Pharmacogenomics
2011; Lippincott Williams & Wilkins; Volume: 123; Issue: 15 Linguagem: Catalão
10.1161/circulationaha.109.914820
ISSN1524-4539
AutoresDan M. Roden, Russell A. Wilke, Heyo K. Kroemer, C. Michael Stein,
Tópico(s)Drug Transport and Resistance Mechanisms
ResumoHomeCirculationVol. 123, No. 15Pharmacogenomics Free AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissionsDownload Articles + Supplements ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toSupplemental MaterialFree AccessResearch ArticlePDF/EPUBPharmacogenomicsThe Genetics of Variable Drug Responses Dan M. Roden, MD, Russell A. Wilke, MD, PhD, Heyo K. Kroemer, PhD and C. Michael Stein, MD Dan M. RodenDan M. Roden From the Departments of Medicine and Pharmacology, Vanderbilt University School of Medicine, Nashville, TN (D.M.R., R.A.W., C.M.S.); and Center of Pharmacology and Experimental Therapeutics, Ernst Moritz Arndt University, Greifswald, Germany (H.K.K.). , Russell A. WilkeRussell A. Wilke From the Departments of Medicine and Pharmacology, Vanderbilt University School of Medicine, Nashville, TN (D.M.R., R.A.W., C.M.S.); and Center of Pharmacology and Experimental Therapeutics, Ernst Moritz Arndt University, Greifswald, Germany (H.K.K.). , Heyo K. KroemerHeyo K. Kroemer From the Departments of Medicine and Pharmacology, Vanderbilt University School of Medicine, Nashville, TN (D.M.R., R.A.W., C.M.S.); and Center of Pharmacology and Experimental Therapeutics, Ernst Moritz Arndt University, Greifswald, Germany (H.K.K.). and C. Michael SteinC. Michael Stein From the Departments of Medicine and Pharmacology, Vanderbilt University School of Medicine, Nashville, TN (D.M.R., R.A.W., C.M.S.); and Center of Pharmacology and Experimental Therapeutics, Ernst Moritz Arndt University, Greifswald, Germany (H.K.K.). Originally published19 Apr 2011https://doi.org/10.1161/CIRCULATIONAHA.109.914820Circulation. 2011;123:1661–1670Not all patients respond to drug therapy in a uniform and beneficial fashion. The goal of this review is to describe the contribution of genetic variation to drug response, with a focus on drugs used in cardiovascular therapy. Genetic approaches used to analyze rare and common adverse effects as well as variability in efficacy are presented first. The challenges and potential solutions to incorporating this body of knowledge into contemporary medical practice are then discussed.History of PharmacogeneticsThe notion that genetic variants might modulate variability in drug actions was first proposed by the English physiologist Archibald Garrod.1 He suggested that enzymatic defects lead not only to accumulation of endogenous substrates in "inborn errors of metabolism" (a term that he coined), but also to accumulation of exogenously administered substrates, such as drugs, foodstuffs, and toxins, with clinical consequences. Initial examples of genetically determined variable drug actions were the identification of pseudocholinesterase deficiency in prolonged paralysis after administration of the muscle relaxant succinylcholine,2 deficient N-acetylation of isoniazid,3 and a high incidence of hemolytic anemia among blacks with glucose-6 phosphate dehydrogenase deficiency receiving antimalarial drugs in the South Pacific during World War II.4 The latter observation highlights the principle in modern genomics and pharmacogenomics that ancestry may play a key role in modulating clinically important phenotypes.The term pharmacogenetics was coined in 1959,5 and the first textbook was published in 1962,6 well before methods for studying DNA sequence variation were available. The term pharmacogenomics has been used more recently to transmit the idea that variable drug response may reflect sets of variants within an individual or across a population. DNA variants can modulate protein function, and hence drug response, through multiple mechanisms. Much of the initial focus in the field was on nonsynonymous DNA variants (ie, those that alter protein function by changing the encoded amino acids). Noncoding variants that modulate gene expression represent another common candidate mechanism for variable drug responses. Contemporary genomics has uncovered multiple other mechanisms regulating gene function and expression, such as epigenetic changes and small interfering RNAs, and a role for these in determining drug response seems likely.7Identifying Genetic Contributors to Variable Drug ActionsHeritability and Drug ResponsesStudies in families can define the extent to which common human disease phenotypes like myocardial infarction or sudden cardiac death include a heritable component. However, it is usually not possible to accumulate well defined drug-response phenotypes across multiple related patients with the same disease; as a result, the heritable component of variability in drug action may not be well defined. An in vitro approach that has been useful to estimate heritability of cytotoxicity caused by anticancer agents is exposure of lymphoblastoid