Key Pharmacogenetic Variants for Cardiovascular Drugs

Journal of the American College of Cardiology Vol. 60, No. 1, 2012
© 2012 by the American College of Cardiology Foundation ISSN 0735-1097/$36.00
Published by Elsevier Inc. http://dx.doi.org/10.1016/j.jacc.2012.01.067
STATE-OF-THE-ART PAPER
Clinical Application of
Cardiovascular Pharmacogenetics
Deepak Voora, MD, Geoffrey S. Ginsburg, MD, PHD
Durham, North Carolina
Pharmacogenetics primarily uses genetic variation to identify subgroups of patients who may respond differently
to a certain medication. Since its first description, the field of pharmacogenetics has expanded to study a broad
range of cardiovascular drugs and has become a mainstream research discipline. Three principle classes of
pharmacogenetic markers have emerged: 1) pharmacokinetic; 2) pharmacodynamic; and 3) underlying disease
mechanism. In the realm of cardiovascular pharmacogenetics, significant advances have identified markers in
each class for a variety of therapeutics, some with a potential for improving patient outcomes. While ongoing
clinical trials will determine if routine use of pharmacogenetic testing may be beneficial, the data today support
pharmacogenetic testing for certain variants on an individualized, case-by-case basis. Our primary goal is to re-view
the association data for the major pharmacogenetic variants associated with commonly used cardiovascu-lar
medications: antiplatelet agents, warfarin, statins, beta-blockers, diuretics, and antiarrhythmic drugs. In addi-tion,
we highlight which variants and in which contexts pharmacogenetic testing can be implemented by
practicing clinicians. The pace of genetic discovery has outstripped the generation of the evidence justifying its
clinical adoption. Until the evidentiary gaps are filled, however, clinicians may choose to target therapeutics to
individual patients whose genetic background indicates that they stand to benefit the most from pharmaco-genetic
testing. (J Am Coll Cardiol 2012;60:9–20) © 2012 by the American College of Cardiology Foundation
“The right dose of the right drug to the right person” is
one of the goals of pharmacogenomics and personalized
medicine. The need for pharmacogenomics in clinical prac-tice
is underscored, for example, by the improved ischemic
outcomes with newer platelet P2RY12 receptor inhibitors
which also have higher risk of adverse events compared to
clopidogrel (1,2). Therefore, there is a critical need to target
therapeutics to individual patients who stand to benefit the
most and suffer the least.
Principles of Pharmacogenetics
A prerequisite for pharmacogenetics is heterogeneity in drug
response. The definitions of drug response are varied and can
include surrogate measurements measured in the laboratory
(e.g., international normalized ratio [INR] for warfarin) or
clinical endpoints (e.g., stent thrombosis for clopidogrel).
A genetic basis for drug response is suggested when
responses are similar within family members (and therefore
are heritable) or significantly different in across ethnic
backgrounds. Underlying genetic variation can be deter-mined
either using a targeted approach where known
variants in candidate genes are hypothesized to influence
drug response or genome-wide association—an “unbiased”
screen for common variants across the entire genome. Rare
genetic variants missed by genome-wide association studies
(GWAS) can be identified through sequencing candidate
genes, exomes, or the entire genome in the case of rare
drug-induced adverse events.
Three broad classes of genetic variants influence drug
response: 1) pharmacokinetic; 2) pharmacodynamic; and
3) those associated with the underlying disease mechanism
(Table 1, Fig. 1). This review will discuss specific drugs, and
in each case, we will apply these pharmacogenetic principles
followed by potential clinical implications.
Statins
Statins (3-hydroxy-3methylglutaryl-coenzyme A reduc-tase
[HMGCR] inhibitors) can elicit 4 types (at least) of
“responses”: 1) low-density lipoprotein cholesterol (LDLc)
lowering; 2) protection from cardiovascular events; 3) mus-culoskeletal
side effects; and 4) statin adherence.
LDLc lowering. The majority of patients respond with
30% to 50% LDLc reduction; however, there is wide (10%
to 70%) variation (3). Two loci appear to affect LDLc
lowering: HMGCR and APOE (Table 2).
HMGCR. Carriers of a minor haplotype (defined by 3
single nucleotide polymorphisms [SNPs]: rs17244841,
rs17238540, and rs3846662) of HMGCR experience a 5%
From the Duke Institute for Genome Science & Policy and Center for Personalized
Medicine, Duke University, Durham, North Carolina. Dr. Voora has reported he has
no relationships relevant to the contents of this paper to disclose. Dr. Ginsburg is a
scientific advisor to CardioDx.
Manuscript received May 12, 2011; revised manuscript received January 5, 2012,
accepted January 18, 2012.
