1
A genetic risk score for hypertension associates with the risk of ischemic stroke in a Swedish
case-control study.
Short title: GRS and stroke
Cristiano Favaa,b, Marketa Sjögrena, Sandra Olssonc, Håkan Lövkvistd, Katarina Joodc, Gunnar
Engströma, Bo Hedblada, Bo Norrvingd, Christina Jernc, Arne Lindgrend and Olle Melandera
SUPPLEMENTARY METHODS
Equation used for the calculation of the count and weighted GRS
All risk alleles, as detected in previous studies, were assigned as coded alleles. For each genotype
subjects: two non coded alleles were computed as 0, heterozygous subjects as 1 and homozygotes
for the coded “risk” allele as 2. The beta coefficient was obtained by previous studies.1-4
wGRS for hypertension:
+ 0.103 * MTHFR_rs173675043 + 0.049 * MOV10_rs29325383 + 0.017 * ULK4_rs37743723
+ 0.035 * SLC4A7_rs130827113+ 0.031 * MDS1_rs4190763 + 0.100 * FGF5_rs169980732 + 0.105 *
SLC39A8_rs131073253 + 0.042 * GUCY1A3_rs131395713 + 0.062 * C5orf174/NPR3_rs1173771 3 + 0.052 *
EBF1_rs119536303 + 0.095 * HFE_rs17999453 + 0.054 * BAT3_rs8053033 + 0.046 * CACNB2_rs43738143 + 0.05 *
C10orf107_rs15304402 + 0.055 * PLCE1_rs9327643 + 0.097 * CYP17A1_rs111915483 + 0.045 * ADM_rs71292203 +
0.062 * PLEKHA7_rs3818153 + 0.07 * TMEM_rs6331853 + 0.13 * ATP2B1_ rs172497543 + 0.07 *
ATXN2SH2B3_rs6531782 + 0.045 * TBX3_rs108504113 + 0.073 * CYP1A1_rs13789423 + 0.059 * FES_rs25215013 +
0.07 * PLCD3_rs129464542 + 0.025 * GOSR2_rs176087663 + 0.06 * ZNF652_rs169480482 + 0.034 *
JAG1_rs13272353 + 0.110 * GNAS_EDN3_rs60154503
2
Please note that the reference assigned to the SNP indicate from which article the beta coefficient
was derived.
Standardization process
After computation of the weighted sum, the score was divided by the number of effectively
genotyped SNP to produce an average measure, which takes into account the amount of effectively
genotyped SNPs. This ratio was the standardized by subtracting the population mean from an
individual raw score and then dividing the difference by the population standard deviation.
POWER analysis
In the combined sample a power analysis estimated that we have 80% power (with an alpha error set
at 0.05) to reject the null hypothesis if the true odds ratio is outside the range 0,830- 1,201 between
subjects in the 2nd tertile vs 1st tertile as well as between subjects in the 3rd tertile vs 1st tertile. In
the LSR we have a power of 80% (with an alpha error set at 0.05) to reject the null hypothesis if the
true odds ratio is outside the range 0,739- 1,332 between subjects in the 2nd tertile vs 1st tertile and
outside 0,734- 1,341 between subjects in the 3rd tertile vs 1st tertile. In the MDC with the same
power and alpha, if the true odds ratio is outside the range 0,711- 1,401 between subjects in the 2nd
tertile vs 1st tertile and outside 0,720- 1,385 between subjects in the 3rd tertile vs 1st tertile. In the
SAHLSIS, if the true odds ratio is outside the range 0,685- 1,446 between subjects in the 2nd tertile
vs. 1st tertile and outside 0,684- 1,447 between subjects in the 3rd tertile vs 1st tertile.
It should be noted that these ranges have been estimated without considering that the association of
GRS tertiles with stroke is modified also by the other covariates entered in the logistic regression
model.
3
Category-less Net Reclassification Improvement (NRI) and Integrated Discrimination
Improvement (IDI) indexes.
