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The usual frequency of difference in single base between two identical
chromosomes of a homologous chromosome pair within the human genome is
1 per 1000 bp. This roughly means that the heterozygous form of a SNP is present
at a frequency of almost 0.1% within an individual (Brookes 1999; Orr and Chanock
2008; Sudmant et al. 2015). Since the human genome is diploid in nature and SNPs
involve the substitution of one nucleotide by another, SNPs usually exist in either of
the two forms based on the presence of either of the two alternative alleles. This
means that a SNP present at specific chromosomal loci can have different alternative
forms.
If the SNP contains an allelic form which is present more commonly within a
population, it is termed as “major allele” or “more common form.” In contrast, if the
SNP contains an allelic formic, which is present rarely or less commonly within a
population, it is termed as “minor allele” or “less common form.” Therefore, a single
human diploid genome can have various genotypic forms for a particular SNP
including homozygous for major allele, homozygous for minor allele, or heterozy-
gous for major and minor alleles (Crawford and Nickerson 2005). Many SNPs are
functionally and phenotypically associated with other SNPs or with different gene
regions through linkage disequilibrium and other possible mechanisms, which
makes it extremely difficult to clearly describe the effect of a particular SNP on a
specific phenotype considering other associated SNPs may also affect that pheno-
type (Abecasis et al. 2010; Sudmant et al. 2015).
The effect of a particular SNP on a specific phenotype varies depending on the
location of that SNP within the genome. Based on the location where single base
substitution occurs within the genome, SNPs can be categorized into two types:
coding region SNPs and noncoding region SNPs. The coding region SNPs are
located in exons, the regions of gene that code for the proteins. In contrast, the
noncoding region SNPs are located in the noncoding regions of the gene, that is, the
regions that do not code for proteins and include 5
0
-untranslated region (5
0
-UTR)
enhancer regions, 5
0
-UTR promoter regions, introns, and 3
0
-untranslated regions
(3
0
UTRs) (Fig. 1.6) (Aerts et al. 2002; Gonzaga-Jauregui et al. 2012).
It has been reported that around 50% of the SNPs are present in noncoding
regions and 50% of the SNPs are located in coding regions of the genome. Of all
5′ UTR
Promoter
5′ 3′
Exon 1
TA
TA
Exon 2 Exon 3
Splice Site
Intron 1 Intron 2
Start Codon
Upstream
Enhancer
Intergenic
Region
Stop Codon
3′ UTR
Coding Region of Gene
Fig. 1.6 The general gene structure and the different sites where SNPs can occur within it (shown
by fire arrows)
12 A. S. Sameer et al.
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the SNPs located in coding regions, 50% SNPs cause missense mutations and the
other 50% SNPs result in silent mutations (Brookes 1999; Sudmant et al. 2015).
Additionally, SNPs can also be categorized as bi-allelic, tri-allelic, or tetra-allelic
depending upon the number of alleles affected by the substitution (Ismail and Essawi
2012).
The coding region SNPs present in exons affect gene transcription and translation
and hence modulate the structure and function of the protein (Aerts et al. 2002; Deng
et al. 2017; Gonzaga-Jauregui et al. 2012; Lupski 2016; Snyder et al. 2010).
Furthermore, SNPs in coding regions can be either synonymous or
non-synonymous. The synonymous SNPs are silent and despite single nucleotide
substitution does not cause any change in the amino acid sequence because of the
degeneracy of the codon. The synonymous SNPs, however, can affect the messenger
RNA (mRNA) splicing, structure and stability, secondary structure conformation,
and translation dynamics (protein translat ion and cotranslational protein folding).
The non-synonymous SNPs affect protein structure and function and result in altered
protein activity. The non-synonymous SNPs cause change in the sense codon at the
substitution site (missense change) and result in the substitution of one amino acid
by another which resul ts in change in the structure, conformation, and functionality
of the protein but the prote in remains viable. Alternatively, these SNPs can change
sense codon into a stop codon, which causes premature termination of polypeptide
chain, and result in the production of truncated, non-functional protein (Ismail and
Essawi 2012).
The SNPs present in the noncoding regions of the genome can affect the gene
expression, especially if these SNPs are located in the regulatory regions of the gene,
which may affect the binding of transcriptional machinery or regulatory proteins.
