The Foundation of Pharmacology in the New


Annu. Rev. Pharmacol. Toxicol. 2000. 40:97–132 Copyright q 2000 by Annual Reviews. All rights reserved

SEQUENCING THE ENTIRE GENOMES OF FREE-LIVING ORGANISMS: The Foundation of Pharmacology in the New Millennium Samuel Broder and J. Craig Venter Celera Genomics, Rockville, Maryland 20850; e-mail: [email protected], [email protected]

Key Words human genomics, comparative genomics, shotgun sequencing, single nucleotide polymorphism (SNP), DNA variation, pharmacogenetics Abstract The power and effectiveness of clinical pharmacology are about to be transformed with a speed that earlier in this decade could not have been foreseen even by the most astute visionaries. In the very near future, we will have at our disposal the reference DNA sequence for the entire human genome, estimated to contain approximately 3.5 billion bp. At the same time, the science of whole genome sequencing is fostering the computational science of bioinformatics needed to develop practical applications for pharmacology and toxicology. Indeed, it is likely that pharmacology, toxicology, bioinformatics, and genomics will merge into a new branch of medical science for studying and developing pharmaceuticals from molecule to bedside.

THE IMPENDING AVAILABILITY OF A COMPLETE REFERENCE SEQUENCE FOR THE HUMAN GENOME Our DNA sequence and its variation provide a special record of human evolution and the migration of populations (1–14). We will learn how this sequence varies among populations and among individuals, including the role of such variation in the pathogenesis of important illnesses and responses to pharmaceuticals. We will localize and annotate every human gene and the regulatory elements that control the timing, tissue-site specificity, and extent of gene expression. For any given physiologic process, we will have a new paradigm for addressing its evolution, its development, its function, and its mechanism. This will revitalize medicine by identifying important new targets for prevention, diagnosis, and therapy. Clinical pharmacologists will be able to approach issues of rational candidate drug design and the reduction of serious side effects by using bioinformatics to analyze the relevant genes and gene variations (polymorphisms), including the promoters and the enhancers, involved. Knowledge regarding the alleles that gov0362–1642/00/0415–0097$14.00




ern the safety and efficacy of pharmaceutical agents (including comparative genomics) will make it possible to streamline the preclinical and clinical development of new drugs and customize interventions to the specific genotypes of patients. Medical progress will be driven more by knowledge of gene structure and function and less by empiricism and intuition. It is important to recall that the first complete genome of any free-living organism (Haemophilus influenzae) was published by scientists at the Institute for Genomic Research roughly 5 years ago (15). During the past 5 years, the sequences of the entire genomes of 23 organisms have been published (Table 1; see also (15–37). What was, a few short years ago, thought to be impossible has become not merely possible but inevitable.

WHOLE GENOME SEQUENCING What has led us to such a future? The development of advanced automation, robotics, and computer software for industrial-scale DNA sequencing has proceeded at a remarkable pace. With the successful sequencing of the H. influenzae genome in its entirety, it became clear that the DNA of entire complex organisms many megabases in size could be accurately and rapidly sequenced by using a ‘‘shotgun’’ sequencing strategy (14). In this strategy, a single random DNA-fragment library is prepared following mechanical or sonic shearing of entire genomic DNA and inserted in suitable vector systems (e.g. plasmids). The ends of a large number of randomly selected fragments are sequenced from both insert ends until every part of the genome has been sequenced several times on average. For any given average sequence read length, the number of end sequences needed can be determined by the Lander and Waterman application of Poisson statistics. This number will depend on the goals of the sequencing project and particularly the degree of tolerance for a small number of gaps in the sequence results (i.e. if the tolerance for gaps is low, the number of end sequences must be high). The sequences are then computationally ‘‘reassembled’’ to provide the complete genome. For higher organisms, which contain one genome from the mother and one genome from the father, this approach yields an important dividend: Points of common DNA variation such as single nucleotide polymorphisms (to which we return at several points in this chapter) become evident. This overall strategy, coupled with the advent of completely automated DNA sequencing machines such as the new ABI Prism 3700 DNA Analyzer (manufactured by PE Biosystems, PE Corp.), will make it possible for a single center to undertake the determination of the reference sequence of the human genome. Indeed, it gives one pause to consider that every organism of pharmacologic or toxicologic interest is now plausibly a candidate for whole genome sequencing.



TABLE 1 Published free-living organism genomes Genome



Size (MB)



Haemophilus influenzae Rd Mycoplasma genitalium Methanococcus jannaschii Synechocystis sp.




Inst. Genomic Res.


G-37 DSM 2661


0.58 1.66

Inst. Genomic Res. Inst. Genomic Res.

16 17

PCC 6803



Kazusa DNA Res. Inst. Univ. Heidelberg Int. Consort. Inst. Genomic Res. Univ. Wisc. Genome Ther. & Ohio State Univ. Int. Consort Inst. Genomic Res. Inst. Genomic Res. Diversa Natl. Inst. of Tech. and Eval. (Japan) Sanger Cent.


Mycoplasma pneumoniae Saccharomyces cerevisiae Helicobacter pylori Escherichia coli Methanobacterium thermoautotrophicum Bacillus subtilis Archaeoglobus fulgidus Borrelia burgdorferi Aquifex aeolicus Pyrcoccus horikoshii

M129 S288C 26695 K-12 Delta H


0.81 13 1.66 4.60 1.75

168 DSM4304 B31 VF5 OT3


4.20 2.18 1.44 1.50 1.80

Mycobacterium tuberculosis Treponema pallidum

H37Rv (lab strain) Nichols





Chlamydia trachomatis

Serovar D (D/UW-3/ Cx) Madrid E




1.10 100

Helicobacter pylori




Chlamydia pneumoniae




Thermotoga maritima Aeropyrum pernix



1.80 1.67

Rickettsia prowazekii Caenorhabditis elegans

Inst. Genomic Res./Univ. Texas Univ. Calif. Berkeley/Stanford Univ. Uppsala Wash. Univ./Sanger Cent. Astra Res. Cent. Boston/Genome Ther. Univ. Calif. Berkeley/Stanford Inst. Genomic Res. Natl. Inst. of Tech. and Eval. (Japan)

19 20 21 22 23 24 25 26 27 28 29 30 31

32 33 34

35 36 37



This is not a vision for the future or a promissory note as to what science may someday bring. The science and supporting technology are here now.

THE CHALLENGE OF MICROBIAL PATHOGENS Microbial pathogens (e.g. tuberculosis, cholera, and malaria) are a source of great suffering and death in many developing countries. Moreover, even in nations with advanced health-care technologies and mature research-based pharmaceutical industries, the emergence of multidrug-resistant pathogens is a serious problem. Indeed, some have argued that we are not too far removed from a return to the prepenicillin era in the fight against infectious disease. The problem for research pharmacologists is formidable and getting worse. The evolving field of genomic sequencing allows unique opportunities for understanding microbial pathogenesis and control by having simultaneously at hand the genomes of the pathogen and host. Knowing the complete genome of microbial pathogens will open up exciting opportunities to develop novel pharmaceuticals and biologics. We will know how many genes are contained in each pathogen, where they are located within that pathogen’s genome, and when two or more similar genes (paralogs) exist in a single microbial genome, thereby creating the potential to confound the research pharmacologist’s search for an Achilles heel. By simultaneously analyzing the microbial genome and the host genome, it will become possible to define which genes are critical for microbial survival and which are optional; why a given pathogen is virulent in the context of a specific host; how and when toxic cytokines are activated within a host; whether a given pathogen has evolved proteins with molecular mimicry capable of frustrating host immunity or inducing autoimmunity; and how a pathogen (e.g. the tubercle bacillus) is able to survive in a state of latency or dormancy, impervious to the host’s immune system. The sum total of this information will make it possible to define vaccines that induce specific and effective immunity against the pathogen while minimizing untoward or toxic side effects in the host. Malaria provides a model for the challenges and research opportunities, and Wahlgren (38) has provided an elegant perspective on this topic. Falciparum malaria is a leading killer in the African countryside, and multidrug-resistant forms of the disease have emerged on a wide scale. The disease is caused by a protozoan (Plasmodium falciparum), whose life cycle involves infecting human erythrocytes, which in turn causes a fulminant hemolytic anemia. One consequence is cerebral malaria, a process facilitated by the patient’s own cytokines, which promote adhesion of malarial organisms or their detritus to the inside walls of blood vessels. The resulting cerebral ischemia is often lethal, and perhaps one million people, many of them children, die from this infectious kind of stroke every year.



