# Brief Overview of a Decade of Genome-Wide Association Studies on Primary Hypertension

Authors: Afifah Binti Azam; Elena Aisha Binti Azizan
Journal: International Journal of Endocrinology
 (2018)

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## Abstract

Primary hypertension is widely believed to be a complex polygenic disorder with the manifestation influenced by the interactions of genomic and environmental factors making identification of susceptibility genes a major challenge. With major advancement in high-throughput genotyping technology, genome-wide association study (GWAS) has become a powerful tool for researchers studying genetically complex diseases. GWASs work through revealing links between DNA sequence variation and a disease or trait with biomedical importance. The human genome is a very long DNA sequence which consists of billions of nucleotides arranged in a unique way. A single base-pair change in the DNA sequence is known as a single nucleotide polymorphism (SNP). With the help of modern genotyping techniques such as chip-based genotyping arrays, thousands of SNPs can be genotyped easily. Large-scale GWASs, in which more than half a million of common SNPs are genotyped and analyzed for disease association in hundreds of thousands of cases and controls, have been broadly successful in identifying SNPs associated with heart diseases, diabetes, autoimmune diseases, and psychiatric disorders. It is however still debatable whether GWAS is the best approach for hypertension. The following is a brief overview on the outcomes of a decade of GWASs on primary hypertension.


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## Body

## 1. Introduction

Hypertension is highly prevalent globally. The estimated number of people with uncontrolled hypertension is nearly 1 billion (around 15% of the world population), with the number predicted to increase to 1.56 billion by the year 2025 [1]. Due to its high prevalence, hypertension is the leading risk factor for cardiovascular disease, stroke, and end-stage kidney diseases. The increased risk of cardiovascular mortality and morbidity has led to the estimation that hypertension causes 13% of all deaths (around 7.5 million deaths worldwide) [2]. Patients are considered to have hypertension when their systolic blood pressure is ≥140 mmHg and/or their diastolic blood pressure is ≥90 mmHg [3]. However, raised blood pressure, even within the normal range, is positively and continuously related to mortality and morbidity—each increment of 20 (systolic)/10 (diastolic) mmHg of blood pressure doubles the risk of cardiovascular diseases [2]. Hence, the number of people at risk is higher as the prevalence of raised blood pressure for adults (aged ≥25 years) is around 40% [2].The majority of hypertension in the general population occur idiopathically with no apparent causes and therefore are categorized as primary hypertension. The remaining hypertensive cases (about 5%) are categorized as secondary hypertension as the raised blood pressure occur secondary to other causes/diseases, for example, hypertension due to aldosteronism, pheochromocytoma, renovascular diseases, or even Mendelian forms of hypertension [4, 5]. However, despite being classified as having no apparent cause, studies of familial aggregation on primary hypertensive patients have found associations of blood pressure among siblings and between parents and children, indicating that genetic factors contribute to the high blood pressure among primary hypertensive patients. Genetic factors have been estimated to explain 30–50% of the interindividual variation in blood pressure which significantly predisposes family (siblings/children) of primary hypertensive patients to hypertension [6]. These heritable genetic factors, in addition to environment and demographic factors, play a major role in interindividual variation in blood pressure [7]. Therefore, extensive genetic research has been conducted over the years, including genome-wide association studies (GWASs), to help elucidate primary hypertension’s heritability.


