ABSTRACT
Current approaches for analysis of longitudinal genetic epidemiological data of quantitative traits are typically restricted to normality assumptions of the trait. We introduce the longitudinal nonparametric test (LNPT) for cohorts with quantitative follow-up data to test for overall main effects of genes and for gene-gene and gene-time interactions. The LNPT is a rank procedure and does not depend on normality assumptions of the trait. We demonstrate by simulations that the LNPT is powerful, keeps the type-1 error level, and has very good small sample size behavior. For phenotypes with normal residuals, loss of power compared to parametric approaches (linear mixed models) was small for the quite general scenarios, which we simulated. For phenotypes with non-normal residuals, gain in power by the LNPT can be substantial. In contrast to parametric approaches, the LNPT is invariant with respect to monotone transformations of the trait. It is mathematically valid for arbitrary trait distribution.