Glossary

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  Adverse drug reaction An unwanted effect caused by the administration of a drug. The onset of the adverse reaction may be sudden or may develop over time.

  
  
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  Allele In association studies , the term allele is most commonly used when referring to markers. An allele would be one specific version of a marker inherited from a parent (e.g., A or T in the case of an A/T SNP). Allele is also used in a broader context when referring to a specific version of an entire gene.

  
  
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  Association testing and association-based approaches A genetic variant is genotyped in a population for which phenotypic information is available (such as disease occurrence or a range of different trait values). If a correlation is observed between genotype and phenotype, there is said to be an association between the genetic variant and the disease or trait.

  
  
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  Causal gene The specific gene responsible for the risk conferred by a genomic region identified by an association study.

  
  
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  Causal variant The specific DNA sequence that functionally gives rise to the increased risk conferred by a genomic region identified by an association study.

  
  
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  Genetic markers Genomic variants used as positional tools to associate specific DNA fragments to certain phenotype or disease.

  
  
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  Genome-wide linkage scan A linkage study performed with markers located across the entire genome. It is traditionally performed with approximately 300 markers of simple sequence length repeats (SSLPs) and, more recently, with approximately 5000 SNPs.

  
  
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  Genotyping A test designed to identify the genetic constitution of an individual—that is, the alleles present at one or more specific loci.

  
  
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  Haplotype A haplotype is a combination or pattern of alleles at multiple linked loci that are transmitted together.

  
  
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  HapMap Genetic resource created by the International HapMap Project ( www.hapmap.org ). The HapMap is a catalogue of common human genetic variants that occur. It describes these variants, where they occur in our DNA, and how they are distributed among people within populations and among populations in different parts of the world.

  
  
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  Linkage disequilibrium (LD) A situation in which alleles on the same chromosome occur together more often than can be accounted for by chance. This indicates that the alleles are physically close on the DNA strand and are most likely to be transmitted together within a population.

  
  
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  Linkage study A study aimed at establishing linkage between a genetic marker and a disease locus. Linkage is based on the tendency for genes and genetic markers to be inherited together because of their location near one another on the same chromosome.

  
  
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  Locus The specific position of a gene or a marker on a chromosome.

  
  
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  Penetrance The likelihood, under given environmental conditions, with which a specific phenotype is expressed by those individuals with a specific genotype. For example, if half (50%) of those with the gene “X” that is known to cause the disease actually have the disease, then the penetrance of the gene is 0.5.

  
  
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  Pharmacodynamics Refers to the study of effects produced by drugs through interactions with their targets (receptors, enzymes, others), their mechanism of action, and the relationship between drug concentrations and effects (what the drug does to the body).

  
  
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  Pharmacokinetics Refers to the study of the time course of a drug and its concentration during its absorption, distribution, metabolism, and excretion in the body (what the body does to a drug).

  
  
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  Poor metabolizer A person who metabolizes a drug at a slower rate than others. A person can be a poor metabolizer for one drug and an extensive metabolizer of another, depending on the enzymes implicated in the metabolism of the drugs and the genotypes of the patient for these enzymes.

  
  
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  Replication study A study designed to test (or replicate) a prior, and explicit, genetic hypothesis.

  
  
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  Single nucleotide polymorphism (SNP) A genomic variant in which a single base in the DNA differs from the usual base at that position.

  
  
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  Tag SNP A representative SNP in a region of the genome with high linkage disequilibrium. This allows genetic variation to be identified without genotyping every single SNP. Tag SNPs are significant in genome-wide association studies in which millions of SNPs across the genome are studied. Tag SNPs allow the number of SNPs to genotype to be reduced while generating the same amount of information. The HapMap Project has catalogued tag SNPs in the entire human genome.

Structure of the Human Genome

The human genome consists of 3.3 billion base pairs of DNA. The recent completion of the human genome sequence provides evidence for 20,000 to 30,000 genes encoded by the genome of every individual nucleated cell of the human body. The genomic code, consisting of four different nucleotides (most often referred to by their single-letter symbols—A, C, G, T), has long been established. More recent studies have demonstrated that there are numerous differences that can be identified when comparing the genome of any two individuals. These are known as genetic variants. Although there are many different forms of genetic variation (e.g., insertions and deletions, rearrangements), the most common form is that of the single nucleotide polymorphism (SNP; pronounced “snip”). There are approximately 10 million SNPs that are common in the human population (i.e., at a frequency of 5% or greater). Because of the recombination patterns in the genome, these SNPs are not all independent; some are in linkage disequilibrium (LD) and are often transmitted together. The recent mapping of this LD, available on the HapMap Project website, has allowed the use of LD between SNPs as a tool in genetic studies. By selecting tag SNPs as proxies for several other SNPs, it is possible to reduce significantly the number of SNPs to be genotyped in a study and thus decrease the costs.