## 2. Outcomes of Genome-Wide Association Studies on Primary Hypertension

GWASs have identified over three hundred plus SNPs/loci associated with blood pressure and/or primary hypertension over the past decade (Table1). Meta-analyses of GWASs have made the biggest contribution as they allowed for larger sample sizes and more extensive imputation panels. Despite these advancements, genetic variation identified so far only explains ~3–6% of the variance for blood pressure, approximately 1 mmHg per allele systolic blood pressure or 0.5 mmHg per allele diastolic blood pressure [8–12]. Further, the vast majority of GWASs were performed predominantly in Caucasian populations with only a few studies assessing or replicating in other populations even though high blood pressure burden risk is ranked number one in Southeast Asia, Central Asia, North Africa, and Middle East [13–40]. This suggests the existence of many more undiscovered SNPs/loci or at the very least SNPs unique to other populations that are not of Caucasian ancestry. For example, one meta-analysis on Oriental populations found five Oriental-specific loci near CAPZA1, FIGN, ENPEP, NPR3, and PTPN11 (near C12orf51) associated with hypertension [22]. Either the differences in environmental exposures/lifestyle factors or genetic background can explain why ethnic/racial susceptibility loci exist. Nevertheless, as even a small increase in blood pressure can increase the risk of cardiovascular diseases, the biological pathways identified by these SNPs would still be useful in resolving many of the open questions regarding blood pressure pathophysiology.Table 1
Loci associated with blood pressure and/or hypertension that have been identified through large-scale studies in the past decade.
Locus name
SNP
Chr ID
Chr position
Coded allele
Best trait
Effect size of best trait (OR beta)
Coded allele frequency
Reporting article
2q36.3
rs2972146
2
226,235,982
T
DBP
0.17
0.19
Surendran et al. [29]
7q32.1
rs4728142
7
128,933,913
A
SBP
−0.224
0.29
Surendran et al. [29]
ABHD17C
rs35199222
15
80,720,696
A
SBP
0.322
0.18
Hoffmann et al. [8], Warren et al. [26]
ABHD17C
rs11634851
15
80,736,624
G
SBP
0.316
0.461
Wain et al. [27]
ABLIM3-SH3TC2
rs9687065
5
149,011,577
A
DBP
0.26
0.16
Kato et al. [20]
ACE
rs4308
17
63,482,264
A
DBP
0.213
0.24
Hoffmann et al. [8], Warren et al. [26]
ACOX1
rs2467099
17
75,952,964
T
SBP
−0.307
0.18
Hoffmann et al. [8], Warren et al. [26]
ADAMTS7-MORF4L1
rs62012628
15
78,777,658
T
DBP
−0.238
0.34
Hoffmann et al. [8], Warren et al. [26]
ADAMTS7-MORF4L1
rs62011052
15
79,156,983
C
PP
−0.28
0.14
Hoffmann et al. [8]
ADAMTS8
rs11222084
11
130,403,335
T
PP
0.337
0.21
Wain et al. [19]
ADAMTS9
rs918466
3
64,724,577
A
DBP
−0.204
0.35
Ehret et al. [12]
ADCY3
rs55701159
2
24,916,727
T
DBP
0.285
0.1
Warren et al. [26]
ADM
rs360157
11
9,732,674
T
SBP
0.413
0.44
Ehret et al. [12]
ADM
rs7129220
11
10,350,538
A
SBP
−0.619
0.058
Ehret et al. [18]
ADM
rs7129220
11
10,350,538
A
DBP
−0.299
0.058
Ehret et al. [18]
ADO
rs10995311
10
62,805,174
G
DPB
−0.20
0.38
Liu et al. [23], Surendran et al. [29]
ADRB1
rs2782980
10
114,021,768
T
PP
−0.338
0.28
Wain et al. [19]
ADRB1-RNU6-709P
rs10787517
10
114,055,047
A
SBP
0.442
0.616
Wain et al. [27]
AGT
rs2004776
1
230,712,956
T
SBP
0.42
0.41
Johnson et al. [30]
AKT2
rs9710247
19
40,254,542
G
DBP
0.252
0.44
Wain et al. [27]
AMH-SF3A2
rs740406
19
2,232,222
A
PP
−0.55
0.21
Kato et al. [20]
ARHGAP12
rs10826995
10
31,793,730
T
PP
−0.212
0.3
Hoffmann et al. [8], Warren et al. [26]
ARHGAP24
rs2014912
4
85,794,517
T
SBP
0.62
0.19
Kato et al. [20]
ARNTL
rs900145
11
13,272,358
G
DBP
−0.25
0.43
Liu et al. [23]
ARVCF
rs12628032
22
19,980,457
T
PP
0.24
0.27
Hoffmann et al. [8], Warren et al. [26]
ARVCF
rs4819852
22
20,000,644
A
PP
0.261
0.29
Wain et al. [27]
ATP2B1
rs2681472
12
89,615,182
A
DBP
0.5
0.83
Levy et al. [9]
ATP2B1
rs2681492
12
89,619,312
T
SBP
1.26
0.21
Levy et al. [9]
ATP2B1
rs17249754
12
89,666,809
A
BP
0.8
0.35
Kelly et al. [31]
BAT2-BAT5
rs805303
6
31,648,589
G
SBP
0.376
0.44
Johnson et al. [30]
BDNF
rs11030119
11
27,706,555
A
DBP
−0.163
0.26
Hoffmann et al. [8], Warren et al. [26]
BLK-GATA4
rs2898290
8
11,576,400
C
SBP
NA
0.38
Ho et al. [33]
C10orf107
rs4590817
10
61,707,795
C
DBP
0.436
0.16
Wain et al. [27]
C10orf107
rs1530440
10
61,764,833
T
DBP
0.19
0.15
Newton-Cheh et al. [10]
C10orf32, C10orf32-ASMT
rs4409766
10
102,856,906
T
SBP
1.24
0.71
Lu et al. [51]
C17orf82-TBX2
rs2240736
17
61,408,032
T
MAP
0.35
0.35
Kato et al. [20]
C20orf187
rs1887320
20
10,985,350
A
SBP
0.78
0.53
Lu et al. [51]
C2orf43
rs2289081
2
20,682,080
C
PP
−0.223
0.31
Hoffmann et al. [8], Warren et al. [26]
C5orf56
rs2188962
5
132,435,113
T
DBP
−0.2
0.14
Liu et al. [23], Surendran et al. [29]
CACNA1D
rs9810888
3
53,601,568
G
DBP
0.39
0.39
Lu et al. [51]
CACNA2D2
rs743757
3
50,438,947
C
DBP
0.245
0.36
Hoffmann et al. [8], Warren et al. [26]
CACNB2
rs1813353
10
18,418,519
C
DBP
0.332
0.34
Wain et al. [27]
CACNB2
rs11014166
10
18,419,869
A
DBP
0.46
0.21
Levy et al. [9]
CAMKV-ACTBP13
rs36022378
3
49,876,272
