Human Molecular Genetics, 2001, Vol. 10, No. 20 2261-2267
© 2001 Oxford University Press
Pharmacogenetics
GlaxoSmithKline, Five Moore Drive – 5616, Research Triangle Park, NC 27709, USA
Received July 11, 2001; Accepted July 26, 2001.
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ABSTRACT |
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Pharmacogenetics is the variability of drug response due to inherited characteristics in individuals. Drug metabolizing enzymes have been studied for decades, first as chemical reactions and, more recently, as specific polymorphisms of known molecules. With the availability of whole-genome single-nucleotide polymorphism (SNP) maps, it will soon be possible to create an SNP profile for patients who experience adverse events (AEs) or who respond clinically to the medicine (efficacy). Proof-of-principle experiments have demonstrated that high density SNP maps in chromosomal regions of genetic linkage facilitate the identification of susceptibility disease genes. Whole-genome SNP mapping analyses aimed at determining linkage disequilibrium (LD) profiles along an ordered human genome backbone are in progress. SNP ‘fingerprints’ or SNP PRINTssm will be used to identify patients at greater risk of an AE, or those patients with a greater chance of responding to a medicine. As LD maps for various ethnic populations are constructed, the number of SNPs necessary to measure for an individual will decrease. Standardized pharmacogenetic maps for drug registration and post-marketing surveillance will result in safer, more effective and more cost-efficient medicines. The timing of these pharmacogenetic applications will occur over the next 5 years. In contrast, the benefits of pharmacogenomic applications such as the identification of new tractable targets will not be visible as new medicines for 7–12 years, due to the lengthy drug development and registration processes.
Pharmacogenetics and pharmacogenomics are frequently interchanged terms and can therefore be confused. For the purpose of clarity in the use of the terms in this review, pharmacogenetics is defined as the study of variability in drug responses attributed to hereditary factors in different populations. Pharmacogenomics is the determination and analysis of the genome (DNA) and its products (RNA and proteins) as they relate to drug response. For example, gene expression profiling using various microarray technologies has enabled the demonstration of distinct sub-sets of genes that may be expressed differentially in disease and healthy tissues. These genomic techniques can be useful for differential diagnosis of patients, particularly for heterogeneous diseases that present with similar clinical phenotypes but differ in molecular expression. Response to treatment can sometimes be recognized at the genomic level by tissue gene expression profiles. Expression profiles, however, differ from the approach of using inherited differences in our genetic information to predict responses to medicines, pharmacogenetics.
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METABOLIZING ENZYMES |
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The term pharmacogenetics was introduced into the literature well before the genomic and genetic advances of the past decade. Differences between the rates of drug metabolism among people, associated with particular polymorphic forms of enzymes involved in drug catabolism, have been established for several decades (1). Biochemical and, later, genetic variants in specific isoenzymes such as cytochrome P450 polymorphisms, were studied to explain the differences in the rates of drug metabolism. New molecular methods now measure the same data more rapidly. This science has been extensively reviewed, and spawned numerous specialty journals devoted to absorption, distribution, metabolism and excretion (ADME) (2,3). This expertise is critical and well integrated into the development of medicines, but is not the subject of this review.
Rather, the focus of this review is the ongoing construction of standardized methods of rapidly distinguishing individuals in the population who, based on their genetic make-up, respond in a particular way to a specific environmental factor such as a medicine. Metabolizing enzymes represent distinct ‘candidate’ genes with recognized functions that can be hypothesized to influence response to a medicine and therefore warrant investigation. This candidate approach has a number of limitations, such as pursuing an attractive, yet incorrect hypothesis. However, profiling of individuals using standardized tools that resemble whole-genome ‘fingerprint’ analyses will be possible in the near future and will begin to obviate the need for hypothesis generation around favored genes by determining genetically associated targets (4,5).
