Multiple SNP testing improves risk prediction of first venous thrombosis

Abstract

There are no risk models available yet that accurately predict a person's risk for developing venous thrombosis. Our aim was therefore to explore whether inclusion of established thrombosis-associated single nucleotide polymorphisms (SNPs) in a venous thrombosis risk model improves the risk prediction. We calculated genetic risk scores by counting risk-increasing alleles from 31 venous thrombosis-associated SNPs for subjects of a large case-control study, including 2712 patients and 4634 controls (Multiple Environmental and Genetic Assessment). Genetic risk scores based on all 31 SNPs or on the 5 most strongly associated SNPs performed similarly (areas under receiver-operating characteristic curves [AUCs] of 0.70 and 0.69, respectively). For the 5-SNP risk score, the odds ratios for venous thrombosis ranged from 0.37 (95% confidence interval [CI], 0.25-0.53) for persons with 0 risk alleles to 7.48 (95% CI, 4.49-12.46) for persons with more than or equal to 6 risk alleles. The AUC of a risk model based on known nongenetic risk factors was 0.77 (95% CI, 0.76-0.78). Combining the nongenetic and genetic risk models improved the AUC to 0.82 (95% CI, 0.81-0.83), indicating good diagnostic accuracy. To become clinically useful, subgroups of high-risk persons must be identified in whom genetic profiling will also be cost-effective.

Full text

THROMBOSIS AND HEMOSTASIS
Multiple SNP testing improves risk prediction of first venous thrombosis
Hugoline G. de Haan,1Irene D. Bezemer,1Carine J. M. Doggen,1,2 Saskia Le Cessie,1,3 Pieter H. Reitsma,4,5
Andre R. Arellano,6Carmen H. Tong,6James J. Devlin,6Lance A. Bare,6Frits R. Rosendaal,1,4,5 and Carla Y. Vossen1,7
1Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands; 2Department of Health Technology & Services Research,
MIRA Institute for Biomedical Technology and Technical Medicine, Enschede, The Netherlands; 3Department of Medical Statistics, Leiden University Medical
Center, Leiden, The Netherlands; 4Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands;
5Department of Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, The Netherlands; 6Celera,Alameda, CA; and 7Department of Medical
Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
There are no risk models available yet
that accurately predict a person’s risk
for developing venous thrombosis. Our
aim was therefore to explore whether
inclusion of established thrombosis-
associated single nucleotide polymor-
phisms (SNPs) in a venous thrombosis
risk model improves the risk prediction.
We calculated genetic risk scores by
counting risk-increasing alleles from
31 venous thrombosis-associated SNPs
for subjects of a large case-control study,
including 2712 patients and 4634 controls
(Multiple Environmental and Genetic As-
sessment). Genetic risk scores based on
all 31 SNPs or on the 5 most strongly
associated SNPs performed similarly (ar-
eas under receiver-operating characteris-
tic curves [AUCs] of 0.70 and 0.69, respec-
tively). For the 5-SNP risk score, the odds
ratios for venous thrombosis ranged from
0.37 (95% confidence interval [CI],
0.25-0.53) for persons with 0 risk alleles
to 7.48 (95% CI, 4.49-12.46) for persons
with more than or equal to 6 risk alleles.
The AUC of a risk model based on known
nongenetic risk factors was 0.77 (95% CI,
0.76-0.78). Combining the nongenetic and
genetic risk models improved the AUC to
0.82 (95% CI, 0.81-0.83), indicating good
diagnostic accuracy. To become clinically
useful, subgroups of high-risk persons
must be identified in whom genetic profil-
ing will also be cost-effective. (Blood.
2012;120(3):656-663)
Introduction
Venous thrombosis is the result of innate thrombotic tendency and
nongenetic triggers. Many common genetic variants, mainly single
nucleotide polymorphisms (SNPs), with modest effects on risk of
venous thrombosis have been reported.1Individual SNPs have little
predictive value because of their modest effect on risk, but
combinations of gene variants may improve the predictive ability
and could be used to model susceptibility to venous thrombosis.
