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Leveraging gene co-expression networks to pinpoint the regulation of complex traits and disease, with a focus on cardiovascular traits

机译:利用基因共表达网络确定复杂性状和疾病的调控,重点关注心血管性状

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摘要

Over the past decade the number of genome-scale transcriptional datasets in publicly available databases has climbed to nearly one million,providing an unprecedented opportunity for extensive analyses of gene co-expression networks. In systems-genetic studies of complex diseases researchers increasingly focus on groups of highly interconnected genes within complex transcriptional networks (referred to as clusters, modules or subnetworks) to uncover specific molecular processes that can inform functional disease mechanisms and pathological pathways. Here, we outline the basic paradigms underlying gene co-expression network analysis and critically review the most commonly used computational methods. Finally, we discuss specific applications of network-based approaches to the study of cardiovascular traits, which highlight the power of integrated analyses of networks, genetic and gene-regulation data to elucidate the complex mechanisms underlying cardiovascular disease.
机译:在过去的十年中,公开数据库中的基因组规模的转录数据集数量已攀升至近一百万,为基因共表达网络的广泛分析提供了前所未有的机会。在复杂疾病的系统遗传学研究中,研究人员越来越关注复杂的转录网络(称为簇,模块或子网络)中的高度相互关联的基因,以发现可以告知功能性疾病机制和病理途径的特定分子过程。在这里,我们概述了基因共表达网络分析的基本范例,并严格审查了最常用的计算方法。最后,我们讨论了基于网络的方法在心血管疾病特征研究中的具体应用,强调了对网络,遗传和基因调控数据进行综合分析以阐明心血管疾病潜在复杂机制的能力。

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