Blood pressure is a heritable trait influenced by several biological pathways and responsive to environmental stimuli. Over one billion people worldwide have hypertension (≥140 mm Hg systolic blood pressure or ≥90 mm Hg diastolic blood pressure). Even small increments in blood pressure are associated with an increased risk of cardiovascular events. This genome-wide association study of systolic and diastolic blood pressure, which used a multi-stage design in 200,000 individuals of European descent, identified sixteen novel loci: six of these loci contain genes previously known or suspected to regulate blood pressure (GUCY1A3-GUCY1B3, NPR3-C5orf23, ADM, FURIN-FES, GOSR2, GNAS-EDN3); the other ten provide new clues to blood pressure physiology. A genetic risk score based on 29 genome-wide significant variants was associated with hypertension, left ventricular wall thickness, stroke and coronary artery disease, but not kidney disease or kidney function. We also observed associations with blood pressure in East Asian, South Asian and African ancestry individuals. Our findings provide new insights into the genetics and biology of blood pressure, and suggest potential novel therapeutic pathways for cardiovascular disease prevention.
Blood pressure is a heritable trait influenced by several biological pathways and responsive to environmental stimuli. Over one billion people worldwide have hypertension (≥140 mm Hg systolic blood pressure or ≥90 mm Hg diastolic blood pressure). Even small increments in blood pressure are associated with an increased risk of cardiovascular events. This genome-wide association study of systolic and diastolic blood pressure, which used a multi-stage design in 200,000 individuals of European descent, identified sixteen novel loci: six of these loci contain genes previously known or suspected to regulate blood pressure (GUCY1A3-GUCY1B3, NPR3-C5orf23, ADM, FURIN-FES, GOSR2, GNAS-EDN3); the other ten provide new clues to blood pressure physiology. A genetic risk score based on 29 genome-wide significant variants was associated with hypertension, left ventricular wall thickness, stroke and coronary artery disease, but not kidney disease or kidney function. We also observed associations with blood pressure in East Asian, South Asian and African ancestry individuals. Our findings provide new insights into the genetics and biology of blood pressure, and suggest potential novel therapeutic pathways for cardiovascular disease prevention.
Genetic approaches have advanced the understanding of biological pathways underlying inter-individual variation in BP. For example, studies of rare Mendelian BP disorders have identified multiple defects in renal sodium handling pathways[4]. More recently two genome-wide association studies (GWAS), each of >25,000 individuals of European-ancestry, identified 13 loci associated with SBP, DBP, and hypertension[5,6]. We now report results of a new meta-analysis of GWAS data that includes staged follow-up genotyping to identify additional BP loci.Primary analyses evaluated associations between 2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and SBP and DBP in 69,395 individuals of European ancestry from 29 studies (Supplementary Materials Sections 1–3, Supplementary Tables 1–2). Following GWAS meta-analysis, we conducted a three-stage validation experiment that made efficient use of available genotyping resources, to follow up top signals in up to 133,661 additional individuals of European descent (Supplementary Fig. 1 and Supplementary Materials Section 4). Twenty-nine independent SNPs at 28 loci were significantly associated with SBP, DBP, or both in the meta-analysis combining discovery and follow up data (Fig. 1, Table 1, Supplementary Figs 2–3, Supplementary Tables 3–5). All 29 SNPs attained association P <5×10−9, an order of magnitude beyond the standard genome-wide significance level for a single stage experiment (Table 1).
Fig. 1
Genome-wide –log10
P-value plots and effects for significant loci.
Genome-wide –log10
P-value plots are shown for systolic (SBP: panel a) and diastolic (DBP: panel b). SNPs within loci reaching genome-wide significance are labeled in red for SBP and blue for DBP (±2.5Mb of lowest P-value) and lowest P-values in the initial genome-wide analysis as well as the results of analysis including validation data are labeled separately. The lowest P-values in the initial GWAS are denoted as an X. The range of different sample sizes in the final meta-analysis including the validation data are indicated as: circle (96–140k), triangle (>140–180k), and diamond (>180–220k). SNPs near unconfirmed loci are in black. The horizontal dotted line is P=2.5 × 10−8. Panel c shows the effect size estimates and 95% confidence bars per BP-increasing allele of the 29 significant variants for SBP (red) and DBP (blue). Effect sizes are expressed in mmHg/allele. GUCY = GUCY1A3-GUCY1B3.