Simulation studies have shown that so-called genetic profiling
may be useful to discriminate between persons with high risk of
disease and those with low risk. The discriminative accuracy of
genetic profiling depends on the heritability and incidence of the
disease and on the frequencies of risk alleles.2,3
Genetic profiling has become a popular aim in epidemiologic
studies of many common diseases because a large amount of data
from genome-wide association studies has become available.2-8 For
recurrent venous thrombosis, we previously investigated the poten-
tial clinical utility of multiple SNP testing for recurrent events.9In
that study, individual SNPs were not significantly associated with
recurrent venous thrombosis. However, when the risk alleles of the
individual SNPs were combined, the risk estimates as well as the
significance of the association increased. The predictive ability of
multiple SNP analysis has not been studied for first events of
venous thrombosis. Genetic profiling may guide decisions on
prophylactic measures in high-risk groups, such as cancer patients,
persons undergoing surgery, persons requiring a plaster cast, or
those subject to prolonged immobilization.
To explore to what extent venous-thrombosis associated SNPs
can be used as predictors for a first venous thrombosis in the
general population and in high-risk groups, we investigated
31 SNPs in 2 large population-based case-control studies, of which
one was used as a validation set. We created genetic risk scores
based on these SNPs and a risk score based on nongenetic risk
factors. We also compared and combined our genetic risk score
with the nongenetic risk score to determine whether genetic
profiling with the currently known SNPs will improve the assess-
ment of venous thrombosis risk.
Methods
Study populations
The Multiple Environmental and Genetic Assessment of risk factors for
venous thrombosis (MEGA study) is a population-based case-control study
of venous thrombosis. Collection and ascertainment of events have
been described in detail previously.10,11 The MEGA analysis included
2712 consecutive patients with a diagnosis of a first deep vein thrombosis of
the leg or arm (with or without pulmonary embolism) and 4634 control
subjects (partners of patients and random population controls).
The Leiden Thrombophilia Study (LETS), another population-based
case-control study of venous thrombosis, was used to validate the risk
scores and included 443 consecutive patients with a diagnosis of a first deep
vein thrombosis of the leg (with or without pulmonary embolism) and
453 control subjects (acquaintances or partners of patients), all without a
Submitted December 9, 2011; acceptedApril 27, 2012. Prepublished online as
Blood First Edition paper, May 14, 2012; DOI 10.1182/blood-2011-12-397752.
There is an Inside Blood commentary on this article in this issue.
The online version of this article contains a data supplement.
The publication costs of this article were defrayed in part by page charge
payment. Therefore, and solely to indicate this fact, this article is hereby
marked ‘‘advertisement’’ in accordance with 18 USC section 1734.
© 2012 by The American Society of Hematology
656 BLOOD, 19 JULY 2012 䡠VOLUME 120, NUMBER 3
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known malignancy. Collection and ascertainment of events have been
described in detail previously.12 Both studies were approved by the Medical
Ethics Committee of the Leiden University Medical Center, Leiden, The
Netherlands.
SNP selection
Initially, we selected 40 SNPs for the genetic risk score, based on the
literature and our previous work. Eighteen SNPs had been reported and
repeatedly confirmed to be associated with venous thrombosis.1,13 Twelve
SNPs were added from the Group Health study13,14; these SNPs were
associated with venous thrombosis in the original study and replicated in
the MEGA study. Nine SNPs were added from a large SNP association
analysis, including subsequent fine mapping that we performed recently in
LETS and MEGA.15,16 Another added SNP was recently identified in a
follow-up study of a genome-wide association study and replicated in the
FARIVE study and the MEGA study.17 Among the 40 SNPs in the initial
selection, we studied linkage disequilibrium and mutually adjusted SNPs
within genes. Four SNPs in PROC (rs1799808, rs1799810, rs2069915, and
rs5937) were explained by rs1799809 in PROC; 4 SNPs in the fibrinogen
genes (rs6050 and rs2070006 in FGA, rs1800788 in FGB and rs2066854 in
FGG) were explained by rs2066865 in FGG; and rs3753305 in F5 was
explained by rs6025 (factor V Leiden). Consequently, we excluded 9 SNP
associations that were explained by other SNPs. The remaining 31 SNPs
(Table 1) were included in the genetic risk score.
Genetic risk score
We defined a genetic risk score that counts the total number of
risk-increasing alleles in persons. To take into account the stronger
association of some SNPs with venous thrombosis, we also constructed
a weighted risk score assigning weights to the risk alleles of each SNP
corresponding to the logarithm of the average risk estimates found in the
literature. In addition to the full genetic model including 31 SNPs, we
constructed a parsimonious model with fewer SNPs. To determine which
SNPs should be included in this model, we added SNPs one-by-one to
create the genetic risk score. We started with the SNP with the highest
odds ratios (ORs) in the literature and assessed whether adding SNPs to
the risk score improved the area under the curve (AUC) after each SNP
addition. The addition of SNPs was stopped when the AUC of the risk
score, including the newly added SNP, did not differ from the AUC of
the full genetic model.