The best known genes for malarial protection are those for which resistance alleles (nucleotide substitutions) in people in endemic regions exist in a state of balanced polymorphism. Thus, people with one copy of the S allele of the beta subunit of hemoglobin have a selective advantage living in malaria-endemic zones compared with individuals with two wild-type alleles, or with two copies of the mutant allele (in which case they would exhibit sickle cell anemia). The a- and b-thalassemias and glucose-6-phosphate dehydrogenase deficiencies are other examples of genotype polymorphisms whose evolution was driven by the selective advantage conferred on account of resistance to malaria. More recently, certain cytokines, most notably tumor necrosis factor (TNF), have been implicated in the pathogenesis of malaria. TNF also probably plays a significant role in leishmaniasis, listeriosis, and other infectious diseases of the developing world. High circulating levels of TNF have been found in patients with cerebral malaria, particularly those whose disease runs a lethal course. TNF, among its many activities, up-regulates endothelial adhesion molecules and thereby increases the tendency of infected red cells to stick to the walls of blood vessels and interrupt blood flow. Knight et al (39) recently found that three polymorphisms in the promoter region of the TNF gene contribute to malarial pathogenesis and poor outcome through a complex dynamic of increased risk and counterbalancing protective effects. One such polymorphism, TNF-376A, increases the secretion of TNF most likely by causing the helix-turn-helix transcription factor OCT-1 to bind to a novel region of complex protein-DNA interactions and alter gene expression in human monocytes. These workers found that a single nucleotide G r A polymorphism at position 376 upstream of the TNF transcriptional initiation site (the OCT-1 binding phenotype) is linked to unfavorable outcome from malarial infection in two ethnically distinct groups of people, one from the Gambia (western African) and the other from Kenya (eastern African). The results underscore the potential to link molecular events to clinical outcomes using the emerging knowledge of whole organism genomics. They are likely to influence future research into the prevention and treatment of malaria and many other diseases. The P. falciparum genome is roughly 30 Mb in size and contains 14 chromosomes. Recently, after overcoming a number of theoretical and practical obstacles owing to the adenine plus thymine richness of its genome, it became possible to obtain the entire genomic DNA sequence of chromosome 2 in P. falciparum (40). Thus, one can expect that in the foreseeable future, the technologies for whole genome sequencing of microbial agents will make it possible to sequence the remaining 13 chromosomes and add this pathogen to the list in Table 1. The coupling of whole genome information from P. falciparum with whole genome information from its human host is likely to initiate an entire range of new approaches for dealing with the problem of malaria. (It is also possible that DNA sequencing of the intermediary host mosquito or a related insect genome may add still another set of strategic targets for attacking malaria.) The development of



knowledge useful in turning the tide against multidrug-resistant malaria is a crucial goal for the science of whole genome sequencing.

DNA SEQUENCE VARIATION We all evolved in an African savanna, and we all share .99.9% of the nucleotide sequence in our genome, so it is remarkable that the extraordinary diversity of human beings is encoded by only 0.1% variation in our DNA. We are predisposed to different diseases, we respond to the environment in variable ways, we metabolize pharmaceutical agents differently, we may show differences in doseresponse relationships for common drugs, and we have a range of susceptibilities to adverse side effects from therapeutic agents (even when there is no discernible difference in individual pharmacokinetics or biochemical pharmacology). Despite the overwhelming similarity in sequence, there are millions of points of DNA variation between any two randomly selected individuals. The most common form of DNA variation is single nucleotide polymorphism (SNP) (1–14, 41), and in our discussion of malaria above we encountered the enormous implications of SNPs for pharmacology and toxicology. Put simply, a SNP is the substitution of one purine or pyrimidine nucleotide at a given location in a strand of DNA for another purine or pyrimidine nucleotide. Such substitutions can affect gene function, or they can be neutral. Neutrality is generally inferred if a SNP does not alter protein coding. In practice, this inference can be wrong. The most common substitution is a transition in which a pyrimidine is substituted for another pyrimidine, and likewise a purine is substituted for a purine. However, transversions (replacement of a purine for a pyrimidine or vice versa) can occur. The nomenclature defining a mutation (a disease-causing change) versus a SNP is arbitrary and relative. By convention, when a substitution is present in more that 1% of a target population, it is called a variant or polymorphism. When a substitution is present in less than 1%, and especially when one can assign a clear phenotype (i.e. a disease or clinical condition), it is called a mutation. SNPs may occur across widely separated populations, or they may be relatively specific for a given population. They are virtually always biallelic. Within populations, in both human and model organisms such as Drosophila, essentially every SNP is in Hardy-Weinberg equilibrium proportions (A Clark, personal communication). SNPs may occur inside or outside of a gene. If they occur within a gene, they may reside in an exon (coding region) or intron (noncoding) region. SNPs in a coding region (sometimes called cSNPs) can be either synonymous (no amino acid–altering effect) or nonsynonymous (amino acid altering). There is some level of natural selection against amino acid–altering changes (12, 13). The average person would be expected to be heterozygous for roughly 40,000 nonsynonymous (amino acid-altering) alleles (12). As we have seen in the case of the TNF-376 allele, SNPs can profoundly affect gene function even if they are at a significant distance upstream of the initiation



site for gene transcription. One needs to keep in mind that enhancers (i.e. the tissue-specific control sequences on which certain regulatory substances act) may operate over at least 3 kb in either orientation (58 r 38 or 38 r 58) from the start point of transcription. It has been proposed that those SNPs that are of keenest interest in the pathophysiology of disease are nonsynonymous cSNPs, which alter the sequence of an encoded protein (12). Coding sequences comprise roughly 3% of the human genome, and therefore, it has been suggested that priority be given to shotgun sequencing of ccDNA libraries from many donors, possibly with an emphasis on specific candidate genes by direct amplification of target sequences. A word of caution is in order. It may not be appropriate to discount synonymous SNPs in the pathogenesis of important illnesses. Correlations between SNPs and clinical phenotypes are in their infancy, and it is critical that we do not assume that we know more than we do. A SNP that is ‘‘synonymous’’ for a protein coding point of view might be ‘‘antonymous’’ with respect to the folding of mRNA, the enzymatic activity on certain RNA molecules (e.g. adenosine to inosine conversion), and the function of small nucleolar RNA(snoRNA). This also applies to the set of genes involved in antisense RNA regulation, such as Tsix, a gene that performs antisense regulatory functions against Xist at the X-inactivation center (42). It is important to recall that splicing variants can also be significantly affected by DNA variation seen only at a genomic level. Moreover, the genes that may be of greatest interest in understanding the pathogenesis of common illnesses or in the development of important new drugs (particularly those that bring about true paradigmatic shifts in therapy) may be dramatically underrepresented (or undetectable) in cDNA libraries. Rarely expressed genes, or genes expressed in an extremely limited range of cells, may be poor candidates for cDNA-based detection systems. Looking for SNPs solely within or near known candidate genes may well result in missed SNPs that would be of great interest to pharmacologists and toxicologists, and scientists generally, in the long run. Any two sets of chromosomes taken at random will differ roughly at one site per 500–1000 bp. There can be up to one potential variable site per 100 bp in an average segment of contiguous DNA if one examines DNA sequence variation among various populations (1, 5). This is one reason why the whole genome shotgun sequencing strategy discussed earlier is so powerful. Any normal individual will have two sets of chromosomes, all of which will be sequenced in their entirety. Thus, providing the full genome sequence from small numbers of normal donors, indeed from even one individual, will yield millions of SNPs for further study. However, correlations between genotypes and phenotypes (clinical conditions) will require association studies with appropriate levels of statistical power (see below). The classic Mendelian model, in which a specific mutation in one gene produces a recognizable disease, may not apply to most common illnesses in our society. It is thought that many common illnesses have a polygenic origin, with several genes (to be more precise, gene variants) playing a comparatively small



