diseases attained by numerous computational approaches and drugs with previous

functional indication.

5.3.1

Linking Target Genes to Clinically Approved Drugs

With the rapid rise of technology, there have been significant upgrades in the study

of human genetics and usage of the output data. Many different programs now exist

to capture genetic information and analytical tools to identify key genetic risk factors

(Bush and Moore 2012). Forming a link between disease traits such as disease-

causing genes and clinically approved drugs provides a greater chance of DR success

as compared to a proposed repositioning with no link to a genetic target (Nelson et al.

2015). Transcriptional data reflect gene regulation profiles of human cells in

responses to disease, toxins, drugs, and more. Such data provides closer insight to

specific isolated situations, thus enhancing the understanding of relationships

between the host, drug, and disease (Iorio et al. 2013). The regulation of specific

genes or disease markers may serve as a basis for successful DR. Inhibitors,

stimulators, or other types of approved drugs may be used to specifically counter

the disease-causing effects by moderating those responses.

5.3.2

Usage of Computational Methods

Modern in silico research for DR employs a combination of databases, software, and

analysis tools to elucidate specific and theoretically functional annotations to serve

as targets or potential therapies (Talevi 2018). With the large amount of information

being gathered from genetic studies, it is important to highlight that no particular

stringency of computational workflow suits all. Rather, fluidity in research method-

ology is dependent on factors such as the analysis required to determine disease-

causing gene variants known as causal genes, data availability of known pathways

related to the gene/disease, and the choice of databases and software. Selecting the

most suitable workflow for computational methods can narrow down the search time

and provide more reliable drug candidates for other diseases.

5.3.2.1 Genome-Wide Association Studies (GWAS)

As the role of genetics in medicine has become more evident in the past years, DR

against specific targets using computational approaches have also grown in popular-

ity. A genome-wide association study (GWAS) is a research approach involving the

evaluation and scanning of genomic profiles of different individuals to identify

genetic markers or specific genetic variations that may be associated with a particular

disease (Genome 2021). Gene variation in GWAS is identified from single-

nucleotide polymorphisms (SNPs) of ill individuals. In the past, the clinical applica-

tion of GWAS was criticized as being limited due to the large number of non-coding

gene variants available in the data set. As an implication, when considered for certain

treatments, they may have reduced potential as efficient targets (Pritchard et al.

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