Patterns of recent natural selection on genetic loci associated with sexually differentiated human body size and shape phenotypes

Audrey M Arner; Kathleen E Grogan; Mark Grabowski; Hugo Reyes-Centeno; George H Perry

doi:10.1371/journal.pgen.1009562

. 2021 Jun 3;17(6):e1009562. doi: 10.1371/journal.pgen.1009562

Patterns of recent natural selection on genetic loci associated with sexually differentiated human body size and shape phenotypes

Audrey M Arner ^1,^*, Kathleen E Grogan ^1,², Mark Grabowski ^3,⁴, Hugo Reyes-Centeno ^3,⁵, George H Perry ^1,^3,^6,^*

Editor: Scott M Williams⁷

PMCID: PMC8174730 PMID: 34081690

Abstract

Levels of sex differences for human body size and shape phenotypes are hypothesized to have adaptively reduced following the agricultural transition as part of an evolutionary response to relatively more equal divisions of labor and new technology adoption. In this study, we tested this hypothesis by studying genetic variants associated with five sexually differentiated human phenotypes: height, body mass, hip circumference, body fat percentage, and waist circumference. We first analyzed genome-wide association (GWAS) results for UK Biobank individuals (~194,000 females and ~167,000 males) to identify a total of 114,199 single nucleotide polymorphisms (SNPs) significantly associated with at least one of the studied phenotypes in females, males, or both sexes (P<5x10^-8). From these loci we then identified 3,016 SNPs (2.6%) with significant differences in the strength of association between the female- and male-specific GWAS results at a low false-discovery rate (FDR<0.001). Genes with known roles in sexual differentiation are significantly enriched for co-localization with one or more of these SNPs versus SNPs associated with the phenotypes generally but not with sex differences (2.73-fold enrichment; permutation test; P = 0.0041). We also confirmed that the identified variants are disproportionately associated with greater phenotype effect sizes in the sex with the stronger association value. We then used the singleton density score statistic, which quantifies recent (within the last ~3,000 years; post-agriculture adoption in Britain) changes in the frequencies of alleles underlying polygenic traits, to identify a signature of recent positive selection on alleles associated with greater body fat percentage in females (permutation test; P = 0.0038; FDR = 0.0380), directionally opposite to that predicted by the sex differences reduction hypothesis. Otherwise, we found no evidence of positive selection for sex difference-associated alleles for any other trait. Overall, our results challenge the longstanding hypothesis that sex differences adaptively decreased following subsistence transitions from hunting and gathering to agriculture.

Author summary

There is uncertainty regarding the evolutionary history of human sex differences for quantitative body size and shape phenotypes. In this study we identified thousands of genetic loci that differentially impact body size and shape trait variation between females and males using a large sample of UK Biobank individuals. After confirming the biological plausibility of these loci, we used a population genomics approach to study the recent (over the past ~3,000 years) evolutionary histories of these loci in this population. We observed significant increases in the frequencies of alleles associated with greater body fat percentage in females. This result is contradictory to longstanding hypotheses that sex differences have adaptively decreased following subsistence transitions from hunting and gathering to agriculture.

Introduction

In many vertebrate species, it is not uncommon for morphological phenotypes to have average size and shape differences between females and males [1]. Traits with average phenotype values that differ by sex but with overlapping trait distributions–such as human height and body fat percentage–are described as ‘sexually differentiated’ traits. Technically, the more commonly-used term ‘sexually dimorphic’ is specific to non-overlapping traits; for example, exaggerated ornamentation in male guppies, peacocks, and mandrills [2–5].

Some sexually differentiated traits are believed by many researchers to be the result of sexual selection. In species with high inter-male competition for mates, larger males may have a competitive advantage that results in increased fitness [6]. Perhaps as a result, the magnitude of sexually differentiated phenotypes are often greater in polygynous species with high competition (e.g. gorillas) and lower in monogamous species (e.g. gibbons) [6–8]. Finally, there are major differences in the degree of body size and shape sex differences between closely related species, suggesting the potential for the relatively rapid evolution of sexually differentiated traits [6].

In humans, females and males exhibit significant but relatively subtle differences in many anthropometric phenotypes [9]. For example, European and African males are an average of approximately 9% taller in height and 15% heavier in body mass than females from the same populations [10,11]. Humans also exhibit sexually differentiated biometric and disease phenotypes, especially those related to immune function [12,13].

There is little consensus regarding the evolutionary history of sexually differentiated traits in our species [11,14,15]. Current levels of human sex differences may partially reflect an evolutionary history of complex interactions between our biology and culture, but the timing, direction, and forces responsible for any adaptive changes in these patterns are debated. For example, it has been hypothesized that the degree of sexual differentiation for body size phenotypes in Europe likely decreased following recent (within the past ~10,000 years) shifts from a foraging-based subsistence strategy to one relying primarily on food production, in response to less pronounced divisions of labor and mobility [16]. However, other scholars suggest that sexually differentiated phenotypes would respond to selection at too slow an evolutionary rate to respond to recent environmental and cultural changes; thus, any such recent changes are more likely to reflect genetic drift and/or non-genetic responses to environmental changes rather than natural selection [17].

In this study, we combined genomic and evolutionary analyses to quantify how recent changes in lifestyle and culture might be affecting the underlying genetic basis of human sexually differentiated traits. First, we applied a genome wide association study (GWAS)-based approach to identify genetic variants associated with sexually differentiated phenotypes based on data from the UK Biobank study [18]. With a very large number of UK Biobank participants (~361,000 individuals in the dataset we analyzed), this analysis represents a powerful extension of several previous GWAS-based analyses of the genetic architecture of human sexually differentiated anthropometric traits [19]. We then used the Singleton Density Score (SDS), a statistic that identifies signatures of polygenic adaptation that acted within the last ~3,000 years [20], a period following the transition to agriculture in present-day United Kingdom [21].

Results

We analyzed genome-wide association study (GWAS) summary statistics for the following five sexually differentiated anthropometric phenotypes that were produced by the Neale Lab [22] using data from the UK Biobank [18]: height, body mass, hip circumference, body fat percentage, and waist circumference. We chose these traits given their relevance to the motivating evolutionary hypothesis and because they have been extensively studied from anthropological and/or genomics perspectives [9,23–25]. We did not include body mass index (BMI) in our set of body size and shape phenotypes following concerns that have been voiced about this metric related to its failure to quantify body shape when indicating obesity and obesity-related health risks [26,27] and other, ethical issues [28]. We instead included the two constituents of BMI (height and body mass) as separate variables in our analysis.

Identification of phenotype-associated SNPs

We analyzed summary statistics from GWAS that were performed separately in ~194,000 females and ~167,000 males of white British genetic ancestry on ~13.8 million autosomal SNPs [22]. SNPs with minor allele frequencies < 0.05 and low imputation quality were filtered out. We further restricted our analysis to SNPs that 1) passed these filters in both females and males and 2) had SDS values available [20], given our motivation to conduct downstream evolutionary analyses. These filtering steps resulted in ~4.4 million genome-wide SNPs for each sex-stratified GWAS. For each phenotype, we identified significantly phenotype-associated SNPs present in females, males, or both using the genome-wide significance threshold of P = 5x10^-8 commonly applied in UK Biobank studies [29–31]. We identified the following total (not yet pruned for linkage disequilibrium) numbers of phenotype-associated SNPs significant in either females, males, or both: 67,738 for height, 15,669 for body mass, 12,580 for hip circumference, 10,538 for body fat percent, and 7,674 for waist circumference (Fig 1A and 1B and S1 Table).

