Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution.
Journal: 2010/November - Nature Genetics
ISSN: 1546-1718
Abstract:
Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10⁻⁹ to P = 1.8 × 10⁻⁴⁰) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10⁻³ to P = 1.2 × 10⁻¹³). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.
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Nat Genet 42(11): 949-960

Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution

+292 authors

RESULTS

Genome-wide significant association of WHR with 14 SNPs

We conducted a two-stage study among individuals of European descent (Supplementary Table 1 and Online Methods). In the discovery stage, up to 2,850,269 imputed and genotyped single nucleotide polymorphisms (SNPs) were examined in 32 GWA studies comprising up to 77,167 participants informative for anthropometric measures of body fat distribution. We performed a fixed-effects meta-analysis of WHR, employing study-specific linear regression adjusted for BMI and age, stratified by gender, and using an additive genetic model. After genomic control adjustment per study and in the meta-analysis, these analyses revealed a substantial excess of low p-values (Figure 1 a, b).

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Genome-wide association analyses for WHR in discovery studies

A. Manhattan plot shows results of the WHR association meta-analysis in discovery studies (P on the y-axis and SNP genomic position on the x-axis). Colored genomic loci indicate significant association (P < 5 × 10) detected previously (blue)13, in our GWA stage (red), and after the meta-analysis combining GWA and follow-up studies (orange). Two loci tested in the follow-up stage did not achieve genome-wide significance (green).

B. Quantile-quantile (QQ) plot of SNPs for the discovery meta-analysis of WHR (black) and after removing SNPs within 1 Mb of either the recently reported LYPLAL1 signal (blue) or the 14 significant associations (green). The grey area represents the 95% confidence interval around the test statistic under the null distribution.

We selected SNPs representing the top 16 independent (> 1 Mb distance) regions of association (discovery P < 1.4 × 10, Table 1) and evaluated them in 29 additional, independent studies (up to 113,636 individuals) using a mixture of in silico data and de novo genotyping. In these follow-up studies, 14 of the 16 showed strong directionally-consistent evidence for replication (P < 1.0 × 10) and ten reached genome-wide significance (P < 5.0 × 10). Joint analysis of the discovery and follow-up results revealed genome-wide significant associations for 14 signals (P between 1.9 × 10 and 1.8 × 10, Table 1).

Table 1

Fourteen SNPs associated with WHR at genome-wide significance levels

SNPChrPosition
(b36)
Nearby genesEA aEAFbDiscoveryFollow-upCombined
PBetaNPBetaNPBeta
SNPs evaluated in follow-up achieving genome-wide significance
rs94916966127,494,332RSPO3G0.5202.10E-140.03777,1643.27E-280.045113,5821.84E-400.042
rs6905288643,866,851VEGFAA0.5624.72E-100.03377,1291.18E-160.03995,4305.88E-250.036
rs9842221119,305,366TBX15-WARS2G0.3653.81E-140.03777,1671.56E-120.031109,6238.69E-250.034
rs1055144725,837,634NFE2L3T0.2101.49E-080.03477,1453.26E-180.043113,6369.97E-250.040
rs101952522165,221,337GRB14T0.5993.23E-100.03177,1193.18E-160.036102,4492.09E-240.033
rs48465671217,817,340LYPLAL1G0.2832.37E-120.03777,1673.15E-100.03291,8206.89E-210.034
rs10117311170,613,171DNM3-PIGCG0.5721.72E-100.03177,0947.47E-090.02692,0189.51E-180.028
rs7183141226,344,550ITPR2-SSPNG0.7412.41E-080.03177,1671.49E-100.030107,5031.14E-170.030
rs129442166,688,148LY86G0.3876.31E-090.02977,1542.69E-100.028102,1891.75E-170.028
rs14435121252,628,951HOXC13A0.2393.33E-080.03177,1652.92E-100.030112,3536.38E-170.031
rs6795735364,680,405ADAMTS9C0.4062.47E-070.02577,1626.75E-080.02684,4809.79E-140.025
rs48230062227,781,671ZNRF3-KREMEN1A0.5694.47E-080.02777,0862.41E-050.01993,9111.10E-110.023
rs6784615352,481,466NISCH-STAB1T0.9413.18E-070.05276,8591.56E-040.036109,0283.84E-100.043
rs68616815173,295,064CPEB4A0.3401.40E-060.02677,1642.13E-040.01985,7221.91E-090.022
Further SNPs evaluated in follow-up but not achieving genome-wide significant in the combined analysis
rs2076529632,471,933BTNL2C0.5702.22E-080.04134,5320.0120.01192,7783.71E-070.020
rs70816781032,030,629ZEB1A0.0855.76E-070.04576,2700.0940.013100,5275.57E-060.027

P-values and beta-coefficients (per change of WHR increasing allele) for the association with WHR on the inverse normal transformed ranked scale in the meta-analyses of discovery studies (up to 77,167), follow-up studies (up to 113,636 subjects), and both combined (up to 190,781 subjects). Fourteen of the 16 SNPs put forward to follow-up show genome-wide significant results (P < 5 × 10) in the combined analysis. P-values in the discovery were genomic control corrected per study and in the meta-analysis. Details on between-study heterogeneity in Supplementary Table 1c.

Effect Allele: WHR increasing allele on the forward strand;
Effect allele frequency

Between-study heterogeneity was low (I < 30%) for all but two signals (GRB14 and LYPLAL1, see Supplementary Note) and all 14 associations remained genome-wide significant in a random-effects meta-analysis (Supplementary Table 2).

One of these SNPs, rs4846567, is in linkage disequilibrium (LD, r = 0.64, D’ = 0.84; HapMap CEU) with the previously reported WHR-associated variant (rs2605100) near the LYPLAL1 gene13. The remaining 13 loci were in or near genes not previously associated with WHR or other measures of adiposity: RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1, and CPEB4 (Figure 2). These 14 loci explain 1.03% of the variance in WHR (after adjustment for BMI, age, and sex), with each locus contributing from 0.02% (ZNRF3-KREMEN1) to 0.14% (RSPO3) based on effect estimates in the follow-up stage.

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Regional plots of 14 loci with genome-wide significant association

SNP association with WHR in meta-analysis of discovery studies for 14 loci (−log10P on the y-axis and SNP genomic position on the x-axis). In each panel, an index SNP is denoted with a purple diamond and plotted using the P attained across discovery and follow-up data (Table 1). Estimated recombination rates are plotted in blue. SNPs are colored to reflect LD with the index SNP (pair-wise r values from HapMap CEU). Gene and microRNA annotations are from the UCSC genome browser.

Sexual dimorphism at several of the WHR loci

Given the known sexual dimorphism of WHR and evidence from variance decomposition studies that this reflects sex-specific genetic effects17, we performed sex-specific meta-analyses for the 14 WHR associated SNPs. These analyses included up to 108,979 women (42,735 discovery, 66,244 follow-up) or 82,483 men (34,601 discovery, 47,882 follow-up). In joint analysis of discovery and follow-up data, 12 of the 14 SNPs reached genome-wide significance in women, but only 3 in men (Table 2). At all but one locus (TBX15-WARS2), effect-size estimates were numerically greater in women. At seven of the loci (those near RSPO3, VEGFA, GRB14, LYPLAL1, HOXC13, ITPR2-SSPN and ADAMTS9), there were marked differences in sex-specific beta-coefficients (P ranging from 1.9 × 10 to 1.2 × 10). All loci displayed consistent patterns of sex-specific differences in both discovery and follow-up studies (Table 2). These 14 loci explain 1.34% of the variance in WHR (after adjustment for BMI and age) in women, but only 0.46% in men.

Table 2

Evidence of sex-differences in the WHR association at seven of the 14 associated loci

SNPNearby
Genes
MenWomenSex
difference
DiscoveryFollow-upCombinedDiscoveryFollow-upCombinedCom-
bined
PBetaPBetaPBetaPBetaPBetaPBetaP
rs9491696RSPO31.68E-040.0266.97E-090.0361.05E-110.0311.62E-120.0478.84E-220.0531.93E-320.0501.94E-03
rs6905288VEGFA0.0660.0132.09E-040.0257.38E-050.0207.72E-130.0523.14E-150.0512.27E-260.0525.20E-06
rs984222TBX15-WARS23.32E-090.0412.43E-050.0299.41E-130.0351.21E-070.0361.33E-080.0331.02E-140.0340.951
rs1055144NFE2L36.00E-040.0295.67E-080.0402.52E-100.0352.34E-060.0407.13E-120.0461.41E-160.0440.270
rs10195252GRB140.2010.0090.1140.0110.0430.0106.33E-150.0534.95E-210.0543.84E-340.0541.41E-11
rs4846567LYPLAL10.1910.0100.9820.0000.3580.0054.84E-180.0648.12E-170.0554.95E-330.0591.18E-13
rs1011731DNM3-PIGC4.88E-070.0341.95E-030.0227.81E-090.0282.13E-050.0287.03E-070.0306.90E-110.0290.855
rs718314ITPR2-SSPN0.1770.0102.02E-030.0221.41E-030.0178.29E-100.0474.21E-090.0382.41E-170.0424.67E-04
rs1294421LY864.18E-030.0207.00E-060.0301.63E-070.0253.44E-080.0387.32E-060.0262.40E-120.0310.357
rs1443512HOXC130.1840.0119.74E-040.0249.45E-040.0181.43E-090.0483.09E-080.0356.38E-160.0402.23E-03
rs6795735ADAMTS90.0110.0170.6140.0040.0270.0117.85E-070.0332.95E-110.0421.92E-160.0388.50E-05
rs4823006ZNRF3-KREMEN16.87E-030.0190.0940.0121.94E-030.0156.86E-080.0373.81E-050.0243.24E-110.0300.032
rs6784615NISCH-STAB11.51E-030.0450.0330.0321.68E-040.0396.23E-050.0571.72E-030.0396.01E-070.0470.574
rs6861681CPEB41.88E-030.0230.0450.0153.03E-040.0192.14E-040.0271.58E-030.0211.55E-060.0240.555

P-values and beta-coefficients (per change of WHR increasing allele in the sex-combined analysis as in Table 1) for the WHR association are given for the discovery (up to 34,601 men, 42,735 women), the follow-up (up to 47,882 men, 65,780 women) and the combined meta-analysis (up to 81,301 men, 107,429 women). Also given are the P-values for testing for difference between sex-specific beta-coefficients in the combined meta-analysis; SNPs with P for sex difference < 3.6E-03 (=0.05/14) were considered to show significant sex difference.

Association with other anthropometric measures

By focusing on WHR after adjustment for BMI, our goal was to detect effects on body fat distribution independent of those influencing overall adiposity. As expected, we found very little evidence that known BMI-associated variants were detected in our WHR analysis. Of the 10 loci identified shown to be associated with BMI in previous genome-wide association studies 141518, only two showed nominally significant (P < 0.05) associations for BMI-adjusted WHR in the discovery analysis (FTO: rs805013614, P = 0.03, N = 77,074; TMEM18: rs654823815, P = 3.0 × 10, N = 77,016).

We also tested the 14 WHR-associated SNPs for their effect on BMI using data from up to 242,530 participants available from the GIANT consortium (including most of the studies available for WHR association). Of the 14 WHR loci, four (near TBX15-WARS2, CPEB4, LYPLAL1 and GRB14) also showed evidence of association with BMI (4.1 × 10P ≤ 3.2×10) with the WHR-increasing allele associated with decreased BMI (Supplementary Table 3). When adding an interaction term of SNP and BMI into the model, we observed that BMI modified the WHR association at the LY86 locus (P for interaction = 9.5 × 10) with a larger WHR effect among the obese compared to the non-obese (see Supplementary Note).

