Genome-Wide Epistasis and Pleiotropy Characterized by the Bipartite Human Phenotype Network
Genome-Wide Epistasis and Pleiotropy Characterized by the Bipartite Human Phenotype Network
复制标题
DOI：
10.1007/978-1-4939-2155-3_14
复制
发表时间：
2015-01-01
期刊：
影响因子：
--
通讯作者：
Moore, Jason H.
中科院分区：
文献类型：
--
作者：
Darabos, Christian;Moore, Jason H.
网络是将高通量遗传技术产生的大量信息转化为可管理的知识来源的核心。它们是表示交互数据的直观方式，但它们提供了一整套复杂的定量工具来分析它们所建模的现象。当结合遗传信息，疾病和表型性状，网络可以揭示和促进在全基因组范围内的多效性和上位性效应的分析。全基因组关联研究数据是公开的，基因和途径数据库也是如此，还有更多，使得全球概览几乎不可能。网络使来自这些多个来源的信息得以涵盖。我们使用网络层之间的连接来表征性状和生物途径之间发生的多效性和上位性效应。基于全局图论的定量方法揭示了多效性和上位性的水平与理论预期一致。网络中放大的“青光眼”区域的结果证实了存在有据可查的相互作用，这些相互作用得到了重叠基因和生物途径以及更模糊关联的支持。它们有可能为尚未表征的相互作用产生新的假设。随着遗传数据的数量和复杂性的增加，将人类疾病和其他物理属性与遗传信息层相结合的二分网络和更普遍的多分网络有可能成为研究复杂遗传和表型相互作用的普遍工具，并可能改善个性化医疗。
Networks are central to turning the colossal amount of information generated by high-throughput genetic technology into manageable sources of knowledge. They are an intuitive way of representing interaction data, yet they offer a full set of sophisticated quantitative tools to analyze the phenomena they model. When combining genetic information, diseases, and phenotypic traits, networks can reveal and facilitate the analysis of pleiotropic and epistatic effects at the genome-wide scale. Genome-wide association study data is publicly available, and so are gene and pathway databases, and many more, making the global overview next to impossible. Networks allow information from these multiple sources to be encompassed. We use connections between the strata of the network to characterize pleiotropy and epistasis effects taking place between traits and biological pathways. The global graph-theory-based quantitative methods reveal that levels of pleiotropy and epistasis are in-line with theoretical expectations. The results of the magnified "glaucoma" region of the network confirm the existence of well-documented interactions, supported by overlapping genes and biological pathways and more obscure associations. They have the potential to generate new hypotheses for yet uncharacterized interactions. As the amount and complexity of genetic data increase, bipartite and, more generally, multipartite networks that combine human diseases and other physical attributes with layers of genetic information have the potential to become ubiquitous tools in the study of complex genetic, phenotypic interactions, and possibly improve personalized medicine.