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Fuzzy clustering in linkage analysis of complex diseases.

机译：复杂疾病关联分析中的模糊聚类。

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摘要

We propose two applications of fuzzy clustering to linkage analysis of complex diseases. The first application consists of a new model-free method of linkage analysis based on using grade of membership scores resulting from a fuzzy clustering procedure to define the dependent variable for the various Haseman-Elston approaches. For a single continuous trait with low heritability, the aim was to identify subgroups such that the grade of membership scores to these subgroups would provide more information for linkage than the original trait. For a multivariate trait, the goal was to provide a means for data reduction and data mining. Simulation studies using continuous traits with relatively low heritability (H = 0.1, 0.2 and 0.3) showed that the new approach does not enhance power for a single trait. However, for a multivariate continuous trait (with 3 components) it is uniformly more powerful than the test proposed by Mangin et al. [1998] when there is pleiotropy. The second application consists of new model-based methods for linkage analysis using grade of membership scores resulting from a fuzzy clustering procedure to define three fuzzy forms of the LOD-score. For a single continuous trait with low heritability, the aim was to identify sub-groups, when such that the grade of membership scores to these subgroups incorporated in the new expression of the likelihood, would provide more information for linkage than using cut-off points to define a binary trait that is used in classic LOD-score methodology. For a multivariate trait case, this test provides a means for data reduction and linkage analysis as well. Simulation studies using continuous traits with relatively low heritability ( H = 0.1, 0.2, 0.3, and 0.4) showed that the new approach enhances power for a single trait as well as for the multivariate trait case (4 continuous components, each with different low heritability, acting pleiotropically).
机译：我们提出了模糊聚类在复杂疾病连锁分析中的两种应用。第一个应用程序包括一种新的无模型的链接分析方法，该方法基于使用模糊聚类过程得出的隶属度评分等级来定义各种Haseman-Elston方法的因变量。对于具有低遗传力的单个连续性状，目的是鉴定亚组，使得这些亚组的成员资格评分等级将比原始性状提供更多的连锁信息。对于多元特征，目标是提供一种数据缩减和数据挖掘的方法。使用遗传力相对较低（ H = 0.1、0.2和0.3）的连续性状进行的模拟研究表明，新方法不会增强单个性状的功效。但是，对于多变量连续性状（具有3个成分），当存在多效性时，它比Mangin等人（1998）提出的测试具有更强大的功能。第二个应用程序包括新的基于模型的链接分析方法，该方法使用从模糊聚类过程得出的会员评分的等级来定义LOD得分的三种模糊形式。对于具有低遗传力的单个连续性状，目的是确定亚组，以使这些亚组的成员资格评分等级纳入新的可能性表达中，比使用截止点能提供更多的连锁信息。定义在经典LOD评分方法中使用的二进制特征。对于多特征的情况，该测试也提供了数据缩减和连锁分析的方法。使用遗传性相对较低的连续性状（ H = 0.1、0.2、0.3和0.4）进行的模拟研究表明，新方法增强了单个性状以及多性状案例的能力（4个连续性成分，每个都具有不同的低遗传力，并具有多效作用）。

著录项

  • 作者

    Kaabi, Belhassen.;

  • 作者单位

    Case Western Reserve University (Health Sciences).;

  • 授予单位 Case Western Reserve University (Health Sciences).;
  • 学科 Biology Genetics.; Biology Biostatistics.; Health Sciences Pathology.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 191 p.
  • 总页数 191
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 遗传学;生物数学方法;病理学;
  • 关键词

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