To determine whether the WHR-associated signals exert their effects primarily through an effect on waist (WC) or hip circumference (HIP), we performed meta-analyses for these specific phenotypes in the discovery and follow-up studies (Supplementary Table 1 and 3). Overall, we observed stronger associations for HIP than for WC. Effect-size estimates were numerically greater for HIP than for WC at eleven of the 14 loci, and there were nominal associations (P < 0.05) with HIP for twelve of the WHR-associated loci but only four associations with WC. In both sexes, the WHR-associated loci displaying nominal association with HIP always featured the WHR-increasing allele associated with reduced HIP. In contrast, we observed sexual dimorphism in the pattern of WC associations. In women, the WHR-increasing allele at all 14 loci was associated with increased WC, whereas this was only true for 6 of these loci in men (Figure 3). At GRB14, for example, the WHR-increasing allele was associated with increased WC in women (P = 3.6 × 10) but decreased WC in men (P = 6.8 × 10). These differences in the relationships between WC, HIP and WHR underlie some of the sexual dimorphism in the patterns of WHR association.

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Association of the 14 WHR loci with waist and hip circumference

Beta-coefficients for waist circumference (WC, x-axis) and hip circumference (HIP, y-axis) in women and men derived from the joint discovery and follow-up analysis. P for WC and HIP are represented by color. In men, grey gene labels refer to those SNPs that were not significant in the male-specific WHR analysis. More details can be found in Supplementary Table 3.

Enrichment of association with metabolic traits

We evaluated the 14 WHR-associated loci for their relationships with related metabolic traits using GWA data provided by trait-specific consortia1921 as well as our de novo genotyped follow-up studies. As expected, given the sample overlap between this GWA data with our WHR GWA data as well as known trait correlations (Supplementary Table 4), we observed directionally consistent enrichment of associations (P < 0.05) between the 14 WHR-associated alleles and increased triglycerides, LDL-cholesterol, fasting insulin, and HOMA-derived measures of insulin resistance (binomial P from 3.2 × 10 to 1.8 × 10; Table 3 and Supplementary Table 5a). For example, the WHR-increasing allele at GRB14 shows strong associations with increased triglycerides (P = 7.4 × 10), fasting insulin levels (P = 5.0 × 10) and insulin resistance (P = 1.9 × 10). Eleven of 14 WHR-associated loci showed directionally consistent associations with T2D, three of these (ADAMTS9, NISCH-STAB1, and ITPR2-SSPN) reaching nominal significance (P < 0.05) (Table 3 and Supplementary Table 5a,b). Because the association signals for correlated traits in this analysis are vulnerable to overestimation given the overlap in GWAS samples examined, we repeated these analyses restricted to our de novo genotyped follow-up studies. Although this also resulted in lower sample size, similar patterns of enrichment were still observed (Supplementary Table 5c).

Table 3

WHR signals show enrichment of association with other traits related to metabolic disorders

TraitSample
sizea
SNPs in concordant
directionb
SNPs in concordant
direction with P<.05c

# SNPsP# SNPsP
Triglycerides43,826146.10E-571.79E-8
HDL-C45,561139.16E-443.20E-4
LDL-C43,889100.09010.298
Fasting glucose63,849100.09010.298
Fasting insulin54,883139.16E-451.62E-5
HOMA-IR53,625139.16E-466.17E-7
2-hr glucose27,01170.60501.000
Type 2 diabetes10,128f110.02934.62E-3

The 14 WHR SNPs were tested for association with other traits by meta-analysis of GWA data from previous reports192139 together with our non-overlapping de novo genotyped follow-up studies. HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; HOMA-IR, index of insulin resistance; 2-hr glucose, glucose levels 2 h after an oral glucose challenge.

Maximum number of subjects available for any of the 14 SNPs
Number of the 14 SNPs for which the WHR increasing allele is associated with the trait in the concordant direction (i.e. increased levels except for HDL-C) and corresponding binomial P-value to test whether this number is greater than by chance not accounting for the correlation between the traits.
Number of SNPs in concordant direction that show P<0.05 for the association with the trait and corresponding binomial P-value as in.
4,549 cases, 5579 controls

Pathway analysis of the WHR-associated loci and potential biological role

To identify potential functional connections and pathway relationships between genes mapping at the WHR-associated loci, we focused on the 95 genes located in a 2 Mb interval centered around each of the 48 independent SNPs that attained a P < 1.0 × 10 in the WHR discovery studies.

First, we performed a survey of the published literature using GRAIL22, to search for connectivity between the genes and specific keywords that describe these functional connections (see Online Methods). Although there was no evidence, after correcting for multiple testing, that the connectivity between these genes was greater than chance, we identified 8 genes with nominal significance (P < 0.05) for potential functional connectivity (PLXND, HOXC10, TBX15, RSPO3, HOXC4, HOXC6, KREMEN1 and HOXC11). The keywords associated with these connections included “vegf”, “homeobox”, “patterning”, “mesenchyme”, “embryonic”, “development” and “angiogenesis”.

Additionally, we performed pathway analyses using the PANTHER database23 based on the same set of 95 genes (Online methods and Supplementary Note). This analysis generated some evidence for over-representation of “developmental processes” (P = 5.8 × 10) and “mRNA transcription regulation” (P = 2.7 × 10), but neither retained nominal significance after adjustment for bias (e.g. due to non-random SNP coverage in relation to genes) and the number of biological processes tested (Supplementary Note, Supplementary Table 6).

Finally, we examined the described functional roles of some of the most compelling candidates based on either proximity to the signal or other analyses described in this paper. These uncovered possible roles in adipocyte development (TBX15), pattern formation during embryonic development (HOXC13), angiogenesis (VEGFA, RSPO3, STAB1), Wnt/beta-catenin signaling (RSPO3, KREMEN1), insulin signaling (ADAMTS9, GRB14, NISCH), lipase activity (LYPLAL1), lipid biosynthesis (PIGC) and intracellular calcium signaling (ITPR2) (see Supplementary Note for details).

Evaluation of copy number variants (CNVs) and nonsynonymous changes

Both common and rare CNVs have been reported to be associated with overall adiposity14152425, but the impact of CNVs on fat distribution has not been evaluated previously. To examine the potential contribution of common CNVs to variation in WHR, we looked for evidence of association in our GWA discovery meta-analysis, using a set of 6,018 CNV-tagging SNPs, which collectively capture >40% of common CNVs >1 kb 2627 (Online Methods, Supplementary Note).

One CNV-tagging SNP (rs1294421, LY86) was observed amongst our 14 WHR-associated loci. This SNP is in strong LD (r = 0.98) with a 2,832 bp duplication variant (CNVR2760.1)27, located 12 kb from an expressed sequence tag ({"type":"entrez-nucleotide","attrs":{"text":"BC039678","term_id":"24901306","term_text":"BC039678"}}BC039678) and 87 kb from LY86, such that the duplication allele is associated with reduced WHR. The duplicated region consists entirely of noncoding sequence but includes part of a predicted enhancer sequence (E.5552.1)28.

To identify other putatively causal variants in our associated regions, we searched for non-synonymous coding SNPs in strong LD (r>0.7) with the most strongly associated SNPs at each locus using data from the HapMap (Build 21) and 1000 Genomes Project (April and August 2009 releases). In this search, one lead SNP (rs6784615, at the NISCH-STAB1 locus) was correlated with non-synonymous changes in two nearby genes, DNAH1 (Val441Leu, Arg1285Trp and Arg3809Cys) and GLYCTK (Leu170Val). Fine-mapping and functional studies will be required to determine whether the DNAH1 or GLYCTK SNPs or the LY86 CNV are causal for the WHR-associations at these loci.

Evaluation of effect of the WHR associations on expression in relevant tissues

Expression-QTL (eQTL) data can implicate regional transcripts that mediate trait-associations, and we therefore examined the 14 WHR-associated loci using eQTL data from human subcutaneous adipose tissue (SAT)29 (two separate sample sets, N=610 and N=603), omental fat30 (N=740), liver30 (N=518), blood29 (N=745), and lymphocytes31 (N=830) (Online methods, Supplementary Note).

At six of the loci, the WHR-associated SNP was either the strongest SNP associated with significant (P < 1.0 × 10) expression of a local (within 1 Mb) gene transcript or explained the majority of the association between the most significant eQTL SNP and the gene transcript in conditional analyses (adjusted P > 0.05; Table 4). For example, the WHR-associated SNP rs1011731 (near DNM3-PIGC) was strongly-associated with expression of PIGC in lymphocytes (P = 5.9 × 10); furthermore, rs1011731 is in high LD (r = 1.00, D’ = 1.00, HapMap CEU) with the SNP with the strongest effect on PIGC expression (rs991790), and this cis-eQTL association is abolished by conditioning on rs1011731. These analyses therefore indicate that these two signals are coincident and that PIGC is a strong candidate for mediating the WHR-association at rs1011731. We found similar evidence for coincidence of the WHR signal with expression for rs984222 (TBX15 in omental fat), rs1055144 (expressed sequence tag {"type":"entrez-nucleotide","attrs":{"text":"AA553656","term_id":"2324195","term_text":"AA553656"}}AA553656 in SAT), rs10195252 (GRB14 in SAT), rs4823006 (ZNRF3 in SAT and omental fat), and rs6784615 (STAB1 in blood)(Table 4). Taken together, the overlap between trait association and gene expression at these loci suggests that the WHR associations are driven through altered expression of PIGC, TBX15, {"type":"entrez-nucleotide","attrs":{"text":"AA553656","term_id":"2324195","term_text":"AA553656"}}AA553656, GRB1, ZNRF3 and STAB1.

Table 4

Expression quantitative trait locus analysis for 11 out of the 14 WHR signals.

