Yi Nengjun, Xu Shizhong, Lou Xiang-Yang, Mallick Himel
Stat Appl Genet Mol Biol. 2014 Feb;13(1):35-48. doi: 10.1515/sagmb-2012-0040.
Multiple comparisons or multiple testing has been viewed as a thorny issue in genetic association studies aiming to detect disease-associated genetic variants from a large number of genotyped variants. We alleviate the problem of multiple comparisons by proposing a hierarchical modeling approach that is fundamentally different from the existing methods. The proposed hierarchical models simultaneously fit as many variables as possible and shrink unimportant effects towards zero. Thus, the hierarchical models yield more efficient estimates of parameters than the traditional methods that analyze genetic variants separately, and also coherently address the multiple comparisons problem due to largely reducing the effective number of genetic effects and the number of statistically "significant" effects. We develop a method for computing the effective number of genetic effects in hierarchical generalized linear models, and propose a new adjustment for multiple comparisons, the hierarchical Bonferroni correction, based on the effective number of genetic effects. Our approach not only increases the power to detect disease-associated variants but also controls the Type I error. We illustrate and evaluate our method with real and simulated data sets from genetic association studies. The method has been implemented in our freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/).
在旨在从大量基因分型变异中检测疾病相关基因变异的基因关联研究中，多重比较或多重检验一直被视为一个棘手的问题。我们通过提出一种与现有方法根本不同的分层建模方法来缓解多重比较问题。所提出的分层模型同时拟合尽可能多的变量，并将不重要的效应向零收缩。因此，与分别分析基因变异的传统方法相比，分层模型能产生更有效的参数估计，并且由于大大减少了基因效应的有效数量和统计上“显著”效应的数量，还能连贯地解决多重比较问题。我们开发了一种在分层广义线性模型中计算基因效应有效数量的方法，并基于基因效应的有效数量提出了一种新的多重比较调整方法——分层邦费罗尼校正。我们的方法不仅提高了检测疾病相关变异的能力，还控制了I型错误。我们用来自基因关联研究的真实和模拟数据集说明了我们的方法并进行了评估。该方法已在我们免费提供的R包BhGLM（http://www.ssg.uab.edu/bhglm/）中实现。