T
DBP
−0.202
0.11
Hoffmann et al. [8], Warren et al. [26]
CAPZA1
rs10745332
1
112,646,431
A
SBP
0.96
0.82
Lu et al. [51]
CASC15
rs6911827
6
22,130,372
T
SBP
0.296
0.30
Hoffmann et al. [8], Warren et al. [26]
CASZ1
rs880315
1
10,736,809
T
SBP
−0.475
0.39
Ehret et al. [12]
CCDC141
rs79146658
2
178,921,341
T
DBP
−0.311
0.03
Hoffmann et al. [8], Warren et al. [26]
CCDC41-CEP83-RN7SL483P
rs139236208
12
94,486,966
A
PP
−0.363
0.04
Hoffmann et al. [8], Warren et al. [26]
CCNE1
rs62104477
19
29,804,084
T
DBP
0.177
0.19
Hoffmann et al. [8], Warren et al. [26]
CD34
rs12731740
1
207,851,475
T
PP
−0.249
0.08
Warren et al. [26]
CDC42BPA
rs10916082
1
227,064,925
A
DBP
−0.177
0.27
Warren et al. [26]
CDH13
rs7500448
16
83,012,185
A
PP
0.329
0.17
Hoffmann et al. [8], Warren et al. [26]
CDH17
rs2446849
8
94,091,269
T
SBP
−0.63
0.22
Zhu et al. [32]
CELA2A
rs1042010
1
15,467,418
A
SBP
0.412
0.19
Hoffmann et al. [8], Warren et al. [26]
CELA2A
rs3820068
1
15,471,702
A
SBP
0.425
0.19
Wain et al. [27]
CEP164
rs8258
11
117,412,960
T
PP
0.236
0.47
Hoffmann et al. [8], Warren et al. [26]
CEP68
rs74181299
2
65,056,838
T
PP
0.23
0.46
Hoffmann et al. [8], Warren et al. [26]
CERS5
rs7302981
12
50,144,032
A
DBP
0.249
0.30
Liu et al. [23], Surendran et al. [29]
CFDP1
rs11643209
16
75,297,146
T
SBP
−0.339
0.47
Hoffmann et al. [8], Warren et al. [26]
CHIC2
rs871606
4
53,933,078
T
PP
0.429
0.21
Wain et al. [19]
chr15mb95
rs12906962
15
94,768,842
T
DBP
−0.221
0.42
Hoffmann et al. [8], Warren et al. [26]
chr1mb25
rs6686889
1
24,703,979
T
DBP
0.185
0.37
Warren et al. [26]
chr1mb9
rs9662255
1
9,381,890
A
PP
−0.207
0.41
Hoffmann et al. [8], Warren et al. [26]
CHST12-LFNG
rs2969070
7
2,472,910
A
DBP
−0.205
0.21
Ehret et al. [12]
CMIP
rs8059962
16
81,540,592
T
DBP
−0.170
0.45
Warren et al. [26]
CNNM2
rs11191548
10
103,086,421
C
SBP
1.082
0.09
Wain et al. [27]
COL21A1
rs1925153
6
56,237,982
T
PP
−0.21
0.44
Liu et al. [23]
CPEB4
rs72812846
5
173,950,633
A
DBP
−0.209
0.11
Hoffmann et al. [8], Warren et al. [26]
CRACR2B
rs7126805
11
828,916
A
PP
0.222
<0.01
Warren et al. [26]
CRK
rs12941318
17
1,430,304
T
SBP
−0.269
0.37
Hoffmann et al. [8], Warren et al. [26]
CRYAA-SIK1-RRP1B
rs12627651
21
43,340,723
A
SBP
0.503
0.19
Ehret et al. [12], Surendran et al. [29]
CSK
rs1378942
15
74,785,026
A
DBP
0.371
0.65
Wain et al. [27]
CYB561-LOC342541
rs4459609
17
63,471,587
A
DBP
0.198
0.61
Wain et al. [27]
CYP17A1-NT5C2
rs1004467
10
102,834,750
A
SBP
1.2
0.16
Levy et al. [9], Newton-Cheh et al. [10]
CYP1A1-ULK3
rs6495122
15
74,833,304
A
DBP
0.45
0.29
Levy et al. [9], Newton-Cheh et al. [10]
CYP2C19
rs4494250
10
94,804,000
A
DPB
0.21
0.22
Liu et al. [23]
DBH
rs6271
9
133,657,152
T
DBP
−0.423
0.04
Ehret et al. [12]
DNM3
rs12405515
1
172,388,301
T
DBP
−0.165
0.47
Hoffmann et al. [8], Warren et al. [26]
DPEP1
rs1126464
16
89,637,957
C
DBP
0.275
0.26
Liu et al. [23], Surendran et al. [29]
EBF1
rs11953630
5
158,418,394
T
DBP
−0.281
0.18
Johnson et al. [30]
EBF2
rs6557876
8
26,043,159
T
SBP
−0.411
0.33
Wain et al. [27]
ENPEP
rs6825911
4
110,460,482
C
DBP
0.39
0.42
Kato et al. [22]
ESR1
rs13192976
6
151,991,280
A
PP
−0.332
0.21
Hoffmann et al. [8], Warren et al. [26]
FAF1
rs147696085
1
50,556,195
G
PP
0.298
0.06
Hoffmann et al. [8]
FAM186B
rs7977389
12
49,587,939
T
PP
0.237
0.18
Hoffmann et al. [8]
FAM208A
rs9827472
3
56,692,618
T
DBP
−0.177
0.46
Hoffmann et al. [8], Warren et al. [26]
FBLN5
rs2244643
14
91,892,678
A
PP
−0.213
0.29
Hoffmann et al. [8]
FBN2
rs6595838
5
128,532,506
A
SBP
0.344
0.41
Hoffmann et al. [8], Warren et al. [26]
FBXL19
rs72799341
16
30,925,422
A
DBP
0.185
0.27
Hoffmann et al. [8], Warren et al. [26]
FER1L5
rs7599598
2
96,686,103
A
DBP
−0.31
0.42
Ganesh et al. [34]
FERMT2
rs9888615
14
52,910,822
T
SBP
−0.318
0.36
Hoffmann et al. [8], Warren et al. [26]
FGD5
rs11128722
3
14,916,619
A
SBP
−0.383
0.41
Ehret et al. [12]
FGF5
rs16998073
4
80,263,187
T
DBP
0.21
0.23
Newton-Cheh et al. [10]
FGGY-HSD52
rs3889199
1
59,188,070
A
PP
0.351
0.14
Hoffmann et al. [8], Warren et al. [26]
FIGN-PRPS1P1
rs16849211
2
164,043,173
T
PP
0.364
0.23
Wain et al. [27]
FIGN-PRPS1P1
rs1446468
2
164,106,976
C
SBP
0.538
0.55
Wain et al. [27]
FIGN-GRB14
rs16849225
2
164,050,310
C
SBP
0.75
0.23
Ehret et al. [18], Kato et al. [22], Wain et al. [19]
FLJ32810-TMEM133
rs633185
11
100,722,807
G
SBP
−0.565
0.36
Johnson et al. [30]
FN1
rs1250259
2
215,435,759
A
PP
−0.314
0.23
Hoffmann et al. [8], Warren et al. [26]
FNDC1
rs449789
6
159,278,093
C
PP
0.359
0.15
Hoffmann et al. [8], Warren et al. [26]
FOSL2
rs7562
2
28,412,873
T
SBP
0.263
0.50
Warren et al. [26]
FRMD3
rs115795127
9
83,378,986
T
BP
NA
NR
Liang et al. [35]
FURIN-FES
rs2521501
15
90,894,158
T
SBP
0.65
0.21
Johnson et al. [30]
GATA2
rs62270945
3
128,483,046
T
PP
0.607
0.01
Hoffmann et al. [8], Warren et al. [26]
GJA1
rs11154027
6
121,460,244
T
PP
0.207
0.38
Warren et al. [26]
GNAS-EDN3
rs6015450
20
59,176,062
G
SBP
0.896
0.10
Johnson et al. [30]
GOSR2
rs17608766
17
46,935,905
T
SBP
−0.556
0.05
Johnson et al. [30]
GPAT2-FAHD2CP
rs2579519
2
96,009,418
T
DBP
−0.197
0.41