WHR SNPTissueGeneEffectaWHR SNP
association
with transcript (P)
Transcript
peak SNP b
rcPeak SNP association
with transcript (P)


Unadj.Adj. for
peak SNP
Unadj.Adj. for
WHR SNP
rs9491696SAT-DRSPO3+1.10E-070.03rs19367950.262.20E-137.40E-08
rs984222OmentalTBX15+7.90E-101.00rs9842221.007.90E-101.00
OmentalWARS2+5.11E-360.03rs108020750.271.31E-1631.33E-88
Subcutaneous fatWARS2+1.67E-250.01rs108020750.223.88E-1101.01E-63
Lymph.WARS24.30E-185.47E-05rs26453050.275.57E-406.88E-26
LiverWARS2+2.57E-170.07rs10579900.266.69E-591.97E-32
SAT-DWARS2+1.10E-180.51rs10579900.265.80E-1305.80E-100
BloodWARS2+6.10E-170.11rs10579900.266.30E-751.10E-54
rs1055144SAT-D{"type":"entrez-nucleotide","attrs":{"text":"AA553656","term_id":"2324195","term_text":"AA553656"}}AA553656d1.20E-110.96rs77980020.957.20E-120.32
SAT-M{"type":"entrez-nucleotide","attrs":{"text":"AA553656","term_id":"2324195","term_text":"AA553656"}}AA553656d2.46E-070.65rs14513850.775.93E-080.38
rs10195252SAT-DGRB14+4.40E-111.00rs101952521.004.40E-111.00
SAT-MGRB14+5.51E-061.00rs101840041.005.51E-061.00
OmentalGRB14+1.02E-131.00rs101952521.001.02E-131.00
SAT-MSLC38A113.93E-060.66rs101841260.187.76E-448.57E-34
SAT-DSLC38A113.70E-090.35rs101841260.182.40E-947.40E-82
rs1011731BloodC1orf105+3.80E-160.20rs21574510.281.30E-338.20E-18
Lymph.PIGC5.87E-101.00rs9917901.005.65E-101.00
rs718314Lymph.ITPR2+1.79E-090.98rs79768770.452.21E-181.91E-06
BloodITPR22.40E-090.20rs25700.412.40E-371.80E-28
rs1294421SAT-M{"type":"entrez-nucleotide","attrs":{"text":"BC039678","term_id":"24901306","term_text":"BC039678"}}BC0396782.43E-070.38rs12944040.641.89E-163.42E-04
Omental{"type":"entrez-nucleotide","attrs":{"text":"BC039678","term_id":"24901306","term_text":"BC039678"}}BC0396781.09E-060.33rs9120560.718.28E-174.26E-05
rs6795735SAT-DADAMTS91.50E-060.04rs73723210.111.10E-092.30E-05
Omental{"type":"entrez-nucleotide","attrs":{"text":"AK022320","term_id":"10433691","term_text":"AK022320"}}AK0223207.99E-150.64rs45212160.025.15E-421.49E-19
SAT-D{"type":"entrez-nucleotide","attrs":{"text":"AK022320","term_id":"10433691","term_text":"AK022320"}}AK0223202.24E-100.98rs45212160.029.62E-377.58E-19
rs4823006SAT-DZNRF32.40E-080.63rs31789150.816.70E-118.90E-04
SAT-MZNRF31.08E-180.93rs60059750.791.59E-190.50
OmentalZNRF39.13E-180.98rs60059750.796.07E-210.27
rs6784615BloodSTAB1+2.80E-090.32rs98460890.839.40E-100.08
rs6861681Lymph.CPEB4+3.79E-220.89rs77055020.874.95E-292.00E-03
BloodHMP19+1.60E-160.97rs105161070.831.10E-214.30E-06

Association between the 14 WHR SNPs and expression of transcripts located within 1 Mb of the WHR SNP in two sets of abdominal subcutaneous adipose tissue (SAT-D from deCODE and SAT-M from Massachusetts General Hospital), omental fat, liver, lymphocytes, and blood (see Supplementary Note): results are given if the unadjusted WHR SNP association showed P < 1.00E-05. Findings are highlighted in bold font, where the WHR SNP was the transcript peak SNP or where the WHR signal and the cis-eQTL signal were considered coincident (i.e. the transcript peak SNP was highly correlated with the WHR SNP, r > 0.7, and the transcript peak association disappeared by adjusting on the WHR SNP, P >0.05); see also Online Methods

Unadj. = unadjusted. Adj. = adjusted.

Effect direction for the WHR increasing allele.
SNP with the strongest association with the transcript in the region (transcript peak SNP).
Correlation (Hapmap CEU, B36) between the WHR SNP and transcript peak SNP.
The transcript labeled {"type":"entrez-nucleotide","attrs":{"text":"AA553656","term_id":"2324195","term_text":"AA553656"}}AA553656 was detected as Contig27623_RC and corresponds to chr7: 25,854,143-25,854,203 (B36)

Differential RNA expression of gluteal compared to abdominal fat tissue

To determine whether genes within the WHR-associated loci showed evidence of differential transcription in distinct fat-depots, we compared expression levels in gluteal or abdominal SAT in 49 individuals. We focused on the 15 genes with the strongest credentials for causal involvement (on the basis of proximity to the lead SNP and/or other biological or functional data: Table 1) for which expression data were available. Five of these (RSPO3, TBX15, ITPR2, WARS2 and STAB1) were differentially expressed between the two tissues (F-test, corrected for false discovery rate across the 15 expressed genes, P < 0.05; Supplementary Table 7). This supported the hypothesis that, at some loci at least, the association with WHR reflects depot-specific differences in expression patterns.

Genome-wide significant association of WHR with 14 SNPs

We conducted a two-stage study among individuals of European descent (Supplementary Table 1 and Online Methods). In the discovery stage, up to 2,850,269 imputed and genotyped single nucleotide polymorphisms (SNPs) were examined in 32 GWA studies comprising up to 77,167 participants informative for anthropometric measures of body fat distribution. We performed a fixed-effects meta-analysis of WHR, employing study-specific linear regression adjusted for BMI and age, stratified by gender, and using an additive genetic model. After genomic control adjustment per study and in the meta-analysis, these analyses revealed a substantial excess of low p-values (Figure 1 a, b).

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Genome-wide association analyses for WHR in discovery studies

A. Manhattan plot shows results of the WHR association meta-analysis in discovery studies (P on the y-axis and SNP genomic position on the x-axis). Colored genomic loci indicate significant association (P < 5 × 10) detected previously (blue)13, in our GWA stage (red), and after the meta-analysis combining GWA and follow-up studies (orange). Two loci tested in the follow-up stage did not achieve genome-wide significance (green).

B. Quantile-quantile (QQ) plot of SNPs for the discovery meta-analysis of WHR (black) and after removing SNPs within 1 Mb of either the recently reported LYPLAL1 signal (blue) or the 14 significant associations (green). The grey area represents the 95% confidence interval around the test statistic under the null distribution.

We selected SNPs representing the top 16 independent (> 1 Mb distance) regions of association (discovery P < 1.4 × 10, Table 1) and evaluated them in 29 additional, independent studies (up to 113,636 individuals) using a mixture of in silico data and de novo genotyping. In these follow-up studies, 14 of the 16 showed strong directionally-consistent evidence for replication (P < 1.0 × 10) and ten reached genome-wide significance (P < 5.0 × 10). Joint analysis of the discovery and follow-up results revealed genome-wide significant associations for 14 signals (P between 1.9 × 10 and 1.8 × 10, Table 1).

Table 1

Fourteen SNPs associated with WHR at genome-wide significance levels

SNPChrPosition
(b36)
Nearby genesEA aEAFbDiscoveryFollow-upCombined
PBetaNPBetaNPBeta
SNPs evaluated in follow-up achieving genome-wide significance
rs94916966127,494,332RSPO3G0.5202.10E-140.03777,1643.27E-280.045113,5821.84E-400.042
rs6905288643,866,851VEGFAA0.5624.72E-100.03377,1291.18E-160.03995,4305.88E-250.036
rs9842221119,305,366TBX15-WARS2G0.3653.81E-140.03777,1671.56E-120.031109,6238.69E-250.034
rs1055144725,837,634NFE2L3T0.2101.49E-080.03477,1453.26E-180.043113,6369.97E-250.040
rs101952522165,221,337GRB14T0.5993.23E-100.03177,1193.18E-160.036102,4492.09E-240.033
rs48465671217,817,340LYPLAL1G0.2832.37E-120.03777,1673.15E-100.03291,8206.89E-210.034
rs10117311170,613,171DNM3-PIGCG0.5721.72E-100.03177,0947.47E-090.02692,0189.51E-180.028
rs7183141226,344,550ITPR2-SSPNG0.7412.41E-080.03177,1671.49E-100.030107,5031.14E-170.030
rs129442166,688,148LY86G0.3876.31E-090.02977,1542.69E-100.028102,1891.75E-170.028
rs14435121252,628,951HOXC13A0.2393.33E-080.03177,1652.92E-100.030112,3536.38E-170.031
rs6795735364,680,405ADAMTS9C0.4062.47E-070.02577,1626.75E-080.02684,4809.79E-140.025
rs48230062227,781,671ZNRF3-KREMEN1A0.5694.47E-080.02777,0862.41E-050.01993,9111.10E-110.023
rs6784615352,481,466NISCH-STAB1T0.9413.18E-070.05276,8591.56E-040.036109,0283.84E-100.043
rs68616815173,295,064CPEB4A0.3401.40E-060.02677,1642.13E-040.01985,7221.91E-090.022
Further SNPs evaluated in follow-up but not achieving genome-wide significant in the combined analysis
rs2076529632,471,933BTNL2C0.5702.22E-080.04134,5320.0120.01192,7783.71E-070.020
rs70816781032,030,629ZEB1A0.0855.76E-070.04576,2700.0940.013100,5275.57E-060.027

P-values and beta-coefficients (per change of WHR increasing allele) for the association with WHR on the inverse normal transformed ranked scale in the meta-analyses of discovery studies (up to 77,167), follow-up studies (up to 113,636 subjects), and both combined (up to 190,781 subjects). Fourteen of the 16 SNPs put forward to follow-up show genome-wide significant results (P < 5 × 10) in the combined analysis. P-values in the discovery were genomic control corrected per study and in the meta-analysis. Details on between-study heterogeneity in Supplementary Table 1c.

Effect Allele: WHR increasing allele on the forward strand;
Effect allele frequency

Between-study heterogeneity was low (I < 30%) for all but two signals (GRB14 and LYPLAL1, see Supplementary Note) and all 14 associations remained genome-wide significant in a random-effects meta-analysis (Supplementary Table 2).

One of these SNPs, rs4846567, is in linkage disequilibrium (LD, r = 0.64, D’ = 0.84; HapMap CEU) with the previously reported WHR-associated variant (rs2605100) near the LYPLAL1 gene13. The remaining 13 loci were in or near genes not previously associated with WHR or other measures of adiposity: RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1, and CPEB4 (Figure 2). These 14 loci explain 1.03% of the variance in WHR (after adjustment for BMI, age, and sex), with each locus contributing from 0.02% (ZNRF3-KREMEN1) to 0.14% (RSPO3) based on effect estimates in the follow-up stage.

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Regional plots of 14 loci with genome-wide significant association

SNP association with WHR in meta-analysis of discovery studies for 14 loci (−log10P on the y-axis and SNP genomic position on the x-axis). In each panel, an index SNP is denoted with a purple diamond and plotted using the P attained across discovery and follow-up data (Table 1). Estimated recombination rates are plotted in blue. SNPs are colored to reflect LD with the index SNP (pair-wise r values from HapMap CEU). Gene and microRNA annotations are from the UCSC genome browser.

Sexual dimorphism at several of the WHR loci

Given the known sexual dimorphism of WHR and evidence from variance decomposition studies that this reflects sex-specific genetic effects17, we performed sex-specific meta-analyses for the 14 WHR associated SNPs. These analyses included up to 108,979 women (42,735 discovery, 66,244 follow-up) or 82,483 men (34,601 discovery, 47,882 follow-up). In joint analysis of discovery and follow-up data, 12 of the 14 SNPs reached genome-wide significance in women, but only 3 in men (Table 2). At all but one locus (TBX15-WARS2), effect-size estimates were numerically greater in women. At seven of the loci (those near RSPO3, VEGFA, GRB14, LYPLAL1, HOXC13, ITPR2-SSPN and ADAMTS9), there were marked differences in sex-specific beta-coefficients (P ranging from 1.9 × 10 to 1.2 × 10). All loci displayed consistent patterns of sex-specific differences in both discovery and follow-up studies (Table 2). These 14 loci explain 1.34% of the variance in WHR (after adjustment for BMI and age) in women, but only 0.46% in men.