Fig 1 — (A) Manhattan plot depicting -log₁₀ P-values for the association of each genome-wide SNP with female height. The black line corresponds to the genome-wide significance threshold of P = 5x10^-8. (B) Manhattan plot for SNP associations with male height. (C) For each of the 67,738 SNPs significantly associated with female and/or male height, we used the equation shown to test whether the SNP was disproportionately associated with height between the sexes (height SexDiff-associated SNPs). The plot depicts -log₁₀ P-values for the t-SexDiff statistic. Gray bars correspond to four different FDR cutoffs. (D) SexDiff association analyses for significant phenotype-associated SNPs (number of SNPs included in each analysis is shown to the right of each plot) for four additional anthropometric traits: body mass, hip circumference, body fat percentage, and waist circumference.

SNPs disproportionately associated with female or male trait variation

From the sets of phenotype-associated SNPs that were significant in females, males, or both for each phenotype (SexDiff-associated SNPs), we identified those SNPs with significant differences in the statistical strengths of association for the female vs. male-specific GWAS results using the t-SexDiff statistic [19]. This statistic estimates the probability of difference between female-specific and male-specific effect sizes given their standard errors and considering the genome-wide correlation between female and male effect sizes for each trait (see Methods). To account for multiple testing, we performed this analysis with four different false-discovery rate (FDR) cutoffs for each phenotype: 0.05, 0.01, 0.005, and 0.001.

At the most stringent FDR cutoff of 0.001, we identified the following number of SexDiff-associated SNPs: 677 for height, 541 for body mass, 808 for hip circumference, 439 for body fat percentage, and 551 for waist circumference (Fig 1C and 1D and S1 Table). In addition to identifying SexDiff-associated SNPs, for each phenotype we calculated the proportion of SexDiff-associated SNPs at the FDR threshold of 0.001 to the number of phenotype-associated SNPs, which ranged from 0.0010 for height to 0.0718 for waist circumference.

Co-localization of SexDiff-associated SNPs and loci with known roles in sexual differentiation

Prior to conducting evolutionary analyses on the anthropometric trait SexDiff-associated SNPs, we assessed their biological plausibility in two ways. First, we tested whether these SNPs are significantly more likely to be located within or nearby (+/- 10,000 base pairs) genes previously known to be involved in sexual differentiation (Gene Ontology term GO:0007548) compared to SNPs that are significantly associated with the phenotype but not significantly associated with sex differences [32,33]. Although not all SexDiff-associated SNPs are expected to be located nearby genes already known to be involved in sexual differentiation, we would expect at least some enrichment if we are identifying true SexDiff associations with our approach.

For each t-SexDiff FDR threshold, we determined the number of unique sexual differentiation (GO:0007548) genes with one or more co-localized SexDiff-associated SNPs (combined across the five phenotypes in our study). We similarly identified the number of all genes (of those in the GO database) that were co-localized with at least one SexDiff-associated SNP and calculated the proportion of the number of GO:0007548 genes to the total number of genes. We conducted the same analysis for the set of SNPs associated with the phenotypes in general but not associated with sexual differentiation. Finally, we estimated the GO:0007548 enrichment ratio for SexDiff-associated to non-SexDiff-associated SNPs by dividing the two proportions. By focusing this analysis on counts of genes rather than counts of SNPs, we limit potential linkage disequilibrium (LD)-based enrichment inflation. That is, a gene would only be counted once regardless of how many SexDiff-associated SNPs are located within or nearby that gene.

For example, at our most stringent FDR threshold (0.001), the 3,016 total SexDiff-associated SNPs were located within or nearby 9 unique GO:0007548 genes and 162 total genes (data included in Dryad Digital Repository deposition). Thus, the proportion of GO:0007548 genes = 0.0556 (9/162). In contrast, the 108,882 non-SexDiff-associated but still phenotype-associated SNPs for this same FDR threshold were located within or nearby 52 unique GO:0007548 genes and 2,544 total genes, for a proportion of 0.0204, resulting in an enrichment ratio = 2.73 (0.0556/0.0204). In other words, GO:0007548 genes with known roles in sexual differentiation are >2.7 times more likely to be co-localized with one or more SexDiff-associated SNPs than with one or more non-SexDiff-associated but still phenotype-associated SNPs at the FDR<0.001 analysis level.

We repeated this analysis for each FDR threshold (Fig 2A and S2 Table). The enrichment ratio steadily increased with increasingly stringent FDR significance thresholds. This pattern is consistent with expectations if our analyses are identifying true SexDiff-associated loci.

To test whether the proportion of GO:0007548 genes co-localized with at least one SexDiff-associated SNP was significantly greater than expected by chance, we used a simple permutation scheme. From the set of 2,570 total GO classified genes that were co-localized with at least one phenotype-associated SNP, we randomly selected the same number of total genes that were co-localized with one or more SexDiff-associated SNPs at the FDR cutoff being considered (e.g. 162 genes at FDR<0.001) and counted the number of GO:0007548 genes represented. We repeated this process 10,000 times for each FDR cutoff and compared the resulting distributions to the actual number of GO:0007548 genes co-localized with at least one SexDiff-associated SNP to calculate empirical P-values. We used this permutation-based approach rather than a Fisher’s exact test because some genes were co-localized with both SexDiff-associated SNPs and non-SexDiff phenotype-associated SNPs. The observed proportion of GO:0007548 genes is very unlikely to be explained by chance at the most stringent FDR<0.001 threshold (P = 0.0041; Fig 2B), with increasing probability for the FDR thresholds of 0.005, 0.01, and 0.05 (P = 0.0513, P = 0.0829, and P = 0.2111, respectively; S1 Fig). Accordingly, all subsequent analyses were limited to SNPs classified based on the FDR<0.001 analysis.

In a second assessment of the biological plausibility of our results, we queried the GWAS Catalog [34] to identify any pleiotropic co-occurrence between our SexDiff-associated (FDR<0.001) or phenotype-associated SNPs and significant associations for all other GWAS Catalog traits. To avoid linkage disequilibrium (LD)-based result inflation of co-occurrence, we selected a maximum of one SNP (zero if no SNPs in the region were significantly associated with a trait) with the lowest t-SexDiff P-value across any of the five studied traits for each of 1,703 approximately LD-independent blocks of the human genome [35]. Of the 693 total SNPs in this curated dataset, 117 SNPs (17%) were significantly associated with sex differences for at least one of the five traits, with the remaining 576 SNPs (83%) associated with one or more of the five phenotypes but not sex differences.

Upon reviewing the overlap with the curated set of SexDiff-associated SNPs and the GWAS Catalog trait associations, we immediately noticed “Sex hormone binding globulin level” annotations [36–38] for 3 of the 117 SexDiff-associated SNPs (2.6%), including the two SNPs with the most extreme t-SexDiff P-values (Fig 2C). In contrast, sex hormone binding globulin level associations were annotated for 4 of the 576 phenotype- but not SexDiff-associated SNPs (0.7%; Fisher’s exact test; P = 0.098). We also used a permutation analysis to estimate the probability of observing 3 SNPs with both SexDiff- and sex hormone binding globulin level-associations, given the occurrence rate for all loci in this curated SNP subset (P = 0.063; Fig 2D).

