The Spherical Evolutionary Multi-Objective (SEMO) Algorithm for Identifying Disease Multi-Locus SNP Interactions

Single-nucleotide polymorphisms (SNPs), as disease-related biogenetic markers, are crucial in elucidating complex disease susceptibility and pathogenesis. Due to computational inefficiency, it is difficult to identify high-dimensional SNP interactions efficiently using combinatorial search methods,...

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Bibliographic Details
Published in:Genes Vol. 15; no. 1; p. 11
Main Authors: Ren, Fuxiang, Li, Shiyin, Wen, Zihao, Liu, Yidi, Tang, Deyu
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 20.12.2023
Subjects:
Algorithms
Breast cancer
breast neoplasms
data collection
Datasets
Disease
Disease susceptibility
Evolution
Genetic aspects
Genomes
Genotype & phenotype
Heuristic
memory
Optimization
pathogenesis
Single nucleotide polymorphisms
Single-nucleotide polymorphism
Canada
biogenetic markers
multi-objective optimization
disease models
single-nucleotide polymorphisms
multi-locus SNP interactions
spherical evolutionary algorithms
ISSN:2073-4425, 2073-4425
Online Access:Get full text
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Summary:Single-nucleotide polymorphisms (SNPs), as disease-related biogenetic markers, are crucial in elucidating complex disease susceptibility and pathogenesis. Due to computational inefficiency, it is difficult to identify high-dimensional SNP interactions efficiently using combinatorial search methods, so the spherical evolutionary multi-objective (SEMO) algorithm for detecting multi-locus SNP interactions was proposed. The algorithm uses a spherical search factor and a feedback mechanism of excellent individual history memory to enhance the balance between search and acquisition. Moreover, a multi-objective fitness function based on the decomposition idea was used to evaluate the associations by combining two functions, K2-Score and LR-Score, as an objective function for the algorithm’s evolutionary iterations. The performance evaluation of SEMO was compared with six state-of-the-art algorithms on a simulated dataset. The results showed that SEMO outperforms the comparative methods by detecting SNP interactions quickly and accurately with a shorter average run time. The SEMO algorithm was applied to the Wellcome Trust Case Control Consortium (WTCCC) breast cancer dataset and detected two- and three-point SNP interactions that were significantly associated with breast cancer, confirming the effectiveness of the algorithm. New combinations of SNPs associated with breast cancer were also identified, which will provide a new way to detect SNP interactions quickly and accurately.
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ISSN:2073-4425
2073-4425
DOI:10.3390/genes15010011