Downloaded From: http://content.onlinejacc.org/pdfAccess.ashx?url=/data/Journals/JAC/24346/ on 09/05/2014

10 Voora and Ginsburg JACC Vol. 60, No. 1, 2012
Clinical Cardiovascular Pharmacogenetics July 3, 2012:9–20
to 20% reduced LDLc lowering
with pravastatin (4) or simvasta-tin
(5) due to an alternatively
spliced HMGCR transcript that
produces a version of HMGCR
that is less sensitive to simvasta-tin
inhibition (6). This haplotype
was not identified in a GWAS
with atorvastatin (7), and there-fore,
whether this association
holds for other statins is unclear.
APOE. Two variants, rs7412
and rs429358, define 3 haplotypes,
namely, 2, 3, and 4, that are
associated with lipid, cognitive,
and thrombotic traits. In general,
the magnitude of LDLc lowering
is greatest for carriers of 2, fol-lowed
by 3, and 4 haplotypes,
respectively. The influence of
APOE haplotypes appears to be
consistent across variety of statin
types (4,7,8) and doses (8), and results in mild (15%)
attenuations of LDLc lowering.
Additional loci. Information on ABCB1 and ABCG2 can
be found in the Online Appendix.
Reduction in cardiovascular events. Statins prevent car-diovascular
disease by lowering LDLc and potential pleio-tropic
effects. There are few genetic predictors of these
pleiotropic benefits, but the rs20455 polymorphism in
kinesin-like protein 6 (KIF6, an underlying disease gene)
may be a candidate. Carriers of the risk allele may receive a
greater benefit from statin therapy compared to noncarriers
despite equal LDLc, triglycerides, or C-reactive protein
reduction (9,10). However, subsequent genome-wide meta-analysis
of large randomized placebo-controlled statin trials
found no evidence for association with coronary artery
disease (11) or any differential treatment benefit with statin
therapy (12,13).
Statin-induced musculoskeletal side effects. Statins have
a well-defined safety profile, but come with a small risk of
musculoskeletal side effects (14). Genetic variants in the
hepatic transporter, SLCO1B1, influence the risk of adverse
events (Table 2); it does not appear that cytochrome P450
(CYP) SNPs influence statin-induced side effects.
SLCO1B1. The solute carrier organic anion transporter
family, member 1B1 gene (SLCO1B1, also referred to as
SLC21A6, OATP-C, or OATP1B1) harbors many genetic
variants, and each variant is numerically and sequentially
labeled beginning with the unmutated copy of the gene, *1
(referred to as “star 1”) followed by *2, *3, *4, and so forth.
The *5 variant (rs4149056, Val174Ala) interferes with the
localization of this transporter to the hepatocyte plasma
membrane (15) and leads to higher plasma statin concen-trations
(16–18). In candidate gene and GWAS, carriers of
*5 are at 4- to 5-fold increased risk of severe, creatine kinase
(CK)- positive simvastatin-induced myopathy and 2- to
3-fold increased risk of CK- negative myopathy (19,20).
In trials of randomly assigned statins as well as in
observational studies, the risk for myopathy with *5 depends
on the statin type: the risk is greatest for simvastatin
atorvastatin pravastatin, rosuvastatin, or fluvastatin (20–23).
These effects parallel the influence of the *5 allele on the
clearance of these statins (16–18,24) and thus appear to be
statin-specific.
Adherence to statin therapy. Often, statin therapy is
hampered by nonadherence. Although the genetics of ad-herence
to statins has not been studied in any great depth,
2 studies (a clinical trial and observational cohort) observed
that carriers of the SLCO1B1*5 allele have a higher rate of
statin nonadherence (20,25).
Clinical implications for statin pharmacogenetics. It is
unlikely that genetic testing for statin efficacy will enter
clinical care since the magnitude of associations is small
(10% to 15% differences in LDLc lowering), and
physicians can reasonably forecast the magnitude of
LDLc lowering based on statin type, dose, and baseline
LDLc.
In contrast, statin-induced side effects and nonadherence
are less predictable. While the current level of evidence
surrounding SLCO1B1*5 may not support prospective
genotyping at this time, the test is currently offered on
consumer-directed whole genome genotyping platforms
(e.g., 23andMe, deCODEME, and so on). A potential strat-egy
for prospective SLCO1B1*5 testing might offer carriers
either pravastatin or rosuvastatin or fluvastatin as first-line
Abbreviations
and Acronyms
ACS acute coronary
syndrome(s)
BP blood pressure
CYP cytochrome P450
GWAS genome-wide
association study
HR heart rate
INR international
normalized ratio
LDLc low-density
lipoprotein cholesterol
LVEF left ventricular
ejection fraction
MI myocardial infarction
PCI percutaneous
coronary intervention
RF reduced function
SNP single nucleotide
polymorphism
SoTuarbclees1of PShoaurmrcaecsoogfenPehtaicrmVaacroiagteionnetic Variation
Category Description Types of Genes Examples
Pharmacokinetic Variability in concentration of drug at site of
drug effect
Drug-metabolizing enzymes
Drug transporters
Warfarin: CYP2C9
Clopidogrel: CYP2C19
Simvastatin: SLCO1B1
Metoprolol: CYP2D6
Pharmacodynamic Variability in drug ability to influence its target Transmembrane receptors
Intracellular enzymes
Clopidogrel: P2RY12
Simvastatin: HMGCR
Metoprolol: ADBR1
Underlying disease mechanisms Variability in disease being treated Often downstream or independent of drug target Hydrochlorothiazide: ADD1
Simvastatin: APOE

JACC Vol. 60, No. 1, 2012 Voora and Ginsburg 11
July 3, 2012:9–20 Clinical Cardiovascular Pharmacogenetics
Figure 1 Categories of Major Cardiovascular Pharmacogenetic Variants
Genetic variants that influence the response to cardiovascular agents fall into 3 broad categories: 1) pharmacokinetic; 2) pharmacodynamic; and
3) underlying disease mechanism.
