Riveros Carlos, Vimieiro Renato, Holliday Elizabeth G, Oldmeadow Christopher, Wang Jie Jin, Mitchell Paul, Attia John, Scott Rodney J, Moscato Pablo A
Centre for Bioinformatics, Biomarker Discovery, and Information-Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia,
Methods Mol Biol. 2015;1253:217-55. doi: 10.1007/978-1-4939-2155-3_12.
We propose here a methodology to uncover modularities in the network of SNP-SNP interactions most associated with disease. We start by computing all possible Boolean binary SNP interactions across the whole genome. By constructing a weighted graph of the most relevant interactions and via a combinatorial optimization approach, we find the most highly interconnected SNPs. We show that the method can be easily extended to find SNP/environment interactions. Using a modestly sized GWAS dataset of age-related macular degeneration (AMD), we identify a group of only 19 SNPs, which include those in previously reported regions associated to AMD. We also uncover a larger set of loci pointing to a matrix of key processes and functions that are affected. The proposed integrative methodology extends and overlaps traditional statistical analysis in a natural way. Combinatorial optimization techniques allow us to find the kernel of the most central interactions, complementing current methods of GWAS analysis and also enhancing the search for gene-environment interaction.
我们在此提出一种方法，以揭示与疾病最相关的单核苷酸多态性（SNP）-SNP相互作用网络中的模块性。我们首先计算全基因组中所有可能的布尔二元SNP相互作用。通过构建最相关相互作用的加权图，并采用组合优化方法，我们找到了连接性最高的SNP。我们表明该方法可以轻松扩展以发现SNP/环境相互作用。使用一个规模适中的年龄相关性黄斑变性（AMD）全基因组关联研究（GWAS）数据集，我们识别出一组仅19个SNP，其中包括先前报道的与AMD相关区域中的SNP。我们还发现了一组更大的基因座，指向受影响的关键过程和功能矩阵。所提出的综合方法以自然的方式扩展并与传统统计分析重叠。组合优化技术使我们能够找到最核心相互作用的内核，补充当前的GWAS分析方法，并增强对基因-环境相互作用的搜索。
