As a female's capacity for reproduction comes to an end, the fat distribution within the female body begins a transition from the gynoid type to more of an android type distribution. This is evidenced by the percentages of android fat being far higher in post-menopausal than pre-menopausal women.

The differences in gynoid fat between men and women can be seen in the typical " hourglass " figure of a woman, compared to the inverted triangle which is typical of the male figure. Women commonly have a higher body fat percentage than men and the deposition of fat in particular areas is thought to be controlled by sex hormones and growth hormone GH.

The hormone estrogen inhibits fat placement in the abdominal region of the body, and stimulates fat placement in the gluteofemoral areas the buttocks and hips. Certain hormonal imbalances can affect the fat distributions of both men and women. Women suffering from polycystic ovary syndrome , characterised by low estrogen, display more male type fat distributions such as a higher waist-to-hip ratio.

Conversely, men who are treated with estrogen to offset testosterone related diseases such as prostate cancer may find a reduction in their waist-to-hip ratio. Sexual dimorphism in distribution of gynoid fat was thought to emerge around puberty but has now been found to exist earlier than this.

Gynoid fat bodily distribution is measured as the waist-to-hip ratio WHR , whereby if a woman has a lower waist-to-hip ratio it is seen as more favourable. It was found not only that women with a lower WHR which signals higher levels of gynoid fat had higher levels of IQ, but also that low WHR in mothers was correlated with higher IQ levels in their children.

Android fat distribution is also related to WHR, but is the opposite to gynoid fat. Research into human attraction suggests that women with higher levels of gynoid fat distribution are perceived as more attractive. cancer ; and is a general sign of increased age and hence lower fertility, therefore supporting the adaptive significance of an attractive WHR.

Both android and gynoid fat are found in female breast tissue. Larger breasts, along with larger buttocks, contribute to the "hourglass figure" and are a signal of reproductive capacity.

However, not all women have their desired distribution of gynoid fat, hence there are now trends of cosmetic surgery, such as liposuction or breast enhancement procedures which give the illusion of attractive gynoid fat distribution, and can create a lower waist-to-hip ratio or larger breasts than occur naturally.

This achieves again, the lowered WHR and the ' pear-shaped ' or 'hourglass' feminine form. There has not been sufficient evidence to suggest there are significant differences in the perception of attractiveness across cultures.

Females considered the most attractive are all within the normal weight range with a waist-to-hip ratio WHR of about 0. Gynoid fat is not associated with as severe health effects as android fat. Gynoid fat is a lower risk factor for cardiovascular disease than android fat.

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Download as PDF Printable version. Female body fat around the hips, breasts and thighs. See also: Android fat distribution. Nutritional Biochemistry , p. Academic Press, London.

ISBN The Evolutionary Biology of Human Female Sexuality , p. Oxford University Press, USA. Relationship between waist-to-hip ratio WHR and female attractiveness". Personality and Individual Differences. doi : Acta Paediatrica. ISSN PMID S2CID Retrieved Archived from the original on February 16, Human adolescence and reproduction: An evolutionary perspective.

c Genetic correlations between body fat ratios and standard anthropometric traits. Sex-stratified summary statistics were generated for each trait by GWAS in the discovery cohort. Body fat ratio-associated SNPs were tested for overlap with associations from previous GWAS for anthropometric traits by determining LD with entries from the GWAS-catalog In total, we identified 29 body fat ratio-associated signals that have not previously been associated with an anthropometric trait Figure 1c , Table 1 , supplementary Figs.

For AFR, the strongest associations were observed at well known BMI and adiposity-associated loci such as: FTO , MC4R , TMEM18 , SEC16B, and TFAP2B supplementary Data 1.

We compared the direction of the effects for overlapping GWAS results by estimating the effects of lead body fat ratio-associated SNPs on the respective overlapping anthropometric traits.

The effects of TFR-associated SNPs were directionally consistent with effects on height and WHR adjusted for BMI WHRadjBMI , while the effects were the opposite for LFR.