cell lines from related subjects to the drug.8,9 Using this method, the heritability of cytotoxicity has been estimated at 0.25 to 0.65; 1 study went on to use linkage analysis to identify a potential locus mediating this toxicity.9One approach when heritability is not well understood is to quantify drug responses in multiple healthy members of a family. For example, very early studies in twins demonstrated far more variability in the urinary excretion of isoniazid within dizygotic than monozygotic twins,10 thus establishing that this trait, now known to reflect genetically determined variable N-acetylation, is heritable. Similarly, digoxin clearance was much better correlated within monozygotic than within dizygotic twins; the heritability of the area under the curve was >79%.11 Adenosine diphosphate–stimulated platelet aggregation was studied before and after clopidogrel in the Amish, a founder population with extensive genealogical records: The investigators reported that heritability was 0.33 at baseline and 0.73 during drug treatment, indicating a strong genetic component in the drug response.12Experimental Approaches in PharmacogeneticsDefining mechanisms underlying variable drug concentrations and effects provides a starting point for identifying candidate genes for further pharmacogenetic study. As a result, many important examples in pharmacogenetics relate to variable drug uptake, metabolism, or elimination. Other contributors to variable drug responses identified by this physiologically based candidate approach include variation in drug target molecules or in disease pathways. In some cases, variants in multiple genes have been implicated, as discussed below (see Combinatorial Pharmacogenetics). More recently, technologies to search for previously unanticipated relationships between phenotypes and hundreds of thousands of common polymorphic sites across the genome (an unbiased approach)13 have been applied to the problem of variable drug actions; these genome-wide association studies (GWAS) have been conducted both in human cohorts and in cellular or model-organ systems.Table 1 lists the experimental approaches that have been used in the field, along with their potential advantages and disadvantages.Table 1. Approaches to Identifying and Validating Genetic Influences on Drug ResponseCandidates and ApproachesAdvantagesDisadvantagesExamplesBiological candidates Candidate gene based on variable pharmacokineticsVariability in these processes logically determine variable drug effectsIdentification and replication of associations between variant genotypes and drug responses may require large populations, depending on the size of the genetic effect and the frequency of the variant allele.Warfarin/CYP2C9,14 Simvastatin/SLCO1B1,15 Clopidogrel/CYP2C19,16–18 Metoprolol/CYP2D6,19 Atorvastatin/CYP3A5,20 Candidate gene based on variable pharmacodynamicsCandidate genes often identifiedIdentification and replication of associations between variant genotypes and drug responses may require large populations, depending on the size of the genetic effect and the frequency of the variant allele.Bucindolol/ADRB1,21 β-blockers in heart failure/ACE,22 Antiarrhythmics in atrial fibrillation/ACE,23 Warfarin/VKORC124,25 Candidate pathway analysisPossibly less biased than single-gene approachesRequires interrogation of large numbers of SNPs; replication may be difficult.HMG-CoA reductase haplotype as a predictor of statin response26Unbiased approaches Candidate gene selected from GWAS or other unbiased approachUnbiasedGWAS result must be available; replication may be difficult.NOS1AP as a predictor of mortality during calcium channel blocker therapy27 GWASUnbiasedSets of cases and controls generally need to be large; replication may be difficult.Simvastatin/SLCO1B1,28 Warfarin/VKORC1, CYP2C9, CYP4F2,29,30 Clopidogrel/CYP2C9/19 locus12 Drug response in model organisms with manipulated genetic backgroundUnbiasedAssay may be difficult to establish; translation from model organism to human may be imperfect.QT prolongation/GINS3 locus31CYP indicates cytochrome P450; SLC01B1, encoding organic anion transporter protein 1B1; ADRB1, β-1-adrenergic receptor 1; ACE, angiotensin-converting enzyme; VKORC1, vitamin K epoxide reductase complex subunit 1; SNPs, single-nucleotide polymorphisms; HMG-CoA, 3-hydroxy-3-methylglutaryl coenzyme A; GWAS, genome-wide association study; and NOS1AP, nitric oxide synthase 1 adaptor protein.Replication of genotype–phenotype relations can be a major issue in modern genomics, both when the effects of single-candidate variants are examined32–34 and with genome-wide approaches.35 Pharmacogenetic studies may be especially difficult to replicate, because large numbers of subjects with well curated drug-response phenotypes are often not available. Other challenges, notably for the use of GWAS, include choice of appropriate control groups matched for factors such as underlying disease and ancestry, contributions by