# Systematic review reveals multiple sexually antagonistic polymorphisms affecting human disease and complex traits

Jon Alexander Harper, Tim Janicke, Edward Morrow

► To cite this version:

Jon Alexander Harper, Tim Janicke, Edward Morrow. Systematic review reveals multiple sexually antagonistic polymorphisms affecting human disease and complex traits. Evolution - International Journal of Organic Evolution, 2021, 75 (12), pp.3087-3097. 10.1111/evo.14394. hal-03388336

HAL Id: hal-03388336

https://hal.science/hal-03388336v1

Submitted on 4 Feb 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Systematic review reveals multiple sexually antagonistic polymorphisms affecting human disease and complex traits

Jon Alexander Harper,¹,² ID, Tim Janicke,³,⁴ and Edward H. Morrow⁵ ID

¹Evolution, Behaviour and Environment Group, School of Life Sciences, University of Sussex, Brighton BN1 9QG, United Kingdom

²E-mail: jah52@sussex.ac.uk

³Centre d'Écologie Fonctionnelle et Évolutive, UMR 5175, CNRS, Université de Montpellier, École Pratique des Hautes Études, Montpellier 34293, France

⁴Applied Zoology, Technical University Dresden, Dresden 01062, Germany

⁵Department of Environmental and Life Sciences, Karlstad University, Karlstad SE-65188, Sweden

Received February 24, 2021

Accepted October 10, 2021

An evolutionary model for sex differences in disease risk posits that alleles conferring higher risk in one sex may be protective in the other. These sexually antagonistic (SA) alleles are predicted to be maintained at frequencies higher than expected under purifying selection against unconditionally deleterious alleles, but there are apparently no examples in humans. Discipline-specific terminology, rather than a genuine lack of such alleles, could explain this disparity. We undertook a two-stage review of evidence for SA polymorphisms in humans using search terms from (i) evolutionary biology and (ii) biomedicine. Although the first stage returned no eligible studies, the second revealed 51 genes with sex-opposite effects; 22 increased disease risk or severity in one sex but protected the other. Those with net positive effects occurred at higher frequencies. None were referred to as SA. Our review reveals significant communication barriers to fields as a result of discipline-specific terminology.

**KEY WORDS:** Evolutionary genomics, fitness, selection—sexual, sexual conflict.

In species with separate sexes, an evolutionary conflict at the level of individual genetic loci can occur, where alleles that are favored by selection in one sex are selected against in the other (i.e., intralocus sexual conflict; Parker 1979). This sexually antagonistic (SA) form of selection is thought to be driven by differences in how the two sexes maximize their fitness and concomitant unequal variances in reproductive success. Research into SA selection has expanded recently with the recognition that it feeds into several important evolutionary processes. It is primarily thought to drive the evolution of sexual dimorphism and trait diversification (Lande 1980; Rice 1984; Pennell et al. 2016), including sex-biased gene expression (Ellegren and Parsch 2007). As a potential driver of balancing selection, it has also been implicated in the maintenance of genetic variation (Connallon and

Clark 2013; Grieshop and Arnqvist 2018) that would otherwise be eroded by directional selection. Most recently, it has been suggested that genetic variation at SA loci could contribute to the occurrence of a number of common human diseases (Morrow and Connallon 2013; Morrow 2015), which also show considerable variation between the sexes in terms of their prevalence, severity, and age of onset (Ober et al. 2008; Rigby and Kulathinal 2015).

Despite the interest in sexual antagonism as an evolutionary force, determining the identity of SA loci remains a major challenge (Ruzicka et al. 2020). An early empirical milestone in the field of sexual conflict was achieved by quantitative genetic studies demonstrating that genomes in a laboratory-adapted population of *Drosophila melanogaster* harbor significant amounts

<figure><img src="image_2.png" /></figure>

SOCIETY for the STUDY of EVOLUTION
of SA standing genetic variation (Rice 1992; Chippindale
et al. 2001). A number of other systems have shown similar
results, albeit using different methods, including invertebrates
and vertebrates from both lab and wild populations (Foerster
et al. 2007; Bonduriansky and Chenoweth 2009; Mills et al.
2012). With the advent of advanced genomic tools, two model
systems report specific examples of SA genetic loci: in Atlantic
salmon (*Salmo salar*) the VGLL3 locus (Barson et al. 2015),
and in *D. melanogaster* the DDT-R locus (Rostant et al. 2015),
the Ala-278-Thr polymorphism in the mitochondrial genome
(Camus et al. 2015), and multiple candidate loci from a recent
genome-wide association study (Ruzicka et al. 2019).

Although humans have also been shown to experience SA
selection for some quantitative traits (Camperio-Ciani et al. 2004;
Garver-Apgar et al. 2011; Stearns et al. 2012; Stulp et al. 2012),
and there are numerous reports of genetic loci with sex-specific
effects (i.e., effects that differ in magnitude between the sexes) on
multiple complex traits or disease phenotypes (Gilks et al. 2014;
Winkler et al. 2015), there are apparently no clear examples of
SA loci in humans, which is inconsistent with theoretical expec-
tations (Connallon and Clark 2014). One potential explanation
is that sexual antagonism is a weak selective force in humans,
with sexual dimorphism being rather limited (Short 1979; Dixson
2009). This may indicate that concordant selection pressures be-
tween the sexes have dominated our evolutionary history. But the
recent quantitative genetic studies challenge this view (Stearns
et al. 2012; Stulp et al. 2012), and indeed theory predicts there
is an inevitability to SA loci occurring in organisms with sepa-
rate sexes (Parker et al. 1972; Connallon and Clark 2014). Thus,
there is a clear disparity between, on the one hand, theoretical
expectations and quantitative genetic evidence that SA selection
does occur in humans, and on the other hand, a complete lack of
specific examples of SA loci. All this in the context of decades
of research into how individual genetic variants influence disease
profiles.

An alternative explanation for the absence of documented
SA loci in humans is that because biomedical science does not
use the same terminology for SA effects that evolutionary biol-
ogy uses, there may in fact be examples that have been misclas-
sified and therefore remain hidden in the literature. For example,
sex-specific effects referred to as sex-different or sex-opposite,
and sex referred to using gender terms (Khramtsova et al. 2019).
Moreover, it is likely that the concepts of (intralocus) sexual con-
flict and sexual antagonism are not generally well known within
biomedical science, and variants that may have SA effects on dis-
ease risk may not be referred to as such. A secondary related hy-
pothesis is that SA effects, when discovered, are discounted as
errors as they do not match expectations that sex differences in
phenotype or genetic architecture are not important (Clayton and
Collins 2014). As a result, that may also lead them to be misclas-

sified or simply go unreported, leading to a general publication
bias.

For these reasons, we propose that a systematic review of
the biomedical literature to identify SA loci in humans requires a
specific and targeted set of search terms that would not normally
be used within the field of evolutionary biology. We test this as-
umption by dividing our systematic review into two stages. In
the first stage, we search for articles identifying specific genetic
loci using terms directly relating to the concept of sexual antag-
onism and others used in evolutionary biology. We supplement
this search with a second stage, where we develop a set of terms
that we hypothesize may capture the same concept as sexual an-
tagonism within the biomedical literature, although not explicitly
stated as such, as well as additional terminology relating to possi-
ble alternatives for describing the two sexes. From our searches,
we also extracted data on sex-specific effect sizes and allele fre-
quencies. We were then able to explore how effect sizes varied for
the same alleles between the sexes, and test the prediction that SA
alleles experiencing net positive selection between the sexes may
achieve higher equilibrium frequencies than alleles that experi-
ence more symmetric or net negative patterns of selection (Mor-
row and Connallon 2013). Our systematic review aims to advance
our understanding of SA genes in humans, which show consid-
erable and largely unexplained diversity in sexual dimorphism
for disease phenotypes (Ober et al. 2006; Rigby and Kulathinal
2015).

