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适用于三联体家系设计的适应性传递不平衡检验。

Adaptive transmission disequilibrium test for family trio design.

作者信息

Yuan Min, Tian Xin, Zheng Gang, Yang Yaning

机构信息

University of Science and Technology of China.

出版信息

Stat Appl Genet Mol Biol. 2009;8:Article30. doi: 10.2202/1544-6115.1451. Epub 2009 Jun 23.

Abstract

The transmission disequilibrium test (TDT) is a standard method to detect association using family trio design. It is optimal for an additive genetic model. Other TDT-type tests optimal for recessive and dominant models have also been developed. Association tests using family data, including the TDT-type statistics, have been unified to a class of more comprehensive and flexable family-based association tests (FBAT). TDT-type tests have high efficiency when the genetic model is known or correctly specified, but may lose power if the model is mis-specified. Hence tests that are robust to genetic model mis-specification yet efficient are preferred. Constrained likelihood ratio test (CLRT) and MAX-type test have been shown to be efficiency robust. In this paper we propose a new efficiency robust procedure, referred to as adaptive TDT (aTDT). It uses the Hardy-Weinberg disequilibrium coefficient to identify the potential genetic model underlying the data and then applies the TDT-type test (or FBAT for general applications) corresponding to the selected model. Simulation demonstrates that aTDT is efficiency robust to model mis-specifications and generally outperforms the MAX test and CLRT in terms of power. We also show that aTDT has power close to, but much more robust, than the optimal TDT-type test based on a single genetic model. Applications to real and simulated data from Genetic Analysis Workshop (GAW) illustrate the use of our adaptive TDT.

摘要

传递不平衡检验(TDT)是一种使用三联体家庭设计来检测关联的标准方法。它对于加性遗传模型是最优的。也已经开发出了适用于隐性和显性模型的其他TDT类检验。使用家庭数据的关联检验,包括TDT类统计量,已被统一到一类更全面、更灵活的基于家庭的关联检验(FBAT)中。当遗传模型已知或被正确设定时,TDT类检验具有较高的效率,但如果模型设定错误,可能会失去效力。因此,更倾向于使用对遗传模型错误设定具有稳健性且高效的检验。约束似然比检验(CLRT)和MAX类检验已被证明具有效率稳健性。在本文中,我们提出了一种新的效率稳健方法,称为自适应TDT(aTDT)。它使用哈迪 - 温伯格不平衡系数来识别数据背后潜在的遗传模型,然后应用与所选模型对应的TDT类检验(或用于一般应用的FBAT)。模拟表明,aTDT对模型错误设定具有效率稳健性,并且在效力方面通常优于MAX检验和CLRT。我们还表明,aTDT的效力接近基于单一遗传模型的最优TDT类检验,但稳健性要强得多。对遗传分析研讨会(GAW)的真实和模拟数据的应用说明了我们的自适应TDT的使用。

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