DNA variants not captured by current platforms (eg, rare variants or copy number variation), and analysis of gene–gene and gene–environment interactions in determining phenotype.Variable Drug Actions and Single Gene VariantsLarge-Effect Variants in Drug-Metabolizing EnzymesIn the 1950s, McKusick and Price-Evans described variable N-acetylation,3 an important contributor to variable isoniazid hepatotoxicity and the lupus syndrome during treatment with procainamide and hydralazine. In the 1970s, 2 groups studying different drugs (debrisoquine, an antihypertensive,36 and sparteine, being assessed as an antiarrhythmic37,38) reported a set of 5% to 10% of subjects with adverse effects due to apparent absence of a key enzyme mediating drug bioinactivation. The enzymes were initially termed debrisoquine 4-hydroxylase and sparteine N-oxidase, but subsequently it became clear that this was the same defect,39 now recognized to represent homozygosity for loss of function of a specific member of the cytochrome P450 (CYP) superfamily of drug metabolizing enzymes, CYP2D6.40 Dozens of variants have now been reported to reduce or eliminate CYP2D6 function (http://www.cypalleles.ki.se/cyp2d6.htm).Coding region variants in other members of this superfamily, such as CYP2C9 and CYP2C19, generate populations of poor metabolizers for substrates of each of these enzymes. Interestingly, CYP3A4, the enzyme most commonly implicated in the metabolism of clinically-used drugs,41 does not include common coding region polymorphisms that alter function; nevertheless, CYP3A4 activity varies widely across individuals, and at least some of this variability likely arises from genetic variation in the regulation of CYP3A4 gene expression.42 Another contributor to variability in CYP3A activity is a common intronic single-nucleotide polymorphism (SNP) in a closely-related gene, CYP3A520,43; the variant CYP3A5*3 allele alters messenger RNA by creating a new splice site.The incidence of functionally-important CYP alleles can vary strikingly by ancestry. For example, poor metabolizers with absent CYP2D6 function are found in 5% to 10% of European and African populations, but are less common in Asian subjects. By contrast, CYP2C19 poor metabolizers are commoner in Asian subjects compared with the other 2 major ancestry groups, and the frequency of the CYP3A5*3 variant is much higher in whites (0.85) compared with blacks (0.55), which correlates with higher hepatic CYP3A5 expression in black subjects.43High-Risk PharmacokineticsWhen drugs are eliminated by multiple pathways, absence of 1 of these (because of genetic variation or because of the presence of interacting inhibiting drugs) is unlikely to produce major variation in drug concentrations at the target site, and thus in drug effect. However, the potential for highly variable drug concentrations increases dramatically when a drug is metabolized by a single pathway, a situation we have termed high-risk pharmacokinetics.44 There are 2 scenarios in which this may occur (Figure 1). The first is the situation in which a prodrug must be metabolized, or bioactivated, to generate pharmacological effects. In situations in which this bioactivation is accomplished by an enzyme with known loss-of-function variants, poor metabolizers will, predictably, display decreased drug action; clopidogrel and losartan are examples of cardiovascular drugs with this attribute (see Table 2), and codeine58,59 and tamoxifen60,61 are other prominent examples. Coadministration of commonly used drugs that inhibit the bioactivating enzyme can result in a phenocopy of the poor metabolizer trait: that is, individuals who are genetically extensive metabolizers may display the same pharmacological outcome as poor metabolizers if administered an interacting drug.Download figureDownload PowerPointFigure 1. High-risk pharmacokinetics. Drugs that are eliminated by a single pathway can generate aberrant responses if that pathway is absent on a genetic basis or because of coadministration of inhibiting drugs. This figure illustrates the 2 scenarios underlying such high-risk pharmacokinetic situations. One (left) is the administration of a drug that is itself not active but requires drug metabolism to generate an active metabolite; the absence of the pathway can lead to failure to generate the desired drug effect. This is thought to underlie variability in response to clopidogrel, tamoxifen, losartan, and codeine, as described in the text. The second scenario (right) is the administration of a drug eliminated by single pathway. Absence of this pathway will result in accumulation of the parent drug and thus drug-related toxicity. Adapted, with permission from the publisher, from Roden and Stein.44 Copyright © 2009, the American Heart Association.Table 2. Genetic Variants Influencing Cardiovascular Drug Therapy: ExamplesGene and DrugsClinical EffectsDrug metabolism CYP2C9 LosartanDecreased bioactivation and effects (PMs)45 WarfarinDecreased dose