Department of Computer Science, Memorial University, St. John's, NL, Canada.
Genet Epidemiol. 2019 Jul;43(5):477-491. doi: 10.1002/gepi.22198. Epub 2019 Mar 11.
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.
复杂疾病（包括癌症）的遗传性通常归因于多种相互作用的遗传改变。这种非线性、非加性的基因-基因相互作用效应，即上位性，使得单变量分析方法在全基因组关联研究中无效。近年来，网络科学在模拟上位性以描述大量遗传变异与表型结果之间的复杂关系方面得到了越来越多的应用。在这项研究中，通过构建结直肠癌（CRC）的统计上位性网络，我们提出了使用多种网络度量来优先考虑通过协同相互作用影响 CRC 疾病风险的基因。我们计算和分析了大型 CRC 上位性网络的几个全局和局部性质。我们利用网络顶点的拓扑性质，如边强度、顶点中心度和不同图的出现，来识别可能对 CRC 具有潜在生物学相关性的基因。我们发现了 512 个排名靠前的单核苷酸多态性，其中 COL22A1、RGS7、WWOX 和 CELF2 是通过所有描述的指标都被优先排序为对 CRC 最有影响的四个易感基因。
