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Single Nucleotide Polymorphisms
and Pharmacogenomics
2
Azher Arafah, Shafat Ali, Sabhiya Majid, Samia Rashid,
Shabhat Rasool, Hilal Ahmad Wani, Iyman Rasool, and
Muneeb U. Rehman
Abstract
Pharmaco-genomics determines the individual genetic mechanism for drug
response and has the ability to transform tailored medication into clinical practice.
A huge number of individuals die every year from adverse drug response since
each person reacts differently to similar drug. The science of pharmaco-genomics
A. Arafah
Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi
Arabia
S. Ali
Cytogenetics and Molecular Biology Laboratory, Centre of Research for Development, University
of Kashmir, Hazratbal, Srinagar, Jammu and Kashmir, India
Department of Biochemistry, Government Medical College (GMC-Srinagar) & Associated SMHS
Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India
S. Majid · S. Rasool · H. A. Wani
Department of Biochemistry, Government Medical College (GMC-Srinagar) & Associated SMHS
Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India
S. Rashid
Department of Medicine, Government Medical College (GMC-Srinagar) & Associated SMHS
Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India
I. Rasool
Department of ENT, Government Medical College (GMC-Baramulla), Kanth Bagh, Baramulla,
Jammu and Kashmir, India
M. U. Rehman (*)
Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi
Arabia
Department of Biochemistry, Government Medical College (GMC-Srinagar) & Associated SMHS
Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India
#
The Author(s), under exclusive license to Springer Nature Singapore Pte
Ltd. 2021
A. S. Sameer et al. (eds.), Genetic Polymorphism and Cancer Susceptibility,
https://doi.org/10.1007/978-981-33-6699-2_2
23
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has emerged as a potential discipline in the development of drugs and clinical
medicine during the last several decades. It has offered a hope of protection for
the patients against lethal health complications that arise from the adverse drug
responses. The new medication approaches utilizing the science of pharmaco-
genomics reduce the patient exposure to less or non-effective drugs or drugs with
adverse effects. Same drugs have been reported to induce specific reactions in
each individual owing to different nucleotide sequences in genes that encode
the essential biological molecules such as drug-metabolizing enzymes, drug
transporters, and drug targets. Single nucleotide polymorphisms (SNPs) are
very helpful in determining the susceptibility of individuals to different diseases
and drug reaction. These polymorphisms are the most prevalent in the genome of
humans and account for 90% of genetic variance amongst individuals. Pharmaco-
genomics may help in understanding the strong effects of inherited single gene
variations on drug mobilization and action. The detection and characterization of
SNPs related with disease risk and adverse drug response (ADR) is essential
before using them as genetic tools. The credibility of SNP application in the
diagnosis of diseases and possible ADR has been increased by the completion of
HapMap project but still there are some challenges associated with it. The present
chapter attempted to present the general role and effect of SNPs on pharmaco-
genomics as well as their utility in clinical practice.
Keywords
Pharmaco-genomics · Single nucleotide polymorphism · Tailored medication ·
Diseases · Sequencing · Drug response
Abbreviations
5-FU 5-fluorouracil
ADR Adverse drug response
APOE Apolipoprotein E
CYP Cytochrome P450
DNA Deoxyribonucleic acid
DPYD Dihydropyrimidine dehydrogenase
LD linkage disequilibrium (LD)