Altogether, these results suggest that the anthropometric trait SexDiff-associated SNPs we identified–especially those discovered with the most stringent FDR<0.001 threshold–are at least enriched for SNPs that do underlie (or are linked to those that do) sexual differentiation variation.

SexDiff-associated SNPs are associated with greater trait effect sizes in the expected sex

Our statistical approach technically identifies genetic variants with significantly different strengths of association for a given trait between males and females. Before proceeding, we sought to confirm that these identified variants are disproportionately associated with greater phenotype effect sizes in the sex with the stronger association value, and not merely with lower trait value variance.

To do so, for each phenotype we first divided our SexDiff-associated SNPs into those with disproportionately stronger associations in females vs. males. To avoid linkage disequilibrium (LD)-based result inflation, we pruned each of these sets of SNPs to include only the one SNP (if any) with the lowest t-SexDiff FDR value located within each of 1,703 approximately LD-independent blocks of the human genome [35]. For comparison to the disproportionate female and male SexDiff-associated SNPs, we prepared similar LD-pruned sets of all SNPs significantly associated with the phenotype, regardless of their associations with sex differences, for each of the five traits (data included in Dryad Digital Repository deposition). Then, for every SNP in each set of pruned SNPs, we calculated the log₂ ratio of the female trait effect size to the male trait effect size.

Each of the five sets of female SexDiff-associated SNPs had strongly positive average ratios, meaning that the female effect sizes for these SNPs were larger than the male effect sizes, and vice versa for the male SexDiff-associated SNPs (Fig 3A). Mean log₂ ratios were significantly different from 0 for each of the 10 SexDiff-associated SNP subset (one-sided t-tests; P = 3.3x10^-5 and lower; FDR = 3.3x10^-5 and lower; S3 and S4 Tables). Conversely, the mean log₂ ratios for the five sets of general trait-associated SNPs were near 0 (Fig 3A). Mean log₂ ratios for each of the 10 SexDiff-associated SNPs were also significantly different from the corresponding mean log₂ ratios of the phenotype-associated SNPs (Permutation analyses [see Methods]; P<0.001, FDR = 0.001). These results confirm that our statistical process successfully and reliably identifies SNPs with sex disproportionate effects on phenotypic trait variation. We thus proceeded to study the evolutionary histories of these SNPs.

Fig 3 — For each anthropometric trait, a maximum of one SNP per each of 1,703 approximately LD-independent blocks of the human genome was included (within each block, the SNP with the strongest statistical significance) from the sets of i) all SNPs associated with trait variation, and the subsets of those SNPs disproportionately associated with variation in ii) females and iii) males (SexDiff-associated SNPs at FDR<0.001). (A) Violin plots of log₂ ratios of female trait effect size to male effect size (calculated separately for each SNP). Mean log₂ ratios for each set of female and male SexDiff pruned SNPs were compared to those for the corresponding phenotype-association set using a permutation scheme (FDR values indicated at the top and bottom of the plot), and to 0 using a one-sided t-test (FDR values indicated in the middle of the plot). (B) Violin plots of trait-SDS values. Positive trait-SDS values reflects recent increases in the frequencies of phenotype-increasing alleles, while negative trait-SDS values reflect increases in the frequencies of phenotype-decreasing alleles. The trait-SDS distributions for each set of female and male SexDiff pruned SNPs were compared to those for the corresponding phenotype-association set using a permutation analysis.

Signatures of positive selection on SexDiff-associated alleles

We next tested whether the SexDiff-associated SNPs identified in our analyses are significantly enriched for signatures of recent (~3,000 years) positive selection, using the Singleton Density Score statistic (SDS) [20]. Briefly, alleles affected by recent positive selection are predicted to be found on haplotypes with relatively fewer singleton mutations; the SDS quantifies this pattern. In turn, the trait-SDS statistic reflects directionality with respect to an associated phenotype. A positive trait-SDS value reflects a recent increase in the frequency of a phenotype-increasing allele, while a negative trait-SDS value reflects an increase in the frequency of a phenotype-decreasing allele [20]. We performed our analyses with previously-published SDS data computed from the whole genome sequences of 3,195 British individuals from the UK10K project [20,39]. This dataset is ideal for integration with the UK Biobank genotype-phenotype association data, given study population similarity.

To examine the evolutionary histories of alleles associated with anthropometric trait sex differences, we aimed to determine whether any signatures of positive selection for SexDiff-associated SNPs differed in either magnitude or direction from any such signatures for alleles associated with the phenotypes (but not necessarily sex differences) themselves. For this analysis, we again considered the sets of LD-pruned SexDiff-associated SNPs divided into those that are disproportionately associated with trait variation in females vs. those disproportionately associated with trait variation in males for each phenotype.

Using a permutation analysis, we tested whether the average trait-SDS values for the pruned female or male SexDiff-associated SNPs were significantly different than the value for the corresponding pruned set of all phenotype-associated SNPs (Fig 3B and S5 Table). For example, there were n = 21 pruned SexDiff-associated SNPs disproportionately associated with female height and n = 532 pruned SNPs associated with height generally (but not necessarily with sex differences). We randomly selected 21 of the 532 pruned height-associated SNPs and calculated the average trait-SDS score. We repeated this process 10,000 times and calculated an empirical P-value based on the proportion (multiplied by two; see Methods) of permuted observations with equal or more extreme average trait-SDS values than the observed average trait-SDS for the actual female height SexDiff-associated SNPs (average trait-SDS = 0.240; P = 0.66). This test was then repeated for the male SexDiff-associated SNPs for height, and the female and male SexDiff-associated SNPs for each of the remaining four anthropometric traits. We also performed a similar permutation but with the additional step of matching minor allele frequencies (+/- 0.05) between the original set of SexDiff-associated SNPs and the set of phenotype-associated SNPs selected in each permutation. The pattern of results was unchanged relative to the above (S6 Table).

For nine of the comparisons, trait-SDS distributions for the sets of pruned SexDiff-associated SNPs were not significantly different (following FDR adjustment for ten tests) from those for SNPs associated with the corresponding general phenotype (Fig 3B and S5 Table). However, compared to all body fat percentage-associated SNPs (n = 181 pruned SNPs; average trait-SDS = -0.124), the average trait-SDS value for the set of female SexDiff-associated SNPs for this phenotype was significantly elevated (n = 9 pruned SNPs; average trait-SDS = 0.827; P = 0.0038; FDR = 0.038). In other words, the average frequencies of alleles associated with greater body fat percentage in females have increased in frequency over the past ~3,000 years at a faster rate than expected based on the pattern for SNPs associated with body fat percentage generally, a potential signature of polygenic selection on this sub-trait.

While our hypothesis testing framework is focused primarily on analyses of recent signatures of positive natural selection, we also investigated whether the sets of LD-pruned SexDiff-associated SNPs were enriched for signatures of relatively more ancient positive selection. To do so we conducted an identical permutation analysis as above with the trait-SDS values, but instead using absolute values of the integrated haplotype score (iHS) statistic, a within-population haplotype-based statistic that can be used to identify putative signatures of positive selection from across the past ~25,000 years [40]. We used previously-published iHS data [41] computed for the Great Britain population (GBR) from the 1000 Genomes Project [42]. Absolute iHS values were not significantly different for any of the sets of SexDiff-associated SNPs relative to the corresponding phenotype-associated SNPs (S2 Fig and S7 Table).

