td><td>Model0</td><td>-14.76</td><td></td><td>34.14</td><td></td><td>34.14 (2.24-76.71)</td><td>9.03</td><td></td><td></td></tr><tr><td></td><td></td><td><strong>Model4</strong></td><td><strong>-15.28</strong></td><td><strong>19.64</strong></td><td><strong>20.45</strong></td><td><strong>29.67</strong></td><td><strong>69.75 (36.62-98.89)</strong></td><td><strong>83.21</strong></td><td><strong>0.41 (0.003-0.92)</strong></td><td><strong>0.09</strong></td></tr><tr><td colspan="11"><strong>Diversity</strong></td></tr><tr><td>All</td><td>17</td><td>Model0</td><td>-45.13</td><td></td><td>26.71</td><td></td><td>26.71 (2.97-60.73)</td><td>11.53</td><td></td><td></td></tr><tr><td></td><td></td><td><strong>Model4</strong></td><td><strong>-46.31</strong></td><td><strong>15.71</strong></td><td><strong>13.76</strong></td><td><strong>34.38</strong></td><td><strong>63.84 (30.49-94.67)</strong></td><td><strong>63.68</strong></td><td><strong>0.50 (0.04-0.96)</strong></td><td><strong>0.16</strong></td></tr><tr><td>All (no pill)</td><td>16</td><td>Model0</td><td>-41.30</td><td></td><td>28.54</td><td></td><td>28.54 (2.15-62.28)</td><td>9.09</td><td></td><td></td></tr><tr><td></td><td></td><td><strong>Model4</strong></td><td><strong>-42.67</strong></td><td><strong>16.73</strong></td><td><strong>14.53</strong></td><td><strong>32.95</strong></td><td><strong>64.21 (33.90-96.71)</strong></td><td><strong>77.24</strong></td><td><strong>0.48 (0.03-0.94)</strong></td><td><strong>0.17</strong></td></tr><tr><td>Human</td><td>10</td><td>Model0</td><td>-25.05</td><td></td><td>27.38</td><td></td><td>27.38 (1.96-65.87)</td><td>8.50</td><td></td><td></td></tr><tr><td></td><td></td><td><strong>Model1</strong></td><td><strong>-24.82</strong></td><td><strong>22.93</strong></td><td><strong>22.19</strong></td><td></td><td><strong>45.12 (10.04-81.55)</strong></td><td><strong>42.10</strong></td><td></td><td></td></tr><tr><td>Human (no pill)</td><td>9</td><td>Model0</td><td>-21.34</td><td></td><td>30.33</td><td></td><td>30.33 (1.65-68.71)</td><td>7.18</td><td></td><td></td></tr><tr><td></td><td></td><td><strong>Model1</strong></td><td><strong>-21.34</strong></td><td><strong>24.39</strong></td><td><strong>23.37</strong></td><td></td><td><strong>47.75 (12.78-84.87)</strong></td><td><strong>33.18</strong></td><td></td><td></td></tr><tr><td>Nonhuman primate</td><td>7</td><td>Model0</td><td>-19.85</td><td></td><td>38.41</td><td></td><td>38.41 (3.64-84.60)</td><td>10.62</td><td></td><td></td></tr><tr><td></td><td></td><td><strong>Model4</strong></td><td><strong>-19.33</strong></td><td><strong>25.80</strong></td><td><strong>17.59</strong></td><td><strong>35.71</strong></td><td><strong>79.09 (47.31-99.44)</strong></td><td><strong>93.10</strong></td><td><strong>0.44 (0.004-0.94)</strong></td><td><strong>0.03</strong></td></tr></tbody></table>

that investigated MHC-mating patterns across vertebrates [dissimilarity Zr (HPD) = 0.064 (-0.080 to 0.193); diversity Zr = 0.113 (-0.004 to 0.237)]. We used this prespecified effect size to represent the minimal biologically significant effect, and we used the observed mean variance of the effect sizes following recommendations of Thomas (1997). We conducted our power analyses for meta-analytic random-effects models for low, medium and high levels of heterogeneity using the methods of Hedges & Pigott (2001).

All statistical analyses except for the *P*-curve method (implemented at http://www.p-curve.com/) were carried out in the *R* environment (version 3.2.1; R Core Team 2015), and all *R* code is provided in the online appendix. The *R* packages we used were *APE* version 3.5 (Paradis *et al.* 2004), *MCMCGLMM* (Hadfield 2010), *METAFOR* (Viechtbauer 2010), *PHYTOOLS* (Revell 2012), *PLOTMCMC* (Magnusson & Stewart 2014) and *SHAPE* (Soetaert 2014).

## Results

The main results are presented in the next four sections, and detailed results of our meta-regression analysis can

be found in the supplementary materials (Tables S1–S17, Figures S1–S11, Supporting information).

