用于分析复杂遗传疾病的图形链模型:在高血压中的应用

C. Serio, Paola Vicard
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引用次数: 3

摘要

现代遗传医学的一项重要任务是了解复杂的遗传疾病。主要的复杂特征是遗传和环境风险因素的结合,感兴趣的表型可能是复杂的。当疾病不再是单基因的,并且潜在的疾病传播模型没有定义时,传统的基于负荷评分的统计技术就失效了。不同类型的关联测试已被证明是检测复杂疾病的“候选基因”的适当和强大的统计工具。然而,需要能够调查表型、基因型和环境风险因素之间直接和间接影响的统计技术来分析复杂疾病的关联结构。在本文中,我们提出图形模型作为一种自然的工具来分析复杂遗传疾病的多因子结构。并举例说明了该模型在原发性高血压数据集中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graphical chain models for the analysis of complex genetic diseases: an application to hypertension
A crucial task in modern genetic medicine is the understanding of complex genetic diseases. The main complicating features are that a combination of genetic and environmental risk factors is involved, and the phenotype of interest may be complex. Traditional statistical techniques based on lod-scores fail when the disease is no longer monogenic and the underlying disease transmission model is not defined. Different kinds of association tests have been proved to be an appropriate and powerful statistical tool to detect a ‘candidate gene’ for a complex disorder. However, statistical techniques able to investigate direct and indirect influences among phenotypes, genotypes and environmental risk factors, are required to analyse the association structure of complex diseases. In this paper, we propose graphical models as a natural tool to analyse the multifactorial structure of complex genetic diseases. An application of this model to primary hypertension data set is illustrated.
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