/  Part IV.3 – Personalized Medicine and Translational Genomics



Personalized Medicine and Translational Genomic

Robert Roberts MD

A. Overview

Personalized medicine is treatment customized to the individual genetic variants and may be measured directly or indirectly through one of its expressed forms, RNA or protein, in any body fluid or tissue. The human DNA variation is due primarily to the 3 million SNPs contained in each genome having evolved over 6 million years from DNA copying errors. The need for personalized medicine varies to pharmacogenetics in the utilization of cardiovascular drugs, such as warfarin, antiplatelet therapy, and beta blockers, for the prevention and treatment of CAD utilizing the recently identified genetic risk variants. Given the 1 million fold decrease in the cost of sequencing the human genome and its potential to benefit mankind we will be forced by both the public and our patients to embrace this genetic evolution which is proceeding at an accelerated pace.

The concept of administering therapy customized to the individual has been discussed for decades. With the sequencing of the human genome in 2001, personalized medicine evolved into a cliché which some regard today as “hype” without substance (1). The potential for personalized medicine is far from generalized and the examples where it has been demonstrated to be highly beneficial are primarily in cancer, and less so in cardiovascular disorders. The current lack of success in many diseases, such as cardiovascular, is a stimulus to pursue it with greater intensity, rather than less. Personalized medicine is best defined as treatment customized on the basis of one’s individual genomic variants. The individual’s DNA variants may be detected directly or indirectly through one of its’ expressed forms of RNA or protein. The DNA, RNA or protein can be sampled in body fluids, such as saliva, blood, and urine or in tissues. The advantages of sampling the DNA directly over that of conventional plasma biomarkers are several, as listed below:

  • Genetic variant does not vary over time
  • Genetic variant does not vary with meals
  • Genetic variant does not vary with drugs
  • Genetic variant does not vary with gender
  • Genetic variant can be determined with a single blood test

This, coupled with the fact that DNA does not change throughout one’s lifetime, makes it possible from a single venipuncture to have a lifetime record of that individual’s DNA variants. Given a mutation rate whereby each gene mutates on average about once every 250 years, DNA is stable, not only in that individual, but also in their parents and their offspring. It is already known that most of the DNA variants that modify gene expression are located in DNA regions that do not code for protein (2). These variants until we know which proteins are affected can only be detected by assessing the DNA directly. Nevertheless, protein represents the expressed form of a gene and is the mediator of the effect of the DNA variant on the phenotype or disease, thus, when known the protein may in certain situations be the preferred biomarker. It is also well recognized that a DNA variant associated with disease may be present but if not expressed would have no effect on the phenotype or disease. In this situation, sampling the protein is recommended. The use of RNA as a biomarker is probably less likely, since they are unstable and their half-life can be very short. Furthermore, many messenger RNAs (mRNA) do not get expressed and their abundance does not necessarily correlate with the protein expression of the particular DNA variant. On the other hand there is great interest in RNA since most of the DNA is expressed as non-protein coding RNA which in turn alters protein coding genes (3). In the future, it is highly likely the DNA variants will be measured and in certain situations, also the protein and RNA, depending on what is known about the effector protein its plasma turn-over rate and that of the RNA. There are many studies on-going, such as electronic Medical Records and Genomics (eMERGE) (4), in which one’s genome will be sequenced and annotated to the medical record containing the individual’s clinical phenotype. It is expected this approach will in the future be integrated into the overall management of cardiovascular disease through its inclusion in diagnosis, prevention and drug therapy.

B. The Need for Personalized Medicine

The desire for personalized medicine relates to its potential to be beneficial in a variety of diseases including; allergies, cancer, predicting non-responders to drugs, predicting adverse drug effects and identifying drug targets for prevention and treatment of diseases such as CAD. Over 100,000 deaths in the United States occur annually due to allergies (5,6). These allergic responses are determined by the individual’s genomic makeup and if the genetic risk variants are identified they could be detected by genetic screening and the allergy avoided. It is estimated that only about 50% of drugs are effective (7). One of the reasons for lack of response to drugs are genetic differences that could be detected and alternative therapies administered. The fifth most common cause of death in the US is adverse effects of drugs, which could benefit from prior genetic screening (5). Genetic predisposition accounts for about 50% of the risk associated with CAD, the number one killer in the world, as it does for most diseases (8). In the past few years over 50 genetic risk variants have been identified to be associated with increased risk for CAD. Therapy for comprehensive prevention and treatment will ultimately have to target the specific genetic risk variants, along with environmental risk factors. The 21st century will probably be known in medical terms for its emphasis on prevention, regenerative medicine and the application of genetics. Comprehensive prevention for most if not all diseases will require targeting genetic and environmental risk. The challenge to identify those genetic risk variants even in polygenic disorders is well underway. Over 2,000 genetic risk variants associated with disease have been identified involving over 160 diseases. A major challenge for the future will be matching the results of genetic screening to the appropriate therapeutic and preventive measures. Elucidating the genetic molecular mechanisms relating genotype to phenotype will undoubtedly lead to new targets for development of improved and novel therapies. This has already occurred in the past few years with the development of the inhibitors of Proprotein convertase subtilisin/kexin type 9, (PCSK9) (9) which is now in Phase III clinical trials and shown to be safe and highly effective in lowering LDL-Cholesterol (LDL-C) and provides therapy complementary to that of statins (9,10,11,12,13). A mutation, discovered in the gene encoding PCSK9, decreases PCSK9 activity and is associated with decreased plasma levels of LDL-C due to increased rate of clearance of LDL-C from the plasma (14). In a Phase III Clinical Trial administering a monoclonal antibody which inhibited PCSK9 was associated with a 57% reduction in plasma LDL-C (15) compared to placebo and a further 48% reduction in those receiving 80mg of atorvastatin.

C. Genetic Variants Associated with Ethnicity are Responsible for Marked Variation in Therapeutic Responses to Many Drugs

It has been known for decades that certain responses to therapy vary markedly between different ethnic groups. A striking example of the role of ethnicity in drug response is that of Bidil (16). In a large clinical trial, Bidil was found to be without effect, but when analyzed for ethnicity, showed a 43% reduction in the incidence of heart failure in African Americans with no effect in Caucasians. It would be the first FDA approved drug to be used solely by African Americans. Many genetic variants responsible for these differences have been identified and documented to modify both the therapeutic effect and the threshold for adverse effects. This field has its own scientific discipline. The study of single genetic influences on drug responses is referred to as pharmacogenetics and that involving many genetic components as pharmacogenomics. The increasing knowledge of the individual human genome is likely to further unravel these variants and provide the impetus to give the right drug in the right dose for the right disease. In this age of globalization, there are mass movements of different ethnic groups migrating, particularly from developing countries to the west. In the USA alone, it is estimated that over one-third of the population is of non-European origin, being from either Hispanic, African or Asian descent. We have in the past referred to the responses of ethnic groups to drugs as fast, intermediate and slow metabolizers of drugs. The liver is the major organ responsible for the metabolism and removal of drugs and the cytochrome P450 (CYP) family of genes is responsible for removal of over 50% of all drugs (17). One single polymorphism CYP2D6 of the CYP genes is recognized to be involved with over 20% of all drugs (18). Polymorphisms in this particular gene can result in an enzyme of no activity, decreased or increased activity (18). CYP2D6 is involved with the metabolism of most beta-blockers with tremendous variation (19,20). In Ethiopians, 29% are ultra-fast metabolizers and 21% of Saudi-Arabians are rapid metabolizers of beta-blockers compared to only 4% of the Caucasian/European population. Given the wide-spread use of beta-blockers for a wide variety of cardiovascular disorders, it is important to recognize that one could considerably over-dose or under-dose populations throughout the world. This enzyme is also responsible for the removal of several antiarrhythmic medications such as Propafenone, and Flecainide which is far less effective in individuals that have the CYP2D6 polymorphism which rapidly metabolizes these drugs (21). Another example of a drug universally used in cardiovascular disease is angiotensin converting enzyme inhibitors. The large insertion/deletion in the angiotensinogen gene explains about 50% of the variation in plasma levels of angiotensin converting enzyme. However, its’ role in determining the dose of drugs remains to be determined (22).