role individually, but with a cumulative effect that leads to a detectable clinical condition or disease. There is considerable interest in using whole genome association studies, as a tool for identifying genes involved in these common disorders, to detect differences in the frequency of DNA sequence variations between unrelated affected individuals and a control group (43). Genetic association strategies may be direct or indirect (8, 43). The directassociation strategy focuses on common variants in coding or regulatory regions. (The identification of the latter is still problematic and will be greatly facilitated by comparative genomics, which is discussed further below.) Frequencies of these variants in patients and control groups are analyzed statistically, with the goal of identifying alleles that serve as morbidity or mortality risk indicators. The indirect strategy relies on a statistical association between an illness or clinical condition. This occurs when an ‘‘innocent’’ variant is linked to the actual risk-producing variant on a stretch of DNA. The probability of a recombinatorial event occurring between any two points of DNA in a chromosome is related to the distance between the two points. If the distance is comparatively small, the two points (variants) are said to be in linkage disequilibrium. During meiosis, they are likely to share a finite journey through time, and in an individual, the presence of one marker would then predict the other. A ‘‘neutral’’ variant can then be used to detect or uncover a disease-causing variant because they are linked together in a shared haplotype (genomic segment), which is longer for new variants in a population and shorter for ancient variants. The length of the shared haplotype determines whether there is a strong or weak level of linkage disequilibrium. Thus, the indirect strategy employs a dense map of anonymous or random polymorphic markers to search the genome for statistical associations with disease. This can provide a unique tool for new gene discovery, and it does not require the investigator to intuit candidate genes or regulatory elements in advance. In some cases, one can identify a marker with a sufficiently strong predictive power for development as a diagnostic test in its own right. This approach can identify the location of genes even when they would not be considered good candidates for a disease association by first principles. Indeed, we expect the availability of the complete sequence for the human genome to yield any number of surprises in terms of assigning function and disease association to gene loci. Although both such approaches have been used on a comparatively small scale to study genes in diseases, we will soon have unprecedented opportunities to apply such studies to the entire genome. It is widely viewed that SNPs provide the key to such approaches because of their high frequency, biallelic nature, low mutation rate, and suitability for industrial-scale automation. The precise number of SNPs necessary is still a matter for debate. Kruglyak (43) has recently proposed that roughly 500,000 evenly spaced SNPs will be required for optimal indirect whole genome association studies. This estimate is at the upper limit of various suggested numbers, and yet it is still easily within the range of SNPs that will emerge as part of the whole human genome shotgun sequencing strategy discussed earlier.



A SURVEY OF SNPS Single base changes may directly cause gross alterations in gene function, but they may also be responsible for subtle correlations with disease. Such SNPs have been linked to a variety of cardiovascular, respiratory, allergic, neurologic, psychiatric, metabolic, bacterial, and neoplastic diseases. Some brief examples of DNA variations that pertain to cancer risk (44–53), infectious disease (38, 39, 54–62), asthma (63–71), and neuropsychiatric diseases (72–81) are summarized in Tables 2–5. In some of the tables areas of controversy are included, as a reminder of the need for more clinical research. A summary of certain alleles associated with drug metabolism is shown in Table 6 (82–100). In some cases, there is an overlap of definitions. Thus, an allele can affect cancer risk (Table 2) but it could also fit in drug metabolism (Table 6). Sometimes, detection of a functional polymorphism leads to unexpected and dramatic biological insights. Thus, apolipoprotein E has been known for many years to play an important role in lipoprotein metabolism. The APOE gene provides an interesting example of genetic polymorphisms, with substantial variation in different groups. This gene affects cholesterol levels. It is surprising that one allelic variant of this gene (APOE4) is a significant risk factor in Alzheimer’s disease (101, 102). APOE4 appears to be directly involved in some way because the protein is seen on immunohistochemical staining in the amyloid plaques, amyloid deposits, and neurofibrillary tangles that characterize the brain lesions of Alzheimer’s disease. In this sense, SNPs offer new opportunities for prevention, diagnosis, and treatment strategies. In the not-too-distant future, physicians may be able to use advanced, miniaturized technologies in their clinics or offices to define a patient’s SNP profile in order to customize a diagnosis and therapy to the specific patient’s needs. We return to a discussion of pharmacogenetics further below, but several recent practical examples of these ideas are worth noting here. The thiopurine methyltransferase gene, which regulates the metabolic inactivation of azathioprine, is a good model for discussion. Substantial hematologic toxicity may accompany the use of this drug in the therapy of rheumatic diseases or transplantation rejection. Patients with a variant allele for thiopurine methyltransferase (TPMT*3A) are at substantially higher risk of hematologic toxicity compared with those with the wild-type allele (100). In another example, dealing with schizophrenia therapy, the best response to the important but potentially toxic drug clozapine was found in patients with two defined genetic polymorphisms in 5-HT2A receptors (80). In another example, polymorphisms of a genetic factor expressed on platelets (PlA2) might determine whether use of aspirin can prevent myocardial infarctions in patients (103). Perhaps even the DNA from a buccal swab would be informative in selecting the right pharmaceutical for the right patient. In functional terms, an SNP can be either good, bad, or neutral, depending on the selective pressures or circumstances faced by the individuals in a population.


TABLE 2 Sequence polymorphisms and cancer risk Gene


Allele frequency

Clinical correlation



0.5% in Ashkenazi Jews 0.1% in Ashkenazi Jews

Associated with increased risk of breast and ovarian cancer. Risk of developing cancer by age 70 is 56% for breast cancer and 16% for ovarian cancer. Male carriers have increased risk of developing prostate cancer, with 16% of carriers developing prostatic cancer by age 70.



Rare in other populations




0.5% in Ashkenazi Jews; rare in other populations

Associated with increased risk of breast and ovarian cancer. Risk of developing cancer by age 70 is 56% for breast cancer and 16% for ovarian cancer. Male breast cancer appears to be more common in BRCA2 mutation carriers than in BRCA1 carriers.



I1307K; Ile to Lys change at residue 1307.

3.5% in Ashkenazi Jews; rare in other populations

Carriage of the I1307K allele is associated with somatic instability in genomic APC DNA and can result in loss of APC expression and the development of cancer. Risk of colorectal and breast cancer are approximately 1.5-fold higher in I1307K carriers than in noncarriers.


NAT2 (N-acetyltransferase 2)

Multiple sequence variant combinations in NAT2 form 11 haplotypes. Subjects carrying the NAT2*4 haplotype (in either the homozygous or heterozygous states) are rapid acetylators; subjects with other haplotypes are slow acetylators.

In a set of 556 German volunteers, 23.4% of alleles were of the NAT2*4 haplotype.

Individuals who were homozygous for the rapid acetylation types NAT2*4/NAT2*4 were overrepresented in lung cancer patients compared with control patients; odds ratio, 2.36 (95% confidence interval 1.05–5.32). See also discussion of NAT1 and NAT2 in Table 6.


MTHFR (methylenetetraydro-folate reductase

677CrT, results in a change from alanine to valine

677C 4 0.58, 677T 4 0.42 in Caucasian populations

The 677T allele of MTHFR is heat labile and results in reduced enzyme activity leading to lower levels of 5-methyltetrahydrofolate. In men with adequate folate levels, homozygosity for the 677T allele reduced the risk of colorectal cancer, but in men with folate deficiency or those drinking more than 10 g of alcohol/day, 677T did not reduce cancer risk.