Table 2

Evidence of sex-differences in the WHR association at seven of the 14 associated loci

SNPNearby
Genes
MenWomenSex
difference
DiscoveryFollow-upCombinedDiscoveryFollow-upCombinedCom-
bined
PBetaPBetaPBetaPBetaPBetaPBetaP
rs9491696RSPO31.68E-040.0266.97E-090.0361.05E-110.0311.62E-120.0478.84E-220.0531.93E-320.0501.94E-03
rs6905288VEGFA0.0660.0132.09E-040.0257.38E-050.0207.72E-130.0523.14E-150.0512.27E-260.0525.20E-06
rs984222TBX15-WARS23.32E-090.0412.43E-050.0299.41E-130.0351.21E-070.0361.33E-080.0331.02E-140.0340.951
rs1055144NFE2L36.00E-040.0295.67E-080.0402.52E-100.0352.34E-060.0407.13E-120.0461.41E-160.0440.270
rs10195252GRB140.2010.0090.1140.0110.0430.0106.33E-150.0534.95E-210.0543.84E-340.0541.41E-11
rs4846567LYPLAL10.1910.0100.9820.0000.3580.0054.84E-180.0648.12E-170.0554.95E-330.0591.18E-13
rs1011731DNM3-PIGC4.88E-070.0341.95E-030.0227.81E-090.0282.13E-050.0287.03E-070.0306.90E-110.0290.855
rs718314ITPR2-SSPN0.1770.0102.02E-030.0221.41E-030.0178.29E-100.0474.21E-090.0382.41E-170.0424.67E-04
rs1294421LY864.18E-030.0207.00E-060.0301.63E-070.0253.44E-080.0387.32E-060.0262.40E-120.0310.357
rs1443512HOXC130.1840.0119.74E-040.0249.45E-040.0181.43E-090.0483.09E-080.0356.38E-160.0402.23E-03
rs6795735ADAMTS90.0110.0170.6140.0040.0270.0117.85E-070.0332.95E-110.0421.92E-160.0388.50E-05
rs4823006ZNRF3-KREMEN16.87E-030.0190.0940.0121.94E-030.0156.86E-080.0373.81E-050.0243.24E-110.0300.032
rs6784615NISCH-STAB11.51E-030.0450.0330.0321.68E-040.0396.23E-050.0571.72E-030.0396.01E-070.0470.574
rs6861681CPEB41.88E-030.0230.0450.0153.03E-040.0192.14E-040.0271.58E-030.0211.55E-060.0240.555

P-values and beta-coefficients (per change of WHR increasing allele in the sex-combined analysis as in Table 1) for the WHR association are given for the discovery (up to 34,601 men, 42,735 women), the follow-up (up to 47,882 men, 65,780 women) and the combined meta-analysis (up to 81,301 men, 107,429 women). Also given are the P-values for testing for difference between sex-specific beta-coefficients in the combined meta-analysis; SNPs with P for sex difference < 3.6E-03 (=0.05/14) were considered to show significant sex difference.

Association with other anthropometric measures

By focusing on WHR after adjustment for BMI, our goal was to detect effects on body fat distribution independent of those influencing overall adiposity. As expected, we found very little evidence that known BMI-associated variants were detected in our WHR analysis. Of the 10 loci identified shown to be associated with BMI in previous genome-wide association studies 141518, only two showed nominally significant (P < 0.05) associations for BMI-adjusted WHR in the discovery analysis (FTO: rs805013614, P = 0.03, N = 77,074; TMEM18: rs654823815, P = 3.0 × 10, N = 77,016).

We also tested the 14 WHR-associated SNPs for their effect on BMI using data from up to 242,530 participants available from the GIANT consortium (including most of the studies available for WHR association). Of the 14 WHR loci, four (near TBX15-WARS2, CPEB4, LYPLAL1 and GRB14) also showed evidence of association with BMI (4.1 × 10P ≤ 3.2×10) with the WHR-increasing allele associated with decreased BMI (Supplementary Table 3). When adding an interaction term of SNP and BMI into the model, we observed that BMI modified the WHR association at the LY86 locus (P for interaction = 9.5 × 10) with a larger WHR effect among the obese compared to the non-obese (see Supplementary Note).

To determine whether the WHR-associated signals exert their effects primarily through an effect on waist (WC) or hip circumference (HIP), we performed meta-analyses for these specific phenotypes in the discovery and follow-up studies (Supplementary Table 1 and 3). Overall, we observed stronger associations for HIP than for WC. Effect-size estimates were numerically greater for HIP than for WC at eleven of the 14 loci, and there were nominal associations (P < 0.05) with HIP for twelve of the WHR-associated loci but only four associations with WC. In both sexes, the WHR-associated loci displaying nominal association with HIP always featured the WHR-increasing allele associated with reduced HIP. In contrast, we observed sexual dimorphism in the pattern of WC associations. In women, the WHR-increasing allele at all 14 loci was associated with increased WC, whereas this was only true for 6 of these loci in men (Figure 3). At GRB14, for example, the WHR-increasing allele was associated with increased WC in women (P = 3.6 × 10) but decreased WC in men (P = 6.8 × 10). These differences in the relationships between WC, HIP and WHR underlie some of the sexual dimorphism in the patterns of WHR association.

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Association of the 14 WHR loci with waist and hip circumference

Beta-coefficients for waist circumference (WC, x-axis) and hip circumference (HIP, y-axis) in women and men derived from the joint discovery and follow-up analysis. P for WC and HIP are represented by color. In men, grey gene labels refer to those SNPs that were not significant in the male-specific WHR analysis. More details can be found in Supplementary Table 3.

Enrichment of association with metabolic traits

We evaluated the 14 WHR-associated loci for their relationships with related metabolic traits using GWA data provided by trait-specific consortia1921 as well as our de novo genotyped follow-up studies. As expected, given the sample overlap between this GWA data with our WHR GWA data as well as known trait correlations (Supplementary Table 4), we observed directionally consistent enrichment of associations (P < 0.05) between the 14 WHR-associated alleles and increased triglycerides, LDL-cholesterol, fasting insulin, and HOMA-derived measures of insulin resistance (binomial P from 3.2 × 10 to 1.8 × 10; Table 3 and Supplementary Table 5a). For example, the WHR-increasing allele at GRB14 shows strong associations with increased triglycerides (P = 7.4 × 10), fasting insulin levels (P = 5.0 × 10) and insulin resistance (P = 1.9 × 10). Eleven of 14 WHR-associated loci showed directionally consistent associations with T2D, three of these (ADAMTS9, NISCH-STAB1, and ITPR2-SSPN) reaching nominal significance (P < 0.05) (Table 3 and Supplementary Table 5a,b). Because the association signals for correlated traits in this analysis are vulnerable to overestimation given the overlap in GWAS samples examined, we repeated these analyses restricted to our de novo genotyped follow-up studies. Although this also resulted in lower sample size, similar patterns of enrichment were still observed (Supplementary Table 5c).

Table 3

WHR signals show enrichment of association with other traits related to metabolic disorders

TraitSample
sizea
SNPs in concordant
directionb
SNPs in concordant
direction with P<.05c

# SNPsP# SNPsP
Triglycerides43,826146.10E-571.79E-8
HDL-C45,561139.16E-443.20E-4
LDL-C43,889100.09010.298
Fasting glucose63,849100.09010.298
Fasting insulin54,883139.16E-451.62E-5
HOMA-IR53,625139.16E-466.17E-7
2-hr glucose27,01170.60501.000
Type 2 diabetes10,128f110.02934.62E-3

The 14 WHR SNPs were tested for association with other traits by meta-analysis of GWA data from previous reports192139 together with our non-overlapping de novo genotyped follow-up studies. HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; HOMA-IR, index of insulin resistance; 2-hr glucose, glucose levels 2 h after an oral glucose challenge.

Maximum number of subjects available for any of the 14 SNPs
Number of the 14 SNPs for which the WHR increasing allele is associated with the trait in the concordant direction (i.e. increased levels except for HDL-C) and corresponding binomial P-value to test whether this number is greater than by chance not accounting for the correlation between the traits.
Number of SNPs in concordant direction that show P<0.05 for the association with the trait and corresponding binomial P-value as in.
4,549 cases, 5579 controls

Pathway analysis of the WHR-associated loci and potential biological role

To identify potential functional connections and pathway relationships between genes mapping at the WHR-associated loci, we focused on the 95 genes located in a 2 Mb interval centered around each of the 48 independent SNPs that attained a P < 1.0 × 10 in the WHR discovery studies.

First, we performed a survey of the published literature using GRAIL22, to search for connectivity between the genes and specific keywords that describe these functional connections (see Online Methods). Although there was no evidence, after correcting for multiple testing, that the connectivity between these genes was greater than chance, we identified 8 genes with nominal significance (P < 0.05) for potential functional connectivity (PLXND, HOXC10, TBX15, RSPO3, HOXC4, HOXC6, KREMEN1 and HOXC11). The keywords associated with these connections included “vegf”, “homeobox”, “patterning”, “mesenchyme”, “embryonic”, “development” and “angiogenesis”.

Additionally, we performed pathway analyses using the PANTHER database23 based on the same set of 95 genes (Online methods and Supplementary Note). This analysis generated some evidence for over-representation of “developmental processes” (P = 5.8 × 10) and “mRNA transcription regulation” (P = 2.7 × 10), but neither retained nominal significance after adjustment for bias (e.g. due to non-random SNP coverage in relation to genes) and the number of biological processes tested (Supplementary Note, Supplementary Table 6).

Finally, we examined the described functional roles of some of the most compelling candidates based on either proximity to the signal or other analyses described in this paper. These uncovered possible roles in adipocyte development (TBX15), pattern formation during embryonic development (HOXC13), angiogenesis (VEGFA, RSPO3, STAB1), Wnt/beta-catenin signaling (RSPO3, KREMEN1), insulin signaling (ADAMTS9, GRB14, NISCH), lipase activity (LYPLAL1), lipid biosynthesis (PIGC) and intracellular calcium signaling (ITPR2) (see Supplementary Note for details).

Evaluation of copy number variants (CNVs) and nonsynonymous changes

Both common and rare CNVs have been reported to be associated with overall adiposity14152425, but the impact of CNVs on fat distribution has not been evaluated previously. To examine the potential contribution of common CNVs to variation in WHR, we looked for evidence of association in our GWA discovery meta-analysis, using a set of 6,018 CNV-tagging SNPs, which collectively capture >40% of common CNVs >1 kb 2627 (Online Methods, Supplementary Note).

One CNV-tagging SNP (rs1294421, LY86) was observed amongst our 14 WHR-associated loci. This SNP is in strong LD (r = 0.98) with a 2,832 bp duplication variant (CNVR2760.1)27, located 12 kb from an expressed sequence tag ({"type":"entrez-nucleotide","attrs":{"text":"BC039678","term_id":"24901306","term_text":"BC039678"}}BC039678) and 87 kb from LY86, such that the duplication allele is associated with reduced WHR. The duplicated region consists entirely of noncoding sequence but includes part of a predicted enhancer sequence (E.5552.1)28.

To identify other putatively causal variants in our associated regions, we searched for non-synonymous coding SNPs in strong LD (r>0.7) with the most strongly associated SNPs at each locus using data from the HapMap (Build 21) and 1000 Genomes Project (April and August 2009 releases). In this search, one lead SNP (rs6784615, at the NISCH-STAB1 locus) was correlated with non-synonymous changes in two nearby genes, DNAH1 (Val441Leu, Arg1285Trp and Arg3809Cys) and GLYCTK (Leu170Val). Fine-mapping and functional studies will be required to determine whether the DNAH1 or GLYCTK SNPs or the LY86 CNV are causal for the WHR-associations at these loci.