The SNPs located in the promoter region of a particular gene can affect its expression
by altering a range of activities including binding of transcription factor(s), promoter
activity, promoter clearance, DNA methylation, and histone modifications.
The SNPs present in introns can alter protein expression through generation of
mRNA splice variants and promote and disrupt the binding and function of
cis-regulatory elements and long noncoding RNAs (lncRNAs). The SNPs in
5
0
-UTRs affect transcriptional activity and protein translation, whereas SNPs in
3
0
-UTRs affect miRNA binding and regulation of its degradation and protein
translation. The SNPs located in regions away from the genes may inhibit or promote
gene transcription through long-range cis effects (Table 1.2) (Fig. 1.7) (Brookes
1999; Deng et al. 2017; Karki et al. 2015; Robert and Pelletier 2018; Shastry 2009).
Therefore, the effect of a particular SNP on the gene expression and the subsequent
effect on various physiological and pathophysiological processes including suscep-
tibility to various diseases depend to a large extent on the location of the SNPs within
the gene or the genome (Deng et al. 2017; Haraksingh and Snyder 2013; Lupski
2016; Snyder et al. 2010).
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1.3.2 Applications of SNPs
Almost half of the SNPs are located in the noncoding regions of the human genome,
which gives them a very low penetrance, and apparently no or very little role in
Table 1.2 Molecular mechanisms of region-based SNP
Location of
SNPs Possible molecular mechanism
Promoter Gene regulation: It occurs through modulation of binding of transcription
factor(s) to promoter region, which affects promoter activity and promoter
clearance
Epigenetic regulation: It occurs through modulation of DNA methylation or
histone modification by affecting, for example, GATC sequence
Exons Non-synonymous coding SNPs: These SNPs affect protein structure and
function and result in altered protein activity
Synonymous coding SNPs: These SNPs affect messenger RNA (mRNA)
splicing, structure and stability, secondary structure conformation, and
translation dynamics (protein translation and cotranslational protein folding)
Introns The intronic SNPs can affect mRNA splicing, genomic imprinting, and
chromatin looping and binding of cis-regulatory elements and long noncoding
RNAs (lncRNAs)
UTRs 5
0
-UTR SNPs: These SNPs affect transcriptional activity and protein
translation
3
0
-UTR SNPs: These SNPs affect miRNA binding and regulation of its
degradation and protein translation
Non-definite
regions
These SNPs affect long-range cis-regulation and protein translation through
modulation of tRNA and rRNA function
Types of SNPs
Non-Coding
Region
Coding Region
Located in the exons
Affects the
expression of gene
Affects the
functionality of the
proteins
In Promoter or
Intronic Regions
Synonymous
SNP
Missense SNP
Results in change of a
codon and hence 1
amino acid in
proteins
Creates a stop codon
& hence causes
truncation of protein
at SNP
Nonsense SNP
Non-Synonymous
SNP
Fig. 1.7 The types of SNPs and their effects
14 A. S. Sameer et al.
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health or development of an individual. However, with the advancement in the
mapping of SNPs coupled with the cataloguing of data from a large number of
meta-analysis studies carried out in different regions of the world, it has emerged that
SNPs play multiple roles. It has been revealed that thousands of SNPs are involved
directly or indirectly in modulating the susceptibility to several diseases including
cancer (Goldstein and Cavalleri 2005; Lander 2011; Sudmant et al. 2015). HapMap
with its databases of around ten million SNPs in human genome has enabled to link
various SNPs as the predisposing risk to various multifactorial diseases like rheuma-
toid arthritis, atherosclerosis, asthma, multiple sclerosis, obesity, diabetes, and
cancers (Table 1.3 lists various public SNP databases available currently)
(Ramírez-Bello 2019; Shastry 2009).
SNPs have emerged as important tool in understanding the population genetics.
SNPs have played a vital role in establishing the evolut ionary divergence of humans
from the other primates (like chimpanzees). It has been reported that most of the
present-day SNPs in human genome have originated before the divergence of human
race into various populations but after their divergence from primates (Mountain
et al. 1992). This has also been established through the discovery that most of the
SNPs in the human genome are not found in the primates though about 85% of those
do exist commonly in all human races (Barbujani et al. 1997). Furthermore, SNPs
have helped in establishing the genealogy and ethnicity within the human popula-
tion, as there exists a substantial heterogeneity among human popula tion of different
races and ethnicities (Goldstein and Cavalleri 2005; Goldstein and Weale 2001;
Weir et al. 2005).
