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14

Role of SNPs and Haplotypes

in Human Disease and

Drug Development

Barkur S. Shastry

Department of Biological Sciences, Oakland University, Rochester, MI 48309

Keywords: genome, sequence, disease, gene, pharmacogenetics, association

14.1. INTRODUCTION

In recent years it has become clear that genetic factors play a major role in every human disease except trauma. In many Mendelian or monogenic disorders, disease genes are identified by linkage and positional cloning methods using pedigrees and simple tandem repeat markers (di-, trior tetra-nucleotides). However, linkage based methods have limited power and are not readily applicable for complex disorders such as cardiovascular, osteoporosis, neuropsychiatric disorders, diabetes, asthma and cancer. This is because multiple loci are involved in these disorders and each locus contributes a small effect to disease etiology. The above complex disorders unfortunately, also occur in higher frequency and are a major social burden.

Now the whole genome sequence is a reality. The challenge faced by many investigators from around the world is how to use this massive information for practical purposes such as improving quality of life. Since many diseases are transmitted from parents to offspring, one immediate goal is to find out which gene (s) predisposes people to various diseases and how the sequence variations in a gene affect functions of its product. The second relevant question is how one could use this genetic information to predict a particular

448	BARKUR S. SHASTRY

drug response or susceptibility to toxic side effects. This is because it is known that only a sub-population of patients experience side effects for certain medications while others do not. This inter-patient variability could be likely due in part to polymorphism in genes encoding drug metabolizing enzymes, drug transporters and/or receptors [3, 32]. Identification of gene defects and the provision of genetic determinants of drug efficacy and toxicity will ultimately enable physicians to optimize the use of medication effectively.

14.2. SNP DISCOVERY

It is known that between any two individuals about 99.9% of the DNA sequence are similar to one another. The remaining 0.1% DNA differs and accounts for diversity among population, disease susceptibility and drug response [71, 126]. The DNA variability is due to mutation, genetic drift, migration and selection. A recent survey suggests that some variants are common to all populations while others are distributed in a more restricted manner [103]. The most common variant is called polymorphism. These are nothing but variants at specific nucleotide positions in the genome among different people (Fig. 14.1) and are designated as single nucleotide polymorphisms (SNPs). They occur once in every thousand base pairs or less throughout the genome [16, 25]. It is estimated that the human genome may contain as many as 10 million such SNPs [104]. These variations can occur either in coding regions (cSNPs, also called non-synonymous SNPs) or outside the coding regions. According to one report, about 50% of SNPs are in the non-coding region, 25% lead to missense mutations (cSNPs) and the remaining 25% are silent mutations [19, 40]. Neutral substitutions in exons are found to occur at a much higher rate (30–60%) compared to that in non-coding regions. This could be due to the presence of relatively rich GC sequences in exons [114].

Although the frequency of SNPs across the genome is greater than any other type of polymorphism, they vary among different genomic regions, genes and different human populations [19]. As mentioned above, it is also not necessary that all SNPs will change the phenotype. However, they could influence gene expression and regulation. For instance, those SNPs that occur in protein coding sequences (cSNPs) cause an amino acid change and possibly contribute to disease susceptibility and drug metabolism. There are approximately 200,000 cSNPs in the human genome [22]. Because of their important role in disease susceptibility and drug metabolism, one of the goals of the human genome project is cataloging these variations [23] and generating an ordered high-density SNP map of the human genome. This map is made available for the public through the web-site http://snp.cshl.org. The database of SNP is also maintained by the National Center for Biotechnology Information (NCBI) and can be found in the web-site http://www.ncbi.nlm.nih.gov/

SNP
GATCAGC	G	ACT		individual A
GATCAGC	A	ACT		individual B

FIGURE 14.1. A Schematic illustration of single nucleotide polymorphism (SNP). Figure shows strings of nucleotides at which individuals A and B differ by just one base.

