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

Abstract

Complex diseases identification through Gene-Gene Interactions (GGIs) plays a significant challenge in Genome-Wide Association Studies (GWAS). A typical indicator of genetic variations in many human diseases is Single Nucleotide Polymorphisms (SNPs). SNPs are the most prevalent sort of genetic variation seen in human beings. The interactions between various SNPs are called Epistasis or genetic interactions. This research paper proposes a two-stage epistasis detection approach based on K-Means clustering and optimization techniques to detect epistasis effects responsible for complex human diseases. In the screening stage, K-Means clustering is adapted to partition the genotype dataset into various clusters. Traditional K-Means clustering algorithms have the flaw of arbitrary selection of the initial k centroid, which leads to inconsistent solutions and traps in the local optimum. We present a hybridized technique based on the K-Means algorithm and Nelder-Mead (NM) optimization (KMeans-NM) to avoid local optima, and all the genotype data falls into a unique collection of clusters for different runs. In the search stage, Salp Optimization with single objective functions (Salp-SO) and Salp Optimization with multi-objective functions (Salp-MO) are employed over the clusters obtained from the screening stage to find disease correlated SNP combinations. The performance of the various proposed algorithms is tested over the simulated datasets. Experimental findings indicated that the KMeans-NM-SalpEpi-SO and KMeans-NM-SalpEpi-MO method is superior to other techniques.