Nongenetic risk factors
We constructed a nongenetic risk score, which included the following
risk factors: recent (within 3 months before the index date) leg injury,
surgery, pregnancy or postpartum, immobilization (ie, plaster cast,
bedridden at home, hospitalization), travel for more than 4 hours in
2 months before the index date, oral contraceptives (OC) use or hormone
replacement therapy (HRT) at the index date, obesity (body mass index
⬎30 kg/m2), and a cancer diagnosis between 5 years before and
6 months after the index date. The index date was defined as date of
diagnosis for patients and their partner controls and the date of
completing the questionnaire for random controls. We also included
family history in the nongenetic risk score. Family history was defined
as positive when a parent or sibling had experienced venous thrombosis
and negative when none of these relatives had experienced venous
thrombosis, or when the participant was not aware of venous thrombosis
in the family. We assigned weights to each nongenetic risk factor
Table 1. The 31 SNP associations with venous thrombosis in MEGA and in the literature13-17,19-26
Gene SNP Chromosome Position
MEGA
Literature
average
OR
Risk allele frequency, %
Cases Controls OR 95% CI
F5 rs6025 1 167.785.673 10 3 4.30 3.70-4.99 3.79
F2 rs1799963 11 46.717.631 6 2 3.01 2.36-3.85 2.78
ABO rs8176719 9 136.132.908 47 34 1.74 1.63-1.87 1.85
FGG rs2066865 4 155.744.726 34 27 1.41 1.32-1.51 1.56
F11 rs2036914 4 187.429.475 59 52 1.35 1.26-1.44 1.32
PROCR rs2069951 20 33.227.425 7 5 1.32 1.16-1.51 1.30
F11 rs2289252 4 187.444.375 48 41 1.36 1.28-1.45 1.26
F9 rs4149755 X 138.451.778 7 6 1.11 0.99-1.24 1.24
PROCR rs2069952 20 33.227.612 64 60 1.21 1.13-1.29 1.21
SERPINC1 rs2227589 1 172.152.839 11 9 1.27 1.15-1.41 1.20
HIVEP1 rs169713 6 11.920.517 22 20 1.10 1.01-1.19 1.20
F2 rs3136516 11 46.717.332 52 49 1.12 1.06-1.20 1.19
F5 rs1800595 1 167.776.972 6 5 1.18 1.03-1.36 1.18
PROC rs1799809 2 127.892.345 47 43 1.17 1.10-1.25 1.17
PROCR rs867186 20 33.228.215 14 12 1.18 1.07-1.29 1.17
VWF rs1063856 12 6.153.534 37 33 1.18 1.10-1.26 1.16
GP6 rs1613662 19 60.228.407 84 82 1.18 1.09-1.29 1.15
F2 rs3136520 11 46.699.808 3 2 1.09 0.89-1.32 1.13
F8 rs1800291 X 153.811.479 85 83 1.12 1.05-1.20 1.13
STXBP5 rs1039084 6 147.635.413 42 45 0.90 0.84-0.96 0.90
NAT8B rs2001490 2 73.781.606 40 37 1.13 1.06-1.20 1.10
F13B rs6003 1 195.297.644 9 10 1.11 1.00-1.24 1.09
RGS7 rs670659 1 239.228.398 67 64 1.14 1.06-1.22 1.09
F9 rs6048 X 138.460.946 72 70 1.09 1.03-1.16 1.08
F5 rs4524 1 167.778.379 79 74 1.31 1.22-1.42 0.92
F13A1 rs5985 6 6.263.794 76 76 1.03 0.95-1.10 0.93
F3 1208 indel 1 94.780.000 46 46 1.02 0.96-1.09 1.06
TFPI rs8176592 2 188.040.937 69 68 1.04 0.97-1.11 1.06
F11 rs3822057 4 187.425.146 55 49 1.31 1.23-1.39 1.06
NR1I2 rs1523127 3 120.983.729 41 38 1.15 1.08-1.23 1.05
CPB2 rs3742264 13 45.546.095 69 68 1.04 0.97-1.11 1.01
MULTIPLE SNP TESTING 657BLOOD, 19 JULY 2012 䡠VOLUME 120, NUMBER 3
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corresponding to the logarithm of the risk estimates in MEGA (supple-
mental Table 1, available on the Blood Web site; see the Supplemental
Materials link at the top of the online article) and constructed a simple
risk scoring system counting the weighted risk factors. We also
constructed a combined risk score, including both the genetic risk score
and the nongenetic risk score using a logistic regression model.