Depending on the nature and location of a sequence difference, genetic variations such as SNP may be silent or may have an influence on the biologic pathway(s) in which the protein product encoded by the gene plays a role. For example, some genetic variations will lead to a difference in the amino acid structure of a protein and disturb its function, whereas others may modulate the levels at which the gene and its protein will be expressed. Furthermore, some of the genetic variations that we have observed with these types of functional consequences may influence normal phenotypic variation (e.g., height, hair color), whereas others may influence clinical phenotypes and thus be considered as risk factors. Certain genetic risk factors modulate an individual’s risk to develop disease; others modulate the ability to respond to a given medication or the propensity to develop specific side effects in response to therapy. It is these latter two issues that are considered within the context of pharmacogenomic studies.

Traditionally, genetic traits have been categorized as monogenic or polygenic. In a monogenic trait, the phenotype observed can be explained by variations in a single gene. Cystic fibrosis, sickle cell disease, and Huntington’s disease are example of monogenic disorders. Conversely, polygenic traits are expressed through the interactions of several genes and are also influenced by the environment; these are commonly named complex traits. Many complex disorders exist such as inflammatory bowel disease, rheumatoid arthritis, systemic lupus erythematosus (SLE), and type 1 diabetes (T1D). CVDs are among the most prevalent complex disorders. Height, skin color, and IQ are also in the complex trait category as are responses to medications, because several factors influence drug pharmacokinetics and pharmacodynamics. Thus, the field of pharmacogenomics tackles the challenge of identifying the drug genes using strategies similar to those used in the search for complex disorders genes.

Genetic Association Studies

Several recent scientific advances have dramatically changed our ability to examine genetic variation as it relates to human disease. The first key advance is the complete determination of the final sequence of the human genome. This advance has provided a direct means to connect a chromosomal region with its DNA sequence and gene content. The second key advance is the successful effort to define DNA sequence variation in the human genome. Specifically, a genome-wide SNP map has grown from an initial version in 1998, with 4000 SNPs, into a current map of approximately 10 million SNPs ( www.ncbi.nlm.nih.gov/SNP ). The third key advance is the definition of the long-range extent of LD among SNPs in the human genome and the identification of common recombination hot spots that punctuate the extent of LD into so-called haplotype blocks across the genome. ^, These advances have facilitated the search for complex trait genes.

Identifying the actual causal gene(s) and causal variant(s) for any given disorder is an important challenge. The most widely used strategy to locate the genes causing a complex trait is association testing because it is the most powerful approach for this purpose. Several genetic variations have been associated with cardiovascular phenotypes or risk factors ( Table 10-1 ). Genetic association studies seek evidence for a statistically significant association between the marker allele and a disease at the population level. Specifically, the association approach involves comparing the frequency of an allele at the marker locus between a sample of unrelated affected individuals and an appropriate, well-matched control sample that is representative of the allelic distribution in the general population. In the context of pharmacogenomics, a group of responder patients is compared with a group of nonresponder patients. Alternatively, the association of a SNP of interest and a continuous trait (e.g., blood pressure, cholesterol) can be investigated. Testing for association to disease is direct (testing the actual mutation) or indirect (testing a genetic variant that acts as a proxy for the mutation). It usually follows one of two common study designs, candidate gene testing or unbiased genome-wide association mapping ( Fig. 10-1 ).

Identifying a susceptibility gene in the context of a clinical trial. The human genome consists of a four-letter code, 3 billion letters long, and is found in each cell in the shape of chromosomes. Approximately 0.3% of these letters are variable and are called SNPs. The challenge is to identify which of these control response to medication. Just like in a complex trait, the interactions among several genes are responsible for the response to medication. The individual risk conferred by each specific susceptibility gene is usually low. As a result, these genes are usually harder to identify than genes causing a monogenic disorder. However, geneticists have developed tools and strategies to search among the ~20,000 genes of the genome. The most common way is to compare the allelic frequencies of genetic markers (e.g., SNPs) between two groups (such as responders to a particular drug and nonresponders). If the frequency is significantly different, it means that the marker allele, and thus the gene within which the marker is located, is associated to the studied trait (e.g., response to drug X). Two main strategies coexist, the candidate gene approach and the GWA approach. In the candidate gene approach, genes are selected through literature searches according to their functions and presumed involvement in the biologic pathways under study (e.g., drug-metabolizing genes). Markers in these genes are then tested for association to the trait. Typically, less than 100 markers are genotyped per study and most of these SNPs are in the protein-coding region of the genes. In the GWA approach, no gene selection is needed. Actually, millions of genetic markers covering the entire genome are tested for association. This relatively new strategy has led to the discovery of several previously unsuspected susceptibility genes.

