Little contribution of the WHR genomic screen

In order to define a set of adiposity-associated variants as the basis of our investigation, we selected variants that showed European ancestry based genome-wide significant association (P < 5 × 10−8) with any of the three adiposity traits, BMI, WHR, and WHRadjBMI from the GIANT consortium (up to N = 322,135, see Methods)5,6. This yielded 159 independent lead variants ( > 500kB or r2 < 0.1): 102, 38, or 53 variants genome-wide significant for BMI, WHR, or WHRadjBMI, respectively. We found a substantial overlap of WHR-derived variants (i.e., variants that are genome-wide significant for WHR) with BMI- or WHRadjBMI-derived variants (genome-wide significant for BMI or WHRadjBMI, respectively), with four being exclusive to the WHR-scan, but no overlap between BMI- and WHRadjBMI-derived variants (Fig. 1, Supplementary Data 1). Thus, the WHRadjBMI-derived variants contributed independently from BMI-derived variants in the GIANT data, whereas the WHR-derived variants contributed little beyond.

Fig. 1 Identification of 159 signals from three genomic scans. The Venn diagram shows the number of independent genome-wide significant (P < 5 × 10−8) signals derived from the BMI−, the WHR−, or the WHRadjBMI-scan, respectively, and their overlap. We found no overlap between BMI- and WHRadjBMI-derived variants Full size image

WHRadjBMI captures relevant aspects of fat distribution

Whether or not a genetic variant has “an expected effect on WHR given the BMI effect” (i.e., as expected by the phenotypic correlation r, e.g. r = 0.5 in the population-based CoLaus study7) or “an unexpected effect” can be determined by evaluating the variant’s co-association with BMI and WHR: the co-association of the 159 variants is visualized in a plane spanned by the genetic effects on WHR and BMI, i.e., b WHR vs. b BMI (Fig. 2a). Variants with a null effect on WHRadjBMI are those with an observed WHR effect to the extent and direction as expected given the variant’s BMI effect and the phenotypic correlation r (located on the line b WHR = r*b BMI, with r = 0.5, gray dashed line); this is in line with a notion of “an expected change in fat distribution given the change in BMI”. Variants with a non-null effect on WHRadjBMI will be those distant from the WHRadjBMI null line. This includes variants with a WHR effect but no effect on BMI, variants with a WHR effect into the opposite direction as their BMI effect, variants with effects on WHR larger than expected from the BMI effect (“supra-expected”), or even BMI effects with no effect on WHR. All are in line with a notion that the observed WHR effect is unexpected given the variant’s BMI effect. We hypothesized important insights from a detailed view of these variants’ position on the b WHR −b BMI -plane and the link of this position to physiology and pathology.

Fig. 2 Classification of 159 signals and overlap by scan. The figure visualizes the classification of the 159 independent signals according to the position on the b WHR -b BMI -plane and their overlap by scan. a The Scatter plot shows the 159 variants on the b WHR -b BMI -plane, where b WHR and b BMI are the variant’s effect on WHR and BMI, respectively. Coloring indicates the four classes: BMI + WHR + (blue, nominal significant effects on BMI and WHR with consistent directions), BMIonly + (green, nominal significant effects on BMI only), WHRonly− (purple, nominal significant effects on WHR only) and BMI + WHR− (red, nominal significant effects on BMI and WHR with opposite directions). Symbols indicate a nominal significance purely for BMI (P BMI < 0.05, P WHR ≥ 0.05, upward triangle), purely for WHR (P BMI ≥ 0.05, P WHR < 0.05, downward triangle), or for both (P BMI < 0.05, P WHR < 0.05, stars). The dashed line indicates a null effect for WHRadjBMI (b WHRadjBMI = 0, estimated as b WHR = r*b BMI , with the correlation between BMI and WHR estimated from the population-based CoLaus study, r = 0.50). b The diagram shows the number of identified signals per class, illustrates the four classes in directed acyclic graphs and shows Venn diagrams per class to distinguish whether the signals were derived with genome-wide-significance by the BMI−, the WHR− or the WHRadjBMI-scan, or by multiple scans. The underlined numbers reflect the 53 genome-wide significant signals identified by the WHRadjBMI-scan Full size image