Finally, because positive selection may occur more often within or nearby genic regions of the genome [40], we also tested whether our sets of pruned SexDiff-associated SNPs and corresponding phenotype-associated SNPs were unevenly distributed among genic (annotated genes +/- 10,000 bp) and intergenic regions. None of the ten sets of SexDiff-associated SNPs were enriched for genic regions. However, we found that SNPs significantly associated with male waist circumference variation were more likely to be intergenic relative to general hip circumference-associated loci (S8 Table).

Discussion

Using a sex-stratified GWAS framework for five sexually differentiated anthropometric phenotypes, we identified 3,016 SNPs that were disproportionately associated with either female or male trait variation at a low false discovery rate (FDR<0.001). We confirmed the biological plausibility of these results by showing that genes with known roles in sexual differentiation are significantly enriched for SexDiff-associated SNPs. Together, these results confirm the importance of considering sex differences when investigating the genetic structure of human polygenic traits [43]. We then used a statistic that quantifies changes in the frequencies of alleles underlying polygenic traits over the past ~3,000 years to identify a signature of recent positive selection on SNPs associated with increased female body fat percentage in the British study population.

We must emphasize that inferring selection signals from GWAS data should be approached with great care, as even subtle uncorrected population structure can impact GWAS and downstream results [44]. For example, data from the GIANT consortium were previously used to identify strong signatures of polygenic selection for height across the genome [20]. However, subtle population structure in the GIANT sample led to effect-size estimate biases, in turn resulting in false signals of polygenic selection for SNPs not crossing the genome-wide significance threshold and impacting results for significant SNPs as well [44]. In contrast, these issues were much less prevalent using GWAS summary statistics from the UK Biobank, in which population structure is minimized [44–46]. In light of these considerations, in our study we have i) used UK Biobank GWAS summary statistics only, ii) focused solely on phenotype-associated SNPs below the genome-wide significance threshold, and iii) restricted our evolutionary analyses to direct comparisons between SNPs significantly associated with individual phenotypes and a sub-phenotype (i.e., sex differences).

Our study further demonstrates the value of GWAS-based approaches for testing anthropological hypotheses [47]. Concerning the evolution of human body size and shape phenotypes, our results fail to provide support for the prevailing notion of recent (i.e., subsequent to agriculture) adaptive reductions in levels of sex differences for such traits. Specifically, using large samples of genomes from British individuals we did not observe significant differences in the recent evolutionary trajectories of SNPs disproportionately associated with female or male variation in height, body mass, hip circumference, and waist circumference relative to the trajectories of SNPs associated with these traits generally.

We note that we made a number of conservative choices (for example, with aggressive pruning to account for linkage disequilibrium) in our analytical approach, meaning that our failure to reject the null hypothesis for each of these four traits should not be interpreted as evidence that no selection on them occurred. Still, even with our conservative analytical approach we did find evidence that the average frequencies of alleles disproportionately associated with greater female body fat percentage significantly increased over the past ~3,000 years, a pattern consistent with polygenic adaptation. Given that females have higher average body fat percentages than men in historic and contemporary populations, the direction of polygenic adaptation in the population we studied would actually be opposite to expectations under hypotheses of recent adaptive reductions in anthropometric trait sex differences in agricultural societies. However, since SNPs can be pleiotropically associated with multiple phenotypes [35], we cannot definitively conclude that positive selection acted directly on female body fat percentage. Regardless, at the very least we did not find positive support for the prevailing hypothesis concerning the evolution of sex differences in recent human evolution.

Methods

Subjects and dataset generation

We used genome-wide association study (GWAS) summary statistics generated from analyses of UK Biobank data [18,22]. The original GWAS analyses were restricted to 361,194 unrelated individuals (194,174 females and 167,020 males) of white British ancestry (based on the combination of self-report and genetic ancestry analysis) who did not have sex chromosome aneuploidies. GWAS summary statistics for each phenotype were computed separately for females and males. Because every phenotype was not defined for all individuals, some of the analyses contributing to our study included fewer than 361,194 individuals (S9 Table). Approximately 40 million SNPs were originally available for analysis, of which only those with minor allele frequencies > 0.001, an INFO score (imputation quality) > 0.8, and a Hardy-Weinberg Equilibrium P-value > 1x10^-10 were retained, resulting in datasets of 13,791,468 SNPs.

We then subsequently filtered out SNPs with minor allele frequencies < 0.05 and those that were not associated with a singleton density score (SDS; for description, see below) [20]. These filters resulted in a total genome-wide dataset of ~4.4 million SNPs for each phenotype.

We chose five sexually differentiated anthropometric phenotypes from the GWAS summary statistics for analysis (S9 Table). To identify phenotype-associated SNPs in female and/or male individuals, we applied the commonly used genome wide significance threshold (i.e., to account for the large number of tested SNPs) of P = 5x10^-8 to the male-specific and female-specific GWAS summary statistics (data included in Dryad Digital Repository deposition).

Scan for SNPs that are disproportionately associated with male or female trait variation

For each genome-wide SNP significantly associated with a phenotype in females, males, or both sexes, we evaluated whether there was a significant difference in the statistical strengths of association for the female vs. male-specific GWAS results using the following t-statistic (t-SexDiff) [19].

t - S e x D i f f = \frac{b_{m a l e} - b_{f e m a l e}}{\sqrt{{S E}_{m a l e}^{2} + S E_{f e m a l e}^{2} - 2 r * {S E}_{m a l e} * S E_{f e m a l e}}}

b_male refers to the male-specific beta value and b_female refers to the female-specific beta value for each genome-wide SNP. SE_male and SE_female refer to the male-specific and female-specific standard error for each genome-wide SNP, respectively. The correlation r (rho) for each phenotype was calculated as the Spearman rank correlation coefficient between b_male and b_female using the cor.test() function in R (version 3.5.1). t-SexDiff was converted to a two-sided P-value using the R function pt(). The effects of multiple testing were considered by computing the False Discovery Rate (FDR) for each t-SexDiff P-value using the R function p.adjust [48].

Of the phenotype-associated SNPs that were significantly associated with a given phenotype for females, males, or both sexes, we identified SexDiff-associated SNPs at four different FDR thresholds: <0.05, <0.01, <0.005, and <0.001 (data included in Dryad Digital Repository deposition).

Assessing the biological plausibility of the SexDiff-associated SNPs

In order to confirm the biological plausibility of our SexDiff-associated SNPs, we tested whether regions of the genome functionally linked to sexual differentiation are more likely than expected by chance to contain one or more of our SexDiff-associated SNPs. Separately for each t-SexDiff FDR threshold, we counted the number of unique genes in the Gene Ontology database [32,33] that contained at least one SexDiff-associated SNP, including within a +/-10,000 base-pair (bp) window around the gene to encompass potential regulatory regions. We then counted the number of these genes with known links to processes of sexual differentiation corresponding to the Gene Ontology (GO) term GO:0007548 and computed the proportion of the number of GO:0007548 genes to the number of all genes co-localized with ≥ 1 SexDiff-associated SNP. We repeated this analysis for significant phenotype- but not SexDiff-associated SNPs. In the absence of enrichment for SexDiff-associated SNPs, the ratio of these two proportions is expected to equal one.