requirements; possible increased bleeding risk (PMs)14,46 CYP2C19 ClopidogrelDecreased bioactivation and effect in PMs16–18 CYP2D6 Metoprolol, carvedilol, timolol, and propafenoneIncreased β-blockade in PMs19,47,48 CYP3A5 AtorvastatinIncreased lipid-lowering efficacy49 SimvastatinIncreased severity of myotoxicity50 Lovastatin NAT2 Hydralazine and procainamideIncreased risk of toxicity in PMs51Drug transport SLCO1B1 SimvastatinVariant nonsynonymous SNP alters efficacy and increases myopathy risk15,28,52 ABCG2 Many statinsAltered pharmacokinetics52Drug targets HMG-CoA reductase PravastatinHaplotype-dependent LDL lowering26 VKORC1 WarfarinDecreased dose requirements with variant promoter haplotype24 ADRB1 and ADRB2 Many β-blockersAltered vascular and heart rate effects53–55 ACE ACE inhibitorsNo effect on drug response34Other genes ACE β-blockers in heart failure, antiarrhythmics in atrial fibrillationDecreased response in subjects with DD genotype22,23 G-protein β3 subunit, kininogen, other loci Thiazide diureticsGreater reduction in diastolic and systolic blood pressure56–58As discussed in the text, there is variability in the size of the genetic effects and in the extent to which these findings have been replicated.Further data at the Pharmacogenetics Research Network/Knowledge base: http://www.pharmgkb.org.CYP indicates cytochrome P450; PMs, poor metabolizers; NAT2, N-acetyltransferase, type 2; SLCO1B1, encoding organic anion transport molecule type 1B1; HMG-CoA, 3-hydroxy-3-methylglutaryl coenzyme A; LDL, low-density lipoprotein; VKORC1, vitamin K epoxide reductase complex subunit 1; ADRB, b1-adrenergic receptor; and ACE, angiotensin-converting enzyme.The second high-risk pharmacokinetic scenario is seen when a substrate drug undergoes bioinactivation via a single metabolic pathway. In the absence of this pathway, much higher concentrations of active parent drug will accumulate. For compounds with a wide therapeutic margin, such accumulation may be without clinical implications; conversely, for other drugs, such accumulation predictably results in serious toxicity. An example is the active S-enantiomer of warfarin, which undergoes CYP2C9-mediated metabolism to inactive forms (Figure 2). As discussed below ("The Warfarin Example"), patients with common reduction-of-function alleles have higher S-warfarin concentrations, and thus lower-dose requirements to achieve steady-state anticoagulation.14,46 However, there are rare patients with near-complete loss of CYP2C9 function (homozygotes for the *3 variant arising from 1075A>C encoding I359L), and these patients may be very difficult to manage clinically because of low, and often unstable, warfarin dose requirements.62Download figureDownload PowerPointFigure 2. A framework for analyzing contributions of multiple genes to a clinical phenotype. The example of warfarin maintenance dose requirement is shown here. A, A simple pathway analysis of the key molecular determinants of warfarin response. The drug is administered as a racemate, and most anticoagulant action is mediated by the more potent S-enantiomer. S-warfarin is bioinactivated primarily by cytochrome P450 269 (CYP2C9). The pharmacological target for the drug is encoded by the vitamin K epoxide reductase complex subunit 1 gene (VKORC1), important for maintaining active vitamin K. The role for other drug-metabolizing pathways and for other enzymes that influence vitamin K metabolism (epoxide hydrolase 1, γ-glutamyl carboxylase) are shown in gray. B, Distribution of CYP2C9 and VKORC1 variants as a function of ancestry.63 For CYP2C9 (top panel), the *1 allele has the highest activity, *2 is a reduction of function variant, and *3 is a near loss of function variant. For the VKORC1 promoter variant shown, the G allele results in greater liver expression than does the A allele.24 These distributions of genotypes largely explain ancestry-dependent variability in warfarin dosing.63C, Contribution of common and rare variants to warfarin dose requirements in a population. The normally distributed dose requirements predominantly reflect the common variants shown in panel B. However, individuals with rare VKORC1 coding region variants and individuals with the rare CYP2C9*3/*3 genotype may display unusually high or low dose requirements.Large-Effect Variants in Other GenesSingle variants in genes not involved in drug metabolism can also confer high risk for variable drug responses. These may involve variants in genes encoding the target molecules or pathways with which drugs interact, or those encoding genes unrelated to the therapeutic effect. In the latter group, one well studied example is variants in the HLA system. Individuals with a single HLA B*5701 variant are at high risk for potentially fatal skin reactions during treatment with the antiretroviral drug abacavir,63,64 and similarly B*1502 (an allele seen primarily among Asians) has been linked to severe skin reactions during treatment with carbamazepine.65 As discussed further