agents, which seem to depend the least on SLCO1B1 for their
clearance.
Thienopyridines
Thienopyridines are effective in patients with acute coronary
syndrome (ACS) and percutaneous coronary intervention
(PCI); however, some patients remain at risk for death,
myocardial infarction (MI), and stent thrombosis. Platelet
function in response to clopidogrel is variable (26), heritable
(27), and reduced inhibition predicts future events (28);
therefore, clopidogrel is a prime candidate for pharmacoge-netics.
Clopidogrel pharmacogenetics centers around 2 loci,
CYP2C19 (pharmacokinetic) and ABCB1 (pharmacoki-netic);
other loci, CYP2C9 (pharmacokinetic), PON1
(pharmacokinetic), and P2RY12 (pharmacodynamic) may
also influence response (Table 3).
Platelet function in response to clopidogrel. CYP2C19.
Clopidogrel is an inactive prodrug activated by several
enzymes, including CYP2C19, to an active metabolite that
inhibits the platelet adenosine diphosphate receptor,
P2RY12 (29). CYP2C19 *2 (rs4244285) is the most com-mon
reduced-function (RF) variant; additional, rare variants
mimic the *2 allele: *3 [rs4986893], *4 [rs28399504], *5
[rs56337013]) (30). An individual person’s genotype can
be characterized in 3 ways: 1) presence of at least 1 RF
allele (carrier vs. noncarrier); 2) the number of RF alleles
(i.e., 0, 1, 2); or 3) the predicted, total CYP2C19 enzymatic
activity (Table 4). Carriers of *2—also referred to as
intermediate metabolizers or poor metabolizers—produce
lower active metabolite and have attenuated platelet inhibi-tion
(31). In general, there is a gene-dose effect where
increasing number of RF alleles (i.e., 0, 1, 2, or extensive
metabolizers, intermediate metabolizers, and poor metabo-lizers)
predicts a decreasing amount of platelet inhibition
(27,31,32). Carriers of another variant *17 (rs3758581,
ultrametabolizers) exhibit increased CYP2C19 activity, pro-duce
more active metabolite, and improved platelet inhibi-tion,
in most reports (33,34).
Higher loading and maintenance doses (e.g., 1,200 mg
and 150 mg/day) appear, in part, to overcome the effects
of the *2 allele (30,32,35,36), although not completely
(37), and can require up to 300 mg/day (38). Ticlopidine
(39), prasugrel (31,40,41), and ticagrelor (42) all produce
uniform platelet inhibition in *2 carriers and noncarriers.
ABCB1. The adenosine triphosphate-binding cassette, sub-family
B (MDR/TAP), member 1 (ABCB1) gene encodes
an apical membrane protein in enterocytes and hepatocytes
and serves to reduce bioavailability. Clopidogrel appears to be
handled by this transporter, and 3 SNPs—C1236T (rs1128503),
G2677T (rs2032582), and C3435T (rs1045642)—capture the
common genetic variation at this locus. Persons who carry a
T allele at each SNPs (i.e., the T-T-T haplotype) produced
reduced active metabolite (43) in an initial report. Despite
this initial observation, the link to reduced platelet inhibi-tion
has been difficult to establish (27,30), although may be
present for persons who carry 2 copies of the T-T-T
haplotype (44). As with CYP2C19 variants, prasugrel-

induced platelet inhibition is not affected by T-T-T haplo-type
(44).
Additional loci. Using GWAS, investigators have unsuc-cessfully
searched for additional variants for clopidogrel
(27). An additional discussion of genetic variation at
ABCB1, P2RY12, CYP2C9, and PON1 can be found in the
Online Appendix.
Clinical response to clopidogrel. CYP2C19. In parallel
with the platelet function data, the CYP2C19*2 allele is
associated with a graded risk of death, MI, or stroke.
