Detecting essential and removable interactions in genome-wide association studies.
Detecting essential and removable interactions in genome-wide association studies.
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DOI：
10.4310/sii.2009.v2.n2.a6
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发表时间：
2009-01-01
影响因子：
0.8
通讯作者：
Hoh J
中科院分区：
文献类型：
--
作者：
Wu C;Zhang H;Liu X;Dewan A;Dubrow R;Ying Z;Yang Y;Hoh J
检测大量单核苷酸多态性（SNP）组合之间的疾病基因相互作用效应是全基因组关联（GWA）研究的下一个前沿。在此，我们根据交互的模式和性质提出了一种新的策略，可以将交互分为必要的（EI）和可移除的（RI）。我们提供了一个分析框架，包括筛选ei / ri的定性条件和用于定量测量效果的ri - ei似然比评分。在分析6个GWA数据集时，我们发现分数遵循指数分布，除了在上10−8尾区域，分数变得不规则和不可预测。我们的方法在概念上简单，计算效率高，并且可以检测到可以可视化和明确解释的相互作用。
Detection of disease gene interaction effects among the enormous array of single nucleotide polymorphism (SNP) combinations represents the next frontier in genome-wide association (GWA) studies. Here we propose a novel strategy on the basis of the pattern and nature of the interaction, which can be classified as essential (EI) or removable (RI). We provide an analytical framework, including the qualitative conditions for screening EIs/RIs and a RI-to-EI likelihood ratio score to quantitatively measure the effect. In analyzing six GWA data sets, we find that the scores follow an exponential distribution, except in the upper 10−8 tail region in which the scores become irregular and unpredictable. Our approach is conceptually simple, computationally efficient and detects interactions that can be visualized and unequivocally interpreted.