Computational Genetics Laboratory, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA.
J Am Med Inform Assoc. 2013 Jul-Aug;20(4):630-6. doi: 10.1136/amiajnl-2012-001525. Epub 2013 Feb 8.
Epistasis has been historically used to describe the phenomenon that the effect of a given gene on a phenotype can be dependent on one or more other genes, and is an essential element for understanding the association between genetic and phenotypic variations. Quantifying epistasis of orders higher than two is very challenging due to both the computational complexity of enumerating all possible combinations in genome-wide data and the lack of efficient and effective methodologies.
In this study, we propose a fast, non-parametric, and model-free measure for three-way epistasis.
Such a measure is based on information gain, and is able to separate all lower order effects from pure three-way epistasis.
Our method was verified on synthetic data and applied to real data from a candidate-gene study of tuberculosis in a West African population. In the tuberculosis data, we found a statistically significant pure three-way epistatic interaction effect that was stronger than any lower-order associations.
Our study provides a methodological basis for detecting and characterizing high-order gene-gene interactions in genetic association studies.
上位性在历史上被用来描述这样一种现象，即给定基因对表型的影响可能依赖于一个或多个其他基因，并且是理解遗传和表型变异之间关联的一个基本要素。由于在全基因组数据中枚举所有可能组合的计算复杂性以及缺乏有效和高效的方法，因此量化高于二阶的上位性是非常具有挑战性的。
在这项研究中，我们提出了一种快速、非参数且无模型的三阶上位性度量方法。
该度量方法基于信息增益，能够将所有较低阶效应与纯三阶上位性区分开来。
我们的方法在合成数据上进行了验证，并应用于来自西非人群结核病候选基因研究的真实数据。在结核病数据中，我们发现了一个具有统计学意义的纯三阶上位性相互作用效应，其强度超过任何较低阶关联。
我们的研究为检测和描述遗传关联研究中的高阶基因-基因相互作用提供了方法学基础。
Zotero 插件
