Association Analysis of Multiple Traits
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. In this presentation, I will explain why it may be advantageous to analyze those phenotypic measurements simultaneously if they share a similar genetic mechanism. Then, I will present a non-parametric approach to studying multiple traits together rather than examining each trait separately. Through simulation, the nominal type I error and power of our proposed test will be compared 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.
This is a joint work with Ching-Ti Liu, Xueqin Wang, and Wensheng Zhu.