Classifying the 159 adiposity variants

We classified the 159 variants according to their location on the b WHR −b BMI -plane (Fig. 2a). We considered an effect as a non-null effect for BMI or WHR, when the effect was nominally significantly different from zero (P BMI < 0.05, P WHR < 0.05, respectively), corresponding to an uncertainty of beta-estimates given by a 95% confidence interval, and as a null effect otherwise. We defined the following four classes: (1) BMI and WHR effects in the same direction (P BMI < 0.05, P WHR < 0.05; BMI + WHR + ), (2) BMI only effects (P BMI < 0.05, P WHR ≥ 0.05; BMIonly + ), (3) WHR only effects (P WHR < 0.05, P BMI ≥ 0.05; WHRonly−), (4) BMI and WHR effects into opposite directions (P BMI < 0.05, P WHR < 0.05; BMI + WHR−). Of note, the WHR effects that were directionally consistent with the BMI effect, but larger than expected (“supra-expected”) were classified as BMI + WHR + . This classification resulted in 82, 25, 28, or 24 variants for each of the four classes, respectively (Fig. 2a,b).

We found the following: (i) of the 159 variants, the 53 WHRadjBMI-derived variants were all in the BMI + WHR− or WHRonly− class (Fig. 2b, Supplementary Fig. 1), except two variants near ANKRD55 and CALCRL with supra-expected WHR effect (BMI + WHR + class). All 53 WHRadjBMI-derived variants were orthogonally distant from the WHRadjBMI null line and can be considered effects of “unexpected change in fat distribution given the effect on BMI”. (ii) The 102 BMI-derived variants were all in the BMI + WHR + or BMIonly + class (Fig. 2b, Supplementary Fig. 1). They scattered closely around the WHRadjBMI null line with some exceptions in the BMIonly + class and are thus, mostly, in line with a notion of a change in fat distribution that is expected given the effect on BMI. (iii) The 38 WHR-derived variants were spread across the classes BMI + WHR + , WHRonly−, or BMI + WHR− (Fig. 2b, Supplementary Fig. 1); the four variants exclusively identified by the WHR-scan were BMI + WHR + or WHRonly−.

We made further important observations regarding the WHRadjBMI-derived variants: (iv) All 53 WHRadjBMI-derived variants had nominally significant effects on WHR (P WHR < 0.05, i.e., no spurious associations, weakest WHR association observed in GIANT P WHR = 7.5 × 10−3, Supplementary Data 1). (v) Of the 53 WHRadjBMI-derived variants, 27 had no effect on BMI (P BMI ≥ 0.05), 24 had a nominally significant effect on BMI (P BMI < 0.05) into the opposite direction. Therefore, WHRadjBMI-derived variants cannot be considered as “independent of BMI”.

We conducted two types of sensitivity analyses. First, we re-classified the variants based on different P-value thresholds instead of the nominal significance level (Supplementary Data 1). A more stringent threshold at P < 3 × 10−4 ( = 0.05/159, Bonferroni-corrected) resulted in 36 of the 53 WHRadjBMI-derived variants retaining the class, 11 variants changing from BMI + WHR− to WHRonly−, and six just missing the P WHR < 3 × 10−4 in the GIANT data (one with BMI effect P BMI < 3 × 10−4, five without any effect). However, these six variants showed a significant association with WHR in the independent UK Biobank data (P WHR < 3 × 10−4, N = 336,107, P WHR ranging from 9.95 × 10−21 to 6.29 × 10−6, Supplementary Data 2). Of note, all 53 WHRadjBMI-derived variants showed a significant WHR association in the UK Biobank data (P WHR < 3 × 10−4, Supplementary Data 2). This supports the notion that none of the WHRadjBMI-derived variants from the GIANT data was a spurious association without effect on WHR.

