MDSN: A Module Detection Method for Identifying High-Order Epistatic Interactions
Epistatic interactions are referred to as SNPs (single nucleotide polymorphisms) that affect disease development and trait expression nonlinearly, and hence identifying epistatic interactions plays a great role in explaining the pathogenesis and genetic heterogeneity of complex diseases. Many method...
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MDPI AG
2022-12-01
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| Series: | Genes |
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| Online Access: | https://www.mdpi.com/2073-4425/13/12/2403 |
| _version_ | 1797457709307527168 |
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| author | Yan Sun Yijun Gu Qianqian Ren Yiting Li Junliang Shang Jin-Xing Liu Boxin Guan |
| author_facet | Yan Sun Yijun Gu Qianqian Ren Yiting Li Junliang Shang Jin-Xing Liu Boxin Guan |
| author_sort | Yan Sun |
| collection | DOAJ |
| description | Epistatic interactions are referred to as SNPs (single nucleotide polymorphisms) that affect disease development and trait expression nonlinearly, and hence identifying epistatic interactions plays a great role in explaining the pathogenesis and genetic heterogeneity of complex diseases. Many methods have been proposed for epistasis detection; nevertheless, they mainly focus on low-order epistatic interactions, two-order or three-order for instance, and often ignore high-order interactions due to computational burden. In this paper, a module detection method called MDSN is proposed for identifying high-order epistatic interactions. First, an SNP network is constructed by a construction strategy of interaction complementary, which consists of low-order SNP interactions that can be obtained from fast computations. Then, a node evaluation measure that integrates multi-topological features is proposed to improve the node expansion algorithm, where the importance of a node is comprehensively evaluated by the topological characteristics of the neighborhood. Finally, modules are detected in the constructed SNP network, which have high-order epistatic interactions associated with the disease. The MDSN was compared with four state-of-the-art methods on simulation datasets and a real Age-related Macular Degeneration dataset. The results demonstrate that MDSN has higher performance on detecting high-order interactions. |
| first_indexed | 2024-03-09T16:27:48Z |
| format | Article |
| id | doaj.art-c3c0ad684a7c44e8a8477a844f5ca1cb |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-09T16:27:48Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Genes |
| spelling | doaj.art-c3c0ad684a7c44e8a8477a844f5ca1cb2023-11-24T15:06:24ZengMDPI AGGenes2073-44252022-12-011312240310.3390/genes13122403MDSN: A Module Detection Method for Identifying High-Order Epistatic InteractionsYan Sun0Yijun Gu1Qianqian Ren2Yiting Li3Junliang Shang4Jin-Xing Liu5Boxin Guan6School of Computer Science, Qufu Normal University, Rizhao 276826, ChinaSchool of Computer Science, Qufu Normal University, Rizhao 276826, ChinaSchool of Computer Science, Qufu Normal University, Rizhao 276826, ChinaSchool of Computer Science, Qufu Normal University, Rizhao 276826, ChinaSchool of Computer Science, Qufu Normal University, Rizhao 276826, ChinaSchool of Computer Science, Qufu Normal University, Rizhao 276826, ChinaSchool of Computer Science, Qufu Normal University, Rizhao 276826, ChinaEpistatic interactions are referred to as SNPs (single nucleotide polymorphisms) that affect disease development and trait expression nonlinearly, and hence identifying epistatic interactions plays a great role in explaining the pathogenesis and genetic heterogeneity of complex diseases. Many methods have been proposed for epistasis detection; nevertheless, they mainly focus on low-order epistatic interactions, two-order or three-order for instance, and often ignore high-order interactions due to computational burden. In this paper, a module detection method called MDSN is proposed for identifying high-order epistatic interactions. First, an SNP network is constructed by a construction strategy of interaction complementary, which consists of low-order SNP interactions that can be obtained from fast computations. Then, a node evaluation measure that integrates multi-topological features is proposed to improve the node expansion algorithm, where the importance of a node is comprehensively evaluated by the topological characteristics of the neighborhood. Finally, modules are detected in the constructed SNP network, which have high-order epistatic interactions associated with the disease. The MDSN was compared with four state-of-the-art methods on simulation datasets and a real Age-related Macular Degeneration dataset. The results demonstrate that MDSN has higher performance on detecting high-order interactions.https://www.mdpi.com/2073-4425/13/12/2403high-order epistatic interactionsmodule detectiongraph clusteringSNP network |
| spellingShingle | Yan Sun Yijun Gu Qianqian Ren Yiting Li Junliang Shang Jin-Xing Liu Boxin Guan MDSN: A Module Detection Method for Identifying High-Order Epistatic Interactions Genes high-order epistatic interactions module detection graph clustering SNP network |
| title | MDSN: A Module Detection Method for Identifying High-Order Epistatic Interactions |
| title_full | MDSN: A Module Detection Method for Identifying High-Order Epistatic Interactions |
| title_fullStr | MDSN: A Module Detection Method for Identifying High-Order Epistatic Interactions |
| title_full_unstemmed | MDSN: A Module Detection Method for Identifying High-Order Epistatic Interactions |
| title_short | MDSN: A Module Detection Method for Identifying High-Order Epistatic Interactions |
| title_sort | mdsn a module detection method for identifying high order epistatic interactions |
| topic | high-order epistatic interactions module detection graph clustering SNP network |
| url | https://www.mdpi.com/2073-4425/13/12/2403 |
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