Genome-wide enrichment analysis between endometriosis and obesity-related traits reveals novel susceptibility loci

Abstract

Endometriosis is a chronic inflammatory condition in women that results in pelvic pain and subfertility, and has been associated with decreased body mass index (BMI). Genetic variants contributing to the heritable component have started to emerge from genome-wide association studies (GWAS), although the majority remain unknown. Unexpectedly, we observed an intergenic locus on 7p15.2 that was genome-wide significantly associated with both endometriosis and fat distribution (waist-to-hip ratio adjusted for BMI; WHRadjBMI) in an independent meta-GWAS of European ancestry individuals. This led us to investigate the potential overlap in genetic variants underlying the aetiology of endometriosis, WHRadjBMI and BMI using GWAS data. Our analyses demonstrated significant enrichment of common variants between fat distribution and endometriosis (P = 3.7 × 10⁻³), which was stronger when we restricted the investigation to more severe (Stage B) cases (P = 4.5 × 10⁻⁴). However, no genetic enrichment was observed between endometriosis and BMI (P = 0.79). In addition to 7p15.2, we identify four more variants with statistically significant evidence of involvement in both endometriosis and WHRadjBMI (in/near KIFAP3, CAB39L, WNT4, GRB14); two of these, KIFAP3 and CAB39L, are novel associations for both traits. KIFAP3, WNT4 and 7p15.2 are associated with the WNT signalling pathway; formal pathway analysis confirmed a statistically significant (P = 6.41 × 10⁻⁴) overrepresentation of shared associations in developmental processes/WNT signalling between the two traits. Our results demonstrate an example of potential biological pleiotropy that was hitherto unknown, and represent an opportunity for functional follow-up of loci and further cross-phenotype comparisons to assess how fat distribution and endometriosis pathogenesis research fields can inform each other.

INTRODUCTION

Endometriosis is a common condition in premenopausal women characterized by chronic pelvic inflammation causing pain and subfertility (1), and has an estimated heritability of 51% (2). The International Endogene Consortium (IEC) performed the largest endometriosis GWAS to date in 3194 surgically confirmed cases (including 1364 moderate–severe—Stage B—cases) and 7060 controls of European ancestry, with replication in a further 2392 cases and 2271 controls (3). One genome-wide significant locus was observed in an intergenic region on chromosome 7p15.2 (rs12700667), primarily associated with Stage B disease (P = 1.5 × 10⁻⁹, OR = 1.38, 95% CI 1.24–1.53). A second locus near WNT4 (rs7521902) was found after meta-analysis with published results from a Japanese GWAS of 1423 cases and 1318 controls (4); a genome-wide meta-analysis confirmed the two loci and found a further five (5).

Rs12700667 on 7p15.2 also marked 1 of 16 reported genome-wide significant loci associated with waist-to-hip ratio adjusted for BMI (WHRadjBMI) in an independent GWAS meta-analysis by the GIANT Consortium involving 77 167 individuals of European ancestry with replication in a further 113 636 individuals (rs1055144: discovery P = 1.5 × 10⁻⁸; meta-analysis P = 1.0 × 10⁻²⁴; r² = 0.5 with rs12700667 in 1000G pilot CEU data) (6,7). This was surprising, as prospective epidemiological studies have suggested consistently that reduced BMI—a measure of overall adiposity—is associated with increased risk of endometriosis, but there is relatively limited evidence for an association with WHRadjBMI—a measure of fat distribution (8,9). We conducted a logistic regression analysis in the IEC dataset of rs1055144 on endometriosis disease status, conditioning on rs12700667, which demonstrated that the SNPs reflected the same association signal (unpublished data; conditional P = 0.65).

The epidemiological evidence of an association between endometriosis and BMI, together with the observed GWAS locus in common between endometriosis and WHRadjBMI, led us to conduct a systematic investigation of overlap in association signals between the IEC endometriosis GWAS and GIANT Consortium WHRadjBMI (N = 77 167) (6,7) and BMI (N = 123 865) (7,10) meta-GWAS datasets through genetic enrichment analyses.

RESULTS

Genetic enrichment analysis of endometriosis with overall adiposity and fat distribution

Using independent, imputed (1000 Genomes pilot reference panel) GWAS datasets of endometriosis (IEC; 3194 cases including 1364 Stage B cases, 7060 controls), BMI (GIANT; 123 865 individuals) and WHRadjBMI (GIANT: 77 167 individuals), we first considered loci genome-wide significantly associated with endometriosis, BMI or WHRadjBMI in each of the individual GWAS. The two genome-wide significant endometriosis loci (intergenic 7p15.2 and WNT4) had significantly lower P-values of association than expected by chance in the WHRadjBMI GWAS (Table 1: rs12700667, P = 4.4 × 10⁻⁵ and rs7521902, P = 1.3 × 10⁻³; binomial P = 1.0 × 10⁻⁴), while 2 of the 16 genome-wide significant WHRadjBMI loci (intergenic 7p15.2 and GRB14) had P < 0.01 in the endometriosis GWAS (binomial P = 0.011). No enrichment between genome-wide significantly associated loci was observed for endometriosis versus BMI (Supplementary Material, Table S1: rs12700667, P = 0.27 and rs7521902, P = 0.92).

Table 1.

Association results of published IEC genome-wide significant endometriosis loci (3) in the GIANT WHRadjBMI GWAS, and of WHRadjBMI loci (6,7) in endometriosis GWAS (lookup results are shown in bold)

GWAS	SNP (proxy; r2)	Ch	Location (B36)	RAF (allele)	Status	Endometriosis all cases		Endometriosis Stage B only		Overall WHRadjBMI		Female-limited WHRadjBMI		Nearest gene
						P-valuec	OR (95% CI)	P-valuec	OR (95% CI)	P-valued	Effect (SE)	P-valuee	Effect (SE)
Endometriosis	rs12700667	7	25 868 164	0.74 (A)	G	5.1 × 10−7	1.21 (1.12–1.31)	3.3 × 10−8	1.36 (1.23–1.50)	4.4 × 10−5	−0.023 (0.005)	3.3 × 10−8	−0.023 (0.005)	Intergenic
Endometriosis	rs7521902	1	22 363 311	0.25 (A)	G	8.9 × 10−5	1.16 (1.08–1.25)	7.5 × 10−5	1.26 (1.14–1.39)	1.3 × 10−3	−0.020 (0.006)	6.1 × 10−3	−0.023 (0.009)	WNT4
WHRadjBMI	rs1055144a	7	25 837 634	0.19 (T)	G	3.7 × 10−5	0.84 (0.77–0.91)	3.1 × 10−4	0.78 (0.70–0.88)	1.5 × 10−8	0.034 (0.006)	2.3 × 10−6	0.039 (0.008)	Intergenic
WHRadjBMI	rs10195252	2	165 221 337	0.41 (C)	G	9.8 × 10−3	0.92 (0.85–0.98)	0.56	0.92 (0.84–1.00)	3.2 × 10−10	−0.031 (0.005)	6.3 × 10−15	−0.053 (0.007)	GRB14
Female WHRadjBMI	rs4684854	3	12 463 882	0.43 (C)	I (0.98)	0.07	1.06 (0.99–1.14)	0.14	1.07 (0.98–1.17)	1.0 × 10−4	0.019 (0.005)	2.3 × 10−8	0.039 (0.007)	PPARG
WHRadjBMI	rs718314	12	26 344 550	0.24 (G)	G	0.11	1.06 (0.99–1.15)	0.054	1.10 (0.99–1.22)	2.4 × 10−8	0.031 (0.005)	8.2 × 10−10	0.047 (0.008)	ITPR2-SSPN
WHRadjBMI	rs6861681	5	173 362 458	0.32 (A)	I (0.96)	0.15	0.95 (0.86–1.04)	0.11	0.93 (0.85–1.00)	1.4 × 10−6	0.026 (0.005)	2.1 × 10−4	0.027 (0.007)	CPEB4
WHRadjBMI	rs6795735	3	64 680 405	0.41 (T)	G	0.21	1.04 (0.98–1.12)	0.32	1.04 (0.96–1.14)	2.5 × 10−7	−0.025 (0.005)	7.8 × 10−7	−0.033 (0.007)	ADAMTS9
WHRadjBMI	rs2820446 (rs4846567, r2 = 1)b	1	21 974 881	0.71 (C)	I (0.99)	0.31	1.04 (0.97–1.12)	0.22	1.06 (0.97–1.17)	5.1 × 10−12	0.037 (0.005)	8.5 × 10−18	0.064 (0.007)	LYPLAL1
WHRadjBMI	rs498778 (rs6784615, r2 = 1)b	3	52 453 893	0.93 (T)	I (0.95)	0.32	1.08 (0.93–1.24)	0.25	1.06 (0.89–1.27)	4.6 × 10−5	0.055 (0.010)	1.1 × 10−3	0.063 (0.019)	NISCH-STAB1
WHRadjBMI	rs1294421	6	6 743 149	0.39 (T)	I (0.96)	0.37	1.03 (0.94–1.10)	0.28	1.03 (0.94–1.13)	6.3 × 10−9	−0.029 (0.005)	3.4 × 10−8	−0.038 (0.007)	LY86
WHRadjBMI	rs9491696	6	127 452 639	0.51 (C)	I (0.99)	0.43	0.97 (0.91–1.03)	0.64	0.98 (0.90–1.06)	2.1 × 10−14	−0.037 (0.005)	3.4 × 10−8	−0.038 (0.007)	RSPO3
WHRadjBMI	rs1443512	12	52 628 951	0.22 (A)	G	0.62	1.02 (0.94–1.10)	0.63	0.97 (0.88–1.08)	3.3 × 10−8	0.031 (0.005)	1.4 × 10−9	0.048 (0.008)	HOXC13
WHRadjBMI	rs984222	1	119 305 366	0.39 (C)	I (0.99)	0.69	0.99 (0.93–1.05)	0.31	0.95 (0.87–1.04)	3.8 × 10−14	−0.037 (0.005)	1.2 × 10−7	−0.036 (0.007)	TBX15-WARS2
WHRadjBMI	rs4823006	22	29 451 671	0.57 (A)	I (0.97)	0.72	1.01 (0.95–1.08)	0.82	1.01 (0.92–1.11)	4.7 × 10−10	0.030 (0.005)	6.9 × 10−8	0.037 (0.007)	ZNRF3
Female WHRadjBMI	rs10478424	5	118 816 619	0.79 (A)	I (0.97)	0.80	1.01 (0.93–1.10)	0.56	1.03 (0.93–1.15)	1.6 × 10−4	0.023 (0.006)	1.0 × 10−5	0.037 (0.009)	HSD17B4
WHRadjBMI	rs1011731	1	170 613 171	0.44 (G)	G	0.81	0.99 (0.93–1.05)	0.77	1.01 (0.93–1.11)	1.7 × 10−10	0.031 (0.005)	2.1 × 10−5	0.028 (0.007)	DNM3-PIGC
WHRadjBMI	rs6905288	6	43 866 851	0.56 (A)	I (0.80)	0.66	0.98 (0.91–1.05)	0.66	0.99 (0.90–1.08)	4.2 × 10−10	0.033 (0.005)	7.7 × 10−13	0.052 (0.007)	VEGFA