Table 1
Summary association results for 29 BP SNPs
Summary association statistics, based on combined discovery and follow-up data, for 29 independent SNPs in individuals of European ancestry are shown. New genome-wide significant findings (17 SNPs) are presented in the top half of the table, data on 12 previously published signals are presented in the lower half.
Locus
Index SNP
Chr
Position
CA/NCA
CAF
nsSNP
eSNP
SBP
DBP
HTN
Beta
P-value
Effect in EA/SA/A
Beta
P-value
Effect in EA/SA/A
Beta
P-value
MOV10
rs2932538
1
113,018,066
G/A
0.75
Y(p)
Y(p)
0.388
1.2*10−9
+/+/−
0.24
9.9*10−10
+/+*/−
0.049
2.9*10−7
SLC4A7
rs13082711
3
27,512,913
T/C
0.78
Y(p)
Y(p)
−0.315
1.5*10−6
−/−/+
−0.238
3.8*10−9
−/−/+
−0.035
3.6*10−4
MECOM
rs419076
3
170,583,580
T/C
0.47
-
-
0.409
1.8*10−13
+/+/+
0.241
2.1*10−12
+/+/−
0.031
3.1*10−4
SLC39A8
rs13107325
4
103,407,732
T/C
0.05
Y
Y(+)
−0.981
3.3*10−14
?/+/+
−0.684
2.3*10−17
?/+/+
−0.105
4.9*10−7
GUCY1A3-GUCY1B3
rs13139571
4
156,864,963
C/A
0.76
-
-
0.321
1.2*10−6
+/−/+
0.26
2.2*10−10
+/−/+
0.042
2.5*10−5
NPR3-C5orf23
rs1173771
5
32,850,785
G/A
0.6
-
-
0.504
1.8*10−16
+*/+/+
0.261
9.1*10−12
+*/+/−
0.062
3.2*10−10
EBF1
rs11953630
5
157,777,980
T/C
0.37
-
-
−0.412
3.0*10−11
+/+/+
−0.281
3.8*10−13
+/+/+
−0.052
1.7*10−7
HFE
rs1799945
6
26,199,158
G/C
0.14
Y
-
0.627
7.7*10−12
+/+/−
0.457
1.5*10−15
+/+/−
0.095
1.8*10−10
BAT2-BAT5
rs805303
6
31,724,345
G/A
0.61
Y(p)
Y(+)
0.376
1.5*10−11
−/−/?
0.228
3.0*10−11
−/−/+
0.054
1.1*10−10
CACNB2(5′)
rs4373814
10
18,459,978
G/C
0.55
-
-
−0.373
4.8*10−11
+/+/−
−0.218
4.4*10−10
−/+/−
−0.046
8.5*10−8
PLCE1
rs932764
10
95,885,930
G/A
0.44
-
-
0.484
7.1*10−16
+/+/−
0.185
8.1*10−7
+/+/−
0.055
9.4*10−9
ADM
rs7129220
11
10,307,114
G/A
0.89
-
-
−0.619
3.0*10−12
?/−/+
−0.299
6.4*10−8
?/−/+
−0.044
1.1*10−3
FLJ32810-TMEM133
rs633185
11
100,098,748
G/C
0.28
-
-
−0.565
1.2*10−17
+*/+/+
−0.328
2.0*10−15
+*/+/−
−0.07
5.4*10−11
FURIN-FES
rs2521501
15
89,238,392
T/A
0.31
-
Y(−)
0.65
5.2*10−19
+*/+/+
0.359
1.9*10−15
+*/+/+
0.059
7.0*10−7
GOSR2
rs17608766
17
42,368,270
T/C
0.86
-
Y(+)
−0.556
1.1*10−10
+/−/+
−0.129
0.017
+/−/+
−0.025
0.08
JAG1
rs1327235
20
10,917,030
G/A
0.46
-
-
0.34
1.9*10−8
+*/+/+
0.302
1.4*10−15
+*/+*/+
0.034
4.6*10−4
GNAS-EDN3
rs6015450