below, the V174A variant in SLCO1B1, which encodes a transport molecule responsible for uptake of simvastatin in liver, has been associated with a markedly increased risk for myopathy.28An example of a large effect of a variant in a drug target molecule is the reported association of the R389G variant in the β1-adrenergic receptor gene with outcomes during treatment of heart failure with the adrenergic receptor blocker bucindolol.21 This variant is known to strongly modulate β1-mediated pharmacological responses in vivo and in vitro, and outcomes in individuals with the G variant were very close to those treated with placebo. This finding suggests that preprescription genotyping could be used to target therapy to those predicted to derive benefit. However, the association is not replicated and other studies have implicated variants in other genes as potential contributors to outcomes of drug therapy in heart failure: examples are angiotensin-converting enzyme,22 CYP2D6,47,66 G-protein coupled receptor kinase 5,67 and α2C receptors.68,69 The relationship between warfarin dose and variants in vitamin K epoxide reductase complex subunit 1 (VKORC1), encoding the warfarin target, is discussed below ("The Warfarin Example") and other examples are listed inTable 2.Combinatorial PharmacogeneticsAnother approach to analyzing variability in complex traits like heart failure and its response to drugs is to study not single genetic variants, but combinations across multiple genes.70 Two recent examples, warfarin and clopidogrel, illustrate how the interrogation of very large clinical datasets for candidate polymorphisms in multiple candidate genes in combination can help establish the role of these variants in determining a drug's action. As discussed further below, the understanding that relatively large effects of single genetic variants modulate the effects of these drugs has prompted the US Food and Drug Administration to include genetic information in the labels for these and other agents, triggering a debate about how this new knowledge can be incorporated into practice.An extension of this idea is interrogation of hundreds of SNPs in multiple genes in a candidate pathway to identify loci modulating a drug response.26 Drug-induced prolongation of the QT interval has also been analyzed in this fashion. Almost all drugs that prolong QT do so by blocking a specific cardiac potassium current, IKr. However, studies examining variation in the QT interval itself71,72 or its response to drug challenge73,74 have implicated multiple other ion channel and other genes. This supports a view in which control of the QT interval relies on diverse mechanisms (almost all unrelated to IKr), and variation in these mechanisms then leads to variability in the extent to which IKr blockers prolong QT. This idea, termed repolarization reserve,75,76 is a specific example of the more general concept that variability in physiological and drug response phenotypes reflects the interplay of multiple biological pathways.The Warfarin ExampleIn 2004, the disease gene for a very rare pharmacogenetic syndrome, warfarin resistance (in which patients displayed little change in international normalized ratio on challenge with extremely high doses of warfarin) was identified.77 The gene, VKORC1, encodes the component of the vitamin K receptor complex that is the warfarin target, thus explaining the rare genetic trait. Identification of VKORC1 as the warfarin target led rapidly to identification of common variant promoter haplotypes, which correlated well with variable liver expression of the protein24 and are associated with decreased steady-state dose requirement and shorter time to therapeutic anticoagulation.24,25 One study in 539 white patients receiving steady-state warfarin therapy reported that 25% of the variability in warfarin dose could be accounted for by common VKORC1 promoter SNPs, and 9% by variants in CYP2C9; these are very large genetic effects.24 Warfarin dose requirements vary strikingly by ancestry (highest in blacks, lowest in Asians), and much of the difference can be attributed to the frequency of common VKORC1 promoter variants (Figure 2B).78 In addition, rare VKORC1 coding region variants have been described in some subjects with unusually high warfarin dosages; for example, in one study, 4.3% of Ashkenazi subjects were found to have a variant resulting in D36Y, associated with high dose requirements (>10 mg/d).79The International Warfarin Pharmacogenetics Consortium studied the relationship between genotypes and steady-state warfarin dose in >5000 patients of diverse ancestries.78 There was clear ancestry-dependent variation in dose requirement (highest in subjects of African descent, lowest in subjects of Asian descent), and the differences could be attributed to variation in VKORC1 and CYP2C9 (Figure 2B). As discussed below, trials are now underway to compare outcomes in patients using genetically versus clinically guided therapy.The