Evaluation of effect of the WHR associations on expression in relevant tissues

Expression-QTL (eQTL) data can implicate regional transcripts that mediate trait-associations, and we therefore examined the 14 WHR-associated loci using eQTL data from human subcutaneous adipose tissue (SAT)29 (two separate sample sets, N=610 and N=603), omental fat30 (N=740), liver30 (N=518), blood29 (N=745), and lymphocytes31 (N=830) (Online methods, Supplementary Note).

At six of the loci, the WHR-associated SNP was either the strongest SNP associated with significant (P < 1.0 × 10) expression of a local (within 1 Mb) gene transcript or explained the majority of the association between the most significant eQTL SNP and the gene transcript in conditional analyses (adjusted P > 0.05; Table 4). For example, the WHR-associated SNP rs1011731 (near DNM3-PIGC) was strongly-associated with expression of PIGC in lymphocytes (P = 5.9 × 10); furthermore, rs1011731 is in high LD (r = 1.00, D’ = 1.00, HapMap CEU) with the SNP with the strongest effect on PIGC expression (rs991790), and this cis-eQTL association is abolished by conditioning on rs1011731. These analyses therefore indicate that these two signals are coincident and that PIGC is a strong candidate for mediating the WHR-association at rs1011731. We found similar evidence for coincidence of the WHR signal with expression for rs984222 (TBX15 in omental fat), rs1055144 (expressed sequence tag {"type":"entrez-nucleotide","attrs":{"text":"AA553656","term_id":"2324195","term_text":"AA553656"}}AA553656 in SAT), rs10195252 (GRB14 in SAT), rs4823006 (ZNRF3 in SAT and omental fat), and rs6784615 (STAB1 in blood)(Table 4). Taken together, the overlap between trait association and gene expression at these loci suggests that the WHR associations are driven through altered expression of PIGC, TBX15, {"type":"entrez-nucleotide","attrs":{"text":"AA553656","term_id":"2324195","term_text":"AA553656"}}AA553656, GRB1, ZNRF3 and STAB1.

Table 4

Expression quantitative trait locus analysis for 11 out of the 14 WHR signals.

WHR SNPTissueGeneEffectaWHR SNP
association
with transcript (P)
Transcript
peak SNP b
rcPeak SNP association
with transcript (P)


Unadj.Adj. for
peak SNP
Unadj.Adj. for
WHR SNP
rs9491696SAT-DRSPO3+1.10E-070.03rs19367950.262.20E-137.40E-08
rs984222OmentalTBX15+7.90E-101.00rs9842221.007.90E-101.00
OmentalWARS2+5.11E-360.03rs108020750.271.31E-1631.33E-88
Subcutaneous fatWARS2+1.67E-250.01rs108020750.223.88E-1101.01E-63
Lymph.WARS24.30E-185.47E-05rs26453050.275.57E-406.88E-26
LiverWARS2+2.57E-170.07rs10579900.266.69E-591.97E-32
SAT-DWARS2+1.10E-180.51rs10579900.265.80E-1305.80E-100
BloodWARS2+6.10E-170.11rs10579900.266.30E-751.10E-54
rs1055144SAT-D{"type":"entrez-nucleotide","attrs":{"text":"AA553656","term_id":"2324195","term_text":"AA553656"}}AA553656d1.20E-110.96rs77980020.957.20E-120.32
SAT-M{"type":"entrez-nucleotide","attrs":{"text":"AA553656","term_id":"2324195","term_text":"AA553656"}}AA553656d2.46E-070.65rs14513850.775.93E-080.38
rs10195252SAT-DGRB14+4.40E-111.00rs101952521.004.40E-111.00
SAT-MGRB14+5.51E-061.00rs101840041.005.51E-061.00
OmentalGRB14+1.02E-131.00rs101952521.001.02E-131.00
SAT-MSLC38A113.93E-060.66rs101841260.187.76E-448.57E-34
SAT-DSLC38A113.70E-090.35rs101841260.182.40E-947.40E-82
rs1011731BloodC1orf105+3.80E-160.20rs21574510.281.30E-338.20E-18
Lymph.PIGC5.87E-101.00rs9917901.005.65E-101.00
rs718314Lymph.ITPR2+1.79E-090.98rs79768770.452.21E-181.91E-06
BloodITPR22.40E-090.20rs25700.412.40E-371.80E-28
rs1294421SAT-M{"type":"entrez-nucleotide","attrs":{"text":"BC039678","term_id":"24901306","term_text":"BC039678"}}BC0396782.43E-070.38rs12944040.641.89E-163.42E-04
Omental{"type":"entrez-nucleotide","attrs":{"text":"BC039678","term_id":"24901306","term_text":"BC039678"}}BC0396781.09E-060.33rs9120560.718.28E-174.26E-05
rs6795735SAT-DADAMTS91.50E-060.04rs73723210.111.10E-092.30E-05
Omental{"type":"entrez-nucleotide","attrs":{"text":"AK022320","term_id":"10433691","term_text":"AK022320"}}AK0223207.99E-150.64rs45212160.025.15E-421.49E-19
SAT-D{"type":"entrez-nucleotide","attrs":{"text":"AK022320","term_id":"10433691","term_text":"AK022320"}}AK0223202.24E-100.98rs45212160.029.62E-377.58E-19
rs4823006SAT-DZNRF32.40E-080.63rs31789150.816.70E-118.90E-04
SAT-MZNRF31.08E-180.93rs60059750.791.59E-190.50
OmentalZNRF39.13E-180.98rs60059750.796.07E-210.27
rs6784615BloodSTAB1+2.80E-090.32rs98460890.839.40E-100.08
rs6861681Lymph.CPEB4+3.79E-220.89rs77055020.874.95E-292.00E-03
BloodHMP19+1.60E-160.97rs105161070.831.10E-214.30E-06

Association between the 14 WHR SNPs and expression of transcripts located within 1 Mb of the WHR SNP in two sets of abdominal subcutaneous adipose tissue (SAT-D from deCODE and SAT-M from Massachusetts General Hospital), omental fat, liver, lymphocytes, and blood (see Supplementary Note): results are given if the unadjusted WHR SNP association showed P < 1.00E-05. Findings are highlighted in bold font, where the WHR SNP was the transcript peak SNP or where the WHR signal and the cis-eQTL signal were considered coincident (i.e. the transcript peak SNP was highly correlated with the WHR SNP, r > 0.7, and the transcript peak association disappeared by adjusting on the WHR SNP, P >0.05); see also Online Methods

Unadj. = unadjusted. Adj. = adjusted.

Effect direction for the WHR increasing allele.
SNP with the strongest association with the transcript in the region (transcript peak SNP).
Correlation (Hapmap CEU, B36) between the WHR SNP and transcript peak SNP.
The transcript labeled {"type":"entrez-nucleotide","attrs":{"text":"AA553656","term_id":"2324195","term_text":"AA553656"}}AA553656 was detected as Contig27623_RC and corresponds to chr7: 25,854,143-25,854,203 (B36)

Differential RNA expression of gluteal compared to abdominal fat tissue

To determine whether genes within the WHR-associated loci showed evidence of differential transcription in distinct fat-depots, we compared expression levels in gluteal or abdominal SAT in 49 individuals. We focused on the 15 genes with the strongest credentials for causal involvement (on the basis of proximity to the lead SNP and/or other biological or functional data: Table 1) for which expression data were available. Five of these (RSPO3, TBX15, ITPR2, WARS2 and STAB1) were differentially expressed between the two tissues (F-test, corrected for false discovery rate across the 15 expressed genes, P < 0.05; Supplementary Table 7). This supported the hypothesis that, at some loci at least, the association with WHR reflects depot-specific differences in expression patterns.

DISCUSSION

Overall, our findings demonstrate that the genetic regulation of body fat distribution involves loci and processes that are largely distinct from those that influence BMI and risk of obesity. This finding is consistent with the evidence that WHR displays substantial heritability even after adjustment for BMI. The loci that emerge from this study display no overlap with those shown to be associated with BMI, either in previous reports141516 or in the expanded meta-analysis recently completed by the GIANT consortium32.

Another point of distinction between our findings and those for BMI relates to the evidence for sexual dimorphism that we observed at several of the WHR-associated loci. Sex differences in the regulation of body fat distribution have long been acknowledged without a clear understanding of the underlying molecular mechanisms. These differences become apparent during puberty and are generally attributed to the influence of sex hormones33. Consistent with our findings, variance decomposition studies have shown that the genetic contribution to the overall variance in WHR, waist or hip circumference is greater in women17. While there is some evidence for loci with differential sex effects influencing lipids34, uric acid levels35 and risk of schizophrenia36, we are unaware of prior reports indicating such strong enrichment of female-specific associations for any other phenotype, including BMI32.

The primary objective of genetic discovery efforts is to characterize the specific mechanisms involved in the regulation of the trait of interest. Despite the considerable challenges associated with moving from common variant association signals to definition of the causal alleles and pathways, we have identified strong candidates at several of the loci. For example, the cis-eQTL data implicate GRB14 as a compelling candidate for the WHR-association on chromosome 2, and we were able to show that the same GRB14 variants are also associated with triglyceride and insulin levels, consistent with previous association of this locus with HDL-cholesterol 37. These inferences about the role of GRB14 are supported by evidence that Grb14-deficient mice exhibit improved glucose homeostasis despite lower circulating insulin levels, and enhanced insulin signaling in liver and skeletal muscle38. The signal near ADAMTS9 overlaps a previously-reported T2D locus39, and the lead SNP for WHR in our study is identical to the SNP displaying the strongest T2D association in an expanded T2D meta-analysis40. Given evidence that ADAMTS9 T2D-risk alleles are associated with insulin resistance in peripheral tissues41, these findings are consistent with a primary effect of ADAMTS9 variants on body fat distribution. At the chromosome 6 locus, VEGFA is the most apparent biological candidate, given the presumed role of VEGFA as a mediator of adipogenesis42 and evidence that serum levels of VEGFA are correlated with obesity4344. Finally, at the TBX15-WARS2 locus, TBX15 emerges as the strongest candidate based on the cis-eQTL data in omental fat, marked depot-specific differences in adipose tissue expression in mice and humans, and associations between TBX15 expression in visceral fat and WHR4546.

Our efforts to use pathway- and literature-mining approaches to look for functional enrichment of the genes mapping to associated regions met with only limited success, but did provide some support for overrepresentation of developmental processes. Developmental genes have been implicated in fat accumulation and distribution4546, and recent evidence supports a link between developmental genes and body fat distribution, including HOXC1347 and TBX154548. Developmental genes may in part determine the adipocyte-specific expression patterns that have been observed in different fat depots45. Taken together, our findings point to a set of genes influencing body fat distribution that have their principal effects in adipose tissue. This is in contrast to the predominantly central (hypothalamic) processes that are involved in the regulation of body mass index and overall adiposity49.