Methods

For this systematic review, we followed PRISMA guidance where
possible (Moher et al. 2009). PubMed (https://pubmed.ncbi.nlm.
nih.gov/) was searched for articles on 2 December 2020 with no
time limit. The searches were carried out in two stages, with the
organism filter set to human in both stages. In Stage 1, eligible
studies were required to report specific genetic variants or haplo-
types that were referred to as SA or were an example of intralocus
sexual conflict. To achieve this, we conducted a Boolean search
for articles that used the terms “sexual antagonism” OR “sexually
antagonistic” OR “intralocus sexual conflict” AND “locus” OR
“loci” OR “gene” OR “snp” OR “polymorphism” OR “variant”
OR “allele” in their abstract or title. The Stage 1 search returned
34 articles in total (full search term in the Supporting Informa-
tion; search output is accessible at https://pubmed.ncbi.nlm.nih.
gov/collections/60255050/?sort=pubdate).

In Stage 2, studies were required to report specific genetic
variants or haplotypes in humans with opposite effects in the two
sexes on either complex traits, the outcome of a medical interven-
tion, or disease risk/severity. We define complex traits as likely
with a polygenic genetic architecture but are not directly related
to a disease phenotype. In this second stage, search terms were
<figure><img src="image_3.png" /><figcaption>**Figure 1.** PRISMA flow diagram for systematic review of sexually antagonistic loci in humans.</figcaption></figure>

specifically designed to include articles from the biomedical literature that may have been missed in the first stage because they do not report their findings with terms normally found within the evolutionary biology literature. Again, we conducted a Boolean search for articles that used terms in their title or abstract to describe an opposite or different effect in the two sexes ("sex dependent," "sex different," "gender dependent," "sex AND opposite," or "gender AND opposite"), or that capture this concept with alternative words for sex ("(male AND female AND opposite" OR "men AND women AND opposite" OR "boys AND girls AND opposite") AND ("locus" OR "loci" OR "gene" OR "snp" OR "polymorphism" OR "variant" OR "allele")). Full details of the search terms used and the numbers of articles returned are provided in the Supporting Information. The Stage 2 search returned 881 articles (Fig. 1) (full search term in the Supporting Information; search output is accessible at https://pubmed.ncbi.nlm.nih.gov/collections/60254985/?sort=pubdate).

The abstracts of the articles from Stage 1 and Stage 2 were then examined, and any articles that had the possibility of reporting an opposite effect of a specific genetic locus on a complex trait, medical intervention, or on disease risk/severity were considered for further screening. From Stage 1, no articles passed the screening. From Stage 2, this screening produced a shortlist of 70

candidate articles (https://pubmed.ncbi.nlm.nih.gov/collections/57906298/?sort=pubdate). Full texts of these candidate articles were then reviewed in detail. Articles were included in the final list if they described a sex-opposite or SA effect linked to a specific genetic locus or loci, and reported the effect to be statistically significant (at a P-value cutoff of <0.05, or with 95% confidence intervals not overlapping 1). One additional article was considered from an outside source. Studies that only reported significant sex-by-variant effects were not automatically included unless they also satisfied the criteria above.

We converted all reported sex-specific effects into a standard effect size (Cohen's *d*) quantifying the magnitude of how a given variant affects the studied trait expressed in the given sex. Specifically, Cohen's *d* was computed based on the reported descriptive statistics (*N*, mean, standard error) or by conversion from other effect sizes (Odds ratio) and test statistics (*F*-values, *t*-values) using formulas reported elsewhere (Borenstein 2009; Gurevitch et al. 2013, pp. 195–206; Lajeunesse 2013). We sought information directly from the authors where these metrics were not possible to extract from the articles themselves (12 authors contacted, five responded, three responded with data, all later excluded as they did not fulfill the criteria for eligibility). We also recorded the gene name, locus (with accession/rs number where possible),
trait affected, and the frequency of the focal allele having the ef-
fect, hereafter referred to as *effect allele frequency*. Identification
for the variants was taken from the studies where possible, but
others necessitated searching the National Centre for Biotechnol-
ogy Information (NCBI) to find the Reference SNP cluster ID for
the variants described. Where effect allele frequencies were not
reported, we used genotype frequencies to calculate effect allele
frequency. Not all studies reported allele or genotype frequencies
and so we attempted to supplement these data with allele fre-
quencies from the 1000 genomes database (The 1000 Genomes
Project Consortium 2015), because these show a strong correla-
tion with effect allele frequencies reported in the studies reviewed
(Pearson correlation: N = 25 r = 0.94, P > 0.001; Fig. S1).
However, using this approach we were only able to supplement
our allele frequency data for one additional locus (rs7341475 in
the *RELN* gene; Table 1). In some cases, loci that affect mul-
tiple different traits were found. In such cases, we calculated
the geometric mean of their effect sizes to account for possible
pseudoreplication.

If $b_f$ and $b_m$ represent the estimated effect of the focal allele in females and males, respectively, and hence can be represented by the male and female D we calculate here, then positive values of $b_f$ or $b_m$ imply that the allele is positively associated with disease expression, whereas negative values imply that the allele is negatively associated with disease expression. SA alleles are therefore defined as those with opposite effects between the sexes (i.e., $b_f < 0 < b_m$ or $b_m < 0 < b_f$). An evolutionary model of evolution at SA loci predicts that as the magnitude of the positive effect that an SA allele has outweighs the negative effect, the higher the frequency that allele can achieve (Morrow and Connallon 2013). We therefore expect a negative relationship between the ratio of positive to negative effect sizes (hereafter referred to as the *effect size ratio*) and observed allele frequency. To investigate this, we calculated the effect size ratio by dividing the positive standardized effect size by the negative standardized effect size; when $b_f < 0 < b_m$, effect size ratio was calculated as $b_m/b_f$, and when $b_m < 0 < b_f$, effect size ratio was calculated as $b_f/b_m$. Thus, SA alleles with a greater beneficial effect will have a smaller, more negative effect size ratio ($<-1$), whereas SA alleles that have a greater deleterious effect will have a larger effect size ratio ($>-1$), up to a maximum value of 0 (positive values occur when the effect is positive or negative in both sexes, but the allele would then no longer be defined as a SA allele).

We modeled how effect allele frequency changes with effect
size ratio using a generalized linear model (GLM), weighted by
the inverse of the variance of the effect sizes such that data points
with smaller variance have a higher weight, because smaller sam-
ple sizes were associated with larger and more variable effect
sizes (Fig. S2). We initially also included trait class and its in-
teraction with effect size ratio as a fixed factor with two levels

(complex trait and disease trait/severity), because alleles that in-
fluence complex traits in opposite directions are not necessarily
under SA selection and so may not behave in the way predicted,
whereas alleles influencing disease traits, unless very late acting,
are more likely to show a closer relationship with marginal ef-
fects on fitness. As a response variable, effect allele frequency
is limited between 0 and 1, so we looked at allele counts to al-
low allele frequency to vary freely. GLMs with binomial error
distribution showed substantial overdispersion, so we used a qua-
sibinomial function to address this issue (Payne et al. 2018). We
used a Chi-squared test to infer significance of the two predic-
tor variables and their interaction. The full model indicated that
the interaction term and trait class have no significant effect, so
that we report the fit of the reduced model that only includes ef-
fect size ratio. We also subsequently modeled the data for the two
trait classes (complex traits and disease risk/severity) separately,
again using a quasibinomial distribution function, to see if the re-
sult was replicated in these smaller subsets of the data. The raw
data and R script are available for replicating the analyses and
figures we present (Supporting Information).