Second, as WHRadjBMI is known for sexually dimorphic genetic effects8,9, we also conducted a sensitivity analysis re-classifying the 53 WHRadjBMI variants based on their sex-specific effects on WHR and BMI (i.e., women-specific or men-specific classification). Among those, 11 variants showed significant sex-difference in the genetic effect on WHRadjBMI in our data (P Sexdiff < 0.05/53). Among those, the 10 variants with women-specific effects retained class in the women-specific, but not in the men-specific classification; similarly, the one variant with men-specific effect retained class in the men-specific, but not in the women-specific classification. For all other variants there was no remarkable pattern by the re-classification for sex-specific effects (Supplementary Data 3).

Generally, with a few exceptions, our classification resulted in splitting the BMI-derived loci into two groups (BMI + WHR + , BMIonly + ), and splitting the WHRadjBMI-derived loci into two groups (BMI + WHR−, WHRonly−).

Computing WHR effect from observed BMI and WHRadjBMI effects

When b WHRadjBMI and b BMI are given for a variant, b WHR can be computed as b WHRadjBMI + r*b BMI (or b WHRadjBMI as b WHR −r*b BM ). We aimed to provide empirical data of how good this computation works by comparing the b WHR estimates computed as described above with the observed b WHR (Fig. 3). When conducting this comparison in one study where we could estimate r directly (interim UK Biobank, N = 116,295, r = 0.44), we found perfect agreement between computed and observed b WHR (Spearman correlation coefficient= 0.98). When conducting this comparison in a meta-analysis setting where r could not be estimated directly (i.e., in GIANT, using r from UK Biobank as a reasonable average across GIANT studies), we found still a strong agreement (Spearman correlation coefficient = 0.88). We were able to improve this agreement even further by using sex-stratified correlation estimates (from UK Biobank, r = 0.46 for women, 0.60 for men, Spearman correlation coefficient > 0.99) and sex-stratified effect estimates (from GIANT, Spearman correlation coefficient = 0.95). Therefore, the formula b WHR = b WHRadjBMI + r*b BMI can very well be used to compute unadjusted estimates from adjusted estimates and BMI estimates; the corresponding standard errors are, however, slightly increased yielding lower power (Supplementary Note 1, Supplementary Fig. 2). As a consequence, for consortia working with obesity traits, such as GIANT5,6, the number of genome-wide traits to be modeled can be limited to two traits as the effect estimate from the third trait can be re-computed with a small loss in precision.

Fig. 3 Comparison of estimated and computed WHR effect sizes. The figure shows a comparison of effect sizes and standard errors for the 38 genome-wide significant WHR-derived lead variants. Using data from the UK Biobank (UKBB, N = 116,295) as a single large study, we compare estimated overall (sex-combined) WHR effects in UKBB data with a computed WHR effects that were calculated from overall BMI and WHRadjBMI effects in UKBB using the overall correlation between WHR and BMI (r = 0.44, in UKBB); and with b WHR effects that were obtained from meta-analysis of computed sex-specific WHR effects that were calculated from sex-specific BMI and WHRadjBMI effects in UKBB using sex-specific correlations (r M = 0.60, r F = 0.46 in UKBB). Using GIANT meta-analysis summary statistics, we compare meta-analyzed overall WHR effects (resulting from meta-analysis of multiple studies) with c computed WHR effects that were calculated from meta-analyzed overall BMI and WHRadjBMI effects using the overall correlation between WHR and BMI (r = 0.44, in UKBB), and with d WHR effects that were obtained from meta-analysis of computed sex-specific WHR effects that were calculated from meta-analyzed sex-specific BMI and WHRadjBMI effects using sex-specific correlations (r M = 0.60, r F = 0.46 in UKBB) Full size image

Anthropometry, fat depots, and cardio-metabolic health

We were interested in whether the four classes characterized meaningful phenotypes with regard to anthropometry, fat depots, and cardio-metabolic health. We thus derived genetic effects of our 159 variants for such measures from genetic consortia and UK Biobank (see Methods, Supplementary Data 4–7). Effects were aligned for BMI-increasing alleles, where appropriate, and for WHR-decreasing alleles for WHRonly− consistent with BMI + WHR− (resulting in an alignment for hip-increasing alleles in all four classes).

