ArticleGenes2021
EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm.
Article in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions.BMC genomics · 2024Article
- Whole genome-wide sequence analysis of long-lived families (Long-Life Family Study) identifies MTUS2 gene associated with late-onset Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Epi-SSA: A novel epistasis detection method based on a multi-objective sparrow search algorithm.PloS one · 2024Article
- FSF-GA: A Feature Selection Framework for Phenotype Prediction Using Genetic Algorithms.Genes · 2023Article
- Article
- Article
- A Novel Multitasking Ant Colony Optimization Method for Detecting Multiorder SNP Interactions.Interdisciplinary sciences, computational life sciences · 2022Article
- Multi-Objective Artificial Bee Colony Algorithm Based on Scale-Free Network for Epistasis Detection.Genes · 2022Article
- A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases.Computational and mathematical methods in medicine · 2022Article
- Article
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8 authors.
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Abstract
In genome-wide association studies, detecting high-order epistasis is important for analyzing the occurrence of complex human diseases and explaining missing heritability. However, there are various challenges in the actual high-order epistasis detection process due to the large amount of data, "small sample size problem", diversity of disease models, etc. This paper proposes a multi-objective genetic algorithm (EpiMOGA) for single nucleotide polymorphism (SNP) epistasis detection. The K2 score based on the Bayesian network criterion and the Gini index of the diversity of the binary classification problem were used to guide the search process of the genetic algorithm. Experiments were performed on 26 simulated datasets of different models and a real Alzheimer's disease dataset. The results indicated that EpiMOGA was obviously superior to other related and competitive methods in both detection efficiency and accuracy, especially for small-sample-size datasets, and the performance of EpiMOGA remained stable across datasets of different disease models. At the same time, a number of SNP loci and 2-order epistasis associated with Alzheimer's disease were identified by the EpiMOGA method, indicating that this method is capable of identifying high-order epistasis from genome-wide data and can be applied in the study of complex diseases.
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