20
57,184,512
G/A
0.12
Y(p)
-
0.896
3.9*10−23
?/+/+
0.557
5.6*10−23
?/+*/+
0.11
4.2*10−14
MTHFR-NPPB
rs17367504
1
11,785,365
G/A
0.15
-
Y(−/r)
−0.903
8.7*10−22
+/+/+
−0.547
3.5*10−19
+/+/+
−0.103
2.3*10−10
ULK4
rs3774372
3
41,852,418
T/C
0.83
Y
Y(r/p)
−0.067
0.39
−/−/+
−0.367
9.0*10−14
+/+/+
−0.017
0.18
FGF5
rs1458038
4
81,383,747
T/C
0.29
-
-
0.706
1.5*10−23
+*/+/+
0.457
8.5*10−25
+*/+*/+
0.072
1.9*10−7
CACNB2(3′)
rs1813353
10
18,747,454
T/C
0.68
-
-
0.569
2.6*10−12
+/+/+
0.415
2.3*10−15
+/+/+
0.078
6.2*10−10
C10orf107
rs4590817
10
63,137,559
G/C
0.84
-
Y(r)
0.646
4.0*10−12
−/+/−
0.419
1.3*10−12
−/−/−
0.096
9.8*10−9
CYP17A1-NT5C2
rs11191548
10
104,836,168
T/C
0.91
-
Y(−)
1.095
6.9*10−26
+*/+*/+
0.464
9.4*10−13
+*/+*/+
0.097
1.4*10−5
PLEKHA7
rs381815
11
16,858,844
T/C
0.26
-
-
0.575
5.3*10−11
+*/+/+
0.348
5.3*10−10
+*/−/+
0.062
3.4*10−6
ATP2B1
rs17249754
12
88,584,717
G/A
0.84
-
-
0.928
1.8*10−18
+*/+*/−
0.522
1.2*10−14
+*/+*/−
0.126
1.1*10−14
SH2B3
rs3184504
12
110,368,991
T/C
0.47
Y
Y(+)
0.598
3.8*10−18
−/−/+
0.448
3.6*10−25
−/−/+
0.056
2.6*10−6
TBX5-TBX3
rs10850411
12
113,872,179
T/C
0.7
-
-
0.354
5.4*10−8
−/+/−
0.253
5.4*10−10
−/−/−
0.045
5.2*10−6
CYP1A1-ULK3
rs1378942
15
72,864,420
C/A
0.35
-
Y(+)
0.613
5.7*10−23
+*/+/+
0.416
2.7*10−26
+*/+/−
0.073
1.0*10−8
ZNF652
rs12940887
17
44,757,806
T/C
0.38
-
Y(−)
0.362
1.8*10−10
+/−/+
0.27
2.3*10−14
+/−/+
0.046
1.2*10−7
Y indicates the BP index SNP is a nsSNP, Y(p) indicates a proxy SNP is a nsSNP. Y(+): indicatesBP index SNP is the strongest known eSNP for a transcript; Y(−): indicates BP index SNP is an eSNP but not strongest known eSNP for any transcript. Y(r): indicates BP index SNP is strongest known eSNP in a regional SNP-RTPCR experiment. Y(p): indicates a proxy SNP (r2 > 0.8) to BP SNP is an eSNP but not the strongest known eSNP. Observed effect directions in East Asian (EA), South Asian (SA), and African (A) ancestry individuals are coded + or − if concordant or discordant with directions in European ancestry results;
denotes significance controlling the FDR at 5% over 58 tests per ancestry (Supplementary Tables 5 and 12). Effect size estimates (beta) correspond to mmHg per coded allele for SBP and DBP and ln(odds) per coded allele for HTN.