First, when evaluating the 159 variants’ co-associations on the components of WHR and BMI, waist and hip circumference, weight, and height (GIANT data, up to N = 253,239), we found a clear separation of the four classes (Fig. 4a–b, Supplementary Data 4). This was supported by enrichment and meta-regression based genetic risk score (GRS) analyses (P Binomial < 3.0 × 10−4, Table 1, P GRS < 8.3 × 10−4, Supplementary Table 1, see Methods). Thus, the variants’ two-dimensional co-association with BMI and WHR effectively summarizes the 2 × 2 co-associations on (height, weight) and (waist circumference, hip circumference). The class-specific view on the variants’ co-association on hip and waist circumference revealed that BMI + WHR + and BMIonly + variants were hip and waist-increasing, WHRonly− variants were enriched for hip increase and waist decrease, and the BMI + WHR− variants were enriched for hip-increasing effects that lacked effects on waist circumference (Table 1). Our results underscore the dual cause for WHR-decreasing effects: decreased waist or increased hip circumference—the role of hip being missed when focusing on “central adiposity” (Supplementary Fig. 3–4; Supplementary Note 2).

Fig. 4 Anthropometry, fat depots and cardio-metabolic traits. The figure shows the co-associations for the 159 variants for pairs of traits (from GIANT, DIAGRAM15, GLGC16, MAGIC17, CARDIoGRAMplusC4D18): a waist circumference (WC) and hip circumference (HIP), b weight (WT) and height (HT), c visceral adipose tissues (VAT) and subcutaneous adipose tissue (SAT), d type 2 diabetes (T2D) and coronary artery disease (CAD), e fasting insulin (FI) and triglycerides (TG). Coloring indicates the four classes: BMI + WHR + (blue), BMIonly + (green), WHRonly− (purple), and BMI + WHR− (red). Symbols indicate nominal significance for y axis trait only (downward triangle), the x axis trait only (upward triangle), or both (stars). In a, the dashed line indicates a null effect for WHR (slope estimated as b WC /b HIP = mean(WHR), mean(WHR) = 0.88 from CoLaus); in b, the dashed line indicates the null effect for BMI (slope estimated as b WT /b HT = 2*mean(height)*mean(BMI)*SD(height)/SD(WT) = 0.54, using estimates from CoLaus) Full size image

Table 1 Results of class-specific enrichment analyses Full size table

Second, we were interested in the variants’ impact on more elaborate measures of fat depots including centrally stored visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT) that is ubiquitously stored with a preference at hip and thigh10,11,12, and pericardial adipose tissue (PAT), which is a VAT-type fat stored in/around the heart13. We evaluated the 159 genetic variants’ association on measures derived by bioelectrical impedance (body fat, trunk fat, leg fat; UK Biobank, N up to 114,367) or imaging techniques (SAT, VAT, PAT, VAT/SAT ratio; Ectopic Fat Traits consortium14, N up to 18,312; Supplementary Data 5, 6). The visualization of the co-association of VAT and SAT was less conclusive (Fig. 4c), whereas enrichment and GRS analyses elucidated a distinct pattern by class (P Binomial < 3.0 × 10−4, P GRS < 8.3 × 10-4, Table 1, Supplementary Table 1) linking BMI + WHR + to VAT and SAT, BMIonly− and BMI + WHR− only to SAT, and WHRonly− to VAT/SAT ratio.

Third, we evaluated the effects of the 159 variants on eight cardio-metabolic traits (DIAGRAM15, GLGC16, MAGIC17, CARDIoGRAMplusC4D18, up to N = 187,135, Supplementary Data 7). The co-associations on T2D and CAD (Fig. 4d) showed a clear pattern for increasing or decreasing disease risk for the two “extreme” classes BMI + WHR + or BMI + WHR−, respectively, but a rather neutral or inconclusive pattern for BMIonly + (except for the known extreme disease effect of TCF7L2 into the opposite direction as expected by the BMI effect) and WHRonly−. This was supported by enrichment and GRS analyses (P Binomial < 3.0 × 10−4, Table 1, P GRS < 8.3 × 10−4, Supplementary Table 1). The joint impact of the class-specific variants on T2D and CAD was substantial and markedly different: the joint BMI + WHR + alleles increased T2D or CAD risk 2.5- or 1.5-fold, respectively; the joint BMI + WHR− alleles decreased T2D risk to a relative risk of 0.10 and CAD risk to 0.43 (Fig. 5). We found a consistent pattern for fasting insulin, triglycerides, and HDL-cholesterol (HDL-C, BMI + WHR + : adverse, BMI + WHR−: protective, Fig. 4e, Table 1, Supplementary Table 1). Overall, the four classes differentiate genetic adiposity effects into metabolically unfavorable (BMI + WHR + ), metabolically neutral or inconclusive (BMI only, WHR only), and metabolically rather favorable adiposity (BMI + WHR−) with some exceptions.

