Department of Electrical Engineering and Computer Science (Montefiore Institute), Grande Traverse, Bioinformatique 4000 Liège 1, Belgium.
Brief Bioinform. 2012 Jan;13(1):1-19. doi: 10.1093/bib/bbr012. Epub 2011 Mar 26.
Over the last few years, main effect genetic association analysis has proven to be a successful tool to unravel genetic risk components to a variety of complex diseases. In the quest for disease susceptibility factors and the search for the 'missing heritability', supplementary and complementary efforts have been undertaken. These include the inclusion of several genetic inheritance assumptions in model development, the consideration of different sources of information, and the acknowledgement of disease underlying pathways of networks. The search for epistasis or gene-gene interaction effects on traits of interest is marked by an exponential growth, not only in terms of methodological development, but also in terms of practical applications, translation of statistical epistasis to biological epistasis and integration of omics information sources. The current popularity of the field, as well as its attraction to interdisciplinary teams, each making valuable contributions with sometimes rather unique viewpoints, renders it impossible to give an exhaustive review of to-date available approaches for epistasis screening. The purpose of this work is to give a perspective view on a selection of currently active analysis strategies and concerns in the context of epistasis detection, and to provide an eye to the future of gene-gene interaction analysis.
在过去的几年中，主要效应遗传关联分析已被证明是一种成功的工具，可用于揭示各种复杂疾病的遗传风险因素。在寻找疾病易感性因素和寻找“缺失的遗传率”的过程中，人们已经开展了补充和辅助工作。这些工作包括在模型开发中纳入几种遗传继承假设，考虑不同来源的信息，并承认疾病潜在的通路和网络。对感兴趣的性状进行上位性或基因-基因相互作用效应的研究呈指数级增长，不仅在方法学发展方面，而且在实际应用、统计学上位性向生物学上位性的转化以及组学信息源的整合方面也是如此。该领域目前的流行程度，以及它对跨学科团队的吸引力，每个团队都有有价值的贡献，有时观点非常独特，这使得不可能对迄今为止可用的上位性筛选方法进行详尽的综述。这项工作的目的是从当前活跃的分析策略和上位性检测方面的关注点的角度提供一个视角，并展望基因-基因相互作用分析的未来。