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FINGERPRINTS: NOT SIMPLE ASSOCIATIONS OR HAPLOTYPES |
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Associations between inherited variants and clinical disease are at the heart of modern medical genetics. When a change in coding sequence always results in a particular disease expression over an observable time frame, the effect of the variant is said to be highly penetrant. If that variant is rare (<1% allele frequency), it is referred to as a mutation. During the past century many diseases have been associated with relatively rare mutations, when either one (dominant) or two variant (recessive) copies are inherited. Science is beginning to recognize less rare gene variations that are associated with more common diseases in a less than cause-and-effect manner and therefore are called susceptibility genes. An example of a rare mutation is the triple repeat variant always associated with Huntington’s disease, although the age of onset varies with the length of the repeat elements (6,7). The association of the APOE4 variant with the age of onset distribution of Alzheimer’s disease, as well as the protective effect of the APOE2 variant, are still the most documented and confirmed examples of susceptibility polymorphisms (8,9). The associations of specific APOE polymorphisms with common forms of Alzheimer’s disease were identified within a chromosome 19 region that had been genetically linked to the disease (10). Combinations of two or more variants (haplotypes) can also be associated with the expression of diseases. Sometimes the variants are within the same gene, sometimes within the same group of genes on a single chromosome, and sometimes on different chromosomes. These variants can be subject to independent selection during meiosis if they are on separate chromosomes, or if recombination events occur between markers on the same chromosome. Thus, the mathematics for identifying disease associations with multiple haplotype markers from separate chromosomes requires very large numbers of patients and controls as well as statistical corrections (e.g. Bonferroni corrections) to obtain statistically significant results.
In contrast to measuring disconnected variants across the genome, such as single-nucleotide polymorphisms (SNPs) that are nominated from a collection of candidate gene variants, thousands of ordered variants along the genome can be measured rapidly as a standardized ‘fingerprint’ or SNP Printsm. The location and order of the variants will always be the same but the marker polymorphisms will vary in different individuals. This phenomenon allows patterns of disease or phenotype-specific associations at each point to be identified at very specific locations along a common, ordered linear array. Multiple closely ordered polymorphisms, especially those within small DNA linkage disequilibrium (LD) regions (50–150 kb) that are inherited together over many generations, can distinguish a region of LD from other combinations. In essence, they date the time in evolution when a recombination event occurred in the neighborhood of a disease susceptibility polymorphism. Therefore, different combinations or haplotypes of stable inherited sequence markers within defined LD regions may mark the DNA strands in the populations that are associated with disease or phenotype susceptibility. This concept has already been demonstrated experimentally with the region of DNA surrounding the APOE4 polymorphism, the well known susceptibility polymorphism associated with a younger age of onset of Alzheimer’s disease. APOE4 was re-identified using a high-density single-nucleotide polymorphism mapping analysis across a four million base region that contained the APOE gene (11–13). A short series of SNP markers that are highly associated with AD (and in LD with APOE4) was demonstrated to define a DNA region of approximately 45 000 bases (Fig. 1). Identification of this small specific region against the common background of no association narrowed the focus to only two genes coded within that relatively small LD region, APOC-1 and APOE. Only the APOE4 variant of APOE at codon 112 (of 299) was associated with the younger age of onset distribution of AD (14). This finding has been verified by other studies of epidemiology in multiple populations with differences in control allele frequencies, immunocytopathology of human brain, analyses of neuronal APOE RNA and protein expression in transgenic mice, among others (15–17). In fact, the APOE2 variant at codon 158 was not related to the expression of AD. Inheritance of each APOE2 allele was associated with an older age of onset distribution as a protective allele. Yet within the larger region of LD several other SNPs not within the APOE gene demonstrated association with AD, but with no biological relevance. These SNPs simply marked the APOE4 ancestral LD region compared with the later appearance of APOE3 and its associated LD region markers that occurred on the APOE3 background DNA strand with evolution. SNP mapping at other known loci support the detection of regions of extensive linkage disquilibrium (18,19).
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Subsequently, high-density SNP mapping was used to identify other previously unknown disease-associated polymorphisms within previously unknown susceptibility genes. Thus, LD association mapping, defined in the context of disease versus control associations for each point along a high-density linear SNP map, allows a small region of DNA that contains very few genes to be identified rapidly. This has been demonstrated for both psoriasis and migraine, where an 80 kb region and a 120 kb region were identified, respectively. Several polymorphisms of a single gene within each small region of LD are associated with the disease. For psoriasis, this is a previously novel gene, designated PAT1 or psoriasis associated transporter 1 (20). It belongs to a class of genes that were previously unrecognized as playing a role in psoriasis pathogenesis (Fig. 2A). Several polymorphisms within the previously well known insulin receptor gene, INSR, on chromosome 19 are also highly associated with migraine (21) (Fig. 2B). These disease-specific associations provide the impetus and direction for further validation related to disease pathogenesis and to pharmaceutical targeting. The LD association methodology can also be modeled to localize more traditional mutations in more traditional highly penetrant diseases in families.