The net reclassification improvement (NRI) index was computed using pold and pnew where pold is the
predicted probability using the beta coefficients as computed in the logistic regression model without
the GRS and pnes is the predicted probability using the beta coefficients as computed in the logistic
regression model with the GRS. In the category-less NRI(>0) subjects are categorized as up and
down according to the differences in the predicted probabilities with the two models and calculated
as:
NRI=P(up|event) - P(down|event) + P(down|non-event) - P(up|non-event)
NRI=
event(NRI)=P(up|event) - P(down|event)=
non-event(NRI)=P(down|non-event) - P(up|nonevent)=
As stated by Pencina et al.5, 6 using the category-less NRI(>0), since the same constant is added to
the logits of models without and with the new marker, and for each subject only the ordering of
predicted probabilities matters, it is easy to show that this ordering remains unchanged after the
adjustment and can be applied directly and remains meaningful for case–control data.
Frequently, in case–control data the ratio of events to non-events is higher than in the true
population. This means that the precision of estimation for events relative to non-events is higher in
this type of case–control data. Thus, in case–control studies the asymptotic standard errors can be
estimated as:
SE(eventNRI)=
4
SE(eventNRI)=
SE(NRI)=
where the respective p’s are estimated based on sample data as discussed by Pencina and colleagues.
=
;
=
=
.
;
=
The IDI was estimated as follows:
=(
new,events
-
where
new,events
is the mean of the new model-based predicted probabilities of an event for those
old,events
who develop events,
)-(
old,events
new,nonevents
-
old,nonevents)
is the corresponding quantity based on the old model,
new,nonevents is
the mean of the new model-based predicted probabilities of an event for those who do not develop
events and
old,nonevents
is the corresponding quantity based on the old model.5
5
SUPPLEMENTARY RESULTS
Supplementary Table S1: Hardy-Weinberg equilibrium for all the SNPs in the stroke case-control project.
Gene
SNP
Chr
MTHFR-NPPB3
rs17367504
1
MOV103
rs2932538
1
SLC4A73
rs13082711
3
ULK43
rs3774372
3
MECOM3
rs419076
3
FGF52
rs16998073
4
SLC39A83
rs13107325
4
GUCY1A3-GUCY1B33
rs13139571
4
NPR3-C5orf233
rs1173771
5
EBF13
rs11953630
5
HFE3
rs1799945
6
BAT2-BAT53
rs805303
6
CACNB2(5’)3
rs4373814
10
c10orf1072
rs1530440
10
PLCE13
rs932764
10
CYP17A1-NT5C23
rs11191548
10
Genomic reference
(GRCh38)
NC_000001.11:g.11
802721A>G
NC_000001.11:g.11
2673921A>G
NC_000003.12:g.27
496418T>C
NC_000003.12:g.41
835922T>C
NC_000003.12:g.16
9383098T>C
NC_000004.12:g.80
263187A>T
NC_000004.12:g.10
2267552C>T
NC_000004.12:g.15
5724361C>A
NC_000005.10:g.32
814922A>G
NC_000005.10:g.15
8418394C>T
NC_000006.12:g.26
090951C>G
NC_000006.12:g.31
648589G>A
NC_000010.11:g.18
131043G>C
NC_000010.11:g.61
764833C>T
NC_000010.11:g.94
136183A>G
NC_000010.11:g.10
3086421T>C
% of valid
genotypes
97.7
C.A.
A.A.
CAF
Expected
heteroz.
0.251
HWE
p-value
0.8096
Kappa
0.853
Observed
heteroz.