The direction of effects for AFR-associated SNPs were consistent with effects on BMI, WC, and WHR supplementary Table 6. Among the loci that have not previously been associated with an adiposity-related anthropometric trait, five overlapped with cardiovascular and metabolic trait-associated loci from previous GWAS: near XKR6 , which is associated with carotid intima thickness 22 , triglycerides 23 , 24 , and systolic blood pressure 25 , 26 ; ZNF : coronary artery disease 27 and diastolic blood pressure 26 , 28 ; RPD Sex-heterogenous effects of associated variants were tested for using the GWAMA software.

This method utilizes summary statistics from sex-stratified GWAS to test for heterogeneity of allelic effects between males and females All replicated lead SNPs were included in these analyses.

SNPs were only tested for heterogenous effects on the traits that they were associated with, which corresponds to 30 variants that were tested for sex-heterogenous effects on AFR, 44 on LFR and 66 on TFR.

A striking heterogeneity in effects between males and females was observed Table 2 , supplementary Data 2. Two variants, near SLC12A2 and PLCE1 , were shown to have larger effects on AFR in males while 37 variants exhibited larger effects in females.

LD score regression LDSC was used to estimate the fraction of variance of body fat ratios that could be explained by SNPs, i. Phenotypic and genotypic correlations were assessed, in males and females separately.

Phenotypic correlations were estimated by calculating squared semi-partial correlation coefficients with ANOVA of nested linear models that were adjusted for age and principal components while genetic correlations were estimated using cross-trait LD score regression 32 see methods.

Overall, the genetic and phenotypic correlations showed a large degree of similarity supplementary Tables 8 — 9 and the correlations between the anthropometric traits and body fat ratios were directionally consistent for phenotypic and genetic correlations for all phenotypes.

In females, BMI and WC was strongly correlated with AFR both with regards to phenotypic and genetic correlations Fig. Height contributed to a moderate degree in explaining the phenotypic variance in LFR and TFR in females In males, anthropometric traits contributed only to a small degree in explaining the phenotypic variance of body fat ratios supplementary Table 8.

Consistent with this result, genetic correlations between body fat ratios and anthropometric traits in males were also quite low Fig. LFR and TFR were inversely correlated, which agrees well with the large overlap in GWAS results for these phenotypes and the fact that the effect estimates from the GWAS was in the opposite direction for LFR and TFR supplementary Data 1.

In total, 31 body fat ratio-associated loci overlapped with an eQTL, and 11 lead SNPs were in LD with a potentially deleterious missense variant. Polyphen and SIFT-scores were used to assess the deleteriousness of the variants. These scores represent the probability for functional effects of missense variants and were estimated through sequence analyses 34 , Missense variants were found in ACAN , ADAMTS17 , FGFR4 and ADAMTS10 , where the lead SNPs were predicted to be damaging supplementary Table The missense variant rs, within FGFR4 , has also previously been shown to be associated with progression of cancer 36 , 37 and to affect insulin secretion in vitro To identify the functional roles of body fat ratio-associated variants and which tissues are mediating the genetic effects, we performed enrichment analyses with DEPICT Data-driven Expression Prioritized Integration for Complex Traits 39 , see method section.

In these analyses we used summary statistics from sex-stratified GWAS on the combined cohort , women and , men in order to maximize statistical power. Results from the enrichment analyses were compared with results from previous GWAS for height, BMI 9 and WHRadjBMI Enrichment analyses of genes at LFR and TFR-associated loci.

a Reconstituted gene-sets that were enriched for TFR- and LFR-associated genes in both males and females were compared to results from previous GWAS on WHRadjBMI 12 , BMI 9 , and height Tissue and cell type enrichment of b TFR- and c LFR-associated genes in females.

Tissue enrichment was observed for LFR and TFR-associated genes in females Fig. For TFR, DEPICT also revealed enrichment of genes associated with adipose tissue cells, female urogenital organs, endocrine organs as well as the arteries Fig.

Tissue enrichment was not seen for the other traits or strata. In the gene set analyses, enrichment was only detected for TFR- and LFR-associated genes in females as well as LFR-associated genes in males supplementary Data 4. Gene sets related to bone morphology and skeletal development were among the most strongly associated with both LFR and TFR.