Application of genetic profiling may be most useful in high-risk groups
(ie, persons exposed to known nongenetic risk factors). We therefore
studied the discriminative accuracy of our genetic risk score as well as the
combined score in high-risk situations of surgery, plaster cast, hospitaliza-
tion, young women (younger than 50 years) using OCs, women using HRT,
pregnancy or postpartum, middle-aged persons (older than 50 years) and
travel. We also studied persons with a positive family history and persons
with malignant disorders.
Statistical analyses
Crude and sex-adjusted (in case SNPs were located on the X chromosome)
ORs and 95% CIs were calculated by logistic regression for individual
SNPs and the genetic, nongenetic, and combined risk scores. When
assessing the magnitude of risk associated with number of risk alleles, we
used the median number of risk alleles among control subjects as the
reference group.
To assess how well a score classifies venous thrombosis patients and
control subjects, we calculated the area under the receiver-operating
characteristic (ROC) curve (AUC). The AUC ranges from 0.5 (no
discrimination between patients and control subjects) to 1.0 (perfect
discrimination). We compared the AUCs of the different genetic and
nongenetic risk models according to the method of Hanley and McNeil.18
Nagelkerke pseudo-r2statistic was used to approximate the proportion of
variability explained by the different risk models. All analyses, including
ROC curves and AUC calculation, were performed in SPSS Version 17.0.2
for Windows (SPSS Inc).
Results
SNPs associated with venous thrombosis
Table 1 lists all associations between SNPs and venous thrombosis
in the MEGA population and the average estimated effect size in
the literature.13-17,19-26 Not all SNPs were associated with venous
thrombosis in our study populations; nevertheless, we included all
31 SNPs in the genetic risk score because these SNPs had been
associated with venous thrombosis in other studies.
Genetic risk score
We first included all 31 SNPs in the genetic risk score. For each
person, we counted the number of risk-increasing alleles. The
number of risk alleles ranged from 13 to 38 with a median of
24 among control subjects and 26 among cases (Figure 1). The risk
for venous thrombosis was estimated for each number of risk
alleles, relative to the median number of risk alleles of 24, and
ranged from an OR of 0.27 (95% confidence interval [CI],
0.13-0.56) for 16 risk alleles to an OR of 3.23 (95% CI, 1.96-5.30)
for 33 risk alleles. At the more extreme ends of the risk distribution,
CIs around risk estimates became very wide because of small
numbers. The average relative risk increase per risk allele, when
treated as an ordinal variable, however, could be estimated with a
high level of precision, and was 1.14 (95% CI, 1.12-1.16). This
corresponds to an about 100-fold difference in risk between the
lowest and the highest number of risk alleles in our population.
We also constructed a weighted risk score thereby assigning
weight to the risk alleles according to their risk estimates found
Figure 1. The 31-SNP risk allele distribution in pa-
tients with venous thrombosis and control subjects
and corresponding ORs. The number of risk alleles was
counted for cases and control subjects (top panel). ORs
(95% CI) for venous thrombosis were calculated relative
to the median number of risk alleles among control
subjects (24 risk alleles; bottom panel). Persons with
15 or less and 36 or more risk alleles were combined for
the calculation of the OR because of the low numbers of
persons with that few or many risk alleles (bottom panel).
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in the literature (Table 1).A few SNPs have only been studied in
the MEGA population; in that case, we used the risk estimate in
MEGA as weight. The ROC curve for the weighted 31-SNP risk
score had an AUC of 0.71 (Table 2: 95% CI, 0.69-0.72; ie, there
is a 71% probability that a randomly chosen patient will have a
higher score than a randomly chosen control subject). The
weighted 31-SNP risk score was a better predictor than the
nonweighted 31-SNP risk score (AUC ⫽0.64, 95% CI, 0.63-
0.65). The average relative risk increase per unit in the risk
score, when treated as an ordinal variable, was 7.89 (95% CI,
6.76-9.21). The proportion of variability explained by the
31-SNP risk score was 16.1% (Nagelkerke pseudo-r2; Table 2).