0.251
A
G
97.4
G
A
0.738
0.390
0.386
0.3822
0.986
98.2
C
T
0.225
0.349
0.349
0.9577
0.970
97.7
C
T
0.150
0.251
0.255
0.2590
0.986
98.0
T
C
0.463
0.500
0.497
0.6140
0.970
98.4
T
A
0.351
0.456
0.455
0.9531
0.985
97.6
C
T
0.956
0.084
0.085
0.5256
0.988
98.0
C
A
0.784
0.338
0.338
0.9758
0.982
98.2
G
A
0.593
0.484
0.483
0.7867
1.000
97.7
C
T
0.657
0.450
0.451
0.8512
0.968
98.3
G
C
0.123
0.213
0.215
0.4954
0.957
98.2
G
A
0.614
0.471
0.474
0.6263
0.978
98.0
C
G
0.432
0.487
0.491
0.5856
0.952
97.4
C
T
0.820
0.294
0.296
0.5977
0.973
97.5
G
A
0.447
0.499
0.494
0.4423
0.982
97.6
T
C
0.896
0.188
0.186
0.4642
0.970
0.996
6
ADM3
rs7129220
11
PLEKHA73
rs381815
11
FLJ32810-TMEM1333
rs633185
11
ATP2B11
rs17249754
12
ATXN-SH2B32
rs653178
12
TBX5-TBX33
rs10850411
12
CYP1A1-ULK33
rs1378942
15
FES3
rs2521501
15
PLCD32
rs12946454
17
GOSR23
rs17608766
17
ZNF6522
rs16948048
17
JAG13
rs1327235
20
GNAS-EDN33
rs6015450
20
NC_000011.10:g.10
328991G>A
NC_000011.10:g.16
880721C>T
NC_000011.10:g.10
0722807G>C
NC_000012.12:g.89
666809G>A
NC_000012.12:g.11
1569952C>T
NC_000012.12:g.11
4949991T>C
NC_000015.10:g.74
785026C>A
NC_000015.10:g.90
894158A>T
NC_000017.11:g.45
130754A>T
NC_000017.11:g.46
935905T>C
NC_000017.11:g.49
363104A>G
NC_000020.11:g.10
988382A>G
NC_000020.11:g.59
176062A>G
97.5
A
G
0.108
0.194
0.193
0.6417
0.994
97.8
T
C
0.282
0.408
0.404
0.4985
0.990
97.5
C
G
0.699
0.418
0.421
0.5908
0.970
97.2
G
A
0.862
0.238
0.238
0.9007
0.977
98.1
G
A
0.488
0.491
0.500
0.1562
0.988
97.8
T
C
0.709
0.412
0.413
0.9301
0.961
97.6
C
A
0.320
0.437
0.436
0.7676
0.968
97.3
T
A
0.328
0.453
0.441
0.0344
0.974
98.5
T
A
0.247
0.370
0.372
0.6983
0.980
98.0
C
T
0.140
0.233
0.241
0.0124
0.963
97.3
G
A
0.385
0.467
0.473
0.3107
0.969
97.9
G
A
0.454
0.480
0.496
0.0126
0.986
98.0
G
A
0.133
0.235
0.231
0.1437
0.989
SNP, Single-nucleotide polymorphism; Chr., chromosome; C.A., coded allele; A.A., Alternate allele; CAF, Coded Allele Frequency; HWE,
Hardy Weinberg equilibrium
7
Supplementary Table S2. Summary association statistics based on all data for 29 independent SNPs in the different samples (MDC,
LSR, SAHLSIS) and combined, after full adjustment (model C).
Combined
Gene
MTHFRNPPB
MOV10
SLC4A7
ULK4
MECOM
FGF5
SLC39A8
GUCY1A3GUCY1B3
NPR3C5orf23
EBF1
HFE
BAT2-BAT5
CACNB2(5')
C10ORF107
PLCE1
CYP17A1NT5C2
ADM
Index SNP
and C.A.
