SAMA: A Fast Self-Adaptive Memetic Algorithm for Detecting SNP-SNP Interactions Associated with Disease.

Ying Yin,Yuhai Zhao,Boxin Guan,Yuan Li

doi:10.1155/2020/5610658

Abstract

Detecting SNP-SNP interactions associated with disease is significant in genome-wide association study (GWAS). Owing to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power and long running time. To tackle these drawbacks, a fast self-adaptive memetic algorithm (SAMA) is proposed in this paper. In this method, the crossover, mutation, and selection of standard memetic algorithm are improved to make SAMA adapt to the detection of SNP-SNP interactions associated with disease. Furthermore, a self-adaptive local search algorithm is introduced to enhance the detecting power of the proposed method. SAMA is evaluated on a variety of simulated datasets and a real-world biological dataset, and a comparative study between it and the other four methods (FHSA-SED, AntEpiSeeker, IEACO, and DESeeker) that have been developed recently based on evolutionary algorithms is performed. The results of extensive experiments show that SAMA outperforms the other four compared methods in terms of detection power and running time.

Highlights

The development of high-throughput sequencing technology makes it possible to analyze single-nucleotide polymorphisms (SNPs) from thousands of individuals [1, 2]
We propose a fast self-adaptive memetic algorithm (SAMA) to detect two-locus SNP-SNP interactions associated with disease
We propose the SAMA algorithm to detect twolocus SNP-SNP interactions associated with disease

Summary

Introduction

The development of high-throughput sequencing technology makes it possible to analyze single-nucleotide polymorphisms (SNPs) from thousands of individuals [1, 2]. GWAS has been successfully applied to identify numerous SNPs associated with diverse diseases, such as about 30 loci associated with schizophrenia [6,7,8]. In the past few years, many methods have been raised for detecting two-locus disease models. These algorithms can be categorized into exhaustive search, stochastic search, heuristic search, and swarm intelligent optimization algorithms [12]. The exhaustive search is a method which evaluates the degree of correlation between all possible SNP-SNP interaction combinations and disease [13, 14] but is often computationally unaffordable for datasets with very large number of SNPs

Methods

Results

Conclusion