We then used the following permutation scheme for each t-SexDiff FDR cutoff. There was a total of 2,570 GO-classified genes overlapping one or more phenotype-associated SNP (whether SexDiff-associated or not; each t-SexDiff FDR cutoff started with the same genome-wide significant set of phenotype-associated SNPs so the total number of 2,570 co-localized genes applies to each FDR cutoff). Given the number of these genes overlapping ≥ 1 SexDiff-associated SNP for a given FDR cutoff, we randomly selected that number of unique genes from the pool of 2,570 genes and counted the number of GO:0007548 genes. We then repeated this procedure 10,000 times and computed an empirical P-value as the proportion of permuted data sets with an equal or greater to number of sexual differentiation genes when compared to our observation for the associated FDR threshold.

We studied pleiotropic relationships between our anthropometric SexDiff and phenotype traits and other traits with the aid of the GWAS Catalog (48; accessed 18 Feb 2021). After concatenating all SexDiff (FDR<0.001) and phenotype-associated SNPs from across the five studied traits, we pruned this total set of SNPs to a maximum of one SNP per each of approximately 1,703 approximately LD-independent blocks of the human genome [35]. For each block, we specifically chose the one SNP with the lowest t-SexDiff P-value (if a block did not have any significant phenotype-associated SNPs for any trait, zero SNPs were included from that block). There were 693 total SNPs in this curated dataset, 117 of which (17%) were significantly associated with sex differences for at least one of the 5 traits, with 576 (83%) significantly associated with one or more of the five phenotypes but not sex differences. After reviewing the full results (data included in Dryad Digital Repository deposition), we used a permutation analysis to estimate the probability that SexDiff-associated SNPs were more likely than expected by chance to also be associated with the “sex hormone binding globulin level” trait. Specifically, from our curated set of 693 SNPs, we randomly selected the number of SNPs equal to the number of observed SexDiff-associated SNPs (117) and counted how many were also associated with sex hormone binding globulin level as per the GWAS catalog. We repeated this procedure 10,000 times and computed the proportion of permuted data sets with equal or more extreme numbers of sex hormone binding globulin level-associated SNPs compared to the result for the SexDiff-associated SNPs.

Assessing the sex-specific effects of SexDiff-associated SNPs

For each phenotype we split our SexDiff-associated SNPs into those that had lower P-values (as identified in the original sex-specific GWAS for phenotype-associated SNPs) in females than males (female SexDiff-associated SNPs) and those that had lower P-values in males than females (male SexDiff-associated SNPs). We separately pruned each set of female SexDiff-associated SNPs and male SexDiff-associated SNPs to account for linkage disequilibrium. Specifically, if there was more than one female SexDiff-associated SNP in one of 1,703 approximately LD-independent segments of the genome [35], we only kept the female SexDiff-associated SNP for that segment with the lowest P-value for association with the phenotype in females. We did the same for male SexDiff-associated SNPs in males. For each set of pruned SexDiff-associated SNPs, we then calculated the log₂ ratio of the female effect size to male effect size.

The set of phenotype-associated SNPs was pruned in a similar fashion to the above, with a maximum of one SNP per each of the 1,703 approximately LD-independent genome segments, chosen as the SNP with the most significant P-value in the segment regardless of whether it was most significant in females or males (data included in Dryad Digital Repository deposition) from among those below the genome-wide significance threshold.

We then used a permutation to estimate the probability that the log₂ ratio of female effect size to male effect size was significantly for each of the ten sets of SexDiff-associated SNPs. From the pruned set of phenotype-associated SNP, we randomly selected a number of SNPs equal to the number of observed SexDiff-associated SNPs for that phenotype. We repeated this procedure 10,000 times and computed the proportion of permuted data sets with equal or more extreme average log₂ ratio. We computed FDR values to account for multiple testing.

Finally, we used a one-sided t-test to determine whether our log₂ ratios for each set of SexDiff-associated SNPs was significantly different from zero. We again computed FDR values to account for multiple testing.

Identification of signatures of positive selection

To test the hypothesis that SexDiff-associated SNPs have been affected by recent (past ~3,000 years) positive selection in recent human evolution, we used the Singleton Density Score (SDS) statistic [20]. We used a database of genome-wide SNP SDS scores that were computed [20] using 3,195 whole genome sequences from British individuals the UK10K project [39]. For the pruned sets of female and male SexDiff-associated SNPs, we fixed the sign of SDS scores so that positive values indicate an increased frequency of the trait-increasing allele.

We used permutations to estimate the probability that the average trait-SDS value for each trait and sex could be observed by chance given the distribution trait-SDS scores for SNPs significantly associated with the corresponding phenotype (regardless of SexDiff-association; S10 Table). From the pruned set of phenotype-associated SNPs, we then randomly selected a number of SNPs equal to the number of observed SexDiff-associated SNPs for that phenotype and the sex and FDR threshold being considered, and we calculated the average trait-SDS score for that set of SNPs. We repeated this procedure 10,000 times and computed the proportion of permuted data sets with equal or more extreme average trait-SDS scores compared to actual result for the observed SexDiff-associated SNPs. This proportion was then multiplied by two to account for the two-tailed nature of this test (i.e., the average trait-SDS values for SexDiff-associated SNPs could have been significantly greater than or less than that for the phenotype-associated SNPs). We computed FDR values to account for multiple testing.

We also performed trait-SDS permutation analysis with minor allele frequency (MAF) matching between SexDiff and phenotype-associated SNPs. Specifically, for each SexDiff-associated SNP we identified all phenotype-associated SNPs with +/- 0.05 MAF. We then randomly selected one of these SNPs for inclusion in the permuted dataset and removed it from our list of phenotype-associated SNPs to be drawn from in the MAF matching to other SexDiff-associated SNPs so that it wouldn’t be represented twice in the same permutation. We repeated this procedure 10,000 times, randomizing the input order of SexDiff-associated SNPs each time.

For our iHS-based analysis, we used a database of genome-wide SNP iHS scores that were previously computed [41] using data from the 1000 Genomes project [42]. We specifically used iHS data from the Great Britain (GBR) population due to population similarity with our GWAS data. We considered the absolute value of the standardized iHS for each SNP, and we removed SNPs from our dataset that did not have a corresponding iHS score. To estimate the probability that the average iHS score for each trait and sex could be observed by chance given the distribution of iHS scores for phenotype-associated SNPs, we used a permutation scheme identical to that used for the primary trait-SDS analysis (the version without consideration of minor allele frequencies).

To test whether there were significant differences in the distributions of pruned SexDiff-associated SNPs and corresponding pruned phenotype-associated SNPs, we determined whether or not each SNP was located within +/- 10,000 bp of a gene annotated in the Gene Ontology database. We compared the ratio of genic:intergenic SNPs for each of our ten sets of SexDiff-associated and corresponding phenotype-associated SNPs for each trait using a permutation analysis (S8 Table). For each set of SexDiff-associated SNPs, we randomly selected the same number of SNPs from the corresponding set of phenotype-associated SNPs and counted the number of SNPs in intergenic regions. We then repeated this procedure 10,000 times and computed an empirical P-value as the proportion of permuted data sets with an equal or greater to number of SNPs in intergenic regions when compared to our observation for the associated set of SexDiff-associated SNPs. This proportion was then multiplied by two to account for the two-tailed test (i.e., the number of intergenic SexDiff-associated SNPs could have been significantly greater than or less than that of phenotype-associated SNPs). We computed FDR values to account for multiple testing.

