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

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 for these covariates in a parametric model for quantitative traits, it is challenging to adjust for covariates when there are 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 dataset on genetics of alcoholism.