NAT 2 N-acetyl transferase 2
SNP Single nucleotide polymorphism
TPMT Thiopurine S-methyltransferase
TPMT Thiopurine methyltransferase
UGT1A1 Uridine diphosphate glucuronosyltransferase 1A1
VDR Variable drug response
24 A. Arafah et al.
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2.1 Introduction
Single nucleotide polymorphism (SNP) occurs when a base of specific nucleotide is
substituted by another base at certain location of genome. Abundant number of SNPs
exist in human genome and about 5,000 pharmaco-genomics associated SNPs have
been reported on Pharmaco-genomics Knowledgebase (PharmGKB). SNPs exist all
through the genome and produce diverse alleles (Zaid et al. 2017). During the whole
genome sequencing in humans mismatched base pairing was observed. DNA is
made of four bases that include adenine, cytosine, thymine, and guanine. Among
these bases adenine commonly forms bonds with thymine and that of guanine with
cytosine. This mispairing of bases was observed around every 1000 base pairs and
considered as SNPs when present in a minimum of 1% of individuals of entire
population (Kirk et al. 2002). SNPs exist in coding as well as non-coding regions of
DNA. SNPs of coding region have been categorized into synonymous and
non-synonymous SNPs. Synonymous SNPs do not alter sequence of amino acids
in proteins while as non-synonymous SNPs modify the sequence of amino acids in
proteins. Non-synonymous SNPs are further classified as missense and non-sense
SNPs (Hunt et al. 2009) (Fig. 2.1). Such genetic polymorphisms even in an individ-
ual gene including drug receptor and transporter coding genes as well as the gene
coding for cell signaling pathways may be a crucial predictor of clinical reaction
(Nebert et al. 2013). The series of SNPs existing together in a specific part of genome
give rise to haplotype. Initially about 300,000 SNPs were estimated. However, the
figure was raised to 1.4 million in 2001 (Thorisson and Stein 2003), 3.1 million in
2007 (Frazer et al. 2007), and 6.4 million currently available in the database of
HapMap project. In human s SNPs represent over 90% of genetic polymorphisms
Fig. 2.1 Classification of single nucleotide polymorphisms (SNPs)
2 Single Nucleotide Polymorphisms and Pharmacogenomics 25
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(Collins et al. 1998; McPherson et al. 2001). Various bio-informatics databases for
SNPs are described in Table 2.1.
A general medical problem nowadays is variable drug response (VDR) against
any drug. These variabilities range from drug failure to adverse drug response
(ADR). It has now become evident that such inter-individual variations are heredi-
tary and caused by genomic variations among individuals (Wang et al. 2012).
Genomic composition is a significant element in the ultimate effect of the
individual’s response to the medication in question. Pharmaco-genomics is indeed
a discipline that originates as a result of junction of the pharmacology with geno-
mics. Simply speaking, pharmaco-genomics study the effect of genomic variations
on the individual drug response (Weng et al. 2013). These changes may be attributed
to differences in drug targets or variations in drug carrying or metabolizing enzymes,
namely CYP450 (Klein and Zanger 2013).
Variations in drug targets may result in differences in potency of drugs, but
variations in enzymes alter either drug efficiency or toxicity. Research in the field
of pharmaco-genomics has acquired tremendous significance with modern
developments in genomic sequencing and molecular genetics. The novel technology
facilitates fast screening of specialized polymorphisms, and also the identification of
target geneti c sequences that code for enzymes, ionic channels, and certain other
kinds of receptors associated with drug response (Lennard et al. 2013; Nelson 2013).
The Human Genome project (HGP) helped in determining the molecular structure of
the enzymes that in turn make it possible to correlate variability between genotypes
and phenotypes. Such developments can enable the identification of persons that
may suffer harmful medication reactions. This all became possible by SNPs identifi-
cation (Chang et al. 2012).
Table 2.1 Bio-informatics databases for SNPs
S
No.