## Results

The Stage 1 search found no articles that described ge-
netic loci in humans with effects that were SA. In contrast,
the Stage 2 search identified 30 articles that described vari-
ants with statistically significant sex-opposite or SA effects
(Fig. 1; https://pubmed.ncbi.nlm.nih.gov/collections/60278165/
?sort=pubdate). From the studies examined, 49 SA variants were
identified (Table 1), affecting 21 different complex traits (30 loci)
and 17 disease risk/severity traits (19 loci), with a large range of
effect sizes. There were no variants affecting medical interven-
tion traits that passed the screening process. The majority of al-
leles had effect sizes of similar absolute values in the two sexes,
with larger effects tending to show greater differences on absolute
effect size (Fig. S3).

The vast majority of studies we identified in the review (Table 1) investigated associations between traits or disease phenotypes and already known candidate genes, with a single genome-wide association study (GWAS). An example candidate gene study is Sainz et al. (2012) who investigated the link between colorectal cancer (CRC) and alleles that are known to be associated with type 2 diabetes in an effort to better understand the link between the two diseases. They genotyped 1798 CRC patients and compared these with controls from a population-based study, focusing on variants previously identified in a GWAS. The study revealed that a SNP in the *LTA* gene was underrepresented in female CRC patients, but more common in males relative to the control population. They reported odds ratios for each sex, which we converted to Cohen's *D*.
<table><caption>**Table 1.** Genetic loci in humans showing sexually antagonistic effects on trait expression.</caption><thead><tr><th>Trait</th><th>Gene</th><th>Locus</th><th>EAF</th><th>M Effect</th><th>M Effect SE</th><th>F Effect</th><th>F Effect SE</th><th>Reference</th></tr></thead><tbody><tr><td colspan="9"><strong>Complex traits</strong></td></tr><tr><td>Concentration performance score</td><td>FADS1/2</td><td>rs1535</td><td>0.672</td><td>0.2275</td><td>0.1065</td><td>-0.2394</td><td>0.1116</td><td>Lauritzen et al. 2017</td></tr><tr><td>Reading speed, number of sentences</td><td>FADS1/2</td><td>rs174448</td><td>0.642</td><td>0.5511</td><td>0.1069</td><td>-0.6074</td><td>0.1129</td><td>Lauritzen et al. 2017</td></tr><tr><td>Reading speed, number of sentences</td><td>ELOVL5</td><td>rs2397142</td><td>0.695</td><td>-0.1963</td><td>0.1042</td><td>0.2364</td><td>0.1095</td><td>Lauritzen et al. 2017</td></tr><tr><td>Reading speed, correct sentences</td><td>FADS1/2</td><td>rs174448</td><td>0.642</td><td>0.6599</td><td>0.1077</td><td>-0.3451</td><td>0.1112</td><td>Lauritzen et al. 2017</td></tr><tr><td>Speed-dating success</td><td>OPRM1</td><td>rs1799971</td><td>0.922</td><td>0.4697</td><td>0.2275</td><td>-0.53</td><td>0.2856</td><td>Wu et al. 2016</td></tr><tr><td>Speed-dating success</td><td>HTR2A</td><td>rs6311</td><td>0.418</td><td>-0.4826</td><td>0.1982</td><td>0.4702</td><td>0.1964</td><td>Wu et al. 2016</td></tr><tr><td>Waist-hip ratio (BMI adjusted)</td><td>SIM1</td><td>rs17185536<sup>a</sup></td><td>0.76</td><td>-0.0166</td><td>0.008</td><td>0.0264</td><td>0.0067</td><td>Winkler et al. 2015</td></tr><tr><td>Waist-hip ratio (BMI adjusted)</td><td>NMU</td><td>rs3805389<sup>a</sup></td><td>0.28</td><td>-0.0139</td><td>0.0067</td><td>0.0261</td><td>0.006</td><td>Winkler et al. 2015</td></tr><tr><td>Waist-hip ratio (BMI adjusted)</td><td>GNPNAT1</td><td>rs4898764<sup>a</sup></td><td>0.53</td><td>0.0208</td><td>0.0066</td><td>-0.0165</td><td>0.0059</td><td>Winkler et al. 2015</td></tr><tr><td>Waist-hip ratio (BMI adjusted)</td><td>SGCZ</td><td>rs17470444<sup>a</sup></td><td>0.71</td><td>0.0345</td><td>0.0081</td><td>-0.0149</td><td>0.0068</td><td>Winkler et al. 2015</td></tr><tr><td>Waist-hip ratio (BMI adjusted)</td><td>IQGAP2</td><td>rs2069664<sup>a</sup></td><td>0.53</td><td>0.0244</td><td>0.0078</td><td>-0.0183</td><td>0.0067</td><td>Winkler et al. 2015</td></tr><tr><td>Waist-hip ratio (BMI adjusted)</td><td>SLC2A3</td><td>rs741361<sup>a</sup></td><td>0.6</td><td>0.0272</td><td>0.0079</td><td>-0.0196</td><td>0.0067</td><td>Winkler et al. 2015</td></tr><tr><td>Waist-hip ratio (BMI adjusted)</td><td>IRS1</td><td>rs2673140<sup>a</sup></td><td>0.38</td><td>0.022</td><td>0.0066</td><td>-0.0209</td><td>0.0059</td><td>Winkler et al. 2015</td></tr><tr><td>Waist-hip ratio (BMI adjusted)</td><td>TTN</td><td>rs2042995<sup>a</sup></td><td>0.23</td><td>0.0198</td><td>0.0066</td><td>-0.022</td><td>0.0078</td><td>Winkler et al. 2015</td></tr><tr><td>Waist-hip ratio (BMI adjusted)</td><td>RXRA</td><td>rs10881574<sup>a</sup></td><td>0.07</td><td>0.0268</td><td>0.008</td><td>-0.0191</td><td>0.0067</td><td>Winkler et al. 2015</td></tr><tr><td>Waist-hip ratio (BMI adjusted)</td><td>PTPRD</td><td>rs7042428<sup>a</sup></td><td>0.98</td><td>0.0244</td><td>0.0071</td><td>-0.0166</td><td>0.0063</td><td>Winkler et al. 2015</td></tr><tr><td>Waist-hip ratio (BMI adjusted)</td><td>CECR2</td><td>rs17809093<sup>a</sup></td><td>0.04</td><td>0.0294</td><td>0.0091</td><td>-0.024</td><td>0.0077</td><td>Winkler et al. 2015</td></tr><tr><td>Reaction time (RT) in the Stroop task, congruent condition</td><td>CHRNA5</td><td>rs3841324</td><td>0.406</td><td>-1.5421</td><td>0.3857</td><td>0.9945</td><td>0.2517</td><td>Schneider et al. 2015</td></tr><tr><td>Reaction time (RT) in the Stroop task, incongruent condition</td><td>CHRNA5</td><td>rs3841324</td><td>0.406</td><td>-1.6215</td><td>0.389</td><td>1.0215</td><td>0.2521</td><td>Schneider et al. 2015</td></tr><tr><td>Reaction time (RT) in the Negative priming task</td><td>CHRNA5</td><td>rs3841324</td><td>0.406</td><td>-1.675</td><td>0.3912</td><td>0.6299</td><td>0.247</td><td>Schneider et al. 2015</td></tr><tr><td>Borderline personality traits after maltreatment</td><td>OXTR</td><td>rs53576</td><td>0.738</td><td>-0.3429</td><td>0.0881</td><td>0.2066</td><td>0.0857</td><td>Cicchetti et al. 2014</td></tr><tr><td>Borderline personality traits after maltreatment</td><td>FKBP5</td><td>CATT haplotype</td><td>-</td><td>0.2041</td><td>0.0877</td><td>-0.195</td><td>0.0857</td><td>Cicchetti et al. 2014</td></tr><tr><td>Fluid intelligence</td><td>OXTR</td><td>rs226849</td><td>0.46</td><td>-0.3949</td><td>0.1869</td><td>0.494</td><td>0.1814</td><td>Kiy et al. 2013</td></tr><tr><td>Face perception</td><td>OXTR</td><td>rs226849</td><td>0.46</td><td>-0.5819</td><td>0.1909</td><td>0.3498</td><td>0.1788</td><td>Kiy et al. 2013</td></tr><tr><td>Face memory</td><td>OXTR</td><td>rs226849</td><td>0.46</td><td>-0.3679</td><td>0.1864</td><td>0.3562</td><td>0.1789</td><td>Kiy et al. 2013</td></tr><tr><td>Hippocampal volume</td><td>SLC6A4</td><td>rs25531</td><td>0.39</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Price et al. 2013</td></tr><tr><td>Sociopathy (in alcoholics)</td><td>SLC6A4</td><td>rs25531</td><td>0.51</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Herman et al. 2011</td></tr></tbody></table>