Computational resources

All analyses were conducted in R (version 3.5.1) with Advanced CyberInfrastructure computational resources provided by The Institute for CyberScience at Pennsylvania State University. All scripts are available at https://github.com/audreyarner/dimorphism-evolution.

Supporting information

S1 Fig. Permutation enrichment distribution at each FDR threshold.

Permutation analysis of the number of genes involved in sexual differentiation for all anthropometric SNPs at every FDR threshold. Data are the frequency of distribution of our results for 10,000 permuted data sets. The empirical P-value represents the probability that the observed value of sexual differentiation genes from our SexDiff-associated SNP pool is equal to or greater than those from a randomly selected set.

(TIF)

Click here for additional data file.^{(1.1MB, tif)}

S2 Fig. Sex-specific iHS scores for anthropometric SexDiff and phenotype-associated SNPs.

The iHS distributions for each set of female and male SexDiff pruned SNPs were compared to those for the corresponding phenotype-association set using a permutation analysis. None of the distributions were significantly different.

(TIF)

Click here for additional data file.^{(9MB, tif)}

S1 Table. Observed number of SexDiff-associated SNPs at each FDR threshold for every phenotype. ^aRatio of SexDiff-associated SNPs at the FDR threshold of 0.001 to the number of phenotype-associated SNPs.

(DOCX)

Click here for additional data file.^{(17KB, docx)}

S2 Table. Observed unique sexual differentiation genes (SDG) and total number of genes for SexDiff-associated SNPs and Non SexDiff-associated SNPs.

^aProportion of SDG genes observed in each SNP group ^bRatio of proportion of SDG genes observed in the group of SexDiff-associated SNPs to the proportion of SDG genes observed in the group of non SexDiff-associated SNPs.

(DOCX)

Click here for additional data file.^{(17KB, docx)}

S3 Table. Observed log₂ ratio of female to male beta values and p-values for each set of Female SexDiff-associated SNPs.

^aNumber of pruned SexDiff-associated SNPs at an FDR threshold of 0.001 ^bMean log₂ ratio of female trait effect size to the male trait effect size ^cOne-sided t-test P-value comparing distribution of the log₂(ratio) ^dPermutation P-value of the probability that the mean log₂(ratio) could be observed by chance when compared to phenotype-associated SNPs.

(DOCX)

Click here for additional data file.^{(17.2KB, docx)}

S4 Table. Observed log₂ ratio of female to male beta values and p-values for each set of Male SexDiff-associated SNPs.

(DOCX)

Click here for additional data file.^{(17.3KB, docx)}

S5 Table. Observed trait-SDS and permutation P-values for each set of Female SexDiff-associated SNPs and Male SexDiff-associated SNPs permuted against phenotype-associated SNPs.

^aNumber of pruned SexDiff-associated SNPs at an FDR threshold of 0.001 ^bAverage trait-SDS score of pruned set of SexDiff-associated SNPs ^cPermutation P-value of the probability that the trait-SDS score for each sex could be observed by chance when compared to phenotype-associated SNPs.

(DOCX)

Click here for additional data file.^{(17.3KB, docx)}

S6 Table. Observed trait-SDS and permutation P-values for each set of Female SexDiff-associated SNPs and Male SexDiff-associated SNPs permuted against phenotype-associated SNPs matched for minor allele frequency.

(DOCX)

Click here for additional data file.^{(17.4KB, docx)}

S7 Table. Observed average |iHS| scores and permutation P-values for each set of Female SexDiff-associated SNPs and Male SexDiff-associated SNPs permuted against phenotype-associated SNPs.

^aNumber of pruned SexDiff-associated SNPs at an FDR threshold of 0.001 ^bAverage |iHS| score of pruned set of SexDiff-associated SNPs ^cPermutation P-value of the probability that the |iHS| score for each sex could be observed by chance when compared to phenotype-associated SNPs.

(DOCX)

Click here for additional data file.^{(17.7KB, docx)}

S8 Table. Observed number of intergenic SNPs and permutation P-values for each set of Female SexDiff-associated SNPs and Male SexDiff-associated SNPs permuted against phenotype-associated SNPs.

^aNumber of pruned SexDiff-associated SNPs at an FDR threshold of 0.001 ^bPermutation P-value of the probability that the number of intergenic SNPs in each set of SexDiff-associated SNPs could be observed by chance when compared to phenotype-associated SNPs.

(DOCX)

Click here for additional data file.^{(17.5KB, docx)}

S9 Table. Phenotype information.

^aAs referred to in the Neale lab manifest released on July 31, 2018 ^b Correlation for each phenotype calculated as the Spearman rank correlation coefficient between beta values of men and women.

(DOCX)

Click here for additional data file.^{(16.8KB, docx)}

S10 Table. Observed trait-SDS for each set of pruned phenotype-associated SNP groups.

(DOCX)

Click here for additional data file.^{(16.1KB, docx)}

Acknowledgments

We thank our colleagues at the DFG Center for Advanced Studies “Words, Bones, Genes, Tools” for their support and S. Marciniak, C. Bergey, J. Tung, and D. Puts and the Sex Differences Interest Group for their helpful discussions.

Data Availability

All data files are available in the Dryad Digital Repository: https://doi.org/10.5061/dryad.nzs7h44rc.

Funding Statement

This work was funded by the National Institutes of Health (NIH) grant R01-GM115656 (to G.H.P.); NIH grant F32-GM123634 (to K.E.G.); Deutsche Forschungsgemeinschaft (DFG) grant FOR-22337 (to A.M.A, M.G, H.R.C., and G.H.P.); and Erickson Discovery (https://urfm.psu.edu/research/erickson-discovery-grant), Presidential Leadership Academy Enrichment (https://academy.psu.edu/current/grants/), and Liberal Arts Enrichment grants (https://la.psu.edu/current-students/undergraduate-students/scholarships-and-funding/enrichment-funding) from Penn State University (to A.M.A). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Genet. doi: 10.1371/journal.pgen.1009562.r001

Decision Letter 0

Scott M Williams

29 Dec 2020

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Dear Dr Arner,

Thank you very much for submitting your Research Article entitled 'Patterns of recent natural selection on genetic loci associated with sexually differentiated human body size and shape phenotypes' to PLOS Genetics.

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Hua Tang

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Reviewer's Responses to Questions

Comments to the Authors:

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Reviewer #1: The manuscript by Arner et al. tests whether genetic variants are associated with sexually differentiated phenotypes in humans using UKBioBank data. The authors first find SNPs associated with different traits in the UK Biobank. They then test whether the strength of the association differs between males and females. Indeed, the authors find that these SexDiff SNPs are enriched in genes involved in sexual differentiation. Lastly, the authors test whether the SexDiff SNPs show greater signals of recent positive selection, as compared to SNPs associated with the overall phenotype. The authors find that alleles associated with increased body fat percentage in only females tend to show signals of positive selection via the SDS statistic more so than SNPs associated with body fat percentage. The other traits examined do not show signals of such sexually differentiated positive selection.