Carriers of 1 allele (intermediate metabolizers) have a
1.5-fold increased risk, and carriers of 2 alleles (poor
metabolizers) experience a 1.8-fold increase. This pattern
also extends to stent thrombosis as well with a 2.6- and
4-fold increased risk in those with 1 and 2 *2 alleles,
respectively (32,45–49). Therefore, the CYP2C19 genetic
associations with platelet function are mirrored in the
clinical response to clopidogrel in the setting of PCI. These
observations formed the foundation for updating the clopi-dogrel
label by the Food and Drug Administration to
include pharmacogenetic information. Similarly, the gain of
function variant, CYP2C19*17, is associated with increased
risk of bleeding (50), and protection from ischemic events
(51) CYP2C19*2 carriers treated with prasugrel or ticagrelor
do not show a heightened risk of cardiovascular death, MI,
stroke, or stent thrombosis (40,52).
ABCB1. Carriers of 2 copies of the ABCB1 T-T-T haplo-type
treated with clopidogrel are at an increased risk for
subsequent death, MI, or stroke compared to persons who
carried none, thus mirroring the platelet function data
(44,45,52). Genetic variation at the ABCB1 locus does not
appear to influence prasugrel- or ticagrelor-treated patients
(40,52).
Clinical implications. The current state of evidence reflects
a strong and consistent association with the CYP2C19*2 allele
and an increased risk for cardiovascular outcomes in ACS/
PCI patients treated with clopidogrel, although not other
settings (51). Alternative antiplatelet agents (i.e., prasugrel
and ticagrelor) mitigate the adverse risk of CYP2C19*2-
associated adverse events. Although CYP2C19 genotyping is
commercially available, whether genotype-guided therapy
will improve outcomes or reduce costs is unknown. Con-sensus
statements (53) currently do not recommend routine
testing. However, there is sufficient evidence to support
physicians who may choose to pursue CYP2C19*2 testing in
selected patients: 1) for diagnosis in patients with compli-cations
of clopidogrel therapy such as stent thrombosis in
compliant clopidogrel users; or 2) for the choice of dual
antiplatelet therapy in the ACS/PCI setting where the
physician believes that additional information regarding
the risk/benefit profile for clopidogrel will influence the
choice of drug therapy (54). Outside of these scenarios,
the ACS/PCI setting, or situations where there are ample
data to guide drug choice (e.g., ST-segment elevation MI
[55], diabetic patients [56], age 75 years, or prior
MTaainblGee2neticMAasinsoGceianteitoincsAWssitohcitahteioRnsesWpoitnhstehteoRSetsaptoinnsse to Statins
Caucasians Africans Asians Type of Study Effect of Variant Statin Type Ref. #
Gene Variant(s)
Statin Response/
Risk Allele/Haplotype Frequencies
Class effect 7,8,145–147
by alleles at rs7412 and
rs429358
LDLc lowering/GWAS, CG 2LDL reduction with 3 vs. 2
APOE 2, 3, and 4 haplotypes defined
2LDL reduction with 4 vs. 2
2: 15%–20%*
3: 90%–95%
4: 20%–30%
3% 6% LDLc lowering/CG 2LDL reduction in H7 carriers Simvastatin 4,5
HMGCR H7 haplotype defined by alleles at
rs17244841, rs17238540,
and rs3846662
Simvastatin, atorvastatin 19,20
1In CK positive symptoms
1In CK negative symptoms with
SLCO1B1 *5: rs4149056 15% 1% 13% Musculoskeletal side
effects/GWAS, CG
risk allele
Simvastatin, atorvastatin 20,25
SLCO1B1 *5: rs4149056 15% 1% 13% Nonadherence/CG 1Risk of nonadherence with
risk allele
*Represents Caucasian population.
CG candidate gene association study; CK creatine kinase; GWAS genome-wide association study; LDLc low-density lipoprotein cholesterol.

transient ischemic attack/stroke), there is minimal ratio-nale
to support CYP2C19 testing.
Based on the available data, it is reasonable that if a
person is found to be a poor metabolizer (Table 4), then an
alternative to clopidogrel, such as prasugrel or ticagrelor,
should be considered. This seems preferable over increased
clopidogrel doses, which have not shown benefit over
standard dose clopidogrel (57–59). The greatest uncertainty
is in the intermediate metabolizer group (i.e., those with 1
loss of function allele) and is where integration of other
clinical risk factors such as diabetes, body mass index, cost,
and other bleeding and thrombosis risk factors need to be
considered in determining the therapy with the optimal
risk:benefit profile.
Aspirin
Aspirin irreversibly inhibits prostaglandin G/H synthase 1
(PTGS1, or COX-1) and the conversion of arachidonic acid
to thromboxane. With adequate dosing and compliance,
aspirin is capable of completely inhibiting COX-1 in 99%
of persons; thus, true “aspirin resistance” is rare (60).
However, alternate agonists such as adenosine diphosphate
and collagen can produce robust aggregation in the face of
complete COX-1 inhibition (61), a response that demon-strates
wide interindividual variability (62), heritability
(63,64), and at high levels, association with future cardio-vascular
events (65,66), therefore making aspirin a candidate
for pharmacogenetic discovery. Because aspirin uniformly
inhibits its target, COX-1, there are no pharmacokinetic or
pharmacodynamic genetic considerations. Instead, investi-gators
have focused on platelet function loci (underlying
disease) and LPA (underlying disease).