(Continued)
<table><caption>**Table 1.** (Continued).</caption><thead><tr><th>Trait</th><th>Gene</th><th>Locus</th><th>EAF</th><th>M Effect</th><th>M Effect SE</th><th>F Effect</th><th>F Effect SE</th><th>Reference</th></tr></thead><tbody><tr><td>Systolic blood pressure S_NIGHT</td><td>PON1</td><td>rs3917542</td><td>-</td><td>-0.1047</td><td>0.0637</td><td>0.1129</td><td>0.0645</td><td>Padmanabhan et al. 2010</td></tr><tr><td>Systolic blood pressure S_NIGHT</td><td>PON3</td><td>rs17884563</td><td>-</td><td>-0.1482</td><td>0.0637</td><td>0.1061</td><td>0.0645</td><td>Padmanabhan et al. 2010</td></tr><tr><td>Systolic blood pressure S_CLIN</td><td>EDNRB</td><td>rs1335803</td><td>-</td><td>-0.1308</td><td>0.0637</td><td>0.1129</td><td>0.0645</td><td>Padmanabhan et al. 2010</td></tr><tr><td>Diastolic blood pressure D_NIGHT</td><td>DRD1</td><td>rs265974</td><td>-</td><td>-0.1482</td><td>0.0637</td><td>0.1502</td><td>0.0646</td><td>Padmanabhan et al. 2010</td></tr><tr><td>Diastolic blood pressure D_HOME</td><td>SLC4A2</td><td>rs2303933</td><td>-</td><td>-0.1482</td><td>0.0637</td><td>0.1325</td><td>0.0646</td><td>Padmanabhan et al. 2010</td></tr><tr><td>Diastolic blood pressure D_CLIN</td><td>LPL</td><td>rs17410577</td><td>-</td><td>-0.1047</td><td>0.0637</td><td>0.1775</td><td>0.0647</td><td>Padmanabhan et al. 2010</td></tr><tr><td>Psychological stress</td><td>BDNF</td><td>rs6265</td><td>0.149</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Shalev et al. 2009</td></tr><tr><td>BMI standard deviation score (children)</td><td>FAS</td><td>rs228305</td><td>0.0312</td><td>-0.552</td><td>0.2259</td><td>0.4427</td><td>0.2075</td><td>Korner et al. 2007</td></tr><tr><td>Pain sensitivity</td><td>OPRM1</td><td>rs1799971</td><td>0.8817</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Fillingim et al. 2005</td></tr><tr><td>Central DBP</td><td>REN</td><td>rs6693954</td><td>0.22</td><td>-0.4444</td><td>0.2132</td><td>1.1052</td><td>0.3923</td><td>Ljungberg et al. 2014</td></tr><tr><td>Disease risk/severity</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Graves ophthalmopathy in GD</td><td>PRR3</td><td>rs2074503</td><td>0.3586</td><td>0.4397</td><td>0.1682</td><td>-0.2185</td><td>0.0851</td><td>Liu et al. 2014</td></tr><tr><td>Colorectal cancer</td><td>LTA</td><td>rs1041981</td><td>0.32</td><td>-0.0722</td><td>0.0368</td><td>0.1027</td><td>0.0453</td><td>Sainz et al. 2012</td></tr><tr><td>Schizophrenia risk</td><td>RELN</td><td>rs7341475</td><td>0.851*</td><td>1.2396</td><td>0.2819</td><td>-0.762</td><td>0.3662</td><td>Ovadia and Shifman 2011</td></tr><tr><td>Colon cancer survival</td><td>ESR2</td><td>(CA)n repeats</td><td>-</td><td>0.2272</td><td>0.0962</td><td>-0.2046</td><td>0.1205</td><td>Press et al. 2011</td></tr><tr><td>Schizophrenia risk</td><td>DAO</td><td>rs2070586</td><td>0.2729</td><td>0.1982</td><td>0.0888</td><td>-0.2537</td><td>0.0908</td><td>Kim et al. 2010</td></tr><tr><td>T2DM, fasting glucose</td><td>SCARB1</td><td>rs9919713</td><td>0.04</td><td>0.1865</td><td>0.0853</td><td>-0.2515</td><td>0.0697</td><td>McCarthy et al. 2009</td></tr><tr><td>Depression</td><td>SLC6A4</td><td>rs25531</td><td>0.4586</td><td>0.5404</td><td>0.2277</td><td>-0.3996</td><td>0.1859</td><td>Sjoberg et al. 2006</td></tr><tr><td>Infertility in cystic fibrosis</td><td>CFTR</td><td>rs213950</td><td>0.417931</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Morea et al. 2005</td></tr><tr><td>Bone mineral density</td><td>APOE</td><td>rs429358</td><td>0.0798</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Wong et al. 2005</td></tr><tr><td>Narcolepsy severity</td><td>COMT</td><td>Long allele</td><td>0.4588</td><td>1.3596</td><td>0.3878</td><td>-2.2152</td><td>0.4871</td><td>Dauvilliers et al. 2001</td></tr><tr><td>Nonsyndromic cleft lip</td><td>IMPK</td><td>rs11142081</td><td>0.863</td><td>1.2156</td><td>0.0937</td><td>-1.1161</td><td>0.0783</td><td>Carlson et al. 2018</td></tr><tr><td>Nonsyndromic cleft lip</td><td>IMPK</td><td>rs72804706</td><td>0.898</td><td>-0.816</td><td>0.0906</td><td>2.0665</td><td>0.0908</td><td>Carlson et al. 2018</td></tr><tr><td>Nonsyndromic cleft lip</td><td>IMPK</td><td>rs77590619</td><td>0.744</td><td>-0.8577</td><td>0.0904</td><td>1.2291</td><td>0.0804</td><td>Carlson et al. 2018</td></tr><tr><td>Allergy</td><td>ACPI</td><td>A* allele (CG haplotype)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Gloria-Bottini et al. 2007</td></tr><tr><td>Hepatitis C susceptibility</td><td>IL-12B</td><td>rs3212227</td><td>0.3463</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Youssef et al. 2013</td></tr><tr><td>RSV severity</td><td>IL-9</td><td>rs2069885</td><td>0.15</td><td>0.1888</td><td>0.0933</td><td>-0.2794</td><td>0.0957</td><td>Schuurhof et al. 2010</td></tr><tr><td>Bone mineral density</td><td>NR3C1</td><td>GCAG haplotype</td><td>-</td><td>0.6854</td><td>0.3676</td><td>-0.9029</td><td>0.4311</td><td>Peng et al. 2008</td></tr><tr><td>Anxiety</td><td>NGF</td><td>rs6330</td><td>0.451</td><td>0.4562</td><td>0.18</td><td>-0.4674</td><td>0.1817</td><td>Lang et al. 2008</td></tr><tr><td>Melanoma risk</td><td>RNASEL, miR-146a</td><td>AG/AA and C haplotype</td><td>-</td><td>0.4525</td><td>0.1564</td><td>-0.3553</td><td>0.174</td><td>Sangalli et al. 2017</td></tr></tbody></table>

EAF = effect allele frequency (asterisk indicates allele frequency derived from 1000 genomes database); M = male; F = female.

