IGENT: efficient entropy based algorithm for genome-wide gene-gene interaction analysis.
IGENT: efficient entropy based algorithm for genome-wide gene-gene interaction analysis.
复制标题
IGENT：用于全基因组基因 - 基因相互作用分析的有效基于熵的算法。
DOI：
10.1186/1755-8794-7-s1-s6
复制
发表时间：
2014
影响因子：
2.7
通讯作者：
Park T
中科院分区：
文献类型：
--
作者：
Kwon MS;Park M;Park T
随着高通量基因分型和测序技术的发展，越来越多的证据表明基因与遗传变异和复杂性状之间存在关联。尽管发现了数千种遗传变异，但这些遗传标记已被证明只能解释复杂性状的潜在遗传变异的一小部分。基因-基因互作（GGI）分析有望揭示复杂性状的大部分无法解释的遗传力。在这项工作中，我们提出了IGENT，信息论为基础的全基因组基因互动方法。IGENT是一种识别全基因组基因-基因相互作用（GGI）和基因-环境相互作用（GEI）的有效算法。为了在全基因组范围内检测显著的GGIs，重要的是显著减少计算负担。我们的方法使用信息增益（IG），并评估其重要性，而无需重新分配。通过我们的仿真研究，IGENT的功率被证明是优于或相当于的BOOST。所提出的方法成功地检测了GGI的双相情感障碍在威康信托病例对照联盟（WTCCC）和年龄相关性黄斑变性（AMD）。该方法用C++语言实现，可在Windows、Linux和MacOSX上运行。
With the development of high-throughput genotyping and sequencing technology, there are growing evidences of association with genetic variants and complex traits. In spite of thousands of genetic variants discovered, such genetic markers have been shown to explain only a very small proportion of the underlying genetic variance of complex traits. Gene-gene interaction (GGI) analysis is expected to unveil a large portion of unexplained heritability of complex traits. In this work, we propose IGENT, Information theory-based GEnome-wide gene-gene iNTeraction method. IGENT is an efficient algorithm for identifying genome-wide gene-gene interactions (GGI) and gene-environment interaction (GEI). For detecting significant GGIs in genome-wide scale, it is important to reduce computational burden significantly. Our method uses information gain (IG) and evaluates its significance without resampling. Through our simulation studies, the power of the IGENT is shown to be better than or equivalent to that of that of BOOST. The proposed method successfully detected GGI for bipolar disorder in the Wellcome Trust Case Control Consortium (WTCCC) and age-related macular degeneration (AMD). The proposed method is implemented by C++ and available on Windows, Linux and MacOSX.