Sixteen of these 29 associations were novel (Table 1). Two associations were near the FURIN and GOSR2 genes; prior targeted analyses of variants in these genes suggested they may be BP loci[7,8]. At the CACNB2 locus we validated association for a previously reported[6] SNP rs4373814 and detected a novel independent association for rs1813353 (pairwise r2 =0.015 in HapMap CEU). Of our 13 previously reported associations[5,6], only the association at PLCD3 was not supported by the current results (Supplementary Table 4). Some of the associations are in or near genes involved in pathways known to influence BP (NPR3, GUCY1A3-GUCY1B3, ADM, GNAS-EDN3, NPPA-NPPB, and CYP17A1;
Supplementary Fig. 4). Twenty-two of the 28 loci did not contain genes that were a priori strong biological candidates.As expected from prior BP GWAS results, the effects of the novel variants on SBP and DBP were small (Fig. 1 and Table 1). For all variants, the observed directions of effects were concordant for SBP, DBP, and hypertension (Fig. 1, Table 1, Supplementary Fig. 3). Among the genes at the genome-wide significant loci, only CYP17A1, previously implicated in Mendelian congenital adrenal hyperplasia and hypertension, is known to harbour rare variants that have large effects on BP[9].We performed several analyses to identify potential causal alleles and mechanisms. First, we looked up the 29 genome-wide significant index SNPs and their close proxies (r2>0.8) among cis-acting expression SNP (eSNP) results from multiple tissues (Supplementary Materials Section 5). For 13/29 index SNPs, we found association between nearby eSNP variants and expression level of at least one gene transcript (10−4 > p > 10−51, Supplementary Table 6). In 5 cases, the index BP SNP and the best eSNP from a genome-wide survey were identical, highlighting potential mediators of the SNP-BP associations.Second, because changes in protein sequence are strong a priori candidates to be functional, we sought non-synonymous coding SNPs that were in high LD (r2 >0.8) with the 29 index SNPs. We identified such SNPsat 8 loci (Table 1, Supplementary Materials Section 6, Supplementary Table 7). In addition we performed analyses testing for differences in genetic effect according to body mass index (BMI) or sex, and analyses of copy number variants, pathway enrichment, and metabolomic data, but we did not find any statistically significant results (Supplementary Materials Sections 7–9, Supplementary Tables 8–10).We evaluated whether the BP variants we identified in Europeans were associated with BP in individuals of East Asian (N=29,719), South Asian (N=23,977), and African (N=19,775) ancestries (Table 1, Supplementary Tables 11–13). We found significant associations in individuals of East Asian ancestry for SNPs at 9 loci and in individuals of South Asian ancestry for SNPs at 6 loci; some have been reported previously (Supplementary Tables 12 and 15). The lack of significant association for individual SNPs may reflect small sample sizes, differences in allele frequencies or LD patterns, imprecise imputation for some ancestries using existing reference samples, or a genuinely different underlying genetic architecture. Because of limited power to detect effects of individual variants in the smaller non-European samples, we created genetic risk scores for SBP and DBP incorporating all 29 BP variants weighted according to effect sizes observed in the European samples. In each non-European ancestry group, risk scores were strongly associated with SBP (P=1.1×10−40 in East Asian, P=2.9×10−13 in South Asian, P=9.8×10−4 in African ancestry individuals) and DBP (P=2.9×10−48, P=9.5×10−15, and P=5.3×10−5, respectively; Supplementary Table 13).We also created a genetic risk score to assess association of the variants in aggregate with hypertension and with clinical measures of hypertensive complications including left ventricular mass, left ventricular wall thickness, incident heart failure, incident and prevalent stroke, prevalent coronary artery disease (CAD), kidney disease, and measures of kidney function, using results from other GWAS consortia (Table 2, Supplementary Materials Sections 10–11, Supplementary Table 14). The risk score was weighted using the average of SBP and DBP effects for the 29 SNPs. In an independent sample of 23,294 women[10], an increase of 1 standard deviation in the genetic risk score was associated with a 21% increase in the odds of hypertension (95% CI 19%–28%; Table 2, Supplementary Table 14). Among individuals in the top decile of the risk score, the prevalence of hypertension was 29% compared with 16% in the bottom decile (odds ratio 2.09, 95% CI 1.86–2.36). Similar results were observed in an independent hypertension case-control sample (Table 2). In our study, individuals in the top compared to bottom quintiles of genetic risk score differed by 4.6 mm Hg SBP and 3.0 mm Hg DBP, differences that approach population-averaged BP treatment effects for a single antihypertensive agent[11]. Epidemiologic data have shown that differences in SBP and DBP of this magnitude, across the population range of BP, are associated with an increase in cardiovascular disease risk[3]. Consistent with this and in line with findings from randomized trials of BP-lowering medication in hypertensive patients[12,13], the genetic risk score was positively associated with left ventricular wall thickness (P=6.0×10−6), occurrence of stroke (P=3.3×10−5) and CAD (P=8.1×10−29). The same genetic risk score was not, however, significantly associated with chronic kidney disease or measures of kidney function, even though these renal outcomes were available in a similar sample size as for the other outcomes (Table 2). The absence of association with kidney phenotypes could be explained by a weaker causal relation of BP with kidney phenotypes than with CAD and stroke. This finding is consistent with the mismatch between observational data that show a positive association of BP with kidney disease, and clinical trial data that show inconsistent evidence of benefit of BP lowering on kidney disease prevention in patients with hypertension[14]. Thus, several lines of evidence converge to suggest that BP elevation may in part be a consequence rather than a cause of sub-clinical kidney disease.
