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稳健的TDT型候选基因关联测试。

Robust TDT-type candidate-gene association tests.

作者信息

Zheng G, Freidlin B, Gastwirth J L

机构信息

National Heart Lung and Blood Institute, Bethesda, MD 20892, USA.

出版信息

Ann Hum Genet. 2002 Mar;66(Pt 2):145-55. doi: 10.1017/S0003480002001045.

Abstract

In studies of association between genetic markers and a disease, the transmission disequilibrium test (TDT) has become a standard procedure. It was introduced originally as a test for linkage in the presence of association and can be used as a test for association under appropriate assumptions. The power of the TDT test for association between a candidate gene and disease depends on the underlying genetic model and the TDT is the optimal test if the additive model holds. Related methods have been obtained for a given mode of inheritance (e.g. dominant or recessive). Quite often, however, the true model is unknown and selection of a single method of analysis is problematic, since use of a test optimal for one genetic model usually leads to a substantial loss of power if another genetic model is the true one. The general approach of efficiency robustness has suggested two types of robust procedures, which we apply to TDT-type association tests. When the plausible range of alternative models is wide (e.g. dominant through recessive) our results indicate that the maximum (MAX) of several test statistics, each of which is optimal for quite different models, has good power under all genetic models. In situations where the set of possible models can be narrowed (e.g. dominant through additive) a simple linear combination also performs well. In general, the MAX has better power properties than the TDT for the study of candidate genes when the mode of inheritance is unknown.

摘要

在基因标记与疾病关联的研究中,传递不平衡检验(TDT)已成为一种标准方法。它最初是作为在存在关联时的连锁检验引入的,并且在适当假设下可作为关联检验使用。TDT检验对于候选基因与疾病之间关联的效能取决于潜在的遗传模型,并且如果加性模型成立,TDT就是最优检验。对于给定的遗传模式(例如显性或隐性)已经获得了相关方法。然而,通常真实模型是未知的,选择单一分析方法存在问题,因为如果另一种遗传模型是真实的,那么使用对一种遗传模型最优的检验通常会导致效能大幅损失。效率稳健性的一般方法提出了两种稳健程序,我们将其应用于TDT型关联检验。当替代模型的合理范围较宽(例如从显性到隐性)时,我们的结果表明,几个检验统计量中的最大值(MAX),每个统计量对于相当不同的模型都是最优的,在所有遗传模型下都具有良好的效能。在可能模型集可以缩小的情况下(例如从显性到加性),简单的线性组合也表现良好。一般来说,当遗传模式未知时,对于候选基因的研究,MAX比TDT具有更好的效能特性。

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