Three main factors that directly influence the success of association studies are the size of the cohorts under study, matching of the different groups, and replication of the discovered association. Traditionally, collections of patients (or cases) and unrelated matched controls were tested for differences in allele frequencies. Initially, association-based genetic studies were generally limited to the testing of modestly sized cohorts of patients with a small number of genetic variants in one gene or in a small number of genes. Unfortunately, this led to a very large number of false-positive results. Today, given the focus on complex traits and the modest effects of individual genes, study design generally involves hundreds to thousands of samples; meta-analyses (i.e., combining the data of several independent studies) are becoming more popular and allow the identification of very modest effects of genes. One other major source of error in case-control studies is that of inadequate matching—that is, differences between the case and control populations that are unrelated to disease, also known as stratification. Careful selection of samples and corrections for stratification are now integrated into association study designs. In addition, replication of the association signals is essential to eliminate false-positive associations. Most studies are designed as two-stage experiments, with large cohorts for both stages. The first stage is for screening to detect associations and the second stage is for replication. More details concerning the limitations of association testing (i.e., statistical design, evaluation of the candidate gene, ethnic variability in disease susceptibility) as well as methods to detect and correct for stratification have been described in detail elsewhere. ^,

Candidate Gene Approach

The candidate gene approach relies on selecting genes based on knowledge of their biologic function as likely to have an influence on disease susceptibility, and testing the known genetic variants in and around the gene for association to disease. The number of SNPs tested in such study, up to a few thousand, keep the cost of this strategy relatively low compared with the genome-wide alternative. For example, the role of apolipoprotein E in the metabolism of cholesterol and triglycerides made it a central candidate for CVD risk and its implication has been confirmed in several association studies. Unfortunately, of the approximately 20,000 genes that are believed to exist in the human genome, few have been annotated for specific function. Moreover, it is not that uncommon for functional annotation of genes to be incomplete. One recent example is that of the nicotinic acetylcholine receptor α7 subunit. The main function of the nicotinic acetylcholine receptor family was initially believed to be the transmission of the signals for the neurotransmitter acetylcholine at the neuromuscular junctions; more recently, it was shown to be an essential regulator of the inflammatory response. Many genes related to the pharmacokinetics and pharmacodynamics of drugs are the subject of candidate gene studies ( Table 10-2 ). However, candidate gene studies are often unsuccessful. This outcome is not that surprising, given the low likelihood that an investigator could select the few causal genes from the human genome, even armed with the best knowledge of disease pathogenesis. Inadequate sample size has also contributed to this lack of success.

TABLE 10–2

Genes Possibly Associated with Responses to Drugs

Source (Year)	Gene	Chromosome	Gene description	Drug
Pharmacokinetics
Ismail and Teh (2006) ; Kirchheiner et al (2007)	CYP2D6	22q13	Cytochrome P-450 2D6	Metoprolol, carvedilol, flecainide, propafenone, codeine
Sconce et al (2005) ; Limdi et al (2008) ; Schwarz et al (2008)	CYP2C9	10q24	Cytochrome P-450 2C9	Warfarin, irbesartan, losartan
Collet et al (2009) ; Mega et al (2009) ; Sibbing et al (2009)	CYP2C19	10q24	Cytochrome P-450 2C19	Clopidogrel
Zheng et al (2003) ; Wilke et al (2005)	CYP3A5	7q22.1	Cytochrome P-450 3A5	Atorvastatin, simvastatin, lovastatin, tacrolimus
Drug Transporters
Pasanen et al (2006) ; Link et al (2008)	SLCO1B1	12p	OATP1B1	Pravastatin, simvastatin, rosuvastatin
Pharmacodynamics/Disease-Modifying Gene
McNamara et al (2004) ; Bhatnagar et al (2007)	ACE	17q23	Angiotensin-converting enzyme	RAAS inhibitors, blockers
Terra et al (2005) ; Liggett et al (2006)	ADBR1	10q24	β 1 -adrenergic receptor	Beta blockers
Kaye et al (2003) ; Lanfear et al (2005)	ADBR2	5q31	β 2 -adrenergic receptor	Beta blockers
Kurland et al (2002)	AGT	1q42	Angiotensinogen	RAAS inhibitors, blockers
Kurland et al (2002)	AGTR1	3q21	Angiotensin II receptor, type 1	RAAS inhibitors, blockers
Mackenzie et al (2005) ; Li et al (2006)	ALDH2	12q24	Aldehyde dehydrogenase	Nitroglycerin
Gerdes et al (2000) ; Chiodini et al (2007)	APOE	19q13	Apolipoprotein E	Statins
Medina et al (2008)	HMGCR	5q13	3-hydroxy-3-methylglutaryl coenzyme A reductase	Statins
Goodman et al (2007)	ITGB3 (GPIIIa)	17q21	Integrin β 3	Aspirin
Iakoubova et al (2008) ; Iakoubova et al (2008)	KIF6	6p21	Kinesin-like protein 6	Statins
Lynch et al (2008)	NPPA	1p36	Atrial natriuretic peptide precursor	Antihypertensive agents
Moore et al (2007)	REN	1q32	Renin	Aliskiren
Sconce et al (2005) ; Schwarz et al (2008)	VKORC1	16p11	Vitamin K epoxide reductase	Warfarin

 

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