^aLogistic regression analysis in the IEC GWAS shows that rs1055144 marks the same locus as rs12700667 (conditional P = 0.65; r² = 0.8).

^bSNP was not genotyped in the endometriosis GWAS dataset; result shown is of proxy SNP.

^cResults are based on an updated GWAS performed using genotype data imputed up to 1000 Genomes pilot reference panel (B36, June 2010).

^dResults are from the GIANT WHRadjBMI discovery GWAS dataset (N = 77 167); 3 of the 14 WHRadjBMI loci have P > 5.0 × 10⁻⁸, however, they reached genome-wide significance combined with replication analyses in up to a further 113 636 individuals (6).

^eResults from the GIANT WHRadjBMI discovery female-limited GWAS dataset (N = 42 969); one of the two female-limited WHRadjBMI loci have P > 5.0 × 10⁻⁸, however, they reached genome-wide significance combined with replication analyses in up to a further 71 295 individuals (7).

To investigate whether statistical enrichment extended beyond genome-wide significant loci, we investigated the most significant (P < 1 × 10⁻³) independent (r² < 0.2) endometriosis GWAS signals for enrichment of WHRadjBMI or BMI signals with P < 0.05 and vice versa (number of lookup SNPs per dataset: n = 717 to 748; see Supplementary Material, Methods). We observed statistically significant enrichment between variants associated with endometriosis (particularly Stage B) and WHRadjBMI (all endometriosis versus WHRadjBMI: P = 3.7 × 10⁻³; Stage B endometriosis versus WHRadjBMI: P = 4.5 × 10⁻⁴), but not between endometriosis and BMI (all endometriosis versus BMI: P = 0.79; Stage B endometriosis versus BMI: P = 0.85) (Fig. 1; Supplementary Material, Table S2). Results were similar when using female-limited WHRadjBMI (N = 42 969 women) and BMI (N = 73 137 women) GWAS summary statistics (7); to optimize power, in the remainder of the paper we therefore focus on sex-combined WHRadjBMI and BMI datasets (Supplementary Material, Fig. S1). Empirical testing of statistical enrichment through permutation (see Supplementary Material, Methods) provided near-identical results (Fig. 1; Supplementary Material, Fig. S1).

Figure 1.

Genetic enrichment analyses between endometriosis, BMI and WHRadjBMI GWAS datasets, using independent (r² < 0.2) SNPs. The panels show (i) The proportion of SNPs nominally associated (P < 0.05) with WHRadjBMI (A) or BMI (B) by significance of overall and Stage B endometriosis association (P < 1.0 × 10⁻³ versus P ≥ 1 × 10⁻³); (ii) The proportion of SNPs nominally associated (P < 0.05) with overall and Stage B endometriosis by significance of WHRadjBMI (C) and BMI (D) association (P < 1.0 × 10⁻³ versus P ≥ 1 × 10⁻³). P-values of χ² tests assessing statistical difference between proportions are shown above each set of bars, and 95% confidence intervals of the proportions are given on each bar. For differences with P_chisq < 0.2, empirical P-values are given in brackets (see Supplementary Material, Methods).

The choice of significance thresholds in the discovery and lookup datasets was based on a balance between applying a sufficiently stringent significance threshold in the discovery dataset that would minimize the proportion of false-positive association signals, while still having sufficient numbers of loci in each of the phenotypic association strata to investigate statistical enrichment, and allow the pursuit of meaningful biological pathway analyses subsequently. We considered the effect of different significance thresholds, for both discovery and lookup, which confirmed results showing enrichment of association signals between endometriosis and WHRadjBMI (Supplementary Material, Table S3), but no enrichment between endometriosis and BMI (Supplementary Material, Table S4).

To investigate potential genome-wide sharing of loci between endometriosis and WHRadjBMI or BMI, we performed polygenic prediction analyses (11) evaluating whether the aggregate effect of many variants of small effect in the WHRadjBMI and BMI GWAS could predict endometriosis status in the IEC GWAS (see Supplementary Material, Methods). There was no significant association between the WHRadjBMI- or BMI-derived profile scores (overall or female limited) and all/Stage B endometriosis (Supplementary Material, Tables S5–S8), suggesting no evidence for a directionally consistent en masse, genome-wide, shared common genetic component.

We next investigated the variants with most significant evidence for association with both endometriosis (P < 1 × 10⁻³) and WHRadjBMI (P < 0.05) for predominance in direction of phenotypic effects (Supplementary Material, Tables S9 and S10 and Fig. S2). No statistically significant directional consistency was observed for these variants (P > 0.47), nor for the 17 loci (Table 1) that were genome-wide significantly associated with either trait (Fig. 2, P > 0.44). Intergenic 7p15.2 and WNT4 showed discordant directions of effect, while the effect of GRB14 was concordant (Fig. 2). This could suggest the presence of multiple biological pathways through which the variants influence the two phenotypes. We next set out to investigate the common biology suggested by genetic variants associated with both endometriosis and WHRadjBMI.

Figure 2.

Directions of effect of 17 independent SNPs genome-wide significantly associated with all (A) or Stage B (B) endometriosis, or WHRadjBMI. Intergenic 7p15.2, WNT4, and GRB14 are shown in red. Linear regression R² and P-values used to test for significant directionality of effects (35) are shown.

Biology of the loci shared between endometriosis and fat distribution

Our analysis showing significant enrichment between SNPs associated with all or Stage B endometriosis (P < 1 × 10⁻³) and WHRadjBMI (P < 0.05) shown in Figure 1 involved 1284 independent (r² > 0.2) loci. We explored the biological function of the loci most strongly associated with WHRadjBMI, at nominal P < 0.005 (n = 16, Table 2; see Supplementary Material, Tables S11 and S12 for all variants associated at P < 0.05). Two novel loci, rs560584 near KIFAP3 (all endometriosis) and rs11619804 in CAB39L (Stage B endometriosis), were significantly associated with WHRadjBMI after Bonferroni correction allowing for 1284 independent tests (P < 3.89 × 10⁻⁵).

Table 2.

Results of the top all/Stage B endometriosis loci (P < 1 × 10⁻³) associated with WHRadjBMI at P < 0.005

