MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring.
MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring.
复制标题
MOBA：使用基于模块化的评分来鉴定与疾病相关的蛋白质子网。
DOI：
10.1186/s13637-015-0025-6
复制
发表时间：
2015-12
期刊：
影响因子：
--
通讯作者：
Koyutürk M
中科院分区：
文献类型：
--
作者：
Ayati M;Erten S;Chance MR;Koyutürk M
基于网络的分析通常被用作功能背景下解释全基因组关联研究（GWAS）结果的强大工具。特别地，识别疾病相关的功能模块，即，具有高度聚集疾病关联的高度连接的蛋白质-蛋白质相互作用（PPI）子网络在揭示与疾病相关的基因和蛋白质之间的功能关系方面被证明是有希望的。在这方面的一个重要问题是通过整合两个量来对子网络进行评分：单个基因产物的疾病关联和蛋白质之间的网络连接。目前的评分方案要么忽略连接性水平，专注于连接蛋白质的聚集疾病相关性，要么使用这两个量的线性组合。然而，这种评分方案可能产生任意大的子网络，这些子网络通常在统计上不显著，或者需要调整用于对网络连接性和疾病关联的贡献进行加权的参数。在这里，我们提出了一个无参数的评分方案，其目的是通过评估基因产物对之间的相互作用的疾病关联来对子网络进行评分。我们还将网络连接性和疾病关联的统计显著性纳入评分函数。我们在GWAS数据集上测试了两种复杂疾病II型糖尿病（T2 D）和银屑病（PS）的评分方案。我们的研究结果表明，通常使用的方法确定的子网络可能会失败的多个假设检验校正后的统计显著性检验。相比之下，所提出的评分方案产生高度显着的子网络，其中包含生物相关的蛋白质，不能单独通过分析全基因组关联数据来识别。我们还表明，所提出的评分方案确定的子网络是可重复的不同的队列，它可以鲁棒地恢复相关的子网络在较低的采样率。
Network-based analyses are commonly used as powerful tools to interpret the findings of genome-wide association studies (GWAS) in a functional context. In particular, identification of disease-associated functional modules, i.e., highly connected protein-protein interaction (PPI) subnetworks with high aggregate disease association, are shown to be promising in uncovering the functional relationships among genes and proteins associated with diseases. An important issue in this regard is the scoring of subnetworks by integrating two quantities: disease association of individual gene products and network connectivity among proteins. Current scoring schemes either disregard the level of connectivity and focus on the aggregate disease association of connected proteins or use a linear combination of these two quantities. However, such scoring schemes may produce arbitrarily large subnetworks which are often not statistically significant or require tuning of parameters that are used to weigh the contributions of network connectivity and disease association. Here, we propose a parameter-free scoring scheme that aims to score subnetworks by assessing the disease association of interactions between pairs of gene products. We also incorporate the statistical significance of network connectivity and disease association into the scoring function. We test the proposed scoring scheme on a GWAS dataset for two complex diseases type II diabetes (T2D) and psoriasis (PS). Our results suggest that subnetworks identified by commonly used methods may fail tests of statistical significance after correction for multiple hypothesis testing. In contrast, the proposed scoring scheme yields highly significant subnetworks, which contain biologically relevant proteins that cannot be identified by analysis of genome-wide association data alone. We also show that the proposed scoring scheme identifies subnetworks that are reproducible across different cohorts, and it can robustly recover relevant subnetworks at lower sampling rates.