### Preference for MHC dissimilarity

The mean effect size calculated over all studies (excluding contraceptive pill users) indicated no significant correlation between MHC dissimilarity and mating outcome (intercept-only posterior mean Zr (95% HPD) = 0.044 (-0.174 to 0.289), *N* = 37). The total heterogeneity (total $I^2$) in effect sizes was large (89%) and could mostly be explained by the two random factors ($I^2_{ID}$ = 37%, $I^2_{phylogeny}$ = 27%), with substantial residual variance remaining ($I^2_{residual}$ = 26%), shown in Table 2. We ran univariate models to identify moderators potentially explaining residual heterogeneity, and while no moderator was identified as significant (Table S1, Supporting information), the moderator 'choosy sex' showed significant contrasts between the categories of males and pairs (contrast *P* = 0.02, Fig. 1a, Table S11, Supporting information). In other words, studies using mated pairs had greater effect sizes for MHC-similar mates than studies investigating male
preferences. This effect was driven by human studies, which had significant contrasts not found for nonhuman primates (human contrast *P* = 0.031, Table S11, Supporting information). Phylogenetic heritability in MHC-dissimilar mating patterns was low (mean *H*² = 0.29 (0.004–0.77), mode *H*² = 0.04, Fig. 2a, Table 2), and we note that random effects are bound to be positive and their posterior distributions will never overlap zero (Wilson *et al.* 2010). Thus, the meaningfulness of the random effect of phylogeny cannot be based on its nonzero posterior distribution. Despite its effect of increasing the HPD intervals for model estimates, we retained phylogeny as a random effect to control for pseudoreplication in multispecies models. We investigated the impact of phylogeny on the meta-mean effect size of MHC dissimilarity by comparing model DICs and posterior estimates for models with and without

phylogeny, but results were qualitatively similar and nonsignificant (Table S13, Supporting information).

When human and nonhuman primate studies were examined separately, neither had significant associations between MHC dissimilarity and mating outcome (human Zr = -0.022 (-0.107 to 0.073), *N* = 31, nonhuman primate Zr = 0.109 (-0.194 to 0.404), *N* = 6). Mean effect sizes calculated for these two taxonomic groups were not statistically differentiable. In intercept-only models, total heterogeneity was high for both humans and nonhuman primates (I²*total* = 88% and 70%, respectively) with substantial residual variance in humans specifically (I²*residual* = 36% vs. 20% in primates, Table 2). Moderators tested separately in univariate models for humans, in models by human choice cue and in models for primates were all nonsignificant and did not reduce residual heterogeneity (Tables S2–S4, S9,

<figure><img src="image_2.png" /><figcaption>**Fig. 1** Effect of moderators on strength of MHC-associated mating outcome. This figure is a forest plot of effect size for categorical moderators for (a) preference for MHC dissimilarity and (b) preference for MHC diversity. Positive posterior estimates of Fisher's Z transform of the correlation coefficient (Zr) indicate positive associations, whereas negative estimates indicate negative associations between MHC target and mating outcome. Boxes show the mean posterior estimate from the model, and error bars represent the 95% highest posterior density (HPD) interval. Numbers on the right-hand side of each panel indicate the number of effect sizes in each subgroup. White boxes indicate model estimates from human data, black boxes are from nonhuman primate data, and grey polygons indicate estimates from models using all data. We removed pill-user effect sizes from the combined primate and human data set to control for its potentially confounding effect (see Methods) and tested pill use as a moderator for studies that investigated its affect or controlled for it. The meta-analytic mean is from the intercept-only model run with study ID and phylogeny as random effects (and study ID as the random effect for the human data model). Dashed boxes highlight mean estimates with HPD intervals that do not overlap zero. Vertical bars indicate significant contrasts between moderator categories for humans and all data (*P*-value < 0.05, see Table S10, Supporting information).</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Fig. 2 Phylogenetic heritability of effect sizes for MHC-dissimilar and MHC-diverse mating patterns. We calculated the residuals of Zr effect sizes due to phylogenetic relatedness and computed the mean for each species to graphically represent the heritability of effect sizes for (a) MHC-dissimilar mating patterns (heritability posterior mean (HPD) = 0.30 (0.01–0.78)) and (b) MHC-diverse mating patterns (heritability posterior mean (HPD) = 0.50 (0.04–0.96)). Colours indicate the strength of phylogenetic signal.</figcaption></figure>

Supporting information, Figs 1a and 3), but the direction of effect sizes for primates was consistent for dissimilarity (Figs 1a and 2a). Phylogenetic heritability in MHC-dissimilar mating patterns among nonhuman primates was low but present (mean $H^2$ = 0.41 (0.003–0.92), mode $H^2$ = 0.09, Table 2).

### Preference for MHC similarity

As a post hoc investigation to explain the large residual heterogeneity in effect sizes among the human dissimilarity data set ($I^2_{residual}$ = 36%, Fig. 4a), we specifically tested for the effects of population ethnic heterogeneity on patterns of MHC-assortative mating among experimental and observational studies (predicted by Rosenberg et al. 1983 and Havlicek & Roberts 2009). We ran mixed-effects models for the subset of dissimilarity effect sizes that were negative (indicating preferences for similarity). We found that preference for MHC similarity was significant in ethnically heterogeneous population samples, but not among those that were homogeneous. This result was found only in observational (mate choice) studies, and not among those using experimental approaches (odour and facial preference combined; Fig. 5, Table S5, Supporting information).

