Genetic Interactions Effects of Cardiovascular Disorder Using Computational Models: A Review

Abstract

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.

KMeans-NM-SalpEpi: Genetic Interactions Detection through K-Means Clustering with Nelder-Mead and Salp Optimization Techniques in Genome-Wide Association Studies

“…It calculates the score for each SNP interaction, and a userspecified threshold is used to detect disease correlated interactions. The epistasis-based algorithms such as Multifactor Dimensionality Reduction (MDR), Boolean Operation-Based Screening and Testing (BOOST), Generalized Multifactor Dimensionality Reduction (GMDR), Efficient Survival-Multifactor Dimensionality Reduction (ES-MDR), PLINK, Generalized Multifactor Dimensionality Reduction-Graphics Processing Unit (GMDR-GPU) are evaluated based on exhaustive analysis [7]. Yang et al [8] developed the method Fuzzy Set-based Multiobjective Multifactor Dimensionality Reduction (FSMOMDR) in 2020 to identify genetic interactions in Coronary Artery Disease (CAD).…”