Variants in several platelet genes, namely, PEAR1,
ITGB3, VAV3, GPVI, F2R, and GP1BA, are associated with
platelet function in response to aspirin (67–70). One with
moderate evidence is rs5918 in ITGB3 where carriers of the
risk allele have heightened platelet function on aspirin
(71–73). Recent GWAS of platelet function in response to
aspirin have identified additional genetic loci that have yet
to be replicated (74). Finally, the most robust association
comes from a large study that found carriers of the minor
allele for an intronic SNP, rs12041331, in PEAR1 have
higher PEAR1 platelet protein content and heightened
platelet function in response to aspirin (75).
The link to an increased risk of clinical events in aspirin
users has not been established despite several large studies
(76,77), although the PEAR1 variant rs12041331 has not
yet been tested. An uncommon variant in LPA (rs3798220)
seems to modify the protective effects of aspirin: carriers
had a more than 2-fold reduction in the risk for cardio-vascular
disease with aspirin, whereas noncarriers (95%
of Caucasians) had none in a large placebo-controlled
clinical trial (78).
Clinical implications. Because of the lack of consistent or
preliminary associations with the variants described in the
preceding text, there is currently no role for genetic testing
for aspirin.
Warfarin
Three loci contribute to the heterogeneity in response:
CYP2C9 (pharmacokinetic), VKORC1 (pharmacodynamic),
and CYP4F2 (pharmacodynamic) (Table 5).
Warfarin dose requirements. CYP2C9. CYP2C9 is re-sponsible
for S-warfarin (the more active enantiomer) clear-ance.
The 2 most common reduced-function variants are
MTaainblGee3neticMAasinsoGceianteitoincsAWssitohcitahteioRnsesWpoitnhstehteoRCelsoppoidnosgeretol Clopidogrel
Gene Variant(s)
Risk Allele/
Haplotype Frequencies
Drug Response/Type of Study
Effect of Variant
Caucasians Africans Asians With Risk Allele Ref. #
CYP2C19 *2 (rs4244285) 16% 14% 27% Drug concentration, platelet function,
recurrent MI, stent thrombosis/
GWAS, DMET, CG
2Active metabolite concentration
2Inhibition of platelet function
1Risk of recurrent MI, stroke,
stent thrombosis
Graded risk with 0, 1, or 2 alleles
31,32,45–47,52
CYP2C19 *17 (rs3758581) 5% 10% 6% Drug concentration, platelet function,
bleeding/CG
1Inhibition of platelet function
1Risk bleeding
32,33,50
ABCB1 T-T-T haplotype defined by
T allele at C1236T
(rs1128503), G2677T
(rs2032582), and
C3435T (rs1045642)
71% 12% 65% Drug concentration, platelet function,
recurrent MI, stroke, death/CG
2No change in inhibition of
platelet function
1Risk of recurrent MI, stroke,
death (in carriers of 2 T-T-T
haplotypes)
27,30,43,45,48,49,52
MI myocardial infarction; DMET drug metabolizing enzyme and transporter panel; other abbreviations as in Table 2.
CYTPab2lCe149 DipClYoPty2pCe1C9laDsispilfioctyapteionC*lassification*
Alleles Classification
*1 and *1 Extensive metabolizer
*2/*3/*4/*5/*6 and *1/*17 Intermediate metabolizer
*2/*3/*4/*5/*6 and *2/*3/*4/*5/*6 Poor metabolizer
*1/*17 or *17/*17 Ultrametabolizer
*Adapted from Ingelman-Sundberg et al. (147) and Scott et al. (148).

*2 (rs1799853) and *3 (rs1057910). The CYP2C9*2 allele
reduces warfarin clearance by 30% and the *3 allele by
90% (79), which translate into 19% and 33% reductions
per allele in warfarin dose requirements compared to
noncarriers (80).
VKORC1. Vitamin K epoxide reductase complex, subunit 1
(VKORC1) is warfarin’s target (81,82), and resequencing
this gene identified 2 haplotypes (A and B defined by the
alleles at 5 SNPS: rs7196161, rs9923231, rs9934438,
rs8050894, and rs2359612) that influence VKORC1 gene
expression and predict warfarin dose requirements (83). Of
these 5 SNPs, an A allele at rs9923231 (also referred to as
1639 GA and the 3673 SNP) in the promoter region is
responsible for reducing: VKORC1 gene expression (84).
Therefore, carriers of an A allele at rs9923231 (or the A
haplotype) require a 30% per allele reduction in warfarin
dose requirements (85).
CYP4F2. Several GWAS have not only confirmed the
observations above but also have identified a novel associa-tion
between rs2108622 in CYP4F2 and reduced hepatic
CYP4F2, higher levels of hepatic vitamin K, and higher
warfarin dose requirements (86–90).
Additional loci. Additional information regarding CALU
and GGCX can be found in the Online Appendix.
