Dawy Zaher, Goebel Bernhard, Hagenauer Joachim, Andreoli Christophe, Meitinger Thomas, Mueller Jakob C
Institute for Communications Engineering (LNT), Munich University of Technology (TUM), Arcisstr. 21, Munich, Germany.
IEEE/ACM Trans Comput Biol Bioinform. 2006 Jan-Mar;3(1):47-56. doi: 10.1109/TCBB.2006.9.
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 promotor region of the associated gene CTLA4. The developed software is freely available at http://www.Int.ei.tum.de/download/InfoGeneMap/.
寻找复杂性状背后的因果基因区域是人类遗传学的主要目标之一。在复杂疾病的背景下，人们认为这些疾病由多个效应和位置 largely unknown 的贡献位点控制，因此开发通用且灵敏的基因定位方法尤为重要。我们讨论了香农信息论在基于群体的离散和数量性状基因定位以及标记聚类中的应用。为了建立一个全面的基因定位分析框架，我们采用了各种互信息度量。提出了一种旨在寻找因果标记所谓相关链的算法。此外，熵度量与多维缩放结合使用，以可视化遗传标记簇。相关链算法在模拟场景中成功检测到了两个因果区域。该方法还应用于一项已发表的关于自身免疫性（格雷夫斯）疾病的临床研究。结果与标准统计方法的结果一致，但在相关基因 CTLA4 的启动子区域发现了一个额外的感兴趣位点。开发的软件可在 http://www.Int.ei.tum.de/download/InfoGeneMap/ 免费获取。