Another example of diversity in genetics is that of anticoagulant therapy. This is one of the most widely used drugs in the world and is associated with serious bleeding as a complication. There are several genetic polymorphisms in CYP2C9 and VKORC1 which are crucial in the metabolism of warfarin. Two of these polymorphisms called *3 and *6 are associated with loss of activity, whereas *2, *4, *5 and *11 are associated with less activity. Carriers of *2 and *3 significantly influence the metabolic turnover of warfarin. In a review by Lee et al (23), it shows that the distribution of the CYP2C9*2 polymorphism varies across ethnic groups. About one-third of the Caucasian population express the *2 or *3 genotype, whereas individuals of Japanese, Chinese, Korean or Taiwanese origin do not express the *2 variant and very few express the *3 variant. The other factor that explains part of the variability in warfarin treatment is that of the Vitamin-K epoxide reductase (VKOR) (24). These polymorphisms decrease the effectiveness of warfarin and when present require an increased dose to achieve efficacy. The variation among ethnic groups of the H1-VKORCI polymorphism is also great being present in about 87% of the Chinese population, 12% of the population in India and 65% of Malaysians. All of these polymorphisms that metabolize very common drugs illustrate the need to detect and adjust for the variation in drug dose induced in their presence or absence.

D. Human Genome

The Human Genome was sequenced in about 2000. This was a major landmark for biology and for the understanding of human disease. The human genome contains about 3.2 billion nucleotides, each nucleotide consists of one of the four nucleotide bases: adenine (A), Cytosine (C), Guanine (G), or Thymine (T) plus a phosphate group and a sugar (ribose in RNA, deoxyribose in DNA).Each of the 23 chromosomes is a single DNA molecule which is formed by the linear joining together of just these four nucleotides. The chromosomes vary in length from 46 million nucleotides to 247 million. The sequence of the nucleotides is transcribed by the cell into mRNA and then into protein. It is important to recognize that the phenotype of the human being is determined by its proteins, whether it is the structural or physical characteristics apparent on visual inspection, or the more subtle features due to metabolism. The DNA containing the genome is localized to the nucleus. mRNA transcribed from the DNA in the nucleus (transcription) goes out into the cytoplasm and provides the template for protein synthesis (translation). Each of the 20 amino acids that composes protein is coded by three nucleotides referred to as a codon. An example is the codon AUG which codes for the amino acid methionine. The mRNA, which contains the codons linearly joined together, is the template for the protein and determines the selection of amino acids and their sequence. An example is the codon AUG which codes for the amino acid methionine. A reasonable estimate of the number of genes coding for proteins is in the range of 21,000. The mean number of nucleotides in a gene is 21,000. Each gene, however, has alternative forms referred to as alleles, which may be formed by having a different start point or through the insertion of mutations or polymorphisms. Less than 1% of human DNA is used to encode for proteins. However, contrary to a decade or more ago when it was assumed that most of the DNA is junk, we now know that at least 90% of the DNA in the human genome is transcribed (25) most of which is transcribed into RNAs which do not code for protein. These are referred to as non-coding RNAs (ncRNA) and play a key regulatory role. One class of non-protein coding RNAs are micro RNAs (18-26 nucleotides) that by binding to mRNA inhibit the formation of protein translation, or in some way alter the rate of decay of mRNAs, both of which influence the function of the proteins. Other ncRNAs of more than 200 nucleotides referred to as long non coding RNAs regulate transcription by binding to the histone complex. The Central Dogma of DNA (Fig. 1) transcribed into mRNA, which in turn is translated into protein remains accurate.

figure 1Figure 1. Shown here is the Central Dogma of Biology whereby DNA contained in the nucleus is transcribed into mRNA which exits the nucleus and provides the template for protein referred to as translation. This leads to the synthesis of a protein which performs the various functions of the cell.

However, over 90% of DNA is transcribed into various non-coding RNAs. The function of ncRNAs, while still unfolding, ultimately affects the function of proteins. Since early knowledge was oriented towards protein coding genes, the terminology developed of introns (intragenic DNA sequences remaining in the nucleus) and exons (DNA sequences which exit the nucleus as mRNA to code for protein). When considering a gene, it is helpful to conceptualize three components the protein coding sequences, the regulatory sequences and the stabilizing sequences as shown in Fig. 2.

figure 2Figure 2. The Genome composed of DNA is enclosed in the nucleus. A gene, which on average, contains 21,000 nucleotides is made of introns and exons. The coding region during transcription joins together the exons which exit the nucleus to become the template for protein synthesis (translation). The regulatory region of the gene are DNA sequences that either promote or decrease transcription in response to the various demands of the body.

DNA is always transcribed from left to right and the left is denoted as 5’ end and the right as 3’ end. The sequences that codes for protein has a start point for transcription which is always the codon that codes for methionine, thus the first amino acid of a protein is always methionine. There are also codons that code to end transcription at the 3’end. As noted in Fig. 2, there are DNA sequences that immediately precede the 5’ end that do not code for protein, but to which a 5′ cap (7-methylguanosine) is attached and similarly the 3’ end has a cap added (adenine sequences). These caps are important to protect the mRNA in the cytoplasm from the various nucleases that induce its decay. There are large DNA stretches of several thousand base pairs, prior to the 5’ end of the gene which are essential in regulating whether the gene is to be transcribed. These regulatory sequences are generally divided for classification purposes into Transcription Factors that initiate and promote transcription, Enhancers which further increase transcription and Silencers which inhibit transcription of the gene. The regulators are very important to human disease. Most genetic risk variants for disease including CAD are located in these non-coding sequences and are targets for development of drug therapy. Many of the drugs we have today that treat cancer will in some way alter DNA transcription, DNA translation, or affect the mRNA. This is probably self-evident since cancer is fundamentally an abnormal growth and must always involve some defect in DNA. The drugs are obviously targeted to inhibit this rapid growth due to abnormal DNA.

The DNA sequence of the human genome is extremely constant with 99.5% of the sequences being identical across all human beings (26). The remaining 0.5% is crucially important in our understanding of human variation and our understanding of genetic predisposition to various diseases. Most of the 0.5% variation is due to large blocks of DNA which vary in the number of repeats in the sequence. However, based on current knowledge, it would appear that most of human variations, such as the color of one’s hair, eyes or skin are due to single nucleotide polymorphisms (SNPs). SNPs consist of a single nucleotide and by definition occur in 1% or more of the human population. We now know from the Hap Map (27) and 1000 Genome Projects (28) that the genome has about 3 million of these SNPs. It is also evident from today’s data that these SNPs account for the majority and probably up to 80% of all human variation including predisposition to disease (26). Determining the function of these SNPs and their association with human variation or disease will in fact be the major challenge in human biology for the next 10 or 15 years. It is the same SNPs that you see in the genes encoding for the clearance of warfarin that play a major role in determining the dose of anticoagulation necessary in individuals (discussed later). However, despite each individual having only 3 million SNPs, these SNPs are selected by the parents from the total population which has well over 20 million different SNPs. Which of these 20 million are carried by each individual through its selection of 3 million further complicates not only identifying these SNPs but also in determining their specific function and their association with a phenotype that may predispose or resist disease. The ultimate implementation of personalized medicine will be when each human genome can be sequenced and the various SNPs present can be annotated to their particular function or phenotype. The tremendous speed of sequencing DNA and the marked decrease in cost would suggest that within a decade, human genome sequencing will be available for most of the population. This would lead to a situation where it would be annotated to their electronic medical record and more intelligent and appropriate choices will be made, to individualize therapy.

E. The Origin of Human DNA Variation – DNA Copying Errors

While the earth planet is estimated to be more than 10 billion years old, life on this planet began only about 3.8 to 4 billion years ago. All life forms utilize the organic elements of carbon, hydrogen, oxygen, phosphorous, sulfur, iron and nitrogen. Phosphorous is common to all living forms since it is the only element which captures energy from the sun and provides it to all living things on this planet. The energy captured from the sun is stored as creatine phosphate, and delivered for tissue utilization as ATP, ADP or NAD. From these elements and for reasons still unknown life originated in its simplest form using the DNA or RNA codes that we know today. The DNA code is the same across all living forms. Life existed for about 4 billion years before the evolution of the human genus, homo. It is estimated that the human genus (homo) originated about 6 million years ago, breaking away from its ancestral upper primates particularly the chimpanzees (29,30). The human genus has evolved through several species. Homo erectus originated about 2 million years ago and quickly spread throughout the planet. Between about 200,000 and 300,000 years ago, the homo sapiens emerged in Africa, referred to as the modern human species, and migrated out of Africa about 100,000 years ago (30,31).