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14.3. DETECTION OF GENETIC VARIATION

A wealth of technologies is available for detecting genomic variations. This short article is not meant to be a review of all methods or their uses that are available to date for SNP analysis. Readers are directed to consult several other reviews on this topic that are already published [39, 50, 52, 53]. Briefly, for small scale studies, assays used for SNP detection include: DNA sequencing, single strand conformational polymorphism [87], restriction fragment length polymorphism (RFLP), denaturing gradient gel electrophoresis (DGGE— uses a chemical denaturing gradient [34, 88]), temperature gradient gel electrophoresis (TGGE—uses a temperature gradient), constant denaturant gel electrophoresis (CDGE— employs a constant amount of denaturants), oligonucleotide ligation assay [123], allelic specific oligonucleotide hybridization [102] and nuclease mutation detection method [38, 131]. These techniques are simple and non-radioactive methods. They can detect as little as a one base difference with a scanning region of 100–1000 base pair. However, the detection limit is approximately 80% and it is likely that mutations at the ends of the fragment as well as in GC rich regions could be missed. Further improvements in these techniques such as addition of a GC rich sequence to the end of the primers (GC clamp), two dimensional DGGE and dideoxy fingerprinting coupled with single strand conformational polymorphism (SSCP), reported to increase the efficiency of detection to nearly 100% [61, 109].

In order to apply any SNP identification technique to a large-scale study, it is necessary that it should be accurate, sensitive, flexible and most importantly cost effective. For this purpose, a variety of reliable high-throughput methods have been developed. These include: structure specific nuclease invader technique [35, 73, 80], primer extension [9, 116], molecular becons—probes that fluoresce on hybridization [75, 119], TaqMan assay [28, 62], denaturing HPLC, genomic mismatch scanning, gene chip [58], nano technology, melting curve single nucleotide polymorphism [14, 70], fluorescent competitive allele specific polymerase chain reaction, constant denaturant capillary electrophoresis [11], highdensity oligonucleotide arrays [29] and ligation rolling-circle amplification [79, 94, 130]. In addition, several academic and private institutions are developing a robotic version of the SNP-IT (offered by Orchid Biosciences) and MassARRAY system (offered by Sequenom). However, as expected for any technique, many of these methods have their own limitations and advantages. For instance, the gene chip method may offer the advantage of minimum sample handling and higher marker throughput but it requires a custom array for each marker set. Similarly, denaturing HPLC technology has flexibility but requires specialized equipment. Moreover, although, Beadedarray [86] and MassARRAY [99], are available for rapid genotyping, further improvements are needed to develop an inexpensive method to screen large numbers of SNPs from hundreds and thousands of patients and controls.

After the identification and mapping of a particular SNP, it is necessary to calculate the allele frequency. This can be accomplished by calculating the percentage of individuals containing the polymorphism within a population. If the frequency is greater than 5–10% then they are considered useful SNPs. A recently reported new assay which makes use of the 3’-to 5’-exonuclease proofreading activity is a useful method for typing, and allele frequency data can be obtained directly from genomic DNA samples [17]. The allele frequency database is available from the Kidd lab home page http://info.med.yale.edu/genetics/kkidd. From these data it appears that SNP allele frequencies vary considerably across ethnic groups and populations. This suggests that different SNP panels will be required for different studies

450	BARKUR S. SHASTRY

depending on the origin of the population. Methods (Bayesian and likelihood) have also been developed for estimating human population growth, migration, population divergence and recombination rates for SNPs [82, 83]. The widely variable SNP frequency between genes also indicates the difference in the recombination rate across the genome [18, 81, 97].