Bio-informatics
Databases Description
1. dbSNP
Kaviar
NCBI (National Centre for biotechnology information) database
for SNPs
Multisource SNP-compendium
2. SNPedia Wiki-type database for annotating, interpreting, and analyzing
individual genome
3. OMIM Database that related polymorphisms with diseases
4. dbSAP Single amino-acid polymorphism-based database detecting
protein variants
5. Human gene
mutation database
Provides functional SNP-related or inherited human disease
associated gene mutations
6. HapMap project Allows tag SNPs identification for haplotype collection
7. GWAS central Allows visual interrogation of exact summary level details in a
single or multiple genome-wide association studies
8. 1000 genomes
project
A catalog with detailed data about human genome variations
9. PharmGKB A data resource of SNPs related with drug reaction and disease
outcome
26 A. Arafah et al.
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Several SNPs are linked with different drug metabolisms (Goldstein 2001; Lee
2004; Yanase et al. 2006). SNPs can occur as deletions that either impede or
facilitate enzymatic activity and result in lower levels of drug metabolism (Butler
2018). The correlation of a broad variety of human disorders such as infectious
diseases (leprosy, hepatits, AIDS, etc.), autoimmune disorders, neuropsychiatry
defects, and several other ailments (Table 2.2) with various SNPs may be render
as important pharmaco-genomic targets for treatmen t (Fareed and Afzal 2013).
2.2 Techniques for the Identification of SNPs
It is necessary to identify and characterize large numbers of SNPs before extensively
utilizing them as genetic tools. In order to carry out association studies optimized
marker sets need to be constructed from several lakh SNPs. Five approaches are
widely used for the identification of SNPs (or mutation) (Wang et al. 1998; Nees and
Woodworth 2001; Khan 2004) (Fig. 2.2).
2.2.1 Single Strand Conformation Polymorphisms (SSCPs)
The DNA segment containing the presumed SNP is amplified with PCR, followed
by denaturation, and run through non-denaturi ng polyacrylamide gel for the identi-
fication of SSCP. The single-stranded DNA fragments formed acquire secondary
structures determined by their sequence of nucleotides while moving through the
gel. The DNA fragments that contain SNPs are recognized due to their unusual
movement behavior and verified via Sequencing. This technique though is com-
monly used and fairly easy but its performance rate for the identification of SNPs
varies, usually varying from 70% to 95% (Wang et al. 1998). This low capacity
technique needs intensive labor, however, highly competent approaches based on
capillary instead of gels are evolving (Wenz et al. 1999).
2.2.1.1 Hetero-Duplex Analysis
This technique detects the hetero-duplex produced when denatured strands of PCR
product obtained from a person heterozygous for the SNP undergo re-ann ealing. The
hetero-duplex is detected either by high-performance liquid chromatography column
where it shows deferential retention or by band shifting on gel. HPLC developed as a
well-known approach for the detection of SNPs by using hetero-duplex because of
its cost effectiveness, simplicity, and high detection rate (95–100%) (O’Donovan
et al. 1998). With the help of Trans-genomic Wave system, a rational output can be
obtained at the rate of 10 min/ sample (Buyse et al. 2000).
2 Single Nucleotide Polymorphisms and Pharmacogenomics 27
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Table 2.2 SNPs in association with human diseases
S. No SNPs
Associated
disease Study technique References
1. Matriptase2 gene/TMPRSS6
(Transmembrane serine protease 6)
Breast cancer TaqMan
®
assay Hartikainen et al. (2006)
2. ALOX5(Archidonate
5-lipooxygenase)
Asthma – Kalayci et al. (2006)
3. DRD2 (dopamine receptor D2) Colorectal
cancer
APEX system Gemignani et al. (2005)
4. UGT1A1
(UDP-glucoronosyltransferase
1A1)
Breast cancer Pyrosequencing™ Shatalova et al. (2006)
5. ADRB2 (Adrenoceptor beta 2) Asthma – Dewar et al. (1997), Matheson et al. (1995)
6. OPCML (Opiod binding protein/
cell adhesion molecule like)
Schizophrenia PCR (polymerase chain reaction), HRM
(high resolution melting) analysis
Panichareon et al. (2012)
7. APOA1/C3/A4/A5 (Apolipoprotein
gene cluster)
Dyslipidemia GWAS (genome-wide association
studies)
Kathiresan et al. (2009b)
8. APOB (Apolipoprotein B) gene Dyslipidemia GWAS (genome-wide association
studies)
Kathiresan et al. (2009b)
9. APOE (Apolipoprotein E) gene Dyslipidemia GWAS (genome-wide association
studies)
Kathiresan et al. (2009b)
10. CYP2D6*4 (cytochrome P450
2D6*4)
Alzheimer’s
disease
– Martinelli-Boneschi et al. (2013), Lu et al.