### Preference for MHC diversity

For diversity models, we present results for effect sizes ($N$ = 17) including one from contraceptive pill-using women because pill use was never shown to have an effect on preferences for MHC-diverse mates, its exclusion did not reduce heterogeneity, and model results

are virtually identical whether this effect size was excluded or not (both results presented in all tables for comparison). Across all effect sizes combining humans and primates, there was a nonsignificant trend for MHC diversity to be positively associated with mating outcome (posterior mean Zr (HPD) = 0.128 (–0.064 to 0.373), $N$ = 17; Fig. 4b shows significant raw mean Zr effect sizes and results from random-effect models without accounting for study ID and phylogeny). Total heterogeneity was large ($I^2_{total}$ = 64%) but was mostly accounted for by differences between studies and phylogeny ($I^2_{ID}$ = 16%, $I^2_{phylogeny}$ = 34%, Table 2). Residual heterogeneity was low ($I^2_{residual}$ = 14%) and could only slightly be reduced by the addition of the moderator relative testes size as a fixed effect (Table S10, Supporting information). Univariate models of moderator effects were nonsignificant (Table S6, Supporting information). The model including relative testes size was also nonsignificant, but the negative association between relative testes size and preferences for MHC diversity was trending towards biological significance (intercept (HPD) = 0.246 (–0.019 to 0.581); posterior mean testes (HPD) = –0.218 (–0.479 to 0.047), Table S6, Supporting information). Phylogenetic heritability in strength of MHC diversity mating patterns was moderate (mean $H^2$ = 0.50 (0.04–0.96), mode $H^2$ = 0.16, Table 2, Fig. 2b).

Examined separately, humans showed a significant association for more MHC-diverse individuals to be preferred as mates (humans = 0.153 (0.020–0.283), $N$ = 10, Fig. 1b) while nonhuman primates had a nonsignificant trend for mate choice for diversity (primates = 0.110 (–0.207 to 0.456), $N$ = 7, Fig. 1b). Total
Preference for human MHC-dissimilarity

<figure><img src="image_4.png" /></figure>

Fig. 3 Effect of moderators on strength of human MHC dissimilarity mating preferences (experimental designs) and mate choice (observational data). Zr effect sizes for human data were investigated by choice cue category, including facial preferences (white/yellow diamond), odour preferences (dark grey/blue diamond), these experimental categories combined (light grey/green polygon), and mate choice studies (black/maroon square). Points show the mean posterior estimate from the model, and error bars represent the 95% HPD interval. Numbers on the right-hand side of each panel indicate the number of effect sizes in each subgroup. We ran all models excluding potentially confounding pill-use effect sizes, except those models specifically testing for pill-use effect (only available for odour preference studies). The cross-diamond model estimates include only studies that had dichotomously classified raters as pill users or not [i.e. Santos *et al.* (2005) provided an effect size for a group of raters in which 30% were taking birth control pills]. The meta-analytic mean is from the intercept-only model run with study ID as a random effect. [Colour figure can be viewed at wileyonlinelibrary.com]

heterogeneity was moderate for humans ($I^2_{total} = 45\%$) and high for nonhuman primates ($I^2_{total} = 79\%$), and mostly explained for primates by the random effects of study ID and phylogeny ($I^2_{ID} = 26\%$, $I^2_{phylogeny} = 36\%$, Table 2). Residual variance was not substantially reduced after the addition of moderators as fixed effects for humans (Table S10, Supporting information) and these mixed models were essentially equivalent to the intercept-only model (all $\Delta$DIC < 2). The addition of the moderator relative testes size slightly reduced residual heterogeneity and DIC for nonhuman primates compared with the intercept-only model (Table S10, Supporting information), but not substantially ($\Delta$DIC < 2). When examining categorical level differences in MHC diversity effect sizes using univariate models, we found no significant moderator for nonhuman primates (Table S8, Supporting information). In contrast, humans showed stronger preferences for MHC-diverse mates for the categories of choosy sex (female rater) and MHC class (when both classes were investigated together, using multiple loci; Table S7, Supporting information, Fig. 1b).

Raw Zr effect sizes for all primates were positively associated with preferences for MHC diversity and showed significant means when not accounting for study ID and phylogenetic pseudoreplication (Fig. 4b). All our mixed models including phylogeny as a random effect had wide 95% confidence intervals for the phylogenetic heritability of effect sizes (proportion of total variance explained by phylogenetic variance, see Table 2). Thus, to test the stability of our results, we reran mixed models for combined data sets and for nonhuman primate data sets with the alternative random-effects ID + species, and ID + species + phylogeny and found qualitatively similar results (Table S13, Supporting information), indicating our conclusions are robust to the random-effect structure employed.

Publication bias and power analysis

We found no evidence for publication bias in the data sets. Egger's Regression tests indicated the intercepts for MHC dissimilarity and diversity data sets were not significant at 90% confidence intervals (Table S14,
(a) Preference for MHC-dissimilarity

<figure><img src="image_5.png" /><figcaption>**Fig. 4** Forest plot of original Fisher's Z-transformed (Zr) effect sizes extracted from each study. Zr effect sizes and associated variance were extracted from each study for (a) preference for MHC dissimilarity (positive Zr values indicate preference for dissimilarity, negative for similarity) and (b) preference for MHC diversity. Black diamonds indicate means from random-effect (RE) models for humans and nonhuman primates presented separately, and for all data combined, and error bars represent the meta-analytic variance $(1/(N_{\text{study}} - 3))$. N is the number of independent targets per study.</figcaption></figure>