14.4. DISEASE GENE MAPPING

By identifying and developing a high-density map of human DNA sequence polymorphism it may be possible to identify disease related genes more effectively by employing the association strategy [54, 55, 68, 98]. This is because such studies do not need a large family. In this approach, a comparison between affected and unaffected individuals will be made for a set of genetic markers. If the marker shows more prevalence in affected individuals than in unaffected, then it is considered as evidence of an association between the marker and the phenotype (reviewed in ref. [107, 108]). For this purpose, an analytical approach has been developed which also incorporates the contribution of environmental factors [132]. This method however, still needs a large sample size [56] and there are other pitfalls such as the generation of millions of genotypes. Despite this limitation, there has been some success recently in identifying the association between the APOE 4 allele and late-onset Alzheimer disease [66, 100], factor V Leiden mutation and venous thrombosis [122] and promoter polymorphism in the insulin gene and type I diabetes [10]. A partial list of diseases associated with SNPs is presented in Table 14.1. Although there are several high-throughput methods that are available for genotyping thousands of samples [45], this type of whole genome approach to mapping is still expensive. A suggested alternative approach in some cases is to use pooled DNA to quantitate the result [13] that will reduce the number of samples to be analyzed. However, such approaches are not recommended for all studies. In addition, this method gives only a rough estimate of allele frequencies and it is possible that some rare alleles (less than 5%) could be easily missed.

14.5. EVOLUTION

Genetic variants are not only considered to be responsible for inter-individual differences and in disease risk but also for molecular evolution. It is estimated that more than 99% of SNPs occur elsewhere in the genome and they do not change amino acid or regulatory sites. Hence they may not have any phenotypic effect. According to the neutral theory of evolution [48], these SNPs are not subjected to natural selection [16, 105]. Therefore, they are considered to be more stable or less mutable. However, those SNPs that do change amino acids may have phenotypic effects and might be subject to natural selection. If these SNPs are retained over time then they must have some selection advantages for individuals. The retention of variants by natural selection is an important step in evolution [60]. Hence raw evolutionary data can be generated by comparing the ratio of non-synonymous to synonymous change in several proteins in different species. This may allow us to trace the branching point of an evolutionary tree. This is an important step in evolution because

TABLE 14.1. A partial list of diseases associated with SNPs

Disease	Gene	Ref	Disease	Gene	Ref
Idiopathic PD and FTD	Tau	[65, 111]	Urinary bladder cancer	Cyclin-D1	[124]
Knee and hip Osteoarthritis	Coll.	[44]	Dyslipidemia	Lipase	[128]
Graft patency of femoropopliteal	PAFAH	[121]	Oxalate stone disease	E-Cad.	[120]
bypass
Atopy and asthma	Chemokine	[36]	Lung cancer	MMP-1 p53	Zho et al., 2001; [15]
Myocardial infarction	Thrombomodulin	[89]	Systemic sclerosis	Fibrillin-1	[118]
	Prostacyclin synthase	[76]	POAG	TIGR	[24]
Severe Sepsis	TNF—α	[85]	Migraine	Insulin Receptor	[69]
Obesity	PAI—1	[41]	Arrhythmia	KCNQ1	[59]
Bipolar affective disorder	HTR 3A	[84]	Eating disorder	Melanocortin	[51]
Rheumatoid arthritis	MIF	[8]	SLE Blood Pressure	Prolactin CPB2	[113, 49]
Hyperbilirubinemia	UGT1A1	[115]	Juvenile idiopathic arthritis	MIF	[30]
Hyperandrogenism and Ovarian	SHBG	[42]	Primary biliary cirrhosis	MBL	[67]
dysfunction

MIF = macrophage migration inhibitory factor; UGT1A1 = UDP glucuronosyl transferase; PAF-AH = plasma platelet-activating factor—acetylhydrolase; TNF = tumor necrosis factor; PAI = plasminogen activator inhibitor; HTR 3A = serotonin receptor gene; PD = Parkinson disease; FTD = frontotemporal dementia; SLE = systemic lupus erythematosus; CPB2 = encodes the thrombin-activable fibrinolysis inhibitor (TAFI); MBL = mannose binding lectin; SHBG = sex hormone binding globulin; MMP-1 = matrix metalloproteinase −1; POAG = primary open-angle glaucoma; TIGR = trabecular meshwork inducible glucocorticoid respose; KCNQ1 = potassium channel; Cad. = cadherin

HAPLOTYPES AND SNPs

451

452	BARKUR S. SHASTRY

at that branch point variance has advantages for the species and fixed in the gene pool. This can be of immense value in understanding the evolution of the human genome in the future.