SNP	Chr	Position (B36)	RAF (allele)	Endometriosis		Overall WHRadjBMI			Female-limited WHRadjBMI			Nearest loci
				P-value	OR (95% CI)	P-value	Effect	SE	P-value	Effect	SE	(distance)
All cases
rs560584	1	168 357 136	0.41 (T)	1.4 × 10−4	1.14 (1.07–1.22)	1.4 × 10−5	−0.021	0.005	1.1 × 10−3	−0.022	0.677	KIFAP3 (46 632)
rs12700667	7	25 868 164	0.74 (A)	5.1 × 10−7	1.22 (1.13–1.32)	4.4 × 10−5	−0.023	0.005	3.4 × 10−4	−0.028	0.284	NFE2L3 (2 90 221)
rs2921188	3	12 387 115	0.64 (A)	5.9 × 10−4	1.13 (1.05–1.21)	1.1 × 10−3	0.017	0.005	1.8 × 10−4	0.026	0.054	PPARG (0)
rs1250248	2	215 995 338	0.27 (A)	1.6 × 10−5	1.17 (1.09–1.26)	1.0 × 10−3	0.018	0.005	9.9 × 10−4	0.025	0.242	FN1 (0)
rs2630787	3	21 847 339	0.52 (C)	9.2 × 10−4	1.12 (1.05–1.19)	1.9 × 10−3	−0.015	0.004	0.38	−0.006	0.030	ZNF659 (79 518)
rs1430788	2	67 721 916	0.31 (C)	9.3 × 10−5	1.15 (1.07–1.23)	2.7 × 10−3	0.016	0.005	3.1 × 10−3	0.022	0.330	ETAA1 (230 878)
rs906721	3	184 687 691	0.41 (A)	6.1 × 10−5	1.16 (1.08–1.24)	4.2 × 10−3	0.015	0.005	1.7 × 10−3	0.023	0.140	KLHL6 (322)
rs1868894	4	187 606 728	0.80 (C)	2.3 × 10−4	1.16 (1.07–1.26)	4.9 × 10−3	−0.018	0.006	0.13	−0.013	0.524	MTNR1A (85 075)
rs3820282	1	22 340 802	0.16 (T)	3.3 × 10−7	1.26 (1.15–1.37)	5.0 × 10−3	−0.019	0.007	0.09	−0.016	0.749	WNT4 (0)
Stage B cases
rs11619804	13	49 888 131	0.53 (C)	4.8 × 10−4	1.17 (1.07–1.28)	1.1 × 10−5	0.022	0.005	2.2 × 10−2	0.016	0.022	CAB39L (0)
rs12700667	7	25 868 164	0.74 (A)	3.3 × 10−9	1.36 (1.23–1.50)	4.4 × 10−5	−0.023	0.005	3.4 × 10−4	−0.028	0.284	NFE2L3 (290 221)
rs2782659	6	45 794 768	0.33 (G)	4.2 × 10−4	1.18 (1.08–1.30)	9.2 × 10−5	0.020	0.005	1.7 × 10−4	0.027	0.108	RUNX2 (167 970)
rs6556301	5	176 460 183	0.63 (G)	7.4 × 10−4	1.17 (1.07–1.28)	1.9 × 10−4	−0.021	0.005	7.8 × 10−3	−0.021	0.845	FGFR4 (2450)
rs1250248	2	215 995 338	0.27 (A)	2.9 × 10−8	1.32 (1.19–1.45)	1.2 × 10−3	0.018	0.005	9.9 × 10−4	0.025	0.242	FN1 (0)
rs4131816	1	161 662 648	0.85 (T)	5.4 × 10−4	1.24 (1.10–1.41)	1.5 × 10−3	0.022	0.007	0.25	0.011	0.072	NUF2 (70 470)
rs9912335	17	77 552 948	0.69 (T)	3.1 × 10−4	1.19 (1.08–1.31)	3.5 × 10−3	−0.021	0.007	0.10	−0.016	0.454	ASPSCR1 (0)
rs10878362	12	64 703 760	0.69 (C)	4.9 × 10−4	1.19 (1.08–1.31)	3.6 × 10−3	0.015	0.005	3.1 × 10−3	0.022	0.204	HMGA2 (57 421)
rs2807357	1	22 364 571	0.64 (A)	9.7 × 10−4	1.16 (1.06–1.27)	3.7 × 10−3	−0.015	0.005	1.0 × 10−3	−0.024	0.081	WNT4 (22 373)
rs906721	3	184 687 691	0.41 (A)	1.4 × 10−4	1.20 (1.09–1.32)	4.2 × 10−3	0.015	0.005	1.7 × 10−3	0.023	0.140	KLHL6 (322)
rs12267660	10	4 419 530	0.85 (G)	7.9 × 10−4	1.24 (1.09–1.40)	4.6 × 10−3	0.02	0.007	8.0 × 10−3	0.030	0.133	CR749391 (191 913)
rs11685481	2	67 590 253	0.15 (C)	8.4 × 10−4	1.23 (1.09–1.38)	4.8 × 10−3	0.018	0.006	1.1 × 10−2	0.022	0.451	ETAA1 (99 215)

The endometriosis risk allele T of rs560584 (OR = 1.14 (1.07–1.22), P = 1.42 × 10⁻⁴) was associated with lower WHRadjBMI (β = −0.021, P = 1.47 × 10⁻⁵), and located in an intergenic region 46 kb downstream of KIFAP3 (Kinesin-associated protein 3). Together with KIF3A and KIF3B, KIFAP3 forms a kinesin motor complex, KIF3, that mediates cellular transport of N-cadherin and β-catenins (12), which are involved in cell adhesion, the Wnt canonical pathway and cell cycle progression (13). The Wnt/β-catenin signalling pathway acts as a molecular switch for adipogenesis (14) and has multiple suggested roles in endometriosis through sex hormone homeostasis regulation (15), its role in development of female reproductive organs (16), molecular mechanisms of infertility (17) and mediation of fibrogenesis (18).

The Stage B endometriosis risk allele C of rs11619804 (OR = 1.17 (1.07–1.28); P = 4.88 × 10⁻⁴), located in CAB39L (Calcium-Binding Protein 39-Like), was associated with increased WHRadjBMI (β = 0.022, P = 1.06 × 10⁻⁵; Table 2). The function of this gene is not well characterized but the encoded protein interacts with a serine threonine kinase (STK11) that functions as a tumour suppressor (19).

Rs12700667 in the intergenic region 7p15.2 remained among the strongest associated shared signals, with the endometriosis risk allele A associated with reduced WHRadjBMI (β = −0.023, P = 4.4 × 10⁻⁵). The association maps to an intergenic high LD region of 48 kb (r² > 0.8) of unknown functionality. Further interesting nearby loci include the miRNA hsa-mir-148a, with a purported role in Wnt/β-catenin signalling (14); NFE2L3 (nuclear factor (erythroid-derived 2)-like 3), a transcription factor suggested to be involved in cell differentiation, inflammation and carcinogenesis (20). The WNT signalling pathway was further highlighted by the nominal association of two independent (r² = 0.06) endometriosis risk variants near WNT4 (wingless-type MMTV integration site family), rs3820282 (genic) and rs2807357 (22.4 kb downstream), with reduced WHRadjBMI (β = −0.019, P = 5.0 × 10⁻³; β = −0.015, P = 3.7 × 10⁻³; Table 2). Of note is that all shared variants implicated in WNT signalling (in/near intergenic 7p15.2, WNT4, KIFAP3) showed consistent—discordant—phenotypic directions of effect.

Risk variant rs10195252, 34.6 kb downstream of GRB14 (growth factor receptor-bound protein 14) was the third locus with significant evidence for association with both overall (not Stage B) endometriosis and WHRadjBMI (Table 1). GRB14 has an inhibitory effect on insulin receptor signalling (21), may have a role in signalling pathways that regulate growth and metabolism and has been shown to interact with fibroblast growth factor receptors (22). This shared variant is also in high LD (r² = 0.93 and = 0.87, respectively) with a type 2 diabetes risk variant rs13389219 (23) and fasting insulin risk variant rs6717858 (24).

Other loci of interest include rs2921188 in PPARG and rs6556301 near FGFR4 (Table 2). The endometriosis risk allele A of rs2921188 (OR = 1.13, 95% CI: 1.05–1.21), P = 5.9 × 10⁻⁴) in PPARG (peroxisome proliferator-activated receptor gamma) is associated with increased WHRadjBMI (β = 0.017; P = 1.1 × 10⁻³). PPARG is a nuclear hormone receptor that regulates fatty acid storage and glucose metabolism. Synthetic ligands, such as insulin sensitizing drugs, target PPARG in treatment of diabetes to lower serum glucose levels (25) and are also documented to have anti-inflammatory, anti-angiogenic and anti-proliferative effects on endometrium, with baboon models suggesting a role in targeting endometriotic disease (26). Stage B endometriosis risk allele G of rs6556301 near FGFR4 (fibroblast growth factor receptor, OR = 1.17 [1.07–1.28], P = 7.4 × 10⁻⁴) is associated with reduced WHRadjBMI (β = −0.021, P = 1.9 × 10⁻⁴). FGFR4 interacts with fibroblast growth factors, which have roles in angiogenesis, wound healing and cell migration (27).

Expression quantitative trait loci analysis of the shared endometriosis and fat distribution loci

We investigated the potential impact of the described 16 genes (Table 2) shared between endometriosis and WHRadjBMI on transcriptional function using three public expression data resources: (i) the Mammalian Gene Expression Uterus database (MGEx-Udb) (28) containing published information on transcriptional activity of specific genes in human endometrial tissue from individuals with and without endometriosis; (ii) the MuTHER study which collected expression and eQTL data from 776 abdominal fat tissues (29); and (iii) the MOLOBB dataset of differential expression levels between abdominal and gluteal fat from 49 individuals (30). Based on the limited available evidence in the MGEx-Udb database, two genes are transcribed in endometrial tissue of women with endometriosis but dormant in those without endometriosis: PPARG and FGFR4 (Supplementary Material, Table S13). Of the 16 genes, 15 had probes present within 1 Mb either side of the SNP in the MuTHER database; however, none showed significant association with nearby transcripts in abdominal fat tissue (Supplementary Material, Table S14). The MOLOBB study data showed cis-eQTL evidence for differential expression of two genes; KIFAP3 (rs560584; fold change = 0.14, adjusted P = 0.04) (Supplementary Material, Table S15). Additional transcriptional evidence relevant to the intergenic 7p15.2 locus includes the presence of an expression QTL associated with a transcript of unknown function, AA553656, in subcutaneous abdominal fat tissue (6), and the differential expression of nearby hsa-miR-148a between gluteal and abdominal fat tissue samples (31).

