New genetic loci link adipose and insulin biology to body fat distribution

Abstract

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.

Figures

Extended Data Figure 1. Overall WHRadjBMI meta-analysis study design

Data (dashed lines) and analyses (solid lines) related to the genome-wide association study (GWAS) cohorts for waist-hip ratio adjusted for body mass index (WHRadjBMI) are colored red and those related to the Metabochip (MC) cohorts are colored blue. The two genomic control (λGC) corrections (within-study and among-studies) performed on associations from each dataset are represented by gray-outlined circles. The λGC corrections for the GWAS meta-analysis were based on all SNPs and the λGC corrections for the Metabochip meta-analysis were based on a null set of 4,319 SNPs previously associated with QT interval. The joint meta-analysis of the GWAS and MC datasets is colored purple. All SNP counts reflect a sample size filter of N ≥ 50,000 subjects. Additional WHRadjBMI meta-analyses included Metabochip data from up to 14,371 subjects of East Asian, South Asian, or African American ancestry from eight cohorts. Counts for the meta-analyses of waist circumference (WC), hip circumference (HIP), and their BMI-adjusted counterparts (WCadjBMI and HIPadjBMI) differ from those of WHRadjBMI because some cohorts only had phenotype data available for one type of body circumference measurement (see Supplementary Table 2).

Extended Data Figure 2. Female- and male-specific effects, phenotypic variances, and genetic correlations

a, Figure showing effect beta estimates for the 20 WHRadjBMI SNPs showing significant evidence of sexual dimorphism. Sex-specific effect betas and 95% confidence intervals for SNPs associated with waist-hip ratio adjusted for body mass index (WHRadjBMI) are shown as red circles and blue squares for women and men, respectively. The SNPs are classified into three categories: (i) those showing a female-specific effect (“Women SSE”), namely a significant effect in women and no effect in men (Pwomen < 5 × 10−8, Pmen ≥ 0.05), (ii) those showing a pronounced female effect (“Women CED”), namely a significant effect in women and a less significant but directionally consistent effect in men (Pwomen < 5 × 10−8, 5 × 10−8 < Pmen ≤ 0.05); and (iii) those showing a male-specific effect (“Men SSE”), namely a significant effect in men and no effect in women (Pmen < 5 × 10−8, Pwomen ≥ 0.05). Within each of the three categories, the loci were sorted by increasing P value of sex-based heterogeneity in the effect betas. b, Figure showing standardized sex-specific phenotypic variance components for six waist-related traits. Values are shown in men (M) and women (W) from the Swedish Twin Registry (N = 11,875). The ACE models are decomposed into additive genetic components (A) shown in black, common environmental components (C) in gray, and non-shared environmental components (E) in white. Components are shown for waist circumference (WC), hip circumference (HIP), waist-hip ratio (WHR), and their body mass index (BMI)-adjusted counterparts (WCadjBMI, HIPadjBMI, and WHRadjBMI). When the A component is different in men and women with P < 0.05 for a given trait, its name is marked with an asterisk. c, Table showing genetic correlations of waist-related traits with height, adjusted for age and body mass index. Genetic correlations of three traits with height were based on variance component models in the Framingham Heart Study and TWINGENE study (see Online Methods). WCadjBMI, waist circumference adjusted for BMI; WHRadjBMI, waist-hip ratio adjusted for BMI; HIPadjBMI, hip circumference adjusted for BMI.

Extended Data Figure 3. Cumulative genetic risk scores for WHRadjBMI applied to the KORA study cohort

a, All subjects (N = 3,440, Ptrend = 6.7 × 10−4). b, Only women (N = 1,750, Ptrend = 1.0 × 10−11). c, Only men (N = 1,690, Ptrend = 0.02). Each genetic risk score (GRS) illustrates the joint effect of the waist-hip ratio adjusted for body mass index (WHRadjBMI)-increasing alleles of the 49 identified variants from Table 1 weighted by the relative effect sizes from the applicable sex-combined or sex-specific meta-analysis. The mean WHRadjBMI residual and 95% confidence interval is plotted for each GRS category (red dots). The histograms show each GRS is normally distributed in KORA (gray bars).

Extended Data Figure 4. Heat map of unsupervised hierarchical clustering of the effects of 49 WHRadjBMI SNPs on 22 anthropometric and metabolic traits and diseases

The matrix of Z-scores representing the set of associations was scaled by row (locus name) and by column (trait) to range from −3 to 3. Negative values (blue) indicate that the waist-hip ratio adjusted for body mass index (WHRadjBMI)-increasing allele was associated with decreased values of the trait and positive values (red) indicate that this allele was associated with increased values of the trait. Dendrograms indicating the clustering relationships are shown to the left and above the heat map. The WHRadjBMI-increasing alleles at the 49 lead SNPs segregate into three major clusters comprised of alleles that associate with: 1) larger waist circumference adjusted for BMI (WCadjBMI) and smaller hip circumference adjusted for BMI (HIPadjBMI) (n = 30 SNPs); 2) taller stature and larger WCadjBMI (n = 8 SNPs); and 3) shorter stature and smaller HIPadjBMI (n = 11 SNPs). The three visually identified SNP clusters could be statistically distinguished with >90% confidence. Alleles of the first cluster were predominantly associated with lower high density lipoprotein (HDL) cholesterol and with higher triglycerides and fasting insulin adjusted for BMI (FIadjBMI). eGFRcrea, estimated glomerular filtration rate based on creatinine; LDL cholesterol, low-density lipoprotein cholesterol; UACR, urine albumin-to-creatinine ratio; BMD, bone mineral density.

Extended Data Figure 5. Regulatory element overlap with WHRadjBMI-associated loci

a, Five variants associated with waist-hip ratio adjusted for body mass index (WHRadjBMI) and located ~77 kb upstream of the first CALCRL transcription start site overlap regions with genomic evidence of regulatory activity in endothelial cells. b, Five WHRadjBMI variants, including rs8817452, in a 1.1 kb region (box) ~250 kb downstream of the first LEKR1 transcription start site overlap evidence of active enhancer activity in adipose nuclei. Signal enrichment tracks are from the ENCODE Integrative Analysis and the Roadmap Epigenomics track hubs on the UCSC Genome Browser. Transcripts are from the GENCODE basic annotation.

Figure 1. Regional SNP association plots illustrating the complex genetic architecture at two WHRadjBMI loci

Sex-combined meta-analysis SNP associations in European individuals were plotted with −log10P values (left y-axis) and estimated local recombination rate in blue (right y-axis). Three index SNPs near HOXC6-HOXC13 (a–c) and four near TBX15-WARS2-SPAG17 (d–g) were identified through approximate conditional analyses of sex-combined or sex-specific associations (values shown as Pconditional <5×10−8, see Methods). The signals are distinguished by both color and shape, and linkage disequilibrium (r2) of nearby SNPs is shown by color intensity gradient.

Figure 2. Gene set enrichment and tissue expression of genes at WHRadjBMI-associated loci (GWAS-only P<10−5)

a, Reconstituted gene sets found to be significantly enriched by DEPICT (FDR<5%) are represented as nodes, with pairwise overlap denoted by the width of connecting lines and empirical enrichment P value indicated by color intensity (darker is more significant). b, The ‘Decreased Liver Weight’ meta-node, which consisted of 12 overlapping gene sets, including adiponectin signaling and insulin sensitivity. c, Based on expression patterns in 37,427 human microarray samples, annotations found to be significantly enriched by DEPICT are shown, grouped by type and significance.