Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study

Abstract

The prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 (P= 3.6 × 10⁻⁸) located near GPR98 and ARRDC3. For SBP, the strongest signal was rs2258119 in C21orf91 (P= 4.7 × 10⁻⁸). The top IBC association for SBP was rs2012318 (P= 6.4 × 10⁻⁶) near SLC25A42 and for DBP was rs2523586 (P= 1.3 × 10⁻⁶) near HLA-B. None of the top variants replicated in additional AA (n = 11 882) or European-American (n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples (SH2B3, P= 0.009; TBX3-TBX5, P= 0.03; and CSK-ULK3, P= 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexity.

INTRODUCTION

In the USA, hypertension is more common among people of African compared with European descent. According to data from the National Health and Nutrition Examination Survey (NHANES) collected between 1999 and 2004, the prevalence of hypertension in African Americans (AAs) was 40%, compared with 27% in European Americans (1,2). The risk of suffering hypertensive end-organ damage including end-stage renal disease, heart failure and stroke is also greater among AAs than European Americans (1,3). Furthermore, in 2004, the death rate from hypertension was three times greater in AAs compared with European Americans (4,5).

A portion of the excess burden of hypertension among AAs may be due to genetic susceptibility. Admixture mapping analysis of hypertension suggested that African ancestry is associated with hypertension (6). Two recent genome-wide association studies (GWASs) of blood pressure, each involving ∼30 000 participants of European descent, have identified common genetic variants at 13 loci that are associated with blood pressure or hypertension. It is unknown at present, however, if these variants confer susceptibility to hypertension in people of African descent. Prior investigations have reported considerable differences in genetic association patterns for blood pressure and other traits across ethnic/racial groups. These association differences may be due to differences in linkage disequilibrium (LD) patterns, allele frequencies, causal pathways, or environmental exposures. Therefore, the relations of genetic variants to blood pressure must be examined within ethnicities.

The first GWAS for blood pressure phenotypes in AAs did not identify any SNPs reaching genome-wide significance (P < 5 × 10⁻⁸) with hypertension, although six were associated with systolic blood pressure (SBP) in a secondary analysis in a subset of 508 normotensive individuals (7). The present study represents the largest GWAS for blood pressure in AAs to date. We also attempted replication of our top findings in individuals of African ancestry and individuals of European ancestry. Understanding genetic contributions to blood pressure may provide insight into the mechanisms underlying ethnic disparities in cardiovascular disease, and findings may assist in more personalized and targeted treatments to prevent target-organ damage and its associated morbidity and mortality.

RESULTS

Study sample

The analyzed study sample included individuals from five cohorts [Atherosclerosis Risk in Communities (ARIC) study (n = 2511); Coronary Artery Risk Development in Young Adults (CARDIA, n = 833); Cleveland Family Study (CFS, n = 489), Jackson Heart Study (JHS, n = 2017) and Multi-Ethnic Study of Atherosclerosis (MESA, n = 1623); total n = 7473] for the GWAS analysis and six cohorts [ARIC (n = 2692), CARDIA (n = 1134), CFS (n = 530), Cardiovascular Health Study (CHS; n = 735), JHS (n = 1916) and MESA (n = 1584); total n = 8591)] for the IBC analysis. For JHS, we excluded these individuals who were overlapped with ARIC participants. The cohort-specific sample characteristics are described in Table 1.

Table 1.

Study sample characteristics

Study	N	Male (%)	Antihypertensive medication (%)	Age (years)		BMI (kg/m2)		DBP (mmHg)		SBP (mmHg)
				Mean	SD	Mean	SD	Mean	SD	Mean	SD
Affymetrix 6.0 genotyping
ARIC	2511	37.1	44.0	53.3	5.8	29.6	6.0	79.7	12.1	128.3	20.8
CARDIA	833	38.1	13.0	39.5	3.9	30.8	7.5	76.9	12.1	116.9	16.4
CFS	489	40.7	38.9	45.7	16.2	34.6	9.6	76.5	10.7	128.2	16.0
JHS	2017	38.7	46.3	49.9	11.9	32.3	7.8	80.0	10.6	124.9	18.0
MESA	1623	45.7	50.5	62.2	10.1	30.2	5.9	74.5	10.2	131.4	21.7
IBC genotyping
ARIC	2692	36.9	44.1	53.2	5.8	29.7	6.1	79.4	12.0	128.0	20.7
CARDIA	1134	40.5	13.8	39.6	3.8	30.7	7.5	77.0	12.4	117.1	16.3
CFS	530	42.1	37.7	45.2	16.1	34.2	9.6	76.4	10.8	127.7	15.8
CHS	735	37.6	51.8	73.0	5.7	28.5	5.6	75.1	11.3	141.8	22.7
JHS	1916	39.2	46.0	49.9	12.0	32.2	7.8	79.9	10.6	124.9	18.0
MESA	1584	46.1	50.9	62.2	10.1	30.2	5.9	74.6	10.3	131.7	21.6

Study characteristics are shown for cohort samples examined in meta-analysis. N, sample size—the number of individuals with genotype and phenotype data available.

Genome-wide association of Candidate Gene Association Resource AA cohorts for blood pressure

Meta-analysis quantile–quantile and Manhattan plots of genome-wide SNPs including both genotyped and imputed for the two blood pressure phenotypes are presented in Supplementary Material, Figure S1. If an SNP was genotyped, we always reported the result based on genotyped data. In the meta-analysis of GWAS data, one SNP for diastolic blood pressure (DBP) and one for SBP attained genome-wide significance (defined as P < 5 × 10⁻⁸; Table 2). The strongest signal for DBP was rs10474346 (P= 3.6 × 10⁻⁸) in the intergenic region of GPR98 and ARRDC3 on chromosome 5q14. This SNP is in tight LD with a missense SNP (rs4377733; pairwise r² = 0.9) in hypothetical gene LOC729040. For SBP, the strongest signal was for rs2258119 in C21orf91 on chromosome 21q21 (P= 4.7 × 10⁻⁸), which is in tight LD with nearby rs2824495 (r² = 1.0), which is a missense SNP in C21orf91. Suggestive evidence of association was detected in the regions of IPO13 (rs1990151, P= 7.4 × 10⁻⁷), FMNL2 (rs13413144, P= 5.6 × 10⁻⁷) and GPD2 (rs592582, P= 4.5 × 10⁻⁷). The regional plots of association for the genome-wide significant loci are presented in Figure 1.