Warren et al. [26]
GPATCH2
rs12408022
1
217,545,447
T
DBP
0.198
0.26
Hoffmann et al. [8], Warren et al. [26]
GPR20
rs34591516
8
141,356,987
T
SBP
0.323
0.05
Surendran et al. [29]
GPR20
rs78192203
8
141,364,973
T
BP
NA
NR
Liang et al. [35]
GPR98/ARRDC3
rs10474346
5
91,268,322
C
DBP
1.1
0.31
Fox et al. [36]
GTF2B
rs10922502
1
88,894,475
A
SBP
−0.382
0.34
Hoffmann et al. [8], Warren et al. [26]
GUCY1A3
rs13143871
4
155,698,052
T
SBP
0.96
0.80
Lu et al. [51]
GUCY1A3-GUCY1B3
rs13139571
4
155,724,361
C
DBP
0.26
0.21
Johnson et al. [30]
GYPA_HHIP
rs4292285
4
144,350,802
T
DBP
0.177
0.41
Hoffmann et al. [8]
HAAO-RNU6-242P-AC016735.1
rs13403122
2
42,851,618
C
DBP
0.226
0.20
Hoffmann et al. [8], Warren et al. [26]
HDAC9
rs2107595
7
19,009,765
A
PP
0.31
0.25
Kato et al. [20]
HFE
rs1799945
6
26,090,951
G
DBP
0.457
0.09
Johnson et al. [30]
HFE
rs1800562
6
26,092,913
A
DBP
0.394
0.06
Wain et al. [27]
HIPK2
rs1011018
7
139,763,465
A
SBP
−0.329
0.35
Warren et al. [26]
HIVEP3
rs7515635
1
41,942,399
T
SBP
0.336
0.47
Ehret et al. [12]
HM13-ID1
rs6060114
20
31,581,870
T
DBP
0.267
0.27
Hoffmann et al. [8]
HNF4G-RNU2-54P
rs1449544
8
75,679,645
A
PP
0.183
0.41
Hoffmann et al. [8]
HOTTIP
rs1859168
7
27,202,740
C
DBP
0.436
0.92
Wain et al. [27]
HOXA3
rs6969780
7
27,119,517
C
BP
NA
NR
Liang et al. [35]
HOXA-EVX1
rs17428471
7
27,298,248
T
SBP
1.2
0.08
Franceschini et al. [24]
HOXB7
rs7406910
17
48,610,894
T
SBP
−0.456
0.12
Surendran et al. [29]
HRCT1
rs76452347
9
35,906,474
T
DBP
−0.25
0.15
Liu et al. [23]
HSD52-LOC105378756
rs10889130
1
59,148,708
A
PP
0.288
0.33
Wain et al. [27]
HSPB7
rs1048238
1
16,015,154
T
SBP
0.366
0.02
Wain et al. [27]
IGFBP3
rs11977526
7
45,968,511
A
DBP
0.3
0.44
Zhu et al. [32], Liu et al. [23]
INPP5B
rs871524
1
37,945,773
A
PP
0.228
0.33
Wain et al. [27]
INSR
rs7248104
19
7,224,420
A
PP
−0.20
0.35
Liu et al. [23]
INSR
rs36047283
19
7,255,690
G
SBP
0.801
0.11
Wain et al. [27]
ITGA11
rs1563894
15
68,343,437
A
SBP
−0.093
0.18
Parmar et al. [37]
JAG1
rs1327235
20
10,988,382
G
DBP
0.302
0.46
Johnson et al. [30]
JAG1-LOC101929395
rs6040076
20
10,678,234
C
PP
0.285
0.49
Wain et al. [27]
KCNH4-HSD17B1
rs79089478
17
42,165,223
T
PP
0.584
0.01
Warren et al. [26]
KCNK3
rs1275988
2
26,691,496
T
SBP
−0.6
0.41
Ganesh et al. [34]
KIAA0753
rs7226020
17
6,570,508
T
PP
−0.256
0.38
Hoffmann et al. [8], Warren et al. [26]
KIAA1462
rs9337951
10
30,028,144
A
PP
0.28
0.26
Hoffmann et al. [8], Warren et al. [26]
L3MBTL4
rs403814
18
6,282,594
A
BP
1.15
NR
Liu et al. [23]
LHFPL2
rs10057188
5
78,541,966
A
PP
−0.205
0.24
Hoffmann et al. [8], Warren et al. [26]
LINC01615-THBS2
rs1322639
6
169,187,008
A
PP
0.316
0.33
Hoffmann et al. [8], Warren et al. [26]
LMO1
rs110419
11
8,231,306
A
DBP
0.159
0.43
Surendran et al. [29]
LOC101928278
rs10932679
2
216,787,868
T
PP
0.226
0.19
Wain et al. [27]
LOC102723446
rs10260816
7
45,970,501
G
PP
0.298
0.43
Wain et al. [27]
LOC105369687-LOC105369688
rs73075659
12
20,220,607
G
SBP
0.357
0.31
Wain et al. [27]
LOC105370003
rs11067763
12
115,760,536
A
DBP
0.51
0.62
Lu et al. [51]
LOC105371811-LOC105371812
rs79917357
17
48,747,312
A
SBP
0.342
0.17
Wain et al. [27]
LOC105374567-LOC102723854
rs72876037
2
42,967,456
T
SBP
0.534
0.12
Wain et al. [27]
LOC105379231
rs9693857
8
9,409,607
T
SBP
0.337
0.45
Wain et al. [27]
LOC107986913-LOC105379224
rs7826238
8
8,529,585
T
SBP
0.335
0.47
Wain et al. [27]
LOC283335
rs73099903
12
53,046,995
T
SBP
0.768
0.06
Wain et al. [27]
LRP12/ZFPM2
rs35783704
8
104,954,030
A
SBP
−0.609
0.03
Wain et al. [27]
LRRC10B-SYT7
rs751984
11
61,510,774
T
MAP
0.33
0.27
Kato et al. [20], Ehret et al. [12]
LSP1-TNNT3
rs661348
11
1,884,062
T
MAP
−0.65
0.42
Johnson et al. [30]
MAP4
rs319690
3
47,885,994
T
DBP
0.282
0.41
Wain et al. [19]
MAPK4-MRO
rs36010659
18
50,757,579
T
PP
0.25
0.12
Hoffmann et al. [8], Warren et al. [26]
MCF2L
rs9549328
13
112,981,842
T
SBP
0.318
0.22
Hoffmann et al. [8], Warren et al. [26]
MECOM
rs419076
3
169,383,098
T
SBP
0.409
0.42
Johnson et al. [30]
METTL21A-AC079767.3
rs55780018
2
207,661,416
T
SBP
−0.391
0.35
Hoffmann et al. [8], Warren et al. [26]
MIR1263
rs16833934
3
164,019,462
G
DBP
−1.63
0.31
Simino et al. [38]
MKLN1
rs13238550
7
131,374,297
A
SBP
0.331
0.33
Warren et al. [26]
MOV10
rs12129649
1
112,688,881
T
DBP
0.548
0.06
Wain et al. [27]
MRAS
rs2306374
3
138,401,110
T
DBP
−0.184
0.08
Hoffmann et al. [8], Warren et al. [26]
MRC2
rs740698
17
62,689,790
T
PP
−0.228
0.41
Warren et al. [26]
MSRA
rs11249992
8
10,362,902
A
SBP
0.293
0.38
Wain et al. [27]
MTAP
rs4364717
9
21,801,531
A
DBP
−0.175
0.43
Warren et al. [26]
MTF1-SF3A3
rs4360494
1
37,990,219
C
PP
0.278
0.38
Hoffmann et al. [8], Warren et al. [26]
MTHFR
rs17367504
1
11,802,721
G
DBP
0.526
0.15
Wain et al. [27]
MTHFR-NPPB
rs4846049
1
11,790,308
T
DBP
−0.55
0.37
Johnson et al. [30]
MYEOV
rs67330701
11
69,312,240
T
DBP
−0.367
0.12
Hoffmann et al. [8], Warren et al. [26]
MYH6
rs452036
14
23,396,676
A
PP
−0.282
0.34
Liu et al. [23], Surendran et al. [29]
NADK-CPSF3L
rs139385870
1
1,754,504
D
SBP
−0.352
0.33
Hoffmann et al. [8], Warren et al. [26]
NFKBIA
rs8904
14