Background colours denote instances where genes and/or variants appear more than once in the list. The superscript "a" denotes alleles from a GWAS (FDR < 5%).
A second example of a candidate gene study is Kiy et al. (2013) who focused on two SNPs: rs4680 in *COMT* and rs226849 in the *OXTR* gene. They genotyped 250 participants, who conducted nine different tasks that measured cognitive traits such as face recognition and memory. In three of these tasks, they discovered a significant, opposite effect of the rs226849 SNP on performance in male and female participants. The effect sizes reported were converted from the R² values for each sex.

The single GWAS by Winkler et al. (2015) aimed to identify variants that affect body size and shape, such as body mass index (BMI) and waist-hip ratio, characterizing how their effects were modulated by age and sex, and included data from 114 studies encompassing up to 320,000 individuals. Forty-four loci that affect waist-hip ratio were found to have different effects in men and women, of which 11 had opposite effects in the two sexes. The data reported were beta values stratified by sex, which we converted into Cohen's *D*.

We found 22 studies provided sufficient data to allow the relationship between effect size ratio and effect allele frequency to be explored by statistical modeling. Trait class and its interaction with effect size ratio were initially included in the model but neither were found to have a significant effect (GLM: trait class dfs = 1,30, deviance = 15.5 × 10³, *P* = 0.968; interaction dfs = 1,29, deviance = 1475, *P* = 0.990) and were therefore excluded from the subsequent model in which effect size ratio was found to relate negatively to effect allele frequency, as predicted (GLM: estimate ± SE = −1.10 ± 0.54, dfs = 1,31, deviance = 38.7 × 10⁶, *P* = 0.036; Fig. 2). For three loci, there were data on effect sizes for more than one trait (Table 1), which introduced a degree of nonindependence between these values in the dataset. However, resampling the data with effect sizes for only one trait for these three loci in turn and modeling these datasets in the same way (18 individual GLMs, dfs = 1,31) did not change the results qualitatively (minimum, maximum: model estimate = −1.0963, −1.0964; deviance = 38.72 × 10⁶, 38.73 × 10⁶; *P* = 0.03618, 0.03620).

Although the interaction term between trait type and effect size ratio was not significant, we wanted to investigate whether the negative relationship between effect size ratio and effect allele frequency across all loci was repeated when the data were divided according to trait class, because although opposite effects on disease risk/severity may show a direct relationship with SA fitness effects, complex traits may or may not be experiencing SA selection, even if effects are in opposite directions in the two sexes. Although the trends were both again negative as predicted, the generalized linear models of these smaller datasets were only marginally nonsignificant (complex traits model estimate ± SE = −1.10 ± 0.69, dfs = 1,19, deviance = 32.6 × 10⁶, *P* = 0.101; disease risk/severity model estimate ± SE = −1.34 ± 0.77, dfs = 1,10, deviance = 5.14 × 10⁴, *P* = 0.053; Fig. S4).

<figure><img src="image_4.png" /><figcaption>**Figure 2.** The relationship between effect allele frequency and effect size ratio. Point size varies according to the variance of effect size ratio—larger points have smaller variance and therefore a larger weighting in the model. The vertical dotted line represents the switch point between a net-negative effect of a particular locus (effect size ratio > −1) and a net positive effect (effect size ratio < −1). The line represents the predicted values derived from the generalized linear model fitted to the data (see Results).</figcaption></figure>

## Discussion

The Stage 1 literature search of our systematic review focused on finding reports of specific genetic loci in humans with evidence for SA effects using terminology normally associated with the evolutionary concepts of sexual conflict or sexual antagonism. The Stage 2 search sought equivalent evidence, but used search terms that we anticipated would be used by scientists outside the field of evolutionary biology, who may not use the same terminology. Although the Stage 1 search did not find any examples of SA loci occurring in humans, the Stage 2 search identified 49 genetic loci across 30 studies that had ostensibly SA effects, but were not described as such. Clearly then the Stage 1 review failed to identify multiple relevant reports going back some 20 years because the terminology used in those reports did not match the conceptual framework of the search. The Stage 2 search may also be a lower limit given searches of other larger databases may also harbor further examples (see below). Although not yet validated, these reports nonetheless represent a substantial body of evidence that humans, like other organisms with separate sexes (Bonduriansky and Chenoweth 2009), inevitably experience SA selection for a wide range of complex traits (Garver-Apgar et al. 2011; Stearns et al. 2012; Stulp et al. 2012; Connallon and Clark 2014), as well as for a range of diseases.

A key prediction from a population genetic model of SA genetic variation is that SA alleles are expected to achieve higher equilibrium frequencies, without necessarily going to
fixation, as the relative magnitude of the positive effect in one sex outweighs the negative effect in the opposite sex, that is, an increasingly negative effect size ratio (Morrow and Connallon 2013). We found clear support for this prediction, with a negative relationship between effect size ratio and allele frequency when examining all complex traits and diseases together (Fig. 2), although this relationship was only marginally significant when complex or disease trait classes were modeled separately (Fig. S3). These results lend support to the view that the loci identified are genuinely experiencing SA selection, and although it is difficult to discern how selection acts on trait size in males and females for complex traits, we would not expect the frequencies of disease-causing alleles to be so high under a mutation-selection balance. We were not able to look at the evolutionary history or age of these alleles due to limited information (n = 8) (Albers and McVean 2018); it would nonetheless be valuable to examine the population dynamics of these alleles over a broad timescale.

The loci showed a very broad range of effect sizes, from small to very large, with a generally symmetrical inverse relationship between effect size in one sex and the other (Fig. S2), which is generally expected, because our screening process necessarily excluded studies reporting effects in the same direction across the two sexes. Nonetheless, it is striking just how large some of the effects were, with very large negative effects in one sex simultaneously occurring with similarly large positive effects. Larger and more variable effect sizes are expected when sample sizes are small, a pattern we also found, which motivates further investigation of the traits and loci included in this review to elucidate more accurate estimates of real effect sizes.

The loci themselves fall within genes that influence a broad range of phenotypes, including morphological, physiological, and behavioral complex traits, and a similarly diverse range of disease types, including various cancers, neurological disorders, and immune system processes (see Table 1). For the most part, the diseases appear to be early-acting rather than late-onset, with the exception of perhaps bone mineral density. As such, it seems reasonable that an allele that increases disease risk or severity in these cases will indeed have a concomitant reduction in marginal fitness. Although a single large study identified several loci related to BMI-adjusted waist-to-hip ratio, there is generally no overall bias toward one particular trait or disease class. The total number of loci is relatively small, however, which may limit the power to identify such biases if they exist. We also did not find any examples of SA loci on the X-chromosome, which may or may not be a hotspot of sexual antagonism (Rice 1984; Fry 2010; Ruzicka and Connallon 2020). This was expected given that it is commonly not included in genome-wide analyses (Wise et al. 2013), although most of the articles we reviewed were candidate gene studies. Nonetheless, we anticipate further examples may

be identified should data from the X-chromosome be included systematically in association studies.

We also found evidence that some traits are influenced by more than one genetic variant in sex-opposite or SA ways (n = 8), or that some specific variants have pleiotropic effects on more than one trait or disease in a sex-opposite or SA way (n = 6; see Table 1). This complexity in the genetic architecture of traits or diseases may make conflict resolution particularly difficult, as a change in the allele at a single genetic locus experiencing SA selection may simultaneously influence multiple genes or phenotypes in both sexes in divergent ways (Fitzpatrick 2004; Pennell and Morrow 2013). Consequently, this may explain the persistence of intralocus sexual conflict at these loci. It may be that some of the remaining loci also have as yet unidentified pleiotropic effects with other traits or there are as yet unidentified SA loci influencing those same traits or diseases. Of the 49 variants identified, four were confirmed to be haplotypes. Such variants, consisting of two or more SNPs in linkage disequilibrium having a joint effect, could also present a problem for conflict resolution, because the ability of selection to act on any individual locus independent of the others in the linkage block is reduced for linked loci. This issue may also extend to other single variants reported here if they also occur in linkage blocks.