The SNPs also find wide application in the field of pharmacogenomics, which
involves the study of how the genes and hence the SNPs affect the metabolism,
response, and efficacy of drugs (Alwi 2005). It has emerged that a number of
uncontrollable factors like the response to drugs, adverse drug reactions, required
drug dosage, etc. affect the metabolism and efficacy of drugs. The treatment
protocols based on earlier notion that “one size fits all” are not fully effective.
Therefore, a tailor-made approach involving precision or personalized medicine is
now being adopted taking into consideration the effect of the human genome on the
metabolism, response, and efficacy of drugs. SNPs of particular pathways especially
the xenobiotic genes [cyto chrome p450 (CYP) genes] (PharmVar SNP database)
have emerged as valuable tools for pharmaceutical industry and they have helped
greatly in predicting the drug response and prevent adverse reactions (Chaudhary
et al. 2015; Katara 2014). Personalized medicine has taken a huge leap with the
advent of HapMap of SNPs and has helped in the assessment of predisposition to
diseases, screening and diagnosis of diseases, disease prognosis, stratification of
patients, estimation of drug dosage, monitoring of drug response, and assessment
and/or prediction of drug resistance (Laing et al. 2011). SNP maps have also
revolutionized the oncology in designing the targeted therapies to treat basic and
aggressive cancers based on the SNPs of the involved genes (Rodríguez-Antona and
Taron 2015).
1 Mutations and Polymorphisms: What Is The Difference? 15
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Table 1.3 Various publicly available SNP databases and their importance
Database Host
Site URL for
SNP searches
Importance and
Usage Content
dbSNP NCBI https://www.
ncbi.nlm.nih.
gov/snp/
SNPs from the
complete human
genome
Almost 6.67
million validated
SNPs
HapMap The HapMap
consortium
https://www.
genome.gov/
10,001,688/
international-
hapmap-project
Contains SNPs
from four
populations
10 million SNPs
Ensembl EMBL-EBI http://asia.
ensembl.org/
info/
Genome/
variation/index.
Html
Contains SNPs
from various
species
Almost six
million validated
SNPs
HGVbase HGVS https://www.
hgvs.org/central-
mutation-snp-
databases
SNPs from the
complete
Genome. Collates
all databases
SNPs from
different
populations
GVS NHLBI http://gvs.gs.
washington.edu/
GVS
Provides access to
dbSNP and
HapMap SNPs
11 million SNPs
PharmGKB Stanford
university
http://www.
pharmgkb.org
SNPs in genes
involved in drug
metabolism
23,910 SNPs
from 167 genes
RTPDB CNHGC http://www.
rtpdb.com/
SNPs in genes
involved in cancers
708 SNPs from
261 genes of
16 tumor types
and 16 treatment
types
NIEHS
SNPs
NIEHS-EGP-
UW
http://egp.gs.
washington.edu
SNPs in genes
involved in
environmental
response
83,000 SNPs
Seattle
SNPs
US NHLBI
(PGA)
https://pga.gs.
washington.edu/
SNPs in candidate
genes and pathways
that underlie
inflammatory
response
38,200 SNPs
from 327 genes
Santa Cruz University of
California
https://genome.
ucsc.edu/
SNPs from the
complete human
genome
Almost six
million validated
SNPs
PharmVar Pharmacogene
variation
(PharmVar)
consortium
http://www.
cypalleles.ki.se
https://www.
pharmvar.org/
SNPs in human
cytochrome P450
genes
SNPs from
30 CYP 450
Genes
(continued)
16 A. S. Sameer et al.
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1.4 Conclusions
Numerous GWAS carried out across the spectrum of diseases in various populations
around the world, have enabled the identification of thousands of disease related
variants. It can easily be said that genetic polymorphisms in general and SNPs in
particular possess a great potential in unraveling the mechanism of how genotype
affects phenotype through gene–gene and gene–environment interactions.Addition-
ally, carrying out large collaborative studies in the field of genetic polymorphisms
will also help in elucidation of complex disease mechanisms, better risk profiling,
diagnosis and prognosis of diseases, and in creating specific and effective
personalized therapeutics.
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