Table 2.

Top associated SNPs for blood pressure in AAs from meta-analysis of Affymetrix 6.0 arrays

BP trait	SNP ID	Chr	Position	Type	Nearest gene	Effect allele	Effect allele frequency	Other allele	CARe meta-analysis, DBP				CARe meta-analysis, SBP
									Beta	SE	P-value	Heterogeneity P	Beta	SE	P-value	Heterogeneity P
SBP	rs1990151	1	44 186 879	Genotyped	IPO13	A	0.05	G	1.21	0.41	3.39 × 10−3	0.56	3.48	0.70	7.39 × 10−7	0.41
SBP	rs13413144	2	153 183 499	Imputed	FMNL2	T	0.05	A	1.75	0.50	4.77 × 10−4	0.67	4.28	0.86	5.55 × 10−7	0.08
SBP	rs592582	2	157 481 632	Imputed	GPD2	G	0.41	T	0.75	0.20	1.20 × 10−4	0.94	1.66	0.33	4.46 × 10−7	0.91
DBP	rs1858309	5	90 592 314	Imputed	GPR98	C	0.29	T	1.09	0.20	8.76 × 10−8	0.04	1.35	0.35	1.03 × 10−4	0.25
DBP	rs7709572	5	90 592 789	Genotyped	GPR98	G	0.30	C	1.10	0.20	7.41 × 10−8	0.04	1.34	0.35	1.05 × 10−4	0.29
DBP	rs7724489	5	90 594 122	Imputed	GPR98	A	0.29	T	1.08	0.21	1.17 × 10−7	0.04	1.35	0.35	1.07 × 10−4	0.25
DBP	rs10474346	5	90599895	Imputed	GPR98/ARRDC3	C	0.33	T	1.10	0.20	3.56 × 10−8	0.11	1.40	0.34	3.73 × 10−5	0.42
SBP	rs243601	21	18 081 637	Imputed	C21orf91	G	0.49	A	0.63	0.21	2.26 × 10−3	0.28	1.80	0.35	2.61 × 10−7	0.36
SBP	rs243603	21	18 082 171	Imputed	C21orf91	C	0.45	T	−0.59	0.20	4.06 × 10−3	0.29	−1.75	0.35	3.91 × 10−7	0.52
SBP	rs243605	21	18 082 991	Imputed	C21orf91	C	0.46	G	−0.60	0.20	3.62 × 10−3	0.29	−1.76	0.35	3.83 × 10−7	0.40
SBP	rs243607	21	18 083 386	Imputed	C21orf91	G	0.44	A	−0.59	0.20	3.84 × 10−3	0.24	−1.76	0.35	1.99 × 10−7	0.46
SBP	rs243609	21	18 083 560	Imputed	C21orf91	T	0.40	C	−0.56	0.22	1.01 × 10−2	0.18	−1.87	0.37	4.41 × 10−7	0.48
SBP	rs2220511	21	18 086 782	Imputed	C21orf91	C	0.50	T	−0.52	0.21	1.17 × 10−2	0.58	−1.75	0.35	4.68 × 10−7	0.47
SBP	rs2258119	21	18089350	Genotyped	C21orf91	C	0.32	T	0.79	0.20	6.90 × 10−5	0.75	1.84	0.34	4.69 × 10−8	0.70

Beta, the effect size on blood pressure in mmHg, per effect allele based on the additive genetic model. Results of the two SNPs with genome-wide significance (P< 5 × 10⁻⁸) are shown in bold.

Figure 1.

Regional plots of top blood pressure loci in AAs from meta-analysis of Affymetrix 6.0 arrays. One locus for diastolic BP (A) and two loci for SBP (B and C). For each locus, we show the region extending to within 500 kb of the associated SNP on either side. Statistical significance of SNPs around each locus are plotted as −log₁₀(P) against chromosomal position. For each locus, the most significant SNP is shown in blue. If the most significant SNP of a locus is imputed SNP (as in A), then the most significant genotype SNP is shown in blue too. Among genotyped SNPs, SNPs in yellow have r² ≥ 0.8 with the most significant genotyped SNP. Imputed SNPs are shown in grey. Superimposed on the plot are gene locations (green) and recombination rate (blue). Chromosome positions are based on HapMap release 22 build 36.

Pooled genotype data analysis was conducted for the five cohorts with Affymetrix 6.0 genotyping data using FamCC (8), on genotyped SNPs only. In general, the results were highly consistent with those from the meta-analysis.

Association of SNPs on the IBC chip with blood pressure

Meta-analysis of Candidate Gene Association Resource (CARe) cohorts with IBC chip data did not identify any SNPs that reached the pre-specified array-wide significance level based on the estimated effective number of independent tests (SNPs) after adjusting for multiple testing (0.05/25 000, P < 2.0 × 10⁻⁶). There was suggestive evidence of association with SBP for two genes NUCB2 (rs214070, P= 8.7 × 10⁻⁶; in LD with missense SNP rs757081, r² = 0.7) and SLC25A42 (rs2012318, P= 6.4 × 10⁻⁶; in LD with missense SNP rs4808907, in SFRS14, r² = 0.6); these results are summarized in Table 3. The top results for DBP were rs2523586 (near HLA-B, P= 1.3 × 10⁻⁶) and rs4930130 (near KCNQ1, P= 3.2 × 10⁻⁶). These and other SNPs with P < 10⁻⁴ are summarized in Supplementary Material, Table S3.

Table 3.

Top associated SNPs for blood pressure in AAs from meta-analysis of IBC arrays

BP trait	SNP ID	Chr	Position	Type	Nearest gene	Effect allele	Effect allele frequency	Other allele	CARe meta-analysis, DBP				CARe meta-analysis, SBP
									Beta	SE	P-value	Heterogeneity P	Beta	SE	P-value	Heterogeneity P
AAs
SBP	rs12408339	1	154 622 134	Imputed	RHBG	A	0.08	G	−1.50	0.44	6.55 × 10−4	0.27	−3.32	0.75	8.64 × 10−6	0.53
SBP	rs214070	11	17 261 893	Genotyped	NUCB2	A	0.06	T	0.83	0.39	3.42 × 10−2	0.16	2.97	0.67	8.65 × 10−6	0.24
SBP	rs6511018	19	19 047 705	Imputed	SLC25A42	G	0.35	A	0.59	0.19	1.69 × 10−3	0.08	1.43	0.32	5.83 × 10−6	0.03
SBP	rs12985799	19	19 048 575	Imputed	SLC25A42	C	0.33	T	0.61	0.19	1.29 × 10−3	0.15	1.48	0.32	3.24 × 10−6	0.09
SBP	rs2012318	19	19 069 240	Genotyped	SLC25A42	C	0.35	T	0.58	0.19	1.90 × 10−3	0.09	1.42	0.31	6.42 × 10−6	0.03
SBP	rs11666627	19	19 072 238	Imputed	SLC25A42	C	0.35	T	0.59	0.19	1.55 × 10−3	0.09	1.47	0.32	3.00 × 10−6	0.03
SBP	rs10417974	19	19 083 070	Imputed	SLC25A42	C	0.35	T	0.59	0.19	1.57 × 10−3	0.06	1.46	0.32	3.71 × 10−6	0.03

Beta, the effect size on blood pressure in mmHg, per allele based on the additive genetic model.

