Investigating the Sexual Dimorphism of Waist-to-Hip Ratio and Its Associations with Complex Traits

Abstract

Background: Obesity significantly impacts disease burden, with waist-to-hip ratio (WHR) as a key obesity indicator, but the genetic and biological pathways underlying WHR, particularly its sex-specific differences, remain poorly understood. Methods: This study explored WHR's sexual dimorphism and its links to complex traits using cross-sectional surveys and genetic data from Giant and UK Biobank (UKB). We analyzed WHR heritability, performed tissue-specific transcriptome-wide association studies (TWAS) using FUSION, and conducted genetic correlation analyses with linkage disequilibrium score regression (LDSC) and Local Analysis of [co]Variant Association (LAVA). Polygenic scores (PGS) for WHR were constructed using the clumping and thresholding method (CT), and associations with complex traits were assessed via logistic or linear models. Results: The genetic analysis showed sex-specific heritability for WHR, with TWAS identifying female-specific (e.g., CCDC92) and male-specific (e.g., UQCC1) genes. Global genetic correlation analysis revealed sex-specific associations between WHR and 23 traits, while local analysis identified eight sex-specific loci across five diseases. Regression analysis highlighted sex-specific associations for 70 traits with WHR and 45 traits with WHR PGS, with stronger effects in females. Predictive models also performed better in females. Conclusions: This study underscores WHR's sexual dimorphism and its distinct associations with complex traits, offering insights into sex-specific biological differences, health management, and clinical advancements.

Keywords: PGS; WHR; genetic architecture; genetic correlation; sex-specific traits; sexual dimorphism.

Figure 1

The flowchart of this study. We performed two stages of research. In the first stage, we mainly explored the sex dimorphism of the WHR genetic structure. Specifically, we used LDSC to evaluate the heritability and genetic correlation of WHR across three sexes and integrated WHR GWAS summary statistics with eQTL data to explore the gene expression of WHR in different sexes using TWAS. In the second stage, we concentrated on sexual dimorphism in the association of WHR with 353 complex traits. Specifically, we firstly integrated GWAS summary statistics of WHR and complex traits, respectively using LDSC to identify sex-specific traits that have a genetic association with WHR and LAVA to explore the local sex-specific shared loci. Secondly, we performed logistic regression analysis to identify sex-specific traits with WHR and its PGS in 308,373 individuals of EUR from UKB, respectively. Finally, we explored the performance of WHR PGS for diseases in prediction models.

Figure 2

The heritability and global genetic correlation of WHR across three sexes. (A) The heritability of WHR of three sexes. The heritability of females is greater than that of males, followed by the mixed sexes. The x-axis represents different genders while the y-axis represents heritability. (B) The global genetic correlation of WHR across three sexes. The genetic correlation between females and mixed sexes is stronger than that between males and mixed sexes. The x-axis represents different gender pairs while the y-axis represents global genetic correlation. WHR, waist-to-hip ratio.

Figure 3

TWAS results of WHR in adipose subcutaneous tissue. (A) TWAS results of WHR in the female set; (B) TWAS results of WHR in the male set. In the adipose subcutaneous tissue, 12 genes showed strong associations with females, while four genes showed strong associations with males. The signals of related genes in females were stronger than those in males. The x-axis represents the chromosome number while the y-axis represents the −log₁₀ of the TWAS p value. Each dot represents a gene. The solid red horizontal line is marked at the Bonferroni threshold of significance for multiple testing, and the black dotted horizontal line represents the p < 0.05. TWAS, transcriptome-wide association analysis; WHR, waist-to-hip ratio.

Figure 4

The relationship of WHR PGS with diseases. We used the odds ratio (95% CI) to estimate the strength of the association. The forest plots show the β values and p values under the different traits for each of the three genders. The WHR PGS had significant associations with 50 diseases for the mixed-sex set, with 27 diseases for the female set, and with 8 diseases for the male set. A total of 23 diseases showed significant sexual dimorphism in association with the WHR PGS, spanning metabolic, cardiovascular, respiratory, musculoskeletal, ocular, and gastrointestinal disorders. The significant threshold was set at < 3.40 × 10⁻⁴ (0.05/147). CI, confidence interval; WHR, waist-to-hip ratio; PGS, polygenic risk score.

Figure 5

The relationship of WHR PGS with measurement traits. We used β (95% CI) to estimate the strength of the association. The forest plots respectively show the β values and p values under the different traits for each of the three genders. The WHR PGS had significant associations with 24 measurement traits for the mixed-sex set, with 33 measurement traits for the female set, and with 22 measurement traits for the male set. A total of 22 measurement traits showed significant sexual dimorphism in association with the WHR PGS, involving 19 body measurements, two blood cell counts, and one biochemical marker. Thehe significant threshold was set at < 3.40 × 10⁻⁴ (0.05/147). CI, confidence interval; WHR, waist-to-hip ratio; PGS, polygenic risk score.

Figure 6

The relationship between global genetic correlation and the effect value of the regression model. (A) The relationship between global genetic correlation and the effect value of the regression model in the female set; (B) The relationship between global genetic correlation and the effect value of the regression model in the male set. The global genetic correlation showed a positive correlation with the effect value for both females and males, and the correlation among males was stronger than that among females. The x-axis represents the global genetic association and the y-axis represents the effect value of the regression model.

Figure 7

The prediction of models in different diseases. We used the facet plots to present the AUC comparison values of the three genders for acute myocardial infarction, acute renal failure, angina pectoris, chronic ischemic heart disease, essential (primary) hypertension, non-insulin-dependent diabetes mellitus, and other chronic obstructive pulmonary diseases. The AUC values ranged from 0.681 to 0.787 in the mixed-sex set, from 0.671 to 0.795 in the female set, and from 0.674 to 0.766 in the male set. The PGS of WHR showed the highest predictive performance for non-insulin-dependent diabetes mellitus in all three genders (0.795 in females, 0.766 in males, and 0.787 in mixed sexes). The y-axis represents the AUC value of models. AUC, area under the curve; WHR, waist-to-hip ratio; PGS, polygenic risk score.