Yuan Min, Tian Xin, Zheng Gang, Yang Yaning
University of Science and Technology of China.
Stat Appl Genet Mol Biol. 2009;8:Article30. doi: 10.2202/1544-6115.1451. Epub 2009 Jun 23.
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.
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。