The source of the DNA sequence variation (rare mutations and polymorphisms) in the human genome originated primarily from copying errors in DNA replication (32,33). DNA, like all molecules in the body turn over every few days and are replicated with great accuracy making only 1 error for every billion of bases added. However, considering that each cell has 3.2 billion bases and is renewed every few days, even an error of only 1 in every billion will lead to significant variation over millions of years. Those errors that occur in the germline tissues (sperm and egg) can be passed on to succeeding generations and become mutations. DNA copying errors that occur in other tissues are referred to as somatic and cannot be transmitted to the offspring but is a major cause of cancer. About 94% of these copying errors are substitutions of a single nucleotide giving rise to the single nucleotide polymorphisms (SNPs) which we referred to earlier that are distributed throughout the genome (32,33). About 4.5% result in deletion of one to four nucleotides and the remainder are insertions of one to four nucleotides. Thus, the 3 million SNPs found in the human genome are essentially due to errors made during copying DNA. A recent estimate of the mutation rate is 1.4 x 10-8 per base pair per generation (34). This mutation rate induces 40 new mutations per generation and with the world’s population of 7 billion results in over 400 billion new mutations in the current generation. When a new mutation is induced by DNA copying errors it is of course rare, and its’ frequency in the genome of each succeeding generation will depend on how it affects the function of the organism. Mutations that are beneficial to survival increase in frequency with each succeeding generation and are referred to as polymorphisms (frequency >1%) while those that are detrimental to survival remain rare (<1%) or are eliminated. These rare mutations are responsible for rare diseases such as hereditary cardiomyopathies and Long QT syndrome.

F. Pharmacogenomics and Cardiovascular Drugs

There has been considerable progress in cardiovascular genomics, however, clinical application is not yet recommended. It was assumed that drugs such as anticoagulants and antiplatelet therapy would be prime targets for pharmacogenetics. There has been considerable clinical research in using genetics and these drugs, however, the results of clinical application have been less than satisfactory. A brief review of the data and the status of a few of the common cardiovascular drugs will be discussed. Progress has been made with respect to defining polymorphisms that predispose to drug-related side effects. The recent discovery of a polymorphism in the ion transporter SLCO1B1 and its relationship to the development of statin-induced myopathy is one such example (1).

G. The Pharmacogenomics of Clopidogrel

Antiplatelet therapy with a thienopyridine such as clopidogrel and aspirin was established as effective therapy in the context of coronary stenting (35), and clopidogrel has become one of the most widely prescribed agents in the cardiovascular arena including the acute coronary syndrome in the absence of coronary stenting (36).

Clopidogrel binds to and inhibits the ADP receptor of the P2Y12 subtype that resides on the surface of the platelet. Clopidogrel is metabolized to its active form through successive oxidative steps in the liver, the first step leads to formation of 2-oxo-clopidogrel and the second to the active metabolite. Studies indicate that cytochromes P450 1A2, P450 2C9 and P450 2C19 are involved in formation of 2-oxo-clopidogrel while cytochromes P450 3A4, P450 2C9 and P450 2C19 are involved in the second oxidative step. Cytochrome P450 3A4 is the major enzyme responsible for conversion to its active metabolite.

It was observed that 32% of patients receiving clopidogrel exhibited no effect on platelet function (37). Factors contributing to clopidogrel hypo-responsiveness (HPR) include genetic factors. The POPULAR study (Do Platelet Function Assays Predict Clinical Outcomes in Clopidogrel-Pretreated Patients Undergoing Elective PCI) demonstrated that patients with HPR as determined by platelet aggregometry were at increased risk for major adverse coronary events (MACE) (38). However, a recent study has shown that while it is possible to significantly augment the degree of platelet inhibition in the HPR group by increasing the dose of clopidogrel it did not mitigate the increased risk conferred by residual platelet activity, i.e. despite increase in platelet inhibition, it did not reduce the risk conferred by HPR following administration of standard dosing regimens (39).

Mega et al hypothesized that carriers of certain clopidogrel polymorphisms, experienced an increased risk of ischemic events (40). The clopidogrel arm CYP4502C of TRITON-TIMI 38 was genotyped for these polymorphisms and assessed for event rates in carriers versus non-carriers. Individuals of cytochrome P450 2C19 polymorphisms, namely *2(rs4244285), *3(rs4986983), *4(rs28399504) and *5(rs56337013) was associated with increased primary end point event rates (death, myocardial infarction and stroke) that were statistically significantly different from non-carriers. Carriers of the *2 allele were found to have primary end point event rates (death, myocardial infarction and stroke) that were most markedly increased hazard ratio (95% confidence interval) 1.53 (1.07-2.19). Pare et al examined the role of CYP4502C19 polymorphisms in the CURE population with acute coronary syndrome and was unable to demonstrate a significant effect on outcomes, among those prescribed clopidogrel (41). It would appear therefore, that genotype-directed therapy with clopidogrel would more likely benefit a population with the greatest risk, such as a population that has undergone coronary stenting. New antiplatelet drugs have been developed, such as Prasugrel and Ticagrelor which may replace clopidogrel and do not require biotransformation. However, these drugs are several fold more expensive then clopidogrel and have been shown to be no more effective. Routine genotyping for antiplatelet therapy is not recommended. However, there does exist a guideline documents (42) that states that if a poor metabolizer is identified, that is a *2 homozygote, replacement of clopidogrel with an alternate agent can be justified providing no contraindication exists (43). Clopidogrel remains the most commonly used drug for antiplatelet therapy and it would seem rational to perform genotyping for the most potent polymorphisms. If the CYP4502C19 polymorphism is present, alternative therapy should be given. This would be a cost effective and therapeutically effective approach based on current knowledge.

Point of care pharmacogenetics testing for clopidogrel

A new technology was developed to perform pharmacogenomics at the bedside. A buccal sample can be obtained by the nurse from the patient by a swab and inserted into a bench top device to test for the CYP450 2C19 polymorphism and results of the analysis are available within 45 minutes. A response is provided as to whether the patient has the appropriate cytochrome P450 gene allele to be responsive to clopidogrel. A prospective, randomized proof-of-concept study (44) enrolled 200 patients undergoing angioplasty and stenting and assigned to rapid point-of-care genetic testing for standard therapy. Upon genetic testing, patients who were not resistant to clopidogrel received 75mg daily and those resistant to clopidogrel received 10mg of Prasugrel daily. After randomization, 187 patients completed follow-up with 91 in the rapid genetic testing arm and 96 in standard treatment. Twenty-three individuals in each group were a carrier of at least one CYP2C19*2 allele. None of the 23 carriers in the rapid genotyping group who received Prasugrel had a Platelet Reactive Unit (PRU) value of more than 234, whereas 7 (30%) of the 23 in the standard treatment arm had a PRU of > 234 on day 7 (P=0.009). The primary endpoint of this study was the PRU value of the two treated groups selected on the basis of genetic testing. The point-of-care genetic testing had a sensitivity of 100% and a specificity of 99.3%. The sample size is too small for outcome, but provides the first proof of concept for point-of-care genetic testing.

Clopidogrel is a relatively inexpensive generic agent that has been proven to be an effective adjunctive antiplatelet agent in the prevention of stent thrombosis in multiple trials. This point of care enables one to identify patients who are nonresponders to clopidogrel, (20 to 30%) and administer alternative antiplatelet therapy. This approach would have significant economic impact, while at the same time providing the most appropriate therapeutic antiplatelet agents to prevent thrombosis following coronary stenting.