Independent replication of top CARe SNPs in cohorts of African and European ancestry

Replication cohorts for the study are described in detail in Supplementary Material, Section II. Nine top SNPs (six selected from the genome-wide meta-analysis, two selected from the candidate gene meta-analysis and one selected from the CARDIA GWAS) in the CARe analyses were submitted for lookup in five AA cohorts [Maywood African-American study (n = 743), Howard University Family Study (HUFS, n = 1016), the International Collaborative Study on Hypertension in Blacks (ICSHIB, n = 1188), the Genetic Epidemiology Network of Arteriopathy (GENOA, n = 845) and the Women Health Initiative (WHI, n = 8090)] and in whites of European ancestry in the International Consortium for Blood Pressure (ICBP; n = 69 899). Criteria for declaring replication was either 5.0 × 10⁻⁸ for final meta-analysis of GWAS SNPs or 2.0 × 10⁻⁶ for final meta-analysis of IBC SNPs. Results of replication for SBP and DBP by replication cohort and those of the final meta-analysis of cohorts of African ancestry are provided in Table 4. None of the top SNPs from the Affymetrix 6.0 or the IBC array met the a priori criteria for replication after correcting for multiple comparisons. Results of replication by cohort are displayed in Supplementary Material, Table S4.

Table 4.

Meta-analysis of CARe and additional African-origin cohorts, as well as the P-values in ICBP

SNP ID	Chr	Position	Type	Nearest gene	SBP		DBP
					Meta P	ICBP P	Meta P	ICBP P
rs10474346	5	90 599 895	Affy6 Imputed	GPR98/ARRDC3	9.96 × 10−3	5.19 × 10−1	1.02 × 10−3	5.65 × 10−1
rs13413144	2	153 183 499	Affy6 Imputed	FMNL2	9.28 × 10−4	4.22 × 10−1	1.06 × 10−1	8.00 × 10−1
rs17610514a	11	55 652 374	CARDIA	Olfactory	8.50 × 10−5	2.59 × 10−1	3.90 × 10−5	2.20 × 10−1
rs1990151	1	44 186 879	Affy6 Genotyped	IPO13	1.14 × 10−3	8.20 × 10−1	8.63 × 10−3	8.14 × 10−1
rs2012318	19	19 069 240	IBC Genotyped	SLC25A42	5.68 × 10−6	5.92 × 10−1	6.21 × 10−3	6.04 × 10−1
rs214070b	11	17 261 893	IBC Genotyped	NUCB2	NA	3.23 × 10−2	NA	1.67 × 10−2
rs2258119	21	18 089 350	Affy6 Genotyped	C21orf91	5.00 × 10−4	5.28 × 10−1	1.41 × 10−2	6.54 × 10−1
rs592582	2	157 481 632	Affy6 Imputed	GPD2	9.15 × 10−3	9.16 × 10−1	1.22 × 10−1	6.59 × 10−1
rs7709572	5	90 592 789	Affy6 Genotyped	GPR98	1.38 × 10−2	4.56 × 10−1	6.49 × 10−4	5.68 × 10−1

Meta P, P-value by combining all cohorts of African ancestry;

ICBP P, one-sided P-value in ICBP data. For SNPs genotyped in Affy6, P-value < 5 × 10⁻⁸ is considered as statistically significant. For SNPs genotyped in IBC chip, P-value <2 × 10⁻⁶ is considered as statistically significant.

^aSNP rs17610514, failed WHI QC due to low concordance rate among duplicates (<98%) and/or low call rate (<95%) and thus was not included in the meta-analysis.

^bSNP rs214070 was not genotyped in the cohorts of African ancestry except CARe.

Lookup of published SNPs from previous studies of people with African ancestry

We examined whether published SNPs from GWAS of blood pressure in people of African ancestry (9) could be replicated in our sample (Supplementary Material, Table S2A). None of the previously reported loci for SBP or DBP replicated in our study.

Lookups of published SNPs from previous studies including populations of European ancestry

Two large-scale GWASs in European populations have been published, and 13 independent loci have been shown to be associated with blood pressure at a genome-wide significant level (10,11). We then performed one-sided test to examine if the association evidence for these published loci can be replicated in the CARe data sets. The one-sided test ensures the effect directions are the same in both discovery and replication samples. Of the SNPs reported in these two European GWASs (Table 5 and Supplementary Material, Tables S2A and S2B), we identified the following association signals in our AA samples: rs3184504 (SH2B3, one-sided P= 0.009), rs2384550 (TBX3-TBX5, one-sided P= 0.03) and rs6495122 (CSK-ULK3, one-sided P= 0.0004).

Table 5.

Lookup of top SNPs for SBP and DBP from the meta-analysis of CHARGE and Global BPgen