(2014)
11. ApoER2 (Apolipoprotein E
receptor 2) gene
Dyslipidemia PCR and HRM analysis Thongket et al. (2015)
12. PPARG (peroxisome proliferator
activated receptor gamma)
(pro12Ala)
T2DM (type
2 diabetes
mellitus)
FRET (fl uorescence resonance energy)
and FP (fluorescence polarization)
methods
Altshuler et al. (2000), Sarhangi et al.
(2020)
13. MEF2D (Myocyte specific
enhancer factor 2D)
Migraine GWAS (genome-wide association
studies)
Freilinger et al. (2012)
28 A. Arafah et al.
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14. TGFBR2 (transforming growth
factor beta receptor 2)
Migraine GWAS (genome-wide association
studies)
Freilinger et al. (2012)
15. PHACTR1(phosphate and actin
regulator 1)
Migraine GWAS (genome-wide association
studies)
Freilinger et al. (2012)
16. ASTN2 (Astroactin 2) Migraine GWAS (genome-wide association
studies)
Freilinger et al., 2012
17. TGFBR2 (transforming growth
factor beta receptor 2)
Monogenic
Marfan’s
syndrome
GWAS (genome-wide association
studies)
Mizuguchi et al. (2004)
18. PHACTR1(phosphate and actin
regulator 1)
Myocardial
infarction
GWAS (genome-wide association
studies)
Kathiresan et al. (2009a)
19. APOE (Apolipoprotein E) gene Alzheimer’s
disease
– Richard et al. (1997)
20. C7orf10 (chromosome 7 poen
reading frame 10) gene
Migraine GWAS (genome-wide association
studies)
Marlaire et al. (2004)
21. MMP16 (matrix metalloproteinase
16) gene
Migraine GWAS (genome-wide association
studies)
Rozanov et al. (2004)
22. ALOX5(Archidonate
5-lipooxygenase)
Lung cancer Genotyping Wei et al. (2020)
23. ALOX12(Archidonate12-
lipooxygenase)
Breast cancer WGA (whole genome amplification) and
genotyping
Connor et al. (2015)
Colorectal
cancer
Genotyping Kleinstein et al. (2013), Habermann et al.
(2013), Kraus et al. (2013), Resler et al.
(2014)
Atherosclerosis Genotyping Burdon et al. (2010)
Schizophrenia Genotyping Kim et al. (2010)
Obesity Genotyping Xiao et al. (2011)
2 Single Nucleotide Polymorphisms and Pharmacogenomics 29
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2.2.2 Direct DNA Sequencing
Presently, the direct DNA sequencing is the recommended high-performance
approach for the detection of SNPs. Wit h the completion of sequencing process
under limited human interference, only one capillary device (e.g. Applied
Biosystems 3700) may produce sequences from over 1500 DNA segments each of
500 bps just within 48 hours. With the help of dye-terminato r sequencing 95%
hetero-zygotes can be identified, however, for the detect ion of all sequences highly
expensive, complex, and labor-intensive dye-primer chemistry is required (Gross
et al. 1999).
2.2.3 Variant Detector Arrays
This approach is comparatively new high-output method used for the identification
of SNPs. It enables the detection of SNPs by hybri dizing PCR products to oligo-
nucleotides placed on glass chip and the calculation of difference in the strength of
hybridization among compatible and incompatible oligo-nucleotides. The rate of
detection of Variant detector arrays (VDA) is equivalent to that of the dye-terminator
sequencing. This technique enables a fast scanning of vast numbers of DNA
sequences. With the application of this approach 2500 SNPs were identified in
2 Mb of DNA in humans by Wang et al. (1998) and 874 SNPs were identified by
Halushka et al. (1999) in 75 hypertension candidate genes.
