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Nonparametric Covariate-Adjusted Association Tests Based on the Generalized Kendall’s Tau *

Content Provider	PubMed Central
Author	Zhu, Wensheng Jiang, Yuan Zhang, Heping
Abstract	Identifying the risk factors for comorbidity is important in psychiatric research. Empirically, studies have shown that testing multiple, correlated traits simultaneously is more powerful than testing a single trait at a time in association analysis. Furthermore, for complex diseases, especially mental illnesses and behavioral disorders, the traits are often recorded in different scales such as dichotomous, ordinal and quantitative. In the absence of covariates, nonparametric association tests have been developed for multiple complex traits to study comorbidity. However, genetic studies generally contain measurements of some covariates that may affect the relationship between the risk factors of major interest (such as genes) and the outcomes. While it is relatively easy to adjust these covariates in a parametric model for quantitative traits, it is challenging for multiple complex traits with possibly different scales. In this article, we propose a nonparametric test for multiple complex traits that can adjust for covariate effects. The test aims to achieve an optimal scheme of adjustment by using a maximum statistic calculated from multiple adjusted test statistics. We derive the asymptotic null distribution of the maximum test statistic, and also propose a resampling approach, both of which can be used to assess the significance of our test. Simulations are conducted to compare the type I error and power of the nonparametric adjusted test to the unadjusted test and other existing adjusted tests. The empirical results suggest that our proposed test increases the power through adjustment for covariates when there exist environmental effects, and is more robust to model misspecifications than some existing parametric adjusted tests. We further demonstrate the advantage of our test by analyzing a data set on genetics of alcoholism.
Related Links	http://dx.doi.org/10.1080/01621459.2011.643707
Ending Page	11
Page Count	11
Starting Page	1
File Format	PDF
ISSN	01621459
e-ISSN	1537274X
Journal	Journal of the American Statistical Association
Issue Number	497
Volume Number	107
Language	English
Publisher Date	2012-03-01
Access Restriction	Open
Subject Keyword	Statistics, Probability and Uncertainty Statistics and Probability Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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