SNP identifier	Chr	Position	Nearest gene	Alleles (coded/other)	CHARGE + Global BPgen meta-analysis			CARe meta-analysis, DBP				CARe meta-analysis, SBP
					Beta	SE	P-value	Effect allele	Other allele	Effect	P-value	Effect allele	Other allele	Effect	P-value
SBP
rs12046278a	1	10 722 164	CASZ	T/C	−0.53	0.12	4.77 × 10−6	C	T	0.40	0.23	C	T	0.06	0.92
rs7571613a	2	190 513 907	PMS1	A/G	−0.54	0.13	1.90 × 10−5	G	A	−0.16	0.46	G	A	−0.28	0.45
rs448378	3	170 583 593	MDS1	A/G	−0.51	0.10	1.18 × 10−7	A	G	0.15	0.42	A	G	0.13	0.68
rs2736376a	8	11 155 175	MTMR9	C/G	−0.48	0.15	9.15 × 10−4	C	G	−0.31	0.13	C	G	−0.47	0.18
rs1910252a	8	49 569 915	EFCAB1	T/C	−0.43	0.13	6.13 × 10−4	T	C	0.03	0.90	T	C	−0.23	0.51
rs11014166a	10	18 748 804	CACNB	A/T	0.50	0.10	7.03 × 10−7	T	A	−0.20	0.44	T	A	−0.30	0.50
rs1004467a	10	104584497	CYP17A	A/G	1.05	0.16	1.28 × 10−10	G	A	0.01	0.97	G	A	−0.31	0.48
rs381815a	11	16858844	PLEKHA	T/C	0.65	0.11	1.89 × 10−9	T	C	0.41	0.07	T	C	0.62	0.10
rs2681492a	12	88537220	ATP2B1	T/C	0.85	0.13	3.76 × 10−11	C	T	−0.18	0.54	C	T	−0.22	0.65
rs3184504a	12	110368991	SH2B3	T/C	0.58	0.10	4.52 × 10−9	T	C	0.53	0.14	T	C	1.61	9.13 × 10−3
DBP
rs13423988a	2	68 764 770	GPR73-ARHGAP25	T/C	0.33	0.08	5.00 × 10−5	T	C	−0.10	0.62	T	C	0.08	0.81
rs13401889a	2	190 618 804	MST	T/C	−0.31	0.08	4.82 × 10−5	T	C	−0.01	0.94	T	C	0.16	0.63
rs9815354a	3	41887655	ULK4	A/G	0.49	0.08	2.54 × 10−9	A	G	0.16	0.49	A	G	0.26	0.40
rs7016759	8	49 574 969	EFCAB1	T/C	0.30	0.08	2.29 × 10−4	C	T	−0.27	0.50	C	T	−0.64	0.44
rs11014166a	10	18748804	CACNB2	A/T	0.37	0.06	1.24 × 10−8	T	A	−0.20	0.44	T	A	−0.30	0.50
rs11024074	11	16 873 795	PLEKHA7	T/C	−0.33	0.07	1.20 × 10−6	C	T	0.45	0.20	C	T	0.52	0.13
rs2681472a	12	88533090	ATP2B	A/G	0.50	0.08	1.47 × 10−9	G	A	−0.40	0.20	G	A	−0.61	0.25
rs3184504a	12	110368991	SH2B3	T/C	0.48	0.06	2.58 × 10−14	T	C	0.53	0.14	T	C	1.61	9.13 × 10−3
rs2384550	12	113837114	TBX3-TBX5	A/G	−0.35	0.06	3.75 × 10−8	A	G	−0.35	0.07	A	G	−0.75	0.03
rs6495122a	15	72912698	CSK-ULK3	A/C	0.40	0.06	1.84 × 10−10	C	A	−0.51	0.02	C	A	−1.32	3.74 × 10−4

SNPs in boldface attained P < 5 × 10⁻⁸ in meta-analysis of CHARGE and Global BPgen.

^aResults of SNPs are from imputed SNPs.

DISCUSSION

This study represents the largest GWAS of blood pressure in AAs to date including a total of 8591 individuals for discovery and 11 882 individuals of African descent and 69 899 of European descent for replication. In a meta-analysis across five US community-based cohorts using the Affymetrix 6.0 array, we identified two novel loci, rs2258119 and rs10474346, that reached genome-wide significance, but did not replicate in independent African-American samples. We replicated several previously reported European-American blood pressure SNPs in our CARe AA samples.

Top loci for the Affymetrix 6.0 array GWAS

We identified a locus on chromosome 5 that reached genome-wide significance for DBP in CARe African-American cohorts. The top SNP (rs10474346, P= 3.6 × 10⁻⁸) is in tight LD with a non-synonymous coding SNP rs4377733. Genes in the region include G protein-coupled receptor 98 (GPR98) and arrestin C (ARRDC3). GRR98 is a very large G-protein coupled receptor expressed in the central nervous system and other tissue and implicated in Usher syndrome characterized by hearing loss and retinitis pigmentosa. SNPs in GPR98 have been associated with markers of hyperglycemia in patients taking the antipsychotic medication olanzapine (12). Arrestin C is a peroxisome proliferator-activated receptor gamma (PPARG) ligand and PPARG activator. PPARs are a family of nuclear receptors that are activated by nutrient molecules and their derivatives (13). PPARG activators may play a role in hypertension and atherosclerosis through modification of inflammation and the innate immunity system in vascular cells (13,14).

Another locus that reached genome-wide significance for SBP in CARe AA cohorts is on chromosome 21, where a region was previously reported in admixture mapping analysis (15). The top SNP at this locus, rs2258119 (P= 4.7 × 10⁻⁸), is in tight LD with missense variant rs2824495 in C21orf91 (pairwise r² = 1.0). The minor allele frequencies of this SNP in HapMap CEU and YRI samples are 21 and 34%, respectively, which suggests that this SNP may contribute to the association signal observed in the admixture mapping analysis (15). This region includes CXADR (Coxsackie and Adenovirus receptors), which encodes a tight junction protein of the intercalated disks between cardiomyocytes. This protein is an entry point for virus uptake in myocarditis and is involved in cardiac remodeling (16). An SNP of interest, rs1990151 on chromosome 1, showed suggestive association with SBP (P= 7.4 × 10⁻⁷). This is an intronic SNP in importin beta (IPO13). Importin beta is a nuclear transport protein that modifies nuclear availability of glucocorticoids through nucleocytoplasmic shuttling (17). There is a potential link proposed between early-onset glucocorticoid exposure and hypertension through changes in gene expression and function in the kidney (18). Of note, another importin beta protein (IPO7) was identified by Adeyemo et al. (7) in a genome-wide association analysis of a normotensive subset of AAs.

Top SNPs from the meta-analysis of the IBC array

In our IBC array analysis, we identified suggestive evidence of association for rs2012318, which is an intronic SNP in SLC25A42, a carrier protein that transports cofactor coenzyme A and adenosine 3′,5′-diphosphate into the mitochondria in exchange for intramitochondrial (deoxy)adenine nucleotides and adenosine 3′,5′-diphosphate (19). SNPs in this region were associated with LDL cholesterol and triglyceride levels in a whole genome analysis of European populations (20).

