Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
Department of Pathology, Virginia Commonwealth University, Richmond, VA, USA.
BMC Bioinformatics. 2020 Oct 21;21(1):473. doi: 10.1186/s12859-020-03821-x.
Phenotypes such as height and intelligence, are thought to be a product of the collective effects of multiple phenotype-associated genes and interactions among their protein products. High/low degree of interactions is suggestive of coherent/random molecular mechanisms, respectively. Comparing the degree of interactions may help to better understand the coherence of phenotype-specific molecular mechanisms and the potential for therapeutic intervention. However, direct comparison of the degree of interactions is difficult due to different sizes and configurations of phenotype-associated gene networks.
We introduce a metric for measuring coherence of molecular-interaction networks as a slope of internal versus external distributions of the degree of interactions. The internal degree distribution is defined by interaction counts within a phenotype-specific gene network, while the external degree distribution counts interactions with other genes in the whole protein-protein interaction (PPI) network. We present a novel method for normalizing the coherence estimates, making them directly comparable.
Using STRING and BioGrid PPI databases, we compared the coherence of 116 phenotype-associated gene sets from GWAScatalog against size-matched KEGG pathways (the reference for high coherence) and random networks (the lower limit of coherence). We observed a range of coherence estimates for each category of phenotypes. Metabolic traits and diseases were the most coherent, while psychiatric disorders and intelligence-related traits were the least coherent. We demonstrate that coherence and modularity measures capture distinct network properties.
We present a general-purpose method for estimating and comparing the coherence of molecular-interaction gene networks that accounts for the network size and shape differences. Our results highlight gaps in our current knowledge of genetics and molecular mechanisms of complex phenotypes and suggest priorities for future GWASs.
身高和智力等表型被认为是多个表型相关基因的集体效应以及它们的蛋白质产物之间相互作用的产物。高/低相互作用程度分别提示相干/随机分子机制。比较相互作用的程度可以帮助更好地理解特定表型分子机制的一致性和治疗干预的潜力。然而，由于表型相关基因网络的大小和结构不同，直接比较相互作用的程度是困难的。
我们引入了一种用于测量分子相互作用网络一致性的度量标准，即内部与外部相互作用程度分布的斜率。内部度分布由表型特异性基因网络内的相互作用计数定义，而外部度分布则计数与整个蛋白质-蛋白质相互作用（PPI）网络中的其他基因的相互作用。我们提出了一种新的归一化一致性估计的方法，使其可以直接比较。
使用 STRING 和 BioGrid PPI 数据库，我们比较了 GWAScatalog 中的 116 个表型相关基因集与大小匹配的 KEGG 途径（高一致性的参考）和随机网络（一致性的下限）的一致性。我们观察到每个表型类别都有一系列的一致性估计值。代谢特征和疾病是最一致的，而精神障碍和智力相关特征是最不一致的。我们证明了一致性和模块性度量可以捕捉到不同的网络属性。
我们提出了一种用于估计和比较分子相互作用基因网络一致性的通用方法，该方法考虑了网络大小和形状的差异。我们的结果突出了当前对复杂表型的遗传学和分子机制的理解中的差距，并为未来的 GWAS 提供了重点方向。