Pathway analysis

To identify potential common biological pathways involved in the aetiology of endometriosis and the variability of fat distribution, we conducted pathway analyses using genes with evidence for enrichment between the traits using (i) the PANTHER database (32) and (ii) GRAIL (33). For the PANTHER analysis, we selected the 91 and 108 genes located in a 1 Mb interval surrounding each independent SNP associated with all endometriosis (P < 1.0 × 10⁻³) and WHRadjBMI (P < 0.05), and Stage B endometriosis (P < 1.0 × 10⁻³) and WHRadjBMI (P < 0.05), respectively (see Supplementary Material, Methods). This excluded intergenic loci without a gene within 1 Mb, such as our top shared locus at 7p15.2. We tested whether the two sets of genes showed significant overrepresentation of a particular pathway, for each of 176 curated pathways and 241 biological processes. The top enriched pathways were ‘developmental processes’ (all endometriosis: P = 1.2 × 10⁻⁵; Stage B: P = 1.25 × 10⁻⁴), ‘WNT signalling’ (all endometriosis: P = 1.07 × 10⁻⁴), ‘gonadotropin-releasing hormone receptor’ (all endometriosis: P = 1.48 × 10⁻³), ‘cadherin signalling’ (Stage B: P = 6.42 × 10⁻⁴), ‘FGF signalling’ (Stage B: P = 2.96 × 10⁻³) and ‘TGF-beta signalling’ (Stage B: P = 1.48 × 10⁻³) pathways (Supplementary Material, Tables S16 and S17). Bonferroni correction for the number of pathways tested (see Supplementary Material, Methods) rendered ‘WNT signalling’, ‘developmental processes’, ‘cellular processes’ and ‘cell communication’ significantly enriched; however, this adjustment is conservative, as exemplified by ‘cadherin signalling’ genes being a subset of those in the ‘WNT signalling’ pathway. Sensitivity analyses exploring the effect of different endometriosis association thresholds on pathway analyses showed very consistent results for threshold P < 1.0 × 10⁻², with the same top three enriched pathways—WNT signalling, Cadherin signalling and Gonadotropin-releasing hormone receptor pathway. No meaningful pathway analyses could be conducted on the limited number of genes passing association threshold P < 1 × 10⁻⁴ (Supplementary Material, Table S18).

We used GRAIL (33) to search for connectivity between the 91 and 108 genes all/Stage B endometriosis and WHRadjBMI-associated genes and specific keywords from the published literature that describe potential functional connections. We identified 17 genes with nominal significance (P < 0.05) for potential functional connectivity for ‘all’ endometriosis and WHRadjBMI and six genes for Stage B endometriosis and WHRadjBMI (Supplementary Material, Fig. S3  and Tables S19 and S20). The keywords associated with these connections included ‘cadherin’, ‘differentiation’, ‘development’ and ‘insulin’ for ‘all’ endo, and ‘development’ and ‘embryos’ for Stage B endometriosis, marking again developmental processes and cadherin signalling as biological pathways shared in the origins of endometriosis and fat distribution.

DISCUSSION

In this study, we have investigated the overlap in genetic association signals from the largest GWA studies to date of endometriosis, overall adiposity (BMI) and fat distribution (WHRadjBMI). Our results demonstrated that there is a shared genetic basis between endometriosis and fat distribution that extends over and above the single genome-wide significant locus that has been reported in GWAS of the separate traits. Our analyses highlight novel loci in/near KIFAP3 and CAB39L, which together with intergenic 7p15.2, WNT4 and GRB14, showed significant evidence of trait association sharing. The strength of evidence of enrichment was similar for overall versus female-limited WHRadjBMI loci, which may be unexpected, given that endometriosis is a female condition. However, the lack of a stronger enrichment between female-specific WHRadjBMI GWAS results and endometriosis, compared with all WHRadjBMI results should be considered against the effects of a reduced sample size used for female-specific WHRadjBMI analyses on power of association detection.

The enrichment of associated variants was generally stronger when the endometriosis cases were restricted to moderate–severe (Stage B) disease, despite the smaller sample size. Indeed, the association of the top intergenic GWAS locus on 7p15.2, also genome-wide significantly associated with WHRadjBMI, is limited to Stage B endometriosis. Stage B—or ASRM Stages III/IV disease (34)—is typically characterized by ovarian (endometrioma) or deep infiltrating (rectovaginal) lesions, which were shown to have a substantially greater underlying genetic contribution than milder, peritoneal disease (ASRM Stage I/II) (3). The particular enrichment between WHRadjBMI and Stages III/IV endometriosis is intriguing, and another reason for further functional work to concentrate on this endometriosis sub-type. There are, however, specific loci that show enrichment of association with WHRadjBMI and overall endometriosis, the analysis of which therefore remains of interest. An example is GRB14, which did not show significant association with Stage B disease, displayed a concordant direction of effect between endometriosis and WHRadjBMI, and the biological function of which also seems to suggest an entirely different contribution to the origins of both phenotypes than the 7p15.2 and WNT4 loci.

The limited available eQTL data showed significant evidence for differential expression of KIFAP3 between different fat depots. The variants with most evidence for enrichment between the traits, in/near intergenic 7p15.2, KIFAP3 and WNT4, were all implicated in WNT signalling and had consistent—discordant—directions of effect, with endometriosis risk alleles associated with a decreased WHRadjBMI. Indeed, biological pathway analyses showed significant evidence for the involvement of developmental processes and WNT signalling in endometriosis aetiology and regulation of fat distribution, a potential pleiotropic connection that has not been reported to date.

The relatively limited epidemiological evidence of phenotypic correlation between endometriosis and WHRadjBMI (8,9) is consistent with the absence of strong directional consistency of phenotypic effects of genetic variants underlying both traits at a genome-wide level. Most studies of genetic pleiotropy between traits to date have focused on genome-wide directional consistency between epidemiologically or clinically (postulated) correlated traits, such as different metabolic traits (6,35) or psychiatric conditions (36). However, genome-wide consistency in directionality of phenotypic effects would most likely apply to traits that share a large proportion of causality, and that epidemiologically lie on the same causal pathway(s) and are thus more likely to be examples of mediated (genetic variants influencing one phenotype indirectly through association with a second phenotype) rather than biological (genetic variants exerting a direct biological influence on more than one phenotype) pleiotropy (37). Thus, our results of genetic enrichment between endometriosis and WHRadjBMI demonstrate an example of the biological complexity of aetiological associations between complex traits, and suggest that the underlying shared loci are potentially biologically pleiotropic, given the absence of phenotypic correlation between endometriosis and WHRadjBMI and absence of en masse directional consistency of shared genetic variants on the phenotypes (37,38). It also demonstrates more generally how potential perturbation of a causal pathway through, for example, drug treatment targeting one trait could have unexpected effects on another, even when there is no clear evidence that these traits are associated clinically or epidemiologically—a problem often encountered in drug development. Systematic exploration of biological pleiotropy of genetic variants marking potential drug targets may help in highlighting the potential of such unwanted or unexpected effects.

While the observed genetic enrichment between endometriosis and WHRadjBMI presents new avenues for exploring common biology, the total absence of any genetic enrichment between endometriosis and BMI (within the limits of power presented by these large datasets) is intriguing given the consistent, prospective, observational epidemiological evidence of phenotypic association between reduced BMI and endometriosis risk (8). Our analyses represent an adaptation of Mendelian randomization analyses (39,40), in which genetic variants robustly associated with BMI in the largest GWAS analyses to date (10) are investigated for association with endometriosis. The total lack of genetic enrichment suggests that reduced BMI is not causally related to endometriosis risk. Rather, it suggests that the observed phenotypic association (8) is either driven by shared environmental factors, or is due to confounding factors related to BMI affecting, for example, diagnostic opportunity for endometriosis.

These novel findings present an entirely new opportunity for functional targeted follow-up of pleiotropic loci between endometriosis and WHRadjBMI in relevant disease tissues such as endometrium and fat tissue, cellular systems, animal models and further cross-trait comparisons, to uncover their biological functions and to assess how studies in the fat distribution research field can inform research into endometriosis pathogenesis, biomarker identification and drug target discovery and validation.

MATERIALS AND METHODS

Genome-wide association studies

IEC endometriosis GWAS

This GWAS included 3194 surgically confirmed endometriosis cases and 7060 controls from Australia and the UK. Disease severity of the endometriosis cases was assessed retrospectively from surgical records using the rAFS classification system and grouped into two phenotypes: Stage A (Stage I or II disease or some ovarian disease with a few adhesions; n = 1686) or Stage B (Stage III or IV disease; n = 1364). We previously showed an increased genetic loading among 1364 cases with Stage B endometriosis compared with 1666 with Stage A disease (3), which led to two GWA analyses, including (i) 3194 ‘all’ endometriosis case and (ii) 1364 Stage B cases (Table 3). The genotyped data were imputed up to 1000 Genomes pilot reference panel (B36, June 2010) and the GWAS was performed again, using a missing data likelihood in a logistic regression model including a covariate representing the Australian and the UK strata, with the imputed data (N = 12.5 million SNPs). The enrichment analysis we present is from this set of results.

Table 3.

Summary description of the GWAS used in the genetic enrichment analysis

GWAS	Consortium	Sample size	No. of SNPs (million)	References
Endometriosis—all cases	IEC	3194 cases, 7060 controls	∼12.5	Painter et al. (3)
Endometriosis—Stage B cases	IEC	1363 cases, 7060 controls	∼12.5	Painter et al. (3)
WHRadjBMI	GIANT	77 167	∼2.85	Heid et al. (6)
Female-limited WHRadjBMI	GIANT	42 969	∼2.85	Randall et al. (7)
BMI	GIANT	123 865	∼2.85	Speliotes et al. (10)
Female-limited BMI	GIANT	73 137	∼2.85	Randall et al. (7)

IEC, International Endogene Consortium; GIANT, Genetic Investigation of Anthropometric Traits Consortium; BMI, body mass index adjusted for age; WHRadjBMI, waist to hip ratio adjusted for BMI and age.

