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Layers of epistasis: genome-wide regulatory networks and network approaches to genome-wide association studies

机译:上位层:全基因组调控网络和用于全基因组关联研究的网络方法

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The conceptual foundation of the genome-wide association study (GWAS) has advanced unchecked since its conception. A revision might seem premature as the potential of GWAS has not been fully realized. Multiple technical and practical limitations need to be overcome before GWAS can be fairly criticized. But with the completion of hundreds of studies and a deeper understanding of the genetic architecture of disease, warnings are being raised. The results compiled to date indicate that risk-associated variants lie predominantly in noncoding regions of the genome. Additionally, alternative methodologies are uncovering large and heterogeneous sets of rare variants underlying disease. The fear is that, even in its fulfillment, the current GWAS paradigm might be incapable of dissecting all kinds of phenotypes. In the following text, we review several initiatives that aim to overcome these limitations. The overarching theme of these studies is the inclusion of biological knowledge to both the analysis and interpretation of genotyping data. GWAS is uninformed of biology by design and although there is some virtue in its simplicity, it is also its most conspicuous deficiency. We propose a framework in which to integrate these novel approaches, both empirical and theoretical, in the form of a genome-wide regulatory network (GWRN). By processing experimental data into networks, emerging data types based on chromatin immunoprecipitation are made computationally tractable. This will give GWAS re-analysis efforts the most current and relevant substrates, and root them firmly on our knowledge of human disease. (C) 2010 John Wiley & Sons, Inc. WIREs Syst Biol Med 2011 3 513-526 DOI: 10.1002/wsbm.132
机译:自从提出以来,全基因组关联研究(GWAS)的概念基础就得到了不断发展。由于尚未完全意识到GWAS的潜力,因此进行修订似乎为时过早。在可以公平地批评GWAS之前,需要克服多种技术和实践限制。但是,随着数百项研究的完成以及对疾病遗传结构的更深入了解,提出了警告。迄今为止收集的结果表明,与风险相关的变异主要位于基因组的非编码区。另外,替代方法正在发现疾病背后的大型且异质的稀有变异集。担心的是,即使在实现上,当前的GWAS范式也可能无法剖析各种表型。在下文中,我们回顾了旨在克服这些局限性的几种举措。这些研究的总体主题是将生物学知识纳入基因分型数据的分析和解释中。 GWAS在设计上并未了解生物学,尽管其简单性有一些优点,但它也是其最明显的缺陷。我们提出了一个框架,在其中以基因组范围的监管网络(GWRN)的形式整合这些新颖的经验和理论方法。通过将实验数据处理到网络中,基于染色质免疫沉淀的新兴数据类型将变得易于计算。这将为GWAS重新分析工作提供最新和相关的内容,并将其牢固地植根于我们对人类疾病的知识。 (C)2010 John Wiley&Sons,Inc.电线Syst Biol Med 2011 3 513-526 DOI:10.1002 / wsbm.132

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