Table 2
Genetic risk score and cardiovascular outcome association results
Association of genetic risk score (using all 29 SNPs at 28 loci, parameterised using the average of SBP and DBP effects [=(SBP effect + DBP effect)/2] from the discovery analysis), tested in results from other GWAS consortia.
Phenotype
Source
Effect
SE
P-value
# SNPs
Contrast top vs. bottom
N case/control or total
(per SD of genetic risk score)
quintiles
deciles
Blood pressure phenotypes
SBP [mmHg]
WGHS
1.645
0.098
(a)
6.5*10−63
29
4.61
5.77
(a)
23,294
DBP [mmHg]
WGHS
1.057
0.067
(a)
8.4*10−57
29
2.96
3.71
(a)
23,294
Prevalent hypertension
WGHS
0.211
0.018
(b)
3.1*10−33
29
1.80
2.09
(b)
5,018/18,276
Prevalent hypertension
BRIGHT
0.287
0.031
(b)
7.7*10−21
29
2.23
2.74
(b)
2,406/1,990
Dichotomous endpoints
Incident heart failure
CHARGE-HF
0.035
0.021
(c)
0.10
29
1.10
1.13
(c)
2,526/18,400
Incident stroke
NEURO-CHARGE
0.103
0.028
(c)
0.0002
28
1.34
1.44
(c)
1,544/18,058
Prevalent stroke
UK-US Stroke Collaborative Group(SCG)
0.075
0.037
(b)
0.05
29
1.23
1.30
(b)
1,473/1,482
Stroke (combined, incident and prevalent)
CHARGE & SCG
NA
NA
NA
3.3*10−5
NA
NA
NA
NA
3,017/19,540
Prevalent CAD
CARDIoGRAM
0.092
0.010
(b)
1.6*10−19
28
1.29
1.38
(b)
22,233/64,726
Prevalent CAD
C4D ProCARDIS
0.132
0.022
(b)
2.2*10−9
29
1.45
1.59
(b)
5,720/4,381
Prevalent CAD
C4D HPS
0.083
0.027
(b)
0.002
29
1.26
1.34
(b)
2,704/2,804
Prevalent CAD (combined)
CARDIoGRAM & C4D
0.100
0.009
(b)
8.1*10−29
29
1.32
1.42
(b)
30,657/71,911
Prevalent chronic kidney disease
CKDGen
0.014
0.015
(b)
0.35
29
1.04
1.05
(b)
5,807/61,286
Prevalent microalbuminuria
CKDGen
0.008
0.019
(b)
0.68
29
1.02
1.03
(b)
3,698/27,882
Continuous measures oftarget organ damage
Left ventricular mass [g]
EchoGen
0.822
0.317
(a)
0.01
29
2.30
2.89
(a)
12,612
Left ventricular wall thickness[cm]
EchoGen
0.009
0.002
(a)
6.0*10−6
29
0.03
0.03
(a)
12,612
Serum creatinine
KidneyGen
−0.001
0.001
(d)
0.24
29
1.00
1.00
(d)
23,812
eGFR (4 parameter MDRD equation)
CKDGen
−0.0001
0.0009
(d)
0.93
29
1.00
1.00
(d)
67,093
Urinary albumin/creatinine ratio
CKDGen
0.005
0.007
(d)
0.43
29
1.01
1.02
(d)
31,580
Units are the unit of phenotypic measurement, either per SD of genetic risk score, or as a difference between top/bottom quintiles or deciles.
Units are ln(odds) per SD of genetic risk score, or odds ratio between top/bottom quintiles or deciles.
Units are ln(hazard) per SD of genetic risk score, or hazard ratio between top/bottom quintiles or deciles.
Units are ln(phenotype) per SD of genetic risk score, or phenotypic ratio between top/bottom quintiles or deciles.