GIANT Consortium

WHR GWAS

A total of 77 167 subjects of European ancestry informative of body fat distribution measurement WHR from 32 GWAS were included (6). The genotype data were imputed up to HapMap 2 CEU reference panel. The associations of 2.85 million SNPs with WHR were examined in a fixed-effects meta-analysis, after inverse normal transformation of WHR and adjusting for BMI and age within each study in an additive genetic model; analyses were conducted for males and females combined (6) and limited to females only (7) (Table 3).

BMI GWAS

A total of 123 865 subjects with overall adiposity measurement BMI from 46 GWAS were included (10). The genotype data were imputed up to HapMap two CEU reference panels. The associations of 2.85 million SNPs with BMI were tested in an inverse-variance meta-analysis, after inverse normally transformation of BMI and adjusting for age and other appropriate covariates in an additive genetic model within each study; analyses were conducted for males and females combined (10) and limited to females only (7) (Table 3).

Genetic enrichment analysis

With one test of association conducted for each SNP, the GWAS analyses produced a genome-wide distribution of P-values of individual SNP associations. Prior to testing enrichment: (i) the overlap of SNPs present in endometriosis GWAS versus WHRadjBMI and BMI GWAS was taken, (ii) all SNPs with MAF ≤ 0.01 were removed, (iii) all SNPs with A/T and C/G base pairs were removed, (iv) correlated SNPs (r² > 0.2) were removed as previously reported (41) by taking the most significantly associated SNP and eliminating all SNPs that have a HapMap CEU pairwise correlation coefficient (r²) > 0.2 with that SNP, then processing to the next strongly associated SNP remaining. This resulted in 173 157 independent SNPs in endometriosis versus WHRadjBMI and 173 223 in endometriosis versus BMI enrichment analyses.

The independent SNPs in the tails (P < 1 × 10⁻³) of the association results distribution of the two endometriosis GWAS (all endometriosis and ‘Stage B’ cases) were investigated for enrichment of WHRadjBMI or BMI low P-value (P < 0.05) association signals; in reversal, SNPs in the tails of WHRadjBMI and BMI GWAS (P < 1 × 10⁻³) were investigated for evidence of nominal association (P < 0.05) in the two endometriosis GWAS. The threshold of P < 1 × 10⁻³ corresponded to the point at which endometriosis GWAS results started to deviate from the null distribution (evidence for association) in the overall and Stage B endometriosis Q–Q plots (Supplementary Material, Fig. S4). Enrichment was assessed in R by means of Pearson's χ² tests with Yates' continuity correction, testing for the difference in proportion of SNPs with association P < 0.05 in the lookup dataset according to association in the discovery dataset (P < 1 × 10⁻³ versus P ≥ 1 × 10⁻³). To test for consistency in directionality of phenotypic effects of the SNPs with evidence of enrichment, linear regression analysis was performed on the effect (β) of each SNP for WHRadjBMI as predictor variable and the effect (β) of endometriosis risk as the outcome variable (35). In addition, a two-sided binomial test was performed with null hypothesis P = 0.50.

Permutation-based enrichment analysis

For those results that showed nominally significant (P < 0.10) evidence for enrichment in χ² tests of contingency tables, we performed permutation-based analyses to obtain empirical estimates of significance of enrichment. We (i) randomly picked the same number of independent SNPs ‘associated’ with the discovery trait at P < 1 × 10⁻³ (e.g. the number of SNPs associated with all endometriosis at P < 1 × 10⁻³ was n = 717) from the WHRadjBMI dataset; (ii) counted how many of the randomly selected SNPs had P-values of association with WHRadjBMI <0.05; (iii) repeated Steps (i) and (ii) 10 000 times; (iv) determined the number of instances among the 10 000 draws in which the number of SNPs associated at P < 0.05 with WHRadjBMI was greater or equal to the number we observed in our original analysis (e.g. ≥52/717). For example, for overall endometriosis and overall WHRadjBMI, we observed this in 26/10 000 instances, corresponding to a P-value of 2.6 × 10⁻³, which was very similar to the P-value obtained from the χ² test (P = 3.7 × 10⁻³).

Polygenic prediction analysis

The independent SNPs in both WHRadjBMI and endometriosis datasets were used to conduct a polygenic prediction analysis (11). The aim of this analysis was to evaluate the aggregate effects of many SNPs of small effect and assess whether subsets of SNPs selected in this manner from one disease/trait GWAS predict disease/trait status in another, thus providing a measure of a common polygenic component with concordant directions of effect underlying the traits. Briefly, subsets of SNPs were selected from the WHRadjBMI GWAS data based on their association with WHRadjBMI using increasingly liberal thresholds, that is, P < 0.01, P < 0.05, P < 0.1, P < 0.2, P < 0.3, P < 0.4, P < 0.5 and P < 0.75. Using these thresholds, we defined sets of allele-specific scores in the WHRadjBMI dataset to generate risk profile scores for individuals in the endometriosis dataset. For each individual in the endometriosis dataset, we calculated the number of score alleles they possessed, each weighted by their effect size (β-value) of association in the WHRadjBMI dataset. To assess whether the aggregate scores were associated with endometriosis risk, we tested for a higher mean score in cases compared with controls. Logistic regression was used to assess the relationship between endometriosis disease status and aggregate risk score.

Expression analyses

MGEx-Udb

The mammalian gene expression uterus database (MGEx-Udb) is a manually curated uterus-specific database created using a meta-analysis approach from published papers (28) that provides lists of transcribed and dormant genes for various normal, pathological (e.g. endometriosis, cervical cancer and endometrial cancer) and experimental (e.g. treatment and knockout) conditions. Each gene's expression status is indicated by a reliability score, derived based on the consensus across multiple samples and studies which highly variable (http://resource.ibab.ac.in/MGEx-Udb/).

MuTHER

The MuTHER resource includes LCLs, skin and adipose tissue-derived simultaneously from a subset of well-phenotyped healthy female twins (29). Whole-genome expression profiling of the samples, each with either two or three technical replicates, was performed using the Illumina Human HT-12 V3 BeadChips (Illumina, Inc.) according to the protocol supplied by the manufacturer. Log2 transformed expression signals were normalized separately per tissue as follows: quantile normalization was performed across technical replicates of each individual followed by quantile normalization across all individuals.

Genotyping was conducted using a combination of Illumina arrays (HumanHap300, HumanHap610Q, 1M-Duo and 1.2MDuo 1 M). Untyped HapMap2 SNPs were imputed using the IMPUTE software package (v2). In total, there were 776 samples with genotypes and expression values in adipose tissue. Association between all SNPs (MAF > 5%, IMPUTE info score > 0.8) within a gene or within 1 Mb of the gene transcription start or end site, and normalized expression values, were performed with the GenABEL/ProbABEL packages (42) using polygenic linear models incorporating a kinship matrix (GenABEL) followed by the mm score test with imputed genotypes (ProbABEL). Age and experimental batch were included as cofactors in the analysis. Benjamini Hochberg corrected P-values are reported.

MolOBB

We performed differential cis-eQTL analysis to compare the expression levels in gluteal and abdominal fat tissue from 49 individuals in the MolOBB dataset (24 with and 25 without metabolic syndrome—MetSyn) (30). We first checked for the presence of the SNP in the MolOBB genotype data and, in the case of absence, selected any proxies (r² > 0.8) available. We then searched for nearby genes (±500 kb) covered by the expression data using the bioconductor R package GenomicRanges (43) and tested for association at each pair using a linear model with the expression level as an outcome and the SNP allelic dosage as a predictor, adjusting for age, gender and MetSyn case–control status. This analysis was carried out for both abdominal and gluteal subcutaneous adipose tissue. To investigate whether genes were differentially expressed between the two tissues, we applied a linear mixed model with tissue, MetSyn case–control status, gender and plate were as fixed effects, and subject as a random effect using MAANOVA (44), as previously described in Min et al. (30). We report the uncorrected and genome-wide FDR corrected Fs test P-values (30).

Biological pathway analysis

PANTHER

We conducted analyses using the PANTHER 8.1 database containing pathway information on 20 000 genes (Homo sapiens) (32). We selected independent SNPs, which had association P-values < 1 × 10⁻³ in the endometriosis datasets and an association P-value of <0.05 in the WHRadjBMI dataset, resulting in (i) 91 SNPs for all endometriosis and WHRadjBMI and (ii) 108 SNPs for Stage B endometriosis and WHRadjBMI. Each SNP was mapped to the closest gene within 1 Mb; 88 of 91 and 103 of 108 genes were present in the PANTHER database, and these subsets were tested for correlation with 241 biological processes and 176 pathways classified in the database (32). For each biological process/pathway, the difference between the observed fraction of genes in that pathway and the number expected by chance was tested using Fisher exact test. A Bonferroni correction was used as a conservative method for adjusting for the maximum number of biological processes (n = 278; P = 1.80 × 10⁻⁴) and pathways (n = 78; P = 6.41 × 10⁻⁴) tested.

SUPPLEMENTARY MATERIAL

Supplementary Material is available at HMG online.