Clinical response to warfarin. RETROSPECTIVE STUDIES.
Standard dosing algorithms (i.e., a 5 mg or 10 mg loading
dose followed by titration based on the INR) lead to an
increased risk of an out-of-range INR (4.0) or a delay in
the time-to-therapeutic range INR in carriers of the
CYP2C9*2 or CYP2C9*3 alleles (91,92), the A haplotype or
A allele of the 1639 SNP in VKORC1 (93), or the T allele
in CYP4F2 (94). Importantly, only variants in CYP2C9 have
been linked to the risk of bleeding (91,92).
PROSPECTIVE STUDIES OF TAILORED WARFARIN THERAPY.
Small randomized pilot studies, parallel cohort studies, and
single arm studies with or without historical controls have
prospectively explored genetically guided warfarin therapy.
Whereas some studies suggest a benefit over standard
therapy (95–97), others failed to show any significant
advantage (98) except for smaller and fewer dosing changes
and INR measurements (99). Definitive clinical trials are
ongoing to assess the benefit of incorporating genetics into
warfarin therapy.
Clinical implications. There are strong genetic associa-tions
surrounding CYP2C9, VKORC1, and CYP4F2 variants
that influence the response to warfarin. Commercial testing
and algorithms that assist in the interpretation of genotypes
are available. Therefore, there is evidence to justify and tools
to enable genotype-guided warfarin therapy. Until large
scale trials demonstrate a benefit for routine testing, physi-cians
may choose to pursue testing in selected patients who
they feel may benefit by: 1) diagnosing those with compli-cations
from warfarin therapy (e.g., hemorrhage); 2) pre-dicting
dose for those at high risk of bleeding (e.g.,
“triple-therapy” with aspirin, clopidogrel, and warfarin); or
3) weighing the costs of newer anticoagulants against
warfarin.
Diuretics
Blood pressure–lowering response. The adducin 1 (alpha)
gene (ADD1, underlying disease) is implicated in animal
and human sodium handling (100), and persons with a
Gly460Trp substitution are more salt sensitive and have
enhanced blood pressure (BP) lowering with thiazide di-uretics
(100).
Clinical outcomes. In an observational study, patients
with Trp460 who were treated with thiazide diuretics
experienced a greater protection from MI and stroke than
patients with Gly460 (101). However, this difference in the
protective effects of diuretics could not be replicated in
several larger studies (102–104).
Clinical implications. Given the inconsistent associations
and lack of a commercially available test, the use of this
genetic variant in guiding treatment options in hypertension
is limited.
MTaainblGee5neticMAasinsoGceianteitoincsAWssitohcitahteioRnsesWpoitnhstehteoRWesaprfoanrisne to Warfarin
Gene Variant(s)
Risk Allele Frequencies
Drug Response/
Type of Study
Effect of Variant in
Caucasians Africans Asians Carriers of Risk Allele Ref. #
CYP2C9 *2 (rs1799853)
*3 (rs1057910)
*2: 10%
*3: 6%
1%
1%
1%
4%
Drug concentration, warfarin
dose requirements,
out-of-range INR values,
hemorrhage/GWAS, CG
2Clearance of S-warfarin
2Dose requirements
1Risk of out-of-range INR values
1Time to stable, therapeutic INR
1Risk of hemorrhage
79,80,88,89,91–93
VKORC1 1639 (rs9923231)* 60% 98% 2% Warfarin dose requirements,
out-of-range INR values/
linkage (human/animal),
GWAS, CG
2Dose requirements
1Risk of out-of-range INR values
1Time to stable, therapeutic INR
81–83,88,89
CYP4F2 rs2108622 23% 6% 25% Warfarin dose requirements/
GWAS, DMET
1Dose requirements 87–89
*Carriers of the risk, A, allele are also carriers of the A haplotype.
INR international normalized ratio; other abbreviations as in Tables 2 and 3.

Beta-Blockers
Beta-adrenergic receptor antagonists (or beta-blockers) are
a diverse class of agents that primarily antagonize the beta-1
adrenergic receptor, encoded by ADBR1. Accordingly,
changes in heart rate (HR), BP, and left ventricular ejection
fraction (LVEF) are considered the surrogate responses in
place of clinical responses such as protection from MI,
death, or heart failure. Variation in CYP2D6 (pharmacoki-netic)
and ADRB1, ADRB2, and GRK5 (all pharmacody-namic)
have received the most attention (Table 6).
Heart rate and blood pressure reduction. CYP2D6. Beta-blockers
are substrates for CYP, and metoprolol is exten-sively
metabolized by hepatic CYP2D6. The most common
RF variant CYP2D6*4 (rs3892097) results in the absence of
CYP2D6 activity. Carriers have a higher systemic exposure
to metoprolol (105), which translates into a greater reduc-tion
in HR and BP (106,107). Beta-blockers such as
atenolol and carvedilol (108) do not require CYP2D6 for
their metabolism.