We also find the TGFβ signaling pathway gene set to be enriched for genes within the TFR and LFR-associated loci in females, as well as SMAD1-, SMAD2-, SMAD3- and SMAD7 protein-protein interaction subnetworks supplementary Data 4 , which act as TGFβ downstream mediators.

There was a substantial overlap of enriched gene sets between TFR and LFR in females as well as moderate overlap with LFR-associated gene sets in males supplementary Fig.

The large fraction of overlapping gene sets between LFR and TFR in females agrees well with the large overlap in GWAS signals. In this study, we performed GWAS on distribution of body fat to different compartments of the human body and identified and replicated 98 independent associations of which 29 have not previously been associated with any adiposity-related phenotype.

In contrast to earlier studies, we have not addressed the total amount of fat but rather the fraction of the total body fat mass that is located in the arms, legs and trunk. Body fat distribution is well known to differ between males and females, which we also clearly show in our study.

We also show that the genetic effects that influence fat distribution are stronger in females compared to males. These results are consistent with previous GWAS that have revealed sexual dimorphisms in genetic loci for adiposity-related phenotypes, such as waist circumference and waist-to-hip ratio 10 , 40 , Phenotypic and genetic correlations, as well as results from GWAS and subsequent enrichment analyses, also revealed that the amount of fat stored in the arms in females is highly correlated with BMI and WC.

This suggests that the proportion of fat stored in the arms will generally increase with increased accumulation of body mass and adipose tissue. In contrast, males exhibited moderate-to-weak phenotypic and genetic correlations between the distributions of fat to different parts of the body and anthropometric traits, which indicates that the proportions of body fat mass in different compartments of the male body remains more stable as body mass and body adiposity increases.

Among the three phenotypes analyzed in this study LFR and TFR were inversely correlated in both males and females. This suggests that LFR and TFR to a large extent describe one trait, i. In contrast, AFR was only weakly correlated with the other two traits. Tissue enrichment revealed an important role in body fat distribution in females for mesenchyme-derived tissues: i.

This suggests that the distribution of fat to the legs and trunk in females is mainly driven by the effects of female gonadal hormones on mesenchymal progenitors of musculoskeletal and adipose tissues.

However, there was also an overlap in the functional aspects between these traits with both height and WHRadjBMI. WHRadjBMI-associated genes 12 were enriched in adipocytes and adipose tissue subtypes. Of particular note, we did not identify any enrichment of body fat ratio-associated genes in CNS tissue gene sets in contrasts to enrichment analyses in previous GWAS for BMI where the CNS has been implicated in playing prominent role in obesity susceptibility 9.

In the GWAS for LFR and TFR in females, we find that several genes that highlight the influence of biological processes related to the interaction between cells and the extracellular matrix ECM , as well as ECM-maintenance and remodeling.

These include ADAMTS2, ADAMTS3, ADAMTS10 , ADAMTS14 , and ADAMTS17 , which encode extracellular proteases that are involved in enzymatic remodeling of the ECM. In addition, possibly deleterious missense mutations in LD with our lead GWAS SNPs were also found for VCAN and ACAN.

Both VCAN and ACAN encode chondroitin sulfate proteoglycan core proteins that constitute structural components of the extracellular matrix, particularly in soft tissues These proteins also serve as major substrates for ADAMTS proteinases ECM forms the three-dimensional support structure for connective and soft tissue.

In fat tissue, the ECM regulates adipocyte expansion and proliferation Remodeling of the ECM is required to allow for adipose tissue growth and this is achieved through enzymatic processing of extracellular molecules such as proteoglycans, collagen and hyaluronic acid.

For example, the ADAMTS2-, 3-, and proteins act as procollagen N-propeptidases that mediate the maturation of triple helical collagen fibrils 45 , We therefore propose that the effects of genetic variation in biological systems involved in ECM-remodeling is a factor underlying normal variation in female body fat distribution.

In summary, GWAS of body fat distribution determined by sBIA reveals a genetic architecture that influences distribution of adipose tissue to the arms, legs, and trunk. Genetic associations and effects clearly differ between sexes, in particular for distribution of adipose tissue to the legs and trunk.