By providing novel insights into the regulation of body fat distribution, the present study raises a number of issues for future investigation. From the genetic perspective, re-sequencing, dense-array genotyping and fine-mapping approaches will be required to characterize causal variants at the loci we have identified, and to support further discoveries that may account for the substantial proportion of genetic variance unexplained by our findings. From the clinical perspective, it will be important to explore the relationship of these variants to more refined measures of body fat distribution derived from detailed imaging studies, to use the variants identified to characterise the causal relationships between body fat distribution and related metabolic and cardiovascular traits, and to explore ethnic differences in patterns of body fat distribution. Efforts to tackle overall obesity through therapeutic or lifestyle-based modulation of overall energy balance have proved extremely challenging to implement, and the manipulation of processes associated with more beneficial patterns of fat distribution offers an alternative perspective for future drug discovery.

Supplementary Material

1

2

3

1

Click here to view.(391K, pdf)

2

Click here to view.(232K, xls)

3

Click here to view.(141K, pdf)

ACKNOWLEDGEMENTS

Academy of Finland (104781, 120315, 129269, 117797, 121584, 126925, 129418, 129568, 77299, 124243, 213506, 129680, 129494, 10404, 213506, 129680, 114382, 126775, 127437, 129255, 129306, 130326, 209072, 210595, 213225, 216374); ADA Mentor-Based Postdoctoral Fellowship grant; Affymetrix, Inc for genotyping services (N02-HL-6-4278); ALF/LUA Gothenburg; Althingi (the Icelandic Parliament); Amgen; AstraZeneca AB; Augustinus Foundation; Becket Foundation; Biocentrum Helsinki; Biomedicum Helsinki Foundation; Boston Obesity Nutrition Research Center (DK46200); British Diabetes Association (1192); British Diabetic Association Research; British Heart Foundation (97020, PG/02/128); Busselton Population Medical Research Foundation; Cambridge NIHR Comprehensive Biomedical Research Centre; CamStrad; Chief Scientist Office of the Scottish Government; Contrat Plan Etat Région de France; Danish Centre for Health Technology Assessment; Danish Diabetes Association; Danish Ministry of Internal Affairs and Health; Danish Heart Foundation; Danish Pharmaceutical Association; Danish Research Council; DIAB Core (German Network of Diabetes); Diabetes UK; Donald W. Reynolds Foundation; Dr Knut Krohn, Microarray Core Facility of the Interdisciplinary Centre for Clinical Research (IZKF), University of Leipzig, Germany; Dresden University of Technology Funding Grant, Med Drive; EMGO+ institute; Emil and Vera Cornell Foundation; Erasmus Medical Center and Erasmus University, Rotterdam; Estonian Government SF0180142s08; European Commission (2004310, 212111, 205419, 245536, DG XII, HEALTH-F4-2007-201413, FP7/2007-2013, QLG1-CT-2000-01643, QLG2-CT-2002-01254, LSHG-CT-2006-018947, LSHG-CT-2006-01947, LSHG-CT-2004-512066, LSHM-CT-2007-037273, EU/WLRT-2001-01254, LSHG-CT-2004-518153, SOC 95201408 05F02, Marie Curie Intra-European Fellowship); Federal Ministry of Education and Research, Germany (01ZZ9603, 01ZZ0103, 01ZZ0403, 03ZIK012, 01 EA 9401); Federal State of Mecklenburg- West Pomerania; Finnish Diabetes Research Foundation; Finnish Diabetes Research Society; Finnish Foundation for Pediatric Research; Finnish Foundation of Cardiovascular Research; Finnish Medical Society; Finska Läkaresällskapet; Finnish Ministry of Education; Folkhälsan Research Foundation; Fond Européen pour le Développement Régional; Fondation LeDucq; Foundation for Life and Health in Finland; GEN-AU "GOLD" from Austria; German Bundesministerium fuer Forschung und Technology (#01 AK 803 A-H, # 01 IG 07015 G); German National Genome Research Net NGFN2 and NGFNplus (01GS0823, FKZ 01GS0823); German Research Council (KFO-152); GlaxoSmithKline; Göteborg Medical Society; Gyllenberg Foundation; Health Care Centers in Vasa, Närpes and Korsholm; Healthway, Western Australia; Helmholtz Center Munich; Helsinki University Central Hospital; Hjartavernd (the Icelandic Heart Association); Ib Henriksen Foundation; IZKF (B27); Jalmari and Rauha Ahokas Foundation; Juho Vainio Foundation; Juvenile Diabetes Research Foundation International (JDRF); Karolinska Institute and the Stockholm County Council (560183); Knut and Alice Wallenberg Foundation; Lundbeck Foundation Centre of Applied Medical Genomics for Personalized Disease Prediction, Prevention and Care; Lundberg Foundation; MC Health; Ministry of Cultural Affairs of the Federal State of Mecklenburg-West Pomerania, Germany; South Tyrol Ministry of Health; Ministry of Science, Education and Sport of the Republic of Croatia (216-1080315-0302); Medical Research Council UK (G0000649, G0601261, G9521010D, G0000934, G0500539, G0600331, PrevMetSyn); Montreal Heart Institute Foundation; MRC Centre for Obesity related Metabolic disease; Municipal Health Care Center and Hospital in Jakobstad; Municipality of Rotterdam; Närpes Health Care Foundation; National Health and Medical Research Council of Australia and the Great Wine Estates Auctions; Netherlands Centre for Medical Systems Biology (SPI 56-464-1419); Netherlands Ministry for Health, Welfare and Sports; Netherlands Ministry of Education, Culture and Science; Netherlands Genomics Initiative; Netherlands Consortium for Healthy Aging (050-060-810); Netherlands Organisation of Scientific Research NWO Investments (175.010.2005.011, 911-03-012, 904-61-090,904-61-193,480-04-004,400-05-717); National Institute on Aging Intramural Research Program; National Institutes of Health ({"type":"entrez-nucleotide","attrs":{"text":"CA047988","term_id":"24354158","term_text":"CA047988"}}CA047988, CA65725, CA87969, CA49449, CA67262, CA50385, , {"type":"entrez-nucleotide","attrs":{"text":"DK075787","term_id":"187613938","term_text":"DK075787"}}DK075787, {"type":"entrez-nucleotide","attrs":{"text":"DK062370","term_id":"187690831","term_text":"DK062370"}}DK062370, DK58845, {"type":"entrez-nucleotide","attrs":{"text":"DK072193","term_id":"187693969","term_text":"DK072193"}}DK072193, K23-{"type":"entrez-nucleotide","attrs":{"text":"DK080145","term_id":"187587877","term_text":"DK080145"}}DK080145, K99HL094535, N01-HC85079 through N01-HC85086, N01-HG-65403; N01-AG-12100, N01-HC-25195, N01-HC35129, N01-HC15103, N01-HC55222, N01-HC75150, N01-HC45133, N01-HC55015, N01-HC55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022, NO1-AG-1-2109, HL71981, HG005581, {"type":"entrez-nucleotide","attrs":{"text":"HG002651","term_id":"548932687","term_text":"HG002651"}}HG002651, {"type":"entrez-nucleotide","attrs":{"text":"HL084729","term_id":"1051655137","term_text":"HL084729"}}HL084729, {"type":"entrez-nucleotide","attrs":{"text":"HL043851","term_id":"1051491008","term_text":"HL043851"}}HL043851, HHSN268200625226C, K23-{"type":"entrez-nucleotide","attrs":{"text":"DK080145","term_id":"187587877","term_text":"DK080145"}}DK080145; {"type":"entrez-nucleotide","attrs":{"text":"MH084698","term_id":"1509333942","term_text":"MH084698"}}MH084698, P30-DK072488, R01-DK075787, R01 HL087652, R01-HL087641, R01-HL59367, R01-HL086694, R01-HL087647, R01-HL087679, R01-HL087700, R01-AG031890, R01-HL088119, R01-DK068336, R01-DK075681, R01-DK-073490, R01-DK075787, R01-MH63706; U01-HL72515, U01-{"type":"entrez-nucleotide","attrs":{"text":"GM074518","term_id":"222009147","term_text":"GM074518"}}GM074518, U01-{"type":"entrez-nucleotide","attrs":{"text":"HL084756","term_id":"1051655164","term_text":"HL084756"}}HL084756, U01-{"type":"entrez-nucleotide","attrs":{"text":"HG004399","term_id":"508081929","term_text":"HG004399"}}HG004399, UO1-{"type":"entrez-nucleotide","attrs":{"text":"CA098233","term_id":"34951540","term_text":"CA098233"}}CA098233, UL1-RR025005, UL1-RR025005, U01-{"type":"entrez-nucleotide","attrs":{"text":"HG004402","term_id":"526250247","term_text":"HG004402"}}HG004402, U01-{"type":"entrez-nucleotide","attrs":{"text":"DK062418","term_id":"187691508","term_text":"DK062418"}}DK062418; U01 {"type":"entrez-nucleotide","attrs":{"text":"HL080295","term_id":"1051650703","term_text":"HL080295"}}HL080295, T32-HG00040, 263-MA-410953; 1RL1-MH083268-01, intramural project 1Z01-{"type":"entrez-nucleotide","attrs":{"text":"HG000024","term_id":"743402349","term_text":"HG000024"}}HG000024); National Institute for Health Research (NIHR); Neuroscience Campus Amsterdam; Novo Nordisk Foundation; Novo Nordisk Inc., Research Foundation of Copenhagen County; Ollqvist Foundation; Paavo Nurmi Foundation; Päivikki and Sakari Sohlberg Foundation; Pew Scholarship for the Biomedical Sciences); Perklén Foundation; Petrus and Augusta Hedlunds Foundation; Research Institute for Diseases in the Elderly (014-93-015, RIDE, RIDE2); Sahlgrenska Center for Cardiovascular and Metabolic Research (CMR, A305:188); Siemens Healthcare, Erlangen, Germany; Signe and Ane Gyllenberg Foundation; Sigrid Juselius Foundation; Social Insurance Institution of Finland; Social Ministry of the Federal State of Mecklenburg-West Pomerania, Germany; South Tyrolean Sparkasse Foundation; State of Bavaria, Germany; Support for Science Funding programme; Swedish Cultural Foundation in Finland; Swedish Foundation for Strategic Research (SSF); Swedish Heart-Lung Foundation; Swedish Medical Research Council (8691, K2007-66X-20270-01-3, K2010-54X-09894-19-3); Swedish Society of Medicine; Swiss National Science Foundation (33CSCO-122661); the Royal Society; the Royal Swedish Academy of Science; Torsten and Ragnar Söderberg's Foundation; Turku University Hospitals; UK Department of Health Policy Research Programme; University and Research of the Autonomous Province of Bolzano; University Hospital Medical funds to Tampere; University Hospital Oulu, Biocenter, University of Oulu, Finland (75617); Västra Götaland Foundation; Wellcome Trust (077016/Z/05/Z, 068545/Z/02, 072960, 076113, 083270, 085301, 079557, 081682, 075491, 076113/B/04/Z, 091746/Z/10/Z, 079895, WT086596/Z/08/Z, WT Research Career Development Fellowship; WT Career Development Award); Western Australian Genetic Epidemiology Resource and the Western Australian DNA Bank (both National Health and Medical Research Council of Australia Enabling Facilities); Yrjö Jahnsson Foundation.