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ANALOGY TO MASS SPECTROSCOPY ANALYSES |
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Mass spectroscopy is emerging as an effective and accurate platform for industrial scale SNP analyses (22,23). As a tool for molecular differentiation, matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectroscopy has considerable advantages because of its accuracy and ability to multiplex many samples (24). The read-out from mass spectroscopy plots the intensity (relative quantity) of the peak on the y-axis, against the mass in Da on the x-axis (Fig. 3). PCR fragments that differ in a single base can easily be differentiated. There are several recent articles describing the emergence of this method for high-throughput SNP analyses (22–24).
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The SNP map of the human genome can also be represented on the x-axis as a linear order (analogous to increasing mass) so that if 200 000 consecutively ordered SNPs are to be analyzed, they can be numbered 1–200 000 sequentially. (The optimum number of SNPs would be the minimum number of SNPs that could define the LD haplotype map for all common ethnic and racial groups.) Similarly, if the association at each SNP for the disease or the adverse event (AE) in patients compared with controls is represented along the y-axis, then the locations of LD peaks could be precisely localized and compared on the map. If association peaks are found in the disease or AE samples, the implication is that genes within that small region are involved in the etiology of the phenotype (Fig. 1). The comparison of SNP maps to determine whether identical peaks of LD along the genome can be identified and associated with a defined phenotype can be considered as an SNP ‘fingerprint’ or an SNP Printsm. Various means of pattern analyses can be applied for rapid identification of disease or AE peaks of LD (Figs 3 and 4).
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ENVIRONMENT AND GENETICS: AE-PHARMACOGENETICS |
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The initial commercial impact of AE-pharmacogenetics will be on the safety of marketed medicines. There are few social or ethical controversies about AEs. Everyone dislikes AEs: patients, physicians, pharmaceutical companies and regulators. They can all agree that AEs are bad, potentially lethal, expensive and should be eliminated. AEs represent the specific interaction of environment and genetics. A drug is introduced into the patient’s environment. In some cases there is an idiosyncratic reaction, an AE that has a stereotypic phenotype and is directly related to the timing of the treatment or environmental insult. The objective of AE-pharmacogenetics is to rapidly identify a genetic profile that characterizes patients who are more likely to suffer the AE, compared with other patients who are likely to respond to the drug safely. The goal is to identify several specific and reproducible regions of LD associations that are characteristic of patients with AEs and are not shared by patients in whom the drug was well tolerated. The panel of widely separated small groups of high-density SNP mapping peaks across the genome can then be abstracted to a much simpler set of SNPs within and adjacent to (as internal controls) those LD regions. This markedly smaller panel of SNPs (e.g. SNP Printsm) can then be used to screen individuals rapidly, in a convenient overnight test before their prescriptions are filled. Thus, with drugs that are highly effective but not used extensively because of the risk of AEs, the safety profile of the medication can be improved. Because the environmental factor, the medicine, and the host genetics are both predictable, the avoidance of AEs can be based on SNP Printssm or predictable patterns of LD (Fig. 5).
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It will also be possible to limit the SNP Printsm to only polymorphisms that are not knowingly uniquely diagnostic of any specific disease, i.e. disease mutations or strong susceptibility polymorphisms. A standardized SNP Printsm without diagnostic constituents would decrease the dissemination of negative collated information to relatives of the patient. In addition, it will be possible to determine which variants within specific genes that are localized within the regions of LD may contribute to the AE. In fact, in cases where the same idiosyncratic clinical phenotype is caused by multiple drugs, it may be possible to discover the panel of susceptibility polymorphisms and target new drugs that do not entail the AE risk for multiple diseases in people who carry a common genetically susceptible SNP Printsm.
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THE NUTS AND BOLTS OF AE-PHARMACOGENETICS |
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AE-pharmacogenetics is not about revolutionary scientific insight into disease mechanisms or cellular interactions. Rather, it is a pattern-recognition scoring system based on genetic variations and LD for rapid selection of individuals at risk of serious side effects upon encountering an environmental factor called a medicine. It is happening now. It is not a wild pipe dream. It is expensive, and therefore the initial proof-of-principle experiments are designed to have some commercial value and are not the type of research undertaken in academic settings.
