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EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm
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, “sma...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7911965/ https://www.ncbi.nlm.nih.gov/pubmed/33525573 http://dx.doi.org/10.3390/genes12020191 |
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author | Chen, Yuanyuan Xu, Fengjiao Pian, Cong Xu, Mingmin Kong, Lingpeng Fang, Jingya Li, Zutan Zhang, Liangyun |
author_facet | Chen, Yuanyuan Xu, Fengjiao Pian, Cong Xu, Mingmin Kong, Lingpeng Fang, Jingya Li, Zutan Zhang, Liangyun |
author_sort | Chen, Yuanyuan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7911965 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79119652021-02-28 EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm Chen, Yuanyuan Xu, Fengjiao Pian, Cong Xu, Mingmin Kong, Lingpeng Fang, Jingya Li, Zutan Zhang, Liangyun Genes (Basel) Article 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. MDPI 2021-01-28 /pmc/articles/PMC7911965/ /pubmed/33525573 http://dx.doi.org/10.3390/genes12020191 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Yuanyuan Xu, Fengjiao Pian, Cong Xu, Mingmin Kong, Lingpeng Fang, Jingya Li, Zutan Zhang, Liangyun EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm |
title | EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm |
title_full | EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm |
title_fullStr | EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm |
title_full_unstemmed | EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm |
title_short | EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm |
title_sort | epimoga: an epistasis detection method based on a multi-objective genetic algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7911965/ https://www.ncbi.nlm.nih.gov/pubmed/33525573 http://dx.doi.org/10.3390/genes12020191 |
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