H. Pharmacogenomics of Aspirin Resistance

Aspirin is a commonly used drug but estimates of the frequency of resistance vary greatly (45). Aspirin exerts its action by irreversible acetylation of cyclooxygenase 1 (COX-1), inhibiting its activity resulting in decreased production of thromboxane A2. Two SNPs, rs10306114 and rs3842787 in close proximity to COX-1 were shown by Maree et al (46) to influence the degree of platelet inhibition in response to aspirin; heterozygotes were shown to have a greater degree of aspirin-induced platelet inhibition than wild-type homozygotes. Kunicki et al (47) were not able to replicate this finding in a similar population.

Studies of the glycoprotein IIIa gene gave contradictory results (48,49). A large meta-analysis, concluded that the PLA1A2 variant is associated with aspirin resistance but only in healthy individuals (50); that is, the effect of this SNP in attenuating aspirin-mediated platelet inhibition may be masked by the co-administration of drugs that are commonly prescribed in the context of CAD. There are certain polymorphisms that have been investigated with respect to the propensity to develop adverse drug-related events, such as upper gastrointestinal hemorrhage (51) and aspirin-induced urticarial (52).

I. The Pharmacogenomics of Warfarin

Warfarin has long been the anticoagulant used throughout the world to prevent thrombosis and acts by inhibiting vitamin K epoxide reductase, an enzyme that catalyzes the reduction of vitamin K; reduction of vitamin K promotes the gamma-carboxylation of the vitamin K-dependent clotting factors, II, VII, IX, X, protein C and protein S. The anti-coagulant response to warfarin is affected by several factors (53), and more recently, it has been recognized that several genetic variants have a major influence on the anti-coagulant response to warfarin. The major genetic risk variants involve alleles of two genes, one that encodes for CYP2C9, a cytochrome P450 enzyme (54,55), and the gene VKORCI which encodes for vitamin K epoxide reductase (54,56). It is claimed that alleles of these two genes account for about 40% of the variation in the anti-coagulant response to warfarin (57,58). S-warfarin is metabolized to an inactive form by CYP2C9 and loss of function alleles CYP2C9*2 and CYP2C9*3 are associated with increased levels of warfarin and increased risk of bleeding (59,60). These findings have been replicated in a large meta-analysis (61).

Studies show patients with the two most common reduced-function CYP2C9 variants, *2(rs1799853) and *3(rs1057910) predisposes to an increased risk of an out-of-range INR, delay in the time-to-therapeutic INR and increased bleeding (60). Algorithms have been proposed for warfarin dosing requirements. These include CYP2C9 and VKORC1 genotype, smoking status, relevant medications, age, sex and body mass index (62). Prospective studies have demonstrated the feasibility of this approach (63,64).

Warfarin metabolism is markedly affected by polymorphisms that vary in frequency with ethnicity (65). The cytochrome CYP2C9 SNP rs1799853 is found in about 2% of Caucasians and less than 1% of Africans or Asians, whereas the CYP2C9 SNP rs1057910 is present in about 3.5% of Caucasians, less than 1% of Africans and 4% of Asians. The VKORC1 SNP rs9923231 is found in about 60% of Caucasians, 98% of Africans and only 2% of Asians. The T-allele of CYP4F232 SNP rs2108622 is claimed to be present in 23% of Caucasians, 6% of Africans and 20% of Asians.

A study (66) compared standard dosing regimen with two genotype-guided algorithms, where subjects were genotyped for VKORC1 and CYP2C9 polymorphisms. Primary outcomes were percentage of out-of-range (OOR) international normalized ratios at 1 and 3 months and percentage of time in therapeutic range (TTR). The combined genotype-guided prescription cohort demonstrated superior outcomes with respect to both primary endpoints at 3 months (30% versus 42% for OOR and 71% versus 59% for TTR). Serious events were significantly less frequent in the genotype-guided cohort (4.5% vs. 9.4% of patients (p<0.001) and but there was no difference in the primary outcome between the two genotype-based algorithms. Other studies have been conducted, but suffer from small sample sizes. A pharmacogenetics algorithm was developed (67) depending on the polymorphisms in the warfarin Candidate genes (CYP2C9 and VKORC1 (http://www.warfarindosing.org) (68). The FDA created a black box warning suggesting efficacy based on genetic testing (67,69). However, these studies did not led to clinical application, in part, because physicians were waiting for results of more appropriate large clinical trials to be performed. The European project, A Pharmacogenetics Approach to Coumadin Anticoagulant Therapy (70) and US Clarification of Optimal Anticoagulant through Genetics Studies (71) have unfortunately provided mixed and somewhat contradictory results. Taking into account these recent results, it is unlikely that routine genetic testing for warfarin dosing will be recommended in the near future.

J. Pharmacogenomics of Statin-Induced Myopathy

The class of drugs referred to as statins, which inhibit Hydroxymethyglutaryl Coenzyme A Reductase, is the main drug utilized in the prevention of atherosclerosis and related cardiovascular events. These drugs are utilized throughout the world and the overall budget for statins alone is over $70 billion. They have been shown to decrease the frequency of heart attacks, strokes and revascularization procedures by approximately 20% for every 1 mmol/l decrease in the serum level of low density lipoprotein (LDL) (1) but marked inter-individual variation in response to statin administration (72) exist.

Kinesin-like protein-6 (KIF6), SNP rs20455, was shown to be associated with improved outcome in a large, randomized, controlled trial examining the effect of pravastatin treatment (73). The role of this protein is not well described. However, the improvement in outcomes appears to be independent of the lipid lowering activity of pravastatin. A subsequent report, found that in a cross-sectional genome-wide association study, rs20455 did not associate with CAD (74). Moreover, a more recent meta-analysis combining nineteen GWAS showed no association of KIF6 with CAD (75).

Administration of statins has been associated with muscle pain and weakness and increased plasma levels of creatine kinase (76). The underlying pathogenesis of statin-induced myopathy is not well understood. A genome-wide association study identified a SNP in SLCO1B1 that encodes the organic anion-transporting polypeptide OATP1B1, to be very strongly associated with a statin-induced myopathy (1). Each copy of the variant allele conferred an odds ratio of 4.5. The odds ratio was 16.9 for homozygotes versus the non-risk genotype. The investigators of this study (1) estimated that carriers of the SNP rs4149056 C allele accounted for 60% of all the statin-induced myopathy cases. The SNP rs4149056 C allele was subsequently re-investigated in independent studies, and results confirmed, however, the studies did not observe a similar association with muscle cramps in patients receiving atorvastatin or pravastatin, raising the possibility of an agent-specific interaction (77).

K. Personalized Prevention of CAD and Myocardial Infarction

It has been known for decades that CAD, the number one killer in the world, is largely preventable. Multiple clinical trials have shown that by modifying known risk factors such as cholesterol or hypertension is associated with 30 to 40% reduction in mortality and morbidity (78). It is also documented that about 50% of the risk associated with CAD and myocardial infarction is due to genetic risk variants (8). In the past two decades there has been tremendous progress in identifying genes responsible for rare single gene disorders such as familial hypertrophic cardiomyopathy and long QT syndrome. It is estimated there are over 7,000 single gene disorders and a gene responsible for over 3,000 have been discovered (79). However, common polygenic disorders such as CAD eluded such attempts until 2005 when the technology was made available to perform Genome Wide Association Studies (80). In 2007, we (81) and the de Code Genetics Group in Iceland (82) independently and simultaneously discovered the first genetic risk variant for CAD located on the small arm of chromosome 9 in band 2.1 now referred to as 9p21. 9p21 was confirmed in multiple studies across the world to be a risk variant for CAD. Heterozygotes have an increased relative risk of 25% and homozygotes of 50%. Individuals with premature coronary artery disease (CAD) have an increased risk from 9p21 of more than twofold. Individuals of African descent showed no risk from 9p21 but all other ethnic groups consistently show 9p21 to be a risk variant for CAD. This was soon followed by one of the largest international collaboration in cardiology, Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM), in which several countries collaborated to pursue genetic risk variants for CAD through a meta-analysis a sample size of over 200,000 cases and controls  (83,84,85). The result has been a major landfall for genetic risk of CAD. These studies resulted in the discovery of more than 50 genetic risk variants associated with CAD and myocardial infarction (recent review (86)) as shown in Table 1. Similar success in identifying genetic risk variants has been observed for other common diseases. In less than 10 years more than 3000 genetic risk variants have been discovered to be associated with more than 300 common polygenic diseases.