47, 48

AR (androgen receptor)

Highly polymorphic CAG repeat present in the first exon of the X-linked androreceptor.

Continuous distribution of CAG repeat length ranging from 12–32 repeats. Modal length of 21 CAG repeats present in 30–40% of women.

Length of the AR CAG repeat is inversely correlated with the transcriptional activity of the AR. AR CAG re-repeat length was found to modify breast cancer risk in women with BRCA1 mutations. Women carrying ARCAG alleles of .28, .29, or .30 repeats were diagnosed with breast cancer 0.8, 1.8, or 6.3 years earlier than women with BRCA1 mutations with shorter AR-CAG alleles.



R72P; Arg to Pro change at residue 72

Allele frequencies have been reported to vary in different study populations. In the largest single normal group studied, 626 control patients from Sweden: R72 4 0.69, P72 4 0.31

The form of p53 carrying an arginine residue at codon 72 is significantly more susceptible to degradation by the HPV E6 protein than is the variant with proline at this position. In one study (50), 72R homozygotes were seven times more likely to develop HPV-associated cancers than were 72R/72P heterozygotes (numbers of 72P homozygotes being too small to analyze separately). This result proved to be controversial, however, and could not be replicated by three subsequent studies examining the effect of the p53 R72P polymorphism on HPV-associated cervical cancer risk.




TABLE 3 Sequence polymorphisms and infectious disease Gene


Allele frequencies

Clinical correlation


Chemokine receptor 2 (CCR2)

Valine at position 64 is replaced by isoleucine (V641).

V64, 90; I64, 10%; n 4 248

In a study of 3003 HIV-positive patients, individuals carrying the I64 allele progressed to AIDS 2 to 4 years later.

Chemokine receptor 5 (CCR5)

32-bp deletion (D32)

` 32 bp, 90.8%, D32, 9.2%; n 4 1408 Caucasians

Carriers of the 32-bp deletion are protected from transmission of HIV.

55, 56

The prevalence of malaria infection (42%) was significantly lower in individuals with the sickle-cell trait compared with their normal-hemoglobin counterparts (68%).

57, 58

The HLA-B*35 haplotype is associated with rapid development of AIDS in HIVinfected Caucasians. The HLS-C*w04 haplotype is associated with rapid development of AIDS in HIV-infected Caucasians.


In a case-control study of 547 subjects in Kenya, a single ICAM-1 mutation was present at high frequency. Genotypes at this locus from samples in this casecontrol study indicated an association of the polymorphism with the severity of clinical malaria such that individuals homozygous for the mutation (M29/M29) have increased susceptibility to cerebral malaria with a relative risk of two.

60, 61


The D32 allele is absent in Western and Central African and Japanese populations. b-Globin

Glycine at position 6 is replaced by valine (G6V).

Human leukocyte antigen (HLA)

HLA-B*35 haplotype HLA-C*w04 haplotype

Intercellular adhesion molecule 1 (ICAM-1)

Lysine at position 29 is replaced by methionine (K29M).

G6, 92%; V6, 8%; American Blacks G6, 60–80%; V6, 20–40%; African Blacks

L29, 67%; M29, 33%; n 4 287 Kenyans (controls) L29, 56%; M29, 44%; n 4 157 Kenyans (cerebral malaria) L29, 76%; M29, 24%; n 4 422 Gambians (controls)

L29, 76%; M29, 24%; n 4 367 Gambians (cerebral malaria)

Natural resistanceassociated macrophage protein 1 (NRAMP1)

Tumor necrosis factor (TNF)


There is a (CA)n repeat variation in the 58 untranslated region. There is a G to C transversion in intron 4 (469 ` 14 G/C). Aspartic acid at position 543 is replaced by asparagine (D543N). There is a 4-bp deletion in the 38 untranslated region (1749 ` 55 DTGTG). The D543 allele is always associated with DTGTG in the 38UTR. There are 3 polymorphisms located in the promoter region of this gene: 1238 G/A, 1308 G/A, 1376 G/A. The 1376 A allele always occurs along with the 1238 A allele.

CA repeat (201 bp), 84%

469 ` 14G, 92%; 469 ` 14C, 8% D543, 93% N543, 7%;

1749 ` 55 (TGTG), 81%; 1749 ` 55 (DTGTG), 19%; n 4 827 Gambians

1376 G, 98.5%; 1376 A, 1.5%; n 4 371 Gambians (controls)

1376 G, 96.9%; 1376 A, 3.1; n 4 384 Gambians (cerebral malaria)

Over 1200 children in The Gambia were typed for polymorphisms of the ICAM-1, gene. None of the polymorphisms typed was significantly associated with severe disease. These data differed significantly from the results of a previous study (Chi 2 4 8.81; P 4 0.003) in which the ICAM-1 gene polymorphism was shown to be significantly associated with cerebral malaria. This suggests that there may be heterogeneity in genetic susceptibility to this condition between these two African populations. Four NRAMP1 polymorphisms were each significantly associated with tuberculosis. Subjects who were heterozygous for two NRAMP1 polymorphisms in intron 4 and the 38 untranslated region of the gene were particularly overrepresented among those with tuberculosis, as compared with those with the most common NRAMP1 genotype (odds ratio, 4.07; 95% confidence interval, 1.86–9.12; chi-square 4 14.58; P 4 0.001).

The -376 polymorphism is located in the OCT-1 binding site of the TNF promoter. Only the 1376 A allele binds OCT-1 and it is associated with a fourfold increased susceptibility to cerebral malaria.



110 TABLE 4 Sequence polymorphisms and asthma Gene


Allele frequencies

Clinical correlation


Beta2-adrenoceptor (B2AR)

Glycine at position 16 is replaced by arginine (G16R). Glutamine at position 27 is replaced by glutamic acid (Q27E). Arginine at position 16 was associated with glutamine at position 27 in 97.8% of the haplotypes determined.

G16, 62%; R16, 38%; n 4 269 Americans Q27, 64%; E27, 36%; n 4 269 Americans

To assess if different genotypes of these two polymorphisms would show differential responses to inhaled B2AR agonists, 269 children who were participants in a longitudinal study of asthma were genotyped. When compared with homozygotes for G16, homozygotes for R16 were 5.3 times (95% confidence interval, 1.6–17.7) and heterozygotes (G16/R16) were 2.3 times (1.3–4.2) more likely to respond to albuterol, respectively. No association was found between the Q27E polymorphism and response to albuterol.


Cystic fibrosis transmembrane regulator (CFTR)

Phenylalanine at position 508 is deleted (DF508).

F508, 97.3%; DF508, 2.7%; n 4 9141 Danes

The CFTR gene encodes a chloride channel found in epithelial cells. Individuals with mutations in both copies of this gene are affected with the chronic lung disease cystic fibrosis. Of the 250 DF408 mutation carriers identified in this study, 9% reported having asthma compared with 6% of the noncarriers. The odds ratio for asthma for carriers of the DF508 mutation was 2.0 (1.2–3.5, P 4 0.02).


Fc(epsilon)RI, beta subunit (highaffinity receptor for immunoglobulin E)

Isoleucine at position 181 is replaced by leucine (I181L).

I181, 97%; L181, 3% (Caucasians)

Valine at position 183 is replaced by leucine (V183L).

I181, 84%; L181, 16% (African Blacks)

Glutamic acid at position 237 is replaced by glycine (E237G).

I181, 38%; L181, 72%; n 4 221 Kuwaitis V183, 38%; L183, 72%; n 4 221 Kuwaitis E237, 80%; G237, 20% (African Blacks) E237, 94.7%; G237, 5.3%; n 4 1004 Australians

A sample of black and white asthmatic and control subjects in South Africa was studied to determine whether these variants contribute to the enhanced immunoglobulin E responses in these groups. There was a significant difference in the frequency of L181 between white asthmatics (28%) and white control subjects (3%) (P 4 0.00001), but no difference in the frequency of I181L was observed between black asthmatics (22%) and black control subjects (16%). I181L might predispose to atopy in the white population but not in the black population.