Fig. 2.2 Techniques used for
the identi fication of single
nucleotide polymorphisms
30 A. Arafah et al.
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2.2.4 DNA Microarray Technology
This technology is the most recent and advanced tool for SNP study (Nees and
Woodworth 2001; Khan 2004). It provides an important biotechnological break-
through for tracking genetic expression of thousands of genes just in one experiment
with the application of optics, chemistry of DNA, and silicon chip technology. In
short, a robot adds 20 thousand to 1 lakh unique molecules of DNA to the silicon
wafer surface and the level of expression of all these genes can be observed in only
one experiment via utilizi ng a standard microarray assay. Now microarray
techniques are commonly utilized to monitor genetic expression of huge figure of
genes, and often used for genotyping, analyzing DNA sequences, and molecular
diagnostics. With these techniques the DNA segments that even differ only by a SNP
can be differentiated which makes the microarray technology as a potent approach
for the identification of new molecular targets for drugs as well as elucidation of drug
action mechanisms. Moreover, the microarray techniques are capable of monitoring
large-scale gene expression profile in respond to pharmacological agents, and thus
furnish data related to drug efficiency and toxicity (Nees and Woodworth 2001).
2.2.4.1 SNPs and Tailored Medication
The genetic variation among human individuals occurs and understood due to SNPs.
The SNPs are the key role players in the concept development of tailored medication
(Wang et al. 2012). In the genome of human beings about 3 billion nucleotide pairs
are present, and a modification in a single nucleotide base pair existing in a minimum
of 1% population is described as a SNP. These minor genetic variations are most
widespread among individuals and produce huge functional differences. Different
biotechnology companies during this current era of Next Generation Sequencing
(NGS) provide relatively cost-effective genotyping of SNPs, and for predicting
SNPs from data already sequenced, numerous computational software systems and
algorithms are available and by now put into practice (Useche et al. 2001; van
Oeveren and Janssen 2009). The relationship of SNPs in humans with different
pharmacologic processes such as drug reaction and toxicity has been observed which
provided some information available on PharmGKB (Whirl-Carrillo et al. 2012), and
Human HapMap about the effect of unique SNPs complement of patient on the
response to drugs.
2.2.4.2 Inter-Individual Variability in Drug Response
The therapeutic response to prescribed drugs remarkably differs among individuals
and the resulting effects are often patient specific (Marchant 1981). This inter-
individual heterogeneity is often challenging for developing a protocol for pre-
scribed dose since majority of medications are quite effective just in about 25% to
60% of cases (Wilkinson 2005). Some patients do not adequately react and gain from
initial medication. For instance, the average percentage of patients with depression
(38%), asthma (40%), diabetes (43%), arthritis (50%), and cancer (75%) do not
respond to preliminary treatment (Spear et al. 2001).
2 Single Nucleotide Polymorphisms and Pharmacogenomics 31
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Different individual patients may react to the same medication and dosage
differently (Fig. 2.3). Often an effective dose of a drug may be fatal for a specific
patient or lead to therapeutic failure in several others (very low concentration of
drugs at regular doses) which leads to significant negative effects or almost no
effects. In case of treatment with drugs that have known adverse side effects and
narrow clinical indices, the constant drug screening is advised to prevent unpredicted
and harmful consequences (Evan and McLeod 2003). The problem may worsen
further due to potential drug-disease and drug–drug interaction when a patient
suffers from many diseases and receives multiple drug treatments (Rettie and Tai
2006). For instance, the regular dose of Warfarin used for the treatment of thrombo-
sis and embolism may vary 20–30 times among patients in several cases of disease
(Rettie and Tai 2006).
In case of Simvastatin similar dose-dependent variations among individual
patients in response to this drug has been observed (Chasman et al. 2004). The
optimal daily dosage of simvastatin for blood cholesterol control is prescribed at
40 mg. A study of 156 cohorts reported that the treatment of patients with
Similar Patients
AB C
Similar
Disease
Severity
SNPs
Variable Drug
Response
Adverse
Response
Positive
Response
No
Response
A
Individual
Patient
Response
Similar Drug
Treatment
BC
Fig. 2.3 Inter-individual
variations in drug response
32 A. Arafah et al.
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