Table 1: Genome-wide significant genetic risk variants associated with Coronary Artery Disease

table 1

* Variant identified only in Japanese; **Variant identified only in Han Chinese
‡ The risk variant at 9q34.2 is associated with MI, but not with coronary atherosclerosis.
A=adenine; C=cytosine; CI=confidence interval; G=guanine; OR=odds ratio; SNP=single-nucleotide polymorphism; T=thymine.

The genetic risk variants for CAD have many features in common with genetic variants for other polygenic disorders (86):

  • Genetic risk variants are very common occurring on average in 50% of the population with a frequency varying from 2 to 91%.
  • The increased risk of each genetic variant for the disease is small with an odds ratio averaging 18% varying from 2 to 90%.
  • The genetic risk associated with CAD is related to the number of risk variants inherited rather than a specific genetic variant. In an analysis of 23 genetic risk variants, the average number inherited her individual case or control was 17 and varied from a minimum of seven to a maximum of 37.
  • Most of the genetic risk variants associated with CAD are located in DNA sequences that do not code for protein. This means the risk variant mediates its increased risk for CAD through regulation of upstream or downstream DNA sequences that code for protein.
  • All DNA risk variants remain the same throughout one’s lifetime.

Only 15 of the 50 genetic risk variants mediated their risk through known risk factors for CAD; seven of which are associated with low density lipoprotein cholesterol, (LDL–C) one with high-density lipoprotein (HDL) two with triglycerides 4 with hypertension and only one with coronary thrombosis. The surprising and important observation is that 35 of these genetic risk variants mediate their risk for CAD and myocardial infarction independent of known risk factors through mechanisms as yet unknown. A major implication being there are multiple pathways other than cholesterol contributing to the pathogenesis of CAD, yet to be discovered. A great opportunity exists to develop new biomarkers for detecting early CAD as well as unique targets for novel therapy. These results indicate that comprehensive prevention and treatment of CAD is unlikely to occur without the elucidation of these genetic risk variants and the molecular pathways mediating their risk. The identification of mutations in the PCSK9 gene has already led to the development of a new therapy for decreasing plasma LDL–C.

Analysis of the genetic risk variants indicate only blood groups A and B are associated directly with a risk for myocardial infarction (MI) (87). Epidemiologists have claimed for decades that blood group O provides protection against heart attacks. The blood groups A, B and O are different alleles of the same gene located at 9q34. We observed a strong association of the A and B blood groups with CAD with myocardial infarction. The O gene was not associated with any increased risk for coronary atherosclerosis or myocardial infarction. The A, B and O genes code for a protein (alpha 1, 3n acetylgalactosaminyltransferase) that transfers a carbohydrate moiety on to von Willebrand factor (vWF) (87). This prolongs the plasma half-life of the vWF predisposing to coronary thrombosis and MI. The blood group O also codes for a protein transferase, but was mutated resulting in the protein having no activity and thus does not alter the lifespan of plasma vWF. This explains why we observed no association between blood group O and CAD.

The genes A and B occur in 57% of Caucasians. In the recent Nurses’ Health Study and Health Professionals Follow-up Study (88) of more than 90,000 individuals, 4,070 developed heart disease. In this 20 year follow-up study subjects with blood group A or B had an increased risk of MI of about 10% and those with the combination a risk of 20%. Plasma levels of the vWF are approximately 25% higher in individuals with A or B blood groups over that of blood group O (89).

These results have important implications for individuals undergoing angioplasty, bypass surgery and other invasive procedures. One may well ask, should blood groups A and B receive some form of antiplatelet therapy, such as aspirin and would it be effective in reducing the risk?

L. Genetic Risk Variants and the Personalized Management of CAD

It has been claimed that CAD could be eliminated or markedly reduced in this century. This of course is based on the observation that CAD is in part due to known risk factors of cholesterol, hypertension, smoking and diabetes that have been shown in randomized clinical trials to be preventable. Many of the genetic risk variants have been discovered and ongoing efforts are likely to complete this task in the near future. A striking fact evolving from discovery of genetic risk variants for CAD, namely, there are novel mechanisms contributing to coronary atherosclerosis and myocardial infarction which, hither to, were not appreciated. A research effort is now ongoing to identify these mechanisms and develop therapies to minimize or eliminate the risk analogous to statins or PCSK9 inhibitors that reduce the risk of cholesterol. Prevention of coronary atherosclerosis and its’ sequelae myocardial infarction and death will require comprehensive prevention of both environmental and genetic risk. In the meantime, should we wait until specific therapies have been developed or can we utilize the knowledge we already have of genetic risk variants. The national guidelines for the management of risk factors predisposing to CAD, such as cholesterol, is predicated on whether other risk factors are present. An example is if the LDL–C is 160 mg/dL, and the individual has one other risk factor it should be reduced to 140 mg/dL and if other risk factors are present further reductions are recommended. The 9p21 genetic risk variant for CAD is proven to be an independent risk factor and could be used as a reason to lower plasma LDL-C if no other risk factor is present. Similarly, other independent risk variants could dictate the management of plasma cholesterol. These recommendation are not part of current guidelines. In individuals with premature CAD and having inherited the 9p21 risk variant the associated increased risk for CAD is twofold and greater than smoking or minor increases in LDL cholesterol. In addition to genetic risk variants associated with CAD that are independent of known risk factors such as cholesterol, there are many genetic risk factors that also determine the plasma levels of lipids. It is well recognized that plasma levels of HDL-C and LDL-C are 60% to 80% determined by genetic variants (90,91,92). In combined studies between CARDioGRAM and the LIPID Consortium 157 genetic variants regulating the plasma levels of LDL–C, HDL–C, triglycerides, and total cholesterol, have been discovered  (93,94). It is highly likely that these genetic risk variants will also play a major role in the management of plasma lipids in preventing CAD. This will also be an area in which it will be possible to personalize the dose and the agent most appropriate for decreasing plasma lipids. A similar approach can be anticipated in the use of genetic variants associated with hypertension (95) and diabetes (96).

M. The Human Genome and its Future Application in Medicine

Implementation of personalized medicine has on until recently been limited by the lack of genetic variants associated with risk for disease. In ten years the effort to discover and unravel genetic risk for common diseases has only been exceeded by the technology to facilitate DNA sequencing. The cost of sequencing DNA in the past 10 years has decreased one million fold (97) while the rate at which DNA sequences can be read has increased one thousand fold. It is also significant to note that our ability to store and interpret data has only increased 16 fold (98,99). Today it is possible to obtain the complete sequence of one’s genome for less than $1,000 within a few days. It is reasonable and feasible to expect within 5 to 10 years ones genome sequences will be as available and as inexpensive as other routine blood test. The results will at first be very concerning both to the medical professionals and the public. The 21st century has provided the technology to make available the sequence of each individual’s genome and the opportunity for its hidden secrets to be harnessed to improve the health of mankind. The medical profession and the public must embrace appropriate education to meet the challenge. There are many beneficial and deleterious effects confronting multiple disciplines including the sciences, ethics, economics and religion as we implement this new technology. We must face and solve the problems so as not to squander its potential to improve mankind.


The author thanks Peggy Offley for efforts in the preparation of this manuscript.

Funding Sources

Dr. Roberts is supported by grants from the Canadian Institutes of Health Research (CIHR#MOP82810-RR), and the Canadian Foundation for Innovation (CFI#11966). Dr. Roberts is a consultant to Cumberland Pharmaceuticals with no conflict of interest.