Clopidogrel ExampleSeveral studies reported in early 2009 that reduction-of-function variants in CYP2C19 (the enzyme responsible for the bioactivation of clopidogrel) increase the risk of cardiovascular events after stent placement.16–18 One of these18 also examined the potential contribution of multiple other candidate genes to variable clopidogrel effects, including those encoding other CYPs, the P2Y12 receptor (the drug target), other molecules known to interact with the receptor, and the drug efflux transporter P-glycoprotein. The latter is encoded by ABCB1, and P-glycoprotein is known to modulate absorption and elimination of many other drugs. The result of that study was that, in addition to the CYP2C19 effect, individuals homozygous for a variant ABCB1 coding region allele were more likely to display failure of efficacy during clopidogrel therapy. Although the role of CYP2C19 is now described in the clopidogrel label, the way in which clinicians should respond to this information remains uncertain.80,81Unbiased Approaches to Identifying Genes Modulating Drug ActionsThe Human Genome Project and subsequent increasingly detailed maps of human genetic variation are providing tools to interrogate the relationship between genetic variation across the human genome and important human physiology and disease traits in a relatively unbiased fashion. A conventional GWAS design generates a set of SNPs associated with the trait under study and then seeks to replicate these associations in other (often larger) clinical datasets.13 The contribution of variants identified by GWAS to susceptibility to common diseases is usually modest: it is likely that high-risk alleles do not accumulate in populations because of the evolutionary disadvantage such accumulation confers.Applying the GWAS paradigm to pharmacogenomics faces the obstacle that very large sets of patients with well defined drug-response phenotypes are unusual (Table 1). On the other hand, because there may be no selection pressure on genes encoding proteins mediating drug action, functionally important variants with large effects may have accumulated in populations. Another distinctive feature of GWAS studies of drug response is the nature of the signals identified. GWAS approaches to disease have often identified new susceptibility loci. By contrast, many (but not all82–84) GWAS studies in pharmacogenomics have yielded signals in previously studied pathways, presumably reflecting large effects readily derived from candidate gene approaches.One notable success using the GWAS approach in cardiovascular pharmacogenomics was a study that examined simvastatin-associated myopathy.28 Among 6033 patients receiving 20 mg/d, there were only 8 possible cases, so the study focused on 98/6031 patients who developed myotoxicity during high-dose (80 mg/d) therapy. A GWAS comparing 85 cases of definite or incipient myopathy to 90 controls identified a single SNP (rs4363657) in SLCO1B1 at genome-wide significance. This SNP is in near perfect linkage disequilibrium with a previously studied nonsynonymous variant (V174A) in OATP1B1, the drug uptake transporter that SLCO1B1 encodes; studies prior to the GWAS showed that V174A impairs elimination of simvastatin acid and thus implicated it as a potential modulator of drug efficacy and toxicity.15 Patients with the rare (2.1%) homozygous phenotype had an 18% 5-year incidence of myopathy compared to 3% among heterozygotes and 0.6% among those lacking the risk allele. The findings were replicated in a separate study of subjects at a lower dose, 40 mg/d, with a smaller effect size (relative risk 2.6 per C allele).28 To date, 1 group has reported a similar finding across multiple statin drugs in a smaller trial.85A recent GWAS examining predictors of therapeutic response to statin therapy in 3 randomized treatment trials has implicated multiple known lipid control loci, as well as identified a novel association near calmin, a gene not previously known to influence lipid homeostasis.86 This analysis of thousands of patients exposed to atorvastatin, pravastatin, or simvastatin used Bayesian statistical approaches to suggest that the relationship between genetic variability at the calmin locus and statin-related change in fasting lipid levels may represent a class effect.GWAS Results to Aid Study DesignIn 2003, the US Food and Drug Administration proposed a prospective evaluation of the utility of CYP2C9 genotyping as a guide to warfarin therapy. This effort was suspended with the recognition that VKORC1 contributes importantly to variability in warfarin action. Although interest in a prospective trial remained high, the new finding raised the question of how many other as-yet-unidentified genes might also contribute to warfarin actions. GWAS studies examining steady-state warfarin dose demonstrated that VKORC1 variants were the most important contributors to this phenotype, and although a nu
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