FUNDING

The endometriosis GWAS was supported by a grant from the Wellcome Trust (WT084766/Z/08/Z) and makes use of WTCCC2 control data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of these data is available from http://www.wtccc.org.uk. Funding for the WTCCC project was provided by the Wellcome Trust under awards 076113 and 085475. The QIMR study was supported by grants from the National Health and Medical Research Council (NHMRC) of Australia (241944, 339462, 389927, 389875, 389891, 389892, 389938, 443036, 442915, 442981, 496610, 496739, 552485 and 552498), the Cooperative Research Centre for Discovery of Genes for Common Human Diseases (CRC), Cerylid Biosciences (Melbourne) and donations from N. Hawkins and S. Hawkins. S.M. was supported by NHMRC Career Development Awards (496674, 613705). D.R.N. was supported by the NHMRC Fellowship (339462 and 613674) and the ARC Future Fellowship (FT0991022) schemes. A.P.M. was supported by a Wellcome Trust Senior Research Fellowship. G.W.M. was supported by the NHMRC Fellowships Scheme (339446, 619667). K.T.Z. was supported by a Wellcome Trust Research Career Development Fellowship (WT085235/Z/08/Z). C.M.L. was supported by a Wellcome Trust Research Career Development Fellow (086596/Z/08/Z). N.R. was supported by an MRC grant (MR/K011480/1). Funding to pay the Open Access publication charges for this article was provided by the Wellcome Trust.

APPENDIX

The International Endogene Consortium

Carl A. Anderson^1,2, Scott D. Gordon³, Qun Guo⁴, Anjali K. Henders³, Ann Lambert⁵, Sang Hong Lee⁶, Peter Kraft⁷, Stephen H. Kennedy⁵, Stuart Macgregor³, Nicholas G. Martin³, Stacey A. Missmer⁴, Grant W. Montgomery³, Andrew P. Morris¹, Dale R. Nyholt³, Jodie N. Painter³, Fenella Roseman⁵, Susan A. Treloar⁸, Peter M. Visscher⁹, Leanne Wallace³, Krina T. Zondervan^1,5.

¹Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK, ²Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK, ³Queensland Institute of Medical Research, Herston, QLD, Australia, ⁴Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA, ⁵Nuffield Department of Obstetrics and Gynaecology, University of Oxford, John Radcliffe Hospital, Oxford, UK, ⁶Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia, ⁷Harvard School of Public Health, Boston, MA, USA, ⁸Centre for Military and Veterans' Health, The University of Queensland, Mayne Medical School, QLD, Australia, ⁹The University of Queensland Diamantina Institute, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia.

The GIANT Consortium

Joshua C. Randall^1,2, Thomas W. Winkler³, Zoltan Kutalik^4,5, Sonja I. Berndt⁶, Anne U. Jackson⁷, Keri L. Monda⁸, Tuomas O. Kilpelainen⁹, Tonu Esko^10,11, Reedik Magi^2,10, Shengxu Li^9,12, Tsegaselassie Workalemahu¹³, Mary F. Feitosa¹⁴, Damien C. Croteau-Chonka¹⁵, Felix R. Day⁹, Tove Fall¹⁶, Teresa Ferreira², Stefan Gustafsson¹⁶, Adam E. Locke⁷, Iain Mathieson², Andre Scherag¹⁷, Sailaja Vedantam^18,19,20, Andrew R. Wood²¹, Liming Liang^22,23, Valgerdur Steinthorsdottir²⁴, Gudmar Thorleifsson²⁴, Emmanouil T. Dermitzakis²⁵, Antigone S. Dimas^2,25,26, Fredrik Karpe²⁷, Josine L. Min², George Nicholson^28,29, Deborah J. Clegg³⁰, Thomas Person³⁰, Jon P. Krohn², Sabrina Bauer³¹, Christa Buechler³¹, Kristina Eisinger³¹, DIAGRAM Consortium, Amelie Bonnefond³², Philippe Froguel^32,33, MAGIC Investigators, Jouke-Jan Hottenga³⁴, Inga Prokopenko^2,27,Lindsay L. Waite³⁵, Tamara B. Harris³⁶, Albert Vernon Smith^37,38, Alan R. Shuldiner^39,40, Wendy L. McArdle⁴¹, Mark J. Caulfield⁴², Patricia B. Munroe⁴², Henrik Gonberg¹⁶, Yii-Der Ida Chen^43,44, Guo Li⁴⁵, Jacques S. Beckmann^46,4, Toby Johnson^4,5,42, Unnur Thorsteinsdottir2^4,47, Maris Teder-Laving¹⁰, Kay-Tee Khaw⁴⁸, Nicholas J. Wareham⁹, Jing Hua Zhao⁹, Najaf Amin4⁹,Ben A. Oostra5^0,51,52, Aldi T. Kraja1⁴, Michael A. Province¹⁴, L. Adrienne Cupples⁵³, Nancy L. Heard- Costa⁵⁴, Jaakko Kaprio^55,56,57, Samuli Ripatti^1,57,58, Ida Surakka^57,58, Francis S. Collins⁵⁹, Jouko Saramies⁶⁰, Jaakko Tuomilehto^61,62,63,64, Antti Jula⁶⁵, Veikko Salomaa⁶⁶,Jeanette Erdmann^67,68, Christian Hengstenberg⁶⁹, Christina Loley^68,70, Heribert Schunkert⁷⁰, Claudia Lamina⁷¹, H. Erich Wichmann^72,73, Eva Albrecht⁷⁴, Christian Gieger⁷⁴, Andrew A. Hicks⁷⁵, Asa Johansson^76,77, Peter P. Pramstaller^75,78,79, Sekar Kathiresan8^0,81,82, Elizabeth K. Speliotes^83,84, Brenda Penninx⁸⁵, Anna-Liisa Hartikainen⁸⁶, Marjo-Riitta Jarvelin^87,88,89,90, Ulf Gyllensten⁷⁶,Dorret I. Boomsma³⁴, Harry Campbell⁹¹, James F. Wilson⁹¹, Stephen J. Chanock⁶, Martin Farrall⁹², Anuj Goel9², Carolina Medina-Gomez^49,52,93, Fernando Rivadeneira^49,52,93, Karol Estrada^49,52,93, Andre G. Uitterlinden4^9,52,93, Albert Hofman^49,52, M. Carola Zillikens^52,93, Martin den Heijer⁹⁴, Lambertus A. Kiemeney^95,96,97, Andrea Maschio⁹⁸, Per Hall¹⁶, Jonathan Tyrer⁹⁹, Alexander Teumer¹⁰⁰, Henry Volzke¹⁰¹, Peter Kovacs¹⁰², Anke Tonjes^103,104, Massimo Mangino¹⁰⁵, Tim D. Spector¹⁰⁵, Caroline Hayward1⁰⁶, Igor Rudan⁹¹, Alistair S. Hall¹⁰⁷, Nilesh J. Samani^108,109, Antony Paul Attwood1^,110, Jennifer G. Sambrook^110,111, Joseph Hung^112,113, Lyle J. Palmer^114,115, Marja- Liisa Lokki¹¹⁶, Juha Sinisalo¹¹⁷, Gabrielle Boucher¹¹⁸, Heikki Huikuri¹¹⁹, Mattias Lorentzon¹²⁰, Claes Ohlsson¹²⁰, Niina Eklund^11,58, Johan G. Eriksson^121,122,123, Cristina Barlassina¹²⁴, Carlo Rivolta⁴, Ilja M. Nolte¹²⁵, Harold Snieder^125,126, Melanie M. Van der Klauw^126,127, Jana V. Van Vliet-Ostaptchouk1^26,127, Pablo V. Gejman^128,129, Jianxin Shi⁶, Kevin B. Jacobs^6,130, Zhaoming Wang^6,130, Stephan J. L. Bakker¹³¹, Irene Mateo Leach¹³², Gerjan Navis¹³¹, Pim van der Harst^132,133, Nicholas G. Martin¹³⁴, Sarah E. Medland¹³⁴, Grant W. Montgomery¹³⁵, Jian Yang¹³⁶, Daniel I. Chasman^137,138, Paul M. Ridker^137,138, Lynda M. Rose¹³⁷, Terho Lehtimaki¹³⁹,Olli Raitakari^140,141, Devin Absher³⁵, Carlos Iribarren¹⁴², Hanneke Basart¹⁴³, Kees G. Hovingh¹⁴³, Elina Hypponen¹⁴⁴, Chris Power¹⁴⁴, Denise Anderson1^45,146, John P. Beilby^113,147,148, Jennie Hui^{113,147,148,149}, Jennifer Jolley¹¹⁰, Hendrik Sager¹⁵⁰, Stefan R. Bornstein¹⁵¹,Peter E. H. Schwarz¹⁵¹, Kati Kristiansson^57,58, Markus Perola^10,57,58, Jaana Lindstrom⁶³, Amy J. Swift⁵⁹, Matti Uusitupa1^52,153, Mustafa Atalay¹⁵⁴, Timo A. Lakka1^54,155, Rainer Rauramaa^155,156, Jennifer L. Bolton⁹¹, Gerry Fowkes⁹¹, Ross M. Fraser⁹¹, Jackie F. Price⁹¹, Krista Fischer¹⁰, Kaarel Krjutaikov¹⁰, Andres Metspalu¹⁰, Evelin Mihailov^10,11, Claudia Langenberg^9,157, Jian'an Luan⁹, Ken K. Ong^9,158, Peter S. Chines⁵⁹, Sirkka M. Keinanen-Kiukaanniemi^159,160, Timo E. Saaristo^161,162, Sarah Edkins¹, Paul W. Franks^163,164,165, Goran Hallmans¹⁶⁵, Dmitry Shungin^163,165,166, Andrew David Morris¹⁶⁷, Colin N. A. Palmer¹⁶⁷, Raimund Erbel¹⁶⁸, Susanne Moebus¹⁷, Markus M. Nothen^169,170, Sonali Pechlivanis¹⁷, Kristian Hveem¹⁷¹, Narisu Narisu⁵⁹, Anders Hamsten¹⁷², Steve E. Humphries¹⁷³, Rona J. Strawbridge¹⁷², Elena Tremoli¹⁷⁴, Harald Grallert¹⁷⁵, Barbara Thorand¹⁷⁶, Thomas Illig^175,177, Wolfgang Koenig¹⁷⁸, Martina Muller-Nurasyid^74,179,180, Annette Peters¹⁷⁶, Bernhard O. Boehm¹⁸¹, Marcus E. Kleber^182,183, Winfried Marz^183,184, Bernhard R. Winkelmann¹⁸⁵, Johanna Kuusisto¹⁸⁶, Markku Laakso¹⁸⁶, Dominique Arveiler¹⁸⁷, Giancarlo Cesana¹⁸⁸, Kari Kuulasmaa⁶⁶, Jarmo Virtamo⁶⁶, John W. G. Yarnell¹⁸⁹, Diana Kuh¹⁵⁸, Andrew Wong¹⁵⁸, Lars Lind¹⁹⁰, Ulf de Faire¹⁹¹, Bruna Gigante¹⁹¹, Patrik K. E. Magnusson¹⁶, Nancy L. Pedersen¹⁶, George Dedoussis¹⁹², Maria Dimitriou¹⁹², Genovefa Kolovou¹⁹³, Stavroula Kanoni¹, Kathleen Stirrups¹, Lori L. Bonnycastle⁵⁹, Inger Njølstad¹⁹⁴, Tom Wilsgaard1⁹⁴, Andrea Ganna¹⁶, Emil Rehnberg¹⁶, Aroon Hingorani¹⁵⁷, Mika Kivimaki¹⁵⁷, Meena Kumari¹⁵⁷, Themistocles L. Assimes¹⁹⁵, Ines Barroso^1,196, Michael Boehnke⁷, Ingrid B. Borecki1⁴, Panos Deloukas¹, Caroline S. Fox¹⁹⁷, Timothy Frayling²¹, Leif C. Groop¹⁹⁸, Talin Haritunians¹⁹⁹, David Hunter^13,22,200, Erik Ingelsson¹⁶, Robert Kaplan²⁰¹, Karen L. Mohlke¹⁵, Jeffrey R. O'Connell³⁹, David Schlessinger²⁰², David P. Strachan²⁰³, Kari Stefansson^24,47, Cornelia M. van Duijn^49,52,204, Gonc çalo R. Abecasis⁷, Mark I. McCarthy^2,27,205, Joel N. Hirschhorn^18,19,20, Lu Qi^13,200, Ruth J. F. Loos^9,206, Cecilia M. Lindgren², Kari E. North⁸, Iris M. Heid^3,74