The Fc(epsilon)RIbeta polymorphism (181/ 183) was investigated in Kuwaiti asthmatic patients and controls. The variant sequence (L181/L183) was detected in 72% (320/442) of chromosomes analyzed. Homozygous LL genotype was detected in 48% (46/96) asthmatic subjects compared with 31% (39/ 125) in nonasthmatics. G237 positive subjects had a significantly elevated skin test response to grass (P 4 0.0004) and house dust mites (P 4 0.04), RAST to grass (P 4 0.002), and bronchial reactivity to methacholine (P 4 0.0009). The relative risk of individuals with G237 having asthma compared with subjects without the variant was 2.3 (95% confidence interval, 1.26–4.19; P 4 0.005). (continued)


112 TABLE 4 Continued Gene


Human leukocyte antigen (HLA)

HLA-B8; HLAS-A10 haplotype; HLA-DQ2 haplotype

5-Lipoxygenase (ALOX5)

(GGGCGG)3-6 repeat variation in the promoter region

Platelet-activating factor; acetylhydrolase (PAFA)

Valine at position 279 is replaced by phenylalanine (V279F).

Allele frequencies

Clinical correlation


In a study of 76 Greek asthmatic patients (35 children/41 adults), increased frequency of HLA-B8 was found in the adults and an increased frequency of HLA-A10 was found in the children. The HLA-DQ2 allele is found more frequently in asthmatic children than in control subjects (60% vs 34%, P 4 0.013) with a relative risk of 2.8.

68, 69

3 repeats, 3.8%; 4 repeats, 172.%; 5 repeats, 77.2%; 6 repeats, 1.8%; n 4 221 Americans

Individuals with asthma carrying the 5repeat allele respond to treatment targeted at this enzyme (18–23% improvement in FEV). Individuals without the 5-repeat allele showed no response.


V279, 82%; F279, 18%; n 4 263 Japanese (controls); V279, 77%; F279, 23%; n 4 266 Japanese (asthmatics)

A missense mutation (V279F) in the PAF acetylhydrolase gene results in the complete loss of activity. The prevalence of PAF acetylhydrolase deficiency is higher in Japanese asthmatics than in healthy subjects and the severity of this syndrome is highest in homozygousdeficient subjects. PAF acetylhydrolase gene is a modulating locus for the severity of asthma.


TABLE 5 Sequence polymorphisms and drug response—central nervous system Gene


D2 dopamine receptor

1241 A/G in 38 UTR

Allele frequencies

1141 C ins/del in the promoter region Valine at position 96 is placed by alanine (V96A)

D4 dopamine receptor

Proline at position 310 is replaced by serine (P310S)

0.004 Caucasians

Serine at position 311 is replaced by cysteine (S311C)

0.16 Southwestern American Indians; 0.03 Caucasian; 0.23 Japanese

A null mutation occurs in ;2% of the general population Exon 1 contains a 12-bp duplication and a 13-bp deletion Exon 3 contains a 48-bp repeat (2–10 copies) and a G194V polymorphism

Clinical correlation


The polymorphism in the promoter region (1141 C) does not affect clozapine response. The serine 310 variant has been reported to be associated with the adverse drug reaction of neuroleptic malignant syndrome. In an in vitro study, the A96 variant showed a reduction of ;50% in the binding of dopamine, chlorpromazine, and clozapine. The binding of other neuroleptic drugs (haloperidol, thioridazine, thiothixene, and risperidone) was not affected. Sequence variation at positions 310 and 311 did not affect drug binding.


Binding site of atypical neuroleptics clozapine and olanzapine. The 7-repeat allele of the exon 3 VNTR is associated with dependence on opiate drugs. The effect of other polymorphisms on drug efficacy is not yet known.


0.64 4-repeat, 0.20 7-repeat, 0.08 2-repeat alleles (continued)



TABLE 5 Continued Gene


D5 dopamine receptor

Leucine at position 88 is replaced by phenylalanine (L88F). Alanine at position 269 is replaced by valine (A269V).

Allele frequencies

Clinical correlation


0.008 Caucasians

In an in vitro study, the N351D polymorphism resulted in a ten-fold decrease in dopamine binding affinity, and a three-fold decrease in R(4)-SKF38393 binding. The L88F polymorphism showed a slight decrease in the binding of SCH-23390 and risperidone, and a small increase in dopamine binding.

480-bp allele has been associated with attention-deficit hyperactivity disorder.


Receptor target of antipsychotics risperidone, ketanserin, clozapine, and olanzapine. Retrospective analysis of studies showed an association between the 102 T/C and H452Y polymorphisms and clozapine response.


s allele: reduced transcriptional efficiency, associated with anxiety-related traits.


Proline at position 330 is replaced by glutamine (P330Q).

0.1 Asians

Asparagine at position 351 is replaced by aspartic acid (N351D).

0.008 Caucasians

Serine at position 453 is replaced by cysteine (S453C).

0.008 Caucasians

Dopamine transporter (DAT1)

VNTR in 38 UTR

5-HT2A serotonin receptor

Thymine at position 102 is replaced by cytosine (102 T/C).

C allele 0.5 in Caucasians

Threonine 25 replaced by asparagine (Thr452Asn)

0.02 Caucasians

Alanine 447 replaced by valine (Ala447Val)

0.01 Caucasians

Histidine at position 452 is replaced by tyrosine (H452Y).

0.09 Caucasians

44-bp insertion/deletion in promoter region

S allele 0.43 in Caucasians

Serotonin transporter (5-HTT)


TABLE 6 Sequence polymorphisms and drug metabolism Gene


Allele frequencies

Clinical correlation

Cytochrome P-450 2D6 (CYP2D6)

There are 48 positions in the CYP2D6 gene where sequence variations have been reported, resulting in 53 different haplotypes. The allele frequencies for the most common polymorphisms are listed in the next column.

V11, 93.3%; M11, 6.7% P34, 81.6%; S34, 18.4% L91, 84.2%; M91, 15.8% H94, 84.2%; R94, 15.8% 1085 C 84.1%; 1085 G 15.9% 1127 C 97.5%; 1127 T, 2.5% 1749 G 47.4%; 1749 C 52.6% 1935 G 82.9%; 1935 A 17.1% 2637 A 98.3%; 2637 DA 1.7% K281, 97.3%; DK281, 2.7% R296, 65.7%; C296, 34.3% 3916 G 98.8%; 3916 A, 1.2% S486, 47.1%; T486, 52.9% Null allele 6.9%; n 4 1344 Europeans

More than 25 commonly prescribed drugs are metabolized by CYP2D6, including codeine, debrisoquine, indoramin, phenformin, and a number of antiarrythmics, antidepressants, betablockers, and neuroleptics.

The wild-type sequence is designated CYP2C9*1.

CYP2C9*1, 79%; CYP2C9*2, 12.5% CYP2C9*3, 8.5%; n 4 100 Caucasians

Cytochrome P-450 CYP2C9 is responsible for the metabolism of S-warfarin. The CYP2C9*2 and CYP2C9*3 allelic variants are associated with impaired hydroxylation of S-warfarin. These patients have difficulty at induction of warfarin therapy and are potentially at a higher risk of bleeding complications.

Cytochrome P-450 2C9 (CYP2C9)

Arginine at position 144 is replaced by cysteine (R144C). This variant allele is designated CYP2C9*2. Isoleucine at position 359 is replaced by leucine (I359L). This variant allele is designated CYP2C9*3.