Overall this paper addresses an interesting question that has not received as much attention in the empirical human genetics literature. The statistical analyses seem solid and carefully thought-out. I have several comments to help improve the manuscript:

Major Comments:

1) Starting on line 154 forward: In this section, the authors test whether the SexDiff SNPs are enriched in genes that have a known role in sexual differentiation. This is a nice addition to the paper and helps strengthen the case that these SNPs are biologically functional. Were the associated SNPs pruned for linkage disequilibrium for these analyses? It was unclear to me at which stage of the analyses the LD pruning was done. Unless I’m missing something (please correct me if I am), the LD pruning should be done prior to testing for enrichments as the enrichment tests are assuming that the SNPs are more or less independent. Further, as genes in different functional categories may have different recombination rates, patterns of LD, etc, it makes sense to include putatively independent SNPs as much as possible.

2) Figure 2 and associated analyses: I appreciate the resampling approach to test for enrichment. Was there a specific reason why the permutation analysis was used instead of Fisher’s exact test? It might be good to clarify more on this.

3) Section starting on line 222: I had a hard time understanding the statistical or biological relevance of this section. What is the purpose of testing for whether the SexDiff variants are disproportionately associated with greater phenotype effect sizes in the sex with the stronger association values? Does this not occur by design? Better motivating the purpose of this analysis would strengthen the manuscript in my view.

4) Figure 3 and tSDS analyses: For each trait, the authors compare the tSDS statistic distribution of the sexDiff SNPs to the tSDS statistic distribution for SNPs associated with the trait. Is this the right null distribution? What if the traits themselves are undergoing positive selection, just not in a sexually differentiated way? Then the SexDiff SNPs may not be expected to look different even if they are under selection too. Additionally, it’s not clear to me what the ideal null distribution for comparison would be in these cases. More explanation as to the rationale for the choice of the null set of SNPs would be good.

5) Figure 3 and tSDS analyses: Are the control SNPs for the tSDS statistic matched for allele frequency and recombination rate to the SexDiff SNPs in each comparison? It would be important to match for these putative confounders that differ across the genome and may influence the tSDS values.

6) Discussion: The authors included a nice discussion about population stratification as a potential confounder. I think it would be good to also discuss whether associative mating might be expected to influence any of these results. Further, on the other side, it would also be good to include some sort of discussion of the power of these analyses. It appears the authors made a number of conservative decisions in their statistical analyses (which I think is good, unless otherwise noted here) and it would be good to put this into context. For example, how strongly do the negative results reject the hypotheses of recent reductions in anthropometric trait sex differences in agricultural societies?

Minor comments:

1. Lines 36-38: This wording was a little confusing. Maybe say something like “SNPs associated with significant differences in association between males and females are enriched genes with known roles in sexual differentiation, as compared to other SNPs associated with the trait”.

2. Line 63: This seems like an odd start to the paper. Why not just start talking about “sexually differentiated traits” and their biology, rather than say that these are not “sexually dimorphic”. That statement can come elsewhere later in the Intro.

3. Lines 137: It would be good to give a little more context and description of the t-SexDiff test here. I realize that it’s fully explained in the Methods, but given that Results come first, I think it would be nice to give a short explanation to those who do not read the Methods.

4). Lines 142-144: Is this a ratio or a proportion?

5) Enrichment analysis in Figure 2: Were 162 or 135 genes selected? Both seem to be used (lines 191 and 206).

6) Figure 2B: To avoid confusion, the “differentiation” label on the x-axis might better be called “Number of GO:00007548 genes per permutation” or something like that. I worry that some may think “differentiation” might refer to the Fst or the population genetic version of differentiation, which of course is not what’s meant.

7) Line 293: Specify how many tests were corrected for with the FDR adjustment.

Reviewer #2: In this manuscript the authors are interested in two related questions: whether there is evidence of significant differential genetic architecture between human males and females across multiple complex traits and, if so, whether these differences can be explained by recent positive selection in the directions expected based on the first set of results. The authors are interested in this question in part due to anthropological theory that states the change in human history to subsistence farming from hunter and gatherers should be accompanied by a reduction in differentiating characteristics between males and females, as the necessary traits in males and females become more homogenous.

The authors approach these questions by analyzing white British individuals in the UK BioBank dataset across multiple complex traits. They conduct sex-differentiated genome-wide association studies and compare both the number of associations between the sexes and the strength of association. They also look at whether genes related to sexual differentiation are enriched among their top hits. Lastly, using their sex-differentiated association SNPs (sex-diff SNPs), they conduct positive selection analyses using the singleton density score, a method that can identify recent positive selection. The authors find evidence for both differential genetic architecture between males and females across the traits analyzed, as well as evidence for at least one trait and sex combination (body fat percentage in females) to be undergoing recent positive selection, though in the opposite direction (increasing in allele frequency) than one would expect from the anthropological theory.

Overall I think this is a good manuscript and study design. I think the analyses and justifications make sense, and the flow of the different parts work well. I also appreciate how the work shown here has a connection to a theory from anthropology, grounding the results as well as potentially broadening their impact too. I do have a few suggestions regarding some additional analyses that might further bolster the positive selection sections, as well as a few minor comments.

Major Comments:

1) For the SDS results, I am wondering whether the presence of older positive selection might affect the results somehow, or at least change our interpretation of the results. For instance, if we find that there is evidence for older positive selection on body fat percentage in females, then maybe our interpretation of the SDS results changes from recent selection due to the change in subsistence strategy to simply ongoing selection for sexual differentiation. In fact maybe we would expect some amount of sexual differentiation to be continuously selected for due to phenomena like assortative mating or sexual conflict. To get at this, could the authors run some other tests for positive selection, such as Berg and Coop’s Qs or statistics for selective sweeps? If there is evidence for older positive selection on the traits that did not show significant SDS results as well, I would wonder then if it’s possible that SDS might be less effective due to presumably lower levels of local genetic variation.

2) Similarly for the SDS results, is there any difference between the distribution of sex-diff SNPs and the non-sex-diff SNPs in terms of genic vs. intergenic regions? Once again I am wondering about other factors that may be affecting the results, and whether if lower levels of local genetic variation for the sex-diff SNPs might somehow impact the SDS results, ie if sex-diff SNPs were somehow more often in genic regions than non-sex-diff SNPs are. If this were the case then maybe some of the negative SDS results for the other traits are a result of being under powered.

3) The authors mention the possibility that pleiotropy could be occurring among the sex-diff SNPs showing evidence for positive selection in female body fat percentage. Since the authors have already used the summary statistics from the Ben Neale UKB GWAS results, I would be curious whether as a group these SNPs are also all associated with any other traits. If as a group we find a pattern of traits these SNPs are associated with, or on the other hand find no evidence these SNPs are associated with any other traits (at least among the traits previously analyzed), that could be an interesting additional piece of information to help with the interpretation.

Minor Comments:

1) For Figure 1 I have a few comments, mostly related to consistency. I don’t believe the red vs. pink motif in 1A continues throughout the rest of the figure (likely due to the combination of red and yellow later on), so I’m not sure if it should be used at all. The Bonferroni p-value threshold line should be labeled in 1A and 1B since the FDR lines are labeled later on as well. And in the legend the FDR lines are described as ‘green bars’, though in the copy of the manuscript I have, the bars are blue. This might just be my copy, but it should be checked and corrected if needed.

2) For lines 233-235, I think “...we prepared similar LD-pruned sets of all SNPs significantly associated with the phenotype for each of the five traits…” would be more clear if it included something such as “...sets of all SNPs significantly associated with the phenotype, /irregardless of sex differentiation or not/,...”. Right now it’s unclear if this comparison set of SNPs includes those that are sex-diff or not. It becomes a bit more clear later on through the analyses, but explicitly delineating this here might help.

