EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm Article Swipe
Yuanyuan Chen
,
Fengjiao Xu
,
Cong Pian
,
Mingmin Xu
,
Lingpeng Kong
,
Jingya Fang
,
Zutan Li
,
Liangyun Zhang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/genes12020191
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/genes12020191
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.
Related Topics
Concepts
Epistasis
Genome-wide association study
Bayesian probability
Sample size determination
Computer science
Heritability
Single-nucleotide polymorphism
SNP
Data mining
Biology
Computational biology
Artificial intelligence
Statistics
Genetics
Mathematics
Genotype
Gene
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/genes12020191
- https://www.mdpi.com/2073-4425/12/2/191/pdf?version=1611836884
- OA Status
- gold
- Cited By
- 17
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3123813045
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3123813045Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/genes12020191Digital Object Identifier
- Title
-
EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-01-28Full publication date if available
- Authors
-
Yuanyuan Chen, Fengjiao Xu, Cong Pian, Mingmin Xu, Lingpeng Kong, Jingya Fang, Zutan Li, Liangyun ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/genes12020191Publisher landing page
- PDF URL
-
https://www.mdpi.com/2073-4425/12/2/191/pdf?version=1611836884Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2073-4425/12/2/191/pdf?version=1611836884Direct OA link when available
- Concepts
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Epistasis, Genome-wide association study, Bayesian probability, Sample size determination, Computer science, Heritability, Single-nucleotide polymorphism, SNP, Data mining, Biology, Computational biology, Artificial intelligence, Statistics, Genetics, Mathematics, Genotype, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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17Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 4, 2023: 2, 2022: 7, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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46Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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