To construct a genetic risk score using the most parsimonious
model, we added SNPs one-by-one to the genetic risk score,
starting with the SNP with the highest OR in literature (factor V
Leiden, rs6025), and calculated the AUC after the addition of
each SNP (Figure 2). The AUC for each single SNP ranged from
0.50 (95% CI, 0.49-0.52) for rs3136520 in F2 to 0.60 (95% CI,
0.59-0.61) for rs8176719 in ABO. The discriminative accuracy
of the model improved rapidly with the addition of each SNP,
until 5 SNPs were included in the model (Figure 2). These SNPs
were rs6025 (F5, factor V Leiden), rs1799963 (F2, 20210
G⬎A), rs8176719 (ABO), rs2066865 (FGG, 10034 C ⬎T),
and rs2036914 (F11). The AUC for this 5-SNP risk score was
0.69 (Table 2, 95% CI, 0.67-0.70). Moreover, a model based on
the 3 most well-known prothrombotic polymorphisms (ie,
rs6025, rs1799963, and rs8176719; AUC ⫽0.65, 95% CI,
0.64-0.66) performed significantly worse than the 5-SNP risk
score. The average relative risk increase per unit in the risk
score, when treated as an ordinal, was 9.50 (95% CI, 7.92-
11.39). The 5-SNP risk score explained 13.5% of the total
variability (Nagelkerke pseudo-r2; Table 2).
The number of risk alleles in the 5-SNP risk score ranged from
0 (OR ⫽0.37, 95% CI, 0.26-0.53) to 8 (OR ⫽7.48, 95% CI,
4.49-12.46 for ⱖ6 risk alleles), with a median number of risk
alleles of 2 among control subjects (Figure 3). The relative increase
in risk per increase in number of risk alleles was 1.61 (95% CI,
1.54-1.68), again corresponding to an over 100-fold difference in
risk between the lowest and the highest number of risk alleles. The
weighted 5-SNP risk score was a better predictor than a non-
weighted model based on number of risk alleles (AUC ⫽0.66,
95% CI, 0.64-0.67).
No difference between the discriminative accuracy of the
5-SNP risk score in men (AUC ⫽0.69, 95% CI, 0.67-0.71) and
women (AUC ⫽0.67, 95% CI, 0.65-0.69) was found. However,
differences were found when we constructed and compared the
5-SNP genetic risk score in patients with DVT in the arm, patients
with DVT in the leg, and patients with DVT in the leg combined
with pulmonary embolism. The AUC of the 5-SNP risk score in
patients with DVT in the arm (AUC ⫽0.62, 95% CI, 0.57-0.67)
was significantly lower than in patients with DVT in the leg
(AUC ⫽0.68, 95% CI, 0.67-0.70) or for DVT combined with
pulmonary embolism (AUC ⫽0.68, 95% CI, 0.67-0.70).
High-risk groups and SNP testing
To explore clinical applications of genetic profiling, we studied
groups exposed to known nongenetic factors in more detail. The
discriminative accuracy of the genetic risk scores in these
subgroups was similar to the discriminative accuracy in the
overall study population, except among cancer patients (Table
3). Subanalysis in cancer patients according to therapy (chemo-
therapy, surgery, radiation) or tumor class (solid vs other) did
not improve the discriminative accuracy of the weighted 5-SNP
risk score (data not shown).
To assess whether the genetic risk score performs better than
the current clinical practice of assessing family history, we
compared the discriminative accuracy of the genetic risk score
with a risk score with family history alone. The AUC of the
5-SNP risk score (0.68, 95% CI, 0.67-0.70) was significantly
higher than the AUC of family history (0.58, 95% CI, 0.57-
0.60), with a similar trend observed among all subgroups of
high-risk persons (Table 3).
Table 2. Venous thrombosis prediction using genetic, nongenetic, and combined risk scores
MEGA (N ⴝ7092) LETS (N ⴝ881)
AUC (95% CI) Nagelkerke pseudo r2AUC (95% CI) Nagelkerke pseudo r2
31-SNP risk score 0.71 (0.69-0.72) 0.161 0.69 (0.65-0.72) 0.149
5-SNP risk score 0.69 (0.67-0.70) 0.135 0.67 (0.64-0.71) 0.138
Nongenetic risk score 0.77 (0.76-0.78) 0.288 0.71 (0.68-0.74) 0.200
Combined risk score 0.82 (0.81-0.83) 0.378 0.77 (0.74-0.80) 0.292
The LETS study was used as a validation set.
Figure 2. Area under the ROC of genetic risk scores
based on increasing numbers of SNPs. SNPs were
added in order of the OR as found in the literature,
starting with rs6025 in the score based on 1 SNP, and
ending with CPB2 included in the score of 31 SNPs
(Table 1).
