Background:
The diseases in 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) focuses extremely on identifying the genetic interactions and genetic architectures of CVD.
Objective:
Genetic interactions or Epistasis infers 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 reveal that various computational models identified the candidate genes such as AGT, PAI-1, ACE, PTPN22, MTHR, FAM107B, ZNF107, PON1, PON2, GTF2E1, ADGRB3, and FTO plays 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.