  1. Link E, Parish S, Armitage J, Bowman L, Heath S, Matsuda F, Gut I, Lathrop M, Collins R, The SEARCH Collaborative Group. SLCO1B1 variants and statin-induced myopathy–a genomewide study. N Engl J Med 2008 Aug 21;359(8):789-99.
  2. Roberts R, Stewart AF. The genetics of coronary artery disease. Curr Opin Cardiol 2012 May;27(3):221-7.
  3. Ravasi T, Suzuki H, Pang KC, Katayama S, Furuno M, Okunishi R, Fukuda S, Ru K, Frith MC, Gongora MM, et al. Experimental validation of the regulated expression of large numbers of non-coding RNAs from the mouse genome. Genome Res 2006 Jan;16(1):11-9.
  4. McCarty CA, Chisholm RL, Chute CG, Kullo IJ, Jarvik GP, Larson EB, Li R, Masys DR, Ritchie MD, Roden DM, et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genomics 2011;4:13.
  5. Couzin J. DRUG SAFETY: Gaps in the Safety Net. Science 2005 Jan 14;307(5707):196-8.
  6. Service RF. PHARMACOGENOMICS: Going From Genome to Pill. Science 2005 Jun 24;308(5730):1858-60.
  7. Spear BB, Heath-Chiozzi M, Huff J. Clinical application of pharmacogenetics. Trends Mol.Med. 2001 May;7(5):201-4.
  8. Chan L, Boerwinkle E. Gene-environment Interactions and Gene Therapy in Atherosclerosis. Cardiol Rev 1994;2(3):130-7.
  9. Ni YG, Di MS, Condra JH, Peterson LB, Wang W, Wang F, Pandit S, Hammond HA, Rosa R, Cummings RT, et al. A PCSK9-binding antibody that structurally mimics the EGF(A) domain of LDL-receptor reduces LDL cholesterol in vivo. J Lipid Res 2011 Jan;52(1):78-86.
  10. Roberts R. PCSK9 Inhibition-A New Thrust in the Prevention of Heart Disease: Genetics Does It Again. Can J Cardiol 2013 Aug;29(8):899-901.
  11. Abifadel M, Varret M, Rabes JP, Allard D, Ouguerram K, Devillers M, Cruaud C, Benjannet S, Wickham L, Erlich D, et al. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat Genet 2003 Jun;34(2):154-6.
  12. Cohen J, Pertsemlidis A, Kotowski IK, Graham R, Garcia CK, Hobbs HH. Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9. Nat Genet 2005 Feb;37(2):161-5.
  13. Cohen JC, Boerwinkle E, Mosley TH, Jr., Hobbs HH. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N Engl J Med 2006 Mar 23;354(12):1264-72.
  14. Stein EA, Mellis S, Yancopoulos GD, Stahl N, Logan D, Smith WB, Lisbon E, Gutierrez M, Webb C, Wu R, et al. Effect of a monoclonal antibody to PCSK9 on LDL cholesterol. N Engl J Med 2012 Mar 22;366(12):1108-18.
  15. Blom DJ, Hala T, Bolognese M, Lillestol MJ, Toth PD, Burgess L, Ceska R, Roth E, Koren MJ, Ballantyne CM, et al. A 52-week placebo-controlled trial of evolocumab in hyperlipidemia. N.Engl.J.Med. 2014 May 8;370(19):1809-19.
  16. Holden C. Race and Medicine. Science 2003;302(5645):594-6.
  17. Rendic S, Di Carlo FJ. Human cytochrome P450 enzymes: a status report summarizing their reactions, substrates, inducers, and inhibitors. Drug Metab Rev. 1997 Feb;29(1-2):413-580.
  18. Clarke JA, Cutler M, Gong I, Schwarz UI, Freeman D, Dasgupta M. Cytochrome P450 2D6 phenotyping in an elderly population with dementia and response to galantamine in dementia: a pilot study. Am.J.Geriatr.Pharmacother. 2011 Aug;9(4):224-33.
  19. Ingelman-Sundberg M. Genetic polymorphisms of cytochrome P450 2D6 (CYP2D6): clinical consequences, evolutionary aspects and functional diversity. Pharmacogenomics.J. 2005;5(1):6-13.
  20. Bernard S, Neville KA, Nguyen AT, Flockhart DA. Interethnic differences in genetic polymorphisms of CYP2D6 in the U.S. population: clinical implications. Oncologist. 2006 Feb;11(2):126-35.
  21. Poolsup N, Li Wan PA, Knight TL. Pharmacogenetics and psychopharmacotherapy. J.Clin.Pharm.Ther. 2000 Jun;25(3):197-220.
  22. Costa-Scharplatz M, van Asselt AD, Bachmann LM, Kessels AG, Severens JL. Cost-effectiveness of pharmacogenetic testing to predict treatment response to angiotensin-converting enzyme inhibitor. Pharmacogenet.Genomics 2007 May;17(5):359-68.
  23. Lee CR, Goldstein JA, Pieper JA. Cytochrome P450 2C9 polymorphisms: a comprehensive review of the in-vitro and human data. Pharmacogenetics 2002 Apr;12(3):251-63.
  24. Lee SC, Ng SS, Oldenburg J, Chong PY, Rost S, Guo JY, Yap HL, Rankin SC, Khor HB, Yeo TC, et al. Interethnic variability of warfarin maintenance requirement is explained by VKORC1 genotype in an Asian population. Clin.Pharmacol.Ther. 2006 Mar;79(3):197-205.
  25. Tay Y, Rinn J, Pandolfi PP. The multilayered complexity of ceRNA crosstalk and competition. Nature 2014 Jan 16;505(7483):344-52.
  26. Stranger BEFM, Dunning M, Ingle CE, Beazley C, Thorne N, et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 2007;315(5813):848-53.
  27. Gibbs RA. The International HapMap Project. Nature 2003 Dec 18;426(6968):789-96.
  28. Pennisi E. Genomics. 1000 Genomes Project gives new map of genetic diversity. Science 2010 Oct 29;330(6004):574-5.
  29. Cavalli-Sforza LL, Feldman MW. The application of molecular genetic approaches to the study of human evolution. Nat.Genet. 2003.Mar.;33.Suppl:266.-75. 2003 Mar.
  30. McDougall I, Brown FH, Fleagle JG. Stratigraphic placement and age of modern humans from Kibish, Ethiopia. Nature. 2005.Feb.17;433.(7027.):733.-6.(7027:733-6).
  31. Levin L, Zhidkov I, Gurman Y, Hawlena H, Mishmar D. Functional recurrent mutations in the human mitochondrial phylogeny: dual roles in evolution and disease. Genome Biol.Evol. 2013.;5(5):876.-90.doi.: 10.1093./gbe./evt058.(5:876-90):doi.
  32. Carlson Chris. Considerations for SNP Selection. In: Michael P.Winer SGJCS, editor. Genetic variation: a laboratory manual. Cold Spring Harbor Laboratory Press; 2007. p. 263-81.
  33. Bhangale TR, Rieder MJ, Livingston RJ, Nickerson DA. Comprehensive identification and characterization of diallelic insertion-deletion polymorphisms in 330 human candidate genes. Hum Mol Genet 2005 Jan 1;14(1):59-69.
  34. Kruglyak L, Nickerson D. Variation is the spice of life. Nature Genetics 2001 Mar;27:234-6.
  35. Bertrand ME, Rupprecht HJ, Urban P, Gershlick AH. Double-blind study of the safety of clopidogrel with and without a loading dose in combination with aspirin compared with ticlopidine in combination with aspirin after coronary stenting : the clopidogrel aspirin stent international cooperative study (CLASSICS). Circulation 2000 Aug 8;102(6):624-9.
  36. Mehta SR, Yusuf S. The Clopidogrel in Unstable angina to prevent Recurrent Events (CURE) trial programme; rationale, design and baseline characteristics including a meta-analysis of the effects of thienopyridines in vascular disease. Eur Heart J 2000 Dec;21(24):2033-41.
  37. Aradi D, Komocsi A, Vorobcsuk A, Rideg O, Tokes-Fuzesi M, Magyarlaki T, Horvath IG, Serebruany VL. Prognostic significance of high on-clopidogrel platelet reactivity after percutaneous coronary intervention: systematic review and meta-analysis. Am Heart J 2010 Sep;160(3):543-51.