14.6. HAPLOTYPES

Identification of millions of SNPs across the genome is an expensive and technologically demanding task. For this reason, a much simpler method called haplotyping and understanding the haplotype structure (HapMap) has been developed [37]. A haplotype is a group of linked SNPs in the same chromosome that are inherited together—a process known as linkage disequilibrium—which is a non-random association of alleles in a chromosomal segment. Thus, a particular SNP will be in linkage disequilibrium with many other SNPs [96, 117]. In a simple sense, a haplotype (blocks of information) is a genotype of a single chromosome and it is likely to be responsible for human genetic diversity. Since a haplotype contains stretches of SNPs in a particular chromosome, they are thought to yield more accurate and predictive information on the complex biology of the genotype—phenotype relationship than a single SNP. This is because polymorphic bases can be distributed differently on the maternal and paternal chromosomes and such differences may affect gene expression and drug response by two patients. In addition, haplotype analysis may significantly reduce the amount of data to be analyzed [47] for gene identification or drug development because only a small number of SNPs are required to map the disease gene.

The haplotype structure often determines the phenotypic consequences and they can be used as genetic markers [6, 26, 27, 46]. In order to determine directly the haplotype structure of genomic DNA, a simple robust method involving a long–range polymerase chain reaction and intra-molecular ligation has been developed [72]. Recently, using gene chip sequencing arrays developed by Perlegen Sciences, it has become possible to define the haplotype structure of chromosome 21 [90]. However, linkage disequilibrium may vary between various populations and across the genome and this requires additional methods [96]. Furthermore, two companies: Parkin Elmer and Sequenom Inc. (San Diego) have launched web sites (www. snpscoring.com and www. realsnp.com) which provide information on population frequencies of SNPs in various populations, how the technology works, how to run the assay and what are the benefits. When the HapMap is completed, a simple comparison of HapMap between normal and patient DNA will provide the region of the genome that is most likely responsible for the disease. Similarly, comparison of HapMap of individuals responding differently to medication will pinpoint the genomic region valuable in personalized treatment. Thus, haplotyping can provide a better understanding of the genetic correlation to clinical response than a single SNP genotype. This is because the specific combination of multiple linked SNPs can have an additive or subtractive effect on a particular trait and this is very useful for designing a drug [5, 108]. Additionally, haplotype based SNP analysis can be applied to crop genetics [95].

14.7. DRUG DEVELOPMENT

Human genetic variation also plays a key role in determining drug toxicity and efficacy. For instance, some weight-loss drugs were found to cause cardiac valve damage

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453

and pulmonary hypertension. Similarly, non-sedating anti-histamine drugs and antibiotics produced cardiac arrhythmias [91, 106]. In fact, the incidence of serious and fatal adverse drug reactions is very high. According to one study, properly prescribed medications caused 100,000 deaths in the United States alone [57]. It has also been reported that the HLA class II haplotype determines the response to cytokine therapy in a subset of patients with renal cell carcinoma [31]. In this regard, rapidly developing pharmacogenomic research and a toxicogenomic assay may shed some light on the response of an entire genome as well as the influence of genetic variation to experimental drugs or class of drugs. Identification of the genotype of patients and correlating that with drug toxicity (Fig. 14.2) may enable clinicians to prescribe most effective drugs with diminished side effects. In short, understanding the relationship between genotypic variation and drug metabolism at the molecular level may lead to customized medications in the future. It will fit each patient’s needs so that clinicians need not have to rely on a guessing game with drugs [12, 20, 59, 63, 92, 112].

