Zhang Heping, Liu Ching-Ti, Wang Xueqin
J Am Stat Assoc. 2010 Jun;105(490):473-481. doi: 10.1198/jasa.2009.ap08387.
In many genetics studies, especially in the investigation of mental illness and behavioral disorders, it is common for researchers to collect multiple phenotypes to characterize the complex disease of interest. It may be advantageous to analyze those phenotypic measurements simultaneously if they share a similar genetic mechanism. In this study, we present a nonparametric approach to studying multiple traits together rather than examining each trait separately. Through simulation we compared the nominal type I error and power of our proposed test to an existing test, i.e., a generalized family-based association test. The empirical results suggest that our proposed approach is superior to the existing test in the analysis of ordinal traits. The advantage is demonstrated on a data set concerning alcohol dependence. In this application, the use of our methods enhanced the signal of the association test.
在许多遗传学研究中,尤其是在对精神疾病和行为障碍的调查中,研究人员通常会收集多种表型来表征所关注的复杂疾病。如果这些表型测量共享相似的遗传机制,同时分析它们可能会有优势。在本研究中,我们提出了一种非参数方法来共同研究多个性状,而不是分别检查每个性状。通过模拟,我们将我们提出的检验的名义I型错误率和检验效能与现有检验(即广义基于家系的关联检验)进行了比较。实证结果表明,在有序性状分析中,我们提出的方法优于现有检验。在一个关于酒精依赖的数据集上证明了这一优势。在这个应用中,我们方法的使用增强了关联检验的信号。