ADRB1. Ethnic differences in the dose response for pro-pranolol
(109) motivated pharmacodynamic genetic inves-tigations
of beta-blockers. Two variants in ADBR1, the
Ser49Gly (rs1801252) and Arg389Gly (rs1801253), lead to
impaired down-regulation (110) and higher signal transduc-tion,
respectively (111). Therefore, carriers of either variant
have enhanced, beta-1-receptor activity and more beta-blocker
sensitivity. Healthy volunteers and patients with
hypertension who carry 2 Arg389 variants have a greater
HR (112) or BP (113) reduction mainly with metoprolol,
although not with all beta-blockers (114).
In patients with systolic heart failure treated with either
metoprolol or carvedilol (115), but not bucindolol (116),
carriers of 2 copies of the ADBR1 Arg389 variant had
significantly greater improvements in LVEF compared to
the Gly389 carriers.
ADRB2. Genetic variation in the beta-2 adrenergic receptor
(ADBR2) centers around 2 variants: Arg16Gly (rs1042713) and
Glu27Gln (rs1042714). Receptors with Gly16 versus Arg16
have enhanced down-regulation of ADBR2. In contrast,
receptors with Glu27 appear to be resistant to down-regulation
(117). Extension to variation in BP or HR
lowering in response to beta-blocker therapy has not been
demonstrated (114).
GRK5. Downstream of the beta-1-adrenergic receptor are
G-protein coupled receptor kinases responsible for desensi-tization
of the beta-1-adrenergic receptor. A Glu41Leu
genetic variant in 1 of these kinases, G protein-coupled
receptor kinase 5 (GRK5), is more prevalent in African
Americans. The Leu41 more effectively uncoupled
isoproterenol-stimulated responses than GRK5-Q41, thus
producing a pharmacological-like “beta-blockade” in mice
(118), although with no differences in atenolol-induced HR
reduction in humans (119).
MTaainblGee6neticMAasinsoGceianteitoincsAWssitohcitahteioRnsesWpoitnhstehteoRBeestpao-AnsderetnoerBgeictaR-AedcreepnteorrgAicntRaegcoenpistotsr Antagonists
Caucasians Africans Asians Carriers of Risk Allele Beta-Blocker Type Ref. #
Risk Allele Frequencies
Drug Response/Type of Study
Gene Variant(s)
Effect of Variant in
Metoprolol 105,106,127,128
2Clearance
1Reduction in BP and HR
1Risk of bradycardia
CYP2D6 *4 (rs3892097) 18% 23% Drug concentration,
HR and BP reduction/CG
113,115,116
ADRB1 Ser49Gly (rs1801252) 20% 1% 1% HR, BP reduction/CG 2Reduction in BP and HR Metoprolol 112
ADRB1 Arg389Gly (rs1801253) 31% 41% 20% HR, improvement in LVEF,
Metoprolol (BP/HR/LVEF)
Carvedilol (LVEF)
Bucindolol (hospitalization/death)
2Reduction in BP and HR
2Improvement in LVEF
BP reduction, hospitalization
or death in CHF/CG
BP blood pressure; CG candidate gene association study; HR heart rate; LVEF left ventricular ejection fraction.

Clinical benefit in patients with cardiovascular disease.
ADRB1. In heart failure patients, when compared to pla-cebo,
ADRB1 Arg389 homozygotes had a greater reduction
in the time to first hospitalization or death when treated
with bucindolol (116) but not carvedilol or metoprolol
(120–125) compared to Gly389 carriers. Whether these
differences are due to drug specific effects (i.e., bucindolol vs.
metoprolol) or the play of chance has not been adequately
tested. In patients with chronic coronary artery disease
randomly assigned to verapamil or atenolol, carriers of at
least 1 copy of the Ser49/ Arg389 haplotype had a 9- versus
2-fold worsened prognosis when assigned to verapamil
versus atenolol (126) despite equivalent BP and HR control
in both groups.
ADRB2. One study examined the influence of genetic
variation at ADRB2 and its influence on ACS patients and
found those homozygous for both the Arg16 and Glu27
alleles had a 20% rate of subsequent death versus 6% of
those homozygous for both Gly16 and Gln27 (125). This
trend has been observed in some studies of patients with
heart failure (124), although not all (122,123,126).
GRK5. Extension of the associations of the Glu41Leu
variant to clinical outcomes of patients was demonstrated in
1 study where Leu41 carriers exhibited improvement in sur-vival
compared to Glu41 carriers (118), although not in
another, larger study (121).
Adverse events. Metoprolol-induced adverse events (e.g.,
bradycardia) are associated with CYP2D6*4 in 2 studies,
each numbering 1,000 treated patients (127,128), al-though
not in smaller studies (129,130).
Clinical implications. In general, carriers of the Arg389
variant have: 1) enhanced reduction in HR and BP; 2) larger
improvements in LVEF; and 3) longer survival when
treated with chronic beta-blocker therapy compared to
persons with the Gly389 variant. Although it is unlikely that
beta-blocker therapy will ever be withheld for carriers of the
Gly389 variant, a potential application of these findings
would be to consider advanced heart failure therapies (e.g.,
left ventricular assist devices, biventricular pacing, or trans-plantation)
at an earlier stage in patients with the Gly389
variant.