Reference 82–84

Sequence changes in CYP2D6 can either increase or decrease the rate at which the enzyme functions. Extensive metabolizers need lower doses to achieve therapeutic response, and may suffer adverse effects if a normal dose is given. Poor metabolizers require greatly elevated doses to achieve response and may not respond to some drugs at all. 85–87

The Vmax values for phenytoin in patients who are heterozygous for the I359/L359 allele are 40% lower than those in patients with the wild-type CYP2C9 allele (I359/I359 homozygotes).




TABLE 6 Continued Gene


Allele frequencies

Clinical correlation


Cytochrome P-450 2C19 (CYP2C19)

M1 allele; 1-bp mutation in exon 5 splice site M2 allele: G636A transition in exon 4

M1 allele: 30.7%, n 4 39 Japanese; 16.7%, n 4 45 Caucasians M2 allele: 8.9%, n 4 39 Japanese; 0%, n 4 45 Caucasians

86, 88, 89

Glutathine Stransferase Mu (GSTMI)

The presence of a null allele results in decreased total GST activity.

Normal, 46.5%; null, 53.5%; n 4 213 whites normal, 72.4%; null, 27.6%; n 4 203 blacks

N-acetyl transferase 1 (NAT1)

There are 29 positions in the NAT1 gene where sequence variations have been reported, resulting in 24 different haplotypes. There are 11 position in the NAT2 gene where sequence variations have been reported, resulting in 26 different haplotypes.

R64, 95%; W64, 5%; n 4 85 Caucasians R187, 96%; Q187, 4%; n 4 85 Caucasians

CYP2C19 catalyzes the 4-hydroxylation of S-phenytoin, hexobarbitone, diazepam, omeprazole, proguanil, and R-warfarin. The M1 1-bp mutation lacks the heme binding domain and is catalytically inactive. It is associated with the poor metabolizer phenotype. The A636 transition in exon 4 creates a stop codon and is also associated with the poor metabolizer phenotype. GSTM1 is a detoxification enzyme with high specificity for epoxides. Individuals who are homozygous for the null allele demonstrated a slightly increased risk for prostate cancer, and smokers who carry the null allele are at increased risk for bladder cancer. NAT1 can further metabolize hydroxylamine metabolites to N-acetoxy derivatives. Normal NAT-1/fast NAT-2 genotype may be protective for susceptibility to smokinginduced bladder cancer. NAT2 polymorphisms are associated with higher incidence or severity of adverse drug reactions to isoniazid, hydralazine, procainamide, and sulfamethazole. Slow acetylators {NAT2*5B (I114T), and NAT2*6A [C282T(Y94Y), R197Q]} who smoke may have an eightfold higher risk of bladder and lung cancer due to their inability to detoxify aromatic amines in tobacco smoke. Rapid acetylators (NAT2*4) are at increased risk of colon cancer from acetylation of heterocyclic amines found in cooked meats.

N-acetyl transferase 2 (NAT2)

I114T: 55:45, n 4 968 Caucasians; 76:33, N 4 61 Indians; 70:30, N 4 214 African Americans; 72:28, n 4 148 Hispanics; 99:1, a n 4 224 Japanese; 95:5, n 4 254 Chinese R197Q: 55:45, n 4 968 Caucasians; 67:33, n 4 61 Indians; 70:30, n 4 214 African Americans; 72:28, n 4 148 Hispanics; 99:1, n 4 224 Japanese; 95:5, n 4 254 Chinese

90, 91



Paraoxonase (PON1)

Methionine at position 55 is replaced by leucine (M55L). Glutamine at position 192 is replaced by arginine (Q192R).

Q192, 84.5%; R192, 15.5%; n 4166 Indians Q192, 63.2%; R192, 36.8%; n 4 105 Turks Q192, 73%; R192, 27%; n 4 248 Saudis

Thiopurine S-methyl transferase (TPMT)

TMPT has several mutant alleles that exhibit low enzyme activity compared with the wild-type enzyme (TPMT*1). Alanine at position 80 is replaced by proline (A80P). This variant allele is designated TPMT*2. Alanine at position 154 is replaced by threonine (A154T). This variant allele is designated TPMT*3B. Tyrosine at position 240 is replaced by cysteine (Y240C). This variant allele is designated TPMT*3C. Variant TPMT*3A contains both the T154 and C240 amino acid changes.

Haplotype frequencies: TPMT*1, 94.5%; TPMT*2, 0.18%; TPMT*3A, 3%; TPMT*3B, 0.35%; TPMT*3C, 0.71%; TPMT*3D, 0.18%; TPMT*4, 0%; TPMT*5, 0.18%; all other, 0.88%; n 4 283 (mixed ethnic groups)

High serum paraoxonase levels protect against the neurotoxic effects of organophosphate insecticides as well as the nerve agents soman and sarin. The R192 allele specifies high enzymatic activity, whereas the Q192 variant specifies low activity. The M55L polymorphism affects the level of mRNA, with M55L heterozygotes showing an excess of the L55 allele. Prevention of transplant rejection and therapy of rheumatoid arthritis by the immunosuppressive drug azathioprine is limited by hemotologic toxicity (leucopenia or agranulosis). This toxicity is a particular problem in patients with low TPMTase activity (homozygotes for variant alleles; ;1% of the population). Other drugs affected by variation in TPMT include mercaptopurine and thioguanine.

96, 97





Evolution affects the phenotypes encoded by genotypes, but it cannot operate through the genotype directly (11). Phenotypes that confer a selective advantage at one point in a population’s history may at a different point do the opposite. Thus, there is an increased risk for deep venous thrombosis in carriers of the prothrombin G r A20210 gene variant (polymorphism). The A allele is deleterious because it is associated with increased prothrombin levels and an approximately threefold increased risk for deep venous thrombosis compared with individuals homozygous for the G allele (104). The median age at the time of the first thrombotic episode is 38 years. Deep venous thrombosis is a serious and potentially life-threatening condition for human beings in western society today. But there was likely a time in human history in which the life expectancy of an individual was well under 38 years. The capacity to undergo blood coagulation quickly and decisively might well have been a selective advantage if wounds from predatory animals or human combatants were a common event in everyday life. Indeed, even as the twentieth century draws to a close, current events do not necessarily support the lack of a selective advantage for the A allele.

PHARMACOGENETICS Research pharmacologists and toxicologists have begun applying the lessons of modern genetics and molecular biology at perhaps a much faster pace than many other medical sciences. This is clear in the rapid practical applications of pharmacogenetics and supporting technology (83, 84, 105). Yet, as with any genetics testing, there are a number of regulatory and policy issues that require further dialogue (106, 107). Pharmacogenetics, a term originally coined in the 1950s, may now be viewed as the study of correlations between an individual’s genotype and that same individual’s ability to metabolize an administered drug or compound. Genotypic variation, often in the form of SNPs, exists for many of the enzymes that metabolize important drugs. Extensive metabolism of a drug is a general characteristic of the normal population. Poor metabolism, which typically is associated with excess accumulation of specific drugs or active metabolic products, is generally an autosomal recessive trait requiring a functional change, such as a frameshift or splicing defect in both copies of the relevant gene. Ultraextensive metabolism, which may have the effect of diminishing a drug’s apparent efficacy in a given individual, is generally an autosomal dominant state derived from a gene duplication or amplification. Some representative examples of genetic polymorphisms that affect drug metabolism are shown in Table 6. For some fields, such as cancer chemotherapy, several common drugs show wide polymorphismrelated metabolic varations, with 30-fold or greater interindividual variability reported (108). Drug metabolism is often divided into two components: phase I (oxidative) and phase II (conjugative). The cardinal phase I enzymes belong to the cytochrome P-450 (CYP) superfamily of inducible mixed-function monooxygenases