rs17367504
A
rs2932538
G
rs13082711
C
rs3774372
C
rs419076
T
rs16998073
T
rs13107325
C
rs13139571
C
rs1173771
G
rs11953630
C
rs1799945
G
rs805303
G
rs4373814
C
rs1530440
C
rs932764
G
rs11191548
T
rs7129220
A
Beta
(SEM)
1.112
(1.003-1.233)
1.080
(0.988-1.181)
0.945
(0.862-1.036)
0.986
(0.885-1.098)
0.977
(0.903-1.056)
1.087
(1.001-1.180)
0.961
(0.823-1.122)
1.052
(0.957-1.155)
1.078
(0.996-1.167)
1.037
(0.957-1.123)
1.041
(0.925-1.172)
1.003
(0.927-1.086)
1.018
(0.942-1.100)
0.991
(0.901-1.091)
1.012
(0.935-1.094)
0.952
(0.837-1.083)
0.993
(0.877-1.125)
LSR
p-value
0.044
0.090
0.225
0.794
0.552
0.046
0.613
0.293
0.062
0.375
0.504
0.936
0.650
0.855
0.773
0.454
0.913
Beta
(SEM)
1.078
(0.919-1.265)
1.058
(0.926-1.209)
0.909
(0.789-1.046)
1.013
(0.860-1.193)
0.969
(0.862-1.089)
1.122
(0.990-1.272)
1.022
(0.810-1.290)
1.099
(0.956-1.265)
0.994
(0.881-1.121)
1.013
(0.896-1.145)
1.029
(0.863-1.228)
1.033
(0.918-1.162)
0.951
(0.845-1.071)
1.043
(0.906-1.199)
0.925
(0.822-1.042)
0.829
(0.680-1.010)
0.921
(0.764-1.109)
MDC
p-value
0.354
0.407
0.181
0.876
0.600
0.071
0.853
0.185
0.922
0.840
0.747
0.593
0.407
0.559
0.199
0.062
0.383
Beta
(SEM)
1.038
(0.860-1.253)
1.128
(0.951-1.337)
1.037
(0.880-1.222)
0.947
(0.777-1.155)
1.030
(0.888-1.193)
1.065
(0.914-1.240)
0.864
(0.657-1.137)
1.049
(0.878-1.255)
1.145
(0.987-1.327)
0.999
(0.866-1.152)
1.070
(0.859-1.333)
1.071
(0.916-1.251)
1.010
(0.878-1.162)
0.933
(0.7781.118)
1.162
(1.001-1.350)
1.115
(0.878-1.415)
1.208
(0.963-1.515)
SAHLSIS
p-value
Beta (SEM)
p-value
0.694
1.278
(1.038-1.575)
1.130
(0.935-1.365)
0.919
(0.760-1.112)
0.966
(0.774-1.205)
0.947
(0.802-1.118)
1.056
(0.892-1.252)
0.991
(0.712-1.380)
0.997
(0.819-1.215)
1.149
(0.973-1.357)
1.083
(0.918-1.278)
1.064
(0.814-1.389)
0.942
(0.800-1.108)
1.097
(0.937-1.285)
1.010
(0.826-1.236)
1.092
(0.928-1.285)
1.006
(0.761-1.330)
0.871
(0.670-1.132)
0.021
0.166
0.666
0.592
0.698
0.421
0.297
0.597
0.073
0.985
0.547
0.392
0.889
0.451
0.049
0.371
0.103
0.208
0.385
0.757
0.520
0.525
0.957
0.979
0.101
0.344
0.651
0.469
0.239
0.920
0.292
0.968
0.301
8
PLEKHA7
FLJ32810TMEM133
ATP2B1
SH2B3
TBX5-TBX3
CYP1A2ULK3
FES
PLCD3
GOSR2
ZNF652
JAG1
Dovr +
GNAS-EDN3
rs381815
T
rs633185
C
rs2681492
G
rs653178
G
rs10850411
T
rs1378942
C
rs2521501
T
rs12946454
T
rs17608766
C
rs16948048
G
rs1327235
G
rs6015450
G
1.080
(0.990-1.179)
1.078
(0.994-1.170)
1.011
(0.912-1.121)
0.974
(0.901-1.053)
1.005
(0.922-1.094)
1.097
(1.008-1.192)
1.067
(0.981-1.161)
1.061
(0.971-1.160)
1.057
(0.948-1.180)
1.041
(0.961-1.128)
0.937
(0.868-1.012)
0.947
(0.829-1.083)
0.084
0.070
0.836
0.506
0.914
0.031
0.129
0.192
0.318
0.321
0.096
0.428
1.122
(0.981-1.282)
1.061
(0.936-1.203)
1.035
(0.894-1.198)
1.005
(0.895-1.129)
0.983
(0.863-1.120)
1.099
(0.967-1.250)
1.076
(0.949-1.221)
1.072
(0.936-1.228)
0.961
(0.812-1.137)
1.037
(0.920-1.169)
1.007
(0.897-1.130)
0.984
(0.825-1.175)
0.093
0.353
0.648
0.934
0.799
0.149
0.251
0.316
0.643
0.547
0.906
0.860
1.048
(0.890-1.235)
1.212
(1.050-1.400)
1.174
(0.964-1.430)
0.915
(0.790-1.060)
1.090
(0.932-1.276)
1.124
(0.957-1.321)
