A method for estimating coherence of molecular mechanisms in major human disease and traits.
A method for estimating coherence of molecular mechanisms in major human disease and traits.
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DOI：
10.1186/s12859-020-03821-x
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发表时间：
2020-10-21
影响因子：
3
通讯作者：
Kendler KS
中科院分区：
文献类型：
--
作者：
Dozmorov MG;Cresswell KG;Bacanu SA;Craver C;Reimers M;Kendler KS
身高和智力等表型被认为是多个表型相关基因及其蛋白质产物之间相互作用的产物。高/低程度的相互作用分别暗示了相干/随机分子机制。比较相互作用的程度可能有助于更好地理解表型特异性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，很难直接比较相互作用的程度。我们引入了一个度量测量的分子相互作用网络的一致性的相互作用的程度的内部与外部分布的斜率。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，而外部度分布则计算整个蛋白质-蛋白质相互作用（PPI）网络中与其他基因的相互作用。我们提出了一种新的方法来规范化的相干性估计，使他们直接可比。使用STRING和BioGrid PPI数据库，我们比较了来自GWAScatalog的116个表型相关基因集与大小匹配的KEGG通路（高一致性的参考）和随机网络（一致性的下限）的一致性。我们观察到一系列的一致性估计的每一类表型。代谢特征和疾病是最一致的，而精神疾病和智力相关的特征是最不一致的。我们证明了一致性和模块化措施捕捉不同的网络属性。我们提出了一个通用的方法来估计和比较分子相互作用基因网络的一致性，占网络的大小和形状的差异。我们的研究结果突出了我们目前对复杂表型的遗传学和分子机制的认识的差距，并为未来的GWAS提出了优先考虑的问题。
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.
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DOI：
10.1038/nrg2579
发表时间：
2009-06
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Cordell HJ
通讯作者：
Cordell HJ
影响因子：
3.7
作者：
Gonçalves JP;Francisco AP;Moreau Y;Madeira SC
通讯作者：
Madeira SC
影响因子：
3.7
作者：
Akula N;Baranova A;Seto D;Solka J;Nalls MA;Singleton A;Ferrucci L;Tanaka T;Bandinelli S;Cho YS;Kim YJ;Lee JY;Han BG;Bipolar Disorder Genome Study (BiGS) Consortium;Wellcome Trust Case-Control Consortium;McMahon FJ
通讯作者：
McMahon FJ
DOI：
10.1088/1742-5468/2005/09/p09008
发表时间：
2005-09-01
影响因子：
2.4
作者：
Danon, L;Díaz-Guilera, A;Arenas, A
通讯作者：
Arenas, A
影响因子：
4.3
作者：
Gillis J;Pavlidis P
通讯作者：
Pavlidis P