期刊
GENES
卷 13, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/genes13122403
关键词
high-order epistatic interactions; module detection; graph clustering; SNP network
资金
- National Natural Science Foundation of China
- [61972226]
- [61902216]
- [62172254]
This paper proposes a module detection method called MDSN for identifying high-order epistatic interactions. By constructing an SNP network and using a node evaluation measure, it can effectively detect high-order interactions associated with diseases.
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.
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