Two tightly linked SNPs, rs4930130 and rs1791926 (r² = 1.0) on chromosome 11, were associated with DBP with P < 1 × 10⁻⁵. They are in proximity to KCNQ1, which encodes a protein for a voltage-gated potassium channel required for the repolarization phase of the cardiac action potential. The gene product is associated with hereditary long QT syndrome, Romano-Ward syndrome, Jervell and Lange-Nielsen syndrome and familial atrial fibrillation (21). Another signal of interest was found for rs1791926, near P2RY2 (purinergic receptor P2Y, G coupled 2) on chromosome 11q13.5-q14.1 that mediates vasoactive and proliferative stimuli. There is evidence that the purinergic system may affect the activity of epithelial sodium channel in the renal collecting duct, which is responsible for re-absorption of sodium (22,23). Genetic defects in this channel in humans have been associated with hypertension in Liddle's syndrome. P2Y2 (a homolog of P2RY2) knockout mice manifest a salt resistant hypertensive phenotype (24). A recent case–control association study by Wang et al. (25) showed an association of P2RY2 with hypertension in Japanese men.

Association evidence of SNPs with blood pressure in CARDIA

It is intriguing that we observed a strong association signal in a 1.26 Mb region on chromosome 11 (smallest P= 3.95 × 10⁻⁹ for rs17610514; Supplementary Material, Table S1) in AAs in the CARDIA cohort only. Although the allele frequencies for these significant SNPs are all relatively small (<4%), the results are unlikely due to the genotyping errors given the number of SNPs reaching genome-wide significance. The sentinel SNP is in tight LD with several missense variants in olfactory receptor genes. The subjects recruited in CARDIA cohort are much younger than in the other cohorts, suggesting that the association is stronger in populations composed of younger individuals.

A particularly important contribution of this study is the generalization of findings from two large meta-analyses of Europeans and European Americans (10,11) to individuals of African ancestry. The three loci, near the SH2B3, TBX3-TBX5 and CSK-ULK3 genes, provide evidence for common genetic variants influencing blood pressure phenotypes in AA and also suggest that at least some loci may confer broad susceptibility to hypertension across race/ethnicities.

Limitations

Because multiple cohorts were used to maximize the sample size in the analyses, heterogeneity in blood pressure measurement across the centers may bias our findings toward the null. Additionally, a substantial proportion of individuals were on blood pressure lowering medications, which may introduce some degree of misclassification of blood pressure. In addition, participants in JHS and ARIC were older with a large number on antihypertensive medications, whereas participants in CARDIA were significantly younger than the other cohorts with only a small percentage of participants on antihypertensive medications. We observed some evidence for heterogeneity across studies, with SNPs in GPR98 region (for DBP) on the Affymetrix 6.0 array (Table 2) and SNPs in the SLC25A42 region (for both DBP and SBP) on the IBC array (Table 3) displaying the smallest heterogeneity P-values. Heterogeneity in the association results across studies may have attenuated association P-values, but also revealed mechanisms of action of genetic variants on blood pressure.

We did not observe clear replication of our two top loci that were genome-wide significant in our CARe GWAS. Our replication cohorts were generally small thus reducing the power to replicate significant findings. We estimated the proportion of variation in blood pressure associated with the two genome-wide significant SNPs at ∼0.4% in CARe samples. Because of the winner's curse and the variation in LD between a true causal SNP and our identified SNP, our effect size may be overestimated, which may contribute to failure to replicate. In addition, population admixture may result in different LD patterns for the African-American samples from different geographical regions because the LD is dependent on the admixture proportion. It has been reported that the admixture proportion rate is different across the African-American population (26,27). Thus, replication analysis can be challenging in African-American populations.

These limitations are leveraged against the advantage of using large community-based cohorts of AAs for this analysis and the implementation of quality-control procedures in individual examination centers and the harmonization of imputation strategies and analytical methods.

CONCLUSIONS

We found evidence of genetic influences on SBP and DBP. Evidence of association in our GWAS was found for DBP (rs10474346 on chromosome 5 near GPR98 and ARRDC3) and for SBP (rs2258119 on chromosome 21in C21orf91). Caution should be paid because the two top SNPs identified in CARe GWAS were not replicated in independent cohorts of African ancestry, and further replication efforts with large sample size are warranted.

Of note, several previously reported EA blood pressure SNPs did replicate in our CARe AA samples. These SNPs are in the regions of SH2B3, TBX3-TBX5 and CSK-ULK3.

Implications

We identified genetic variants that reached genome-wide significance for SBP and DBP in a large number of AAs from the CARe consortium that did not replicate in a meta-analysis of cohorts of African ancestry. To our knowledge, these genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. Hypertension represents the leading cause of death from cardiovascular disease in AAs. Our study lends support to prior admixture analyses, which indicate that blood pressure represents a complex disease trait with genetic underpinnings within the AA community. Further investigation of the genetic loci identified in our analysis including replication efforts is warranted. Identification of potential genetic loci implicated in hypertension represents a unique opportunity to introduce new treatment and management strategies for this high-risk population.

MATERIALS AND METHODS

Study sample

NHLBI's CARe Study includes six cohort studies with AA representation: the ARIC Study, the CHS, the CARDIA, the CFS, the JHS and MESA (see Supplementary Material, Section I, for sampling details). Each study adopted collaboration guidelines and established a consensus on phenotype harmonization, covariate selection and an analytical plan for within-study genetic association and prospective meta-analysis of results across studies. Each study received institutional review board approval of its consent procedures, examination and surveillance components, data security measures and DNA collection and its use for genetic research. All participants in each study gave written informed consent for participation in the study, and the conduct of genetic research. AA samples from five cohorts (ARIC, CARDIA, CFS, JHS and MESA) had genome-wide genotyping using the Affymetrix Genome-Wide Human SNP Array 6.0 array and blood pressure data for association analysis. Six cohorts (ARIC, CARDIA, CFS, CHS, JHS and MESA) had candidate gene genotyping in AAs using the Illumina iSelect HumanCVD bead array (28). We excluded individuals younger than 18 years of age.

Genotyping and quality control

Quality control of genotyping data was performed using PLINK (29). Quality-control efforts were conducted at two levels: exclusion of individuals and exclusion of SNPs. Samples with a genotyping success rate of <95% were removed. An inbreeding coefficient was calculated and used as a measure of heterozygosity. Outliers for heterozygosity (defined as less than −4 SD or >4 SD beyond the mean) were removed because of possible DNA contamination or poor DNA quality. For population-based cohorts, pair-wise identity-by-descent score was calculated and for each pair of identical samples, the sample with the lowest genotyping success rate was removed. In addition, samples that shared 5% or more of their genome with other samples also were excluded. Multidimensional scaling (MDS) was used to estimate population substructure and the identified outliers were removed.