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Combining nongenetic and genetic risk scores
We assessed the discriminative accuracy of a nongenetic risk score
based on known nongenetic risk factors for venous thrombosis (leg
injury, surgery, pregnancy, plaster cast, bedridden at home, hospital-
ization, travel, OC use, HRT, obesity, and malignancy) and family
history. For the individual components, the AUC ranged from
0.50 (95% CI, 0.48-0.51) for recent travel to 0.67 (95% CI,
0.65-0.69) for OC use by women. The AUC for the nongenetic risk
score including family history was 0.77 (95% CI, 0.76-0.78). When
we added the genetic risk score to the nongenetic score, the AUC
significantly increased to 0.82 (Figure 4: 95% CI, 0.81-0.83)
compared with the nongenetic risk score alone (P⬍.0001) using
either the 31-SNP or the 5-SNP risk score. In addition, 28.8% of the
total variability in venous disease risk was explained by the
nongenetic risk score, which significantly improved to 37.8%
(Nagelkerke pseudo r2; Table 2) when combining the nongenetic
and genetic risk scores. Both the nongenetic and the combined risk
score models performed better in women than in men (nongenetic
risk score: AUC ⫽0.81, 95% CI, 0.80-0.83 for women and
AUC ⫽0.74, 95% CI, 0.72-0.75 for men; combined risk score:
AUC ⫽0.85, 95% CI, 0.83-0.86 for women and AUC ⫽0.80,
95% CI, 0.78-0.81 for men).
We also studied the discriminative accuracy of the combined
risk score model in the high-risk groups. For all subgroups, the
AUC improved when using the combined risk score compared with
the nongenetic risk score, which was significant for persons using
OCs, persons with a positive family history of venous thrombosis,
and persons older than 50 years old (Table 3).
Validation of the risk scores
To validate the genetic, nongenetic, and combined risk scores, we
studied their discriminative accuracy in subjects from another
population, the LETS population. As described in “Study popula-
tions,” LETS and MEGA are both population-based case-control
studies and are similar with respect to mean age at index of patients
(45 years in LETS, 47 years in MEGA) or control subjects
(45 years in LETS, 48 years in MEGA) and sex distribution
(43% men in LETS, 47% men in MEGA). Associations between
the 31 SNPs and venous thrombosis in LETS can be found in
supplemental Table 2. The discriminative accuracy of the weighted
31-SNP and 5-SNP risk scores in LETS were 0.69 (95% CI,
0.65-0.72) and 0.67 (95% CI, 0.64-0.71), respectively, which are
similar to those found in MEGA (Table 2).
We also constructed the nongenetic risk score weighted accord-
ing to the risk estimates of each risk factor from MEGA, except for
malignancies as having cancer was an exclusion criterion in LETS.
In addition, information of some nongenetic risk factors (ie, HRT,
recent travel, leg injury and plaster cast) was not assessed in LETS
or not in such detail as in MEGA. Therefore, these risk factors were
excluded from the nongenetic risk score. The discriminative
accuracy of the nongenetic risk score in LETS was 0.71 (95% CI,
0.68-0.74) and improved to 0.77 (95% CI, 0.74-0.80) when
combined with the genetic risk score. Both risk scores performed
slightly better in MEGA than in LETS (Table 2).
Table 3. Risk score prediction in subgroups of persons exposed to known nongenetic risk factors
High-risk group Patients, N
Control
subjects, N
Family history
risk score,
AUC (95% CI)
5-SNP risk
score, AUC
(95% CI)
Nongenetic
risk score,
AUC (95% CI)
Combined risk
score, AUC
(95% CI)
Surgery 292 111 0.60 (0.55-0.66) 0.66 (0.60-0.72) 0.67 (0.61-0.72) 0.73 (0.67-0.78)
Plaster cast 111 18 0.61 (0.48-0.73) 0.73 (0.59-0.87) 0.70 (0.56-0.84) 0.78 (0.64-0.91)
Hospitalization 278 93 0.57 (0.50-0.63) 0.66 (0.59-0.72) 0.60 (0.53-0.66) 0.66 (0.59-0.72)
Oral contraceptives* 513 327 0.58 (0.54-0.62) 0.71 (0.68-0.75) 0.73 (0.69-0.76) 0.81 (0.78-0.84)
HRT 58 90 0.59 (0.49-0.68) 0.71 (0.63-0.80) 0.74 (0.66-0.82) 0.80 (0.72-0.87)
Pregnancy/postpartum* 67 46 0.54 (0.44-0.65) 0.70 (0.60-0.79) 0.68 (0.57-0.79) 0.76 (0.66-0.85)
Age ⬎50 y 944 1534 0.57 (0.55-0.60) 0.68 (0.66-0.70) 0.73 (0.71-0.75) 0.79 (0.77-0.81)
Travel 379 610 0.58 (0.54-0.62) 0.70 (0.67-0.73) 0.77 (0.73-0.80) 0.82 (0.80-0.85)
Family history 659 551 NA 0.68 (0.65-0.71) 0.74 (0.71-0.76) 0.81 (0.78-0.83)
Malignancies 156 65 0.57 (0.49-0.65) 0.60 (0.52-0.68) 0.71 (0.64-0.78) 0.72 (0.65-0.80)
NA indicates not applicable.
