Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Abstract

Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.

Fig. 1. Miami plots of sex-specific associations.

a FI sex-specific associations, b FG sex-specific associations showing women on upper panel (all y axis values are positive) and men on lower panel (all x axis values are negative). Established or novel loci with sex-dimorphic effects (P_{sex-dimorphic} ≤ 5.0 × 10⁻⁸) and nominal sex heterogeneity (P_{heterogeneity} < 0.05) are shown in magenta (larger effect in women) or cyan (larger effect in men). Novel genome-wide significant loci from sex-combined analyses with sex-homogeneous effects (P_sex-combined ≤ 5.0 × 10⁻⁸) are shown in yellow. Established loci reaching genome-wide significance in sex-combined analyses and showing no sex heterogeneity (P_{heterogeneity} > 0.05) are colored in purple. All remaining established loci (i.e. no significant sex-dimorphic or sex-homogeneous effects) are marked in orange.

Fig. 2. Plots for ZNF12 locus with sex-dimorphic effects on FI.

a female-specific regional plot, b male-specific regional plot, c ZNF12 whole blood RNA expression data in n = 3,621 Netherlands Twin Register and Netherlands Study of Anxiety and Depression studies. Beta ± SD (error bars) represent the sex effect in the linear regression analysis where the average gene expression by all probes in the gene was predicted by sex, as well as the following covariates: age, smoking status, RNA quality, hemoglobin, study, time of blood sampling, month of blood sampling, time between blood sampling and RNA extraction, and the time between RNA extraction and RNA amplification. A positive value represents an upregulated expression in women and a negative value an upregulated expression in men. The P value represents the significance of sex effect from the linear models (P values are not corrected for multiple testing). d ZNF12 tissue expression relative to three housekeeping genes (PPIA, B2M, and HPRT). For beta cell (n = 3) and islets (n = 3) data, lines are means. Quantitative RT-PCR was carried out using cDNAs from three human donors (beta-cells and islets). The other tissues were commercial cDNAs (one point observation).

Fig. 3. Genetic correlations and causality.

a Genetic correlations for FI, b genetic correlations for FG. Phenotypes with statistically significant (P < 0.001) genetic correlations (calculated by LD score regression) with FI/FG in either women or men are plotted. The outer track shows estimates for all together, followed by those for women and men. Traits with I² (sex heterogeneity) ≥50% are labeled with asterisks. Gray color indicates traits that do not show significant genetic correlation with the given glycemic trait. Estimates in black color indicate statistically significant associations. c bi-directional MR analysis between WHRadjBMI and FI with betas, standard errors of the estimates and P values from random-effect inverse-variance weighted regression given for men and women. AN anorexia nervosa, BMI body-mass index, EA educational attainment as of years of schooling 2016, FVC forced vital capacity, HbA1c glycated hemoglobin, HC hip circumference, HDL high-density lipoprotein cholesterol, HOMA-B homeostatic model assessment of beta cell function, HOMA-IR homeostatic model assessment of insulin resistance, leptin adjBMI leptin adjusted for BMI, Leptin not adjBMI leptin not adjusted for BMI, Obesity 1 obesity class 1, Obesity II obesity class II, Obesity III obesity class 3, T2D type 2 diabetes, TG triglycerides, WC waist circumference, WHR adjBMI waist-to-hip ratio adjusted for BMI, UKBB UK Biobank.

Fig. 4. Power of tests for detecting sex heterogeneity through simulations.

The power of sex-combined, sex-dimorphic and female-specific analyses, as well as Cochran’s Q-test was evaluated under three scenarios of sex-effects: no sex heterogeneity at a CAF = 0.05 and b CAF = 0.1, effects on both sexes with the presence of heterogeneity between them at c CAF = 0.05 and d CAF = 0.1, an effect specific to one sex only, e.g., women at e CAF = 0.05 and f CAF = 0.1. The power at P < 5 × 10⁻⁸ is given for all three tests: sex-combined, sex-dimorphic and female-specific. The power for the heterogeneity test implemented in GWAMA (Cochran’s Q-test) is also given. Simulations are based on 70,000 men and 70,000 women. For each parameter setting, 10,000 replicates of data were generated. CAF is the causal variant allele frequency and beta is the effect size in SD units in women. Within each scenario, we considered two CAFs (0.05 and 0.1) and a range of betas (from 0 to 0.1) representing the effect size in SD units in women. For the no sex heterogeneity setting, the beta in men is the same as in women; for the sex-dimorphic setting, the beta in men is fixed at 0.05 SD units; for the female-specific setting, the beta in men is fixed at zero.