35,402,011
A
SBP
0.377
0.40
Wain et al. [27]
NME7
rs7519279
1
169,238,123
G
PP
0.218
0.13
Hoffmann et al. [8]
NOS3
rs3918226
7
150,993,088
T
DBP
0.83
0.03
Johnson et al. [30]
NOTCH3
rs10418305
19
15,167,997
C
PP
−0.282
0.13
Hoffmann et al. [8]
NOV
rs2071518
8
119,423,572
T
PP
0.312
0.32
Wain et al. [19]
NOX4
rs2289125
11
89,491,285
A
PP
−0.377
0.32
Hoffmann et al. [8], Warren et al. [26]
NPNT
rs13112725
4
105,990,585
C
SBP
0.435
0.34
Hoffmann et al. [8], Warren et al. [26]
NPPA-AS1, NPPA
rs12744757
1
11,846,764
T
SBP
0.695
0.06
Wain et al. [27]
NPR1
rs35479618
1
153,689,947
A
SBP
1.34
0.01
Liu et al. [23]
NPR3-C5orf23
rs1173771
5
32,814,922
C
SBP
0.63
0.34
Johnson et al. [30], Kato et al. [22]
OBFC1
rs4387287
10
103,918,139
A
SBP
0.338
0.32
Surendran et al. [29]
OR5B12
rs11229457
11
58,439,730
T
SBP
−0.312
0.22
Surendran et al. [29]
OSR1
rs1344653
2
19,531,084
A
PP
−0.27
0.38
Kato et al. [20]
PABPC4
rs4660293
1
39,562,508
G
DBP
0.27
0.10
Liu et al. [23]
PALLD-chr4mb174
rs1566497
4
168,795,997
A
PP
0.236
0.23
Hoffmann et al. [8], Warren et al. [26]
PAPPA2
rs61823001
1
176,664,440
G
PP
0.31
0.03
Hoffmann et al. [8]
PAX2
rs112184198
10
100,844,757
A
SBP
−0.659
0.05
Hoffmann et al. [8], Warren et al. [26]
PDE10A
rs147212971
6
165,764,963
T
DBP
−0.360
0.13
Hoffmann et al. [8], Warren et al. [26]
PDE3A
rs12579720
12
20,020,830
C
DBP
−0.32
0.46
Kato et al. [20]
PDE5A
rs66887589
4
119,588,124
T
DBP
−0.215
0.50
Hoffmann et al. [8], Warren et al. [26]
PHACTR1
rs9349379
6
12,903,725
A
SBP
0.289
0.38
Surendran et al. [29]
PHIP
rs10943605
6
78,945,760
A
DBP
0.18
0.49
Liu et al. [23]
PIK3CG
rs17477177
7
106,771,412
T
PP
−0.418
0.17
Wain et al. [19]
PKHD1
rs13205180
6
51,967,696
T
DBP
0.168
0.34
Hoffmann et al. [8], Warren et al. [26]
PKN2-AS1
rs61767086
1
88,600,899
G
PP
0.413
0.14
Wain et al. [27]
PLCB1
rs6108168
20
8,645,624
A
DBP
−0.211
0.38
Warren et al. [26]
PLCD3
rs12946454
17
45,130,754
T
SBP
0.28
0.21
Newton-Cheh et al. [10]
PLCE1
rs932764
10
94,136,183
G
SBP
0.484
0.43
Johnson et al. [30]
PLCE1
rs932764
10
94,136,183
G
SBP
0.484
0.44
Ehret et al. [18]
PLEKHA7
rs177542
11
16,901,107
A
DBP
0.243
0.50
Wain et al. [27]
PLEKHA7-NUCB2
rs381815
11
16,880,721
T
SBP
0.84
0.21
Levy et al. [9]
PLEKHG1
rs17080102
6
150,683,634
C
DBP
−0.74
0.12
Franceschini et al. [24]
PNPT1
rs1975487
2
55,581,918
A
DBP
−0.217
0.32
Ehret et al. [12]
POC5-SV2C
rs10078021
5
75,742,606
T
DBP
−0.164
0.46
Hoffmann et al. [8], Warren et al. [26]
PPL
rs12921187
16
4,893,018
T
DBP
−0.174
0.41
Hoffmann et al. [8],Warren et al. [26]
PPP2R5E
rs8016306
14
63,461,828
A
SBP
0.335
0.41
Warren et al. [26]
PRDM11
rs11442819
11
45,186,590
I
PP
−0.279
0.13
Hoffmann et al. [8], Warren et al. [26]
PRDM16
rs2493292
1
3,412,095
T
SBP
0.42
0.13
Liu et al. [23]
PRDM6-SUMO1P5
rs337100
5
123,210,816
A
PP
0.277
0.40
Wain et al. [27]
PRDM6-CSNK1G3
rs13359291
5
123,140,763
A
SBP
0.53
0.28
Kato et al. [20]
PRDM8-FGF5
rs1902859
4
80,236,549
C
SBP
1.34
0.41
Lu et al. [51]
PRDM8-FGF5
rs1458038
4
80,243,569
T
DBP
0.403
0.30
Wain et al. [27]
PREX1
rs6095241
20
48,692,260
A
DBP
−0.168
0.46
Surendran et al. [29]
PRKAG1
rs1126930
12
49,005,349
C
PP
0.5
0.02
Surendran et al. [29]
PRKCE
rs11690961
2
46,136,197
A
PP
0.34
0.04
Hoffmann et al. [8], Warren et al. [26]
PRKD3
rs13420463
2
37,290,423
A
SBP
0.356
0.49
Hoffmann et al. [8], Warren et al. [26]
PROCR
rs867186
20
35,176,751
A
DBP
0.265
0.11
Surendran et al. [29]
PRRC2A-BAG6
rs151168737
6
31,638,615
A
DBP
0.249
0.46
Wain et al. [27]
PSMD5
rs10760117
9
120,824,459
T
SBP
0.334
0.42
Ehret et al. [12], Liu et al. [23]
PYY
rs62080325
17
43,983,263
A
PP
−0.186
0.21
Warren et al. [26]
RABGAP1
rs10818775
9
122,993,292
C
PP
0.254
0.30
Hoffmann et al. [8]
RAPSN, PSMC3, SLC39A13
rs7103648
11
47,440,232
A
DBP
−0.203
0.33
Ehret et al. [12]
RBM47
rs35529250
4
40,426,074
T
SBP
−1.537
<0.01
Surendran et al. [29]
RCOR2
rs4980532
11
63,913,247
T
PP
0.301
0.56
Wain et al. [27]
RGL3
rs167479
19
11,416,089
T
DBP
−0.33
0.49
Liu et al. [23], Surendran et al. [29]
RNF207
rs709209
1
6,218,354
A
PP
0.199
0.36
Surendran et al. [29]
RP11-273G15.2
rs62524579
8
142,979,538
A
DBP
−0.175
0.48
Hoffmann et al. [8], Warren et al. [26]
RP11-321F6.1
rs7178615
15
66,576,734
A
DBP
−0.179
0.36
Warren et al. [26]
RP11-435J9.2-TLN2
rs956006
15
62,516,340
C
PP
0.188
0.23
Hoffmann et al. [8]
RP11-439C8.2
rs143112823
3
154,990,178
A
DBP
−0.403
0.06
Hoffmann et al. [8], Warren et al. [26]
RP11-61O1.1
rs9323988
14
98,121,293
T
PP
−0.212
0.29
Hoffmann et al. [8], Warren et al. [26]
RP4-710M16.1-PPAP2B-PLPP3
rs112557609
1
56,111,252
A
PP
0.227
0.22
Hoffmann et al. [8], Warren et al. [26]
RPL34P18-CDH17
rs7006531
8
94,098,516
G
BP
NA
NR
Liang et al. [35]
RPL35P4-LOC107986733
rs10279895
7
27,288,591
G
DBP
0.7553
NR
Liang et al. [35]
RPL35P4-LOC107986733
rs11563582
7
27,312,031
A
BP
NA
NR
Liang et al. [35]
RPL6-PTPN11-ALDH2
rs11066280
12
112,379,979
T
DBP
1.01
0.04
Kato et al. [22]
RPS29P9-LOC102724714
rs3845811
2
207,656,788
G
SBP
0.284
0.43
Wain et al. [27]
RRAS
rs61760904
19
49,636,675
T
SBP
1.499
<0.01
Surendran et al. [29]