located within hepatic endoplasmic reticulum. Thirty or more forms of P-450 enzymes have been identified, each with distinct enzymatic activity. It is difficult to overstate the importance of these enzymes in clinical practice. More than 25 prescription drugs are metabolized by one member of the family, the CYP2D6, alone. It has been estimated that genetic polymorphisms of the CYP2D6 gene alter clinical care and outcome of nearly one fifth of patients in some ethnic groups. In the case of certain drugs or carcinogens, the P-450 enzymes play a role in generating active moieties from otherwise inactive or poorly active starting compounds (pro-drugs or pro-carcinogens), which may add a layer of complexity in any global overview of these enzymes. Phase II enzymes include the glutathione transferases, N-acetyltransferases, UDP-glucuronyl-transferases, and the sulfotransferases. Genetic polymorphisms in these enzymes can also have important pharmacologic and clinical sequelae. They may also participate in relative cancer risk and causation, depending on their effectiveness in eliminating certain carcinogens (109) (see also Tables 2 and 6). However, with our current state of knowledge, there may be complex or counter-intuitive relationships between cancer and certain polymorphisms, such as those seen in the N-acetyltransferases. In a sense, every clinical pharmacologist already relies on the tools of medical genetics and genotyping. The common technologies for determining alleles of pharmacogenetic interest are readily adaptable to most reference labs. There are two commonly used techniques. The first is amplification of a specific genetic region by polymerase chain reaction, followed by an analysis of restriction fragment length polymorphism (which reflects alterations in nucleotide sequence). The second involves allele-specific polymerase chain reaction, which depends on oligonucleotides capable of hybridization with common or variant alleles. Only a successful (i.e. precise) hybridization to the known target sequence yields an amplification product, which can in turn be detected on agarose gels. However, it is expected that improvements in microfluidic and chip-based technologies will make it even easier and more convenient, and thus more important, to assess genotypes of important metabolic enzymes. These technologies may also eventually make it possible to address the metabolic pathways for biologic response modifiers, for which very little is known. The availability of the complete sequence for the human genome, including new knowledge regarding DNA variation, will alter the scope and definition of pharmacogenetics. New genomic knowledge will make it possible to examine families of drug efflux pump genes (e.g. P-glycoprotein) in the gut epithelium and central nervous system for their effects on oral bioavailability and central nervous protective effects, particularly with respect to xenobiotic agents (110). This knowledge may make it possible to clearly predict important pharmacokinetic parameters of certain drugs well in advance of empirical testing. In addition to the possibility of identifying new genes (and new alleles) from known metabolic gene families, pharmacologists will gain unprecedented ways of analyzing polymorphisms in the target receptors of important drugs, as well as in genes involved in the metabolic pathways affected by new agents. As an example, about



6% of patients with asthma do not carry a wild-type allele at the 5-lipoxygenase (ALOX5) core promoter locus (see Table 4). In such patients, the ALOX5 pathway agents (i.e. leukotrienes) do not make a major contribution to their small airway disease, and these asthmatics do not improve with a drug whose mechanism is to inhibit this pathway (70). Also, a common polymorphism of the leukotriene C4 synthase promoter appears to be a risk factor for aspirin-induced asthma (111). This clearly illustrates the principle that variants in the promoter region of a therapeutic target gene can predict clinical response. This concept is a significant expansion of pharmacogenetics beyond polymorphisms in drug-metabolizing enzymes. Thus, on several fronts, pharmacologists and toxicologists will be able to address issues of efficacy and safety not reflected in the traditional pharmacokinetic or pharmacodynamic profiles of drugs. Even when patients have identical pharmacokinetic profiles for a given drug, we currently have no way of knowing that there will be comparable interpatient efficacy or safety because the science of pharmacogenetics can now only look at one part of the picture, and even that picture generally excludes important classes of therapeutics, such as biologicresponse modifiers and monoclonal antibodies. Some examples of polymorphisms in a target receptor, with important pharmacologic consequences, are shown in Tables 4 and 5. In the future, the growing knowledge, based on the foundation of the complete human genome, will make it possible for pharmaceutical developers to select therapeutic agents according to the individual allele profiles of the intended patient. Said another way, patients will someday get only the drugs they need, and no other. But what is certain now is that pharmacology and toxicology will become dependent on the emerging bioinformatics and computational sciences linked to genomics. Data acquisition and management across multiple disciplines will require new tools and skill sets, necessitating changes in the curricula of pharmacology teaching centers toward a much more computational orientation. It will also be necessary for new cross-relational databases to emerge to serve the needs of pharmacologists and toxicologists in the new genomics era.

COMPARATIVE GENOMICS The availability of whole genome sequence information in the human is important in its own right, but the full power of this knowledge requires the additional availability of whole genome sequence information from model organisms, especially Drosophila melanogaster (common fruit flies) and mice. Drosophila has been at the forefront of genetics research for nearly 80 years. This species is an important model for combining genetics, electrophysiology, and molecular biology. A brief listing of some medically important fruit fly genes is shown in Table 7. We expect that our group, in collaboration with the Berkeley Drosophila Genome Project, will publish the complete sequence of Drosophila in the near future. Many gene families, ranging literally from A to Z (aldolases to zinc finger



TABLE 7 Model organisms, gene names, and human disease Fruit fliesa



Congenital heart defects Tinman Huntington’s polyglutamine repeats cause neural degeneration Alzheimer’s APPL Amyotrophic lateral sclerosis SOD Cancer Cdx(2) caudal Diabetes insulin receptor

Csx/NKx2.5 Polyglutamine repeats

Csx/NKx2.5 Polyglutamine repeats

APP SOD1 cdx2 Insulin receptor (knockout mice) Rhodopsin NF2 ATM

APP SOD1 cdx2 Insulin receptor

Retinitis pigmentosa rhodopsin NF MERLIN Ataxia telangiectasia mei-41

Rhodopsin NF2 ATM


Human disease. APP, amyloid precursor protein; SOD, superoxide dismutase, NF, neurofibromatosis.

transcription factors), suggest that a single invertebrate gene corresponds to a handful of equally related vertebrate genes on different chromosomes. Thus, gene duplication is the engine that drove vertebrate evolution (112). In the case of vertebrate organisms, not only does homology to human genes exist, there is also something more meaningful called synteny. This means there are related genes arrayed on chromosomes with an evolutionary history common to human counterparts, in a comparable order in terms of exons and gene regulatory elements. Thus, a mouse chromosomal region with such a common evolutionary history and genetic arrangement is said to be syntenic, and the relevant mouse and human genes are said to be orthologs. Synteny and the capacity to overlay complete human and mammalian genome sequences (especially mouse) will affect gene discovery and our understanding of gene structure and function in ways without precedent. We expect the completion of other genomes with importance to pharmacology and toxicology, e.g. rat and canine genomes, not too far behind those of humans and mice. These advances will allow an integration of information from transgenic animals and gene knockout models in far-reaching ways and will stimulate novel strategies for currently intractable therapeutic problems. The implications of these advances can perhaps be illustrated with a few examples. The homeobox genes are an interesting case. An excellent website by Gaunt can be consulted ( gaunt.html). We can examine homeobox (Hox) genes (113), a family of genes that are conserved in evolution in detail. These genes direct the development of the body plan and body parts of many morphologically distinct species. The homeobox is a highly conserved 180-bp nucleotide sequence shared by this family of transcription factors. The homeobox in turn encodes the DNA binding region, called the homeodomain, a DNA binding motif.