Although we have identified multiple genetic loci with either sex-opposite or SA effects, these are likely outnumbered by those with either sex-specific (same direction but different magnitude) or sex-limited effects. For instance, Winkler et al. (2015) report 44 loci with sex-specific or sex-limited effects but only 11 with sex-opposite effects. Several other genome-wide association studies report sex-specific effects in several human diseases (Khramtsova et al. 2019), and a recent (nonsystematic) review identified 37 SNPs with sex-dependent effects (Gilks et al. 2014). Detection of loci with SA versus sex-specific or sex-limited effects may differ systematically because we expect SA loci are more likely to persist at intermediate frequencies than loci with sex-specific effects (i.e., that differ between the sexes in magnitude but not sign). A larger systematic review that targeted variants with sex-specific or sex-dependent effects would therefore be a valuable contribution and enable us to more clearly understand how important SA alleles are relative to the broader context of genes with nonidentical effects in the two sexes.

A key gap in our knowledge is whether the putative examples of sex-opposite or SA alleles presented here can be validated using independently derived datasets in the same or different subpopulations. For this, focusing on those variants associated with sex-differential disease risk would naturally be the most fruitful for advancing our knowledge of sexual antagonism in human disease. We therefore encourage specialists for the particular disease groups presented in Table 1 to include in the future the potential for SA genetic effects to occur when designing studies and
analyzing data. It is also important for all studies to report re-
sults in sufficient detail so that effect sizes can be calculated (if
not given) as well as allele frequency data. Unfortunately, eight
studies (representing 17 loci) failed to do this, so could not be
included in our review and subsequent test of the evolutionary
model. More generally, our study reinforces the view that sex is
an important factor in shaping genetic associations with human
traits and disease, even in divergent and contradictory ways, and
so should always be considered when investigators examine ge-
netic associations with phenotypes (Ober et al. 2008; Lee 2018).

There is a possibility that some bias exists in the PubMed
database against articles from the field of evolutionary biology,
and if we had used another database then both stages would have
returned valid articles. We subsequently (14 December 2020)
sought to investigate this possibility with additional searches of
Scopus and Web of Science. Repeating the Stage 1 search ini-
tially returned 85 articles in Web of Science (Basic search of
Topic, refined by “Human” within results) and 71 articles in Sco-
pus (include keywords “Human,” exclude “Non-Human”), but
again no articles made it through the screening process. Repeat-
ing the Stage 2 search returned a much larger number of studies
in both Web of Science (4573; Basic search of Topic, refined by
“Human” within results) and Scopus (6616; Advanced search of
Title-Abstract, filter: include human and humans, exclude nonhu-
mans, Articles only), with 8604 unique records. This large num-
ber of records is too many to currently screen and may harbor
further examples of SA loci in humans, but it is noteworthy that
the majority of the initial list of studies found during Stage 2 us-
ing PubMed were also found in the other two databases (712 out
of 881) as well as 28 out of the screened list of 32 studies identi-
fied in the current systematic review.

The disparity between the two stages of the review process
provides a remarkable example of how discipline-specific ter-
minology and concepts can hinder scientific communication be-
tween fields for substantial periods of time. For many of the stud-
ies identified in this review, the focus of the research was not
on quantifying or even identifying sex-specific genetic effects in
traits or disease, although they were tested for. The responses to
such findings varied. Some framed these results as a major find-
ing, whereas others merely made note of them, with one suggest-
ing that because the genetic associations were in opposite direc-
tions in the two sexes then it should be regarded as a false positive
(Wong et al. 2005). It is possible or even likely then that there ex-
ists a publication bias in the biomedical sciences against studies
with apparently incongruous sex-opposite or SA effects.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

AUTHOR CONTRIBUTIONS

JAH and EHM conceived the study. JAH performed the searches and
created the shortlists. JAH, EHM, and TJ screened the shortlisted articles.

JAH and TJ collected and curated data. JAH, EHM, and TJ performed the
analysis. JAH and EHM drafted this article, with all authors commenting
on revisions.

ACKNOWLEDGMENTS

We thank J. Abbott, T. Connallon, and F. Ruzicka for comments on a pre-
vious version of the MS. Funding was provided by the Swedish Research
Council (Grant number: 2019-03567) and by the Royal Society to EHM
as a University Research Fellowship and Enhancement Award. TJ was
funded by the Centre National de la Recherche Scientifique (CNRS) and
the German Research Foundation (DFG grant number: JA 2653/2-1).

DATA ARCHIVING

Stage 1 search output can be found at: https://pubmed.ncbi.nlm.nih.gov/collections/60255050/?sort=pubdate.

Stage 2 search output can be found at: https://pubmed.ncbi.nlm.nih.gov/collections/60254985/?sort=pubdate.

Stage 2 shortlist can be found at: https://pubmed.ncbi.nlm.nih.gov/collections/57906298/?sort=pubdate.

Stage 2 selected articles can be found at: https://pubmed.ncbi.nlm.nih.gov/collections/60278165/?sort=pubdate.

Data and scripts are provided as part of the Supporting Information.

Data and code have been uploaded to Dryad: https://doi.org/10.5061/dryad.rv15dv48k.

LITERATURE CITED

Albers, P. K., and G. McVean. 2018. Dating genomic variants and shared ancestry in population-scale sequencing data (preprint). Genomics https://doi.org/10.1101/416610.

The 1000 Genomes Project Consortium. 2015. A global reference for human
genetic variation. Nature 526:68–74.

Barson, N. J., T. Aykanat, K. Hindar, M. Baranski, G. H. Bolstad, P. Fiske, C. Jacq, A. J. Jensen, S. E. Johnston, S. Karlsson, et al. 2015. Sex-dependent dominance at a single locus maintains variation in age at maturity in salmon. Nature 528:405–408.

Bonduriansky, R., and S. F. Chenoweth. 2009. Intralocus sexual conflict. Trends Ecol Evol 24:280–288.

Borenstein, M., L. V. Hedges, J. P. T. Higgins, and H. R. Rothstein. 2009. Converting among effect sizes. Pp. 45–49 *in* M. Borenstein, L. V. Hedges, J. P. T. Higgins, and H. R. Rothstein, eds. Introduction to meta-analysis. John Wiley & Sons, Ltd, Hoboken, NJ.

Camperio-Ciani, A., F. Corna, and C. Capiluppi. 2004. Evidence for mater-
nally inherited factors favouring male homosexuality and promoting fe-
male fecundity. Proc. R. Soc. Lond. B Biol. Sci 271:2217–2221.

Camus, M. F., J. B. W. Wolf, E. H. Morrow, and D. K. Dowling. 2015. Single nucleotides in the mtDNA sequence modify mitochondrial molecular function and are associated with sex-specific effects on fertility and aging. Curr. Biol 25:2717–2722.

Carlson, J. C., N. L. Nidey, A. Butali, C. J. Buxo, K. Christensen, F. W.-D. Deleyiannis, J. T. Hecht, L. L. Field, L. M. Moreno-Uribe, I. M. Orioli, et al. 2018. Genome-wide interaction studies identify sex-specific risk alleles for nonsyndromic orofacial clefts. Genet. Epidemiol 42:664–672.

Chippindale, A. K., J. R. Gibson, and W. R. Rice. 2001. Negative genetic correlation for adult fitness between sexes reveals ontogenetic conflict in Drosophila. Proc. Natl. Acad. Sci. USA 98:1671–1675.

Cicchetti, D., F. A. Rogosch, K. F. Hecht, N. R. Crick, and S. Hetzel. 2014. Moderation of maltreatment effects on childhood borderline personality
symptoms by gender and oxytocin receptor and FK506 binding protein 5 genes. Dev. Psychopathol 26:831-849.

Clayton, J. A., and F. S. Collins. 2014. Policy: NIH to balance sex in cell and animal studies. Nature 509:282-283.

