Kozlitina Julia, Schucany William R
Stat Appl Genet Mol Biol. 2015 Nov;14(5):443-64. doi: 10.1515/sagmb-2014-0050.
In association studies of quantitative traits, the association of each genetic marker with the trait of interest is typically tested using the F-test assuming an additive genetic model. In practice, the true model is rarely known, and specifying an incorrect model can lead to a loss of power. For case-control studies, the maximum of test statistics optimal for additive, dominant, and recessive models has been shown to be robust to model misspecification. The approach has later been extended to quantitative traits. However, the existing procedures assume that the trait is normally distributed and may not maintain correct type I error rates and can also have reduced power when the assumption of normality is violated. Here, we introduce a maximum (MAX3) test that is based on ranks and is therefore distribution-free. We examine the behavior of the proposed method using a Monte Carlo simulation with both normal and non-normal data and compare the results to the usual parametric procedures and other nonparametric alternatives. We show that the rank-based maximum test has favorable properties relative to other tests, especially in the case of symmetric distributions with heavy tails. We illustrate the method with data from a real association study of symmetric dimethylarginine (SDMA).
在数量性状的关联研究中，通常使用F检验并假定加性遗传模型来检验每个遗传标记与感兴趣性状之间的关联。在实际中，真实模型很少是已知的，指定错误的模型可能会导致检验效能的损失。对于病例对照研究，已证明加性、显性和隐性模型最优检验统计量的最大值对模型误设具有稳健性。该方法后来被扩展到数量性状。然而，现有程序假定性状呈正态分布，当正态性假设被违反时，可能无法保持正确的I型错误率，并且检验效能也可能降低。在此，我们引入一种基于秩的最大（MAX3）检验，因此它是无分布的。我们使用正态和非正态数据的蒙特卡罗模拟来检验所提出方法的性能，并将结果与常用的参数程序和其他非参数方法进行比较。我们表明，基于秩的最大检验相对于其他检验具有良好的性质，特别是在具有重尾的对称分布情况下。我们用来自对称二甲基精氨酸（SDMA）真实关联研究的数据说明了该方法。
