An Association Test for Multiple Traits Based on the Generalized Kendall's Tau

• 成果类型: Article
• 署名作者: Zhang, Heping; Liu, Ching-Ti; Wang, Xueqin
• 署名单位: Yale University; Boston University; Sun Yat Sen University; Sun Yat Sen University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.ap08387
• 发表日期: 2010
• 页码: 473-481
• 关键词: genomewide linkage scan; ordinal traits; sib pairs; disequilibrium; genes; dependence; alcohol; variant; loci

摘要

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, that is, 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 dataset concerning alcohol dependence. In this application, the use of our methods enhanced the signal of the association test.