The distribution of body fat in women has previously been suggested as a causal factor leading to lower risk of cardiovascular and metabolic disease, as well as cardiovascular mortality for women in middle age 5 and genetic studies have identified SNPs that are associated with a favorable body fat distribution 47 , i.

The capacity for peripheral adipose storage has been highlighted as one of the components underlying this phenomenon Resolving the genetic determinants and mechanisms that lead to a favorable distribution of body fat may help in risk assessment and in identifying novel venues for intervention to prevent or treat obesity-related disease.

Imputed genotype data from the third UK Biobank genoype data release were used for replication. Participants who self-reported as being of British descent data field and were classified as Caucasian by principal component analysis data field were included in the analysis.

Genetic relatedness pairing was provided by the UK Biobank Data field After filtering, , participants remained in the discovery cohort and , in the replication cohort. All participants provided signed consent to participate in UK Biobank Genotyping in the discovery cohort had been performed on two custom-designed microarrays: referred to as UK BiLEVE and Axiom arrays, which genotyped , and , SNPs, respectively.

Imputation had been performed using UK10K 49 and genomes phase 3 50 as reference panels. Prior to analysis, we filtered SNPs based on call rate --geno 0. The third release of data from the UK Biobank contained genotyped and imputed data for , participants partly overlapping with the first release.

For our replication analyses, we included an independent subset that did not overlap with the discovery cohort. Genotyping in this subset was performed exclusively on the UK Biobank Axiom Array. The phenotypes used in this study derive from impedance measurements produced by the Tanita BCMA body composition analyzer.

Participants were barefoot, wearing light indoor clothing, and measurements were taken with participants in the standing position. Height and weight were entered manually into the analyzer before measurement.

The Tanita BCMA uses eight electrodes: two for each foot and two for each hand. This allows for five impedance measurements: whole body, right leg, left leg, right arm, and left arm Fig.

Body fat for the whole body and individual body parts had been calculated using a regression formula, that was derived from reference measurements of body composition by DXA Fig.

This formula uses weight, age, height, and impedance measurements 51 as input data. Arm, and leg fat masses were averaged over both limbs. Arm, leg, and trunk fat masses were then divided by the total body fat mass to obtain the ratios of fat mass for the arms, legs and trunk, i.

These variables were analyzed in this study and were named: AFR, LFR, and TFR. Phenotypic correlations between fat distribution ratios and anthropometric traits were estimated by calculating squared semi-partial correlation coefficients for males and females separately, using anova.

glm in R. Adipose tissue ratios AFR, LFR or TFR were set as the response variable. BMI, waist circumference, waist circumference adjusted for BMI, waist-to-hip ratio, height, or one of the other ratios were included as the last term in a linear model with age and principal components as covariates.

The reduction in residual deviance, i. A two-stage GWAS was performed using a discovery and a replication cohort.

Sex-stratified GWAS were performed in the discovery cohort for each trait. A flowchart that describes the steps taken for the genetic analyses is included as supplementary Fig.

Prior to running the GWAS, body fat ratios were adjusted for age, age squared and normalized by rank-transformation separately in males and females using the rntransform function included in the GenABEL library GWAS was performed in PLINK v1. A batch variable was used as covariate in the GWAS for the discovery analyses to adjust for genotyping array UKB Axiom and UK BiLEVE as well as for other differences between UK BiLEVE and UKB Axiom-genotyped participants.

We also included the first 15 principal components and sex in the sex-combined analyses as covariates in the GWAS. LD score regression intercepts see further information below , calculated using ldsc 17 , were used to adjust for genomic inflation, by dividing the square of the t -statistic for each tested SNP with the LD-score regression intercept for that GWAS, and then calculating new P -values based on the adjusted t -statistic.

The --clump function in PLINK was used to identify the number of independent signals in each GWAS. This function groups associated SNPs based on the linkage disequilibrium LD pattern.

After running --clump in PLINK, conditional analyses were also performed for each locus conditioning for the lead SNP, but no further signals were identified.

Several associations were found in more than one of the three body fat ratios AFR, TFR, or LFR or strata males, females, or sex-combined and different lead SNPs were observed for different traits and strata at several loci. To assess whether these represented the same signal, we assessed the LD between overlapping lead SNPs in PLINK.