There were 1176 SNPs that mapped to more than one locus in the human genome that were excluded from analysis. Individual SNPs were also excluded if they had a call rate of less than 90% or were monomorphic. For family data, Mendelian inconsistency was checked using PLINK and the corresponding SNPs were removed. No SNPs were removed due to significant deviation from Hardy–Weinberg equilibrium (HWE) because the African-American population is an admixed population, which may result in departure from HWE.

Genotype imputation

SNP imputation was performed using MACH and the HapMap phase 2 data sets (build 36 release 22) employing a similar strategy as that used by Kang et al. (9). In order to address the admixture component of our African-American population, a reference panel consisting of equal proportions of the YRI and CEU HapMap-phased haplotypes (using only SNPs found in both YRI and CEU panels, i.e. ∼2.2 million SNPs) was constructed. Because the CARe project had both IBC array and Affymetrix 6.0 data genotypes on the ∼8500 individuals of African ancestry, it was possible to assess the quality of the imputation process. The observed concordance was 95.6%, which is comparable to previous studies (30). Imputation was performed for the Affymetrix 6.0 data only.

Phenotype modeling

SBP and DBP were modeled at the first examination for ARIC, CHS, MESA and JHS, and at the most recent examination for CARDIA and CFS in order to minimize the effect of extreme age differences between the cohorts. For ARIC and JHS, seated blood pressure was measured with a random-zero sphygmomanometer three times with the last two measurements averaged. For CARDIA, seated BP was measured on the right arm following 5min rest using a random-zero sphygmomanometer. SBP and DBP were recorded as Phase I and Phase V Korotkoff sounds. Three measurements were taken at 1min intervals with the average of the second and third measurements taken as the blood pressure value. For CFS, blood pressure was measured using a mercury sphygmomanometer and was the average of nine readings (three each made over three intervals in an 18 h period). Three measures were made supine before bed, three measures were made awake supine after bed and three were measured awake while sitting. For MESA, resting seated blood pressure was measured three times at 1min intervals using an automated oscillometric sphygmomanometer (Dinamap PRO 100, Critikon); the average of the second and third blood pressure measurements was used for these analyses. For individuals taking antihypertensive medication, we added 10 and 5 mmHg to the measured SBP and DBP (31), respectively, to account for treatment effect. Continuous DBP and SBP were adjusted for age, age², sex and body mass index (BMI) in linear regressions. Residuals were calculated and applied within cohort for analysis of genotype–phenotype associations.

Statistical analyses

Within each cohort, the first 10 main eigenvectors from principal components (PCs) were calculated and included in the model testing genotype–phenotype association. The PCs were calculated based on selected ancestry informative markers. For comparison, we also calculated the PCs using the method described in Zhu et al. (8), in which the eigenvectors were calculated based on only unrelated individuals. PCs were then calculated for all individuals, including family members. Additionally in this method, all SNPs were used to calculate PCs. The results between the two methods were consistent, except for a few individuals (Supplementary Material, Figure S2). We did not find that the discrepancy affected final association results. For all data sets except CFS, which includes family data sets, association of SNPs with SBP and DBP was tested by linear regression with additive genetic model using PLINK; for CFS, association was tested using a linear mixed-effect model that accounted for family structure (32).

Meta-analysis of results was carried out using the inverse-variance weighting method in METAL (http://www.sph.umich.edu/csg/abecasis/metal/). Genomic control was carried out on cohort-specific test statistics and used to adjust results within each study.

For comparison, analysis of pooled raw data from the five cohorts genotyped with the Affymetrix 6.0 array was carried out with FamCC (8). Cohort-specific genotypes and standardized DBP or SBP residuals were pooled together. PCs were calculated for all unrelated individuals and predicted for related individuals. Genotype–phenotype association was tested using a linear regression model with adjustment for the first 10 PCs.

Previously published genome-wide significant SNP associations with blood pressure 7, 9 and 10 were examined. If the published SNPs were not available in either genotyped SNPs or imputed SNPs in the current study, we used SNPs in a strong LD with the sentinel SNPs as proxies.

Loci with a P-value of <1 × 10⁻⁶ for the GWAS data and of <1 × 10⁻⁵ for IBC data were selected for replication analysis in independent cohorts of African and European ancestry. SNPs in LD (r² ≥ 0.5) were considered to represent the same signal; consequently, the SNP with the smallest P-value at a locus was selected for replication analysis.

Conflict of Interest statement. None declared.