3) There are a few sentences that are awkwardly constructed. I would recommend revising them.

a) Lines 75-77: “/Large ranges in the degree of body size and shape sexually differentiated traits/ are repeatedly observed among…” (unclear here)

b) Lines 101-104: “/With the recent availability of a greatly increased participant sample size, this analysis represents a powerful extension of the several previous GWAS-based approaches/ that have studied the genetic architecture of…’ (feels clunky)

c) Lines 278-281: “...for the corresponding pruned set of all phenotype-associated SNPs (Figure 3B; Table S6), /using a permutation analysis/” (maybe start the sentence with this?)

4) This may not be necessary for the paper, but could the authors explain what the concern with BMI is? The paper cited has this at the end of its conclusion: “ABSI expresses the excess risk from high WC in a convenient form that is complementary to BMI and to other known risk factors.” Is this mostly a reflection of body shape being a better complementary phenotype to traits such as waist and hip circumference, whereas BMI may be too correlated? If so, the authors may want to be more explicit with their reasoning beyond ‘concerns about this metric’ -- this makes it seem like BMI is a potentially ‘bad metric’, which is maybe not the intention here.

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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PLoS Genet. 2021 Jun 3;17(6):e1009562. doi: 10.1371/journal.pgen.1009562.r002

Author response to Decision Letter 0

14 Mar 2021

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PLoS Genet. doi: 10.1371/journal.pgen.1009562.r003

Decision Letter 1

Scott M Williams

20 Apr 2021

Dear Dr Arner,

We are pleased to inform you that your manuscript entitled "Patterns of recent natural selection on genetic loci associated with sexually differentiated human body size and shape phenotypes" has been editorially accepted for publication in PLOS Genetics. Congratulations!

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----------------------------------------------------

Comments from the reviewers (if applicable):

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors were very responsive to my previous comments and have adequately addressed them. I think this is a really interesting and important paper and the analyses are well-done.

I just have a couple of minor comments to help improve the presentation.

1) Lines 146-149: This part is still a little confusing and may be missing some words. Or, maybe change “calculated” to “compared”.

2) Line 231: Thank you for the clarification in the response to reviewers about the rationale for comparing the trait effect size estimates between males and females. However, the part about “lower trait value variance” is still a little unclear. What is the variance of? The effect sizes? The traits? The difference in effect sizes? A few more words of clarification would go a long way here.

3) The authors might consider using a subscript on their t-sexDiff statistic. The dash could look like a minus sign and lead to confusion. For example, line 402, it could look like “t minus SexDiff=”.

Reviewer #2: Thank you to the authors for their responses to my previous suggestions and for the edits made to the manuscript. In particular I think the inclusion of the iHS and the GWAS catalog analyses provide additional, interesting perspectives to the results. I believe the authors have addressed my concerns adequately and that the manuscript has become stronger as a result of their additional work. As I was already mostly satisfied with the manuscript before, I feel that it is suitable for publication now as well. I have no additional comments.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Reviewer #1: Yes

Reviewer #2: Yes

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PLoS Genet. doi: 10.1371/journal.pgen.1009562.r004

Acceptance letter

Scott M Williams

12 May 2021

PGENETICS-D-20-01472R1

Patterns of recent natural selection on genetic loci associated with sexually differentiated human body size and shape phenotypes

Dear Dr Arner,

We are pleased to inform you that your manuscript entitled "Patterns of recent natural selection on genetic loci associated with sexually differentiated human body size and shape phenotypes" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Permutation enrichment distribution at each FDR threshold.

(TIF)

Click here for additional data file.^{(1.1MB, tif)}

S2 Fig. Sex-specific iHS scores for anthropometric SexDiff and phenotype-associated SNPs.

(TIF)

Click here for additional data file.^{(9MB, tif)}

(DOCX)

Click here for additional data file.^{(17KB, docx)}

S2 Table. Observed unique sexual differentiation genes (SDG) and total number of genes for SexDiff-associated SNPs and Non SexDiff-associated SNPs.

(DOCX)

Click here for additional data file.^{(17KB, docx)}

S3 Table. Observed log₂ ratio of female to male beta values and p-values for each set of Female SexDiff-associated SNPs.

(DOCX)

Click here for additional data file.^{(17.2KB, docx)}

S4 Table. Observed log₂ ratio of female to male beta values and p-values for each set of Male SexDiff-associated SNPs.

(DOCX)

Click here for additional data file.^{(17.3KB, docx)}

S5 Table. Observed trait-SDS and permutation P-values for each set of Female SexDiff-associated SNPs and Male SexDiff-associated SNPs permuted against phenotype-associated SNPs.

(DOCX)

Click here for additional data file.^{(17.3KB, docx)}

(DOCX)

Click here for additional data file.^{(17.4KB, docx)}

S7 Table. Observed average |iHS| scores and permutation P-values for each set of Female SexDiff-associated SNPs and Male SexDiff-associated SNPs permuted against phenotype-associated SNPs.

(DOCX)

Click here for additional data file.^{(17.7KB, docx)}

S8 Table. Observed number of intergenic SNPs and permutation P-values for each set of Female SexDiff-associated SNPs and Male SexDiff-associated SNPs permuted against phenotype-associated SNPs.

(DOCX)

Click here for additional data file.^{(17.5KB, docx)}

S9 Table. Phenotype information.

^aAs referred to in the Neale lab manifest released on July 31, 2018 ^b Correlation for each phenotype calculated as the Spearman rank correlation coefficient between beta values of men and women.

(DOCX)

Click here for additional data file.^{(16.8KB, docx)}

S10 Table. Observed trait-SDS for each set of pruned phenotype-associated SNP groups.

(DOCX)

Click here for additional data file.^{(16.1KB, docx)}

Attachment

Submitted filename: Arner et al Response to Reviewers.docx

Click here for additional data file.^{(9.4MB, docx)}

Data Availability Statement

All data files are available in the Dryad Digital Repository: https://doi.org/10.5061/dryad.nzs7h44rc.

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PERMALINK

Patterns of recent natural selection on genetic loci associated with sexually differentiated human body size and shape phenotypes

Audrey M Arner

Kathleen E Grogan

Mark Grabowski

Hugo Reyes-Centeno

George H Perry

Roles

Abstract

Author summary

Introduction

Results

Identification of phenotype-associated SNPs

Fig 1. SexDiff-associated SNPs for five anthropometric phenotypes.

SNPs disproportionately associated with female or male trait variation

Co-localization of SexDiff-associated SNPs and loci with known roles in sexual differentiation

Fig 2. Co-localization of SexDiff-associated SNPs and loci with known roles in sexual differentiation.

SexDiff-associated SNPs are associated with greater trait effect sizes in the expected sex

Fig 3. Sex-specific effect size ratios and trait-SDS scores for anthropometric trait-associated SNPs.

Signatures of positive selection on SexDiff-associated alleles

Discussion

Methods

Subjects and dataset generation

Scan for SNPs that are disproportionately associated with male or female trait variation

Assessing the biological plausibility of the SexDiff-associated SNPs

Assessing the sex-specific effects of SexDiff-associated SNPs

Identification of signatures of positive selection

Computational resources

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Scott M Williams

Roles

Author response to Decision Letter 0

Decision Letter 1

Scott M Williams

Roles

Acceptance letter

Scott M Williams

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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RESOURCES

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