The reason for this is that, it is well known that concentration of certain drugs in a patient is determined by drug metabolizing enzymes such as cytochrome P450, N-acetlytransferase and other key enzymes [33, 74]. For instance, a variation in cytochrome CYP 2D6 activity has been linked to liver cancer [2] and genetic polymorphism has been reported in this enzyme [78, 107, 108]. As mentioned earlier, an SNP map of the human chromosome suggests that some SNPs are in the exons [4, 77]. Therefore, identification of non-synonymous SNPs (those that cause amino acid change) and other SNPs in the promoter region are very useful in understanding drug response because they have a functional effect [92, 112]. Additionally, it is known that some patients respond poorly to a given medication (Fig. 14.2) while the other patients either experience incompatibility or no response at all [21, 101]. By using a high-density SNP map and comparing the pattern of those individuals who respond poorly, adversely and positively, it is possible to identify responsible polymorphism in a gene and to develop safer and more effective drugs. In summary, SNPs and haplotypes are valuable markers to identify certain individuals susceptible to certain diseases such as Alzheimer, depression and diabetes. They are also helpful to understand individual variability in drug response.

Individual A		Individual B				Individual C
(Fast responder)		(Slow responder)				(Toxic responder)
			*						*
							*
Metabolizes the		Metabolizes the drug				Metabolizes the
drug more		slowly (lower doses				drug very poorly (it
efficiently (high		are needed to avoid				may have fatal
doses are needed		side-effect or toxicity).				effect).
to treat).

FIGURE 14.2. Highly hypothetical representation of the relationship between the genotype and drug response. Two horizontal lines denote a pair of homologous genes, mark indicates polymorphism in the gene which is responsible for different types of drug response.

454	BARKUR S. SHASTRY

CONCLUDING REMARKS

The human genome project is a big step forward for the identification of genes responsible for complex diseases. One major challenge for the future is to understand how genetic factors contribute to more common and complex conditions such as cancer, psychiatric disorders, diabetes and asthma. By identifying and understanding the genome—wide polymorphisms, a more complete medicinal response (adverse effect and efficacy) profile can be developed for each drug. This will ultimately enable clinicians to provide an effective therapy using the individual patient’s genetic background. It is hoped that these genetic approaches will significantly reduce long-term hospitalization and care in the near future.

The use of SNP in genetic testing and development of individualized medicine is truly exciting. However, to meet this challenge, several underlying problems should be considered. For instance (a) only a small fraction (15%) of SNPs are characterized to date to carry out meaningful experiments, (b) a huge number of SNPs are needed to make a connection between disease and SNP, (c) they are not randomly distributed in the genome,

(d) it is possible that some of them may not be allelic variants but cismorphism [7]. In addition, a large number of studies are required to identify haplotypes that are in linkage disequilibrium. Identification of a haplotype associated with type II diabetes mellitus [43] is a good example of this problem. It is clear that some SNPs do change the amino acid sequence or regulatory site but it is not clear how useful they are in diagnostic testing. This is because of the complexity of human disease and the involvement of environmental factors that determine the phenotype. It is also unknown how genotype alone can predict disease susceptibility. For instance, although identical twins have identical genotypes, they exhibit 50% concordance for more common diseases [64, 93, 125, 127]. Moreover, we should also consider the influence of developmental programs such as DNA methylation, X- inactivation and environmental conditions during postnatal development in causing disease susceptibility and phenotype.

Furthermore, individualized drug development also needs more challenging approaches. This is because, drug-metabolizing enzymes such as cytochrome P450 family have been well characterized but the drug receptors (or membrane transporter) have not been well studied. For instance, estrogen β and cyclooxygenase receptors and their therapeutic values are largely unknown. Additionally, these receptors are highly variable or genetically diverse in a given population which adds another limitation. Moreover, several drugs have multiple genetic effects that make the study design even more complex. Although it will be a long time before personalized medicine becomes a reality [129], pharmacogenomics plays a major role in reducing or preventing drug related deaths and hospitalization costs. Meanwhile, future research will uncover methods to make SNP markers as useful tags for medical testing.

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