*Women younger than 50 years.
Figure 3. The 5-SNP risk allele distribution in patients with venous thrombosis
and control subjects and corresponding ORs. The number of risk alleles was
counted for cases and control subjects (top panel). ORs (95% CI) for venous
thrombosis were calculated relative to the median number of risk alleles among
control subjects (score 2; bottom panel). Persons with 6 or more risk alleles were
combined for the calculation of the OR because of the low numbers of persons with
that few or many risk alleles (bottom panel).
660 de HAAN et al BLOOD, 19 JULY 2012 䡠VOLUME 120, NUMBER 3
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Discussion
We calculated a genetic risk score based on SNPs consistently
associated with venous thrombosis and observed a “dose-response”
relationship between this score and the risk of venous thrombosis.
The more risk alleles or genotypes present, the higher the risk of
venous thrombosis. A score constructed of the 5 most strongly
associated SNPs appeared to differentiate between patients and
control subjects equally as well as the initial genetic risk score
based on 31 SNPs. The discriminative accuracy of both the 5-SNP
and 31-SNP risk score was replicated in another study (LETS)
suggesting robustness of the genetic models.
When preventive measures after a positive test are invasive or
can have harmful side effects, strict discrimination is required
between those at high risk and low risk of developing a specific
disease. In the case of venous thrombosis, indiscrimination may
lead to an increased risk of thrombosis in high-risk persons
receiving insufficient prophylactic anticoagulant treatment, whereas
persons at low risk receiving treatment are at an increased risk of
major bleeding. We investigated the extent to which genetic risk
scores can improve the accuracy of thrombosis risk assessment by
ROC curves. The 5-SNP genetic risk score performed better than
family history assessment, which is the current clinical practice of
risk assessment in persons exposed to known nongenetic risk
factors. However, the 5-SNP genetic risk score performed worse
than a risk score of nongenetic risk factors. A recent study by
Hippisley-Cox and Coupland27 showed that an algorithm of
nongenetic risk factors is able to discriminate between patients and
control subjects with an AUC of 0.75. This is similar to the AUC
observed with our nongenetic risk score (0.77). However, the AUC
may be an overestimation because we used (the logarithm of) the
risk estimates from MEGA as weights.
Here, we showed that addition of the 5-SNP genetic risk score
to the nongenetic risk score model significantly improved the AUC
to 0.82, indicating good diagnostic accuracy. In our validation
study, information on the nongenetic risk factors was less complete,
which explains the lower discriminative accuracy of both the
nongenetic risk score (0.71) and the combined risk score (0.77).
Identification of persons at risk of developing venous thrombo-
sis is most useful in high-risk populations. This is because the
incidence of venous thrombosis in the general population is too low
(1 per 1000 persons a year28) to justify genotyping of all persons. In
all subgroups of high-risk persons, the combined risk score
performed better than the nongenetic score alone, which may
indicate the potential clinical value of genetic profiling in these
high-risk persons.
We defined a basic genetic risk score that counts the total
number of risk-increasing alleles in persons. To take into
account the stronger association of some SNPs with venous
thrombosis, we assigned literature-based weights to each SNP,
which discriminated patients better from controls than a non-
weighted genetic risk score. Although the proportion of variabil-
ity explained by the 5-SNP risk score is smaller than by the
31-SNP risk score, we showed that the discriminative accuracy
of the 5-SNP and 31-SNP risk scores was similar. The genetic
risk score is still limited, though, by its assumption that all SNPs
act independently and in an additive manner in venous thrombo-
sis susceptibility. An additive effect was assumed for the
different genotypes, whereas we cannot exclude a multiplicative
effect. Gene-gene interaction and gene-environment interaction
are not taken into account, although in reality many interactions
exist. Examples for venous thrombosis are the synergistic
effects between factor V Leiden (rs6025) and OC use29 and
between the F13A1 Val34Leu variant (rs5985) and fibrinogen
levels.30 We chose to include SNPs on their contribution to risk
(effect size) and gave weights corresponding to the logarithm of
this effect size. This is the most relevant for a person who has a
certain genotype. One could argue that, on a population level,
the prevalence of risk alleles is of relevance. However, this
would not be expected to improve the performance of the risk
prediction model, and indeed a genetic risk model based on the
5 SNPs with the highest risk allele frequency in MEGA
performed worse than the nonweighted 5-SNP risk score, which
is based on the 5 SNPs with the highest effect size (AUC ⫽0.54,
95% CI, 0.53-0.56; and AUC ⫽0.66, 95% CI, 0.64-0.67,
respectively).