What is required for demonstrating proof of principle for AE-pharmacogenetics? There are essentially two elements, the patients and the tools. While the protocols for obtaining DNA samples from both AE-patients and patients who have taken the drug with no AEs are not trivial, the use of the tools and bioinformatic applications are more germane to the readership of Human Molecular Genetics. First, a dense, ordered map of easily measured SNP variants defining regions of LD across the genome is needed. Sufficient SNPs were placed into the public domain by The SNP Consortium, and this resource continues to expand (25). Secondly, there should be rapid, accurate, automated, high-throughput and inexpensive ($0.001/SNP) genotyping assays available to measure approximately 100 000–200 000 SNPs from each patient and control, and tens of millions of SNPs from each experiment. Competition is very active in the biotechnology sector to develop industrial genotyping capacity, and the retail price per SNP has come down from $1 to $0.10 per SNP over the past year. The technology will continue to develop and the cost will continue to decrease to less than $0.01 per SNP, just as it has with DNA sequencing.
Also critical is the need for high-capacity bioinformatic analyses of the huge databases containing clinical phenotypes as well as SNP data. Databases with the capacity to handle and analyze large amounts of data will also require investment in high capacity computing structure. Parallel to industrialization of SNP mapping, bioinformatic platforms for handling the data are also being developed. The construction of a readable interpretation of the data (e.g. an SNP Print) that can be used for registration purposes with regulatory authorities and as a pre-prescription test will result in a commercial product that can be used optimally.
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PROOF-OF-PRINCIPLE EXPERIMENTS |
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It has taken only 4 years to go from the first high-density SNP maps that demonstrated rapid localization of disease susceptibility genes to the completion of an ordered whole-genome SNP map. The next phase, which is currently in progress at GlaxoSmithKline, is the selection of SNPs to be used for whole-genome mapping, and the SNP measurement techniques and bioinformatic capability to analyze the data. In parallel to the building of a technical base, patient DNA has been collected to enable proof-of-principle studies to be conducted.
Abacavir is a very useful and effective medicine used for HIV treatment. A recognizable hypersensitivity syndrome occurs as an AE in 
5% of patients who use the drug. The HIV community is well aware of the hypersensitivity AE, resulting in limitation of the use of this very effective medicine. During the last 2 years, DNA samples have been collected using appropriate informed consent and IRB approvals. A large series of patients with hypersensitivity to abacavir, and control patients who have been dose-matched but who did not develop hypersensitivity are ready to be tested. Following the construction of the SNP map and PCR primer set, the whole-genome mapping experiment will be executed in 2002 with anticipated data available for publication early in 2003. The time frame for the prosecution of the first proof-of-principle experiments for AE-pharmacogenetics is less ‘protracted’ that most commentators suggest (26).
It is, of course, far more likely that AE-pharmacogenetics will be developed in industry. The investment associated with clinical ascertainment, data and tissue collection and the capability to SNP map is more than academic institutions would invest. The return on investment for a pharmaceutical company is the increased safety profile of the marketed medicine and the competitive advantage that safety provides. In the case of abacavir, the medicine is a component of a triple therapy combination that allows a patient to decrease the cost and increase the convenience of being treated. Rather than ingesting tens of pills at various times of the day, some with meals and some without, Trizivir® can be taken twice a day and reduces the heavy pill burden, if the patient can tolerate abacavir. Thus, the patient’s benefit, the costs of drugs for treatment are reduced, and the company that made the investment in AE-pharmacogenetics derives a return by marketing a safer and more competitive product.
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EFFICACY PHARMACOGENETICS |
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The drivers for efficacy pharmacogenetics differ in several major respects from those for AE-pharmacogenetics. First, unlike AEs there is currently no social consensus to support efficacy-based SNP Printssm. While patients may want to be prescribed and take medicines that work, regulators are more concerned with limiting AEs than enhancing efficacy and cost effectiveness. In addition, the potential medical and marketing impact of efficacy pharmacogenetics warrants careful consideration on a case-by-case basis. If phase III trials are limited to those patients with a drug-responsive SNP Printsm defined in phase II, then clinical trials could be performed faster, with fewer patients and less expense. This would, of course, segment the patient group for which the drug is indicated. Physicians would also prefer to prescribe efficacious drugs defined by an evidence-based SNP Printsm, but pharmaceutical companies may worry about limiting their markets. Efficacy pharmacogenetics can be cost-effective and profit making, while eventually serving the whole population. Molecules for non-responders in a phase II trial could be developed in real time, when non-responders are identified, rather than the current system of trial and error in the market place for many years. In the business world, this is referred to as mass customization, but it is usually based on what automobile or computer is preferred, not whether the purchased product is optimized for your health.