¹Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK, ²Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK, ³Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany, ⁴Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland, ⁵Swiss Institute of Bioinformatics, Lausanne, Switzerland, ⁶Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA, ⁷Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA, ⁸Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, ⁹MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK, ¹⁰Estonian Genome Center, University of Tartu, Tartu, Estonia, ¹¹Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia, ¹²Department of Epidemiology, Tulane School of Public Health and Tropical Medicine, New Orleans, LA, USA, ¹³Department of Nutrition, Harvard School of Public Health, Boston, MA, USA, ¹⁴Department of Genetics, Washington University School of Medicine, St. Louis, MI, USA, ¹⁵Department of Genetics, University of North Carolina, Chapel Hill, NC, USA, ¹⁶Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, ¹⁷Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany, ¹⁸Divisions of Genetics and Endocrinology and Program in Genomics, Children's Hospital, Boston, MA, USA, ¹⁹Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA, ²⁰Department of Genetics, Harvard Medical School, Boston, MA, USA, ²¹Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, UK, ²²Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA, ²³Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA, ²⁴deCODE Genetics, Reykjavik, Iceland, ²⁵Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland, ²⁶Biomedical Sciences Research Center Al. Fleming, Vari, Greece, ²⁷Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK, ²⁸Department of Statistics, University of Oxford, Oxford, UK, ²⁹MRC Harwell, Harwell, UK, ³⁰University of Texas Southwestern Medical Center, Dallas, TX, USA, ³¹Regensburg University Medical Center, Innere Medizin I, Regensburg, Germany, ³²CNRS UMR8199-IBL-Institut Pasteur de Lille, Lille, France, ³³Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK, ³⁴Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands, ³⁵Hudson Alpha Institute for Biotechnology, Huntsville, AL, USA, ³⁶Laboratory of Epidemiology, Demography, Biometry, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA, ³⁷Icelandic Heart Association, Kopavogur, Iceland, ³⁸University of Iceland, Reykjavik, Iceland, ³⁹Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA, ⁴⁰Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA, ⁴¹School of Social and Community Medicine, University of Bristol, Bristol, UK, ⁴²Clinical 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, London, UK, ⁴³Department of OB/GYN and Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA, ⁴⁴Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, CA, USA, ⁴⁵Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA, ⁴⁶Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV) University Hospital, Lausanne, Switzerland, ⁴⁷Faculty of Medicine, University of Iceland, Reykjav ik, Iceland, ⁴⁸Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, UK, ⁴⁹Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands, ⁵⁰Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands, ⁵¹Centre for Medical Systems Biology & Netherlands Consortium on Healthy Aging, Leiden, The Netherlands, ⁵²Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands, ⁵³Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA, ⁵⁴Department of Neurology, Boston University School of Medicine, Boston, MA, USA, ⁵⁵National Institute for Health and Welfare, Unit for Child and Adolescent Psychiatry, Helsinki, Finland, ⁵⁶Finnish Twin Cohort Study, Department of Public Health, University of Helsinki, Helsinki, Finland, ⁵⁷Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland, ⁵⁸National Institute for Health and Welfare, Department of Chronic Disease Prevention, Unit of Public Health Genomics, Helsinki, Finland, ⁵⁹Genome Technology Branch, National Human Genome Research Institute, NIH, Bethesda, MD, USA, ⁶⁰South Karelia Central Hospital, Lappeenranta, Finland, ⁶¹Red RECAVA Grupo RD06/0014/0015, Hospital Universitario, La Paz, Madrid, Spain, ⁶²Centre for Vascular Prevention, Danube-University Krems, Krems, Austria, ⁶³National Institute for Health and Welfare, Diabetes Prevention Unit, Helsinki, Finland, ⁶⁴South Ostrobothnia Central Hospital, Seinajoki, Finland, ⁶⁵National Institute for Health and Welfare, Department of Chronic Disease Prevention, Population Studies Unit, Turku, Finland, ⁶⁶National Institute for Health and Welfare, Department of Chronic Disease Prevention, Chronic Disease Epidemiology and Prevention Unit, Helsinki, Finland, ⁶⁷Nordic Center of Cardiovascular Research (NCCR), Lu¨beck, Germany, ⁶⁸Universita¨t zu Lu¨beck, Medizinische Klinik II, Lu¨beck, Germany, ⁶⁹Institut fu¨r Medizinische Biometrie und Statistik, Universita¨t zu Lu¨beck, Universita¨tsklinikum Schleswig-Holstein, Campus Lu¨beck, Lu¨beck, Germany, ⁷⁰Deutsches Herzzentrum Mu¨nchen and DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany, ⁷¹Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria, ⁷²Institute of Epidemiology I, Helmholtz Zentrum Mu¨nchen – German Research Center for Environmental Health, Neuherberg, Germany, ⁷³Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universita¨t, and Klinikum Grosshadern, Munich, Germany, ⁷⁴Institute of Genetic Epidemiology, Helmholtz Zentrum Mu¨nchen – German Research Center for Environmental Health, Neuherberg, Germany, ⁷⁵Center for Biomedicine, European Academy Bozen/Bolzano (EURAC), Bolzano/Bozen, Italy, Affiliated Institute of the University of Lu¨beck, Lu¨beck, Germany, ⁷⁶Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden, ⁷⁷Uppsala Clinical Research Center, Uppsala University Hospital, Uppsala, Sweden, ⁷⁸Department of Neurology, General Central Hospital, Bolzano, Italy, ⁷⁹Department of Neurology, University of Lu¨beck, Lu¨beck, Germany, ⁸⁰Cardiovascular Research Center and Cardiology Division, Massachusetts General Hospital, Boston, MA, USA, ⁸¹Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA, ⁸²Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA, ⁸³Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA, ⁸⁴Department of Internal Medicine, Division of Gastroenterology, University of Michigan, Ann Arbor, MI, USA, ⁸⁵Department of Psychiatry, University Medical Centre Groningen, Groningen, The Netherlands, ⁸⁶Department of Clinical Sciences/Obstetrics and Gynecology, University of Oulu, Oulu, Finland, ⁸⁷Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK, ⁸⁸Institute of Health Sciences, University of Oulu, Oulu, Finland, ⁸⁹Biocenter Oulu, University of Oulu, Oulu, Finland, ⁹⁰National Institute for Health and Welfare, Oulu, Finland, ⁹¹Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK, ⁹²Cardiovascular Medicine, University of Oxford, Wellcome Trust Centre for Human Genetics, Oxford, UK, ⁹³Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands, ⁹⁴Department of Internal Medicine, VU University Medical Centre, Amsterdam, The Netherlands, ⁹⁵Department of Epidemiology, Biostatistics and HTA, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands, ⁹⁶Department of Urology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands, ⁹⁷Comprehensive Cancer Center East, Nijmegen, The Netherlands, ⁹⁸Istituto di Neurogenetica e Neurofarmacologia del CNR, Monserrato, Cagliari, Italy, ⁹⁹Department of Oncology, University of Cambridge, Cambridge, UK, ¹⁰⁰Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany, ¹⁰¹Institute for Community Medicine, Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany, ¹⁰²Interdisciplinary Centre for Clinical Research, University of Leipzig, Leipzig, Germany, ¹⁰³University of Leipzig, IFB Adiposity Diseases, Leipzig, Germany, ¹⁰⁴Department of Medicine, University of Leipzig, Leipzig, Germany, ¹⁰⁵Department of Twin Research and Genetic Epidemiology, King's College London, London, UK, ¹⁰⁶MRC Human Genetics Unit, Institute for Genetics and Molecular Medicine, Western General Hospital, Edinburgh, UK, ¹⁰⁷Division of Cardiovascular and Neuronal Remodelling, Multidisciplinary Cardiovascular Research