Homeobox genes are found in clusters, as shown at the top of Figure 1 (see color insert). The 58 r 38 order is reflected in the spatial correspondence of the genes along the posterior r anterior axis in D. melanogaster, and this is likewise conserved in other species. There is a rigid correspondence between the order of Hox genes within their clusters and that of their expression domains along the body of the embryo. Homeobox genes have a high degree of sequence similarity (denoted by color coding) in Figure 1 and can be found thoughout the animal kingdom at the same positions in the cluster. Shown below Drosophila in Figure 1 are the corresponding clusters of orthologs in mice, demonstrating a general preservation of these genes, along with the expected pattern of gene duplication in vertebrates in different chromosomes. Verterbrates have four Hox clusters, called Hoxa, Hoxb, Hoxc, and Hoxd. A more complete understanding of these genes will offer the pharmacologist many new strategies for preventing and treating birth defects and possibly organ or even limb repair. Cross-species homologues can show similar function, even in organisms separated by hundreds of millions of years of evolution. Thus, dysfunction of the homeotic gene Pax-6 can cause an eyeless phenotype in Drosophila and small eye syndrome in mice, whereas in children, mutations in Pax-6 results in a complete loss of the iris and also in disturbances of the lens, cornea, and retina, which can contribute to blindness (114, 115). One must use caution in extrapolating these observations too broadly, but they serve as a reminder of the remarkable power of comparative genomics from fruit fly to human. Knowledge from comparative genomics may make it possible to rethink therapeutic strategies for currently untreatable disorders, including those that arise from cytogenetic abnormalities. For example, is it possible that if an appropriate intervention were made early enough, Down’s syndrome (trisomy, critical region chromosome 21q22.2–q22.3) might not inevitably lead to mental retardation? Asked another way, do the models of phenylketonuria and congenital hypothyroidism apply? Is there a way of testing whether an extra dose of a gene causes mental retardation, and could a pharmaceutical agent somehow neutralize the effects of such an extra dose if given early enough? One could note, with some justification, the futility of such hypotheticals in the absence of knowledge of the genes responsible for the disorder, and this underscores the importance of completing the human genome sequence effort quickly. By utilizing available knowledge of synteny and comparative genomics, a novel family of protein kinases, called Dyrk, was found to represent interesting candidates for a role in this syndrome (116). Human chromosome 21 and the syntenic region of mouse chromosome 16 are shown in Figure 2 (see color insert) (117, 118). The gene Dyrk1A is located in the Down’s syndrome critical region of chromosome 21. Dyrk1A reveals homologies with minibrain, a gene in Drosophila whose mutations yield reduced neuronal number and defective learning behavior. Mouse models of Down’s syndrome, which involve a partial trisomy 16, have been created (119, 120). And perhaps most interesting, mice transgenic for a 180-kb DNA segment derived from the human Down’s syndrome critical



region had defects in learning and memory (121, 122). Thus, it may soon be possible to identify both the gene(s) responsible for the most significant feature of the syndrome and in vivo systems for designing and testing interventions. These types of research opportunities will be multiplied thousands of times with the completion of the reference human genome program.

THE CHIMPANZEE GENOME Approximately 5 million years ago, humans and chimpanzees shared a common ancestor. At the nucleotide sequence level, humans and chimpanzees differ by approximately 1.5% (50 Mb), on average. It has been argued that portions of human and chimpanzee genes are sometimes so similar that differences may fall within the range of normal DNA sequence variation (123). The overlapping synteny between humans and chimpanzees means that the completion of a reference human genome will greatly simplify the tasks of sequencing and assembling a reference genome for chimps. Chimp DNA variation casts light on human DNA variation. Of nearly all human SNPs studied, one or another of the nucleotide allele set is shared with chimpanzees, thus making it possible to differentiate between ancestral and more recent alleles. Hacia et al (124) found that at the vast majority of SNP sites scored, all three nonhuman primates (chimps, pygmy chimps, and gorillas) exhibited one or the other of the human nucleotides, and roughly 75% of the time, the more common allele in humans was the ancestral form. On the basis of considerable DNA sequence information (especially from the noncoding sequences of the genomic region called the beta-globin gene cluster), Goodman (125) has proposed creating a new phylogenetic classification of primates. In this proposed classification, humans would share their genus with chimpanzees and bonobos (pygmy chimpanzees). Thus, the subtribe Homina would contain Homo (Homo), humans, and Homo (Pan), chimpanzees and bonobos. The science of comparative primate genomics can yield a number of interesting insights into the molecular events that contributed to human evolution (125–129). For example, mutations in cis-regulatory control elements changed c-globin gene expression from strictly embryonic to fetal in profile, and there is evidence that natural selection favored such a distinct fetal hemoglobin during the evolution of the anthropoid primates. There were also amino acid–changing substitutions, including those in codons specifying 2,3-diphosphoglycerate–binding sites. A noteworthy consequence at the protein level was a loss of 2,3-diphosphoglycerate–binding capacity, yielding a fetal hemoglobin molecule that binds oxygen with increased affinity. Such a change facilitates the transfer of oxygen from mother to fetus. These changes, taken together, made a prolonged gestation and extensive prenatal brain development possible in anthropoid primates. The changes in both regulatory elements and coding exons that permitted a prolonged fetal gestation provide but a glimpse of what can be learned when



complete genomic information becomes available. These changes are particularly instructive because they are not in any immediate or obvious way associated with brain structure or related anatomical development, and yet these differences in hemoglobin structure and kinetics of expression enabled the evolution of intelligence in primates. Two questions emerge at this point. What are the genes that define human beings as a unique primate species and how can we apply this knowledge to medical science? McConkey & Goodman (130) have suggested that the first priority would be to identify genes that are situated in close proximity to the archaic chromosomal rearrangements occurring in human evolution after the divergence of humans and chimps from their last common ancestor. The fusion of two ape chromosomes to form human chromosome 2 and a small pericentric inversion in human chromosomes 1 and 18 might be excellent clues about where to search for candidate genes and regulatory elements that uniquely facilitated human evolution (131, 132). In any event, there will be exceptional opportunities to study the genes and pathways involved in affect and emotion; cognition and memory; language, speech, and gesticulation; and attention span, sexuality, craniofacial and neurodevelopment, gait, aging, and resistance to infectious agents (notably retroviruses). The implications for pharmaceutical sciences and such disciplines as rehabilitative medicine are profound.

SOCIETAL RESPONSIBILITY It is worth discussing the limits of genomics. Understanding the human genome will change science and medicine in profound ways. It will, however, not solve or explain every important problem of the public health. Other components of society will need to express determination and commit resources for solutions. One such problem is the need to respect patient privacy. Another is racism in whatever guise, which is invariably an enemy of science. In our efforts to provide a reference sequence for the human genome, we believe there is neither ‘‘good’’ nor ‘‘bad’’ DNA, only human DNA. Every individual will have a finite number of genetic flaws in his or her genome. Our task is to use the modern tools of pharmacology and genomics to ameliorate these flaws or undo their consequences. There is very good reason for optimism on this front, as we have tried to convey in this chapter. Yet many societies, including our own, have at various times embraced eugenics and other irrational genetic theories of race or ethnicity as the justification for neglect, oppression, or worse. However, the embrace of these negative philosophies is not inevitable. Opening incomparable opportunities for preventing and curing illnesses through genomic science is one way of refuting these pseudoscientific viewpoints. We believe that various governmental and private agencies, especially the National Institutes of Health, need to redouble their efforts to provide resources in the arena of ethics, education, and genomic research. Furthermore, some illnesses have their roots in certain external envi-



ronmental factors for which genes may not affect outcomes in practical terms. Poverty may be viewed as one such factor. It is, therefore, important for society at large to recognize that advances in science alone, including the reference human genome project, will need to be coupled with programs to address these larger societal issues. ACKNOWLEDGMENTS We wish to thank Mary Whiteley, Trevor Woodage, Anibal Cravchik, and Emily Winn-Deen for their helpful scientific input, and Beth Hoyle for her excellent editorial assistance in preparing this manuscript. Visit the Annual Reviews home page at

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Figure 1 Synteny of homeobox (Hox) genes. (Color coding) Shows evolutionary relationships of orthologs. Abd-B, Abdominal B; Abd-A, abdominal A; Ubx, ultrabiothorax; Antp, antennapedia; Scr, sex combs reduced; Dfd, deformed; Pb, proboscipedia; Lab, labial.


Figure 2 Synteny (red) between human chromosome 21 and mouse chromosome 16. Dyrk1A protein kinase is in a Down’s syndrome critical region and is a candidate gene for causation. (See also


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