Connallon, T., and A. G. Clark. 2014. Evolutionary inevitability of sexual antagonism. Proc. R. Soc. B Biol. Sci 281:20132123.

Connallon, T., and A. G. Clark. 2013. Antagonistic versus nonantagonistic models of balancing selection: characterizing the relative timescales and hitchhiking effects of partial selective sweeps. Evolution 67:908-917.

Dauvilliers, Y., E. Neidhart, M. Lecendreux, M. Billiard, and M. Tafti. 2001. MAO-A and COMT polymorphisms and gene effects in narcolepsy. Mol. Psychiatry 6:367-372.

Dixson, A. F. 2009. Sexual selection and the origins of human mating systems. Oxford Univ. Press, Oxford, U.K.

Ellegren, H., and J. Parsch. 2007. The evolution of sex-biased genes and sex-biased gene expression. Nat Rev Genet 8:689-698.

Fillingim, R. B., L. Kaplan, R. Staud, T. J. Ness, T. L. Glover, C. M. Campbell, J. S. Mogil, and M. R. Wallace. 2005. The A118G single nucleotide polymorphism of the mu-opioid receptor gene (OPRM1) is associated with pressure pain sensitivity in humans. J. Pain Off. J. Am. Pain Soc 6:159-167.

Fitzpatrick, M. J. 2004. Pleiotropy and the genomic location of sexually selected genes. Am. Nat 163:800-808.

Foerster, K., T. Coulson, B. C. Sheldon, J. M. Pemberton, T. Clutton-Brock, and L. E. B. Kruuk. 2007. Sexually antagonistic genetic variation for fitness in red deer. Nature 447:1107-1110.

Fry, J. D. 2010. The genomic location of sexually antagonistic variation: some cautionary comments. Evolution 64:1510-1516.

Garver-Apgar, C. E., M. A. Eaton, J. M. Tybur, and M. Emery Thompson. 2011. Evidence of intralocus sexual conflict: physically and hormonally masculine individuals have more attractive brothers relative to sisters. Evol. Hum. Behav 32:423-432.

Gilks, W. P., J. K. Abbott, and E. H. Morrow. 2014. Sex differences in disease genetics: evidence, evolution, and detection. Trends Genet 30:453-463.

Gloria-Bottini, F., N. Bottini, G. Renzetti, and E. Bottini. 2007. ACP1 and Th class of immunological disease: evidence of interaction with gender. Int. Arch. Allergy Immunol 143:170-176.

Grieshop, K., and G. Arnqvist. 2018. Sex-specific dominance reversal of genetic variation for fitness. PLOS Biol 16:e2006810.

Gurevitch, J., J. Koricheva, and K. Mengersen. 2013. Handbook of meta-analysis in ecology and evolution. Princeton University Press, Princeton, NJ.

Herman, A. I., T. S. Conner, R. F. Anton, J. Gelernter, H. R. Kranzler, and J. Covault. 2011. Variation in the gene encoding the serotonin transporter is associated with a measure of sociopathy in alcoholics. Addict. Biol 16:124-132.

Khramtsova, E. A., L. K. Davis, and B. E. Stranger. 2019. The role of sex in the genomics of human complex traits. Nat. Rev. Genet 20:173-190.

Kim, B., H. Kim, Y. H. Joo, J. Lim, C.-Y. Kim, and K. Song. 2010. Sex-different association of DAO with schizophrenia in Koreans. Psychiatry Res 179:121-125.

Kiy, A., O. Wilhelm, A. Hildebrandt, M. Reuter, and W. Sommer. 2013. On the genetic basis of face cognition and its relation to fluid cognitive abilities. Genes Brain Behav 12:438-445.

Korner, A., L. Ma, P. W. Franks, W. Kiess, L. J. Baier, M. Stumvoll, and P. Kovacs. 2007. Sex-specific effect of the Val1483Ile polymorphism in the fatty acid synthase gene (FAS) on body mass index and lipid profile in Caucasian children. Int. J. Obes. 31:353-358.

Lajeunesse, M. 2013. Recovering missing or partial data from studies: a survey of conversions and imputations for meta-analysis. Pp. 195-206 in J. Koricheva, J. Gurevitch, and K. Mengersen, eds. Handbook of meta-

analysis in ecology and evolution. Princeton University Press, Princeton, NJ.

Lande, R. 1980. Sexual dimorphism, sexual selection, and adaptation in polygenic characters. Evolution 34:292-305.

Lang, U. E., R. Hellweg, M. Bajbouj, V. Gaus, T. Sander, and J. Gallinat. 2008. Gender-dependent association of a functional NGF polymorphism with anxiety-related personality traits. Pharmacopsychiatry 41:196-199.

Lauritzen, L., L. B. Sorensen, L. B. Harslof, C. Ritz, K. D. Stark, A. Astrup, C. B. Dysisgaard, N. Egelund, K. F. Michaelsen, and C. T. Damsgaard. 2017. Mendelian randomization shows sex-specific associations between long-chain. Am. J. Clin. Nutr 106:88-95.

Lee, S. K. 2018. Sex as an important biological variable in biomedical research. BMB Rep 51:167-173.

Liu, Y.-H., Y.-J. Chen, H.-H. Wu, T.-Y. Wang, and F.-J. Tsai. 2014. Single nucleotide polymorphisms at the PRR3, ABCF1, and GNL1 genes in the HLA class I region are associated with Graves' ophthalmopathy in a gender-dependent manner. Ophthalmology 121:2033-2039.

Ljungberg, L. U., C. J. Ostgren, F. H. Nystrom, and T. Lanne. 2014. Associations of genetic polymorphisms in the renin-angiotensin system with central aortic and ambulatory blood pressure in type 2 diabetic patients. J. Renin-Angiotensin-Aldosterone Syst. 15:61-68.

McCarthy, J. J., A. Somji, L. A. Weiss, B. Steffy, R. Vega, E. Barrett-Connor, G. Talavera, and R. Glynne. 2009. Polymorphisms of the scavenger receptor class B member 1 are associated with insulin resistance with evidence of gene by sex interaction. J. Clin. Endocrinol. Metab 94:1789-1796.

Mills, S. C., E. Koskela, and T. Mappes. 2012. Intralocus sexual conflict for fitness: sexually antagonistic alleles for testosterone. Proc. R. Soc. B Biol. Sci. 279:1889-1895.

Moher, D., A. Liberati, J. Tetzlaff, D. G. Altman, and T. P. Group> 2009. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLOS Med 6:e1000097.

Morea, A., M. Cameran, A. G. Rebuffi, D. Marzenta, O. Marangon, L. Picci, F. Zacchello, and M. Scarpa. 2005. Gender-sensitive association of CFTR gene mutations and 5T allele emerging from a large survey on infertility. Mol. Hum. Reprod 11:607-614.

Morrow, E. H. 2015. The evolution of sex differences in disease. Biol. Sex Differ 6:5.

Morrow, E. H., and T. Connallon. 2013. Implications of sex-specific selection for the genetic basis of disease. Evol. Appl 6:1208-1217.

Ober, C., D. A. Loisel, and Y. Gilad. 2008. Sex-specific genetic architecture of human disease. Nat Rev Genet 9:911-922.

Ober, C., L. Pan, N. Phillips, R. Parry, and L. M. Kurina. 2006. Sex-specific genetic architecture of asthma-associated quantitative trait loci in a founder population. Curr. Allergy Asthma Rep 6:241-246.

Ovadia, G., and S. Shifman. 2011. The genetic variation of RELN expression in schizophrenia and bipolar disorder. PloS One 6:e19955.

Padmanabhan, S., C. Menni, W. K. Lee, S. Laing, P. Brambilla, R. Sega, R. Perego, G. Grassi, G. Cesana, C. Delles, et al. 2010. The effects of sex and method of blood pressure measurement on genetic associations with blood pressure in the PAMELA study. J. Hypertens 28:465-477.

