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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Gene mapping and marker clustering using Shannon's mutual information
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Gene mapping and marker clustering using Shannon's mutual information

机译：利用Shannon的互信息进行基因定位和标记聚类

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Finding the causal genetic regions underlying complex traits is one of the main aims in human genetics. In the context of complex diseases, which are believed to be controlled by multiple contributing loci of largely unknown effect and position, it is especially important to develop general yet sensitive methods for gene mapping. We discuss the use of Shannon's information theory for population-based gene mapping of discrete and quantitative traits and for marker clustering. Various measures of mutual information were employed in order to develop a comprehensive framework for gene mapping analyses. An algorithm aimed at finding so-called relevance chains of causal markers is proposed. Moreover, entropy measures are used in conjunction with multidimensional scaling to visualize clusters of genetic markers. The relevance chain algorithm successfully detected the two causal regions in a simulated scenario. The approach has also been applied to a published clinical study on autoimmune (Graves') disease. Results were consistent with those of standard statistical methods, but identified an additional locus of interest in the promoter region of the associated gene CTLA4. The developed software is freely available at http://www.lnt.ei.tum.de/download/InfoGeneMap/.
机译：寻找构成复杂特征的因果遗传区域是人类遗传学的主要目标之一。在复杂疾病的背景下，据信这些疾病是由多个作用基因位点控制的，这些基因位点和作用在很大程度上是未知的，因此开发通用而敏感的基因作图方法尤为重要。我们讨论了香农信息理论在基于人群的离散和定量性状基因作图以及标记聚类中的应用。为了建立用于基因作图分析的综合框架，采用了各种互信措施。提出了一种旨在寻找因果标记的相关链的算法。此外，将熵测度与多维标度结合使用以可视化遗传标记簇。关联链算法在模拟场景中成功检测到两个因果区域。该方法也已应用于已发表的有关自身免疫性疾病（Graves'）的临床研究。结果与标准统计方法的结果一致，但是在相关基因CTLA4的启动子区域中发现了其他感兴趣的基因座。可从http://www.lnt.ei.tum.de/download/InfoGeneMap/免费获得开发的软件。

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