Regensburg University Medical Center, Department of Epidemiology and Preventive Medicine, 93053 Regensburg, Germany
Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany
Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts 02115, USA
Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
deCODE Genetics, 101 Reykjavik, Iceland
Department of Internal Medicine, Erasmus MC, Rotterdam, 3015GE, The Netherlands
Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA)
Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA
CNRS UMR8199-IBL-Institut Pasteur de Lille, F-59019 Lille, France
University Lille Nord de France, 59000 Lille, France
Laboratory of Epidemiology, Demography, Biometry, National Institute on Aging, National Institutes of Health, Bethesda, Maryland 20892, USA
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892, USA
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden
Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, EX1 2LU, UK
MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
Divisions of Genetics and Endocrinology and Program in Genomics, Children's Hospital, Boston, Massachusetts 02115, USA
Estonian Genome Center, University of Tartu, Tartu 50410, Estonia
Estonian Biocenter, Tartu 51010, Estonia
Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia
Department of Medical Genetics, University of Lausanne, 1005 Lausanne, Switzerland
Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, USA
Department of Pharmacy and Pharmacology, University of Bath, Bath, BA1 1RL, UK
MRC Harwell, Harwell Science and Innovation Campus, Oxfordshire, OX11 0RD, UK
Department of Statistics, University of Oxford, Oxford OX1 3TG, UK
Harvard Medical School, Boston, Massachusetts 02115, USA
MGH Weight Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA
Human Genetics, Leiden University Medical Center, Leiden 2333, The Netherlands
National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK
Merck Research Laboratories, Merck &amp; Co., Inc., Boston, Massachusetts 02115, USA
Pacific Biosciences, Menlo Park, California 94025, USA
Sage Bionetworks, Seattle, Washington 98109, USA
Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, OX3 7BN, Oxford
Department of Genetics, Washington University School of Medicine, St Louis, Missouri 63110, USA
Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
On behalf of the MAGIC (Meta-Analyses of Glucose and Insulin-related traits Consortium) investigators
Department of Epidemiology, Erasmus MC, Rotterdam, 3015GE, The Netherlands
University of Melbourne, Parkville 3010, Australia
Department of Primary Industries, Melbourne, Victoria 3001, Australia
Montreal Heart Institute, Montreal, Quebec, H1T 1C8, Canada
Department of Medicine, Université de Montréal, Montreal, Quebec, H3T 1J4, Canada
Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
Neurogenetics Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia
Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
Department of Psychiatry, Harvard Medical School, Boston, Massachusetts 02115, USA
Icelandic Heart Association, Kopavogur, Iceland
University of Iceland, Reykjavik, Iceland
Queensland Statistical Genetics Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia
Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
Hudson Alpha Institute for Biotechnology, Huntsville, Alabama 35806, USA
Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK
Department of Medicine, University of Washington, Seattle, Washington 98101, USA
Cardiovascular Health Research Unit, University of Washington, Seattle, Washington 98101, USA
MRC Human Genetics Unit, Institute for Genetics and Molecular Medicine, Western General Hospital, Edinburgh, EH4 2XU, Scotland, UK
Department of Neurology, Boston University School of Medicine, Boston, Massachusetts 02118, USA
Department of Biological Psychology, VU University Amsterdam, 1081 BT Amsterdam, The Netherlands
Department of Genetics and Pathology, Rudbeck Laboratory, University of Uppsala, SE-75185 Uppsala, Sweden
Department of Cancer Research and Molecular Medicine, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, N-7489, Norway
Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary, University of London, London, UK
Clinical Pharmacology and Barts and The London Genome Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
Institute of Health Sciences, University of Oulu, 90014 Oulu, Finland
Biocenter Oulu, University of Oulu, 90014 Oulu, Finland
Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA
Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria
Departments of Biostatistics, University of Washington, Seattle, Washington 98195, USA
Andrija Stampar School of Public Health, Medical School, University of Zagreb, 10000 Zagreb, Croatia
Gen-Info Ltd, 10000 Zagreb, Croatia
Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, OX3 7LJ, UK
Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014, Helsinki, Finland
National Institute for Health and Welfare, Department of Chronic Disease Prevention, Unit of Public Health Genomics, 00014, Helsinki, Finland
Istituto di Neurogenetica e Neurofarmacologia del CNR, Monserrato, 09042, Cagliari, Italy
Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-Arndt-University Greifswald, 17487 Greifswald, Germany
National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, 14558 Nuthetal, Germany
Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
Hagedorn Research Institute, 2820 Gentofte, Denmark
Regensburg University Medical Center, Clinic and Policlinic for Internal Medicine II, 93053 Regensburg, Germany
MRC Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, Oakfield House, Bristol, BS8 2BN, UK
Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, USA
Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
Centre for Genetic Epidemiology and Biostatistics, University of Western Australia, Crawley, Western Australia 6009, Australia
Department of Physiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden
Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden
Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, 20502 Malmö, Sweden
Zentrum für Zahn-, Mund- und Kieferheilkunde, 17489 Greifswald, Germany
Department of Medicine III, University of Dresden, 01307 Dresden, Germany
Division of Endocrinology, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
Department of Internal Medicine B, Ernst-Moritz-Arndt University, 17475 Greifswald, Germany
MRC-HPA Centre for Environment and Health, London W2 1PG, UK
Department of General Practice and Primary health Care, University of Helsinki, Helsinki, Finland
National Institute for Health and Welfare, 00271 Helsinki, Finland
Helsinki University Central Hospital, Unit of General Practice, 00280 Helsinki, Finland
Folkhalsan Research Centre, 00250 Helsinki, Finland
Vasa Central Hospital, 65130 Vasa, Finland
Center for Neurobehavioral Genetics, University of California, Los Angeles, California 90095, USA
Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
Department of Medicine III, Pathobiochemistry, University of Dresden, 01307 Dresden, Germany
Department of Clinical Sciences/Obstetrics and Gynecology, University of Oulu, 90014 Oulu, Finland
National Institute for Health and Welfare, Department of Chronic Disease Prevention, Chronic Disease Epidemiology and Prevention Unit, 00014, Helsinki, Finland
Institute of Biomedicine, Department of Physiology, University of Oulu, 90014 Oulu, Finland
Department of Psychiatry, Kuopio University Hospital and University of Kuopio, 70210 Kuopio, Finland
Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano/Bozen, 39100, Italy. Affiliated Institute of the University of Lübeck, Lübeck, Germany
PathWest Laboratory of Western Australia, Department of Molecular Genetics, J Block, QEII Medical Centre, Nedlands, Western Australia 6009, Australia
Busselton Population Medical Research Foundation Inc., Sir Charles Gairdner Hospital, Nedlands, Western Australia 6009, Australia
National Institute for Health and Welfare, Department of Chronic Disease Prevention, Population Studies Unit, 20720 Turku, Finland
Hospital for Children and Adolescents, Helsinki University Central Hospital and University of Helsinki, 00029 HUS, Finland
National Institute for Health and Welfare, Diabetes Prevention Unit, 00271 Helsinki, Finland
Interdisciplinary Centre for Clinical Research, University of Leipzig, 04103 Leipzig, Germany
Institut für Pharmakologie, Universität Greifswald, 17487 Greifswald, Germany
Croatian Centre for Global Health, School of Medicine, University of Split, Split 21000, Croatia
Department of Medicine, University of Kuopio and Kuopio University Hospital, 70210 Kuopio, Finland
HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, 7600 Levanger, Norway
Finnish Institute of Occupational Health, 90220 Oulu, Finland
Institut inter-regional pour la sante (IRSA), F-37521 La Riche, France.
Centre National de Genotypage, Evry, Paris 91057, France
Transplantation Laboratory, Haartman Institute, University of Helsinki, 00014, Helsinki, Finland
Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge CB2 2SR, UK
Avon Longitudinal Study of Parents and Children (ALSPAC) Laboratory, Department of Social Medicine, University of Bristol, Bristol, BS8 2BN, UK
Division of Health, Research Board, An Bord Taighde Sláinte, Dublin, 2, Ireland
Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario L8N3Z5, Canada
Amgen, Cambridge, Massachusetts 02139, USA
Department of Cardiovascular Medicine, University of Oxford, Level 6 West Wing, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU
Finnish Twin Cohort Study, Department of Public Health, University of Helsinki, 00014, Helsinki, Finland
Obesity Research unit, Department of Psychiatry, Helsinki University Central Hospital, Helsinki, Finland
Department of Medicine, Levanger Hospital, The Nord-Trøndelag Health Trust, 7600 Levanger, Norway
National Institute for Health and Welfare, 90101 Oulu, Finland
Department of Clinical Sciences, Lund University, 20502 Malmö, Sweden
Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, LE3 9QP, UK
Leicester NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester, LE3 9QP, UK
South Karelia Central Hospital, 53130 Lappeenranta, Finland
Division of Cardiology, Cardiovascular Laboratory, Helsinki University Central Hospital, 00029 Helsinki, Finland
Department of Psychiatry/EMGO Institute, VU University Medical Center, 1081 BT Amsterdam, The Netherlands
Atherosclerosis Research Unit, Department of Medicine, Solna,Karolinska Institutet, Karolinska University Hospital, 171 76 Stockholm, Sweden
Department of Human Genetics, Leiden University Medical Center, 2333 ZC Leiden, the Netherlands
Center of Medical Systems Biology, Leiden University Medical Center, 2333 ZC Leiden, the Netherlands
Institut für Klinische Chemie und Laboratoriumsmedizin, Universität Greifswald, 17475 Greifswald, Germany
Steno Diabetes Center, 2820 Gentofte, Denmark
Department of Physiatrics, Lapland Central Hospital, 96101 Rovaniemi, Finland
School of Pathology and Laboratory Medicine, University of Western Australia, Nedlands, Western Australia 6009,Australia
School of Medicine and Pharmacology, University of Western Australia, Perth, Western Australia 6009, Australia
Department of Clinical Physiology, University of Tampere and Tampere University Hospital, 33520 Tampere, Finland
Department of Clinical Chemistry, University of Tampere and Tampere University Hospital, 33520 Tampere, Finland
Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520 Turku, Finland
The Department of Clinical Physiology, Turku University Hospital, 20520 Turku, Finland
Department of Medicine, University of Leipzig, 04103 Leipzig, Germany
LIFE Study Centre, University of Leipzig, Leipzig, Germany
Coordination Centre for Clinical Trials, University of Leipzig, Härtelstr. 16-18, 04103 Leipzig, Germany
Department of Medicine, University of Turku and Turku University Hospital, 20520 Turku, Finland
INSERM CESP Centre for Research in Epidemiology and Public Health U1018, Epidemiology of diabetes, obesity and chronic kidney disease over the lifecourse, 94807 Villejuif, France
University Paris Sud 11, UMRS 1018, 94807 Villejuif, France
Department of Social Medicine, University of Bristol, Bristol, BS8 2PS, UK
The London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
South Asia Network for Chronic Disease
Department of Genomics of Common Disease, School of Public Health, Imperial College London, W12 0NN, London, UK
Faculty of Health Science, University of Southern Denmark, 5000 Odense, Denmark
Klinik und Poliklinik für Innere Medizin II, Universität Regensburg, 93053 Regensburg, Germany
Regensburg University Medical Center, Innere Medizin II, 93053 Regensburg, Germany
Department of Social Services and Health Care, 68601 Jakobstad, Finland
Research Centre for Prevention and Health, Glostrup University Hospital, 2600 Glostrup, Denmark
Faculty of Health Science, University of Copenhagen, 2100 Copenhagen, Denmark
NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, OX3 7LJ, UK
Department of Endocrinology, Diabetology and Nutrition, Bichat-Claude Bernard University Hospital, Assistance Publique des Hôpitaux de Paris, F-75018 Paris, France
Cardiovascular Genetics Research Unit, Université Henri Poincaré-Nancy 1, 54000, Nancy, France
Institute of Human Genetics, Klinikum rechts der Isar der Technischen Universität München, 81675 Munich, Germany
Institute of Human Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany
Institute of Biomedical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
Faculty of Health Science, University of Aarhus, 8000 Aarhus, Denmark
Department of Medicine III, Prevention and Care of Diabetes, University of Dresden, 01307 Dresden, Germany
Department of Medicine, Helsinki University Central Hospital, 00290 Helsinki, Finland
Research Program of Molecular Medicine, University of Helsinki, 00014 Helsinki, Finland
Hjelt Institute, Department of Public Health, University of Helsinki, 00014 Helsinki, Finland
South Ostrobothnia Central Hospital, 60220 Seinajoki, Finland
Collaborative Health Studies Coordinating Center, Seattle, Washington 98115, USA
Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV) University Hospital, 1011 Lausanne, Switzerland
Human Genetics Center and Institute of Molecular Medicine, University of Texas Health Science Center, Houston, Texas 77030, USA
Division of Research, Kaiser Permanente Northern California, Oakland, California 94612, USA
Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California 94107, USA
Department of Epidemiology and Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
National Institute for Health and Welfare, Department of Mental Health and Substance Abuse Services, Unit for Child and Adolescent Mental Health, 00271 Helsinki, Finland
Department of Clinical Genetics, Erasmus MC, Rotterdam, 3015GE, The Netherlands
Department of Psychiatry, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands
Department of Psychiatry, University Medical Centre Groningen, 9713 GZ Groningen, The Netherlands
Department of Neurology, General Central Hospital, Bolzano, Italy
Department of Neurology, University of Lübeck, Lübeck, Germany.
Departments of Epidemiology, Medicine and Health Services, University of Washington, Seattle, Washington 98195, USA
Group Health Research Institute, Group Health, Seattle, Washington 98101, USA
Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, Maryland 21201, USA
Uppsala University / Dept. of Medical Sciences, Molecular Medicine, 751 85 Uppsala, Sweden
Institut für Community Medicine, 17489 Greifswald, Germany
Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV) University Hospital, 1011 Lausanne, Switzerland
Division of Biostatistics, Washington University School of Medicine, St.Louis, Missouri 63110, USA
Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, USA
Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York 10461, USA
Carolina Center for Genome Sciences, School of Public Health, University of North Carolina Chapel Hill, Chapel Hill, North Carolina 27514, USA
Department of Medical Genetics, University of Helsinki, 00014 Helsinki, Finland
Laboratory of Genetics, National Institute on Aging, Baltimore, Maryland 21224, USA
Division of Community Health Sciences, St George's, University of London, London, SW17 0RE, UK
Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
Klinikum Grosshadern, 81377 Munich, Germany
Ludwig-Maximilians-Universität, Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, 81377 Munich, Germany
Faculty of Medicine, University of Iceland, 101 Reykjavík, Iceland
University of Cambridge Metabolic Research Labs, Institute of Metabolic Science Addenbrooke's Hospital, CB2 OQQ, Cambridge, UK
Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham Heart Study, Framingham, Massachusetts 01702, USA
Corresponding Authors: Cecilia M. Lindgren, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK, ku.ca.xo.llew@ilec, Karen L. Mohlke, University of North Carolina, Chapel Hill, NC 27599-7264, ude.cnu.dem@eklhom, Caroline S. Fox, NHLBI’s Framingham Heart Study, 73 Mt Wayte Ave Suite #2, Framingham MA 01702, vog.hin.iblhn@acxof, Mark I. McCarthy, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford OX3 7LJ, United Kingdom, ku.ca.xo.lrd@yhtraccm.kraM, Iris M. Heid, Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Franz-Josef-Strauss Allee 11, 93055 Regensburg, Germany, ed.grubsneger-inu.kinilk@dieh.siri
These authors contributed jointly.
These senior authors jointly directed this work.
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Abstract

Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body-mass-index (up to 77,167 participants), following up 16 loci in an additional 29 studies (up to 113,636 subjects). We identified 13 novel loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1, and CPEB4 (P 1.9 × 10 to 1.8 × 10), and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex-difference 1.9 × 10 to 1.2 × 10). These findings provide evidence for multiple loci that modulate body fat distribution, independent of overall adiposity, and reveal powerful gene-by-sex interactions.

Keywords: genome-wide association, waist-hip-ratio, body fat distribution, central obesity, meta-analysis, genetics, visceral adipose tissue, metabolism, body composition, Expression Quantitative Trait Loci, sex difference
Abstract

Central obesity and body fat distribution, as measured by waist circumference (WC) and waist-hip-ratio (WHR), are associated with individual risk of type 2 diabetes (T2D)12 and coronary heart disease3, and with all-cause mortality4. These effects are independent of overall adiposity as measured by body mass index (BMI). WHR is of particular interest as a measure of body fat distribution, since it integrates the adverse metabolic risk associated with increasing WC with the more protective role of gluteal fat deposition with respect to diabetes, hypertension, and dyslipidemia56.

There is abundant evidence that body fat distribution is influenced by genetic loci distinct from those regulating BMI and overall adiposity. First, even after accounting for BMI, individual variation in WHR is heritable78, with estimates ranging from 22–61%710. Second, the striking abnormalities of regional fat deposition associated with lipodystrophic syndromes demonstrate that genetic variation can have dramatic effects on the development and maintenance of specific fat depots1112. Third, in a previous genome-wide association analysis, we identified a locus near LYPLAL1 strongly associated with WHR independent of any effects on BMI13, providing proof-of-principle for the genetic control of body fat distribution, distinct from that of overall adiposity.

Within the GIANT (Genetic Investigation of Anthropometric Traits) consortium, we performed a large-scale meta-analysis of genome-wide association (GWA) studies informative for WHR, using adjustment for BMI to focus discovery towards genetic loci associated with body fat distribution rather than overall adiposity1416.

Abbreviations

BMIBody-mass-index
WCwaist circumference
WHRwaist-hip ratio
HIPhip circumference
GWAgenome-wide association
SNPsingle-nucleotide polymorphism
LDlinkage disequilibrium
eQTLExpression Quantitative Trait Loci
Abbreviations

Footnotes

AUTHOR CONTRIBUTION

Writing group: IB, CSF, IMH (lead), CML (lead), MIM, KLMoh, LQ, VSte, GT, MCZ

Waist phenotype working group: TLA, NB, IB, LAC, CMD, CSF, TBH, IMH, AUJ, CML (lead), RJFL, RM, MIM, KLMoh, LQ, JCR, EKS, VSte, KSte, GT, UT, CCW, TW, TWW, HEW, MCZ

Data cleaning &amp; analysis: SIB, IMH (lead), EI, AUJ, HL, CML (lead), RJFL (lead), JL, RM, LQ, JCR, EKS, GT, SV, MNW, EW, CJW, TWW, TW

Sex-specific analyses: SIB, TE, IMH, AUJ, TOK, ZK, SL, CML, RJFL, RM, KLMon, KEN, LQ, JCR (lead), VSte, GT, TWW (lead)

eQTL and expression analyses: SIB, ALD, CCH, JNH, FK, LMK, CML, LL, RJFL, JL, MFM, JLM, CM, GN, EES, EKS, VSte, GT, KTZ

Pathway and CNV analyses: CML, SAM, MIM, JN, VSte, GT, BFV

Secondary analyses: SIB, IBB, NC, KE, TMF, MFF, TF, MEG, JNH, EI, GL, CML, HL, RM, MMas, MIM, KLMon, DRN, JRO, SP, JRBP, JCR, AVS, EKS, PMV, MNW, CJW, RJW, EW, ARW, JY

Study-specific analyses: GRA, DA, NA, TA, TLA, NB, CC, PSC, LC, LAC, DIC, MNC, CMD, TE, KE, EF, MFF, TF, APG, NLG, MEG, CHay, NLH, IMH, JJH, AUJ, ÅJ, TJoh, JOJ, JRK, MKaa, KKap, SKet, JWK, PKra, ATK, ZK, JKet, CLam, RJFL, CLec, HL, MFL, CML, JL, RWL, RM, MMas, BM, KLMon, APM, NN, KEN, DRN, JRO, KKO, CO, MJP, OPol, IPro, NP, MP, LQ, JCR, NWR, SR, FR, NRR, CS, LJS, KSil, EKS, KSta, SS, AVS, NS, US, VSte, DPS, IS, MLT, TMT, NJT, AT, GT, AU, SV, VVit, LV, PMV, RMW, RW, RJW, SW, MNW, CCW, CJW, TWW, ARW, JY, JHZ, MCZ

Study-specific genotyping: DA, TLA, LDA, NB, IB, AJB, EB, LLB, IBB, HC, DIC, INMD, MDei, MRE, PE, KE, NBF, MF, APG, HG, CG, EJCG, CJG, THan, ALH, NH, CHay, AAH, JJH, FBH, DJH, JH, WI, MRJ, ÅJ, JOJ, JWK, PKov, ATK, HKK, JKet, PKra, RNL, CML, RJFL, JL, MLL, MAM, MMas, WLM, MIM, JBJM, MJN, MN, DRN, KKO, CO, OPed, LP, MJP, GP, ANP, NP, LQ, NWR, FR, NRR, CS, AJS, NS, ACS, MT, BT, AU, GU, VVat, PMV, HW, PZ

Study-specific phenotyping: HA, PA, DA, AMA, TLA, BB, SRB, RB, EB, IBB, JPB, MDör, CMD, PE, MFF, CSF, TMF, MF, SG, JG, LCG, THan, ASH, CHen, ALH, ATH, KHH, AHof, FBH, DJH, BI, TI, TJor, PJ, MRJ, ÅJ, AJ, ALJ, JOJ, FK, LK, JKuu, KKva, RK, SKet, JWK, IK, SKos, VK, MKäh, PKov, OL, RNL, BL, JL, GML, RJFL, TL, MMas, MIM, CO, BMP, OPed, CGPP, JFP, IPic, KP, OPol, AP, LQ, MR, IR, OR, VSal, JSar, PEHS, KSil, NJS, JHS, TDS, DPS, RS, HMS, JSin, TT, AT, MU, PV, CBV, LV, JV, DRW, GBW, SHW, GW, JCW, AFW, LZ, PZ

Study-specific management: GRA, AMA, BB, YBS, RNB, HB, JSB, SB, MB, EB, DIB, IBB, JPB, MJC, FSC, LAC, GD, CMD, SE, GE, PF, CSF, TMF, LCG, VG, UG, MEG, THan, CHen, KH, AHam, TBH, ATH, AHof, FBH, DJH, BI, TI, CI, TJor, MRJ, ALJ, FK, KTK, WHLK, RK, JKap, MKäh, ML, DAL, LJL, CML, RJFL, TL, MMar, TM, AMET, KM, MIM, KLMoh, PBM, KEN, MSN, DRN, BO, CO, OPed, LP, BWP, PPP, BMP, LJP, TQ, AR, IR, OR, PMR, VSal, PS, DS, ARS, NS, TDS, KSte, DPS, ACS, MS, TT, JT, UT, AT, MU, AU, TTV, PV, HV, JV, PMV, NJW, HEW, JFW, JCW, AFW

Steering committee: GRA, TLA, IB, SIB, MB, IBB, PD, CMD, CSF, TMF, LCG, THar, JNH (Chair), DJH, EI, RK, RJFL, MIM, KLMoh, KEN, JRO, LP, DS, DPS, UT, HEW

FINANCIAL DISCLOSURES

The authors declare competing financial interests. Details can be found in the Supplementary Note.

Footnotes

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