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层叠的上位性:全基因组调控网络和全基因组关联研究的网络方法。

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

机构信息

Department of Genetics, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, NH, USA.

出版信息

Wiley Interdiscip Rev Syst Biol Med. 2011 Sep-Oct;3(5):513-26. doi: 10.1002/wsbm.132. Epub 2010 Dec 31.

Abstract

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.

摘要

全基因组关联研究(GWAS)自概念提出以来,其理论基础一直在不断发展。尽管 GWAS 的潜力尚未完全实现,但此时对其进行修正似乎还为时过早。在 GWAS 能够得到公正的批评之前,需要克服多个技术和实际的限制。但是,随着数百项研究的完成以及对疾病遗传结构的深入了解,人们开始提出警告。迄今为止汇总的结果表明,与风险相关的变异主要存在于基因组的非编码区域。此外,替代方法学揭示了疾病背后存在大量异质的罕见变异。人们担心的是,即使在其实现之后,当前的 GWAS 范式也可能无法剖析所有类型的表型。在接下来的文本中,我们将回顾旨在克服这些限制的若干举措。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 在设计上不了解生物学,尽管其简单性具有一定的优点,但这也是其最明显的缺陷。我们提出了一个框架,以将这些新颖的方法,包括经验和理论方法,以基因组范围的调控网络(GWRN)的形式进行整合。通过将实验数据处理为网络,可以使基于染色质免疫沉淀的新兴数据类型在计算上变得可行。这将为 GWAS 重新分析工作提供最新和最相关的底物,并使它们牢固地扎根于我们对人类疾病的了解。

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