Centre, Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds, UK, ¹⁰⁸Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, UK, ¹⁰⁹Leicester NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester, UK, ¹¹⁰Department of Haematology, University of Cambridge, Cambridge, UK, ¹¹¹NHS Blood and Transplant, Cambridge Centre, Cambridge, UK, ¹¹²School of Medicine and Pharmacology, The University of Western Australia, Nedlands, WA, Australia, ¹¹³Busselton Population Medical Research Foundation, Inc., Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia, ¹¹⁴Genetic Epidemiology and Biostatistics Platform, Ontario Institute for Cancer Research, Toronto, Canada, ¹¹⁵Prosserman Centre for Health Research, Samuel Lunenfeld Research Institute, Toronto, Canada, ¹¹⁶Transplantation Laboratory, Haartman Institute, University of Helsinki, Helsinki, Finland, ¹¹⁷Division of Cardiology, Cardiovascular Laboratory, Helsinki University Central Hospital, Helsinki, Finland, ¹¹⁸Montreal Heart Institute, Montreal, QC, Canada, ¹¹⁹Institute of Clinical Medicine, Department of Internal Medicine, University of Oulu, Oulu, Finland, ¹²⁰Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, ¹²¹Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland, ¹²²National Institute for Health and Welfare, Helsinki, Finland, ¹²³Helsinki University Central Hospital, Unit of General Practice, Helsinki, Finland, ¹²⁴University of Milan, Department of Medicine, Surgery and Dentistry, Milano, Italy, ¹²⁵Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands, ¹²⁶LifeLines Cohort Study, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands, ¹²⁷Department of Endocrinology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands, ¹²⁸University of Chicago, Chicago, IL, USA, ¹²⁹Northshore University Healthsystem, Evanston, IL, USA, ¹³⁰Core Genotyping Facility, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD, USA, ¹³¹Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands, ¹³²Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands, ¹³³Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands, ¹³⁴Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, QLD, Australia, ¹³⁵Molecular Epidemiology Laboratory, Queensland Institute of Medical Research, QLD, Australia, ¹³⁶Queensland Statistical Genetics Laboratory, Queensland Institute of Medical Research, QLD, Australia, ¹³⁷Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA, ¹³⁸Harvard Medical School, Boston, MA, USA, ¹³⁹Department of Clinical Chemistry, University of Tampere and Tampere University Hospital, Tampere, Finland, ¹⁴⁰Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland, ¹⁴¹The Department of Clinical Physiology, Turku University Hospital, Turku, Finland, ¹⁴²Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA, ¹⁴³Department of Vascular Medicine, Academic Medical Center, Amsterdam, The Netherlands, ¹⁴⁴Centre For Paediatric Epidemiology and Biostatistics/MRC Centre of Epidemiology for Child Health, University College of London Institute of Child Health, London, UK, ¹⁴⁵Telethon Institute for Child Health Research, West Perth, WA, Australia, ¹⁴⁶Centre for Child Health Research, The University of Western Australia, Perth, Australia, ¹⁴⁷PathWest Laboratory of Western Australia, Department of Molecular Genetics, QEII Medical Centre, Nedlands, WA, Australia, ¹⁴⁸School of Pathology and Laboratory Medicine, University of Western Australia, Nedlands, WA, Australia, ¹⁴⁹School of Population Health, The University of Western Australia, Nedlands, WA, Australia, ¹⁵⁰Medizinische Klinik II, Universita¨ t zu Lu¨beck, Lu¨beck, Germany, ¹⁵¹Department of Medicine III, University of Dresden, Medical Faculty Carl Gustav Carus, Dresden, Germany, ¹⁵²Department of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland, ¹⁵³Research Unit, Kuopio University Hospital, Kuopio, Finland, ¹⁵⁴Institute of Biomedicine/Physiology, University of Eastern Finland, Kuopio Campus, Kuopio, Finland, ¹⁵⁵Kuopio Research Institute of Exercise Medicine, Kuopio, Finland, ¹⁵⁶Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland, ¹⁵⁷Department of Epidemiology and Public Health, University College London, London, UK, ¹⁵⁸MRC Unit for Lifelong Health & Ageing, London, UK, ¹⁵⁹Faculty of Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland, ¹⁶⁰Unit of General Practice, Oulu University Hospital, Oulu, Finland, ¹⁶¹Finnish Diabetes Association, Tampere, Finland, ¹⁶²Pirkanmaa Hospital District, Tampere, Finland, ¹⁶³Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmo¨, Lund University, Malmo¨, Sweden, ¹⁶⁴Department of Nutrition, Harvard School of Public Health, Boston, MA, USA, ¹⁶⁵Department of Public Health & Clinical Medicine, Umeå University, Umeå, Sweden, ¹⁶⁶Department of Odontology, Umeå University, Umea, Sweden, ¹⁶⁷Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK, ¹⁶⁸Clinic of Cardiology, West German Heart Centre, University Hospital of Essen, University Duisburg-Essen, Essen, Germany, ¹⁶⁹Institute of Human Genetics, University of Bonn, Bonn, Germany, ¹⁷⁰Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany, ¹⁷¹HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger, Norway, ¹⁷²Atherosclerosis Research Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden, ¹⁷³Cardiovascular Genetics, British Heart Foundation Laboratories, Rayne Building, University College London, London, UK, ¹⁷⁴Department of Pharmacological Sciences, University of Milan, Monzino Cardiology Center, IRCCS, Milan, Italy, ¹⁷⁵Unit for Molecular Epidemiology, Helmholtz Zentrum Munchen – German Research Center for Environmental Health, Neuherberg, Germany, ¹⁷⁶Institute of Epidemiology II, Helmholtz Zentrum Munchen – German Research Center for Environmental Health, Neuherberg, Germany, ¹⁷⁷Hannover Unified Biobank, Hannover Medical School, Hannover, Germany, ¹⁷⁸Department of Internal Medicine II – Cardiology, University of Ulm Medical Center, Ulm, Germany, ¹⁷⁹Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universitat, Munich, Germany, ¹⁸⁰Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universitat, Munich, Germany, ¹⁸¹Division of Endocrinology and Diabetes, Department of Medicine, University Hospital, Ulm, Germany, ¹⁸²LURIC Study nonprofit LLC, Freiburg, Germany, ¹⁸³Mannheim Institute of Public Health, Social and Preventive Medicine, Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany, ¹⁸⁴Synlab Academy, Mannheim, Germany, ¹⁸⁵Cardiology Group, Frankfurt-Sachsenhausen, Germany, ¹⁸⁶Department of Medicine, University of Kuopio and Kuopio University Hospital, Kuopio, Finland, ¹⁸⁷Department of Epidemiology and Public Health, Faculty of Medicine, Strasbourg, France, ¹⁸⁸Department of Clinical Medicine, University of Milano-Bicocca, Monza, Italy, ¹⁸⁹Centre for Public Health, Queen's University, Belfast, UK, ¹⁹⁰Department of Medical Sciences, Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden, ¹⁹¹Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden, ¹⁹²Department of Dietetics-Nutrition, Harokopio University, Athens, Greece, ¹⁹³First Cardiology Department, Onassis Cardiac Surgery Center, Athens, Greece, ¹⁹⁴Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway, ¹⁹⁵Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA, ¹⁹⁶University of Cambridge Metabolic Research Labs, Institute of Metabolic Science Addenbrooke's Hospital, Cambridge, UK, ¹⁹⁷Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham Heart Study, Framingham, MA, USA, ¹⁹⁸Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmo¨, Sweden, ¹⁹⁹Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA, ²⁰⁰Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA, ²⁰¹Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA, ²⁰²Laboratory of Genetics, National Institute on Aging, Baltimore, MD, USA, ²⁰³Division of Community Health Sciences, St George's, University of London, London, UK, ²⁰⁴Center of Medical Systems Biology, Leiden University Medical Center, Leiden, The Netherlands, ²⁰⁵Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, UK, ²⁰⁶Genetics of Obesity and Related Metabolic Traits Program, The Charles Bronfman Institute of Personalized Medicine, Child Health and Development Institute, Mount Sinai School of Medicine, NY, USA.