Because certain beta-blockers such as atenolol and carve-dilol
are minimally handled by CYP2D6 (131), these may be
reasonable alternates for carriers of CYP2D6*4 with
metoprolol-induced bradycardia. Commercial testing for
CYP2D6*4 is available (e.g., Labcorp).
Antiarrhythmic Drugs
Digoxin and calcium-channel blockers. Many antiar-rhythmics
are known ABCB1 (described in preceding text)
substrates including verapamil, diltiazem, and digoxin. Ge-netic
variation in ABCB1 is inconsistently associated with
altered pharmacokinetic profiles (132–134), and there are
no reports of different clinical outcomes.
Procainamide. Procainamide is rapidly converted by acet-ylation
into N-acetylprocainamide (NAPA, an active me-tabolite)
by hepatic acetyltransferases (NAT2, primarily).
Variability in hepatic acetylation capacity has long been
observed (135), and “slow acetylators” produce less NAPA.
Common genetic variants that decrease NAT2 activity are
*5/rs1801280, *6/rs1799930, *7/rs1799931, and *14/
rs1801279 (136,137). Although efficacy does not appear to
be related to variation in its pharmacokinetics, the induction
of drug-induced lupus-associated autoantibodies (138,139),
but not symptomatic, drug-induced lupus (138,139) has
been linked to the slow acetylator status.
Propafenone. The hepatic hydroxylation by CYP2D6 of
propafenone into 5-hydroxypropafenone demonstrates wide
interindividual variability (140). Carriers of CYP2D6*4 (a
reduced function allele) have reduced propafenone clearance
compared to noncarriers (140–142).
Antiarrhythmic efficacy. At low doses of propafenone,
CYP2D6*4 carriers have greater reduction in exercise- or
isoproterenol-induced HR compared to noncarriers (143),
although not with higher doses or without the “stress” of
exercise/isoproterenol (140,142,143). Further, CYP2D6*4
carriers have enhanced suppression of atrial and ventricular
arrhythmias compared to noncarriers in some studies
(141,144), although not in all (140,142) Therefore, there are
insufficient data to make any conclusions regarding
CYP2D6 genotype and antiarrhythmic efficacy.
Toxicity. Central nervous system side effects with
propafenone, as well as excessive beta-blockade, are correlated
with a higher concentration of systemic propafenone and,
accordingly, the CYP2D6 genotype (140).
Clinical implications. Antiarrhythmic drug pharmacoge-netics
represents a mixed collection of associations that do
not sufficiently translate to consistent associations of mean-ingful
clinical outcomes. Therefore, there is currently no
role for pharmacogenetic testing in the clinical use of these
medications.
Future Directions
The data to date support many common variants that alter
the pharmacologic properties of cardiovascular agents.
Newer medications such as prasugrel, ticagrelor, and apxi-ban
as well as older medications such as enalapril, spirono-lactone,
and angiotensin-receptor blockers that have variable
clinical effects are also candidates for a pharmacogenetic approach.
Novel systems approaches may elucidate additional deter-minants
of drug response. Broad “omics” approaches have
several advantages over traditional genetics-based research,
for example, 1) responding to the environment, and 2)
elucidating how the activity of entire pathways, instead of
individual genes, influence drug response.
The field of pharmacogenomics would benefit from the
development of thresholds of evidence for testing and
coverage by insurance. With the expanding number and
complexity of “-omic” markers of drug response is an equally

profound evidentiary gap between association studies and
those that demonstrate clinical utility. It may be unrealistic
to wait (or to require) a prospective, events-driven random-ized
controlled trial of each drug-marker combination. Even
when such trials are conducted, the results may be made
obsolete by new observations and/or approval of new drugs.
Therefore, genetic substudies of clinical trials and registries
may become the highest levels of evidence for the potential
benefits of using pharmacogenetic testing. Such standards
are common in many fields of medicine where there is often
a lack of randomized clinical trial data. Observational
studies and comparative effectiveness research will lay the
foundation for broad-based recommendations and transla-tion
of pharmacogenetic observations into a clinical para-digm
of personalized medicine to improve health outcomes.
Reprint requests and correspondence: Dr. Deepak Voora or Dr.
Geoffrey S. Ginsburg, Institute for Genome Sciences Policy,
Center for Genomic Medicine, 101 Science Drive, Duke Univer-sity,
DUMC Box 3382, Durham, North Carolina 27708. E-mail:
deepak.voora@duke.edu or geoffrey.ginsburg@duke.edu.
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Key Words: clopidogrel y pharmacogenetics y polymorphism y statins
y warfarin.
APPENDIX
For additional loci, see the online version of this article.

Key Pharmacogenetic Variants for Cardiovascular Drugs

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