Background:
The diseases in the heart and blood vessels such as heart attack, Coronary
Artery Disease, Myocardial Infarction (MI), High Blood Pressure, and Obesity, are generally referred
to as Cardiovascular Diseases (CVD). The risk factors of CVD include gender, age, cholesterol/
LDL, family history, hypertension, smoking, and genetic and environmental factors. Genome-
Wide Association Studies (GWAS) focus on identifying the genetic interactions and genetic architectures
of CVD.
Objective:
Genetic interactions or Epistasis infer the interactions between two or more genes where
one gene masks the traits of another gene and increases the susceptibility of CVD. To identify the
Epistasis relationship through biological or laboratory methods needs an enormous workforce and
more cost. Hence, this paper presents the review of various statistical and Machine learning approaches
so far proposed to detect genetic interaction effects for the identification of various Cardiovascular
diseases such as Coronary Artery Disease (CAD), MI, Hypertension, HDL and Lipid
phenotypes data, and Body Mass Index dataset.
Conclusion:
This study reveals that various computational models identified the candidate genes
such as AGT, PAI-1, ACE, PTPN22, MTHR, FAM107B, ZNF107, PON1, PON2, GTF2E1, ADGRB3,
and FTO, which play a major role in genetic interactions for the causes of CVDs. The benefits,
limitations, and issues of the various computational techniques for the evolution of epistasis responsible
for cardiovascular diseases are exhibited.
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