We then performed conditional analysis in PLINK, conditioning on the most significant SNPs across all phenotypes and strata. For each independent signal, the lead SNP lowest P -value was taken forward for replication. Meta analyses of results from the discovery and replication cohorts was performed with the METAL software 53 for all independent associations that were taken forward for replication.

We estimated SNP heritability and genetic correlations using LD score regression LDSC , implemented in the ldsc software package Only SNPs that were included in HapMap3 were included in these analyses.

LDSC uses LD patterns and summary stats from GWAS as input. For genetic correlations, we performed additional sex-stratified GWAS in the UK biobank using the same covariates as for the ratios for standard anthropometric traits, BMI, height, WC, WHR, WCadjBMI, and WHRadjBMI, in the discovery cohort.

GWAS summary stats were filtered for SNPs included in HapMap3 to reduce likelihood of bias induced by poor imputation quality.

After this filtering, 1,, SNPs remained for LDSC analyses. Genetic correlations between the three body fat ratios and anthropometric traits were assessed by cross-trait LD score regression.

Lead SNPs from all independent signals in our analyses were cross-referenced with the NHGRI-EBI catalog of published genome-wide association studies GWAS Catalog—data downloaded on 23 April 19 to determine whether body fat ratio-associated signals overlapped with previously identified anthropometric associations from previous GWAS.

LD between data in the GWAS catalog and our lead SNPs were calculated using PLINK v1. Associated loci were investigated for overlap with eQTLs from the GTEx project The threshold for significance for the eQTLs was set to 2.

The strongest associated SNP for each tissue and gene in the GTEx dataset was identified. We then estimated the LD between the top eQTL SNPs and the lead SNP for each independent association from our analysis. Polyphen and SIFT-scores for the missense variants extracted from Ensembl— www.

org were used to assess the deleteriousness of the body fat ratio-associated variants. To identify the functional roles and tissue specificity of associated variants, we performed tissue and gene-set enrichment analyses using DEPICT For the gene-set enrichment in DEPICT, gene expression data from 77, samples have been used to predict gene function for all genes in the genome based on similarities in gene expression.

In comparison to standard enrichment tools that apply a binary definition to define membership in a set of genes that have been associated with a biological pathway or functional category genes are either included or not included , in DEPICT, the probability of a gene being a member of a gene set has instead been estimated based on correlation in gene expression.

This membership probability to each gene set has been estimated for all genes in the human genome and the membership probabilities for each gene have been designated reconstituted gene sets. A total of 14, reconstituted gene sets have been generated which represent a wide set of biological annotations Gene Ontology [GO], KEGG, REACTOME, Mammalian Phenotype [MP], etc.

For tissue enrichment in DEPICT, microarray data from 37, human tissues have been used to identify genes with high expression in different cells and tissues. For the enrichment analyses, we performed sex-stratified GWAS for AFR, LFR and TFR on the combined cohort, i.

The clump functionality in PLINK is used to determine associated loci. In the enrichment analyses, DEPICT assesses whether the reconstituted gene sets are enriched for genes within trait-associated loci The false discovery rate FDR 55 was used to adjust for multiple testing.

We used the GWAMA software 31 to test for heterogenous effects of associated SNPs between sexes. In GWAMA, fixed-effect estimates of sex-specific and sex-combined beta coefficients and standard errors are calculated from GWAS summary statistics to test for heterogeneous allelic effects between females and males.

GWAMA obtains a test-statistic by subtracting the sex-combined squared t -statistic from the sum of the two sex-specific squared t -statistics.

This test statistic is asymptotically χ 2 -distributed and equivalent to a normal z -test of the difference in allelic effects between sexes. Lead SNPs that replicated were tested for heterogeneity between sexes for the trait that they were associated with. This corresponds to 30 tests for AFR, 44 for LFR, and 66 for TFR.

Summary statistics from the replication cohort were used in order to maximize statistical power. Restrictions apply to the availability of these data, which were used under license for the current study Project No. Data are available for bona fide researchers upon application to the UK Biobank.

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