The phenotype of efficacy may be more uncertain clinically than the occurrence of AEs. The drug discovery process will still need to progress to phase II studies before a well-defined efficacy profile in a sufficient number of patients could be tested. Those molecules with obvious efficacy in a large proportion of patients would not necessarily benefit from SNP profiling, but those with clear efficacy in limited sub-groups may benefit, especially if the sub-group would be considered too small to develop a commercially viable product. By including only those patients with the apparent efficacy SNP Printsm, the size of larger, double-blind, clinical development studies would be reduced. Development of new medicines, including high efficacy potential blockbusters could be accelerated by smaller, faster clinical trials for response-defined patient groups.
Another confounding variable is the placebo effect. For genetic analyses, a strong placebo effect would be indistinguishable from a genetic susceptibility effect, thereby making the determination of an ‘efficacy’ SNP Printsm more difficult. For genetic analyses, the placebo effect is the equivalent of pseudo-genes. Modeling studies are currently underway to examine these variables: disease by disease, molecule by molecule. The commercial advantage is actually quite simple: clinically non-responsive patients could be identified early in the clinical studies, so that additional lead molecules may be targeted to them (early drug discovery customization). Traditionally, this market is generally not identified until years after a medicine is used extensively. One can envision a series of medicines for each disease that are based on SNP Printssm. Fewer trial and error prescriptions would be better for the practice of medicine as well as for those companies that market safe and effective drugs. While pharmaceutical companies could create value through the mass customization of medicines, the total drug bill is anticipated to decrease and the rate of effective treatment would increase. This results from mass segmentation with more patients being prescribed the correct medication for them, and fewer individual patients being prescribed medicines which are poorly tolerated or non-effective. Changes in the drug discovery process would occur to meet the need of multiple customized products, with several lead molecules and several analogues carried through drug discovery.
The first examples of efficacy pharmacogenetics are in clinical use today. Perhaps the best known example is the use of the DAKO Hercep TestTM, which is a semi-quantitative assay for testing breast tumor tissue for over-expression of the HER-2/neu protein. Herceptin® is a biologic (antibody) approved by regulators for therapeutic use only in patients whose tumors test positive for the HER-2/neu protein. Thus, breast tumor patients were sub-divided into predictable responders and non-responders for clinical trials and subsequent marketing. The clinical effect of the product was demonstrated through the testing of breast cancer patients (approximately one-third test positive), whereas the clinical effect may have seemed marginal if all patients were analyzed as a whole (27,28).
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ETHICS AND PRIVACY |
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Many of the technologies and strategies described above will require an in-depth examination of ethical and data privacy issues. For example, false positives for AE-pharmacogenetics (i.e. the patient is incorrectly predicted to be a poor candidate for the medicine) performed in patients with a disease prior to receiving a drug adds no diagnostic information. In fact, in such an example, a false positive would only indicate that a person should not take a medicine. As long as the SNP Printsm contained no SNP that would provide primary diagnostic information for any disease, the risk of accidental discovery of unwanted medical information would be minimal (29). Of greater significance, concerns about insurance companies taking advantage of the test for undisclosed diagnostic information would be largely minimized. A separate set of SNPs and other polymorphisms could be used for disease diagnostic purposes, the use of which would remain to be debated publicly with respect to ethics and data privacy. As an aside, the largest variable in that debate is whether the pertinent population of patients has medical coverage guaranteed for all or whether the risk for disease diagnostic capability is only a ‘problem’ for those who must qualify for medical insurance.