(b) Preference for MHC-diversity

<figure><img src="image_6.png" /></figure>

Figure S2 and S3, Supporting information). The slope for MHC dissimilarity was significantly negative, indicating that studies with larger sample sizes (and less variance) showed stronger preference for MHC similarity (Table S14, Figure S2a, Supporting information). This effect was largely due to human correlative studies that had large sample sizes but did not control for ethnicity and assortative mating biases. Trim and fill analyses on mixed-model residuals (following Nakagawa & Santos 2012) were nonsignificant and suggested there were no missing studies on the left-hand side of the funnel plots (Table S14, Figures S2–S3, Supporting information). The sensitivity adjustment of 0.002 to the original intercept-only mean for human MHC diversity results would increase the significance (adjusted meta-mean (95% HPD) = 0.155 (0.022–0.285)). We found no evidence for

temporal bias using year as a moderator, nor using Spearman's rank correlation between effect size and year of publication (all *P* > 0.05, Table S16, Supporting information). Finally, *P*-curve analyses for MHC dissimilarity, similarity and diversity data sets suggest that studies contained significant evidential value and showed no evidence of intense p-hacking (Table S16, Supporting information). However, study sets in general had low likelihoods to detect a true effect (average power = 24% for dissimilarity, 57% for similarity, 33% for diversity).

Power analyses on our meta-analytic results indicated that the combined primate and human data sets for MHC dissimilarity and diversity had high power (77.2%–98.5%) to detect a biologically significant mean effect of 0.15 at an alpha value of 0.05 across low,
<figure><img src="image_7.png" /></figure>

**Fig. 5** Effect of ethnic homogeneity of the population on the strength of human preference for MHC similarity. We investigated the effect of the moderator 'population' on the subset of Zr effect sizes that were negative (indicating preference for similarity). Models with the moderator 'population' were run between choice cue categories for combined experimental studies (odour and facial preference studies, light grey/green diamond) and for mate choice studies (black/maroon square). Points show the mean posterior estimate from the model, and error bars represent the 95% HPD interval. Numbers on the right-hand side of each panel indicate the number of effect sizes in each subgroup. Effect sizes from ethnically heterogeneous populations for mate choice studies (but not experimental studies) had a significant posterior mean estimate for similarity (mean Zr (95% HPD) = -0.142 (-0.275 to -0.017)). [Colour figure can be viewed at wileyonlinelibrary.com]

medium and high heterogeneity values (Table S17, Supporting information). Power was lower for nonhuman primate data sets (dissimilarity = 43.8%; diversity = 58.2% power at high heterogeneity) and a total of 17 and 13 effect sizes, respectively, would be required to reach 80% power for an overall effect size of 0.15 at $\alpha = 0.05$ at high heterogeneity (Table S17, Supporting information). Splitting the human data set by experimental choice cue category, we should have high power to detect a biologically meaningful effect size, if it were present, for mate choice and facial preference sample sizes even at high heterogeneity (98.8% and 64.2% power, respectively). This indicates our non-significant meta-analytic results for human mate choice and facial preference category were likely not due to sample size limitations. In contrast, given the observed variation in effect sizes, we would need 84 odour preference effect sizes to detect biological significance at $\alpha = 0.05$ and high heterogeneity (Table S17, Supporting information).

## Discussion

There is substantial evidence that immunity genes play direct and (less well-supported) indirect roles in mate choice across vertebrates (Kamiya et al. 2014). These roles could function to promote diverse immunological repertoires in mates and offspring to respond to diverse parasite attack. Yet nearly four decades of research into this phenomenon in humans has yielded puzzling results. Taking a quantitative meta-analysis approach to put these results in their evolutionary context, we sought to identify patterns of consistency across studies

in humans and nonhuman primates. We found a suggestive trend for choice of MHC-diverse mates across primates and clear support in humans, but inconsistency for MHC-dissimilar mating preferences.

### Humans select more MHC-diverse mates

Overall, we found a systematic trend for primates and a significant association for humans to prefer more heterozygous mates at MHC sites. In humans, congruent to findings from a range of nonhuman vertebrates (Kamiya et al. 2014), we found stronger evidence for female preferences for MHC diversity, and when multiple MHC classes and loci were considered together. This may indicate that females receive greater evolutionary fitness benefits than males from selecting more MHC-diverse mates, and thus, the ability to identify heterozygosity of potential mates is particularly important. Moderate phylogenetic heritability observed in our study implies there may be evolutionary constraints on the expression of mate choice for MHC heterozygous mates. This could be related to olfactory signalling potential that varies among species (Niimura 2009). Our findings also suggest that power to detect significant effects increases when using more MHC classes and loci – allowing for greater variation in individual diversity. Nonhuman primate studies did not use both MHC classes and they rarely used multiple loci in their mate choice research, so the comparatively weaker effect sizes could be due to the lower variation available compared with human studies. Additionally, nonhuman primate effect sizes measured mating outcomes where MHC-mating effects, if existing, are likely to be smaller
than those from more controlled human experimental
studies that measured mating preferences. We did not
find mean effect size differences between human odour
and facial preference tests, suggesting that humans may
be similarly sensitive to both odour and visual cues of
diversity (Penn & Potts 1998).

