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A network approach to prioritizing susceptibility genes for genome‐wide association studies
Author(s) -
Kafaie Somayeh,
Chen Yuanzhu,
Hu Ting
Publication year - 2019
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.22198
Subject(s) - epistasis , missing heritability problem , biology , computational biology , genetic association , genetics , genome wide association study , biological network , gene regulatory network , gene , centrality , genome , single nucleotide polymorphism , genotype , mathematics , gene expression , statistics
Abstract The heritability of complex diseases including cancer is often attributed to multiple interacting genetic alterations. Such a non‐linear, non‐additive gene–gene interaction effect, that is, epistasis , renders univariable analysis methods ineffective for genome‐wide association studies. In recent years, network science has seen increasing applications in modeling epistasis to characterize the complex relationships between a large number of genetic variations and the phenotypic outcome. In this study, by constructing a statistical epistasis network of colorectal cancer (CRC), we proposed to use multiple network measures to prioritize genes that influence the disease risk of CRC through synergistic interaction effects. We computed and analyzed several global and local properties of the large CRC epistasis network. We utilized topological properties of network vertices such as the edge strength, vertex centrality, and occurrence at different graphlets to identify genes that may be of potential biological relevance to CRC. We found 512 top‐ranked single‐nucleotide polymorphisms, among which COL22A1 , RGS7 , WWOX , and CELF2 were the four susceptibility genes prioritized by all described metrics as the most influential on CRC.