MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

Abstract

Genome-wide association studies have succeeded in identifying genetic variants associated with complex diseases, but the findings have not been well interpreted biologically. Although it is widely accepted that epistatic interactions of high-order single nucleotide polymorphisms (SNPs) [(1) Single nucleotide polymorphisms (SNP) are mainly deoxyribonucleic acid (DNA) sequence polymorphisms caused by variants at a single nucleotide at the genome level. They are the most common type of heritable variation in humans.] are important causes of complex diseases, the combinatorial explosion of millions of SNPs and multiple tests impose a large computational burden. Moreover, it is extremely challenging to correctly distinguish high-order SNP epistatic interactions from other high-order SNP combinations due to small sample sizes. In this study, a multitasking harmony search algorithm (MTHSA-DHEI) is proposed for detecting high-order epistatic interactions [(2) In classical genetics, if genes X1 and X2 are mutated and each mutation by itself produces a unique disease status (phenotype) but the mutations together cause the same disease status as the gene X1 mutation, gene X1 is epistatic and gene X2 is hypostatic, and gene X1 has an epistatic effect (main effect) on disease status. In this work, a high-order epistatic interaction occurs when two or more SNP loci have a joint influence on disease status.], with the goal of simultaneously detecting multiple types of high-order (k1-order, k2-order, …, kn-order) SNP epistatic interactions. Unified coding is adopted for multiple tasks, and four complementary association evaluation functions are employed to improve the capability of discriminating the high-order SNP epistatic interactions. We compare the proposed MTHSA-DHEI method with four excellent methods for detecting high-order SNP interactions for 8 high-orderepistatic interaction models with no marginal effect (EINMEs) and 12 epistatic interaction models with marginal effects (EIMEs) (*) and implement the MTHSA-DHEI algorithm with a real dataset: age-related macular degeneration (AMD). The experimental results indicate that MTHSA-DHEI has power and an F1-score exceeding 90% for all EIMEs and five EINMEs and reduces the computational time by more than 90%. It can efficiently perform multiple high-order detection tasks for high-order epistatic interactions and improve the discrimination ability for diverse epistasis models.

Detecting protein complexes with multiple properties by an adaptive harmony search algorithm

“…Additionally, most protein complex detection methods have a common disadvantage: their parameters are specified by users, making it difficult to deal with various PPI networks effectively. In recent years, the harmony search algorithm has paid much attention in the fields of bioinformatics, such as the detection of high-order SNP epistasis and protein interactions [48], combinations, and epistasis [49][50][51], etc. Therefore, the improved harmony search algorithm is used to determine the parameters of the protein complex detection method in this paper.…”

SimHOEPI: A resampling simulator for generating single nucleotide polymorphism data with a high‐order epistasis model

“…It has been widely accepted that epistasis affects complex diseases through nonlinear interactions between SNPs [1,2]. To analyze epistatic interactions, many epistasis detection methods have been proposed.…”

A novel intelligent global harmony search algorithm based on improved search stability strategy

“…In medicine and genetics, Shouheng Tuo et al developed and applied some excellent modified harmony search algorithm to detect higher-order SNP epistatic interactions. Through experimental comparison, it is proved that this algorithm can effectively perform multiple high-order detection tasks for high-order epistatic interaction, and improve the recognition ability of different epistatic models 68 – 70 .…”