1.013
(0.8631-1.19)
1.068
(0.903-1.263)
1.207
(0.993-1.468)
1.031
(0.886-1.201)
0.844
(0.729-0.978)
0.932
(0.754-1.153)
0.574
0.009
0.111
0.236
0.280
0.154
0.878
0.443
0.059
0.693
0.024
0.518
1.017
(0.849-1.219)
0.965
(0.812-1.148)
0.889
(0.705-1.120)
1.025
(0.870-1.208)
0.980
(0.815-1.177)
1.118
(0.945-1.322)
1.180
(0.986-1.413)
1.132
(0.943-1.359)
1.041
(0.832-1.302)
1.041
(0.880-1.232)
0.920
(0.785-1.080)
1.011
(0.798-1.280)
0.851
0.862
0.318
0.766
0.980
0.194
0.071
0.182
0.728
0.636
0.309
0.928
SEM, standard error of the mean. After full adjustment [regression model C= age, sex, diabetes mellitus, hypertension, smoking habit]; C.A.
coded allele
9
Supplementary Table S3. Subjects with number of missing genotypes:
Number of missing genotypes
Number of subjects
No missing genotypes
4,685
1 missing genotype
1,148
2 missing genotypes
259
3 or more missing genotypes
602
Subjects with 3 or more missing genotypes were excluded from the analysis
10
Supplementary table S4. Reclassification (NRI>0) among people who experience an ischemic stroke and those who do not experience
an ischemic stroke.
N. of subjects changing categories
Participants who experience a IS Event
Number of events moving up
Number of events moving down
Participants who do not experience a IS Event
Number of events moving up
Number of events moving down
1809
1694
3503
1155
1234
2389
Probability
P(up|IS=1)
P(down|IS=1)
NRI events
0,516415
0,483585
0,032829
P(up|IS=0)
0,483466
P(down|IS=0)
0,516534
0,033068
NRI non events
0,065897
NRI total
n.b 174 cases and 26 controls not included in the analysis due to lack of covariates specified in model C.
P(up|IS=1)= Predicted Probability augmented, given that IS occurred.
P(down|IS=1)= Predicted Probability diminished, even if IS occurred.
P(up|IS=0)= Predicted Probability augmented, even if IS did not occur.
P(down|IS=0)= Predicted Probability diminished, given that IS occurred.
11
Supplementary Table S5. Different O.R. (95% C.I.) derived by the association analysis between GRS and different subtypes of
stroke according to the TOAST classification in the SALSHIS and LSR samples.
TOAST subtype
SHALSIS
LSR
SHALSIS+LSR
Large-artery atherosclerosis
1.319 (0.725-2.400)
1.037 (0.615-1.747)
1.085 (0.738-1.595)
Cardioembolic stroke
1.004 (0.610-1.652)
1.187 (0.870-1.619)
1.130 (0.870-1.468)
Small-vessel occlusion
1.763 (1.074-2.895)*
0.992 (0.746-1.319)
1.124 (0.881-1.435)
Cryptogenic stroke
2.008 (1.304-3.090)**
1.502 (0.782-2.884)
1.809 (1.266-2.585)**
1.176 (0.901-1.535)
1.286 (1.011-1.635)*
All the remaining causes including other determined 2.009 (1.124-3.588)*
cause of stroke and undetermined stroke
*<0,05; **<0,01
12
Supplementary Figure S1. Histogram showing the distribution of subjects with different GRS
and according to valid genotypes before standardization.
Average: 0,07 SD: 0.007 N=6,092
The boundaries for the inclusion in different tertiles were set as follows: 1st tertile: 0.03-0,0641
2nd tertile: 0.0642-0.0706;
3rd tertile 0.07061-0,09;
13
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