FUNDING

The CARe authors wish to acknowledge the support of the National Heart, Lung, and Blood Institute and the contributions of the research institutions, study investigators, field staff and study participants in creating this resource for biomedical research. The following nine parent studies have contributed parent study data, ancillary study data and DNA samples through the Broad Institute (N01-HC-65226) to create this genotype/phenotype database for wide dissemination to the biomedical research community. Atherosclerotic Risk in Communities (ARIC): University of North Carolina at Chapel Hill (N01-HC-55015), Baylor Medical College (N01-HC-55016), University of Mississippi Medical Center (N01-HC-55021), University of Minnesota (N01-HC-55019), Johns Hopkins University (N01-HC-55020), University of Texas, Houston (N01-HC-55017), University of North Carolina, Forsyth County (N01-HC-55018); Cardiovascular Health Study (CHS): University of Washington (N01-HC-85079), Wake Forest University (N01-HC-85080), Johns Hopkins University (N01-HC-85081), University of Pittsburgh (N01-HC-85082), University of California, Davis (N01-HC-85083), University of California, Irvine (N01-HC-85084), New England Medical Center (N01-HC-85085), University of Vermont (N01-HC-85086), Georgetown University (N01-HC-35129), Johns Hopkins University (N01 HC-15103), University of Wisconsin (N01-HC-75150), Geisinger Clinic (N01-HC-45133), University of Washington (N01 HC-55222, U01 HL080295); Cleveland Family Study (CFS): Case Western Reserve University (RO1 HL46380-01-16); Cooperative Study of Sickle Cell Disease (CSSCD): University of Illinois (N01-HB-72982, N01-HB-97062), Howard University (N01-HB-72991, N01-HB-97061), University of Miami (N01-HB-72992, N01-HB-97064), Duke University (N01-HB-72993), George Washington University (N01-HB-72994), University of Tennessee (N01-HB-72995, N01-HB-97070), Yale University (N01-HB-72996, N01-HB-97072), Children's Hospital-Philadelphia (N01-HB-72997, N01-HB-97056), University of Chicago (N01-HB-72998, N01-HB-97053), Medical College of Georgia (N01-HB-73000, N01-HB-97060), Washington University (N01-HB-73001, N01-HB-97071), Jewish Hospital and Medical Center of Brooklyn (N01-HB-73002), Trustees of Health and Hospitals of the City of Boston, Inc. (N01-HB-73003), Children's Hospital-Oakland (N01-HB-73004, N01-HB-97054), University of Mississippi (N01-HB-73005), St Luke's Hospital-New York (N01-HB-73006), Alta Bates-Herrick Hospital (N01-HB-97051), Columbia University (N01-HB-97058), St Jude's Children's Research Hospital (N01-HB-97066), Research Foundation, State University of New York-Albany (N01-HB-97068, N01-HB-97069), New England Research Institute (N01-HB-97073), Interfaith Medical Center-Brooklyn (N01-HB-97085); Coronary Artery Risk in Young Adults (CARDIA): University of Alabama at Birmingham (N01-HC-48047), University of Minnesota (N01-HC-48048), Northwestern University (N01-HC-48049), Kaiser Foundation Research Institute (N01-HC-48050), University of Alabama at Birmingham (N01-HC-95095), Tufts-New England Medical Center (N01-HC-45204), Wake Forest University (N01-HC-45205), Harbor-UCLA Research and Education Institute (N01-HC-05187), University of California, Irvine (N01-HC-45134, N01-HC-95100); Framingham Heart Study (FHS): Boston University (N01-HC-25195); Jackson Heart Study (JHS): Jackson State University (N01-HC-95170), University of Mississippi (N01-HC-95171), Tougaloo College (N01-HC-95172); Multi-Ethnic Study of Atherosclerosis (MESA): University of Washington (N01-HC-95159), Regents of the University of California (N01-HC-95160), Columbia University (N01-HC-95161), Johns Hopkins University (N01-HC-95162), University of Minnesota (N01-HC-95163), Northwestern University (N01-HC-95164), Wake Forest University (N01-HC-95165), University of Vermont (N01-HC-95166), New England Medical Center (N01-HC-95167), Johns Hopkins University (N01-HC-95168), Harbor-UCLA Research and Education Institute (N01-HC-95169); Sleep Heart Health Study (SHHS): Johns Hopkins University (U01 HL064360), Case Western University (U01 HL063463), University of California, Davis (U01 HL053916), University of Arizona (U01 HL053938), University of Minnesota (relocating in 2006 to University Arizona) (U01 HL053934), University of Pittsburgh (U01 HL077813), Boston University (U01 HL053941), MedStar Research Institute (U01 HL063429), Johns Hopkins University (U01 HL053937). The Women's Health Initiative (WHI) program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts N01WH22110, 24152, 32100–32102, 32105, 32106, 32108, 32109, 32111–32113, 32115, 32118, 32119, 32122, 42107–42126, 42129–42132, and 44221. Genetic Epidemiology Network of Arteriopathy (GENOA) study is supported by the National Institutes of Health, grant numbers HL087660 and HL100245 from National Heart, Lung, Blood Institute, and MD002249 from National Institute on Minority Health and Health Disparities. M.J.C. and T.J.'s contribution was facilitated by National Institute for Health Research support of the Barts and The London Cardiovascular Biomedical Research Unit. A.C. and a portion of the genotyping supported by HL086694 from National Heart, Lung, Blood Institute. Maywood African-American study are supported by the National Institutes of Health, grant number HL074166 from the National Heart, Lung, Blood Institute. Y.L. and X.Z. are supported by HL086718 from National Heart, Lung, Blood Institute and HG003054 from the National Human Genome Research Institute. The Howard University Family Study (HUFS) was supported by NIGMS/MBRS/SCORE grants to C.N.R. and A.A. with additional support from the Coriell Institute for Biomedical Sciences and the Intramural Research Program in the Center for Research in Genomics and Global Health, NHGRI/NIH (Z01HG200362). The ICBP-GWAS consortium was supported by many funding bodies including NIH/NHLBI, European and private funding agencies. Many of the participating studies and authors in ICBP-GWAS are members of the CHARGE and Global BPgen consortia. Details are provided in ref. 11. Funding to pay the Open Access publication charges for this article was provided by Herman Taylor, University of Mississippi Medical Center.