Attractiveness has been shown to correlate with
health across cultures (Gangestad & Buss 1993; Hume
& Montgomerie 2001; Gray & Boothroyd 2012; Rantala
et al. 2012) and universal standards of attractiveness
based on health and fitness cues (Langlois et al. 2000)
may be advertising protective immunogenetic diversity
(Hamilton & Zuk 1982). Facial attractiveness may be a
particularly effective indicator of health and condition,
and of individual heterozygosity (Roberts et al. 2005b;
Lie et al. 2008). To illustrate, the faces of mixed-ethnicity
individuals, who tend to be more heterozygous than
average, have been shown to be more attractive than
single-ethnicity faces across genders and cultures
(Rhodes et al. 2005; Lewis 2010; Little et al. 2012). In
addition, work using facial image manipulations
demonstrated that cues to heterozygosity are them-
selves attractive independent of other accompanying
indicators of heterosis (Lewis 2010; Little et al. 2012).
How much MHC heterozygosity would make an indi-
vidual attractive? The majority of human studies (9/10)
measured MHC diversity categorically, with individuals
homozygous at one or more loci classified as ‘homozy-
gous’ and all others as ‘heterozygous’. Therefore, if
increasingly higher levels of heterozygosity or if some
optimal level of heterozygosity is favoured remains to
be determined. Furthermore, preferences for heterozy-
gosity and dissimilarity need not be exclusive. A prefer-
ence for both heterozygosity and some degree of
similarity is possible (where for each level of similarity,
relative heterozygotes are preferred), as demonstrated
empirically and by models of correlations between
heterozygosity and measures of genetic similarity
(Roberts et al. 2005b, 2006).

*Mate choice for MHC dissimilarity is not consistent*

Nonhuman primates tend to consistently prefer MHC-dissimilar mates (Fig. 4a), but our power to detect a significant average effect is limited by our small sample size (N = 6) and by the addition of random effects to control for pseudoreplication. However, meta-analytic power analyses predict that 11 additional effect sizes (total N = 17) should be sufficient to detect a biologically relevant effect (Zr = 0.15, explaining 2.25% variance) of MHC dissimilarity on mating patterns at 80% power (Table S17, Supporting information). In contrast to the unidirectional trend for dissimilarity in nonhuman primates (posterior mean Zr (HPD) = 0.109

(-0.194 to 0.404)), humans show great variation in
direction and magnitude of effect sizes (Fig. 4a). Direc-
tional variation could partly be explained by the unit of
investigation; we found pairs had significantly stronger
preferences for MHC similarity than males. Human
males are thought to be less choosy than females as
they tend to invest less in offspring, can reproduce at a
faster rate and have higher reproductive variance
(Trivers 1972; Puts 2012). Thus, this significant contrast
may reflect assortative mating in pairs vs. male indiffer-
ence. The contrast between pairs and females was
not significant, as females had a greater range of
preferences.

One interpretation is that pairs represent mate choice
outcomes, in contrast to individual preferences of males
and females. Because actual choice of partners is influ-
enced by many socio-economic conditions including
ethnicity, nationality, family relatedness, phenotypic
similarity and spatial segregation (reviewed in Kalmijn
1998; Bovet et al. 2012; Nojo et al. 2012) that can result
in genetically assortative pairings, the apparent prefer-
ence for MHC similarity in pairs may be coincidental.
In effect, ethnic heterogeneity within a sample will pro-
duce patterns of positive assortative mating at the eth-
nic level, where spouses are more similar at a
genomewide level than random pairs of individuals
(Chaix et al. 2008; Laurent & Chaix 2012). We found
strong support for this explanation in our findings of
significant effects for similarity within couples sampled
from ethnically heterogeneous populations, but not
from homogeneous populations or from experimental
studies that control for potential ethnic biases (Fig. 5).
A way to rule out spurious MHC associations stem-
ming from population stratification – differences in sub-
population background allele frequencies (Cardon &
Palmer 2003; Derti et al. 2010; Laurent & Chaix 2012) –
would be to provide measures of neutral genetic simi-
larity of couples. One study adopting this design was
able to determine that MHC dissimilarity in couples of
European ancestry was independent of genomewide
effects, while MHC similarity observed in couples of
Yoruban ancestry was most likely a consequence of kin-
ship-based assortative mating (Chaix et al. 2008; Laurent
& Chaix 2012). Neutral genetic markers would also
allow for testing whether the MHC is specifically tar-
geted or whether there is hitch-hiking during assorta-
tive or disassortative mating for other traits (Rosenberg
et al. 1983; Redden & Allison 2006; Laurent & Chaix
2012).

The variation in human MHC dissimilarity preference
but single-direction trend in nonhuman primates sug-
gests that selection pressure for MHC-dissimilar mates
may be sensitive to environmental or demographic per-
turbations primarily affecting humans. In contrast to
nonhuman primates, humans have undergone a recent population expansion (Kaessmann *et al.* 2001) and show extensive levels of admixture (Lawson *et al.* 2012), while most nonhuman primate populations are more genetically isolated and homogeneous, as are, for example, wild great apes (Prado-Martinez *et al.* 2013). Considering that mate choice relies on interpreting cues about traits relative to their background frequency, large changes in the genetic composition of the mating pool may distort signals. For example, if population-specific targets of optimal offspring diversity exist, mixing those populations would produce conflicting optimal targets.