Another issue concerns the commercial use of research data and informed consent considerations. AE-pharmacogenetics will undoubtedly involve patients who are prescribed a medication and have provided informed consent regarding the use of their information for the purpose of analyzing AEs, should they occur. Patients participating in disease-specific genetic studies that are designed to research common diseases will continue to be consented as they have for decades. Well phenotyped patient groups will be in demand, as more drug discovery and development pipelines will be based on human genetic targets. The immediate danger is quite obvious. Most studies of patient populations with specific diseases are performed in academic medical centers, often without specific informed consent for commercial uses. Some time later, when a project has been successful, the data derived from studies of those patients may be commercialized in a biotechnical company or used by a large pharmaceutical company. Care must be taken to inform patients and controls of these commercial implications in a clear manner that does not threaten their medical care.
There are many other points worthy of discussion regarding privacy, ‘trusted third party’ DNA banks, informed consent issues, medical data access and the differences between disease-specific genetic studies and AE and efficacy pharmacogenetic studies. Additionally, the role of regulators will need to be modified as systematic methods for identifying patients at greater risk of AEs will provide evidence-based data that will need to be incorporated into surveillance and case reporting systems. The practice of medicine will continue to evolve based on better diagnostics, safer and more effective medicines, reductions in unmet medical needs, use of appropriate technologies and increasing sensitivity to individuals’ interests and data privacy. Publicity tends to focus on the negative and shocking scenarios but, as the science is explained and incorporated into health care, education and the anticipation of positive scenarios will defeat major medical and iatrogenic problems.
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FOOTNOTES |
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+ Tel: +1 919 483 7418; Fax: +1 919 315 6013; Email: adr69412@gsk.com
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REFERENCES |
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1 Poolsup, N., Li Wan Po, A. and Knight, T.L. (2000) Pharmacogenetics and psychopharmacotherapy. J. Clin. Pharm. Ther., 25, 197–220.[ISI][Medline]
2 Caldwell, J., Gardner, I. and Swales, N. (1995) An introduction to drug disposition: the basic principles of absorption, distribution, metabolism, and excretion. Toxicol. Pathol., 23, 102–114.[Medline]
3 Vesell, E.S. (2000) Advances in pharmacogenetics and pharmacogenomics. J. Clin. Pharmacol., 40, 930–938.[Abstract]
4 Roses, A.D. (2000) Pharmacogenetics and future drug development and delivery. Lancet, 355, 1358–1361.[ISI][Medline]
5 Roses, A.D. (2000) Pharmacogenetics and the practice of medicine. Nature, 405, 857–865.[Medline]
6 The Huntington’s Disease Collaborative Research Group (1993) A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell, 72, 971–983.[ISI][Medline]
7 Gusella, J.F. and MacDonald, M.E. (1994) Huntington’s disease and repeating trinucleotides. New Engl. J. Med., 330, 1450–1451.
8 Saunders, A.M., Strittmatter, W.J., Schmechel, D., George-Hyslop, P.H., Pericak-Vance, M.A., Joo, S.H., Rosi, B.L., Gusella, J.F., Crapper-MacLachlan, D.R., Alberts, M.J. et al. (1993) Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer’s disease. Neurology, 43, 1467–1472.
9 Corder, E.H., Saunders, A.M., Risch, N.J., Strittmatter, W.J., Schmechel, D.E., Gaskell, P.C.,Jr, Rimmler, J.B., Locke, P.A., Conneally, P.M., Schmader, K.E. et al. (1994) Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease. Nat. Genet., 7, 180–184.[ISI][Medline]
10 Pericak-Vance, M.A., Bebout, J.L., Gaskell, P.C.,Jr, Yamaoka, L.H., Hung, W.-Y., Alberts, M.J., Walker, A.P., Bartlett, R.J., Haynes, C.A., Welsh, K.A. et al. (1991) Linkage studies in familial Alzheimer’s disease: evidence on chromosome 19 linkage. Am. J. Hum. Genet., 48, 1034–1050.[ISI][Medline]
11 Lai, E., Riley, J., Purvis, I. and Roses, A.D. (1998) A 4-Mb high-density single nucleotide polymorphism-based map around human APOE. Genomics, 54, 31–38.[ISI][Medline]
12 Martin, E.R., Lai, E.H., Gilbert, J.R., Rogala, A.R., Afshari, A.J., Riley, J., Finch, K.L., Stevens, J.F., Livak, K.J., Slotterbeck, B.D. et al. (2000) SNPing away at complex diseases: analysis of single nucleotide polymorphisms around APOE in Alzheimer disease. Am. J. Hum. Genet., 67, 383–394.[ISI][Medline]