In the future, adding newly discovered predictive SNPs to the
model may further improve discrimination. In a simulation study,
Janssens et al showed that the AUC depends on the number of
SNPs included, and their OR and risk allele frequency.2The
heritability of a disease determines the maximum obtainable AUC.
For venous thrombosis, the heritability is estimated to be approxi-
mately 60%.31,32 The simulation study indicated that at this level
Figure 4. ROC (AUC) curves of the weighted 5-SNP risk score (light
gray line), the nongenetic risk score (dotted gray line), and the
combined risk score (black line). The striped black line represents the
reference line (no discrimination).
MULTIPLE SNP TESTING 661BLOOD, 19 JULY 2012 䡠VOLUME 120, NUMBER 3
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high AUCs (⬎0.90) can be obtained, given that all genetic
contributors are in the prediction model. Identification of new
genetic predictors and validation of the genetic risk score in other
study populations will reveal whether genetic profiling is useful in
venous thrombosis.
In conclusion, we demonstrated that addition of a 5-SNP risk
score to a risk scoring system based on nongenetic risk factors
significantly improved the risk prediction of venous thrombosis.
Although additional predictive markers may be required for a risk
score to be clinically useful in the general population, the 5-SNP
risk score may aid the management of subgroups of high-risk
persons.
Acknowledgments
The Leiden Thrombophilia Study was supported by The Nether-
lands Heart Foundation (grant 89.063). The Multiple Environmen-
tal and Genetic Assessment of Risk Factors for Venous Thrombosis
was supported by The Netherlands Heart Foundation (grant NHS
98.113), the Dutch Cancer Foundation (RUL99/1992), and The
Netherlands Organization for Scientific Research (grant 912-03-
033l 2003).
Authorship
Contribution: C.Y.V. had full access to all of the data in the study,
takes full responsibility for the integrity of the data and the
accuracy of the data analysis, performed statistical analysis, and
supervised the study; F.R.R. had full access to all of the data in the
study, takes full responsibility for the integrity of the data and the
accuracy of the data analysis, conceived and designed the study,
acquired the data, performed statistical analysis, obtained funding,
and supervised the study; I.D.B. conceived and designed the study,
acquired the data, drafted the manuscript, and performed statistical
analysis; J.J.D. conceived, designed, provided administrative,
technical, or material support, and supervised the study; P.H.R.
conceived, designed, and supervised the study; A.R.A. acquired the
data and provided administrative, technical, or material support;
H.G.d.H. drafted the manuscript, performed statistical analysis;
S.L.C. performed statistical analysis; C.J.M.D. conceived and
designed the study, acquired the data, provided administrative,
technical, or material support and supervised the study; C.H.T.
provided administrative, technical, or material support; L.A.B.
provided administrative, technical, or material support and super-
vised the study; and all authors analyzed and interpreted the data
and critically revised the manuscript for important intellectual
content.
Conflict-of-interest disclosure: L.A.B., J.J.D., P.H.R., I.D.B.,
and F.R.R. hold or have applied for patents related to SNPs in this
manuscript (notably rs6025, rs2066865, and rs2036914). A.R.A.,
C.H.T., J.J.D., and L.A.B. are employees of Celera USA. The
remaining authors declare no competing financial interests.
Correspondence: Frits R. Rosendaal, Department of Clinical
Epidemiology, C7-P, Leiden University Medical Center,
PO Box 9600, 2300 RC Leiden, The Netherlands; e-mail: f.r.
rosendaal@lumc.nl.
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online May 14, 2012 originally publisheddoi:10.1182/blood-2011-12-397752
2012 120: 656-663
Y. Vossen
Andre R. Arellano, Carmen H. Tong, James J. Devlin, Lance A. Bare, Frits R. Rosendaal and Carla
Hugoline G. de Haan, Irene D. Bezemer, Carine J. M. Doggen, Saskia Le Cessie, Pieter H. Reitsma,
Multiple SNP testing improves risk prediction of first venous thrombosis
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