## *Methodological differences among studies*

Even considering only experimental studies with much greater control over the statistical design, there was still high heterogeneity among human dissimilarity effect size magnitude and direction (Table S3, Supporting information, Fig. 3). It could be argued that differences among studies in methods or statistical design are responsible (Havlicek & Roberts 2009). However, the majority of experimental human studies used the same statistical design of Wedekind *et al.* (1995) which treated the chosen individual as the unit of analysis. Only two odour studies used different designs: one (Jacob *et al.* 2002) repeated the analysis using a within-donor design and found that the analysis 'yields virtually identical results' (McClintock *et al.* 2002). The other (Santos *et al.* 2005) used a chi-squared design, but had also had a percentage of participants on birth control, so this effect size along with other effect sizes of pill users was ultimately removed from the data set and could not influence the results.

Sources of heterogeneity can arise through study design, how the outcome is measured, and through real biological differences between populations. Yet we emphasize that studies that differ in their methodologies can be combined for a meaningful meta-analysis. The meta-analysis of Kamiya *et al.* (2014) is a case in point, which combined studies employing a diversity of methods across a range of vertebrates and found a clear general pattern for individuals to prefer MHC-dissimilar and MHC-diverse mates. In fact, combining research based on different methodologies insures that the variance in effect size patterns reflects this process and not any one methodological artefact (Lajeunesse 2010). We can then explore the factors that contribute to variation in MHC mate choice research to synthesize discordant results. One main source of heterogeneity we detected with statistical support was from combining different ethnic groups in a sample for studies that compared observed pairs to randomly created pairs. Another methodological source of heterogeneity we tested was

choice cue because preferences based on different stimuli could show different patterns, but we found no strong evidence of this and other studies have found positive correlation between facial and scent attractiveness (Thornhill & Gangestad 1999; Thornhill *et al.* 2003). Additional methodological differences between human studies and their implications for results have been thoroughly discussed elsewhere (Wedekind *et al.* 2002; Havlicek & Roberts 2009; Derti *et al.* 2010; Winternitz & Abbate 2015). We note that we cannot unanimously differentiate whether the differences between study outcomes are caused by methodology or biology, and it is likely that both types of mechanisms are in effect.

## *Study limitations*

One potential source of type I (false-positive) errors is biases in published effect sizes. Publication bias testing showed no evidence for p-hacking but did reveal that the average power of the studies analysed was low, ranging between 24% and 57% between data sets, indicating that true effects may have gone undetected in those studies. Based on our mean effect sizes for dissimilarity (Zr = 0.044) and diversity (Zr = 0.153), we recommend study samples sizes of ~4051 and ~260, respectively, to detect true effects in primates 80% of the time. The magnitude of these mean effect sizes is typical for ecological data (Møller & Jennions 2002) but this translates to dissimilarity only explaining approximately 0.2% and diversity 2.3% of the variation in private mating patterns. Clearly, MHC-mediated mate choice in humans and other primates is just one relatively small consideration of many involved in choosing a mate. Another issue regarding type I error is multiple testing. In our study, we used a large number of predictors, the majority being those from Kamiya *et al.* (2014) to get comparable results across different taxa (mammals, nonhuman primates and humans). We also performed a large number of tests for each MHC target. We appreciate that type I errors may occur when testing for multiple predictors, but highlight that the magnitude of the effects may be suggestive for designing future studies. It is reassuring to see that a significant variable in our study (choosy sex) was also found to be significant in a previous meta-analysis on MHC diversity-based mating in vertebrates (Kamiya *et al.* 2014).

Regarding type II (false-negative) errors, retrospective power analysis of our own results indicates we have sufficient sample sizes to have enough power to detect an effect of Zr = 0.15 (explaining ~2.2% variation in MHC-mating patterns) for human MHC diversity model and models of all human MHC dissimilarity choice cue categories excluding odour preference studies. Sample sizes for nonhuman primates were too low to have a good
chance of detecting true effects, but high power could
easily be achieved with less than 20 additional effect
sizes. Noise in the data may increase the risk of not
finding biological effects. Therefore, we have tried to
draw attention to models where power was limited and
new data would be very helpful (i.e. human odour pref-
erence models, nonhuman primate models), to help
advance the field of MHC-linked mate choice.

Another challenge linked with interpreting meta-analyses conducted across heterogeneous samples and study designs is the issue of confounding factors. Confounding factors not accounted for in original studies pose a substantial problem for the interpretation of all meta-analytical approaches, as they can increase the risk of missing a true effect. Specifically, in our study, only four studies in our data set had (i) controlled for Pill effects, (ii) used subjects of the same ethnicity and (iii) conducted an experimental study to control for confounding factors. Two of these studies (Wedekind et al. 1995; Wedekind & Furi 1997) investigated odour preferences and found positive effects of MHC dissimilarity ($r = 0.3347, r = 0.11$, respectively). The two other studies (Roberts et al. 2005a, b) investigated facial preferences and found negative effects of MHC dissimilarity ($r = -0.2632, r = -0.2067$). Therefore, even studies that fulfil the strictest of conditions still find opposing effects of MHC dissimilarity on human mate preferences (which may have a biological explanation if the two modalities work in complementary ways to optimize level of MHC diversity for offspring (Roberts et al. 2005a)). Our meta-analysis has shown that various moderators can impact the sizes and directions of results investigating MHC-linked primate mating and therefore can point to gaps in the research field to be addressed by future studies. Unfortunately, given our limited sample size, we were not able to incorporate multiple moderators within the same models. Thus, we cannot rule out a significant effect of MHC dissimilarity on primate mate choice under certain conditions and a definitive answer awaits further studies.