  38. Breet NJ, van Werkum JW, Bouman HJ, Kelder JC, Ruven HJ, Bal ET, Deneer VH, Harmsze AM, van der Heyden JA, Rensing BJ, et al. Comparison of platelet function tests in predicting clinical outcome in patients undergoing coronary stent implantation. JAMA 2010 Feb 24;303(8):754-62.
  39. Parodi G, Marcucci R, Valenti R, Gori AM, Migliorini A, Giusti B, Buonamici P, Gensini GF, Abbate R, Antoniucci D. High residual platelet reactivity after clopidogrel loading and long-term cardiovascular events among patients with acute coronary syndromes undergoing PCI. JAMA 2011 Sep 21;306(11):1215-23.
  40. Mega JL, Close SL, Wiviott SD, Shen L, Hockett RD, Brandt JT, Walker JR, Antman EM, Macias W, Braunwald E, et al. Cytochrome p-450 polymorphisms and response to clopidogrel. N Engl J Med 2009 Jan 22;360(4):354-62.
  41. Pare G, Mehta SR, Yusuf S, Anand SS, Connolly SJ, Hirsh J, Simonsen K, Bhatt DL, Fox KA, Eikelboom JW. Effects of CYP2C19 genotype on outcomes of clopidogrel treatment. N Engl J Med 2010 Oct 28;363(18):1704-14.
  42. Bell AD, Roussin A, Cartier R, Chan WS, Douketis JD, Gupta A, Kraw ME, Lindsay TF, Love MP, Pannu N, et al. The use of antiplatelet therapy in the outpatient setting: Canadian Cardiovascular Society guidelines. Can J Cardiol 2011 May;27 Suppl A:S1-59.
  43. Scott SA, Sangkuhl K, Gardner EE, Stein CM, Hulot JS, Johnson JA, Roden DM, Klein TE, Shuldiner AR. Clinical Pharmacogenetics Implementation Consortium guidelines for cytochrome P450-2C19 (CYP2C19) genotype and clopidogrel therapy. Clin Pharmacol.Ther. 2011 Aug;90(2):328-32.
  44. Roberts JD, Wells GA, Le May MR, Labinaz M, Glover C, Froeschl M, Dick A, Marquis JF, O’Brien E, Goncalves S, et al. Point-of-care genetic testing for personalisation of antiplatelet treatment (RAPID GENE): a prospective, randomised, proof-of-concept trial. Lancet 2012 Mar 28.
  45. Hovens MM, Snoep JD, Eikenboom JC, van der Bom JG, Mertens BJ, Huisman MV. Prevalence of persistent platelet reactivity despite use of aspirin: a systematic review. Am Heart J 2007 Feb;153(2):175-81.
  46. Maree AO, Curtin RJ, Chubb A, Dolan C, Cox D, O’Brien J, Crean P, Shields DC, Fitzgerald DJ. Cyclooxygenase-1 haplotype modulates platelet response to aspirin. J Thromb Haemost. 2005 Oct;3(10):2340-5.
  47. Kunicki TJ, Williams SA, Nugent DJ, Harrison P, Segal HC, Syed A, Rothwell PM. Lack of association between aspirin responsiveness and seven candidate gene haplotypes in patients with symptomatic vascular disease. Thromb Haemost. 2009 Jan;101(1):123-33.
  48. Li Q, Chen BL, Ozdemir V, Ji W, Mao YM, Wang LC, Lei HP, Fan L, Zhang W, Liu J, et al. Frequency of genetic polymorphisms of COX1, GPIIIa and P2Y1 in a Chinese population and association with attenuated response to aspirin. Pharmacogenomics. 2007 Jun;8(6):577-86.
  49. Jefferson BK, Foster JH, McCarthy JJ, Ginsburg G, Parker A, Kottke-Marchant K, Topol EJ. Aspirin resistance and a single gene. Am J Cardiol 2005 Mar 15;95(6):805-8.
  50. Goodman T, Ferro A, Sharma P. Pharmacogenetics of aspirin resistance: a comprehensive systematic review. Br.J Clin Pharmacol. 2008 Aug;66(2):222-32.
  51. Piazuelo E, Fuentes J, Garcia-Gonzalez MA, Jimenez P, Lanas A. A case-control study of the association between polymorphisms of the endothelial nitric oxide synthase and glycoprotein IIIa genes and upper gastrointestinal bleeding in users of low-dose aspirin. Clin Ther. 2008 Jan;30(1):121-30.
  52. Choi JH, Kim SH, Cho BY, Lee SK, Kim SH, Suh CH, Park HS. Association of TNF-alpha promoter polymorphisms with aspirin-induced urticaria. J Clin Pharm.Ther. 2009 Apr;34(2):231-8.
  53. Cairns JA, Connolly S, McMurtry S, Stephenson M, Talajic M. Canadian Cardiovascular Society atrial fibrillation guidelines 2010: prevention of stroke and systemic thromboembolism in atrial fibrillation and flutter. Can J Cardiol 2011 Jan;27(1):74-90.
  54. Furuya H, Fernandez-Salguero P, Gregory W, Taber H, Steward A, Gonzalez FJ, Idle JR. Genetic polymorphism of CYP2C9 and its effect on warfarin maintenance dose requirement in patients undergoing anticoagulation therapy. Pharmacogenetics 1995 Dec;5(6):389-92.
  55. Rettie AE, Haining RL, Bajpai M, Levy RH. A common genetic basis for idiosyncratic toxicity of warfarin and phenytoin. Epilepsy Res 1999 Jul;35(3):253-5.
  56. Carlquist JF, Horne BD, Muhlestein JB, Lappe DL, Whiting BM, Kolek MJ, Clarke JL, James BC, Anderson JL. Genotypes of the cytochrome p450 isoform, CYP2C9, and the vitamin K epoxide reductase complex subunit 1 conjointly determine stable warfarin dose: a prospective study. J Thromb Thrombolysis. 2006 Dec;22(3):191-7.
  57. Takeuchi F, McGinnis R, Bourgeois S, Barnes C, Eriksson N, Soranzo N, Whittaker P, Ranganath V, Kumanduri V, McLaren W, et al. A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet 2009 Mar;5(3):e1000433.
  58. Cooper GM, Johnson JA, Langaee TY, Feng H, Stanaway IB, Schwarz UI, Ritchie MD, Stein CM, Roden DM, Smith JD, et al. A genome-wide scan for common genetic variants with a large influence on warfarin maintenance dose. Blood 2008 Aug 15;112(4):1022-7.
  59. McDonald MG, Rieder MJ, Nakano M, Hsia CK, Rettie AE. CYP4F2 is a vitamin K1 oxidase: An explanation for altered warfarin dose in carriers of the V433M variant. Mol Pharmacol. 2009 Jun;75(6):1337-46.
  60. Higashi MK, Veenstra DL, Kondo LM, Wittkowsky AK, Srinouanprachanh SL, Farin FM, Rettie AE. Association between CYP2C9 genetic variants and anticoagulation-related outcomes during warfarin therapy. JAMA 2002 Apr 3;287(13):1690-8.
  61. Lindh JD, Holm L, Andersson ML, Rane A. Influence of CYP2C9 genotype on warfarin dose requirements–a systematic review and meta-analysis. Eur J Clin Pharmacol. 2009 Apr;65(4):365-75.
  62. Carlquist JF, Anderson JL. Using pharmacogenetics in real time to guide warfarin initiation: a clinician update. Circulation 2011 Dec 6;124(23):2554-9.
  63. Epstein RS, Moyer TP, Aubert RE, DJ OK, Xia F, Verbrugge RR, Gage BF, Teagarden JR. Warfarin genotyping reduces hospitalization rates results from the MM-WES (Medco-Mayo Warfarin Effectiveness study). J Am Coll Cardiol 2010 Jun 22;55(25):2804-12.
  64. McMillin GA, Melis R, Wilson A, Strong MB, Wanner NA, Vinik RG, Peters CL, Pendleton RC. Gene-based warfarin dosing compared with standard of care practices in an orthopedic surgery population: a prospective, parallel cohort study. Ther.Drug Monit. 2010 Jun;32(3):338-45.
  65. Ross KA, Bigham AW, Edwards M, Gozdzik A, Suarez-Kurtz G, Parra EJ. Worldwide allele frequency distribution of four polymorphisms associated with warfarin dose requirements. J Hum Genet 2010 Sep;55(9):582-9.
  66. Anderson JL, Horne BD, Stevens SM, Woller SC, Samuelson KM, Mansfield JW, Robinson M, Barton S, Brunisholz K, Mower CP, et al. A randomized and clinical effectiveness trial comparing two pharmacogenetic algorithms and standard care for individualizing warfarin dosing (CoumaGen-II). Circulation 2012 Apr 24;125(16):1997-2005.