Parker, G. A. 1979. Sexual selection and sexual conflict. Pp. 123-166 in M. S. Blum and N. A. Blum, eds. Sexual selection and reproductive competition in insects. Elsevier, Amsterdam, The Netherlands.

Parker, G. A., R. R. Baker, and V. G. F. Smith. 1972. The origin and evolution of gamete dimorphism and the male-female phenomenon. J. Theor. Biol 36:529-553.

Payne, E. H., M. Gebregziabher, J. W. Hardin, V. Ramakrishnan, and L. E. Egede. 2018. An empirical approach to determine a threshold for
assessing overdispersion in Poisson and negative binomial models for
count data. Commun. Stat. Simul. Comput 47:1722-1738.

Peng, Y.-M., S.-F. Lei, Y. Guo, D.-H. Xiong, H. Yan, L. Wang, Y.-F. Guo, and H.-W. Deng. 2008. Sex-specific association of the glucocorticoid receptor gene with extreme BMD. J. Bone Miner. Res. Off. J. Am. Soc. Bone Miner. Res 23:247-252.

Pennell, T. M., F. J. H. de Haas, E. H. Morrow, and G. S. van Doorn. 2016. Contrasting effects of intralocus sexual conflict on sexually antagonistic coevolution. Proc. Natl. Acad. Sci 113:E978-E986.

Pennell, T. M., and E. H. Morrow. 2013. Two sexes, one genome: the evolutionary dynamics of intralocus sexual conflict. Ecol. Evol 3:1819-1834.

Press, O. A., W. Zhang, M. A. Gordon, D. Yang, C. A. Haiman, M. Azuma, S. Iqbal, and H.-J. Lenz. 2011. Gender-related survival differences associated with polymorphic variants of estrogen receptor-beta (ERbeta) in patients with metastatic colon cancer. Pharmacogenomics J 11:375-382.

Price, J. S., J. Strong, J. Eliassen, T. McQueeney, M. Miller, C. B. Padula, P. Shear, and K. Lisdahl. 2013. Serotonin transporter gene moderates associations between mood, memory and hippocampal volume. Behav. Brain Res 242:158-165.

Rice, W. R. 1992. Sexually antagonistic genes: experimental evidence. Science 256:1436-1439.

Rice, W. R. 1984. Sex chromosomes and the evolution of sexual dimorphism. Evolution 38:735-742.

Rigby, N., and R. J. Kulathinal. 2015. Genetic architecture of sexual dimor-
phism in humans. J. Cell. Physiol 230:2304-2310.

Rostant, W. G., C. Kay, N. Wedell, and D. J. Hosken. 2015. Sexual conflict maintains variation at an insecticide resistance locus. BMC Biol 13:34.

Ruzicka, F., and T. Connallon. 2020. Is the X chromosome a hot spot for sexually antagonistic polymorphisms? Biases in current empirical tests of classical theory. Proc. R. Soc. B Biol. Sci. 287:20201869.

Ruzicka, F., L. Dutoit, P. Czuppon, C. Y. Jordan, X.-Y. Li, C. Olito, A. Runemark, E. I. Svensson, H. P. Yazdi, and T. Connallon. 2020. The search for sexually antagonistic genes: practical insights from studies of local adaptation and statistical genomics. Evol. Lett 4:5:398-415.

Ruzicka, F., M. S. Hill, T. M. Pennell, I. Flis, F. C. Ingleby, R. Mott, K. Fowler, E. H. Morrow, and M. Reuter. 2019. Genome-wide sexually antagonistic variants reveal long-standing constraints on sexual dimorphism in fruit flies. PLoS Biol 17:e3000244.

Sainz, J., A. Rudolph, M. Hoffmeister, B. Frank, H. Brenner, J. Chang-
Claude, K. Hemminki, and A. Forsti. 2012. Effect of type 2 diabetes pre-
disposing genetic variants on colorectal cancer risk. J. Clin. Endocrinol.
Metab 97:E845-E851.

Sangalli, A., E. Orlandi, A. Poli, A. Maurichi, M. Santinami, M. Nicolis, S. Ferronato, G. Malerba, M. Rodolfo, and M. Gomez Lira. 2017. Sex-specific effect of RNASEL rs486907 and miR-146a rs2910164 polymorphisms' interaction as a susceptibility factor for melanoma skin cancer. Melanoma Res 27:309-314.

Schneider, K. K., L. Hule, A. B. Schote, J. Meyer, and C. Frings. 2015. Sex matters! Interactions of sex and polymorphisms of a cholinergic receptor gene (CHRNA5) modulate response speed. Neuroreport 26:186-191.

Schuurhof, A., L. Bont, C. L. E. Siezen, H. Hodemaekers, H. C. van Houwelingen, T. G. Kimman, B. Hoebee, J. L. L. Kimpen, and R. Janssen. 2010. Interleukin-9 polymorphism in infants with respiratory syncytial virus infection: an opposite effect in boys and girls. Pediatr. Pulmonol 45:608-613.

Shalev, I., E. Lerer, S. Israel, F. Uzefovsky, I. Gritsenko, D. Mankuta, R. P. Ebstein, and M. Kaitz. 2009. BDNF Val66Met polymorphism is associated with HPA axis reactivity to psychological stress characterized by genotype and gender interactions. Psychoneuroendocrinology 34:382-388.

Short, R. V. 1979. Sexual selection and its component parts, somatic and geni-
tal selection, as illustrated by man and the great apes. Adv. Study Behav
9:131-158.

Sjoberg, R. L., K. W. Nilsson, N. Nordquist, J. Ohrvik, J. Leppert, L. Lindstrom, and L. Oreland. 2006. Development of depression: sex and the interaction between environment and a promoter polymorphism of the serotonin transporter gene. Int. J. Neuropsychopharmacol 9:443-449.

Stearns, S. C., D. R. Govindaraju, D. Ewbank, and S. G. Byars. 2012. Constraints on the coevolution of contemporary human males and females. Proc. R. Soc. B Biol. Sci 279:4836-4844.

Stulp, G., B. Kuijper, A. P. Buunk, T. V. Pollet, and S. Verhulst. 2012. Intralo-
cus sexual conflict over human height. Biol. Lett. 8:976-978.

Winkler, T. W., A. E. Justice, M. Graff, L. Barata, M. F. Feitosa, S. Chu, J. Czajkowski, T. Esko, T. Fall, T. O. Kilpeläinen, et al. 2015. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLOS Genet 11:e1005378.

Wise, A. L., L. Gyi, and T. A. Manolio. 2013. eXclusion: toward integrating the X chromosome in genome-wide association analyses. Am. J. Hum. Genet 92:643-647.

Wong, S. Y. S., E. M. C. Lau, M. Li, T. Chung, A. Sham, and J. Woo. 2005. The prevalence of Apo E4 genotype and its relationship to bone mineral density in Hong Kong Chinese. J. Bone Miner. Metab 23:261-265.

Wu, K., C. Chen, R. K. Moyzis, E. Greenberger, and Z. Yu. 2016. Gender interacts with opioid receptor polymorphism A118G and serotonin receptor polymorphism -1438 A/G on speed-dating success. Hum. Nat. Hawthorne N 27:244-260.

Youssef, S. S., A. M. Abd El Aal, A. S. Nasr, T. el Zanaty, and S. M. Seif. 2013. Interleukin-12B gene polymorphism frequencies in Egyptians and sex-related susceptibility to hepatitis C infection. J. Interferon Cytokine Res. 33:415-419.

Associate Editor: N. G. Prasad

Handling Editor: T. Chapman

Supporting Information

Additional supporting information may be found online in the Supporting Information section at the end of the article.

**Figure S1.** Study reported effect allele frequencies and database allele frequencies are strongly correlated.

**Figure S2.** Relationship between study sample size and average extracted effect size across all studies.

**Figure S3.** Female effect size and male effect size are negatively correlated in SA alleles.

**Figure S4.** The relationship between effect allele frequency and effect size ratio, grouped by trait class.