Fig. 5 Different disease implications. Shown are the class-specific variants’ co-associations for BMI and T2D or CAD (from DIAGRAM15and CARDIoGRAMplusC4D18) and the meta-regression line that can be interpreted as the association of the genetic risk score of BMI-increasing alleles (GRS) and disease (slope estimate indicated as “a”, p value as P GRS ): a BMI + WHR + variants on T2D, b BMIonly + variants on T2D; c BMI + WHR− variants on T2D; d BMI + WHR + variants on CAD, e BMIonly + variants on CAD; f BMI + WHR− variants on CAD. Although the higher GRS for BMI is significantly associated with increased T2D and CAD risk for BMI + WHR + variants, it is associated with decreased T2D and CAD risk for BMI + WHR− variants Full size image

Evidence of gene expression in digestive system tissue

Finally, we explored whether our four classes distinguished the underlying physiological pathways. For this, we used DEPICT19 to search for enriched pathways among the genes overlapping association signals (P < 10−5 for any of BMI, WHR, or WHRadjBMI, excluding metabochip data as done previously5,6, to avoid enriching for known metabolic regions by chip design, see Methods). We applied Data-Driven Expression Prioritized Integration for Complex Traits (DEPICT) for different sets of variants: (i) by the scan that a variant was selected for or (ii) by class. Our scan-specific DEPICT analyses replicated previous findings5,6 (highlighting central nervous system, CNS, for BMI-derived variants and adipose tissue for WHRadjBMI). Previous work had not investigated the WHR-derived variants and we found here that they provided an inconclusive pattern without any significant pathway enrichment (judged at false-discovery rate, FDR, < 5%, Supplementary Fig. 5, Supplementary Data 8, Supplementary Note 3). The lack of enriched pathways for WHR-loci suggests that WHR signals capture less-distinct biology than WHRadjBMI or BMI.

Our class-specific DEPICT analyses yielded a pattern for CNS and adipose tissue that was similar to the pattern observed previously by Locke et al. and Shungin et al. for three of our four classes5,6 (Supplementary Fig. 6, Supplementary Data 9). WHRonly− variants were not only significantly enriched (at FDR < 5%) for adipocyte-related cells and tissues as reported previously6, but also in physiological systems labeled ‘digestive’ (rectum, cecum, upper GI, esophagus, stomach) and ‘urogenital’ (genitals, uterus, endometrium, myometrium) (Fig. 6a, Supplementary Data 9). This WHRonly- class finding was robust, even more pronounced, after excluding known height loci (to remove effects of the known strong height locus around GDF5 and other height regions), after excluding all five RSPO3 signals (to limit the strong contribution of multiple RSPO3 signals in this class), or after using a wider locus definition treating the RSPO3 signals as a single region in the DEPICT analyses (to limit the contribution of multiple signals like RSPO3, Supplementary Fig. 7, Supplementary Data 10-12).