  67. Johnson JA, Cavallari LH, Beitelshees AL, Lewis JP, Shuldiner AR, Roden DM. Pharmacogenomics: application to the management of cardiovascular disease. Clin.Pharmacol.Ther. 2011 Oct;90(4):519-31.
  68. Verschuren JJ, Trompet S, Wessels JA, Guchelaar HJ, de Maat MP, Simoons ML, Jukema JW. A systematic review on pharmacogenetics in cardiovascular disease: is it ready for clinical application? Eur Heart J 2012 Jan;33(2):165-75.
  69. Ganesh SK, Arnett DK, Assimes TL, Basson CT, Chakravarti A, Ellinor PT, Engler MB, Goldmuntz E, Herrington DM, Hershberger RE, et al. Genetics and genomics for the prevention and treatment of cardiovascular disease: update: a scientific statement from the American Heart Association. Circulation 2013 Dec 24;128(25):2813-51.
  70. Pirmohamed M, Burnside G, Eriksson N, Jorgensen AL, Toh CH, Nicholson T, Kesteven P, Christersson C, Wahlstrom B, Stafberg C, et al. A randomized trial of genotype-guided dosing of warfarin. N.Engl.J.Med. 2013 Dec 12;369(24):2294-303.
  71. Kimmel SE, French B, Kasner SE, Johnson JA, Anderson JL, Gage BF, Rosenberg YD, Eby CS, Madigan RA, McBane RB, et al. A pharmacogenetic versus a clinical algorithm for warfarin dosing. N.Engl.J.Med. 2013 Dec 12;369(24):2283-93.
  72. Mangravite LM, Thorn CF, Krauss RM. Clinical implications of pharmacogenomics of statin treatment. Pharmacogenomics.J 2006 Nov;6(6):360-74.
  73. Lakoubova OA, et al. Association of the Trp719Arg Polymorphism i Kinesin-like protein 6 with Myocardial Infarction and Coronary Artery Disease in 2 Prospective Trials. Journal of American College of Cardiology 2008;51(4):435-43.
  74. Stewart AF, Dandona S, Chen L, Assogba O, Belanger M, Ewart G, LaRose R, Doelle H, Williams K, Wells GA, et al. Kinesin family member 6 variant Trp719Arg does not associate with angiographically defined coronary artery disease in the Ottawa Heart Genomics Study. J Am Coll Cardiol 2009 Apr 21;53(16):1471-2.
  75. Assimes TL, Holm H, Kathiresan S, Reilly MP, Thorleifsson G, Voight BF, Erdmann J, Willenborg C, Vaidya D, Xie C, et al. Lack of association between the Trp719Arg polymorphism in kinesin-like protein-6 and coronary artery disease in 19 case-control studies. J Am Coll Cardiol 2010 Nov 2;56(19):1552-63.
  76. Mancini GB, Baker S, Bergeron J, Fitchett D, Frohlich J, Genest J, Gupta M, Hegele RA, Ng D, Pope J. Diagnosis, prevention, and management of statin adverse effects and intolerance: proceedings of a Canadian Working Group Consensus Conference. Can J Cardiol 2011 Sep;27(5):635-62.
  77. Voora D, Shah SH, Spasojevic I, Ali S, Reed CR, Salisbury BA, Ginsburg GS. The SLCO1B1*5 genetic variant is associated with statin-induced side effects. J Am Coll Cardiol 2009 Oct 20;54(17):1609-16.
  78. Yusuf S, Hawken S, +unpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. The Lancet 2004;364(9438):937-52.
  79. Ku CS, Naidoo N, Pawitan Y. Revisiting Mendelian disorders through exome sequencing. Hum.Genet. 2011.Apr;129.(4):351.-70.doi.: 10.1007./s00439.-011.-0964.-2.Epub.2011.Feb.18.(4:351-70):doi-011.
  80. Roberts R. New Gains in Understanding Coronary Artery Disease. Interview with Dr. Robert Roberts. Affymetrix Microarray Bulletin 2007;3(2):1-4.
  81. McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox DR, Hinds DA, Pennacchio LA, Tybjaerg-Hansen A, Folsom AR, et al. A common allele on chromosome 9 associated with coronary heart disease. Science 2007 Jun 8;316(5830):1488-91.
  82. Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A, Jonasdottir A, Sigurdsson A, Baker A, Palsson A, et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 2007 Jun 8;316(5830):1491-3.
  83. Preuss M, Konig IR, Thompson JR, Erdmann J, Absher D, Assimes TL, Blankenberg S, Boerwinkle E, Chen L, Cupples LA, et al. Design of the Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) Study: A Genome-wide association meta-analysis involving more than 22 000 cases and 60 000 controls. Circ Cardiovasc Genet 2010 Oct;3(5):475-83.
  84. Samani NJ, Deloukas P, Erdmann J, Hengstenberg C, Kuulasmaa K, McGinnis R, Schunkert H, Soranzo N, Thompson J, Tiret L, et al. Large scale association analysis of novel genetic loci for coronary artery disease. Arterioscler Thromb Vasc Biol 2009 May;29(5):774-80.
  85. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007 Jun 7;447(7145):661-78.
  86. Roberts R. Genetics of coronary artery disease. Circ.Res. 2014 Jun 6;114(12):1890-903.
  87. Reilly MP, Li M, He J, Ferguson JF, Stylianou IM, Mehta NN, Burnett MS, Devaney JM, Knouff CW, Thompson JR, et al. Identification of ADAMTS7 as a novel locus for coronary atherosclerosis and association of ABO with myocardial infarction in the presence of coronary atherosclerosis: two genome-wide association studies. Lancet 2011 Jan 29;377(9763):383-92.
  88. He M, Wolpin B, Rexrode K, Manson JE, Rimm E, Hu FB, Qi L. ABO blood group and risk of coronary heart disease in two prospective cohort studies. Arterioscler Thromb Vasc Biol 2012 Sep;32(9):2314-20.
  89. Gill JC, Endres-Brooks J, Bauer PJ, Marks WJ, Jr., Montgomery RR. The effect of ABO blood group on the diagnosis of von Willebrand disease. Blood 1987 Jun;69(6):1691-5.
  90. Roberts R. Genetics of premature myocardial infarction. Curr Atheroscler.Rep 2008 Jun;10(3):186-93.
  91. Rader DJ, Maugeais C. Genes influencing HDL metabolism: new perspectives and implications for atherosclerosis prevention. Mole Med Today 2000;6:170-5.
  92. Cohen JC, Kiss RS, Pertsemlidis A, Marcel YL, McPherson R, Hobbs HH. Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science 2004 Aug 6;305(5685):869-72.
  93. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, Pirruccello JP, Ripatti S, Chasman DI, Willer CJ, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 2010 Aug 5;466(7307):707-13.
  94. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet 13 A.D. Nov 11;(2013 Nov;45(11):1274-83. doi: 10.1038/ng.2797. Epub 2013 Oct 6.).
  95. Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, Chasman DI, Smith AV, Tobin MD, Verwoert GC, Hwang SJ, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 2011 Oct 6;478(7367):103-9.
  96. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segre AV, Steinthorsdottir V, Strawbridge RJ, Khan H, Grallert H, Mahajan A, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat.Genet.012.Sep.;44.(9.):981.-90.doi.: 10.1038./ng.2383.Epub.2012.Aug.12.(9:981-90):doi.
  97. Roberts R, Marian AJ, Dandona S, Stewart AF. Genomics in cardiovascular disease. J Am Coll Cardiol 2013 May 21;61(20):2029-37.
  98. Stein LD. The case for cloud computing in genome informatics. Genome Biol 2010;11(5):207.
  99. Zerbino DR, Paten B, Haussler D. Integrating genomes. Science 2012 Apr 13;336(6078):179-82.


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