Our discovery meta-analysis (Supplementary Fig. 2) suggests an excess of modestly significant (10−5
15] that there are 116 (95% CI 57–174) independent BP variants with effect sizes similar to those reported here, which collectively explain ≈2.2% of the phenotypic variance for SBP and DBP, compared with 0.9% explained by the 29 associations discovered thus far (Supplementary Fig. 6, Supplementary Materials Section 13).
Most of the 28 BP loci harbour multiple genes (Supplementary Table 15, Supplementary Fig. 4), and although substantial research is required to identify the specific genes and variants responsible for these associations, several loci contain highly plausible biological candidates. The NPPA and NPPB genes at the MTHFR-NPPB locus encode precursors for atrial- and B-type natriuretic peptides (ANP, BNP), and previous work has identified SNPs, modestly correlated with our index SNP at this locus, that are associated with plasma ANP, BNP, and BP[16]. We found the index SNP at this locus was associated with opposite effects on BP and on ANP/BNP levels, consistent with a model in which the variants act through increased ANP/BNP production to lower BP[16] (Supplementary Materials Section 14).Two other loci identified in the current study harbour genes involved in natriuretic peptide and related nitric oxide signalling pathways,[17,18] both of which act to regulate cyclic guanosine monophosphate (cGMP). The first locus contains NPR3, which encodes the natriuretic peptide clearance receptor (NPR-C). NPR3 knockout mice exhibit reduced clearance of circulating natriuretic peptides and lower BP[19]. The second locus includes GUCY1A3 and GUCY1B3, encoding the alpha and beta subunits of soluble guanylatecyclase (sGC); knockout of either gene in murine models results in hypertension[20].Another locus contains ADM, encoding adrenomedullin, which has natriuretic, vasodilatory, and BP-lowering properties[21]. At the GNAS-EDN3 locus, ZNF831 is closest to the index SNP, but GNAS and EDN3 are two nearby compelling biological candidates (Supplementary Fig. 4, Supplementary Table 15).We identified two loci with plausible connections to BP via genes implicated in renal physiology or kidney disease. At the first locus, SLC4A7 is an electro-neutral sodium bicarbonate co-transporter expressed in the nephron and in vascular smooth muscle[22]. At the second locus, PLCE1 (phospholipase-C-epsilon-1 isoform) is important for normal podocyte development in the glomerulus; sequence variation in PLCE1 has been implicated in familial nephrotic syndromes and end-stage kidney disease[23].Missense variants in two genes involved in metal ion transport were associated with BP in our study. The first encodes a His/Asp change at amino acid 63 (H63D) in HFE and is a low penetrance allele for hereditary hemochromatosis[24]. The second is an Ala/Thr polymorphism located in exon 7 of SLC39A8, which encodes a zinc transporter that also transports cadmium and manganese[25]. The same allele of SLC39A8 associated with BP in our study has recently been associated with high-density lipoprotein (HDL) cholesterol levels[26] and BMI[27] (Supplementary Table 15).In conclusion, we have shown that 29 independent genetic variants influence BP in people of European ancestry. The variants reside in 28 loci, 16 of which were novel, and we confirmed association of several of them in individuals of non-European ancestry. A risk score derived from the 29 variants was significantly associated with BP-related organ damage and clinical cardiovascular disease, but not kidney disease. These loci improve our understanding of the genetic architecture of BP, provide new biological insights into BP control and may identify novel targets for the treatment of hypertension and the prevention of cardiovascular disease.
Methods summary
Supplementary Materials provide complete methods and include the following sections: study recruitment and phenotyping, adjustment for antihypertensive medications, genotyping, data quality control, genotype imputation, within-cohort association analyses, meta-analyses of discovery and validation stages, stratified analyses by sex and BMI, identification of eSNPs and nsSNPs, metabolomic and lipidomic analyses, CNV analyses, pathway analyses, analyses for non-European ancestries, association of a risk score with hypertension and cardiovascular disease, estimation of numbers of undiscovered variants, measurement of natriuretic peptides, and brief literature reviews and GWAS database lookups of all validated BP loci.
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Authors: N Matsukawa; W J Grzesik; N Takahashi; K N Pandey; S Pang; M Yamauchi; O Smithies Journal: Proc Natl Acad Sci U S A Date: 1999-06-22 Impact factor: 11.205
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Fig. 1