Fig. 6 Tissue-specific gene expression for WHRonly− variants. Shown are results of DEPICT and FUMA tissue-specificity analyses based on variants that were selected from GWAS-only meta-analyses of GIANT (P < 10−5) and that were classified as WHRonly−. Significant results within the digestive system are marked with green arrows. a DEPICT results for WHRonly− with significant enrichments highlighted in blue (FDR < 5%). Results are grouped by type and ordered alphabetically by MeSH term within a specific system, cell type, or tissue (details in Supplementary Data 9). Results for the other three classes showed no significance with DEPICT (Supplementary Figure 6). b FUMA results with significant enrichments highlighted in red (adjusted P < 0.05, Bonferroni-corrected, details in Supplementary Data 13). The -log10(Pvalues) in the graph refer to the probability of the hypergeomteric test. Results for the other three classes showed only little enrichment with FUMA (Supplementary Figure 8) Full size image

To follow-up this finding, we used FUMA20 to examine data from GTEx21 for tissue-specific enrichments of expression effects of genes overlapping our association results (P < 10−5 for any of BMI, WHR, or WHRadjBMI, excluding metabochip data), again separating the variants by class. Consistent with the class-specific DEPICT analyses, genes harboring WHRonly− variants were significantly enriched (Bonferroni-adjusted P < 0.05) for expression effects in an adipocyte-related tissue (‘Adipose_Subcutaneous’) as well as in digestive tissues (‘Colon_Sigmoid’ and ‘Esophagus_Gastroesophageal_Junction’, Fig. 6b, Supplementary Data 13, Supplementary Fig. 8). In contrast to DEPICT analyses, there was no significant enrichment for expression effects in urogenital tissue in FUMA analyses; there was an additional significant finding for ‘tibial nerve’ in FUnctional Mapping and Annotation (FUMA), which is a tissue not included in DEPICT. We found an overlap of nine genes (BARX1, FOXP2, HOXA13, LAMB1, PCK1, PPARG, RGMA, RSPO3, and VEGFA) that contributed to the significant digestive system results in both DEPICT and FUMA tissue-specificity analyses of WHRonly− class variants.

In summary, we identified the digestive system as a pathway for obesity genetics, which highlights an important biology underlying the WHRonly− class variants.

A wrap-up of the class-specific adiposity phenotypes

When summarizing the results of our data and analysis, we are able to characterize our four adiposity genetics classes with regard to anthropometry, fat depots, metabolic consequences, and implicated pathways (Supplementary Table 2): (i) BMI + WHR + alleles increased waist, hip, SAT, VAT as well as T2D and CAD risk consistent with the observed adverse lipids and insulin profile. This would be in line with a biological model of a CNS-triggered increase in fat mass and a metabolically unfavorable genetic pre-disposition to store fat subcutaneously and viscerally (metabolically unfavorable adiposity, e.g., MC4R and FTO22,23). (ii) BMIonly + alleles presented a similar pattern with increased hip, waist, and SAT, but without VAT storage consistent with an observed neutrality toward T2D or CAD (except TCF7L2, Fig. 4d, Fig. 5b). This would be in line with a CNS-triggered increase in fat mass and a metabolically neutral genetic pre-disposition to store fat subcutaneously rather than viscerally on both belly and lower body (metabolically neutral adiposity). (iii) WHRonly− alleles increased hip, but decreased waist, without any effect on BMI, total fat mass, VAT or SAT, but a decreased VAT/SAT ratio and a tendency toward a favorable metabolic profile (e.g., loci around PPARG, PLXND1, MAP3K1, RSPO3, PLXND1, JUND, Fig. 4d/e). This would be in line with a mechanism of fat redistribution as described for PPARG or RSPO324,25,26 (redistributing adiposity). At least one WHRonly− variant pointed to a different mechanism of enhanced bone growth: the variant near GDF5-UQCC is a known height locus11 and got grasped by WHRonly− due to increased hip probably from bone growth rather than adiposity. For the genes within WHRonly− signals, we found enrichment of expression in digestive systems in DEPICT and FUMA analyses. (iv) BMI + WHR− alleles increased hip and SAT, but had no effect on waist or VAT, and a markedly favorable metabolic profile (metabolically rather favorable adiposity, e.g., GRB14-COBLL1). Our Mendelian Randomization approach27 restricting the instruments to the BMI + WHR− variants showed that their BMI-increasing effect was causally linked to a favorable metabolic profile, particularly decreased risk of T2D and CAD. We also showed that the BMI increase of BMI + WHR− variants was causally linked to increased hip circumference and SAT, but had no effect on waist circumference or VAT. This would be in line with a direct beneficial effect of SAT stored on hip, possibly through adipokines12, for this subtype of adiposity effects.