RSPO3
rs13209747
6
126,794,309
T
DBP
0.56
0.35
Franceschini et al. [24]
RYK
rs9859176
3
134,281,183
T
SBP
0.322
0.25
Hoffmann et al. [8], Warren et al. [26]
SBNO1
rs1060105
12
123,321,672
T
DBP
−0.182
0.18
Surendran et al. [29]
SCAI-PPP6C
rs72765298
9
125,138,717
T
PP
−0.374
0.06
Hoffmann et al. [8], Warren et al. [26]
SDCCAG8
rs953492
1
243,307,890
A
DBP
0.22
0.49
Hoffmann et al. [8], Warren et al. [26]
SENP2
rs12374077
3
185,599,886
C
DBP
0.163
0.42
Hoffmann et al. [8], Warren et al. [26]
SEPT9
rs57927100
17
77,321,218
G
SBP
−0.489
0.01
Wain et al. [27]
SETBP1
rs12958173
18
44,562,012
A
SBP
0.386
0.25
Ehret et al. [12]
SH2B3
rs3184504
12
111,446,804
T
SBP
0.75
0.33
Levy et al. [9], Newton-Cheh et al. [10]
SLC12A9
rs7801190
7
100,860,471
C
BP
1.31
0.72
Lettre et al. [39]
SLC14A2
rs7236548
18
45,517,785
A
PP
0.352
0.3
Hoffmann et al. [8], Warren et al. [26]
SLC20A2
rs2978456
8
42,467,247
T
PP
−0.188
0.45
Hoffmann et al. [8], Warren et al. [26]
SLC24A3
rs6081613
20
19,485,263
A
PP
0.263
0.31
Hoffmann et al. [8], Warren et al. [26]
SLC35F1
rs9372498
6
118,251,323
A
DBP
0.334
0.07
Hoffmann et al. [8], Warren et al. [26]
SLC39A8
rs13107325
4
102,267,552
T
DBP
−0.684
0
Johnson et al. [30]
SLC4A7
rs11716531
3
27,415,717
A
DBP
0.213
0.237
Wain et al. [27]
SLC4A7
rs13082711
3
27,496,418
T
DBP
−0.238
0.12
Johnson et al. [30]
SLC8A1
rs4952611
2
40,340,603
T
DBP
−0.157
0.34
Warren et al. [26]
SMARCA2-VLDLR
rs872256
9
2,496,480
T
SBP
0.096
0.43
Parmar et al. [37]
SNORD32B
rs926552
6
29,580,312
T
DBP
−0.31
0.07
Liu et al. [23]
SNX31
rs2978098
8
100,664,447
A
DBP
0.165
0.34
Warren et al. [26]
SOX6
rs4757391
11
16,281,393
C
DBP
0.49
0.28
Lu et al. [51]
SSPN
rs6487543
12
26,285,256
A
SBP
0.3
0.46
Warren et al. [26]
ST7L-CAPZA1-MOV10
rs2932538
1
112,673,921
G
DBP
0.24
0.17
Johnson et al. [30]
STK39
rs6749447
2
168,184,876
G
SBP
3
0.48
Wang et al. [40]
SUGCT
rs76206723
7
40,408,372
A
PP
−0.346
0.18
Hoffmann et al. [8], Warren et al. [26]
SULT1C3
rs6722745
2
108,258,788
C
SBP
0.28
0.4
Liu et al. [23]
SVEP1
rs111245230
9
110,407,495
C
SBP
0.94
0.03
Liu et al. [23]
SWAP70
rs2649044
11
9,742,422
T
DBP
0.2
0.547
Wain et al. [27]
TBC1D1-FLJ13197
rs2291435
4
38,385,774
T
SBP
−0.378
0.4
Ehret et al. [12]
TBX5-TBX3
rs2384550
12
114,914,926
A
DBP
−0.35
0.29
Levy et al. [9], Kato et al. [22]
TCF7L1
rs11689667
2
85,264,242
T
PP
0.176
0.28
Hoffmann et al. [8], Warren et al. [26]
TCF7L2
rs34872471
10
112,994,312
T
PP
−0.226
0.24
Hoffmann et al. [8]
TEX41
rs1438896
2
144,888,505
T
DBP
0.234
0.3
Hoffmann et al. [8], Warren et al. [26]
TEX41
rs55944332
2
144,969,054
G
DBP
0.267
0.24
Wain et al. [27]
TFAP2D
rs78648104
6
50,715,296
T
SBP
−0.481
0.09
Warren et al. [26]
TM6SF1
rs2034618
15
83,130,880
C
DBP
0.21
0.22
Hoffmann et al. [8]
TMEM161B
rs10059921
5
88,218,698
T
SBP
−0.526
0.06
Hoffmann et al. [8], Warren et al. [26]
TMEM194B-NEMP2-NAB1
rs7592578
2
190,574,865
T
DBP
−0.240
0.18
Hoffmann et al. [8], Warren et al. [26]
TNRC6A
rs11639856
16
24,777,324
A
SBP
−0.37
0.17
Liu et al. [23]
TNRC6B
rs470113
22
40,333,610
A
PP
−0.253
0.21
Surendran et al. [29]
TNS1
rs1063281
2
217,804,009
T
DBP
−0.200
0.43
Hoffmann et al. [8], Warren et al. [26]
TNXB
rs2021783
6
32,077,074
C
DBP
0.49
0.79
Lu et al. [51]
TNXB
rs185819
6
32,082,290
C
SBP
0.365
0.513
Wain et al. [27]
TP53-SLC2A4
rs78378222
17
7,668,434
T
PP
0.904
0
Hoffmann et al. [8], Warren et al. [26]
TRAPPC9
rs4288356
8
140,045,627
A
PP
0.224
0.615
Wain et al. [27]
TRAPPC9
rs4454254
8
140,049,929
A
PP
−0.261
0.45
Warren et al. [26]
TRIM36
rs10077885
5
115,054,424
A
DBP
−0.194
0.42
Ehret et al. [12]
UBA52P4-LOC105377005
rs820430
3
27,507,409
A
SBP
0.76
0.32
Lu et al. [51]
ULK4
rs7651190
3
41,724,463
G
BP
NA
NR
Liang et al. [35]
ULK4
rs9815354
3
41,912,651
A
DBP
0.6
0.17
Levy et al. [9]
ULK4
rs7372217
3
41,948,630
G
BP
NA
NR
Liang et al. [35]
UMOD
rs13333226
16
20,354,332
NA
HTN
NA
0.24
Padmanabhan et al. [25]
VAC14
rs117006983
16
70,721,707
A
PP
0.986
0
Warren et al. [26]
WNT3A
rs2760061
1
228,003,374
A
DBP
0.23
0.35
Hoffmann et al. [8], Warren et al. [26]
XKR6
rs10107145
8
10,900,703
G
SBP
0.361
0.528
Wain et al. [27]
XRCC6
rs73161324
22
41,642,782
T
PP
0.496
0.02
Warren et al. [26]
ZBTB38
rs16851397
3
141,415,976
A
DBP
−0.493
0.05
Surendran et al. [29]
ZC3HC1
rs11556924
7
130,023,656
T
DBP
−0.214
0.27
Ehret et al. [12]
ZFAT
rs894344
8
134,600,502
A
SBP
−0.258
0.47
Warren et al. [26]
ZNF101
rs2304130
19
19,678,719
A
DBP
−0.292
0.11
Surendran et al. [29]
ZNF318-ABCC10
rs10948071
6
43,312,975
T
PP
−0.38
0.43
Ganesh et al. [34]
ZNF385B
rs13407401
2
179,850,979
A
SBP
0.434
0.291
Wain et al. [27]
ZNF638
rs3771371
2
71,400,409
T
PP
−0.160
0.37
Hoffmann et al. [8], Warren et al. [26]
ZNF652
rs12940887
17
49,325,445
T
DBP
0.26
0.374
Wain et al. [27]
ZNF652
rs16948048
17
49,363,104
G
DBP
0.39
0.29
Newton-Cheh et al. [10]
ZNRF3
rs4823006
22
29,055,683
G
SBP
−0.33
0.45
Liu et al. [23]
SBP: systolic blood pressure; DBP: diastolic blood pressure; PP: pulse pressure; MAP: mean arterial pressure; HTN: hypertension; NR: not recorded; NA: not available.