13 Roses, A.D. (2000) Pharmacogenetics and the practice of medicine. Nature, 405, 857–865.
14 Roses, A.D. (1998) Genetic associations. Lancet, 351, 916.
15 Relkin, N.R., Tanzi, R., Breitner, J., Farrer, L., Gandy, S., Haines, J., Hyman, B., Mullan, M., Poirer, J., Strittmatter, W. et al. (1996) Apolipoprotein E genotyping in Alzheimer’s disease (consensus statement of the National Institute of Aging/Alzheimer’s Association Working Group). Lancet, 347, 1091–1095.[ISI][Medline]
16 Roses, A.D. (1997) Apolipoprotein E, a complex gene with biological interactions in the aging brain. Neurobiol. Dis., 4, 170–185.[ISI][Medline]
17 Xu, P.-T., Schmechel, D., Rothrock-Christian, T., Burkhart, D.S., Qiu, H.-L., Popko, B., Sullivan, P., Maeda, N., Saunders, A.M., Roses, A.D. and Gilbert, J.R. (1996) Human apolipoprotein E2, E3 and E4 isoform specific transgenic mice: human-like pattern of neuronal immunoreactivity in central nervous system not observed in wild type mice. Neurobiol. Dis., 3, 229–245.[ISI][Medline]
18 Bonnen, P.E., Story, M.D., Ashorn, C.L., Buchholz, T.A., Weil, M.M. and Nelson, D.L. (2000) Haplotypes at ATM identify coding-sequence variation and indicate a region of extensive linkage disequilibrium. Am. J. Hum. Genet., 67, 1437–1451.[ISI][Medline]
19 Koch, H.G., McClay, J., Loh, E.W., Higuchi, S., Zhao, J.H., Sham, P., Ball, D. and Craig, I.W. (2000) Allele association studies with SSR and SNP markers at known physical distances within a 1 Mb region embracing the ALDH2 locus in the Japanese, demonstrates linkage disequilibrium. Hum. Mol. Genet., 9, 2993–2999.
20 Hewett, D., Samuelsson, L., Polding, J., Enlund, F., Cantone, K., See, C.G., Smart, D., Chadha, S., Inerot, A., Enerback, C. et al. (2001) Identification of a psoriasis susceptibility candidate gene by linkage disequilibrium mapping with a localised single nucleotide polymorphism map. Genomics, in press.
21 McCarthy, L.C., Hosford, D.A., Riley, J.H., Bird, M.I., White, N.J., Hewett, D.R., Peroutka, S.J., Griffiths, L.R., Boyd, P.R., Lea, R.A. et al. (2001) Single nucleotide polymorphism (SNP) alleles in the Insulin Receptor (INSR) gene are associated with typical migraine. Genomics, in press.
22 Jarman, K.H., Daly, D.S., Petersen, C.E., Saenz, A.J., Valentine, N.B. and Wahl, K.L. (1999) Extracting and visualizing matrix-assisted laser desorption/ionization time of flight mass spectral fingerprints. Rapid Commun. Mass Spectr., 13, 1586–1594.
23 Bakhtiar, R. and Nelson, R.W. (2000) Electrospray ionization and matrix-assisted laser desorpiton ionization mass spectrometry. Emerging technologies in biomedical sciences. Biochem. Pharmacol., 59, 891–905.[ISI][Medline]
24 Leuschner, J. (2000) MALDI TOF mass spectroscopy: an emerging platform for genomics and diagnostics. Expert. Rev. Mol. Diagn., 1, 11–18
25 The International SNP Map Working Group (2001) A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature, 409, 928–933.[Medline]
26 Post, G. (2000) Molecular diagnostics: a powerful new component of the healthcare value chain. Expert. Rev. Mol. Diagn., 1, 1–5.
27 Wang, S., Saboorian, M.H., Frenkel, E., Hynan, L., Gokaslan, S.T. and Ashfaq, R. (2000) Laboratory assessment of the status of Her-2/neu protein and oncogene in breast cancer specimens: comparison of immunohistochemistry assay with fluorescence in situ hybridisation assays. J. Clin. Pathol., 53, 374–381.
28 Baselga, J. (2000) Current and planned clinical trails with trastuzumab. Semin. Oncol., 27, 27–32.
29 Roses, A.D. (2001) How pharmacogenetics will affect the practice of neurology. Arch. Neurology, in press.
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