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...
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MDPI AG
2021-01-01
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| Series: | Genes |
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| Online Access: | https://www.mdpi.com/2073-4425/12/2/191 |
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| author | Yuanyuan Chen Fengjiao Xu Cong Pian Mingmin Xu Lingpeng Kong Jingya Fang Zutan Li Liangyun Zhang |
| author_facet | Yuanyuan Chen Fengjiao Xu Cong Pian Mingmin Xu Lingpeng Kong Jingya Fang Zutan Li Liangyun Zhang |
| author_sort | Yuanyuan Chen |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T03:24:29Z |
| format | Article |
| id | doaj.art-6e1f4da2c8654240a016dc705927d037 |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-09T03:24:29Z |
| publishDate | 2021-01-01 |
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| series | Genes |
| spelling | doaj.art-6e1f4da2c8654240a016dc705927d0372023-12-03T15:03:58ZengMDPI AGGenes2073-44252021-01-0112219110.3390/genes12020191EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic AlgorithmYuanyuan Chen0Fengjiao Xu1Cong Pian2Mingmin Xu3Lingpeng Kong4Jingya Fang5Zutan Li6Liangyun Zhang7Department of Mathematics, College of Science, Nanjing Agricultural University, Nanjing 210095, ChinaDepartment of Mathematics, College of Science, Nanjing Agricultural University, Nanjing 210095, ChinaDepartment of Mathematics, College of Science, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Agriculture, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Agriculture, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Agriculture, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Agriculture, Nanjing Agricultural University, Nanjing 210095, ChinaDepartment of Mathematics, College of Science, Nanjing Agricultural University, Nanjing 210095, ChinaIn 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.https://www.mdpi.com/2073-4425/12/2/191genome-wide association studieshigh-order epistasisgenetic algorithmsmulti-objective optimizationAlzheimer’s disease |
| spellingShingle | Yuanyuan Chen Fengjiao Xu Cong Pian Mingmin Xu Lingpeng Kong Jingya Fang Zutan Li Liangyun Zhang EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm Genes genome-wide association studies high-order epistasis genetic algorithms multi-objective optimization Alzheimer’s disease |
| 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 | genome-wide association studies high-order epistasis genetic algorithms multi-objective optimization Alzheimer’s disease |
| url | https://www.mdpi.com/2073-4425/12/2/191 |
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