## 3. Biological Pathways Involved with Blood Pressure Pathophysiology

Mendelian forms of hypertension and germline mutations causing early-onset hypertension have highlighted biological pathways that involve renal salt handling (WNK1, WNK4, KLHL3, and CUL3), ion transport (CACNA1D, CACNA1H, KCNJ5, SCNN1B, and SCNN1G), corticosteroidogenesis (CYP11B2, HSD11B2, NR3C2, CYP11B1, and CYP17A1), and vascular tone (PDE3A) to regulate blood [41–44]. Thus, not surprisingly, GWASs have identified SNPs in or near to genes involved with these biological pathways associated with primary hypertension. In fact, one of the first few high-throughput genotyping was performed on only genes underlying monogenic hypertension and hypotension (not genome-wide) which found two renal sodium regulatory genes (KCNJ1 and NR3C2) to have SNPs associated with blood pressure in the general population [45].


### 3.1. Renal Salt Handling

One interesting SNP putatively involving renal salt-handling pathway was only linked to hypertension in an extreme case-control GWAS design [25]. This SNP, rs13333226, on chromosome 16 is in the 5′ region of UMOD (combined P value of 3.6 × 10−11). The minor G allele of this SNP had an OR of 0.87 (95% CI: 0.84–0.91) for hypertension, with the subject having the minor G allele having decreased urinary uromodulin and better renal function. The exclusive expression of uromodulin, the protein encoded by UMOD, in the thick portion of the ascending limb of Henle suggests that the SNP exerts its effect through sodium homeostasis [25]. Also based on renal expression, SNPs in or near to PAPPA2 and ADAMTS7 (rs61823001 and rs62011052, resp., [8]) are expected to play a role in the renal salt-handling pathway. Interestingly in regard to the protein encoded by ADAMTS7, angiotensin II stimulation induced renal expression of the protein [46]. Similarly, renal cortex expression of PAPPA2 in Dahl salt-sensitive rats responded to changes of salt diet supporting a role of the SNP in the renal salt-handing pathway [47]. SNPs in FAM186B and ARHGAP24 on the other hand are postulated to play a role in renal function based on involvement with kidney diseases. Combining whole exome sequencing and homozygosity mapping in consanguineous families, FAM186B was identified as a novel candidate gene for monogenic, recessive nephronophthisis-related ciliopathies [48]. ARHGAP24 on the other hand is thought to play a role in renal cell carcinoma and focal segmental glomerulosclerosis most likely through RhoA and Rac1 signaling pathways [49, 50].


### 3.2. Ion Transport

Several SNPs in genes involved with ion transport have been associated with blood pressure (e.g.,ATP2B1, CACNA1D, CACNA2D2, CACNB2, KCNK3, SLC4A7, and SLC39A8; Table 1). Of these, the one most studied and replicated are SNPs in ATP2B1 [9, 18, 22, 51]. Confirming the role of ATP2B1 in blood pressure regulation is the vascular smooth muscle cell-specific knockout of ATP2B1 mice which had higher systolic blood pressure and significantly increased phenylephrine-induced vasoconstrictions [52]. Similarly, silencing of ATP2B1 through injection of an SiRNA complex into mouse tail veins led to an increase in blood pressure and an increase in contractile response to phenylephrine [53]. These results support that ATP2B1 genetic variants are the causative gene for the association with blood pressure seen in GWASs. The other gene encoding an ion channel with significant supporting evidence is CACNA1D. This is because gain of function mutations in CACNA1D have been found to be causal for primary aldosteronism and for aldosterone-producing cell clusters [42, 54, 55]. As aldosterone is a key regulator of blood pressure, even small changes which may not pass the clinical threshold for primary aldosteronism may be causal for increase in blood pressure. Elevation of aldosterone may also be the mechanism of action for the other ion channels associated with primary hypertension as mutations in the ATPase Na+/K+ transporting subunit alpha 1 and G protein-activated inward rectifier K+ channel 4 have also been found causal for primary aldosteronism and aldosterone-producing cell clusters [55, 56].


### 3.3. Corticosteroidogenesis

Surprisingly in the sense that corticosteroids can highly affect blood pressure, only 2 cytochrome P450 enzyme genes involved with corticosteroidogenesis have been linked to hypertension by GWASs—CYP17A1 and CYP21A2. And of that, only SNPs in the CYP17A1 gene have been replicated, though even then with inconsistent results. CYP17A1 encodes 17α-hydroxylase which is essential to the synthesis of cortisol precursors. Therefore, alteration of this gene can cause a deficiency in 17α-hydroxylase and thus cortisol, which affects blood pressure [57]. Supporting the role of CYP17A1 in blood pressure regulation is the SNP rs11191548, a SNP near the CYP17A1 gene that has been consistently associated with blood pressure in both East Asian cohorts and Caucasian cohorts [10, 17, 18, 58–60]. Patients harboring the risky C allele had lower PRA and K+ levels similar to patients with 17α-hydroxylase deficiency, suggesting that the SNP (which is actually in the noncoding region of the gene CNNM2) has an effect on the enzymatic activity of CYP17A1 [58]. One hypothesis as to why inconsistent results occur with GWAS is if the association found between the lead SNP is indirect whereby the signal produced is actually caused by a synthetically linked rarer variant in linkage disequilibrium with the identified tag SNP. This could be the case with the lead SNP rs1004467 which was identified from the CHARGE + Global BPgen meta-analysis [9]. In an Oriental cohort (from Korea), rs1004467 was found to have a modest association with hypertension in prediabetic subjects and a significant association with augmentation index in diabetic subjects [61]. However, in another Oriental cohort with similar ethnic background (from China), rs1004467 association with hypertension/blood pressure was not found in children [62]. As such, perhaps the causal SNP is not rs1004467 as identified by the initial GWAS meta-analysis but a tag SNP with poor penetrance. Interestingly, rs1004467 is in linkage disequilibrium with rs138009835, a functional SNP located 1800 bases upstream of the transcription site of CYP17A1. In vitro gene reporter gene assays and clinical functional experiments found the minor alleles to have reduced mRNA expression of CYP17A1 and reduced aldosterone excretion [63]. To note, both rs1004467 and rs11191548 are associated with a reduction in both visceral and subcutaneous fat mass in Japanese women [64].


### 3.4. Vascular Tone

Interestingly, although only one of the fifteen monogenic hypertension genes is postulated to mediate an effect through the vasculature, SNPs associated with blood pressure and primary hypertension are enriched in genes that are expressing their proteins in vascular smooth muscle and endothelial cells [11, 12, 65–67]. This is consistent with vascular tone playing a primary role in blood pressure regulation. Many of these genes, however, may have been reported as the causal genes due to their proximity to the SNP in question and their likelihood of playing a role in blood pressure regulation rather than due to real functional data [68]. For example, the reported gene for rs7129220, a SNP downstream to the ADM gene in the noncoding RNA CAND1.11 gene, was the ADM gene as adrenomedullin the protein encoded by ADM plays a role in vasodilation [69]. Oppositely, the reported genes for rs633185 are FLJ32810-TMEM133, even though the SNP is within the intron of ARHGAP42 (Table 1). As a candidate gene for blood pressure regulation, ARHGAP42 has many functional evidence to be the causal gene as reduced expression of ARHGAP42 in mice elevated blood pressure [70]. To note, rs633185 is in high linkage disequilibrium with rs604723, another SNP in the intron of ARHGAP42, and the minor T allele is a functional variant that increases ARHGAP42 expression by promoting serum response factor binding to a smooth muscle-selective regulatory element [71]. Based on this strong functional data, rs604723 is most likely the causative SNP at this locus. rs6271 in exon 11 of the DBH gene on the other hand is one of the rare times where GWASs had managed to directly identify a missense variant which is probably damaging to the protein dopamine β-hydroxylase according to PolyPhen-2 prediction [72]. Concurringly, severe orthostatic syndrome (postural hypotension) were found to be caused by truncating, splice site, or missense mutations in the DBH gene [73].