Appendix

Patricia B. Munroe, Kenneth M. Rice, Murielle Bochud, Andrew D. Johnson, Daniel I. Chasman, Albert V. Smith, Martin D. Tobin, Germaine C. Verwoert, Shih-Jen Hwang, Vasyl Pihur, Peter Vollenweider, Paul F. O'Reilly, Najaf Amin, Jennifer L. Bragg-Gresham, Alexander Teumer, Nicole L. Glazer, Lenore Launer, Jing Hua Zhao, Yurii Aulchenko, Simon Heath, Siim Sõber, Afshin Parsa, Jian'an Luan, Pankaj Arora, Abbas Dehghan, Feng Zhang, Gavin Lucas, Andrew A. Hicks, Anne U. Jackson, John F. Peden, Toshiko Tanaka, Sarah H. Wild, Igor Rudan, Wilmar Igl, Yuri Milaneschi, Alex N. Parker, Cristiano Fava, John C. Chambers, Meena Kumari, Min Jin Go, Pim van der Harst, Wen Hong Linda Kao, Marketa Sjögren, D.G. Vinay, Myriam Alexander, Yasuharu Tabara, Sue Shaw-Hawkins, Peter H. Whincup, Yongmei Liu, Gang Shi, Johanna Kuusisto, Mark Seielstad, Xueling Sim, Khanh-Dung Hoang Nguyen, Terho Lehtimäki, Giuseppe Matullo, Ying Wu, Tom R. Gaunt, N. Charlotte Onland-Moret, Matthew N. Cooper, Carl G.P. Platou, Elin Org, Rebecca Hardy, Santosh Dahgam, Jutta Palmen, Veronique Vitart, Peter S. Braund, Tatiana Kuznetsova, Cuno S.P.M. Uiterwaal, Harry Campbell, Barbara Ludwig, Maciej Tomaszewski, Ioanna Tzoulaki, Nicholette D. Palmer, CARDIoGRAM consortium, CKDGen consortium, KidneyGen consortium, EchoGen consortium, CHARGE-HF consortium, Thor Aspelund, Melissa Garcia, Yen-Pei C. Chang, Jeffrey R. O'Connell, Nanette I. Steinle, Diederick E. Grobbee, Dan E. Arking, Dena Hernandez, Samer Najjar, Wendy L. McArdle, David Hadley, Morris J. Brown, John M. Connell, Aroon D. Hingorani, Ian N.M. Day, Debbie A. Lawlor, John P. Beilby, Robert W. Lawrence, Robert Clarke, Rory Collins, Jemma C. Hopewell, Halit Ongen, Joshua C. Bis, Mika Kähönen, Jorma Viikari, Linda S. Adair, Nanette R. Lee, Ming-Huei Chen, Matthias Olden, Cristian Pattaro, Judith A. Hoffman Bolton, Anna Köttgen, Sven Bergmann, Vincent Mooser, Nish Chaturvedi, Timothy M. Frayling, Muhammad Islam, Tazeen H. Jafar, Jeanette Erdmann, Smita R. Kulkarni, Stefan R. Bornstein, Jürgen Grässler, Leif Groop, Benjamin F. Voight, Johannes Kettunen, Philip Howard, Andrew Taylor, Simonetta Guarrera, Fulvio Ricceri, Valur Emilsson, Andrew Plump, Inês Barroso, Kay-Tee Khaw, Alan B. Weder, Steven C. Hunt, Richard N. Bergman, Francis S. Collins, Lori L. Bonnycastle, Laura J. Scott, Heather M. Stringham, Leena Peltonen, Markus Perola, Erkki Vartiainen, Stefan-Martin Brand, Jan A. Staessen, Thomas J. Wang, Paul R. Burton, Maria Soler Artigas, Yanbin Dong, Harold Snieder, Xiaoling Wang, Haidong Zhu, Kurt K. Lohman, Megan E. Rudock, Susan R. Heckbert, Nicholas L. Smith, Kerri L. Wiggins, Daniel Shriner, Gudrun Veldre, Margus Viigimaa, Sanjay Kinra, Dorairajan Prabhakaran, Vikal Tripathy, Carl D. Langefeld, Annika Rosengren, Dag S. Thelle, Anna Maria Corsi, Andrew Singleton, Terrence Forrester, Gina Hilton, Colin A. McKenzie, Tunde Salako, Naoharu Iwai, Yoshikuni Kita, Toshio Ogihara, Takayoshi Ohkubo, Tomonori Okamura, Hirotsugu Ueshima, Satoshi Umemura, Susana Eyheramendy, Thomas Meitinger, H.-Erich Wichmann, Yoon Shin Cho, Hyung-Lae Kim, Jong-Young Lee, James Scott, Joban S. Sehmi, Weihua Zhang, Bo Hedblad, Peter Nilsson, George Davey Smith, Andrew Wong, Narisu Narisu, Alena Stančáková, Leslie J. Raffel, Jie Yao, Sekar Kathiresan, Chris O'Donnell, Steven M. Schwartz, M. Arfan Ikram, Will T. Longstreth Jr, Sudha Seshadri, Nick R.G. Shrine, Louise V. Wain, Mario A. Morken, Amy J. Swift, Jaana Laitinen, Inga Prokopenko, Paavo Zitting, Jackie A. Cooper, Steve E. Humphries, John Danesh, Asif Rasheed, Anuj Goel, Anders Hamsten, Hugh Watkins, Stephan J.L. Bakker, Wiek H. van Gilst, Charles S. Janipalli, K. Radha Mani, Chittaranjan S. Yajnik, Albert Hofman, Francesco U.S. Mattace-Raso, Ben A. Oostra, Ayse Demirkan, Aaron Isaacs, Fernando Rivadeneira, Edward G. Lakatta, Marco Orru, Angelo Scuteri, Mika Ala-Korpela, Antti J. Kangas, Leo-Pekka Lyytikäinen, Pasi Soininen, Taru Tukiainen, Peter Würz, Rick Twee-Hee Ong, Marcus Dörr, Heyo K. Kroemer, Uwe Völker, Henry Völzke, Pilar Galan, Serge Hercberg, Mark Lathrop, Diana Zelenika, Panos Deloukas, Massimo Mangino, Tim D. Spector, Guangju Zhai, James F. Meschia, Michael A. Nalls, Pankaj Sharma, Janos Terzic, M.J. Kranthi Kumar, Matthew Denniff, Ewa Zukowska-Szczechowska, Lynne E. Wagenknecht, F. Gerald R. Fowkes, Fadi J. Charchar, Peter E.H. Schwarz, Caroline Hayward, Xiuqing Guo, Michiel L. Bots, Eva Brand, Nilesh J. Samani, Ozren Polasek, Philippa J. Talmud, Fredrik Nyberg, Diana Kuh, Maris Laan, Kristian Hveem, Lyle J. Palmer, Yvonne T. van der Schouw, Juan P. Casas, Karen L. Mohlke, Paolo Vineis, Olli Raitakari, Tien Y. Wong, E. Shyong Tai, Markku Laakso, Dabeeru C. Rao, Tamara B. Harris, Richard W. Morris, Anna F. Dominiczak, Mika Kivimaki, Michael G. Marmot, Tetsuro Miki, Danish Saleheen, Giriraj R. Chandak, Josef Coresh, Gerjan Navis, Veikko Salomaa, Bok-Ghee Han, Jaspal S. Kooner, Olle Melander, Paul M. Ridker, Stefania Bandinelli, Ulf B. Gyllensten, Alan F. Wright, James F. Wilson, Luigi Ferrucci, Martin Farrall, Jaakko Tuomilehto, Peter P. Pramstaller, Roberto Elosua, Nicole Soranzo, Eric J.G. Sijbrands, David Altshuler, Ruth J.F. Loos, Alan R. Shuldiner, Christian Gieger, Pierre Meneton, Andre G. Uitterlinden, Nicholas J. Wareham, Vilmundur Gudnason, Rainer Rettig, Manuela Uda, David P. Strachan, Jacqueline C.M. Witteman, Anna-Liisa Hartikainen, Jacques S. Beckmann, Eric Boerwinkle, Michael Boehnke, Martin G. Larson, Marjo-Riitta Järvelin, Bruce M. Psaty, Gonçalo R. Abecasis, Paul Elliott, Cornelia M. van Duijn and Christopher Newton-Cheh.