Exploration of gene-gene interaction effects using entropy-based methods
Exploration of gene-gene interaction effects using entropy-based methods
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DOI：
10.1038/sj.ejhg.5201921
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发表时间：
2008-02-01
影响因子：
5.2
通讯作者：
Li, Yixue
中科院分区：
文献类型：
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作者：
Dong, Changzheng;Chu, Xun;Li, Yixue
基因-基因相互作用在复杂疾病研究中可能发挥重要作用，其中相互作用效应与单基因效应相结合是活跃的。自上个世纪初以来，人们提出了许多相互作用模型。然而，现有的方法，包括统计和数据挖掘方法，很少考虑遗传交互作用模型，这使得交互作用结果缺乏生物学或遗传学意义。在这项研究中，我们开发了一种基于熵的方法，整合了两个基因座遗传模型来探索这种相互作用的影响。我们对模拟数据和真实数据进行了评估。仿真结果表明，该方法能有效地检测基因与基因之间的相互作用，并能从各种相互作用模型中识别出最适合的模型。此外，当我们的方法应用于疟疾数据时，成功地揭示了镰状细胞性贫血和阿尔法(+)地中海贫血之间的负上位性效应。
Gene-gene interaction may play important roles in complex disease studies, in which interaction effects coupled with single-gene effects are active. Many interaction models have been proposed since the beginning of the last century. However, the existing approaches including statistical and data mining methods rarely consider genetic interaction models, which make the interaction results lack biological or genetic meaning. In this study, we developed an entropy-based method integrating two-locus genetic models to explore such interaction effects. We performed our method to simulated and real data for evaluation. Simulation results show that this method is effective to detect gene-gene interaction and, furthermore, it is able to identify the best-fit model from various interaction models. Moreover, our method, when applied to malaria data, successfully revealed negative epistatic effect between sickle cell anemia and alpha(+)-thalassemia against malaria.