## 3.1. Renal Salt Handling

One interesting SNP putatively involving renal salt-handling pathway was only linked to hypertension in an extreme case-control GWAS design [25]. This SNP, rs13333226, on chromosome 16 is in the 5′ region of UMOD (combined P value of 3.6 × 10−11). The minor G allele of this SNP had an OR of 0.87 (95% CI: 0.84–0.91) for hypertension, with the subject having the minor G allele having decreased urinary uromodulin and better renal function. The exclusive expression of uromodulin, the protein encoded by UMOD, in the thick portion of the ascending limb of Henle suggests that the SNP exerts its effect through sodium homeostasis [25]. Also based on renal expression, SNPs in or near to PAPPA2 and ADAMTS7 (rs61823001 and rs62011052, resp., [8]) are expected to play a role in the renal salt-handling pathway. Interestingly in regard to the protein encoded by ADAMTS7, angiotensin II stimulation induced renal expression of the protein [46]. Similarly, renal cortex expression of PAPPA2 in Dahl salt-sensitive rats responded to changes of salt diet supporting a role of the SNP in the renal salt-handing pathway [47]. SNPs in FAM186B and ARHGAP24 on the other hand are postulated to play a role in renal function based on involvement with kidney diseases. Combining whole exome sequencing and homozygosity mapping in consanguineous families, FAM186B was identified as a novel candidate gene for monogenic, recessive nephronophthisis-related ciliopathies [48]. ARHGAP24 on the other hand is thought to play a role in renal cell carcinoma and focal segmental glomerulosclerosis most likely through RhoA and Rac1 signaling pathways [49, 50].


## 3.2. Ion Transport

Several SNPs in genes involved with ion transport have been associated with blood pressure (e.g.,ATP2B1, CACNA1D, CACNA2D2, CACNB2, KCNK3, SLC4A7, and SLC39A8; Table 1). Of these, the one most studied and replicated are SNPs in ATP2B1 [9, 18, 22, 51]. Confirming the role of ATP2B1 in blood pressure regulation is the vascular smooth muscle cell-specific knockout of ATP2B1 mice which had higher systolic blood pressure and significantly increased phenylephrine-induced vasoconstrictions [52]. Similarly, silencing of ATP2B1 through injection of an SiRNA complex into mouse tail veins led to an increase in blood pressure and an increase in contractile response to phenylephrine [53]. These results support that ATP2B1 genetic variants are the causative gene for the association with blood pressure seen in GWASs. The other gene encoding an ion channel with significant supporting evidence is CACNA1D. This is because gain of function mutations in CACNA1D have been found to be causal for primary aldosteronism and for aldosterone-producing cell clusters [42, 54, 55]. As aldosterone is a key regulator of blood pressure, even small changes which may not pass the clinical threshold for primary aldosteronism may be causal for increase in blood pressure. Elevation of aldosterone may also be the mechanism of action for the other ion channels associated with primary hypertension as mutations in the ATPase Na+/K+ transporting subunit alpha 1 and G protein-activated inward rectifier K+ channel 4 have also been found causal for primary aldosteronism and aldosterone-producing cell clusters [55, 56].


## 3.3. Corticosteroidogenesis

Surprisingly in the sense that corticosteroids can highly affect blood pressure, only 2 cytochrome P450 enzyme genes involved with corticosteroidogenesis have been linked to hypertension by GWASs—CYP17A1 and CYP21A2. And of that, only SNPs in the CYP17A1 gene have been replicated, though even then with inconsistent results. CYP17A1 encodes 17α-hydroxylase which is essential to the synthesis of cortisol precursors. Therefore, alteration of this gene can cause a deficiency in 17α-hydroxylase and thus cortisol, which affects blood pressure [57]. Supporting the role of CYP17A1 in blood pressure regulation is the SNP rs11191548, a SNP near the CYP17A1 gene that has been consistently associated with blood pressure in both East Asian cohorts and Caucasian cohorts [10, 17, 18, 58–60]. Patients harboring the risky C allele had lower PRA and K+ levels similar to patients with 17α-hydroxylase deficiency, suggesting that the SNP (which is actually in the noncoding region of the gene CNNM2) has an effect on the enzymatic activity of CYP17A1 [58]. One hypothesis as to why inconsistent results occur with GWAS is if the association found between the lead SNP is indirect whereby the signal produced is actually caused by a synthetically linked rarer variant in linkage disequilibrium with the identified tag SNP. This could be the case with the lead SNP rs1004467 which was identified from the CHARGE + Global BPgen meta-analysis [9]. In an Oriental cohort (from Korea), rs1004467 was found to have a modest association with hypertension in prediabetic subjects and a significant association with augmentation index in diabetic subjects [61]. However, in another Oriental cohort with similar ethnic background (from China), rs1004467 association with hypertension/blood pressure was not found in children [62]. As such, perhaps the causal SNP is not rs1004467 as identified by the initial GWAS meta-analysis but a tag SNP with poor penetrance. Interestingly, rs1004467 is in linkage disequilibrium with rs138009835, a functional SNP located 1800 bases upstream of the transcription site of CYP17A1. In vitro gene reporter gene assays and clinical functional experiments found the minor alleles to have reduced mRNA expression of CYP17A1 and reduced aldosterone excretion [63]. To note, both rs1004467 and rs11191548 are associated with a reduction in both visceral and subcutaneous fat mass in Japanese women [64].


## 3.4. Vascular Tone

Interestingly, although only one of the fifteen monogenic hypertension genes is postulated to mediate an effect through the vasculature, SNPs associated with blood pressure and primary hypertension are enriched in genes that are expressing their proteins in vascular smooth muscle and endothelial cells [11, 12, 65–67]. This is consistent with vascular tone playing a primary role in blood pressure regulation. Many of these genes, however, may have been reported as the causal genes due to their proximity to the SNP in question and their likelihood of playing a role in blood pressure regulation rather than due to real functional data [68]. For example, the reported gene for rs7129220, a SNP downstream to the ADM gene in the noncoding RNA CAND1.11 gene, was the ADM gene as adrenomedullin the protein encoded by ADM plays a role in vasodilation [69]. Oppositely, the reported genes for rs633185 are FLJ32810-TMEM133, even though the SNP is within the intron of ARHGAP42 (Table 1). As a candidate gene for blood pressure regulation, ARHGAP42 has many functional evidence to be the causal gene as reduced expression of ARHGAP42 in mice elevated blood pressure [70]. To note, rs633185 is in high linkage disequilibrium with rs604723, another SNP in the intron of ARHGAP42, and the minor T allele is a functional variant that increases ARHGAP42 expression by promoting serum response factor binding to a smooth muscle-selective regulatory element [71]. Based on this strong functional data, rs604723 is most likely the causative SNP at this locus. rs6271 in exon 11 of the DBH gene on the other hand is one of the rare times where GWASs had managed to directly identify a missense variant which is probably damaging to the protein dopamine β-hydroxylase according to PolyPhen-2 prediction [72]. Concurringly, severe orthostatic syndrome (postural hypotension) were found to be caused by truncating, splice site, or missense mutations in the DBH gene [73].


## 4. Conclusion

Although some of the SNPs identified by GWAS on primary hypertension associates with similar biological pathways as Mendelian or early-onset forms of hypertension (validating the study approach), none of the SNPs identified had a large size effect (≤1 mmHg) to be of significance to an individual patient. The ultimate goals of performing these GWASs are to determine the genetic factors regulating blood pressure that can be used to make predictions about who is at risk of developing hypertension and to identify the biological pathways of the disease allowing for identification of novel targets for treatment or even prevention strategies. As currently no direct clinical application of these GWAS findings can be made, it is still debatable whether GWAS is the best approach to identify the biological underpinnings of primary hypertension. Even though yet-to-be-discovered Oriental-specific loci or rare SNPs that might have larger effect size may increase the variance for blood pressure that can be explained by genetic variation, information on epigenetic modulation (e.g., DNA methylation, posttranslational modifications of proteins, or even gut microbiota [20, 74–78]) may still be needed to explain the total heritability of raised blood pressure which cannot be captured by GWASs.



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*Source: 7259704-2018-01-30.xml*