*Evidence for mate choice for MHC optimality?*

Considering only experimental studies that could con-
trol for potential socio-ethnic assortative biases we still
found wide variation in effect sizes for MHC dissimilar-
ity. It seems prudent to consider that an alternative bio-
logical explanation may be at least partially responsible
for the variation. The optimality hypothesis predicts
that direction of preference either for MHC dissimilarity
or some degree of allele matching may depend on the
relative allelic diversity in the pool of potential mates
(Aeschlimann et al. 2003; Milinski 2006). For example,
Aeschlimann et al. (2003) showed that sticklebacks

preferred dissimilar partners in simulated inbred popu-
lations and optimally dissimilar partners in simulated
outbred populations. High heterogeneity in MHC-dis-
similar mating preferences could reflect differences
among individuals attempting to achieve 'optimal dis-
similarity' by preferring similar or dissimilar mates
depending on ecological and demographic context
(Jacob et al. 2002; Milinski 2006; Roberts 2009). Thus,
MHC-based mate choice may be stronger or easier to
detect in settings where there is less population genetic
diversity and less heterogeneity in other factors which
influence mate choice (Ober et al. 1997; Jacob et al. 2002;
Chaix et al. 2008). Considering that the diversity of dis-
tinct HLA haplotypes (multilocus set of linked alleles)
per population typically ranges from 100s to 1000s (Grä-
gert et al. 2013), individuals from isolated populations
would most frequently encounter only a fraction of the
diversity of haplotypes common in more outbred popu-
lations. For example, only 10 haplotypes make up the
high-frequency majority for the Hutterite community
($N$ = 1891 sampled) where a significant preference for
MHC haplotype-dissimilar mating was detected (Ober
et al. 1997). Matching at six alleles (for six-locus haplo-
types) could severely limit the potential antigenic detec-
tion range and/or interfere with maternal-foetal
interactions (Ober et al. 1988, 1997; Lashley et al. 2015).
The Hutterite results are in line with the hypothesis of
mate selection disfavouring extreme MHC similarity
(Derti et al. 2010), and our results also show that experi-
mental study preferences for MHC-similar individuals
had relatively few matching alleles between mates (av-
erage of 1–3 alleles). It would be helpful if tests for
MHC dissimilarity preferences considered a higher
range of potentially matching alleles (e.g. allele match-
ing at 0–6 loci). Odour preference studies in particular
could employ synthetic peptides that mimic individual
alleles to have greater control of the range of allelic
diversity (Milinski et al. 2005, 2013).

*Importance of direct and indirect fitness benefits*

Our finding of greater mean effect size for MHC diversity compared with dissimilarity is in line with evidence from across all vertebrates (Zr = 0.113 vs. Zr = 0.064, respectively (Kamiya et al. 2014)). Detecting MHC diversity in a mate may be easier than detecting dissimilarity, as diversity is expected to correlate positively with the mate's phenotypic condition, including health status (Penn et al. 2002) or perception of health status through skin condition (Roberts et al. 2005), body mass (Thoß et al. 2011) and coloration dependent on infection status (Milinski & Bakker 1990), among others. Dissimilarity, on the other hand, should not be reflected by the mate's phenotype alone, as it depends
only on the combination of both mates genotypes. Thus, this type of preference would require more sophisticated sensory mechanisms including self-referential capabilities. The evolutionary benefits of human mate choice for MHC diversity may include prolonged parental care and reduced risk of contracting disease for a partner and the offspring (Roberts et al. 2005b), in addition to the potential indirect benefits from transmission of advantageous genes to offspring by diverse mates (Brown 1997, 1999; Kempenaers 2007). If preferences for MHC-diverse mates are primarily for direct fitness benefits, then resource-based mating systems where direct benefits (e.g. mate protection, provisioning and paternal care) are important should show stronger effects for mate choice. Alternatively, if diversity preferences are primarily for indirect fitness benefits, then nonresource based mating systems should show stronger effects for mate choice. We found preliminary support favouring direct fitness benefits in our trend of more promiscuous mating systems (with larger relative testes size) correlating with weaker mate choice for MHC diversity in primates (Tables S6 and S10, Supporting information). Another piece of support for direct benefits is that our results show stronger mate choice for MHC diversity in humans, where direct benefits are important, than in nonhuman primates. For instance, the expression of a major direct fitness benefit, paternal care, is intense for humans but rare in nonhuman primates (reviewed in Fernandez-Duque et al. 2009). This association suggests that more promiscuous mating systems which tend to provide fewer resource-based benefits to females (Clutton-Brock 1989) have weaker effects of mate choice for MHC diversity.

