Cuckoo search epistasis: a new method for exploring significant genetic interactions

Abstract

The advent of high-throughput sequencing technology has resulted in the ability to measure millions of single-nucleotide polymorphisms (SNPs) from thousands of individuals. Although these high-dimensional data have paved the way for better understanding of the genetic architecture of common diseases, they have also given rise to challenges in developing computational methods for learning epistatic relationships among genetic markers. We propose a new method, named cuckoo search epistasis (CSE) for identifying significant epistatic interactions in population-based association studies with a case-control design. This method combines a computationally efficient Bayesian scoring function with an evolutionary-based heuristic search algorithm, and can be efficiently applied to high-dimensional genome-wide SNP data. The experimental results from synthetic data sets show that CSE outperforms existing methods including multifactorial dimensionality reduction and Bayesian epistasis association mapping. In addition, on a real genome-wide data set related to Alzheimer's disease, CSE identified SNPs that are consistent with previously reported results, and show the utility of CSE for application to genome-wide data.

An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions

“…Detection power is one of the generally used evaluation measures in the field of the detection of SNP-SNP interactions, and various forms of detection power have been proposed depending on what is desired to measure [ 4 , 7 , 9 – 11 , 21 , 31 , 37 ]. In this study, two types of detection power are introduced, namely, Power 1 and Power 2.…”

“…Recently, many swarm intelligence based algorithms have been proposed for the detection of SNP-SNP interactions [ 17 – 31 ]. Among them, particle swarm optimization (PSO) appears promising and some related works have been reported [ 22 – 30 ].…”

Stochastic Fractal Search: A powerful metaheuristic algorithm

“…Before starting the second procedure, once again, all points obtained from the first statistical procedure are ranked based on Eq. (15). Similar to the first statistical process, if the condition Pa i < e is held for a new point P 0 i , the current position of P 0 i is modified according to Eqs.…”

“…Cuckoo Search (CS) [2] is another successful metaheuristic which mimics the cuckoo behavior reproduction strategy. Over the last decades, uses of metaheuristic algorithms have increased [10][11][12][13][14][15][16][17]. These algorithms are used to solve complex computational optimization problems, however, fast convergence along with accuracy is not guaranteed.…”

Investigation the Effect of Particle Swarm Optimization in Performance of Mixture of Experts

“…Nowadays, the applications of metaheuristic in solving problems are increased dramatically [15][16][17][18]. Among the all metaheuristic algorithms, PSO is a population (swarm) based optimization tool.…”