## Conclusions and suggestions for future research

We found clear support for humans and a trend for nonhuman primates choosing more MHC-diverse mates. In contrast, we found extremely high heterogeneity and no such clear pattern in humans for choice for MHC-dissimilar mates. A key driver of this heterogeneity was whether or not ethnic heterogeneity in studies on couples was controlled. High heterogeneity among nonhuman primate studies still showed a consistent direction for MHC dissimilarity, which could stem from methodological differences but also from socio-ecological differences between populations and species (Setchell & Huchard 2010). In fact, we found preliminary evidence that the expression of MHC-based mate choice could depend on the mating system and the reproductive strategies of individuals within those systems, as well as the phylogenetic history among species.

Results of this study show clear priorities in how to design future human and nonhuman primate studies, including studies that: (i) are large scale (≥200 individual targets) and include power analysis, (ii) focus on individuals from (ethnically) homogeneous populations with limited MHC diversity (Ober et al. 1997; Jacob et al. 2002; Chaix et al. 2008) and test socio-ecologically sensitive hypotheses (e.g. how expression of preferences for diversity/dissimilarity/ optimality may vary according to mating system or demography; Setchell & Huchard 2010), (iii) explicitly test the optimality hypothesis (with the prediction of less variance around an optimal parental combination of alleles than combination under random mating; Forsberg et al. 2007) and consider experimentally adjusting MHC peptide diversity/overlap (Milinski et al. 2013), (iv) use multiple loci, including different MHC classes (I and II; Kamiya et al. 2014), (v) control for non-MHC variability-key in determining incidental or adaptive MHC-assortative mating (Jiang et al. 2013), and (vi) control for biologically confounding variables (e.g. ovulatory status and contraceptive pill use; Wedekind et al. 1995).

Finally, we emphatically call for more nonhuman primate studies to improve understanding of the evolutionary trajectory of human mate choice. We hope that our synthesis highlights the need for additional studies of the selective pressure of MHC genotype on mating decisions and provides direction for future research.

## Acknowledgements

We thank T. Lenz, M. Milinski and the four anonymous reviewers for constructive feedback that substantially improved our final manuscript. Funding support for JW came from the Alexander von Humboldt Foundation, the European Social Fund (ESF) and the state budget of Czech Republic through the Operational Program Education for Competitiveness (OPEC), Reg. No. CZ.1.07/2.3.00/30.0048. Funding support for JA came from the University of Berne. JH is supported by the Czech Science Foundation Grant (14-02290S). LZG was supported by funds from the Ministry of Economy and Competitiveness in Spain (CGL2015-70639-P), and by the National Research, Development and Innovation Office in Hungary (NKFIH, K-115970).

## Conflicts of interest

The authors report no conflicts of interest in this work.

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J.W. initiated the study; J.W., J.A. and L.Z.G. designed the study; J.W., J.A. and L.Z.G. compiled data sets; J.W. and L.Z.G. analysed data; and J.W. wrote the manuscript with the substantial contribution of J.A., E.H., J.H. and L.Z.G.

## Data accessibility

The data sets compiled for our analyses and R code are deposited on Dryad (doi: 10.5061/dryad.5003g).

## Supporting information

Additional supporting information may be found in the online version of this article.

**Table S1** Moderator estimates for MHC-dissimilar mating patterns.

**Table S2** Moderator estimates for human MHC-dissimilar mating patterns.

**Table S3** Moderator estimates for human MHC-dissimilar mating patterns by experimental design.
**Table S4** Moderator estimates for non-human primate MHC-dissimilar mating patterns.

**Table S5** Population heterogeneity moderator estimates for human MHC-similar mating patterns.

**Table S6** Moderator estimates for MHC-diversity mating patterns.

**Table S7** Moderator estimates for human MHC-diversity mating patterns.

**Table S8** Moderator estimates for non-human primate MHC-diversity mating patterns.

**Table S9** Heterogeneity estimates for MHC-dissimilarity meta-regression models.

**Table S10** Heterogeneity estimates for MHC-diversity meta-regression models.

**Table S11** Moderator contrasts for MHC-dissimilarity.

**Table S12** Moderator contrasts for MHC-diversity.

**Table S13** Mixed-model results using alternative random effects.

**Table S14** Publication bias and sensitivity analysis results.

**Table S15** Testing for temporal bias in MHC mate choice datasets.

**Table S16** P-curve analysis results.

**Table S17** Power analysis.

**Fig. S1** Primate phylogenetic tree and mating system.

**Fig. S2** Publication bias and sensitivity analysis for MHC dissimilarity.

**Fig. S3** Publication bias and sensitivity analysis for MHC diversity.

**Fig. S4** Raw data for moderators of MHC-dissimilarity effect sizes (yi).

**Fig. S5** Raw data for moderators of human MHC-dissimilarity effect sizes (yi).

**Fig. S6** Raw data for moderators of non-human primate MHC-dissimilarity effect sizes (yi).

**Fig. S7** Raw data for moderators of MHC-diversity effect sizes (yi).

**Fig. S8** Raw data for moderators of human MHC-diversity effect sizes (yi).

**Fig. S9** Raw data for moderators of non-human primate MHC-diversity effect sizes (yi).

**Fig. S10** P-curve plots.

**Fig. S11** Correlation between MHC and neutral marker effect size for mate choice.